UNIVERSITY OF GHANA

CASH FLOW RISK MANAGEMENT IN THE GHANAIAN INSURANCE INDUSTRY

(A Dynamic Factor Modelling Approach)

BY

FRANCIS KOJO SEMANU AFENYO TSEVI

(10551122)

THIS THESIS IS SUBMITTED TO UNIVERSITY OF GHANA, LEGON IN

PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL

RISK MANAGEMENT AND INSURANCE DEGREE

JULY, 2017

i

DECLARATION

I hereby do declare that this thesis is original and a result of my own research except for those

literature, quotations, explanations and summarizations which are duly identified and

acknowledged.

I bear sole responsibility for any shortcomings.

……………………………………………………. ….……………………………

FRANCIS KOJO SEMANU AFENYO TSEVI DATE

(10551122)

ii

CERTIFICATION

I certify hereby that this thesis was supervised in accordance with procedures laid down by the

University.

.............................................................. .….…................................

DR. CHARLES ANDOH DATE

(LEAD SUPERVISOR)

………………………………………… …………………………..

DR. SAINT KUTTU DATE

(CO-SUPERVISOR)

iii

DEDICATION

This work is dedicated to my parents Mr Daniel Dodzi Tsevi and Mrs Margaret Tsevi for their

unflinching support and prayers throughout the entire period of my studies and to Ms Gloria Aseye

Beli for her words of encouragement during my difficult moments of study.

iv

ACKNOWLEDGEMENT

The success of this work would not have been possible without the useful contributions and help

of some special people. I, therefore, would like to take this great opportunity to acknowledge all

manner of persons who contributed enormously to this successfully completed thesis work.

My greatest gratitude I extend to God Almighty for His unfailing love, strength, guidance and

favour throughout my period of study.

I also thank fervently Dr Charles Andoh and Dr Saint Kuttu, my supervisors, who dedicated time

out of their busy schedules to guide me during this research through their useful comments,

encouragements, contributions and suggestions.

I would again like to express my gratefulness to my parents, Mr Daniel Dodzi Tsevi and Mrs

Margaret Tsevi as well as my lovely sisters Irene, Celestine, Florence, Ethel and Adelaide Tsevi

for their prayers and support during my study.

Next, I would like to express my profound gratefulness to Mr Godfred Nsabo who dedicated time

to assist me throughout the period of my data collection. To the management and staff of NIC and

all the various non-life insurance companies who made available valuable data for this study, I say

a big thank you.

Finally, my warmest gratitude goes to all and sundry who contributed in diverse ways whom I may

have inadvertently missed out, I say a big thank you.

God bless.

v

TABLE OF CONTENT

CONTENT

DECLARATION ........................................................................................................................................... i

CERTIFICATION ........................................................................................................................................ ii

DEDICATION ............................................................................................................................................. iii

ACKNOWLEDGEMENT ........................................................................................................................... iv

CONTENT .................................................................................................................................................... v

LIST OF TABLES ..................................................................................................................................... viii

LIST OF FIGURES ................................................................................................................................... viii

ABSTRACT ................................................................................................................................................. ix

CHAPTER ONE ........................................................................................................................................... 1

INTRODUCTION ........................................................................................................................................ 1

1.1 Background to the study ............................................................................................................... 1

1.1.1 The Ghanaian Insurance Industry ......................................................................................... 3

1.2 Research Problem ......................................................................................................................... 6

1.3 Research Purpose .......................................................................................................................... 7

1.4 Research Objectives ...................................................................................................................... 7

1.5 Research questions ........................................................................................................................ 7

1.6 Research Hypotheses .................................................................................................................... 8

1.7 Significance of the Research ......................................................................................................... 8

1.8 Definition of Scope and Limitation of Study ................................................................................ 9

1.9 Chapter Outline ........................................................................................................................... 10

1.10 Chapter Conclusion ..................................................................................................................... 11

CHAPTER TWO ........................................................................................................................................ 12

LITERATURE REVIEW ........................................................................................................................... 12

2.1 Introduction ....................................................................................................................................... 12

2.2 Overview of Cash Flow in the Insurance Industry ...................................................................... 13

2.3 Theories on Cash Holding Practices of Firms............................................................................. 14

2.3.1 Trade-off Theory ................................................................................................................. 15

2.3.2 Pecking Order Theory ......................................................................................................... 18

2.3.3 Free Cash Flow Theory ....................................................................................................... 20

vi

2.4 The importance of Future Cash Flow Prediction. ....................................................................... 21

2.5 Review on Determinants of Cash Holdings of Firms ................................................................. 22

2.5.1 Firm Characteristics ............................................................................................................ 23

2.6 Determinants of Cash Flow in the Insurance Industry ................................................................ 29

2.7 Parent Disciplines: Accrual and Cash Accounting ..................................................................... 30

2.7.1 Accrual Accounting ............................................................................................................ 31

2.7.2 Cash accounting .................................................................................................................. 33

2.8 Empirical Literature Review on Cash Flow Risk of Firms ......................................................... 36

2.9 Chapter Conclusion ..................................................................................................................... 38

CHAPTER THREE .................................................................................................................................... 39

METHODOLOGY ..................................................................................................................................... 39

3.1 Introduction ................................................................................................................................. 39

3.2 Research approach ...................................................................................................................... 39

3.3 Research Design .......................................................................................................................... 40

3.3.1 Research Methodology: Quantitative versus Qualitative. ................................................... 41

3.4 Conceptual Framework for Prediction Models Development..................................................... 43

3.5 Research population .................................................................................................................... 45

3.6 Sample and Sampling Technique ................................................................................................ 45

3.7 Data collection ............................................................................................................................ 47

3.8 Specification of the Model .......................................................................................................... 47

3.8.1 Correlation Evaluation ........................................................................................................ 47

3.8.2 Regression Analysis ............................................................................................................ 48

3.8.3 Principal Component Analysis ............................................................................................ 49

3.8.4 Dynamic Factor Modelling ................................................................................................. 50

3.9 Variables Construction and Expected Signs ............................................................................... 54

3.10 Limitation to Methodology ......................................................................................................... 55

3.11 Chapter Conclusion ..................................................................................................................... 56

CHAPTER FOUR ....................................................................................................................................... 57

ESTIMATIONS AND DISCUSSION OF RESULTS ............................................................................... 57

4.1 Introduction ................................................................................................................................. 57

4.2 Industry Descriptive Statistics .................................................................................................... 57

4.2.1 Pearson’s Correlation Matrix .............................................................................................. 59

4.3 Regression Analysis .................................................................................................................... 61

4.3.1 Determinants of Cash Flow in the Ghanaian Insurance Industry ........................................ 63

vii

4.3.2 The impact of Insurers’ management practices on their Cash Flow ................................... 67

4.4 Empirical Results: Illustration of FAAR (DFM) Applications in Cash Flow Forecasting ......... 68

4.4.1 Principal Components Results: ........................................................................................... 69

4.4.2 Choice of a Baseline Model [AR(n) model] ....................................................................... 72

4.4.3 Comparison between Baseline Model AR2 and the FAAR Model .................................... 73

4.4.4 Identifying an Optimum number of Principal Components ................................................ 76

4.4.5 Forecasting Cash Flow using FAAR (DFM) Model ........................................................... 81

4.5 Chapter Conclusion ..................................................................................................................... 82

CHAPTER FIVE ........................................................................................................................................ 83

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............................................................... 83

5.1 Introduction ................................................................................................................................. 83

5.2 Summary of Findings .................................................................................................................. 83

5.2.1 Determinants of Cash Flow ................................................................................................. 83

5.2.2 Summary of Findings on hypotheses formulated. ............................................................... 85

5.2 Conclusions ................................................................................................................................. 88

5.3 Contribution to theory and knowledge ........................................................................................ 88

5.4 Implications for Policy and Practice ........................................................................................... 89

5.5 Recommendations ....................................................................................................................... 89

5.6 Limitations and Directions for Future Research ......................................................................... 90

5.7 Chapter Conclusion ..................................................................................................................... 91

REFERENCES ........................................................................................................................................... 93

APPENDIX ............................................................................................................................................... 100

viii

LIST OF TABLES

Table 1.1: Market Capitalizations of Financial Service firms ........................................................ 4

Table 1.2: Distribution of the number of complaints from the general public ............................... 5

Table 2.1: Differences between Accrual and Cash Accounting Principles .................................. 35

Table 3.1: Definition of Primary Variables with Expected Signs ................................................ 55

Table 4.1: Descriptive Statistics of the Insurance Industry .......................................................... 58

Table 4.2: Pearson’s Correlation Matrix for Industry Cash Flow Variables ................................ 60

Table 4.3: Variance Inflation Factor (VIF) for Predictors ............................................................ 61

Table 4.4: Result of Diagnostic Tests ........................................................................................... 62

Table 4.5: Cash Flow models using past Cash Flows and identified observed variables ............. 63

Table 4.6: Impact of management activities on Cash Flow .......................................................... 68

Table 4.7: Pearson Correlated matrix for firm F1 (Correlation between predictor variables) ..... 70

Table 4.8: Empirical Result of PCA, Firm F1 .............................................................................. 71

Table 4.9: Comparison between Baseline Models: AR1, AR2 and AR3 ..................................... 72

Table 4.10: Results of FAARM; Number of Models that Outperform the Baseline Model ........ 74

Table 4.11: Empirical Results of FAAR (DFM) for Firm F1 ....................................................... 77

Table 4.12: Results of FAARM: The Optimum Number of Principal Components .................... 79

Table 4.13: Forecasting Values of FAAR Model ......................................................................... 81

Table 4.14: Forecast Values for Holdout Period with calculated MAPE ..................................... 82

Table 5.1: PCA Loading Matrix for Industry Observed Variables ............................................. 100

Table 5.2: Forecasting Values Using FAAR Model (2015Q1) .................................................. 101

Table 5.3: Forecasting Values Using FAAR Model (2015Q2) .................................................. 101

Table 5.4: Forecasting Values Using FAAR Model (2015Q3) .................................................. 102

Table 5.5: Forecasting Values Using FAAR Model (2015Q4) .................................................. 102

LIST OF FIGURES

Figure 3.1: Research Design Overview ........................................................................................ 41

Figure 3.2: Cash Flow model for future Cash Flow prediction .................................................... 53

Figure 4.1: Time-varying movement of PC1, PC2 and PC5 ........................................................ 78

Figure 5.1: Residual Plots for Cash Flow (CF)........................................................................... 100

ix

ABSTRACT

This study investigated determinants of Cash Flow in the insurance industry, explained the time-

varying patterns of Cash Flows and forecasted future Cash Flows for Cash Flow risk management.

Sample financial data of 21 non-life insurance firms in Ghana from the year 2007 to 2015 was

used for this Study. Statistical analyses were made using a combination of pooled ordinary least

squares methods, principal component analysis and a novel Factor Augmented Autoregressive

(FAAR) Model to incorporate capital management, risk management, financial (investment)

management, firm characteristics, underwriting activities, and macroeconomic variables to

forecast future Cash Flows. The study provided new evidence regarding the relationship of firms’

management activities with Cash Flow risk management. Findings indicate that Cash Flow in the

Ghanaian insurance industry is significantly driven by capital ratio, short term and long term

investment ratios and industry-specific variables such as reinsurance ratio and net premium

received ratios. The results also validated the acute forecasting ability of FAAR (DFM) models in

predicting future Cash Flows.

In addition, the identification of possible variations in Cash Flows using the FAAR forecasting

model can help firms to further apply different financial instruments to manage and control Cash

Flow variations. i.e. Cash Flow Risks (Shortfalls).

Findings imply that firms can generate favourable Cash Flows to meet future obligations such as

dividend payments and unexpected insurance claims by controlling variables such as capital ratio,

short term and long term ratios, reinsurance and net premium received to assets ratios.

Keywords: Cash Flow Risk, Dynamic Factor Model (DFM), Factor Augmented Autoregressive

(FAAR), Cash Flow Volatility, Cash Accounting

1

CHAPTER ONE

INTRODUCTION

1.1 Background to the study

Cash is king. It is true for entrepreneurs and it is also true for managers of financial institutions

(Born, Lin, & Wen, 2014). It is true that both financial and non-financial businesses are constantly

confronted with the risk of variations in their Cash Flow. In times of extreme economic volatilities,

prior knowledge on the trend of future Cash Flows would be very beneficial if it is available in

order to control for any associated risks. Proper Cash Flow forecasting and Cash Flow risk

management can, therefore, make huge differences between a surviving firm and one that will go

bankrupt or insolvent. There is much evidence suggesting that the high level of volatilities that

currently exist in the world of business is going to get worse in the years and decades ahead (Elahi,

2013). Younger firms most especially face uncertainties about their growth prospects. This

uncertainty exhibited in time through the realisation of future Cash Flow provide new information

about the health of a firm (Alti, 2003). This current trend of rising uncertainties that result in risks

for businesses calls for great attention to proper risk management with the potential of providing

a competitive advantage as well as promote firm solvency.

Differently stated, the life-blood of any business can be likened to its Cash Flow and if a business

in the course of operations runs out of cash and is unable to obtain new finance, it will become

insolvent. The level of Cash Flow within a firm is a clear indication of the future growth and

prospects of that firm. The Cash Flow of a firm can thereby be seen as a measure of the health of

that firm. Volatilities in the level of Cash Flow for a firm, therefore, need to be critically analysed

and projections accurately made as a first step in managing or controlling for inherent financial

risks that might be eminent in the firm’s operating activities. For any business, therefore, accurate

Cash Flow forecasting is essential at regular periods to provide information regarding the amount

2

of cash required at any point in time as well as determine how Cash Flow will be affected as a

result of a change in the risk factors that influence the level of cash.

With this in mind, the risks to Cash Flow thus have long been one most essential considerations

managers of firms make in their attempts to manage a variety of other risks. Management of firms

within the insurance industry are likewise keenly interested in Cash Flow statements, particularly

due to the unique underwriting risks uncommon to other industries but faced by this industry. By

this, forecasting Cash Flow, Cash Flow management as well as planning for future cash

requirements by businesses, has become vital to the process of financial management in a bid to

avoiding a crisis of insolvency and other financial risks. Essentially, the bottom line is the need for

firms to accurately forecast (predict) what is going to happen to their Cash Flow in the near future

so as to make sure they are able to service debts in time as a means to their very survival. The

frequency of forecasting Cash Flow, however, will depend strongly on the financial security of a

particular business under consideration, thus, whether a business is struggling with its finances or

its finances are stable (safe).

Cash Flows in the insurance industry can be generated mainly through underwriting activities,

financing and investment choices and even through risks management (Born et al, 2014). In

banking, one main operating activity, lending or deposits when demanded by customers leads to

an outflow of cash while loans when they are paid back or deposits lead to an inflow of cash to the

firm. Interests received or paid on these loans and deposits also contributes significantly to the

Cash Flow of these firms (Torfason, 2013).

Cash Flow as used in this study hence refers to the overall incomings and outgoings of cash within

an organisation as pertaining to its day to day activities. It thus refers to the sum of money that is

being transferred out of and into a business, particularly as affecting the liquidity of that business.

3

1.1.1 The Ghanaian Insurance Industry

According to the Annual Report of National Insurance Commission (NIC, 2015), the Ghanaian

insurance industry is currently made up of 25 Non-Life Companies; 19 Life Companies; 64

Broking Companies; 1 Loss Adjusting Company; 1 Reinsurance Broking Company and; 4

Reinsurance Companies. Despite these numbers, insurance penetration in Ghana is still very low.

Insurance penetration in Ghana for 2013 was 1.42% below the African average of 3.5%. This is

very low as compared to jurisdictions like Kenya and South Africa where penetration stood at

3.4% and 15.4% respectively (NIC, 2013).

In Damodaran (2013), any firm providing financial services or products to either individuals or

other firms is classified as a financial service firm. With this notion, financial service businesses

were categorised into four main groups from the viewpoint of how they make their money. Among

this classification was insurance firms which they classified as firms which make their income

primarily through premiums they receive from policyholders who seek insurance protection from

them. The study also identified income from investment portfolios as a second primary source of

income to insurance firms in order to service genuine claims. In measuring how big the financial

services sector is in the United States, the study summarised the market capitalisation of all

publicly traded financial service firms at the end of 2007 and the proportion of the overall equity

they represent in the market at the time. The results are presented in Table 1.1.

From the table, the proportion of overall market capitalization for insurance firms is 8.00 percent

equivalent to a market value of $4,029,009 in millions of US dollars. This stated value accounts

for about one-half of the overall market, in terms of market capitalization of the financial services

sector.

4

Table 1.1: Market Capitalizations of Financial Service Firms

Source: Damodaran (2013)

Thus, although insurance contributes highly to the financial sector of most jurisdictions such as

the United States of America, South Africa and Kenya among others, insurance penetration in

Ghana still remains very low. In Ghana, a majority of the insuring public deal primarily with Non-

Life Insurance companies mainly because of the Motor Vehicle Insurance, which was made

compulsory by the Motor Vehicle (Third Party) Act, 1958 (Act 42). This study posits that the low

insurance penetration in Ghana can partly be linked to the lack of public trust and confidence in

insurance companies reinstating policyholders to their former financial positions in an event of an

occurrence of a fortuitous loss which was insured against.

Fast forward to some unfolding occurrences in the Ghanaian insurance industry, the National

Insurance Commission in 2001, filed a court case for the winding up of “Great African Insurance

Company Limited” and further filed an application in 2003 for an order to audit the company.

Similarly, the Commission took some enforcement actions against Phoenix Insurance Company

Ltd owing to poor finances and bad management practices (NIC, 2005).

The Commission again in pursuit of its statutory function, served a notice to the general public

stating that, “Adamas Life Assurance Company Limited is undergoing regulatory intervention to

improve its operational efficiency”. This notice coupled with regulatory decisions taken by NIC

Sector Market Cap Number Proportion of

market

Banking $2,404,664 550 4.78%

Financial Services $1,153,793 294 2.29%

Insurance $4,029,009 353 8.00%

Securities Brokerage $731,343 31 1.45%

Thrift $156,596 234 0.31%

All financial services $8,475,404 1462 16.83%

5

restricted the company from writing any new insurance business, renewing or varying any existing

ones among others (NIC, 2016). According to the draft NIC Annual Report (2015), the

Commission received 520 complaints during the year 2014 as against 380 in 2013 of which 407

of these were resolved. Out of these number of complaints, 193 were made against non-life

insurance companies while 327 of these complaints were filed against life insurance companies.

35 of these complaints were, however, associated with uninsured vehicles owners which in this

study can be likened to the class of individuals who have little trust in the insurance business. The

total number of complaints year on year as illustrated in Table 1.2 indicates a steady increase from

the year 2011 to 2014. The areas of common complaints in the area of non-life insurance as

reported in the NIC annual report, however, bother on the pursuit of motor claims with specific

relation to repudiation of claims by insurers, delay in settlement of claims, dispute over quantum

and delay in payment of settled claims. Table 1.2 highlights the distribution of claims from the

year 2010 to 2014.

Table 1.2: Distribution of the number of complaints from the general public

Source: NIC (2015)

This study posits that these failures recorded in the insurance industry may be due to poor Cash

Flow management as a number one contributing factor. The study also conjectures that these let-

downs if not appropriately resolved may lead to a total breakdown of the entire insurance industry

as policyholders will lose total confidence in insurance as a buffer for financial losses. This

Companies

Number of Complaints per Year

2010 2011 2012 2013 2014

Non-life Insurance

companies 236 114 165 199 193

Life Insurance companies 80 24 67 113 327

Others (uninsured vehicles) 129 74 83 68 35

Totals 445 212 315 380 520

6

research, therefore, sets to model Cash Flow which is the primary medium to claims settlement

and forecast future cash requirement of insurers as a first step to detecting and managing future

risks so as to protect policyholder and other stakeholder interests. The study focuses on Non-Life

Insurance which is the predominant class of insurance patronised in the Ghanaian market.

1.2 Research Problem

Future Cash Flow risks have been a major concern for many firms and the insurance industry

which faces, particularly, unique underwriting risks which are not observed in other industries is

no exception. Cash Flow of firms is determined by various activities that contribute to the overall

cash position. Cash Flow for the insurance industry is thus generated mainly through activities in

underwriting, financial and investment choices, capital management and risks management (Born

et al., 2014). Cash Flow generated within this industry thus dynamically interrelates with these

activities. Currently, the insurance industry in Ghana is faced with challenges relating to

policyholder complaints about insurer repudiations and delay in reimbursements, poor industry

capitalisation coupled with challenges in investment choices, as well as financial and risk

management. These challenges among other things bring to bear the insolvencies (NIC, 2005)

recorded within the industry over the decades. The current trend of uncertainty in firms’ Cash Flow

as related to their financial health, therefore, calls for a study to model Cash Flow. The bottom line

is, thus, any model developed to forecast insurers’ Cash Flow, must take into consideration all

activities related to the cash generating process of financing and investment, underwriting, capital

management, and risk management in order to manage the current Cash Flow risks of insurers in

the Ghanaian market.

7

1.3 Research Purpose

This study is targeted at ensuring the solvency of insurance firms within the Ghanaian insurance

industry by forecasting future Cash Flows with the intention of protecting the interest of

policyholders as well as increasing insurance penetration in Ghana. The forecasted Cash Flow of

a typical insurance firm will thus provide relevant guidelines for necessary risk management

practices.

1.4 Research Objectives

This study focuses on identifying Cash Flow risks of insurance firms by forecasting future net

Cash Flow as the primary indicator for Cash Flow variations (risk). Specifically, this research

identifies determinants of Cash Flow, as well as the impact of their management on future Cash

Flows. The primary objectives of this study are:

a. To identify the major determinants of Cash Flow in the cash generating process

b. To investigate the relationship between Cash Flow and capital management, investment

(financial) management, risk management, firm characteristics and underwriting activities

c. To forecast future Cash Flow of a typical insurance firm in Ghana.

1.5 Research questions

In order to have an in-depth understanding of this research, the key guiding questions are:

a. What are the main determinants of Cash Flow in the insurance industry?

b. What is the relationship between Cash Flow and capital management, investment

management, risk management, firm characteristics and underwriting activities?

c. What is the forecasted Cash Flow for an insurance firm at a given time (t)?

8

1.6 Research Hypotheses

Following the above research questions, the study tested the following hypotheses;

a. Capital management does not impact Cash Flow level

b. Firm characteristics do not impact Cash Flow level

c. Investment management does not impact Cash Flow level

d. Risk management does not impact Cash Flow level

e. Underwriting activities do not impact Cash Flow level

1.7 Significance of the Research

The significance of this study can be viewed from three (3) angles namely:

a. Research: This study goes beyond currently existing research on Cash Flow management

which largely had been conducted in the European and American countries by applying a

quantitative method to estimate and forecast future Cash Flow for insurance firms in

Ghana. Existing literature on Cash Flow risk management in Ghana and perhaps West

Africa is presently rare. This research serves as a useful contribution to the discussion of

the use of Cash Flow as an indicator of firm’s financial risk in developing countries and

especially in Ghana where insolvency is a major concern to insurers, insurance regulators

and policyholders aside other stakeholders.

It will also be of great worth to individuals and organisations interested in carrying out

similar or related studies on Cash Flow risk management in the future.

b. Practice: This study provides necessary signal and useful remedying guidelines for

insurance companies and stakeholders by estimating future cash requirements, subsequent

future financial health of a firm and drawing attention to activities that need critical

considerations. Findings will further inform the general public and insurance regulators

about the vulnerability of some insurance firms and hence protect customers and

9

beneficiaries. In effect, critical assessment and projection of future Cash Flow of insurance

firms will provide relevant insight and understanding to meaningful risk management

needs in insurance companies to prevent insolvencies

c. Policy: This study will provide valuable intuition which can help the regulator – the

National Insurance Commission (NIC), to enact policies and regulations to protect the

interest of both insurers and the insuring public in Ghana.

1.8 Definition of Scope and Limitation of Study

The scope within which this study is conducted is discussed in this section. It examines the

contextual validity of methods employed and the extent to which findings in this study can be

generalised.

In line with the research objectives, the study steps out to investigate the various drivers of Cash

Flow, particularly in the insurance industry. The methods employed are particularly appropriate as

extant empirical studies validate the diverse implication of dissimilar characteristics and

volatilities in forecasting future Cash Flows (Born et al., 2014; Stock & Watson, 2006;

McNicholas, 2002; Palepu, Healy, & Bernard, 2000). Thus, by concentrating on one

sector/industry, the influences of different dynamics that exist in other industries are minimised

and the reliability of findings is consequently greatly enhanced.

The sample used for this research considers financial ratios from licensed non-life insurance

companies within the Ghanaian insurance industry. Findings may thus not be generalizable easily

to life insurance or other industries within or outside Ghana due to some unique factors (such as

compulsory third party motor vehicle insurance and currently compulsory fire insurance for

commercial buildings) characterising the non-life insurance sector.

10

Although findings are aimed at fully informing insurers, insurance regulators and other

stakeholders of the various determinants of Cash Flow, the effective management of these

determinants is beyond the scope of this study. Also, although this study aims at assisting insurers,

insurance regulators and other capital market analysts to forecast more accurately future Cash

Flows as a means to the detection of future possible Cash Flow risks, the optimum management

procedures of possible risks in future Cash Flow are beyond the scope of this study. The detection

of these risks through projected Cash Flows will, however, assist firms to make improved financial

decisions.

Secondary accounting data from the financial reports of insurance firms were utilised in this study.

The sampled data is historic in nature and covers the period of 2007 to 2015. This period was

particularly chosen to reflect the current economic condition of non-life insurance in Ghana and

the regulatory impact of the 2006 ACT (ACT 724) which prohibited the forming and existence of

composite insurance companies. The study, however, made no comparable inference on the

performance of non-life companies before and after the enactment if this ACT (ACT 724). The

validity and robustness in the forecastability of Cash Flow using dynamic prediction models were,

however, authenticated using a holdout sample (2015 quarterly data).

1.9 Chapter Outline

The rest of the study is organised in the following manner:

Chapter two comprises a review of relevant literature on Cash Flow and cash holding behaviour

of firms so as to set a foundation for the research. It explores extant literature pertinent to this study

to ensure familiarity with existing body of knowledge and the position of this current study. The

literature review also highlights the conceptual framework for the study, clearly outlining the

research gap to be filled.

11

Chapter three explains the research methodology and discusses the population and sampling

undertaken as well as the data collection methods employed with relevant justification for each

selected research method.

The fourth chapter covers estimations, presentation, analysis of the results obtained using some

descriptive and inferential statistics.

The fifth chapter summarises the research findings, conclusions and make recommendations based

on findings with respective implications.

1.10 Chapter Conclusion

This chapter contained a comprehensive overview of the background, a brief description of the

Ghanaian insurance industry, a statement of the research problem and the research purpose. The

objectives and research questions guiding the research as well as hypotheses facilitating an in-

depth understanding and direction for the study were also provided. A justification to the

significance and the contribution of this study to the current body of literature, to policy, practice

of insurance as well as the conduct of future research was also provided. This chapter concluded

with a discussion of the scope of study and a comprehensive outline to the rest of the study.

The ensuing chapter presents a thorough examination of relevant literature related to the study.

12

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter reviews the theoretical and empirical basis upon which the thoughts and

opinions developed in this study are constructed. The chapter reviews relevant extant literature

associated with the research topic as expressed by various authors and researchers and some

regulatory bodies. Specifically, the chapter contains literature on the background and

developments in the insurance industry of Ghana, the regulatory structure and bodies in order to

provide an overview of the financial reporting and operative environment of insurance in Ghana.

The chapter reviews relevant literature on the importance of forecasting future Cash Flows and

highlights on the accrual and cash accounting principles as two parent disciplines in financial

information reporting. Thus, literature relating to the use of accruals as well as Cash Flows as

either aggregated or single predictors of future Cash Flows are reviewed.

This chapter also contains an overview of Cash Flow in the insurance industry as well as literature

on some grounding theories on cash holding practices of firms. By this, the chapter reviews

literature to examine the major determinants of the Cash Flow of firms and the relationships

existing between Cash Flow and key management practices of firms.

Also, prior studies on the value relevance of accounting information in explaining share market

returns and the ability of derived financial ratios from this accounting information as proxies for

the prediction of financial distress were reviewed.

This chapter forms the basis for the regression models used in the next chapter for empirical

estimation and finally ends with a conclusion.

13

2.2 Overview of Cash Flow in the Insurance Industry

Insurance companies are financial institutions with financial objectives. In financial accounting, a

Cash Flow statement refers to a statement on finances showing how changes in income and balance

sheet accounts affect cash and cash equivalents, as a result of transactions in operating, investing

and financing activities (IASB, 2001). In essence, the Cash Flow statement for firms concerns with

the flow of cash into and out of the business. It captures both current operating results as well as

accompanying changes in the balance sheet. Cash Flow statements are useful as analytical tools in

determining the short-term viability of a company, principally its ability to pay bills since bills are

paid not against income but against cash. Timely availability of cash, therefore, can distinguish

between successful operations and closure (Beck, 1994). Insurers’ overall risk of insolvency,

therefore, links strongly with Cash Flow volatility which results through a myriad of individual

underwriting, financing and investment decisions of firms in the midst of macroeconomic variables

like inflation, interest rate, unemployment and GDP growth rates.

The net Cash Flow of a firm over a period (typically a quarter, half year or a full year) is equivalent

to the change in cash balance over this period. It is positive if the cash balance increases (more

cash becomes available) and negative, if the cash balance decreases. In general, the net of Cash

Flows of a project are in the following classifications:

a. Operational Cash Flows: This is simply, cash received or expended as a result of the

internal business activities in a firm.

b. Change in Net Working Capital: This is the flow of cash into or out of a company as a

result of the cost or revenue accruing to a company through its short-term assets.

c. Capital Spending: This is the cost or gains associated with the company’s fixed asset,

such as cash used to purchase a new equipment or cash which is gained from selling an old

equipment.

14

In order to maintain a smooth Cash Flow, therefore, manipulating practically every facet of a

business is required (Bodunrin, 2016). In insurance, activities in underwriting, financing, investing

as well as risk managing are essential in a successful Cash Flow management of regulating the

amount of money flowing into and out of a business. Increasing Cash Flow in a venture will imply

a reduction in the amount of fixed capital needed to support a given level of business operations.

An increased and consistent Cash Flow in a business also creates a predictable pattern in

businesses, enabling planning and budgeting for future growth.

2.3 Theories on Cash Holding Practices of Firms

The holding of cash and other liquid assets in a world of perfect capital markets are totally

irrelevant (Opler, Pinkowiz, Stulz, & Williamson, 1999). Thus, if the level of Cash Flow

unexpectedly turns out to be low, to the extent that a firm will have to raise external funds to

maintain their operations as well as invest, it can do exactly so at an absolutely zero cost. This will

be so since there is no opportunity cost for holding liquid assets in such a world due to the absence

of liquidity premium. The shareholder wealth for a firm will, therefore, remain unchanged if a firm

invests borrowed funds in liquid assets. Alternatively, if it becomes costly to run short of liquid

assets, firms will compare the marginal benefit of holding liquid assets to the marginal cost of

holding those assets. A firm running short of liquid assets is the situation in which it has to reduce

investments, retain dividends, raise external funds or sell its assets or securities. Consequently, the

importance of efficiently and effectively managing cash and other liquid assets by organisations

has become an important ingredient to a competitive financial environment and an imperative

research space in recent years.

Decisions regarding dividend payout, Cash Flow management, working capital and investment

plans are all very significant in corporate cash management, principally, in maintaining an optimal

level of cash (Tahir, Alifiah, Arshad, & Saleem, 2016). The optimal level of cash holding

15

behaviour of firms is grossly related to some grounding theories which are much more relevant to

cash management practices of these firms. Based on these financial theories, several studies have

been conducted across different economies around the globe. Initially, the majority of these studies

have been conducted with focus on firm cash holding trends and behaviour principally in the

United States (Chang-Soo, Mauer, & Sherman, 1998; Faulkender & Wang, 2006; Bates, Kahle, &

Stulz, 2009; Gao, Harford, & Li, 2013). Conclusions from these strand of literature are, however,

mixed relatively and will be challenging to generalise these results in other contexts due to varied

financial and economic environments. Nonetheless, other studies such as Chen and Mahajan

(2010), found some similarities in the practices of firms in cash management across developed

countries while Iskander-Datta and Jia (2012), in their comparative study of US firms with other

firms in developed countries, found differences in cash management behaviour of firms across

these countries as mainly attributing to institutional differences in these firms. Again, Kusnadi and

Wei (2011), in a study found great variations in the cash holding level of firms in developed and

developing countries. Although these findings could hardly be generalised, several theoretical

perspectives have been framed in defining firms’ cash holding behaviour and practices. These

theories include the trade-off theory, pecking order theory and free Cash Flow theory

(Wasiuzzaman, 2014).

2.3.1 Trade-off Theory

According to this theory, firms cash holding decisions are made considering the marginal cost and

benefits associated with the level of cash to hold in order to maximise shareholder’s wealth

(Dittmar, Mahrt-Smith, & Servaes, 2003). The benefits of holding cash according to the theory of

Keynes (1936), result from the motives of liquidity assets: transaction cost motive, precautionary

motive, and speculative motive.

16

2.3.1.1 Transaction Cost Motive

In relation to this theory, firms are able to avoid or save transaction costs that may arise as a result

of raising funds or liquidating assets by holding enough cash. According to Tobin (1956), Miller

and Orr (1966) and Dittmar et al. (2003) as cited in Tahir, Alifiah, Arshad, & Saleem (2016), firms’

transaction motives for holding cash are only to overcome the high opportunity cost of raising

funds in the absence of higher or enough cash levels.

2.3.1.2 Precautionary Motive

Precautionary motive for holding cash is another motivation for the amount of cash held by firms.

This motive reveals that the level of cash held by firms enables them to finance their investments

and projects using cash when other sources of financing fail or are unavailable. Additionally, firms

invest in liquid assets aside enhancing their cash level, as a means to overcome the probability of

very high cost of external financing to their activities (Ozkan & Ozkan, 2004).

2.3.1.3 Speculative Motive

Speculation as one of the motives for holding cash argues that economic agents hold cash or

marketable securities so as to make gains from future increments in an interest rate. By this,

benefits are firstly weighed against alternative costs of holding cash, as liquid assets in general,

generate comparatively very low rates of return. Efficient management of cash, however, holds a

significant potential for reducing financial distress (Ferreira & Vilela, 2004).

Despite the numerous benefits of holding cash, there exist also several drawbacks. As stated by

Jensen (1986), cash holdings of firms could result in an increase in agency cost. Also, firms with

very high cash holdings are highly restricted in their access to the capital market for financing.

This situation could empower managers to pursue their own interests rather than that of

shareholders as corporate managers can afford to stay away from the capital market monitoring.

17

In addition, the rate of return on such liquid assets is low due to liquidity premium which explains

the differences between two separate types of financial instruments that have the same qualities

but liquidity. Also, double taxation according to Chang-Soo et al. (1998) can be counted as one of

the drawbacks to cash holding at the corporate and individual level if it is distributed to

shareholders.

In order to reflect the trade-off theory in the cash holding practices of firms, extant empirical

studies incorporated different proxies as determinants of holding cash. Wasiuzzaman (2014), Uyar

and Kuzey (2014), Al-Najjar and Belghitar (2011) and Ferreira and Vilela (2004) for example, in

their empirical studies employed leverage, firm size, dividend pay-out, liquidity and risk, to

examine firm’s cash holding standpoint as related to the trade-off theory. The findings in these

studies, however, provided mixed results and which can create problems when generalisations are

made due to the unique macro environment of other economies. Afza and Adnan (2007),

Faulkender and Wang (2006) and Ozkan and Ozkan (2004), emphasised that the output of

investment and financing activities is cash. Firms that generate positive Cash Flows from their

operations, therefore, finance their investments and projects with these internal funds and remains

reliant on keeping large cash reserves on their balance sheets. Likewise, Gao et al. (2013)

conducted a comparative study of the cash policies in private and public firms in the United States

and found out that, private firms hold high Cash Flows as well as liquid assets.

Their arguments, however, are inconsistent with real trade-off prediction which predicts firms with

high Cash Flows to focus more on debt in order to minimise tax liabilities. Contrary to this, studies

such as Ozkan and Ozkan (2004), Alles, Lian, and Xu (2012), Azmat (2014), Uyar and Kuzey

(2014) and Wasiuzzaman (2014) saw significance in the role of optimal cash level as a support to

the trade-off theory. Cash holding mechanisms in relation to the trade-off theory as studied in

extant literature using mainly firm-level data, however, could be different across sectors as they

18

are influenced by various degrees of dynamics and competition within an industry (Tahir, Alifiah,

Arshad, & Saleem, 2016). This theory, therefore, needs to be validated empirically at sector levels

in the future.

2.3.2 Pecking Order Theory

Another grounding theory pertinent to Cash Flow management is the pecking order theory. This

theory was first proposed by Myers (1984) and Myers and Majluf (1984). According to them, firms

follow some kind of order in deciding the source of funds to use in financing their investments.

Based on this theory, firms firstly prefer financing their projects using internal funds. Secondly,

firms will adjust dividend levels of shareholders, even if dividends lean towards or follow some

sticky policies. Firms will thereafter decide to sell liquid assets in the absence of the earlier

mentioned sources before finally considering external capital sources as a last resort in financing

investments and projects. Following this order, firms will prefer debt to hybrid securities if

external financing is needed and finally resort to the issuance of equity (Myers, 1984). Tahir,

Alifiah, Arshad and Saleem (2016), explained this order as coming from the theory of asymmetric

information and managers by their superior knowledge on investments have an obligation to

minimise costs related to this issue.

Also, managers are agents who are supposed to act in the sole interest of the firm’s current owners

and will consequently try to issue new shares at the highest possible price. Equity investors,

however, who are conscious of this issue of information asymmetry will demand higher risk

premiums and thereby increase the cost of investment financing using equity. This logic, therefore,

is the reason behind firm’s preference for debt over equity (Myers & Majluf, 1984).

19

Thus, the level of cash holding is highly a result of the investment and financing decisions faced

by firms. The Cash Flow of firms is used to finance investment projects, repay debts when they

are due and thereafter accumulate any unused Cash Flow as cash balance if possible. Firms will,

however, resort to cash reserves as a safeguard to avoid external sources of financing when Cash

Flow is unable to cover expenditure. Hence, an additional financing is required if cash and

Operating Cash Flow are insufficient to cover all expenses. This stands to reason that, the amount

of cash holding of a firm highly depends on the cash inflow and cash outflow variations in order

to identify an optimal cash holding level. In a study to test the validity of both the trade-off and

pecking order theory and their influence on the cash holding behaviour of firms, results indicate

that both theories significantly explain the level of cash holdings of firms.

Again, Ferreira and Vilela (2004) argued that firms use cash for investment activities besides

paying debts and therefore, in return, firms hold higher liquidity. Likewise, Dittmar et al. (2003)

stressed that firms that have high Cash Flows distribute dividend smoothly and conversely, rely

on debt financing as well as holding high cash reserves.

Importantly, however, Wasiuzzaman (2014) while investigating the cash management behaviour

of firms in the context of Malaysian firms, indicated that both pecking order and static trade-off

theories have still been unable to explain the behaviour of firms fully. In contrast, Kim et al. (2011)

in their sector level study argued that firms with high growth opportunities have a tendency to hold

high levels of cash while firms with high capital and expenditures have lower cash ratio. These

results, however, contradict with pecking order theory.

In essence, firms which practice the pecking order theory of holding cash, therefore, tend to hold

more liquid cash as they prefer internal sources to external ones in financing investment projects

and hence reduces their Cash Flow risks.

20

2.3.3 Free Cash Flow Theory

Managers of corporate organisations are basically agents to shareholders who are the principal

owners of the organisation. An agent representing a principal, therefore, has the sole obligation to

serve the interest of the principal. The problem, however, at hand is that the agent might have

objectives and interests other than that of the principal and could act to accomplish these objectives

to the detriment of the principal (Tahir et al., 2016). The level of cash held by a manager can also

lead to an agency problem between shareholders and managers.

The analysis of such conflicts relating to free cash flow theory is gaining major attention in

financial literature. The free cash flow theory according to Jensen (1986), states that managers

prefer to hold high cash levels so as to enhance the size of total assets within their control. These

managers by this try to gain distinctive powers in the investment and financing decisions of their

firms which may lead to overinvestment issues (Ferreira & Vilela, 2004). Ferreira and Vilela

(2004) further argued that firms which have strong affiliations with banks and firms who practice

in superior investor protection countries tend to hold lower cash levels.

In a similar vein, Afza and Adnan (2007) described the optimal level of liquid assets as very

significant to the smooth functioning of the firm’s activities. With regards to the free cash theory,

it is argued that management of firms may hoard cash as it does not want to make payouts to

shareholders so as to hold these funds within the firm. By this, Drobetz and Grüninger (2007)

supported this argument and revealed that dividend payment to shareholders are related to cash

reserves as management may accumulate cash by reducing dividend or does not make payouts at

all to shareholders, in order to keep funds within the firm.

21

Based on both empirical and theoretical studies relating to cash holding practices of firms, it is

clear that trade-off, pecking order and free cash flow theories though not mutually exclusive,

predict uncertain roles of diverse financial environments in cash holding behaviour of firms.

However, no study in available extant literature gives preference to any single theory for

determining cash holding behaviour at both firm and industry level. This, therefore, warrants an

empirical study to analyse both theory and financial factors that best describe the cash holding

mechanism at the firm and industry levels.

2.4 The importance of Future Cash Flow Prediction.

The availability of an optimum level of Cash to a firm is “king”. This is because a firm can be

operating at a profit but suddenly go insolvency if it runs out of cash in the course of business and

cannot obtain cash (Keown, Martin, Petty & Scott, 2005; Born et al., 2014). Both financial and

non-financial organisations are constantly faced with the risk of variations in their flow of cash.

The receipts coming into an organisation must exceed their cash payments in the long run so as to

ensure the solvency of that organisation. For firms, the timing of cash receipts and when it can be

invested as well as when cash can be paid back to shareholders as a dividend is of great concern

(Keown et al., 2005).

Financial information users are, therefore, primarily concerned with the ability of firms to generate

Cash Flows that are favourable to future financial obligations. Financial decisions of firms are,

however, linked to the timing, volume and uncertainties in future Cash Flows (FASB, 1978).

According to Elahi (2013), the current high level of volatilities in the world of business will grow

worse in the years to come. It, therefore, becomes imperative for analysts and business players

especially younger firms which most especially face uncertainties in their growth prospects to

obtain prior knowledge on the trend of their future Cash Flows. The uncertainties in the growth of

22

young firms are exhibited through the passage of time with the realisation of future Cash Flow and

make available evidence about their financial health (Alti, 2003).

Accounting information reported by firms is increasingly gaining usefulness to their users in

forecasting future Cash Flows as a foundation for making sound financial decisions (Obinata,

2002). In the United States, the call for Cash Flow prediction by investors in the capital market

has been on the rise especially for firms with unstable earnings, large accruals, varied accounting

practices choices and high capital intensiveness (DeFond & Hung, 2003). Financial analysts, as

part of their main activities, make use of financial information in order to forecast the performance

of a firm as related to earnings, Cash Flows beside share prices (Ramnath, Rock, & Shane, 2008).

Previous studies have, therefore, validated the use of accounting information as a crucial source of

data for executing the decision-making process by analysts (Chang, Khana, & Palepu, 2000; Alti,

2003). Forecasting future Cash Flows with data obtained from reported accounting information,

therefore, aids in the appraisal of investments from the long-term and short-term perspectives,

valuation of securities, valuing credit and facilitating the process of decision making in an

organisation with the ultimate goal of ensuring the profitability and solvency of a firm.

2.5 Review on Determinants of Cash Holdings of Firms

The factors influencing the inflow and outflow of cash within a firm to a large extent determines

the level of cash held by that firm. There are various motives based on theory that determine the

amount of cash held by firms at any point in time. These motives include for example the

precautionary and the transactionary motives considered by Keynes (1936) as the main reasons

defining the level of cash in a firm. Bates et al. (2009), Foley et al. (2007) also identified the tax

motive while the agency motive was also pointed in Jensen (1986). With all of this said, however,

the ultimate goal in cash management whatever the motive, should be to have just enough cash but

not a cedi more. There exist an increasing number of research conducted with a focus on analysing

23

the cash management behaviour and define the determinants of cash holdings in relation to the

idea of a target cash level of firms, which stays in line with the capital structure analysis (Jani et

al., 2004). In Drobetz, Grüninger, and Hirschvogl (2010), the assumption of frictionless capital

markets makes cash holdings irrelevant, however, the trend of corporate cash holdings demands

the relaxation of this assumption. The trade-off theory, pecking order theory, and the Free Cash

Flow hypotheses become prominent in the debate on corporate cash holdings if information

asymmetry, transaction costs and agency costs are considered. These theories play the best part in

determining the influence of various firm and board characteristics on the level of cash a firm

holds.

2.5.1 Firm Characteristics

As cited in Wasiuzzaman (2014), Opler, Pinkowiz, Stulz, & Williamson (1999) in their cash model

identified some firm characteristics which to a large extent determine the normal level of cash

held. The ensuing section discusses some variables influencing the level of cash held by firms with

their expected relationship with cash holdings of firms.

Size: The size of a firm plays significant roles in determining their financial performance. Due to

the existence of economies of scale in cash management, smaller firms have the greater

possibilities of being financially distressed aside very high and expensive external sources of

financing (Ferreira & Vilela, 2004). This is primarily due to the higher information asymmetry

(Ozkan & Ozkan, 2004) these smaller firms suffer. According to prior studies, larger firms, in

reality, have better credit ratings and bank credit lines which together help them assess funds when

the need be. Again, the ability of these firms to raise large amounts of capital places them at an

advantage to exploit economies of scale (D'Mello, Krishnaswami, & Larkin, 2008).

24

Consequently, the trade-off theory foretells an inverse relationship between the size of a firm and

the level of cash holdings of that firm. In line with this argument, the negative relationship

predicted by the trade-off theory points strongly to the transaction motive of holding cash (Bates

et al., 2009). In a different argument, (Opler et al., 1999; Ferreira & Vilela, 2004; Jani et al., 2004)

asserts that large firms are doubtlessly more successful than their counterpart small ones and hence

have higher levels of cash after they control for investments. By their argument, there exists a

positive relationship between firm size and the level of cash holding.

Thus, judging from the perking order theory perspective, large firms tend to have higher cash

levels due to their preference for internal funds as the first priority in financing their investments.

In a similar argument, Ferreira and Vilela (2004) using the agency theory predicts that the

relationship between firm size and the amount of cash holding remains positive due to the fact that

large firms possess larger shareholder dispersion and therefore a poorer chance of takeover

attempts. This condition hence offers managers the discretionary authority over the firm’s financial

and investment policies and decisions, giving them the edge to hold large amounts of cash and

other liquid assets.

According to the predictions of these three theories (trade-off, pecking order and agency theories)

therefore, the effect of the size of a firm on the level of cash holding can either be negative or

positive depending on the theory applied.

Capital expenditure: Firms with high capital expenditure according to Oppler et al. (1999) tend to

have increased levels of liquid assets. By this reasoning, they explained stating that, by applying

the static trade-off theory, the positive relationship expected between the level of capital

expenditure and cash holding will arise since firms will require more cash or liquid assets in order

to pursue its capital requirement needs. Bates et al., (2009) in a similar argument predicted that

25

capital expenditure of firms can be used as a good proxy for firms’ financial distress costs and/or

investment opportunities hence leading to a direct relationship.

Jani et al. (2004 highlighted that firms that have high capital expenditure levels will as a way of

avoiding extra transaction costs associated with raising external capital coupled with the

opportunity costs that come with holding an insufficient level of resources tend to hold more cash.

In a dissimilar argument, however, Opler et al. (1999) point out that, relating this relationship with

the pecking order theory, firms that have higher capital expenditure will expend available cash or

liquid assets for this purpose and thereby have little internal resources. These firms will hence

accumulate less cash leading to an expected negative relationship.

Bates et al. (2009), further explained that if a firm uses capital expenditure as a means of creating

assets that can be later used as collateral, then this enables managers to take up more debt. This

increase in their debt capacity will lead to a reduction in their demand for cash and/or liquid assets.

Likewise, they argued that when there arises an unexpected need to invest in operations due to a

positive shock in productivity, a temporary increment in capital investments will lower the

capacity of firms to accumulate cash.

By these arguments, the expected relationship between the level capital expenditure and the

amount of cash holding may either be negative or positive.

Leverage. The leverage of a firm possibly will lead a firm into financial distress or into bankruptcy

(Wasiuzzaman, 2014). Holding high volumes of debt or liabilities as against the assets available

to that firm increases the probability of bankruptcy. As stated by Deloof (2003), a firm that is

highly leveraged will need as a precautionary tool to hold more cash so as to reduce the possibility

of running into financial distress. Here again, using the trade-off theory, it is expected that, firms

that are highly leveraged will have high cash holdings. To this end, the relationship between the

26

level of cash holding and leverage is thus positive. However, taking leverage of firms as a proxy

for their ability to issue debt, the existing relationship then tends to be a negative one since firms

with high leverage, hold a lesser amount of cash (Ferreira & Vilela, 2004; D’Mello et al., 2008).

Analysing this relationship from the viewpoint of the pecking order theory, Jani et al. (2004)

explained that, since the issue of debt by firms is done only after they finish using up all retained

earnings for investment and repaid in the situation of an excess of retained earnings, the cash

holding of a firm falls when the level of investment surpasses retained earnings while the level of

cash holding actually increases when the level of investment goes below retained earnings. In view

of the agency theory, however, managers of firms prefer to hold an increased amount of cash when

the leverage of a firm is less (Ferreira & Vilela, 2004).

Leverage, as predicted by theory thus suggests that its relationship with cash holding is uncertain.

By this, the predictions of the three theories propose an association which can either be negative

or positive.

Cash Flow: According to Ferreira and Vilela (2004), having a Cash Flow in a firm is equivalent

to having a ready and an alternative source of available liquid assets which reduces the necessity

to hold very high levels of cash. Using the trade-off theory, Ferreira and Vilela (2004) predicted

an inverse relationship between the Cash Flow of a firm and the amount of cash holding of that

firm. Thus a firm with high Cash Flow all things being equal will hold very low cash level judging

from the predictions of a trade-off theory. On the other hand, the relationship between Cash Flow

and the amount of cash held by a firm using the perking order theory indicates a positive

relationship. By this argument, firms with high Cash Flow will thus hold most of this Cash Flow

as cash. Deloof (2003) iterates that, if firms finance new investments using liquid reserves such

as cash and marketable securities, then since they will keep Cash Flows in the form of liquid

reserves to be freed up easily when needed, higher Cash Flow will, therefore, mean higher liquid

27

reserves. Garcia-Teruel and Martinez-Solano (2008), refers to this phenomenon as the financing

motive to holding cash. Deloof (2003), complements this saying, maintaining higher Cash Flows

can also be interpreted as a precautionary move towards shortfalls in future Cash Flow in the course

of operations.

From the ensuing review and analysis, since the trade-off theory forecasts an inverse relationship

between Cash Flow and the pecking order theory predicts a direct one, the expected relationship

or impact of Cash Flow on the level of cash holdings, therefore, remains uncertain.

Cash-flow volatility: Volatilities in Cash Flows implies the possibility of shortages in possible

future cash of firms. Firms with high uncertainty in their level of Cash Flow tend to keep higher

levels of cash in an anticipation of future Cash Flow shortages (D’Mello et al., 2008; Bates et al.,

2009). Thus, by the trade-off theory, it is expected that a direct relationship must exist between

Cash Flow volatility and the level of cash holdings.

The shortage in the Cash Flow for a firm can have long-term consequences on the firm’s

investment opportunities. The motive for the amount of cash holding due to this possibility will,

therefore, be highly related to the precautionary motive for holding cash. There exists evidence

supporting the fact that firms that experience shortfalls in their Cash Flow do sacrifice valuable

growth opportunities. Ozkan and Ozkan (2004) argued that firms which have ever experienced

shortages in their Cash Flow will be prepared to rather let go of the investment opportunity open

to them than react to cash-flow shortages by changing the discretionary timing of that investment.

Also, if a firm continues to experience shortfalls in its Cash Flow due to their high Cash Flow

volatility, the cost of obtaining external funds tends to be higher. They hence tend to keep more

cash reserves with the intention of relaxing their reliance on these costly external debts and equity

in financing. From the perspective of the trade-off theory, there exists a positive relationship

28

between the level of cash holding and the volatility in Cash Flow. According to Opler et.al. (1999)

and Jani et al. (2004), the relationship existing between the volatility in Cash Flow and cash

holding is however unclear. Further, according to D’Mello et al. (2008), the volatility in Cash Flow

for a firm indicates to a large extent the expected cost of financial distress. Similarly, Garcia-Teruel

and Martinez-Solano (2008) added that while financial distress may increase the level of cash

holding of firms as a way of reducing their default risk, these firms or those most likely to be

financially distressed may very well have less liquidity. Thus, to be financially distressed as a firm

means to be unable to meet payment commitments and hence it is logical to conclude that

financially distressed firms would be without a capacity to accrue cash. In line with these

arguments, therefore, the relationship between the level of cash holding and Cash Flow volatility

of a firm remains indeterminate.

Investment and Investment Opportunities: The volume of Cash Flow generated as a result of both

short term and long term activities in investment impacts the level of cash for firms. The

cumulative change in the cash position through investments of a firm primarily manifests as a

result of the gains and/or losses accruing from the financial markets. Further, subsidiary changes

in cash as a result of investments in such capital assets like plant and equipment and investments

in research and developments influence the level of cash holding of firms. Particularly the

discussion about research and development investments relates well to that for capital

expenditures. According to D’Mello et al. (2008), investments in research and development can

be used as a proxy for the measurement of growth opportunities or the cost of financial distress.

Opler et al. (1999) in examining the determinants and implications of corporate cash holdings

contended that the existence of information asymmetry is of most importance in determining the

impact of research and development on corporate cash holding behaviour. Thus, in situations

where asymmetric information is deemed crucial, a shortfall in Cash Flow may result in a reduction

29

of investment which will lead to an increased cost of financial distress. It is for this reason expected

that firms with a higher cost of financial distress as a result of high expenses on research and

development will hold increased levels of cash. This stance as argued from the static trade-off

theory point of view stands to reason that there exists a positive relationship between investment

and the level of cash holdings (Bates et al., 2009). Alternatively, the pecking order theory

hypothesises a negative relationship between investment and cash holdings as firms tend to

accumulate less cash when they spend more on investments (Opler et al., 1999).

Similarly, the presence of opportunities in investment impacts the level of target cash holding of

firms. According to Alles, Lian, and Xu (2012), investment opportunities for firms relates to the

level of target cash held positively as a result of different reasons. According to them, the

opportunity cost of a shortage in the level of cash is high when a firm has investment opportunities.

This is because these firms tend to forego lucrative ventures if they lack immediate cash in order

to respond speedily to such opportunities due to market competition. Delays in ceasing such

opportunities could imply a perpetual foregoing of these opportunities (Baskin, 1987).

Also, holding enough cash readily available could reduce drastically transaction costs connected

with lengthy processes involved in accessing external capital market for funds when opportunities

for investment are eminent (Alles et al., 2012).

2.6 Determinants of Cash Flow in the Insurance Industry

When investigating the determinants of Cash Flow for a firm, it is essential to consider all the

various activities that contribute to the overall cash position of that firm. For the insurance industry,

the generation of Cash Flows can be realised through activities in underwriting, financing and

investment choices, capital management and even through risks management (Born et al., 2014).

30

This assumes that Cash Flow generated within the industry dynamically interrelates with these

activities and consequently any modelling of Cash Flow essential to forecasting and managing

future Cash Flows must recognise this dynamic process by which financial, investment and

underwriting risks are generated. In their empirical study of Cash Flow risk management, Born et

al. (2014), modelled Cash Flow using a Dynamic Factor Model which recognises the dynamic

process of cash generation is insurance firms. According to them, the net Cash Flow of an

insurance firm goes through a generation process which incorporates activities in financing,

investment and underwriting as well as risk management. This approach is particularly unique as

it combines underwriting risks which are peculiar to insurance firms with financial and investment

activities which are common to both financial and non-financial institutions while incorporating

macroeconomic variables as well.

Unlike other previous studies related to forecasting cash and Cash Flow management (Stock &

Watson, 2006; Forni, Hallin, Lippi, & Reichlin, 2005; Jang, Park, & Lee, 2011; Geweke, 1977),

the study of Born et al. (2014), applied a novel DFM (FAAR) approach and focuses on analysing

the health of insurance firms using various Cash Flow ratios in a dynamic interaction to forecast

Cash Flows of insurance firms while incorporating macroeconomic variables. The findings in this

study present a novel forecasting model for each insurance firm depending on its size within the

industry.

2.7 Parent Disciplines: Accrual and Cash Accounting

In an effort to set a foundation for this study, literature is reviewed on two broad disciplines in

order to present a bigger picture and establish gaps in previous research. Literature, as viewed from

a broader perspective relating to Cash and Accrual Accounting, is thus reviewed and later

31

narrowed to an immediate discipline. This section presents an evaluation of two parent disciplines

considered as the primary basis for the preparation of financial statements.

2.7.1 Accrual Accounting

According to the International Accounting Standard Board (IASB) (2001), the primary objective

of financial statements is to make available information regarding the financial position,

performance and the variations in these measurements over time in order to enable users of this

information to make sound economic decisions. Accrual accounting is one of the generally

accepted conventions in accounting recommended by the International Accounting Standard

Board (IASB, 2001) for the preparation of financial statements.

The accrual accounting convention to reporting financial information identifies and reports

accordingly revenues and expenses for accounting periods for which they occur irrespective of

when cash is received for the revenue or when cash is paid for the corresponding expense incurred

(Keown et al., 2005; Elliott & Elliott, 2007). Thus, transactions under this convention of financial

reporting are divided into periods in which the economic activity occurred but not automatically

the same period in which Cash Flows of these transactions are related. Financial information

reported using accrual accounting consequently provides superior information not only on receipt

of cash and payment of expenses but also on the continuing ability of a firm to generate future

Cash Flows. Financial information reported under this basis, therefore, furnishes users with

historical transactional information involving cash the receipt and payment of cash as well as

information on future financial obligations of cash payments and cash benefits (IASB, 2001; Elliott

& Elliott, 2007). The derivation of net incomes or earnings, assets and liabilities and accounting

for historical costs under the accrual accounting convention, however, rely on certain principles

32

such as revenue recognition, matching principle in addition to historical cost conventions and the

going concern principles which are beyond the scope of this study.

2.7.1.1 The subjectivity of accrual-based accounting information to cash-based ones

In accrual-based accounting reports, information such as earnings has the potential of being higher

subjective relative to Cash Flow measurements. Estimations of future Cash Flow and the valuation

and allocation of deferred past Cash Flows that are characterised with the accrual-based financial

accounting are often subject to the discretion and estimation of management (Dechow, Richardson,

& Sloan, 2008). Estimations made under the accrual-based convention may, however, encompass

several and complicated assumptions concerning future events and these make critics of this

convention of accounting information reporting argue the inability of inexperienced users to

comprehend fully information presented therein (Athukorala & Reid, 2003). Contrary to this

approach, the information presented using cash accounting is more objective and simple in nature

as it presents information on only payments and receipts of cash as they occur.

Other arguments against the use of accrual-based accounting also make clear the possibilities of

income manipulations or earnings management by firms in the course of allocating periodic Cash

Flows. According to Graham, Harvey, & Rajgopal (2005), earnings management refers to the

intentional structuring of accounting practices or transactions in an attempt to manipulate the

reporting of financial information to deceive stakeholders on their perception of the real economic

performance of a firm. Prior empirical literature examining a large cross-section of companies

shown that firms overstate the value of their earnings before equity offerings (Erickson & Wang,

1999; Rangan, 1998; Teoh, Welch, & Wong, 1998). These issues and many others not stated here

relating to the subjectivity of the accrual-based accounting reporting notwithstanding does not

provide necessarily an absolute superiority of the cash-based accounting over the accrual-based

33

one. For instance, cash information about the dates of payment and receipt of cash reported in

cash-based accounting can also be manipulated by choosing favourable policies in order to classify

current items as capital one and vice versa (Diamond, 2002).

2.7.2 Cash accounting

The principle governing cash-based accounting reporting identifies only cash transactions

involving, for example, the cash payments for expenses incurred and the receipt of cash as income

(Birt, Chalmers, Beal, Brooks, Byrne & Oliver, 2008). With this convention, earnings are recorded

as pertaining to a given accounting period only when cash is received and likewise expenses

documented with the payment of cash. Cash-based accounting convention to financial reporting

as noted by Elliot and Elliot (2007), stands to be more prudent as compared to accrual-based

accounting as it only accounts for Cash Flow when they are realised devoid of any form of

anticipation of the occurrence of Cash Flows. In view of the nature of this method, very few

assumptions are required in cash accounting reporting as cash financial transactions reported can

be checked and verified with much ease as opposed to accrual accounting. As a result, the

requirements for the disclosure of accounting policies such as the methods for calculating

depreciation are fewer with this method (Elliott & Elliott, 2007). Most information captured in

accrual-based financial reporting often becomes difficult to comprehend by policymakers,

financial decision makers, credit rating agencies, the media and most especially the general public

(Athukorala & Reid 2003). Conversely, cash-based reported financial information is relatively

more simply comprehensible especially by unsophisticated users of this information.

2.7.2.1 Objectivity of Cash Flow information to the accrual-based information

Arguably, cash accounting convention to financial report reporting is more objective than accrual-

based accounting since it lends itself to less subjectivity seen in through management judgments

34

in accrual accounting in the determination of values reported in financial statements (Athukorala

& Reid 2003; Dechow et al., 2008). The value of the net income that is reported in the financial

statement of firms under the accrual-based accounting lends itself to high subjectivity as the

assumptions and subsequent estimations may be greatly erroneous owing to biasedness of

management (Elliot & Elliot, 2007). This assertion is further strengthened through the prudence

concept which biasedly recommends under the accrual-based accounting that, in the presence of

uncertainty, firms must avoid overstating assets and gains along with avoiding the understatement

of liabilities and losses (Elliott & Elliott, 2007). Financial information reported using cash

accounting thus to a large extent based on facts about the performance of a firm without the

influence of management preferences. The Financial Accounting Standards Board (FASB) posits

that detailed disclosure of Cash Flow data using cash accounting will enable users of this

information to better assess the amount, timing and uncertainties of future cash-flows (Casey &

Bartczak, 1985). Users of financial information reported using cash accounting are thus placed in

a better position to evaluate the ability of firms to generate future Cash Flows in order to meet

future obligations such as dividend payments and payment for genuine claims in the case of

insurance firms (Elliott & Elliott, 2007). Also, the objectivity present in reported Cash Flow

information assists in the comparison of firms’ performance and overcome differences in

accounting treatments that may arise in accrual accounting (Chotkunakitti, 2005).

Information on the current and historic Cash Flow of firms is very crucial and forms the foundation

for future forecasting of Cash Flows. This informs users to assess the accuracy of past Cash Flow

forecasts in validating the applicability of past Cash Flows in significantly predicting future Cash

Flow and also thoroughly assess the relationship between changes in the net Cash Flow of firms

and their profitability as established in previous studies (Barth, Cram, & Nelson, 2001; Bowen,

Burgstahler, & Daley, 1986; Waldron & Jordan, 2010). Table 2.1 summarises some differences

35

between cash and accrual accounting conventions to accounting reporting as developed in extant

literature

Table 2.3: Differences between Accrual and Cash Accounting Principles

No. Accrual Accounting Cash Accounting

1 All financial transactions relating to a

financial period are reported irrespective of

when that cash is received.

Only realised cash transactions within

a particular financial period are

reported.

2 Difficult to prepare as it requires subjective

judgement of the firm.

Easier to prepare and are more

objective and factual.

3 Revenue is documented and matched with

corresponding expenses, irrespective of Cash

Flow timing.

Revenue is documented only when

cash received and expenses

documented when cash is paid.

4 Better measurement in terms of performance

as a result of the smoothening of reported

earnings when linking revenue and expenses.

Mismatch of receipts and payments

may arise due to an overlap of cash

transactions over periods, causing

variations in earnings and cause

further distortions in performance

measurement.

5 More comprehensive as it includes cash aside

other relevant data, as assets and liabilities

which is useful in resource management.

Less comprehensive as it only reports

on cash transactions.

6 Recommended as a primary basis for

accounting standards for the preparation of

financial statements.

Recognised in accounting standards

as a document providing only

supplementary information.

7 Complex and governed by numerous policies.

Simpler and governed by fewer

policies.

8 Information on asset utilisation provided. No information provided.

9 Relevant but less reliable Reliable but less relevant.

Source: Extracted from extant literature (See for example Chotkunakitti, 2005; Chong, 2012)

36

2.8 Empirical Literature Review on Cash Flow Risk of Firms

The availability of cash in any business, small or large is a vital and a continued crucial requirement

for the survival of that business (Ryu & Jang, 2004). The importance of cash-flow as a crucial need

for many businesses in a variety of industries have been discussed in extant literature over the

decades (Casey & Bartczak, 1985; Almeida et al., 2004; Alti, 2003; Born et al, 2014). According

to Beck, (1994), a firm must close if it runs out of cash even though it might be operating at a

profit. This is because bills are paid not against income but cash, and this is critical every day and

distinguishes between successful operations and closure. Users of financial statements in addition

to regulators of publicly reported financial accounting data, therefore, need on regular basis

detailed information on the current operating Cash Flow of firms. This reason necessitated a call

by FASB in November 1987, for all financial statements to include a statement of Cash Flows in

addition to the traditional income statement and balance sheet (Ryu & Jang, 2004) and this further

heightened the topic of Cash Flow hence Cash Flow ratios analysis as they provide additional

information and understanding in measuring the real operational position of a business.

Analysts in business have identified poor Cash Flow management as the foremost reason for

failure in businesses (Caux, 2005). Effective management of Cash Flow is, therefore, one key

aspect of financial management in planning future cash requirements of businesses in today’s

world of increasing economic and financial crisis. The primary motive for Cash Flow forecast,

therefore, is to identify in advance potential shortfalls in the balances as it serves as an “early

warning system” (Bodunrin, 2016).

Just as the banking industry, insurance plays a crucial role in modern financial systems. In order

to perform their role effectively, insurance firms must be financially safe and be perceived as such.

One of the most important guarantees of this is the economic value of assets to be significantly

more than liabilities. Due to the recent financial crisis, however, there is the need for a second type

37

of cushion, the “liquidity” to cover unexpected cash outflows. This is because, a firm can be

solvent, holding economic assets exceeding liabilities on an accrual accounting basis, but still die

suddenly if its customers and other stakeholders lose confidence in that firm (Elliott, 2014).

Various estimating methods for assessing a firm’s financial health and the likelihood of its

financial distress have evolved over the decades. Jang, Park, and Lee (2011), used the comparable

approach to Cash-Flow-at-Risk (CFaR) to analyse Cash Flow data from publicly listed U.S.

restaurant firms from 1988 to 2007. In Ryu and Jang (2004), the performance of commercial and

casino hotel companies were measured using Cash Flow ratios and traditional financial ratios from

income statements and balance sheets. According to this study, traditional ratio analysis often fails

to make known severe liquidity difficulties that may result in the insolvency of firms. Results

indicate that traditional ratios produce different outcomes from Cash Flow ratios in liquidity.

Similar studies on firm performance stressed the importance of Cash Flow in predicting the

likelihood of insolvency (Almeida et al., 2004; Alti, 2003; Casey & Bartczak, 1985). According

to Guth & Sepetys (2001), the risks to a firm are evident in shocks to expected Cash Flow which

in turn spells the survivorship of the firm depending on the adequacy of cash available. Despite

the fact that Cash Flow risks pose a substantial danger to most companies, the detection and

management of such risks are not a common practice in most firms especially in the insurance

industry of Ghana. This current study, therefore, applies the novel FAAR (DFM) modelling

employed by Born et al. (2014) to forecast Cash Flow of Ghanaian insurance firms. This study is,

however, different from existing literature as it goes beyond forecasting future Cash Flow to

further determining insurance activities and factors that significantly impact Cash Flow. The study

also investigates the impact of management practices of insurers in underwriting, financing and

investment, risk management and capital management on Cash Flow. Thus, to the best of the

writer’s knowledge although there are numerous extant literature on Cash Flow risks, these studies

38

use various models to forecast future Cash Flow and measure future Cash Flow risks without

necessarily studying the relationship between factors impacting Cash Flow and Cash Flow levels.

This current work, therefore, goes beyond what has been documented to further explore the

relationships between identified individual factors and insurers’ Cash Flow as well as the

relationship between some management practices of insurers and their Cash Flow.

2.9 Chapter Conclusion

Included in this chapter is a demonstration of the significance of future Cash Flows prediction as

well as a review of literature pertaining to some determinants of Cash Flow and cash holding

practices of firms. The chapter also reviewed literature on some grounding theories regarding cash

holding practices of corporate entities and highlighted two parent disciplines specifically accrual

and cash accounting principles regarding the reporting of financial accounting information.

Issues in prior research were also identified and the significance of this current research was

established based on current gaps identified. The ensuing chapter develops the research design and

methodology to test hypotheses and provide answers to the research questions posed.

39

CHAPTER THREE

METHODOLOGY

3.1 Introduction

In this chapter, the various tools and estimation procedures applied in achieving the set aims and

objectives are clearly identified and explained. It also outlines the scope of the study and the

various sources of data required for analysis and accomplishment of the research objectives. This

chapter further specifies and justifies the econometric model adopted and clearly explains the

variables used in the models employed. Thus, the chapter lays strong emphasis on the data sources

and their scope, the conceptual and functional economic models employed as well as the estimation

procedures employed in the choice of methodology and finally the conclusion.

3.2 Research approach

The two generally main ways of carrying a research study involve the deductive and inductive

approaches. According to Sauders, Lewis, and Thornhill (2009), the deductive approach, allows a

researcher to develop a theory and hypothesis (hypotheses) and design a research strategy or

strategies to test those hypotheses. In their view, deductive approach to research remains dominant

in the natural sciences as a basis for explaining and proving laws and permit the anticipation of

phenomena as they predict their occurrences and allow for their control.

Inductive approach, on the other hand, starts with the researcher collecting data and developing a

theory as a result of the data analysis. This approach supports the study of human behaviours based

on context. This approach thus allows a researcher to decipher human understandings and

behaviours within their social environment (Ghauri & Grønhaug, 2005). This study, however,

benefits from the rich qualities of a deductive approach to research as it seeks to test hypotheses

around the determinants and factors influencing the Cash Flow levels of non-life insurance

40

companies in the insurance industry of Ghana and finding a suitable model to project and forecast

the net Cash Flow level of an insurance firm for a one-time future period using relevant data.

3.3 Research Design

This section outlines the plan that guides the researcher in the organisation of the research activity.

The methodology regarding what, where and how to gather relevant data and analyse it so as to

achieve the preconceived purpose of the research is outlined here. It is the detailed plan aimed at

guiding the researcher in the step by step organisation of the research activity of investigating a

given phenomenon. Thus the identification of a suitable methodology about what, where, when

and how to gather data and analyse it are defined herein (Easterby, Thorpe, & Lowe, 1991).

Bryman and Bell (2003), indicated that data collection methods and design of a research should

be aligned with the paradigm adopted such that they are consistent with each other. According to

them, there are three main classifications of business research which are exploratory, descriptive

and explanatory research. This current research employs the rich qualities of an explanatory

research by extending findings of extant literature in investigating determinants of Cash Flow in

the cash holding practices of insurance firms as well as predicting future Cash Flows of non-life

insurance firms in the Ghanaian insurance industry using accounting information.

Neuman (2006), explained explanatory research to be a type of research that builds on existing

research in explaining an existing phenomenon in order to validate or reject predictions or

explanations given or test an existing theory or extend it. This explanation fits into this study as it

extends previous research by examining the predictability of future Cash Flows using past financial

data. This approach falls within the positivist paradigm as it is most suitable for examining cause

and effect relationships predicted by theory to describe an observed phenomenon (Neuman, 2006),

using a quantitative approach.

41

The study used pooled ordinary least squares (OLS) method to develop multiple regression models

to identify determinants of Cash Flow in the insurance industry. Further, a novel DFM (FAAR)

Model as used in Born et al. (2014), is employed to empirically forecast future Cash Flows in order

identify and manage possible Cash Flow risks.

Statistical techniques were applied to sampled data in order to test hypotheses and analyse findings.

Conclusions and inferences were drawn with reference to relevant literature. The overview of the

design is presented as in Figure 3.1.

Source: Developed for this study

Figure 3.1: Research Design Overview

3.3.1 Research Methodology: Quantitative versus Qualitative.

The two main research methodologies guiding the conduct of every research are the qualitative

and the quantitative approaches to research (Bryman & Bell, 2003; Neuman, 2006). This section

42

contrasts and compares the qualities of both orientations and selects the most appropriate

methodology suitable to achieve the set objectives in this study.

Qualitative research as explained by Neuman (2006), relies on interpretive or critical social science

in describing various influencing factors of a social phenomenon as well as complexities associated

in an iterative and a nonlinear research path within a specific historical context.

This research path largely emphasises inductive processes between existing theory and research in

an attempt to build further theories (Bryman & Bell, 2003; McMurray, 2008). According to

McMurray (2008), qualitative research stresses on descriptive words and their implicative images

in the collection and analyses of data instead of numerical information. These type of research

recognises individual perspectives regarding a research matter, expresses intimate participation of

the researcher as well as an interpretation of the theme within the particular context of study

(Neuman, 2006). Nonetheless, the values, beliefs, preconceptions or experience of these

researchers have to a large extent the potential of impacting the credibility or reliability of these

studies.

Alternatively, qualitative research concerns with measuring variables with great precision and by

employing a deductive approach, test hypotheses in a linear path to explain a phenomenon

(Neuman, 2006). By this, quantitative studies seek to lessen the human factor present in qualitative

studies by emphasising the need for objectivity in the conduct of a research. The methods selected

in a quantitative study bases on standardised data sets and numerical analyses highly statistically

based (Bryman & Bell, 2003; McMurray, 2008). Thus, quantitative research supports minimal

researcher involvement as much as possible in order to give room for replicability and

generalizability in different settings aside the confines within which the study was piloted. These

studies are cross-sectional, longitudinal, correlation and survey studies (McMurray, 2008).

43

Quantitative studies by their approach seek to offer fresh intuitions to existing theories or

extending to new settings as they formulate and test relationships using standardised numerical

evidence assigned to variables identified (Neuman, 2006).

In this study, the researcher makes use of quantitative methodology to research as it is most suited

to and aligned with explanatory research to examine as well as provide empirical evidence to the

extent to which historic Cash Flow data combined with latent variables related to insurance

activities help forecast future Cash Flow of insurers.

3.4 Conceptual Framework for Prediction Models Development

This section describes the conceptual framework aligned with a quantitative methodology to

develop predictive models. According to Bryman and Bell (2003), the concepts around a research

are the ideas which are the very foundations of theory. The deductive approach to research used

by quantitative researchers start with concepts and thereafter formulate measurement procedures

to capture the concepts in a quantitative form to link the abstract constructs with numerical

evidence about empirical reality (Neuman, 2006). After getting these concepts operationalized,

they can be then classified as either dependent variables which respond to changes in other

variables and for which the researcher needs explanations to or independent variables or predictor

variables which explain variations in the dependent variables or the phenomenon that needs

investigation (Bryman & Bell, 2003; McMurray, 2008; Saunders et al., 2003)

Extant literature used numerous statistical approaches in developing predictive models for future

Cash Flows of firms. Many of such studies made use of the least square method to develop simple

or multiple regression models to predict Cash Flows. This method strives to fit observed data to a

model that predicts them by reducing to the barest minimum the sum of squared errors between

the actual observed data and the predicted data (Field, 2009).

44

Previous studies examined the relative predictability of earnings and operating Cash Flows in

forecasting future Cash Flows. These studies employed regressing analysis by using current

operating Cash Flows as proxy for future ones by considering period-lagged earnings or Cash

Flows or a combination of period-lagged Cash Flows with accruals (Barth et al., 2001;

Chotkunakitti, 2005; Kim & Kross, 2005; Arthur & Chuang, 2006; Farshadfar et al., 2008). Some

previous studies also assumed random walk models where Cash Flows in the current period is

assumed to persist into both the current and future periods (Barth et al., 2001; Yoder, 2006).

In line with these ideas (Barth et al., 2001; Arthur & Chuang, 2006; Cheng & Hollie, 2008;

Chotkunakitti, 2005), this study employs ordinary least squares methods to develop multiple

regression models to investigate and statistically test hypotheses around the determinants of Cash

Flow in the insurance industry using past values of Cash Flow. In 2006, Manning and Munro

pointed out that, ordinary least square method is most suitable when the main objective of a

research is to investigate causal relationships between variables having a dichotomous nominal

scale, ratio or interval scales and also satisfies normality assumptions.

Additionally, this study also adopts the dynamic factor modelling approach applied by Born et al.

(2014) by augmenting all latent factors related to insurers’ activities in underwriting, financing

(investing), risk management and capital management in the presence of macroeconomic variables

to model the dynamic interactions of all these activities to forecast future Cash Flows of firms in

the insurance industry of Ghana.

It is important to add that, although the models developed in this study builds strongly on the

approach used by Born et al. (2014) and Wen, Lin, Born, Yang, and Wang (2018), this study also

goes beyond forecasting future Cash Flow to also study the relationships between identified

variables related to insurers’ activities in underwriting, financing (investing), risk management and

capital management and Cash Flow. Results presented, therefore, shows relationships between key

45

variables related to activities in insurance and their effect on Cash Flow levels aside augmenting

these variables in a dynamic structure to predict future Cash Flow as applied in Born et al. (2014)

3.5 Research population

The population for this study focusses on the insurance sector of the financial services industry in

Ghana. Specifically, in a quest to manage Cash Flow risks, secondary quarterly data from financial

statements of non-life insurance companies in Ghana was extracted. By this, Cash Flow data

relating to underwriting, financing or investment, risk management and capital management in

non-life insurance companies of Ghana were considered. As earlier stated, data was hence obtained

taking into consideration all the 25 non-life insurance companies in Ghana (NIC, 2015).

3.6 Sample and Sampling Technique

In order to apply pooled ordinary least square and dynamic factor models for a comprehensive and

explicit examination of Cash Flow management by insurers, time series financial data of non-life

insurance business pertaining to firm-level variables such as capital, liabilities and assets and other

variables relating to activities in underwriting, financing and investing, risk management etc. were

collected and analysed empirically. Information relating to insurers’ assets, liabilities, capital

(equity), Cash Flows, investments, income statements etc., from the first quarter of the year 2007

to the fourth quarter of the year 2015 was extracted into Microsoft Excel worksheets for feasible

time series modelling with sufficient observations over periods.

The study applied purposive sampling technique to obtain feasible data of insurance firms. Thus

in order to gather sufficiently relevant data for all firms within the study period (2007 – 2015), the

study selectively chose firms with available non-negative assets and premiums written in each year

for at least seven (7) years (Barth et al., 2001; Yang, 2000; Chotkunakitti, 2005). This period

46

(2007-2015) particularly had been considered for data collection due to the new insurance act

(ACT 724) which prohibited the forming and existence of composite insurance companies and

thereby calling for the separation of existing ones for a better regulation and supervision of

insurance business in the country. Secondly, the time period selected also provides recent time-

series observations to capture the current financial climate of the economy marking the major

change in regulation (enactment of the 2006 ACT). The study period ended 2015 since this

particular year provides data for the most recent financial data available for the study. Data relating

to the financial activities of these firms were obtained from the National Insurance Commission as

well as individual insurance companies in Ghana. Furthermore, in order to incorporate macro

factors relating to economic growth into the analysis, quarterly interest rates, inflation rates,

unemployment and GDP growth rates were as well collected. These macroeconomic variables

were retrieved from AFDB (African Development Bank) Group (Africa Information Highway,

2017) and also the World Development Indicators (World Development Indicators, 2017)

The data collection and sampling procedure thus resulted in a sample of 21 non-life insurance

firms within Ghana with feasible and available data over the period sample period. The procedure

resulted in this number due to the fact that, some of these firms came into existence barely a few

years ago relative to the sampling period under consideration.

Previous studies investigated differing research period lengths, ranging from two (2) to twenty

(20) years. This current research period is at par with Chotkunakitti (2005) and Ebaid, (2011) while

exceeding other previous studies like Arthur and Chuang (2006) and Quirin, O'Bryan, Wilcox, &

Berry (1999). The length of 9 years period as examined in this research is thus also acceptable.

With this sample, multiple linear regression and principal component analysis are conducted to

apply ordinary least squares panel analysis for the entire industry and dynamic factor time-series

47

models for each of the sample firms over the nine (9) year (36-quarter) time period under

consideration to make an inference from the empirical results.

3.7 Data collection

In obtaining data for this study, secondary data relating to firms’ Cash Flows were relied upon.

These data are quantitative in nature and based on financially accepted information presentation

standards. For the purpose of this study, financially accepted information presentation standard

refers to the International Financial Reporting Standards (IFRSs) adopted by Ghana as of 1 January

2007 in place of the Ghana National Accounting Standards, for all financial and non-financial

companies.

3.8 Specification of the Model

In selecting a methodology, this study employed linear regression models to determine factors

impacting Cash Flow of insurance forms. The study adopted variables identified in the study of

Born et al. (2014) as key variables in determining Cash Flow of insurance firms. The study also

used a novel FAAR (DFM) model (Born et al., 2014) to dynamically model the interrelations

between core insurance activities and their dynamic relations in order to forecast future Cash Flow

and identify possible Cash Flow risks.

Thus, the ability to accurately forecast future Cash Flow with minimal error will serve a

foreknowledge in identifying any solvency risks inherent and thereby manage those risks as

applicable.

3.8.1 Correlation Evaluation

In order to ensure the existence of a relationship between dependent and independent variables,

the study tested the association between these variables prior to the conduct of a regression (Collins

48

& Hussey, 2003). Field (2009), stressed that in order to make the regression model generalizable,

there should not be a perfect linear relationship between two or more of the predictor variables.

The presence of this condition will bring about the problem of multicollinearity leading to a

difficulty in evaluating the contribution of each variable in the model. According to Field (2009),

multicollinearity exists when the coefficient of correlation for any two pair of predictors exceeds

0.90. Pearson correlation was thus conducted to assess the size and direction of the association

between the variables used in the study.

3.8.2 Regression Analysis

In order to determine factors that influence the Cash Flow level of an insurance firm, this study

identified variables that contribute to Cash Flow of insurance firms. These variables were

identified as either dependent or independent variable. Dependent or response variables respond

to changes in other variables referred to as independent variables or predictors (Saunders et al.,

2003; McMurray, 2008). Dependent variables represent variables to which a researcher seeks

explanations while independent variables are the ones that provide explanations to the

phenomenon being examined. They are the predicted variables in a model. In this study, multiple

regression models were developed to seek explanations to Cash Flow of firms. The regression

model developed considered predictors relating to the core activities of insurers. These variables

were combined in multiple linear regression models using ordinary least square estimation method

to determine Cash Flow. Previous studies investigated mostly determinants of cash holdings of

firms and the predictive strength of operating Cash Flows as well as earnings in predicting future

Cash Flows by regressing current operating Cash Flows on period-lagged operating Cash Flows,

period lagged operating Cash Flow combined with accruals and period-lagged earnings (Barth et

al., 2001; Chotkunakitti, 2005; Arthur & Chuang, 2006; Farshadfar et al., 2008). Some previous

49

studies also considered the random walk model which suggests that current Cash Flow levels

persist into both current and future periods (Barth et al., 2001; Dechow et al., 2008; Yoder, 2006).

Consistent with prior studies, this study measures the determinants of Cash Flow using identified

observed variables as specified in equation 3.1.

Multiple linear regression has been highly recommended by some researchers to test theory

(Studenmund & Cassidy, 1987). This method is most suited for testing causal relationships

between variables which are dichotomous nominal scale, interval or ratio and satisfy normality

assumptions (Manning & Munro, 2006). The generic regression equations used in this study to

evaluate the relationship between Cash Flow and its predictors is as stated in equation 3.1.

CFi,t = β0 + β1Cap_ATi,t + β2Leveragei,t + β3Sizei,t + β4IT_Ri,t + β5Sterm_Ri,t + β6LTerm_Ri,t +

β7ReInsAsset_Ri,t + β8NetPremRec_Ri,t + β9UW_R1i,t + β10NPW_GPWi,t + εi.t (3.1)

Where β0 is the value of Cash Flow from the model when all predictors are zero, βi for i starting

from 1,2,…,10 are the regression coefficients for the various predictors and ε the error term. The

predictors remain the same as described in Table 3.1.

3.8.3 Principal Component Analysis

In order to identify latent factors which are unobserved but related to insurers’ activities in

investing, underwriting and risk management at the firm level, this study performs an analysis of

principal components to extract these factors (Born et al., 2014). Similarly, latent variables

relating to macroeconomic variables as well as industry-specific variables such as regulatory

requirements and underwriting cycles were also identified through principal component analysis

50

denoted, Ft, through observed (Xt) variables identified in insurers underwriting, investment and

financial choices, capital and risk management activities. In terms of the choice of observed

variables Xt, this study followed the analysis of Born et al. (2014) and categorised variables mainly

pertaining to (1) capital management, (2) financial (investment) management, and (3) risk

management. Furthermore, variables capturing insurers’ (4) firm characteristics, (5) underwriting

activities, in addition to variables relating to the macroeconomic environment, were chosen. Each

category of variables depicts different risk factors of an insurance firm.

3.8.4 Dynamic Factor Modelling

Dynamic Factor Modelling (DFM) is one of the most frequently used methods by policymakers

and economic researchers while considering key macroeconomic variables for the purposes of

forecasting. According to Born et al. (2014), DFM was developed by Geweke in 1977 and further

revised by Giannone, Reichlin and Sala (2004) and Watson (2004). DFM is an extension of factor

analysis and has the capability to depict the time-varying nature of relationships by taken into

consideration dynamic patterns. Following Born et al. (2014), this study applied Factor Augmented

Autoregressive (DFM) modelling to forecast future Cash Flow of insurers at the firm-level taking

into consideration macroeconomic variables and firm-specific factors.

The choice of methodology is additionally suitable because its application can resolve the problem

of multicollinearity when considering too many variables in multiple regression models. Besides,

there exists the possibility of structural changes as a result of incorporating macro business cycles

as well as underwriting cycles. As suggested by Rochet & Villeneuve (2011), there exists a non-

monotonic theoretical impact of investment management and risk management decisions on the

level of Cash Flow within a firm. Further, and as suggested by Fairley (1979), the expected

earnings of an insurance firm is made up of profit margin as a result of underwriting and returns

51

from investment activities. This important highlight, therefore, points to the need of considering a

model that utilises the interactions between investment, underwriting and other actions of the firm

that generates or influences the Cash Flow for that firm.

DFM application hence enables the study of Cash Flow in the presence of underwriting cycles to

explicitly depict embedded structural changes while modelling theoretically factors and

empirically analysing observed data (Belviso & Milani, 2006). This study, therefore, considers a

model that makes use of all variables relevant to the generation of Cash Flow in a dimension-

reduction technique, incorporating more information in the calculation and hence depicting the

dynamic patterns of Cash Flows for an insurance firm.

By applying the factor forecasting model proposed by Stock and Watson in 2005 (Born et al.,

2014), the latent factors influencing the Cash Flow of a firm were constructed using predictors in

a dynamic structural equation of the form:

Xt = ΛFt + et (3.2)

Where Xt represents the observable variables, Ft is the estimated unobservable latent factors, Λ

representing the factor loading matrix and et being the error term. Stated differently, the variable

F explains variable X through the estimation of factor loadings with e as the error term in the

estimation. Specifically, Ft comprises both industry and firm-level factors. Firm-level factors are

latent variables associated with activities in underwriting, risk management, investing and

financing by insurers. Industry-level factors comprise latent variables related to macroeconomic

variables such as growth rate, interest rate, unemployment and inflation and industry-specific

variables, such as regulatory requirements and underwriting cycles. The factor loading matrix (Λ)

thus captures the dynamic interactions in Xt which explicitly describes observed insurance

underwriting and investment asset allocations. In total, the dynamic interactions between

underwriting, financial and investment asset allocation activities in the presence of

52

macroeconomic and firm-specific variables are captured in equation (3.2) based on dynamic factor

modelling.

Following Born et al. (2014) and applying FAAR (DFM) model, therefore, this study distinguishes

itself from other previous studies as the FAAR model does not necessitate strong assumptions on

the objective function. Thus, the DFM model applied in this study avoids strong assumptions such

as firm value maximisation or total cost minimization that are associated with profit and cost

efficiencies discussed in previous literature (Cummins & Weiss, 2000; Cummins & Xie, 2008).

Using the dynamic Cash Flow model, a Factor-Augmented Autoregressive Model is then

developed after integrating into it the Cash Flow generating process, as an equation by allowing

for cross-sectional characteristics and time-series factors (Born et.al., 2014). The model in

equation (3.3) is applied to depict how Cash Flows are revealed after simultaneously accounting

for the abovementioned interactions (Almeida et al., 2004). That is, by incorporating variables that

depict the principal components identified through significance comparison from the first step’s

analysis, future Cash Flows are predicted using the model in (3.3).

CF1t+1= α +γ 'Ft + Φ(L)CFt + ut+1 (3.3)

Where CF1t+1 is a one period forecasted Cash Flow at time t, Ft the estimated unobservable factors

from equation (3.3), (L)CFt being lagged Cash Flows at time t and ut+1 a random error for the

estimated dependent variables.

The conceptual Cash Flow model that guides the development of the dynamic model specified in

equation 3.3 is illustrated in figure 3.2. Thus, findings in this model estimation should indicate the

predictive ability of past Cash Flows on future Cash Flow.

53

Source: Developed for this study

Figure 3.2: Cash Flow model for future Cash Flow prediction

Based on this model set-up, the functional process is extended to a measurable system to capture

the dynamic interactions between activities and draw an optimal status for risk management,

investment and underwriting strategies. Particularly, by the use of PCA, which explains Cash Flow

variables relating to the principal activities of insurers, the generated significant PCs were picked

as the main factors and integrated into equation (4.5) along with an autoregression (AR) model to

forecast Cash Flows for the next period. In order to infer comparable results between firms, a

normalized CF variable of the form ln(CFi,t – min(CFi,t)) was constructed where, CFi,t is the Cash

Flow of firm i at time t and min(CFi,t) is the minimal Cash Flow among all observations. Since the

net Cash Flow for firms was negative for some time periods, a constant positive figure was added

to CF in order to make it positive to enable log normalisation. This is applied following the

conventional method used in the study of profit frontier in normalising key variables. The

normalised Cash Flow was then used as a key variable in an autoregressive Cash Flow model

together with estimated factors to predict future Cash Flow. Thus, the estimate �̂�T from equation

(3.2), along with equation (3.3) enables us to derive the empirical autoregression model (FAARM)

described as in equation (3.4):

𝐶�̂�FAAR, n𝑇 + 1⃓𝑇

= α̂ + 𝛾'�̂�T + Φ̂T(L)CFT (3.4)

54

In equation four (4), 𝐶�̂�FAAR, n𝑇 + 1⃓𝑇

represents a one period forecasted Cash Flow at time T + 1

using Cash Flow at time T. FAAR signifies a factor augmented autoregressive model with n

standing for an appropriate order for the AR process. α̂ is the estimated constant term, while 𝛾

and Φ̂T represent estimated coefficients.

3.9 Variables Construction and Expected Signs

In order to empirically carry out the application of dynamic factor modelling through the use of a

Factor-Augmented Auto Regressive (FAAR) models in forecasting and managing Cash Flow of

insurance firms, variables relating to capital management, firm characteristics, investment

(financial) management, risk management, underwriting activities as well as macroeconomic

factors were identified. The variables identified are then converted into ratios which measure to a

large extent the economic performance of financial firms. For the insurance industry, the Cash

Flow generated is primarily associated with their core activities. According to Rochet &

Villeneuve (2011), the Cash Flow generating process of an insurance firm can be described as a

function of underwriting profit which comprises expected profitability per unit of time as well as

the volatilities in primary earnings, the rate of return on investment portfolios as a result of

allocation of assets and hence the total investment cash inflows. Cash Flow for an insurance firm

also emanates from cash generated through risk management activities such as reinsurance and the

use of derivatives in managing risk. Firms’ activities in financing through the issue of securities

and financing costs corresponding to these financing activities have also been identified as the

determinants of Cash Flow for insurance firms. The variables constructed therefore considered all

the core activities of insurance firms relating to underwriting, financing or investment, risk

management, capital management and firm characteristics in the Cash Flow generating process.

55

Table 3.1 summarises the primary variables considered as affecting the Cash Flow of non-life

insurance companies with their expected relationship (signs) with Cash Flow.

Table 3.1: Definition of Primary Variables with Expected Signs

Variable Definition Expected Sign

Capital Management Variables

Cap_AT Capital/surplus(Equity) to asset ratio (+/-)

Leverage The ratio of liability to total assets (+/-)

Firm Characteristics Variables

Size Natural logarithm of the total assets (+/-)

IT_R The ratio of computer and equipment to total

assets (+/-)

Investment (Financial) Management Variables

Sterm_R Short-term investment to total assets ratio (+)

LTerm_R Long-term investment to total assets ratio (-)

Risk Management Variable

ReInsAsset_R The ratio of reinsurance premium written to

total premium written

(-)

Underwriting Activities Variables

NetPremRec_R Net premium to total assets ratio +

UW_R1 Underwriting gain (loss) to total assets ratio +/-

NPW_GPW The ratio of net premium written to gross

premium written (+)

Macroeconomic Variables

Growth Economic growth rate based on Ghana GDP +

Interest rate 91-Day Treasury Bill rate +/-

Unemployment Ghana unemployment rate N/A

Inflation Inflation rate based on Ghana CPI (-)

Source: Adapted from Born et al. (2014)

3.10 Limitation to Methodology

One major limitation encountered in the estimation using this methodology is the availability of

enough data points so as to ensure generalisability. In order to estimate the individual time series

56

data models, there was the need for available data with enough observations for each firm.

Available time series data after the enactment of the 2006 ACT (ACT 724) which prohibited the

forming and existence of composite insurance companies, however, makes available an annual

time series data with only nine (9) observations (2007 – 2015). In order to deal with this limitation,

however, the study resorted to a quarterly time series financial data form the first quarter of the

year 2007 (2007q1) through to the fourth quarter of the year 2015 (2015q4). Although this study

would have employed a more frequent data (like a monthly data) or data over an extended period

of time, this is unavailable. This study, therefore, dealt with this challenge of insufficient data by

making use of quarterly financial data of non-life insurance firms within the context of Ghana for

the time series estimation and analysis.

3.11 Chapter Conclusion

The methodology in this study made use of quarterly time series data of 21 non-life insurance

companies in Ghana with feasible data. The methodology also incorporated economy-wide macro

variables which significantly affect the Cash Flow level of a firm. Observed variables relating to

insurers’ activities in underwriting, risk management, capital management, financial and

investment management were thus collected and latent factors extracted using principal component

analysis. These factors were further incorporated in a factor-augmented autoregressive process

with Cash Flow (CF) as the key variable at each stage of the estimation. The study made use of

principal components, as well as lagged Cash Flow values in a factor, augmented autoregressive

model to capture dynamic interactions between firms’ activities. All estimations were done using

Stata 13.1 Statistical Package.

57

CHAPTER FOUR

ESTIMATIONS AND DISCUSSION OF RESULTS

4.1 Introduction

This chapter presents the results as well as discuss the econometric estimations of the specified

model in chapter three. The chapter is organised into sections to reflect the steps involved in the

investigation process.

The presentation and analysis of the results, discussion of various estimates and extraction of latent

factors from observed variables to explain Cash Flow variations are done in this chapter. The

chapter towards the later part exhibits the forecasting power of the FAAR model and various

determinants of Cash Flow in the insurance industry. The chapter ends with a conclusion.

4.2 Industry Descriptive Statistics

In order to showcase how observed variables in insurers’ activities can be related to principal

components in the application of FAAR (DFM) modelling, this section presents a descriptive

statistics of the insurance industry as applied to the panel data. The analysis of the industry

variables presents the number of observations, mean and standard deviation for the various

variables measured. The statistics also present the minimum and maximum observations for all

variables of interest in order to present a clearer image of the industry in terms of its performance

in relation to the variables measured. Table 4.1 hence presents statistics on the variables used in

the model.

Table 4.1 presents two Cash Flow statistics to reflect actual observed net Cash Flow which is

negative for some observations and a log normalised net Cash Flow. The mean observation for the

not normalised net Cash Flow is GHC 7,615,073 with a standard deviation of 1.20E+07. Standard

58

deviation indicates the magnitude by which the Cash Flow of firms within the sample frame differs

from the mean value. This also gives a fair idea about the variations in CF of various firms in the

study. The values of CF ranged from a minimum of – GHC 1,785,030 to a maximum of GHC

9.28E+07. In order to take the natural log of the net Cash Flow for all firms, a constant positive

number (1,785,032) was added to all net cash values observed. CF (normalised) measured as

ln(CFit - min(CFit)) subsequently reported a mean average of 15.50828 and a standard deviation of

0.955158.

Table 4.1: Descriptive Statistics of the Insurance Industry

Variable Obs. Mean Std. Dev. Min Max

CF 178 7615073 1.20E+07 -1785030 9.28E+07

CF (Normalised) 188 15.50828 0.955158 13.86912 18.36526

Cap_AT 178 0.418811 0.180045 0.018379 0.936735

Leverage 179 0.573097 0.179352 0.000763 0.981621

Size 179 16.54193 1.109537 13.87983 19.15516

IT_R 178 0.128045 0.127579 0.00835 0.595331

Sterm_R 175 0.353953 0.211772 6.65E-05 0.855224

Lterm_R 172 0.21281 0.200093 0.003842 0.953704

ReInsAsset_R 176 0.34073 0.186261 0.016012 0.835089

NetPremRec_R 177 0.482022 0.196044 0.006488 0.989124

UW_R1 178 -0.02198 0.13586 -0.67463 0.557727

NPW_GPW 177 0.657581 0.178687 0.164913 0.983988

Growth 189 6.777778 3.267072 3.4 14

Interest Rate 189 18.13667 3.849303 11.3 22.9

Unemployment 189 3.377778 1.12128 1.8 5.3

Inflation 189 12.44444 3.828259 8.3 19.2 Source: Developed for this study from statistical results using annual panel data

The mean average for the variable UW_R1 (underwriting gain/loss) among the sampled firms

stood at -0.02198. This value coupled with a minimum and maximum values extracted and

presented as -2.36207 and 0.557727 respectively reveal that non-life insurance companies on the

average were making underwriting losses (negative profits) over the period studied. This stands to

59

suggest that the revenue generated in underwriting falls below the associated cost of underwriting

and can only be supplemented with positive returns from other activities in either financial and

investment opportunities or capital management activities. This can also be associated with under-

pricing of insurance products (Undercutting of premiums to attract more clients).

Analysing Table 4.1, the mean of CF, however, shows that Ghanaian insurance firms on average

have positive Net Cash Flows as a result of continuing activities in business. This characteristic is

at par with the study of Chotkunakitti (2005) and Barth et al. (2001). In the category of

macroeconomic variables, the mean of Growth is 6.777778 which indicates a growth rate of

approximately 6.78% as against interest, unemployment and inflation rates of 18.14%, 3.38% and

12.44% respectively. Details on the other variables are as stated in Table 4.1.

4.2.1 Pearson’s Correlation Matrix

The relationship between the predicted and predictor variables as described in Section 3.6.1 is

presented in Table 4.2 using Pearson’s correlation coefficient matrix. The study used pairwise

exclusion of cases to ensure there was a corresponding variable for each observation. The result of

the correlation matrix revealed the existence of significant association among variables (p −

value < 0.05). The dependent variable CF shows significant correlation with variables Size, IT_R,

Sterm_R, ReInsAsset_R and UW_R1 with corresponding levels of significance indicated beneath.

From the matrix, there exists a fairly strong negative correlation between Cap_AT and Leverage

with a magnitude of -0.636. However, there exists no correlating between IT_R and

NetPremRec_R as seen with a magnitude of 0.000. These notwithstanding, there was no evidence

of multicollinearity among predictors. Although some independent variables were significantly

autocorrelated with each other (p-value < 0.05), there were no problems of multicollinearity since

correlation coefficients are less than the 0.9 threshold recommended by Field (2009). This,

therefore, predicts the generalizability of regression models using these variables devoid of any

60

problem of multicollinearity. This is confirmed using the variance inflation factor (VIF) with their

corresponding tolerance estimates for predictor variables in a regression model. The result is

presented in Table 4.3 with a mean VIF of 2.06 (< 2.5).

Table 4.2: Pearson’s Correlation Matrix for Industry Cash Flow Variables

Source: Developed for this study from results of Pearson Correlation

Key: 1 = CF, 2 = Cap_AT, 3 = Leverage, 4 = Size, 5 = IT_R, 6 = Sterm_R, 7 = Lterm_R, 8 = ReInsAsset_R, 9 =

NetPremRec_R, 10 = UW_R1, 11 = NPW_GPW

In summary, the significant associations between the dependent variable (CF) and predictors may

result in strong predictive abilities of these variables in determining Cash Flow in the regression

model. These variables are thus statistically acceptable in a regression analysis to determine the

drivers of Cash Flow in the insurance industry.

1 2 3 4 5 6 7 8 9 10 11

1 1

2 -0.029 1

0.699 3 0.083 -0.636 1

0.269 0.000 4 0.157 -0.030 -0.039 1

0.036 0.692 0.608 5 -0.389 0.291 -0.074 -0.358 1

0.000 0.000 0.327 0.000 6 0.176 -0.059 0.152 -0.134 0.030 1

0.020 0.436 0.045 0.077 0.695 7 -0.082 0.319 -0.219 0.183 -0.052 -0.339 1

0.288 0.000 0.004 0.016 0.503 0.000 8 0.285 -0.011 0.026 0.342 -0.280 -0.265 0.299 1

0.000 0.888 0.736 0.000 0.000 0.000 0.000 9 -0.003 -0.206 0.238 -0.120 0.000 0.295 -0.285 -0.413 1

0.973 0.006 0.001 0.110 0.999 0.000 0.000 0.000 10 0.160 -0.006 -0.030 0.213 -0.109 0.012 0.036 0.037 0.129 1

0.034 0.933 0.692 0.004 0.149 0.877 0.638 0.624 0.086 11 -0.122 -0.148 0.101 -0.071 -0.013 -0.097 -0.002 -0.039 0.086 0.015 1

0.105 0.048 0.179 0.348 0.862 0.201 0.980 0.608 0.256 0.844

61

Table 4.3: Variance Inflation Factor (VIF) for Predictors

Source: Developed for this study from statistical results

4.3 Regression Analysis

This section presents results from the analysis of regression models developed to examine the

determinants of Cash Flow and test hypotheses proposed in section 1.5.

In order to choose between pooled OLS and random effect, the study conducted the Breusch and

Pagan Lagrange Multiplier test for random effect. From Table 4.4, the test result suggests the

preference of pooled OLS to a random effect model. Similarly, the study performed both Jarque-

Berra and Shapiro-Wilk diagnostic tests for normal data in addition to the Pearson Correlation and

VIF tests for multicollinearity. The study also conducted the Breusch-Pagan/Cook-Weisberg test

for heteroskedasticity to ensure there is no heteroscedasticity associated with the data. Results of

these tests are presented in Table 4.4.

Variable VIF 1/VIF

ReInsAsset_R 4.33 0.23074

NPW_GPW 4.14 0.24159

Cap_AT 2.12 0.47083

Leverage 2.01 0.49860

NetPremRec_R 1.53 0.65173

Lterm_R 1.44 0.69386

IT_R 1.35 0.74332

Sterm_R 1.33 0.75263

Size 1.3 0.77109

UW_R1 1.08 0.92749

Mean VIF 2.06

62

Table 4.4: Result of Diagnostic Tests

Source: Developed for this study from statistical results

Both the Jarque Berra and Shapiro-Wilk test for normality estimated p-values greater than 5%

significance level which shows that residuals are independent and uncorrelated to confirm

normality and linearity as required in OLS models. Also, the Breusch-Pagan/Cook-Weisberg’s test

for heteroskedasticity confirms the presence of constant variance in the data with a p-value of

0.3156 (> 0.05). A graphical plot for of these assumptions are presented in Appendix A. A visual

examination of the histogram graphs against a normal distribution curve reveals normality in the

data. Also, the distribution of the residuals as plotted in the scatter plot in Appendix “A” reveals a

random and evenly spread one suggesting the adherence to the assumptions of homoscedasticity

and linearity (Tabachnick & Fidell, 2007). Table 4.5 presents results of the pooled OLS regression.

The application of the FAAR (DFM) model which incorporates all firm and industry factors as

well as economy-wide factors using a dimension reduction technique is, however, presented and

explained in section 4.4.

Test Statistic

Breush-Pagan Lagrange Multiplier

Chi-square statistic 1.05

Prob. > chi2 0.1527

Jarque-Berra

chi2 3.959

Prob. > chi2 .1382

Shapiro-Wilk

z 1.264

Prob. > z 0.10305

Breusch-Pagan/Cook-Weisberg

chi2 1.01

Prob. > chi2 0.3156

63

Table 4.5: Cash Flow models estimation using observed industry variables

Variable Coef. Robust Std. Error

Cap_AT 0.273277*** 0.092710

Leverage 0.114946 0.078932

Size 0.070528 0.056481

IT_R 0.055639 0.075855

Sterm_R 0.185997*** 0.070365

Lterm_R -0.29274*** 0.089978

ReInsAsset_R 0.318987*** 0.069277

NetPremRec_R -0.6202*** 0.131937

UW_R1 0.13179 0.077678

NPW_GPW 0.038174 0.056593

_cons 0.019273 0.074365

R Square 0.3459

Adj. R Square 0.3042

Prob. > F 0.0000

Obs. 168

*** and ** denote significance at the 1% and 5% levels respectively

Source: Developed from the regression analysis.

4.3.1 Determinants of Cash Flow in the Ghanaian Insurance Industry

Table 4.5 presents results for a Cash Flow predictive model using observed variables pertaining to

insurers’ core activities in capital management, financial (investment) management, risk

management, firm characteristics and underwriting activities as predictors. Results from the table

indicate that 34.59% (30.42% adj. R2) of the overall variability in Cash Flow of insurance firms is

explained by the independent observed variables. The p-value of the F-statistic as estimated from

the regression model is 0.000. This value shows the joint significance of the explanatory variables

and shows the explanatory power of the model under a strong statistical significance of 1 %. Also,

Table 4.5 summarises results of coefficients with corresponding standard errors for variables

considered to impact Cash Flow. The variable capital to asset ratio (Cap_AT) indicates an

64

estimated coefficient of 0.273277 with a strong statistical significance at the 1% significance level.

This result suggests that a 10% improvement in the capital ratio will contribute a 27.33% to Cash

Flow of firms. This result supports the theoretical prediction of the impact of capital on the Cash

Flow level of a firm as discussed in section 2.6.

Variables related to both short term and long term investment activities indicate a statistically

significant relationship with Cash Flow. Intuitively, this result suggests a more rapid movement of

cash within a firm through the positive coefficient of short-term investment ratio (Sterm_R) as

opposed to the negative coefficient of long-term investment ratio (Lterm_R) to indicate a less

frequent movement of long-term investment cash. Thus an increase in long-term investment leads

to a decrease in Cash Flow of insurers. The coefficient for risk management variable,

ReInsAsset_R, is estimated to be 0.318987. This indicates that a 10% increase in ReInsAsset_R

will lead to a corresponding 31.90% increase in Cash Flow. This estimate is also statistically

significant at 1% level of significance with an estimated standard error of 0.069277.

The relationship between variables relating to underwriting activities which are unique to the

insurance industry and Cash Flow of insurers is also summarised in Table 4.5. For these variables,

only NetPremRec_R (net premium received to assets ratio) is statistically significant in

determining Cash Flow. These results suggest that although UW_R1 and NPW_GPW influence

Cash Flow, their impact is statistically insignificant. The impact of NetPremRec_R as presented

in Table 4.5 is, however, strongly significant statistically at the 0.01 level of statistical significance.

In line with objective one and the results of this model, the determinants of Cash Flow for the

insurance industry as also found in some studies (Duchin, 2010; Bates et al., 2009; Jani et al.,

2004; Opler et al., 1999) and supported by theory are capital ratio, short term and long term

investment ratios, and industry specific variables related to reinsurance as a ratio of total assets

65

and net premium received as a ratio of total assets (Cap_AT, Sterm_R, Lterm_R, ReInsAsset_R,

NetPremRec_R).

Consistent with objective two, variables in this study are linked to various management activities

of insurers. These are capital management, firm characteristics, financial (investment)

management, risk management and underwriting activities. By this, it can be inferred from the

results from objective one that;

a. Capital management

Following Born et al. (2014), the two main variables considered as relating to capital management

in insurance are capital to asset ratio (Cap_AT) and leverage. From the regression results, Cap_AT

reveals a statistically significant relationship with Cash Flow through an estimated coefficient at

5% significance level. On the contrary, Leverage is statistically insignificant in determining Cash

Flow. These two opposing results seem to suggest a neutral effect of capital management on Cash

Flow. However, as displayed in Table 4.5, the magnitude of the contribution of Cap_AT to Cash

Flow (0.273277) as against leverage (0.114946) suggests an overall positively significant influence

of capital management on Cash Flow. This result suggests that firms can manage their Cash Flow

significantly by managing their capital to asset ratio. The statistical significance of this activity is

however reconsidered in subsequent sections using an extracted latent factor that captures capital

management of firms.

b. Firm Characteristics

Firm characteristics in this study are represented by variables size and IT_R. These variables

considered separately in the regression estimation, however, were statistically insignificant in

managing Cash Flow. The result of these variables considered individually hence fails to reject the

null hypothesis that firm characteristics do not impact Cash Flow.

66

c. Financial (Investment) Management

Embodied in this category are the variables pertaining to both short-term and long-term

investments. Although both variables statistically significantly influence Cash Flow, their

individual effects are opposing. Thus, as explained earlier, the impact of short-term investments

(Sterm_R) on Cash Flow illustrates a positive one (0.185997) while that of long-term investments

(Lterm_R) indicates a negative relationship (-0.29274). The net effect of these variables on Cash

Flow suggests an indeterminate impact of financial management on insurers’ Cash Flow. This

notwithstanding, firms can manage Cash Flow risks by concentrating on individual components to

achieve the required impact of financial management on Cash Flow. Here again, the aggregated

statistical impact of both short and long-term investments on Cash Flow is illustrated in section

4.3.4 using a latent factor extraction pertaining to financial management.

d. Risk management

Risk management practices of insurers are measured by their reinsurance with other insurance

companies. As exhibited in Table 4.5, risk management through reinsurance is statistically

significant at the 1% level of significance in determining Cash Flow. The study thus rejects at the

1% significance level the null hypothesis that risk management does not impact Cash Flow.

e. Underwriting activities

Lastly, variables uniquely pertaining to insurers’ underwriting activities were also summarised

with their corresponding level of statistical significance. These variables are NetPremRec_R,

NetPremRec_R and UW_R1. The contributory impact of NetPremRec_R on Cash Flow as shown

in Table 4.5 is statistically significant at 1% level of significance. This variable shows a negative

relationship with Cash Flow through its estimated coefficient. The disaggregated analysis of this

result also presupposes that underwriting activities impact Cash Flow through net premium

received to assets ratio as opposed to results of both underwriting gain/loss and the ratio of net

67

premium written to gross premium written (UW_R1, NPW_GPW). The aggregated impact taking

all three variables together is however re-examined using an extracted latent factor that combines

all three variables.

4.3.2 The impact of Insurers’ management practices on their Cash Flow

In order to investigate the statistical impact of key management practices of insurers on Cash Flow,

this section employed principal component analysis to capture latent variables pertaining to capital

management, firm characteristics, risk management, financial (investment) management and

underwriting activities (Born et al., 2014). Table 4.6 displays the results of the statistical

estimation of insurers’ Cash Flow using principal components pertaining to capital, risk, financial

management as well as underwriting activities and firm characteristics. The association and

contribution of observed variables to principal components are however displayed in Appendix B.

From the results, all activities in capital management, risk management, financing (investing) and

underwriting activities coupled with firm-specific characteristics were found to be statistically

significant in determining Cash Flow of insurers. The results of this estimation answer the

questions regarding the second objective (b) and provide empirical evidence to make statistical

decisions regarding the hypotheses formulated in section 1.6. The result of the estimations provides

statistical evidence to reject all the null hypotheses as stated in section 1.6. Thus, capital

management, firm characteristics, investment management, risk management, underwriting

activities do statistically significantly impact Cash Flow of firms at the 1% level of statistical

significance. Findings in this estimation also provide evidence on the explanation strength of latent

factors related to firms’ activities in capital management, financial (investment) management, risk

management and underwriting. These activities are jointly significant at 1% significant level and

contribute to 79.47% variability in insurers’ Cash Flow.

68

Table 4.6: Impact of management activities on Cash Flow

CF Coef. Robust Std. Err.

PC1 -0.037316*** 0.004779

PC2 -0.086549*** 0.005556

PC3 0.030211*** 0.005843

PC4 0.118844*** 0.006812

PC5 0.032568*** 0.007971

_cons 0.257332*** 0.007535

R Square

0.8008

Adj. R Square

0.7947

Prob. > F

0.000

Obs.

168

*** denotes significance level at 1%

Source: Developed for this study from regression analysis

Consistent with Born et al. (2014), the next section demonstrates the application of a novel FAAR

(DFM) model that extracts latent factors pertaining to industry and economy-wide variables and

augmenting it with lagged Cash Flows to forecast future Cash Flow of individual insurance firms.

4.4 Empirical Results: Illustration of FAAR (DFM) Applications in Cash Flow Forecasting

For every individual insurance firm in this study, a time-series analysis for 32 quarter time periods

(with a four-quarter time period reserved as a holdout period) was conducted for the period 2007

to 2014. Time series results were obtained for all 21 firms with feasible data. Also, there was the

need to categorise these firms based on certain defining features. In this study, the size of a firm

was used as a basis to categorise firms into groups. The conduct and analysis of results were thus

based on: (i) entire group of 21 non-life insurance firms while sub-sample analysis was also

conducted by categorising the entire sample into (ii) large-firm (greater than 250 employees), (iii)

medium-firm (50-249 employees) and (iv) small-firm groups (below 50 employees).

69

In order to better demonstrate the procedures in the application of these models, each step in

applying DFM is illustrated using a randomly selected firm, F1. The results provide enough

evidence supporting the applicability and convenience of FAAR (DFM) models in forecasting and

subsequently managing future Cash Flows.

4.4.1 Principal Components Results:

As a first step to the application of DFM, Principal Component Analysis (PCA) was performed for

all firms to extract latent factors pertaining to insurer’s activities in capital management, financial

management, risk management and underwriting activities while taking into consideration

macroeconomic factors such as growth rate, interest rate, unemployment rate and inflation. In

order to establish the applicability of PCA, a Pearson’s correlation was performed on observed

variables to measure the correlation between explanatory variables.

The result of this matrix is presented in Table 4.7. From the matrix, there exists strong associations

between predictors and thus establishes the applicability of PCA as a variable reduction in order

to avoid a problem of multicollinearity. For example, the correlation between the size of a firm

and the net premium received ratio (NetPremRec_R) indicates a strong positive correlation

coefficient of 0.951. Similarly, there exists a strong negative correlation between short-term

investment ratio (Sterm_R) and size with a coefficient of -0.973. Evidence for the existence of

enough correlation for the application of PCA is presented as in Table 4.7 with correlation

coefficients and their corresponding significance levels indicated underneath.

Again, as applied to a typical firm F1, the results of PCA is presented in Table 4.8. Since PCA is

performed on a standardised data set, the contribution of each variable to a PC is the estimated

coefficient for that variable. The estimation resulted in the extraction of five (5) PCs with

eigenvalues greater than or equal to one ( ≥ 1)

70

Table 4.7: Pearson Correlated matrix for firm F1 (Correlation between predictor variables) 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1

2 0.268 1

0.144

3 0.345 0.973 1

0.057 0.000

4 -0.455 0.707 0.601 1

0.010 0.000 0.000

5 -0.577 -0.579 -0.520 -0.177 1

0.001 0.001 0.002 0.332

6 -0.383 -0.978 -0.973 -0.598 0.645 1

0.034 0.000 0.000 0.000 0.000

7 0.373 0.936 0.960 0.542 -0.543 -0.924 1

0.039 0.000 0.000 0.001 0.001 0.000

8 0.245 0.941 0.951 0.690 -0.553 -0.934 0.952 1

0.184 0.000 0.000 0.000 0.001 0.000 0.000

9 0.191 0.331 0.372 0.056 0.074 -0.289 0.527 0.368 1

0.304 0.064 0.036 0.760 0.688 0.109 0.002 0.038

10 0.463 0.223 0.210 -0.057 -0.343 -0.267 0.050 0.052 -0.270 1

0.009 0.228 0.257 0.760 0.059 0.147 0.790 0.783 0.142

11 -0.045 0.230 0.315 0.144 0.369 -0.285 0.150 0.186 0.232 0.141 1

0.811 0.205 0.079 0.433 0.038 0.114 0.414 0.307 0.202 0.448

12 0.147 0.305 0.394 0.054 -0.204 -0.365 0.433 0.443 0.163 -0.066 0.115 1

0.431 0.096 0.028 0.773 0.271 0.044 0.015 0.013 0.381 0.724 0.539

13 0.039 0.421 0.381 0.283 0.069 -0.435 0.234 0.189 0.091 0.316 0.677 0.018 1

0.837 0.016 0.032 0.117 0.706 0.013 0.198 0.300 0.622 0.083 0.000 0.925

14 0.250 0.251 0.396 -0.141 -0.245 -0.304 0.415 0.387 0.162 0.027 0.027 0.441 -0.254 1

0.174 0.173 0.027 0.450 0.183 0.096 0.020 0.031 0.384 0.884 0.888 0.013 0.168 Source: Developed from results of Pearson Correlation. Key: 1 = Cap_AT, 2 = Leverage, 3 = Size, 4 = IT_R, 5 = Sterm_R, 6 = Lterm_R, 7 =

ReInsAsset_R, 8 = NetPremRec_R, 9 = UW_R1, 10 = NPW_GPW, 11 = Growth,12 = Interest rate, 13 = Unemployment, 14 = Inflation

71

Table 4.8: Empirical Result of PCA, Firm F1

In Table 4.8, the first principal component (PC1) is more significantly related to variables in the

categories of investment management and risk management with a fairly significant relationship

with macroeconomic variables through their contribution to PC1. For instance, the estimated

coefficient on the short-term investment ratio (Sterm_R) is 0.3421, and that for long-term

investment ratio (LTerm_R) is 0.3583. Reinsurance to asset ratio (ReInsAsset_R) contributes

0.3619 and more related to PC1 than any other PC.

Similarly, PC2 is more significantly related to variables in the category of underwriting activities

while variables size and IT_R in the category of firm characteristics loads more strongly onto PC3

than any other PC. From Table 4.8, PC4 is more related to capital management variables through

PC1 PC2 PC3 PC4 PC5

Capital Management

Cap_AT 0.1304 0.1886 -0.1808 0.6484** -0.1058

Leverage -0.243* 0.1952 0.0394 -0.4684** -0.2977*

Firm Characteristics

Size 0.1622 0.0798 -0.4811** 0.0327 0.2951*

IT_R 0.2231* 0.1725 0.6286** -0.1 0.1163

Investment (Financial)

Management

Sterm_R 0.3421** 0.301** -0.0391 -0.0141 -0.0972

LTerm_R 0.3583** 0.2751* 0.2035* 0.0743 -0.0136

Risk Management

ReInsAsset_R 0.3619** -0.057 -0.2505* -0.2051* -0.2639**

Underwriting Activities

NetPremRec_R -0.1675 0.4782** -0.1249 0.1465 -0.2124*

UW_R1 0.0049 0.4163** 0.238* -0.0963 0.0989

NPW_GPW -0.1552 0.4798** 0.0325 0.3002** -0.3028**

Macroeconomic Variables

Growth -0.296* 0.1974 -0.043 0.1297 0.577**

Interest rate 0.0833 0.3273** -0.2025* 0.1895 -0.3813**

Unemployment -0.3134** 0.2137* 0.1961 -0.1161 0.2901*

Inflation 0.2305* 0.272* -0.1707 0.0695 0.408** Coefficients that are 0.20 and larger are labelled with an asterisk “*” sign while those that are 0.30 and lager

are marked with a double asterisk “**” sign.

Source: Developed from PCA results.

72

the contrasting contributions of Cap_AT (0.6484) and Leverage (-0.4684). However,

macroeconomic variables are more described by PC5 as indicated in Table 4.8. The outcome of

the principal component analysis illustrates how PCs can be linked to observed variables and this

relationship demonstrate the feasibility of a factor-augmented autoregressive model in explaining

Cash Flow variations in insurance firms.

The next step in the application of FAAR (DFM) modelling involves incorporating the results

from PCA with an autoregressive model to validate the forecasting power of the model.

4.4.2 Choice of a Baseline Model [AR(n) model]

The next step in the FAAR (DFM) model development is a choice of an appropriate baseline

autoregressive (AR) model. The time series autoregressive model estimation also necessitated the

unit root test in order to appropriately infer a suitable order for the AR model. By this, a time series

AR process up to order three was conducted for all 21 sampled firms. The result is summarised in

Table 4.9.

Table 4.9: Comparison between Baseline Models: AR1, AR2 and AR3

Source: Developed for this study from empirical results

In order to select an appropriate order for the baseline model, the Akaike Information Criterion

(AIC) was used to estimate an appropriate order for the AR models for all firms. AR1 beats AR2

All Firms Large Firms Medium Firms Small Firms

N* % N* % N* % N* %

AR1 beats AR2 & AR3 3 14.29% 0 0.00% 0 0.00% 3 37.50%

AR2 beats AR1 & AR3 14 66.67% 4 80.00% 6 75.00% 4 50.00%

AR3 beats AR1 & AR2 4 19.05% 1 20.00% 2 25.00% 1 12.50%

Total 21 100% 5 100% 8 100% 8 100%

*N represents the number of firms in each category.

73

and AR3 implies that the Akaike information criterion chose an AR1 order over both AR2 and

AR3 for the number of firms indicated. The results in Table 4.9, however, demonstrate the

preference for an AR model with order two (2) with 66.67% for the ‘all-firm’ category, 80%, 75%

and 50% majorities for large, medium and small firm categories respectively.

As a result of the analysis above, it is concluded that AR2 is a more appropriate baseline model

for a FAAR (DFM) model for the groups of entire sample as well as large-firm, medium-firm and

small-firm sub-samples.

4.4.3 Comparison between Baseline Model AR2 and the FAAR Model

After identifying AR2 as the appropriate baseline model, the study examined the forecasting

accuracy of the FAAR models as compared to the baseline AR2 model. In doing this, the

corresponding root mean square errors (RMSE) for all the estimated models were analysed and

compared. Without pre-specifying the optimum number of PCs to be added to the baseline model

to form the DFM model, Table 4.10 summarises the RMSE of AR2 and that of different DFM

models. Specifically, the study identified five DFM models using different numbers of significant

PCs extracted along with the AR2 baseline model. Thus: AR2+1PC, AR2+2PCs, AR2+3PCs,

AR2+4PCs and finally AR2+5PCs. Again, the number of PCs is based on significant principal

components with eigenvalues greater than or equal to one ( ≥ 1)

From Table 4.10, “5 models beat AR2” means that any one of the five FAAR (DFM) models has

RMSEs smaller than that of the baseline AR2 model. Similarly, “at least 4 models beat AR2”

implies that RMSEs of four to five models within the FAAR (DFM) models are smaller than the

RMSE estimated for the baseline AR2 model etc. For the entire sample of 21 firms, approximately

74

33.33 percent of the entire sample of firms can improve the forecasting power of the baseline

model through the application of any one of the five DFM models identified.

Table 4.10: Results of FAARM; Number of Models that Outperform the Baseline Model

All Firms Large Firms Medium Firms Small Firms

N* % N* % N* % N* %

5 models beats

AR2

7 33.33% 0 0.00% 1 12.50% 6 75.00%

At least 4 models

beat AR2

10 47.62% 1 20.00% 3 37.50% 6 75.00%

At least 3 models

beat AR2

14 66.67% 3 60.00% 4 50.00% 7 87.50%

At least 2 models

beat AR2

16 76.19% 3 60.00% 6 75.00% 7 87.50%

At least 1 model

beats AR2

18 85.71% 4 80.00% 6 75.00% 8 100.00%

No model beat

AR2

3 14.29% 1 20.00% 2 25.00% 0 0.00%

Total 21 100% 5 100% 8 100% 8 100%

* N represents the number of models in each category. Note: The vertical summation of N* does not add up to

21.

Source: Developed for this study from empirical results

Differently stated, for all 7 firms (33.33%) in the entire sample, FAAR models improve Cash Flow

forecasting no matter the number of principal components utilised in the estimation. Also, about

47.62 percent of firms within the “All Firm” category can utilise at least four of the DFM models

to increase the forecasting supremacy as compared to the application of the simple AR2 model.

Finally, for most firms, specifically 18 out of the 21 firms (85.71%) can utilise either one, two, …

or five DFM models to produce an RMSE estimate which is lower regardless of the number of

principal components included. Performing the same analysis on sub-samples of different firm

sizes indicated that, for the three categories, FAAR (DFM) models which consider principal

components yield better forecasting ability of Cash Flow compared to the baseline AR2 model.

From the results, at least 80% ( 4 out of 5), 75% (6 out of 8) and 100% (8 out of 8) of large, medium

75

and small firms groups respectively can improve the forecasting power in as much as principal

components are augmented with the baseline AR2 model. It is important, however, to note that

although results from Table 4.10 illustrate the forecasting power of DFM models, they do not

specifically recommend the optimum number of PCs to be included in a FAAR model to further

enhance the performance of the model.

Remarkably, by comparing results across the different sub-sample groups, it is observed that 75%t

of firms in the small-firm sub-sample are featured with better FAAR models with

any of the five models with different numbers of principal components. Conversely, in the large-

firm sub-sample category, the number of firms featured with a better FAAR model with any

different number of PCs in the five FAAR models is an absolute 00.00 percent. This, however, can

be associated with the fact that the variation in Cash Flows of large firms is generally larger than

that of small firms or that the quality of the explanatory variables is better. It is, therefore, possible

to make use of fewer PCs to capture variations in Cash Flows of large firms as compared to small

firms which generally may need more PCs to explain possible variations in their Cash Flow.

Largely, the results as displayed in Table 4.10 suggest that an AR2 baseline model alongside

principal components enhances the forecasting accuracy of FAAR models.

The next step identifies the optimum number of PCs to be incorporated in the FAAR model to

bring the RMSE estimate to the barest minimum and thus enhance the forecasting accuracy of the

dynamic factor model.

76

4.4.4 Identifying an Optimum number of Principal Components

The previous steps illustrate how preferable FAAR (DFM) models are in predicting future Cash

Flows. This section identifies the optimum number of PCs that can be incorporated in a FAAR

model. The study conducted five FAAR models namely, FAAR 1: AR2 + 1 PC, FAAR 2: AR2 +

2 PCs, …, FAAR5: AR2 + 5 PCs for each firm. Thus, FAAR models of the form AR2 + ∑ 𝑃𝐶5𝑗=1 j,

were conducted for each firm to determine the model with the least RMSE estimate. j is the

optimum number of PCs. Table 4.11 summarises the results obtained from the DFM application

with different numbers of PCs based on a sample firm, F1. From Table 4.11, AR2 is the chosen

baseline autoregressive model of lag order two (2) while FAAR1, FAAR2, …, FAAR5 signifies

the FAAR (DFM) models with one (1) up to five (5) PCs incorporated respectively. Table 4.11

also exhibits the relative changes in both RMSE and R-squared measures as a result of including

an extra PC. Statistically, the more the number of PCs included in the model, the higher the

expected R-squared value indicating the further acute forecastability of the model. This

notwithstanding, it is not necessary to include more PCs in the model if their inclusion does not

significantly improve the model’s accuracy. In this regard, the table (Table 4.11) also reports the

adjusted R-squared in order to account for the contribution of each extra PC included in the model.

Among the five FAAR models, the models including the first and second lags of Cash Flow were

consistently significant at least at 5% significance level. PC 5 as indicated in Table 4.11 also

indicates statistical significance at 1% significant level. As seen in Table 4.11, the inclusion of

PC2 in FAAR2 has increased the forecasting error (RMSE) from 0.16136 to 0.16268. However,

the inclusion of PC3 in FAAR3 improves the adjusted R2 from 0.7218 to 0.7617 as well as reduces

the RMSE to 0.15058 representing -7.44% change in RMSE. The significant contribution of PC4

to both R-squared and a corresponding reduction in RMSE is also indicated in Table 4.11.

77

Table 4.11: Empirical Results of FAAR (DFM) for Firm F1

***, ** and * denote the significance levels at 1%, 5% and 10% respectively

Source: Summarised from results of estimations from this study

Application of FAAR model of the form �̂�FAAR, n𝑇 + 1⃓𝑇

= α̂ + 𝛾'�̂�T + Φ̂T(L)CT

AR FAAR1 FAAR2 FAAR3 FAAR4 FAAR5

AR(2) AR(2) + PC1 AR(2) + ∑ 𝑷𝑪𝟐𝒋=𝟏 j AR(2) + ∑ 𝑷𝑪𝟑

𝒋=𝟏 j AR(2) + ∑ 𝑷𝑪𝟒𝒋=𝟏 j AR(2) + ∑ 𝑷𝑪𝟓

𝒋=𝟏 j

Coef. Robust

Std. Err.

Coef. Robust

Std. Err. Coef. Robust

Std. Err.

Coef. Robust

Std. Err.

Coef. Robust

Std. Err.

Coef. Robust

Std. Err.

Constant 4.9715*** 1.9602 6.7926*** 2.6344 6.8887*** 2.6555 6.5139*** 2.3905 7.0554*** 2.4029 8.2661*** 2.1773

lnCF(t-1) 1.1851*** 0.1057 1.1543*** 0.1161 1.1376*** 0.1185 1.0596*** 0.0975 0.9914*** 0.1219 0.9271*** 0.1005

lnCF(t-2) -0.4862*** 0.1586 -0.5659*** 0.1793 -0.5550*** 0.1738 -0.4546*** 0.1329 -0.4196** 0.1258 -0.4287*** 0.0940

PC1 -0.0250* 0.0122 -0.0255* 0.0122 -0.0220 0.0111 -0.0226* 0.0104 -0.0271** 0.0101

PC2 -0.0173 0.0279 -0.0091 0.0207 -0.0035 0.0204 -0.0063 0.0176

PC3 -0.0565** 0.0279 -0.0687** 0.0323 -0.0663*** 0.0178

PC4 0.0443* 0.0303 0.0455** 0.0240

PC5 -0.0686*** 0.0250

R-squared 0.7238 0.7546 0.7602 0.8027 0.8297 0.8762

Adj. R-

squared

0.7033 0.7263 0.7218 0.7617 0.7852 0.8368

RMSE .1680 0.16136 0.16268 0.15058 0.14293 0.12461

R-square

change % 4.26% 0.74% 5.59% 3.36% 5.60%

RMSE

change % -3.95% 0.82% -7.44% -5.08% -12.82%

78

The significance of these components in the five FAAR models indicate the relative importance

of their inclusion in the models. It can thus be seen that including more PCs in the model

improves both the explanation power measured by the adjusted R2 as well as the forecasting

accuracy measured by the reduction in the RMSE.

Referring to Table 4.8, the first PC is more significantly related to financial management

through its positive relations with both short-term and long-term investments. PC2 relates more

to underwriting variables while PC5 captures variables relating to macroeconomic variables

through its negative relations with interest rate and direct relations with growth, unemployment

and inflation. The time-varying movements of PC1, PC2 and PC5 are depicted in Figure 4.1 to

illustrate their time-varying patterns.

Source: Developed for this studies. Key: 2007q1 is the first quarter of the year 2007 etc

Figure 4.1: Time-varying movement of PC1, PC2 and PC5

Approximately, the long run routes of both PC1 and PC2 exhibits a decrease after the year

2008 with PC2 recording the greatest fall in the first quarter of the year 2009. While PC2

illustrates a bounce back after that year with a relative increase, PC1 continued to decrease till

the third quarter of 2009 at which point it began to increase. PC1 and PC2, however, continued

-4-2

02

4

2007q1 2009q1 2011q1 2013q1 2015q1Quarter Time Periods

PC1 PC5

PC2

79

to exhibit similar waves indicating decreases and increases as depicted in Figure 4.1. The

relative rise and fall in these components can be likened to their relations with macroeconomic

risks to reflect the economic instability in the Ghanaian insurance market. This study conjecture

that the small variation in PC1 and PC2 relative to PC5 may represent the underwriting cycles

of this firm due to its relations with underwriting variables. The variation in PC5 which

indicated the biggest wave as depicted in the line plot can be linked mainly to macroeconomic

variables which demonstrate a decrease from the first quarter of the year 2008 all the way to

somewhere the first quarter of 2011 and thereafter an increase till the year 2014 after which it

begins to fall again.

As a result of the significant changes observed in the RMSE and R-square with the inclusion

of different numbers of PCs, the study concludes that the FAAR model with five PCs with the

least RMSE and largest adjusted R2 can be best suited for the forecast of Cash Flow for this

firm.

Performing a similar analysis for all 21 sample firms, the results for the optimum number of

PCs to be included in the forecast of Cash Flow for each firm under the various firm categories

were inferred. The results are summarised in Table 4.12.

Table 4.12: Results of FAARM: The Optimum Number of Principal Components

All Firms Large Firms Medium Firms Small Firms

Model Best % Best % Best % Best %

FAAR1: (AR2+1PC) 4 19.05% 1 20.00% 3 37.50% 0 0.00%

FAAR2: AR2+2PCs) 1 4.76% 0 0.00% 0 0.00% 1 12.50%

FAAR3: AR2+3PCs) 2 9.52% 2 40.00% 0 0.00% 0 0.00%

FAAR 4: AR2+4PCs 2 9.52% 0 0.00% 1 12.50% 1 12.50%

FAAR5 (AR2+5PCs) 12 57.14% 2 40.00% 4 50.00% 6 75.00%

Total 21 5 8 8

Note: The number in each cell represents the number and percentage of firms with that best

model. E.g., There are 4 out of 21 firms featured with FAAR1as the best model, which is 19.05%

of the entire sample.

80

In this analysis, the RMSE corresponding to each model within a firm is compared.

Accordingly, the best FAAR model for a firm is identified as the model with the minimum

RMSE among the five FAAR models. Thus for each firm in the sample, the estimated RMSEs

were compared and ordered. RMSEij, therefore, refers to the RMSE estimate for firm i under

model j. i, in this case, ranges from 1 to 21 to represent the total number of firms while j stands

for models with principal components 1 up to 5. Thus, for each firm i, the minimum RMSE

from model j can be identified for all j from 1 to 5.

Table 4.12, however, shows that the inclusion of additional PCs does not necessarily lower the

corresponding RMSEs. This can be seen for example in the category of all firms where the

inclusion of PC2, PC3 and PC4 resulted in decreased percentages of firms with those models

as the best models. In the category of all firms, the inclusion of PC2 example for reduces the

number and percentage of firms with FAAR2 as the best model from 4 (19.05%) to 1 firm

representing 4.76%. Similarly, in the category of medium firms, the inclusion of PC2 and PC3

in FAAR2 and FAAR3 respectively resulted in no firm within that category having the

corresponding model as best models. For all category of firms, FAAR5 has been identified as

the best model for forecasting Cash Flows. Specifically, within the “all firm” category, a total

of 12 firms represented by 57.14% are featured with FAAR5 (AR2 plus five PCs) as the

optimum number of PCs through significant comparison of RMSEs. Similarly, about 50% and

75% of firms within the category of medium and small firms respectively support an optimum

number of five PCs in forecasting Cash Flows. For large firm sub-samples however, FAAR3

turns to be the best model for about 40% of the sample and FAAR5 also features as the best

model for another 40% of the same sample. Thus, for this category of firms either FAAR3 or

FAAR5 can be used as forecasting models for Cash Flow.

81

4.4.5 Forecasting Cash Flow using FAAR (DFM) Model

The forecasting power of FAAR5 model identified in the previous section is illustrated in this

section. The study performed an in-sample Cash Flow forecast for the fourth quarter of 2014

as well as a holdout sample forecast for the first, second, third and fourth quarters of the year

2015 to validate the robustness of the model. The result of this estimation is demonstrated in

Table 4.13.

Table 4.13: Forecasting Values of FAAR Model

Source: Developed for this study

Panel A, in Table 4.13 captures observed variables with observed values. By incorporating the

results of PCA, the estimated values of each PCs together with past two period Cash Flow

values for the fourth quarter of the year 2014 are also reported in Panel B. Consequently, the

AR model alongside 5 PCs in the form AR2 + ∑ 𝑃𝐶5𝑗=1 j were utilised to forecast Cash Flow

for the fourth quarter of the year 2014. As exhibited, the forecasting error is 0.00582415 and

equivalent to GHC 1.005841. In order to check the robustness of this procedure, the study also

conducted forecasts using the holdout sample and compared the results to observed values. The

results of this step are presented in Table 4.14.

Panel A: Observed Values

2014Q3

Panel B: Cash Flows of

2014Q3 and Estimated

PC

Panel C: Observed and

Forecasted Cash Flow

Variable

Observed

value

Cap_AT 0.4032 lnCFt-1 16.5733 Observed lnCF 2014Q4 15.7783

Leverage 0.1360 lnCFt-2 16.52628 Forecasted lnCF 2014 4th

quarter 15.7841

Size 15.0253 𝑃𝐶1̂ -1.24698 Difference in GHC 1.0058

IT_R 0.0413 𝑃𝐶2̂ -1.35233

Sterm_R 0.4264 𝑃𝐶3̂ -0.3721

LTerm_R 0.5047

0.0672

𝑃𝐶4̂ -0.68476

ReInsAsset_R 𝑃𝐶5̂ 1.36845

NetPremRec_R 0.0369

UW_R1 -0.1182

NPW_GPW 0.6922

growth 3.68

Interest rate 21.34

Unemployment 2.5

Inflation 16.5

82

Table 4.14: Forecast Values for Holdout Period with calculated MAPE

Time

Period

Observed

Value

Forecast

Value

Error Diff. in

GHC

Absolute

Percent Error

Mean Absolute

Percent Error

2015q1 16.7133 16.6945 0.0187 1.0189 0.11% 2015q2 16.8209 16.6945 0.1264 1.1347 0.75% 0.70%

2015q3 17.2236 17.0628 0.1608 1.1744 0.93% 2015q4 17.1753 17.0032 0.1721 1.1878 1.00%

Source: Developed for this study from estimated results

Overall, the empirical results as demonstrated with this sampled firm (F1) provides evidence

supporting the applicability and feasibility of DFM in predicting Cash Flow. Also, the model

exhibits a robust forecasting power of future Cash Flow as displayed in Table 4.14. Although

these models estimated with very minimal RMSEs, one wonders how small RMSE estimates

have to be to ensure acute forecasting ability of their corresponding models since RMSE values

are not bounded. The study, therefore, employed the Mean Absolute Percentage Error (MAPE)

which is bounded between 0% and 100%. The result of this estimate as presented in Table 4.14

further confirms the robustness of FAAR (DFM) models in forecasting future Cash Flows. The

tables demonstrating the holdout period forecast is displayed in Appendix C.

4.5 Chapter Conclusion

This chapter covered a detailed analysis of the descriptive statistics of the sample data collected

and utilised. The chapter also presented results of a Pearson’s correlation matrix of variables

to demonstrate the applicability of PCA. Regressions were estimated with key reference to the

estimation models developed from Chapter 3. The results from these statistical estimations

were analysed and findings provided empirical evidence to supplement existing literature and

provide new knowledge. The chapter demonstrated the significance of observed variables and

period-lagged Cash Flows in estimating current Cash Flow of insurers and also validated the

applicability of a novel FAAR (DFM) model in predicting future Cash Flow.

Chapter 5 presents the summary, conclusions and recommendations to the study.

83

CHAPTER FIVE

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This study models Cash Flow for insurance companies using past Cash Flow ratio data to

predict future Cash Flows. The study also determined various drivers that statistically

significantly impact Cash Flow in the insurance industry. Findings in this study will hence pre-

inform insurers on future Cash Flow needs and help them take necessary steps to manage Cash

Flow risks.

This chapter establishes how findings fit prior studies and draw conclusions and implications

for the insurance industry.

5.2 Summary of Findings

This section presents a summary of the data analysis regarding the research questions and the

various hypotheses constructed. It summarises findings of the determinants of Cash Flow in

the insurance industry as well as the relationship between principal activities in insurance and

Cash Flow.

5.2.1 Determinants of Cash Flow

As pointed by Knyazeva, Yuzvovich, Smorodina, Fomenko, and Katochikov (2016), the

activities undertaken by firms give rise to the emergence of Cash Flow but the efficiency and

effectiveness depend on how they are managed.

This study found that five (5) indicating variables are statistically significant in determining

insurers’ Cash Flow and thus its subsequent management. These are capital ratio, short term

and long term investment ratios, industry-specific variables relating to reinsurance as a ratio of

total assets and net premium received as a ratio of total assets (Cap_AT, Sterm_R, Lterm_R,

ReInsAsset_R and NetPremRec_R). These findings answer the research question concerning

84

the first objective (a) which sought to identify key determinants of Cash Flow in the insurance

industry.

Empirical results based on financial data of non-life insurers, however, found no statistical

support for variables relating to leverage, the size of firm nor firms’ use of IT and other

equipment (Size, Leverage, and IT_R) as impacting Cash Flow.

From the statistical estimations, capital to asset ratio (Cap_AT) under capital management is

statistically significant at 1% level of significance (p < 0.01) in determining insurers’ Cash

Flow. The estimated positive coefficient (0.2733) suggests that an improvement in capital to

asset ratio of insurance firms will lead to a corresponding increase in Cash Flow.

Similarly, both short term and long term investment related variables (Sterm_R and Lterm_R)

demonstrated statistical significance at 1% (p<0.01). The coefficient estimates for these two

variables indicates opposing directions relative to Cash Flow of insurers. Intuitively, the

positive coefficient estimate for Sterm_R variable suggests a more rapid movement of cash

while the negative coefficient estimate for Lterm_R variable indicates a less rapid Cash Flow.

Also, reinsurance premium to total premium written ratio (ReInsAsset_R) is significant at 1%

in determining insurers’ Cash Flow. The estimated positive sign of the coefficient for this

variable also points to a more rapid movement of Cash Flow with a corresponding increase in

ReInsAsset_R.

Finally, NetPremRec_R is statistically significant (p < 0.01) in impacting Cash Flow of

insurers. The estimated coefficient is negative with a magnitude of -0.6202 and suggests an

inverse relationship with insurers’ Cash Flow.

Also, as demonstrated in Table 4.12 the result of the autoregressive process also showed a

significant impact of past Cash Flow on the current Cash Flow of insurers. Unambiguously,

past Cash Flows relating to the first and second period-lagged Cash Flows were found to be

85

statistically significant (at least at 5% significant level) in explaining current Cash Flow. This

finding supports prior studies (Barth et al., 2001; Dechow et al., 2008; Yoder, 2006) which

assumed the random walk model, suggesting that past period Cash Flows persevere into both

current and future time periods. This result is also consistent with the findings of Born et al.

(2014), Chong (2012), Chotkunakitti (2005) and Habib (2010).

5.2.2 Summary of Findings on hypotheses formulated.

Consistent with the second objective, statistical analyses were made regarding the significance

of core management practices on Cash Flow. In this light, direct inferences were made taking

into consideration variables relating to these core practices under the pooled OLS estimation.

Also, the study employed PCA before OLS to investigate the aggregated impact of core

management practices on Cash Flow. The ensuing sections summarise the result thereof.

Hypothesis 1 (H0: Capital management does not impact Cash Flow)

In line with this hypothesis, the study made direct inferences from variables (Cap_AT and

Leverage) relating to capital management activities of firms. Although Cap_AT was significant

in determining Cash Flow, Leverage was not. Using PCA, the study found that, capital

management measured by PC2 is statistically significant (p<0.05) in determining future Cash

Flows. PC2 is an extracted principal component pertaining to capital management activity of

firms. The study, therefore, rejects the null hypothesis (H0) at the five percent (5%) level of

significance and concludes that capital management statically significantly impacts Cash Flow.

Using the same analysis, the results of the other hypotheses are as below:

Hypothesis 2 (H0: Firm characteristics do not impact Cash Flow)

The results of the statistical analyses indicate that both Size and IT_R are insignificant in

explaining Cash Flow. This outcome is inconsistent with some studies which found that the

86

size of a firm plays significant roles in determining their Cash Flow and financial performance

(Ferreira & Vilela, 2004; Ozkan & Ozkan, 2004; D’Mello et al., 2008). According to these

studies, larger firms have better credit ratings and bank credit lines which together impact their

Cash Flow. Also, the ability of these firms to raise large amounts of capital places them at an

advantage to exploit economies of scale which ultimately contribute to their Cash Flow.

In this study, the joint effect of these variables on Cash Flow measured by the fifth principal

component (PC5) using PCA found that firm characteristics are statistically significant at 99%

confidence interval in determining Cash Flow. That is, although the size of a firm and their use

of information technology (IT) and other related software processing equipment (IT_R) do not

individually impact Cash Flow, their joint effect does. We thus reject the null hypothesis and

conclude that firm characteristic statistically significantly determine Cash Flow.

Hypothesis 3 (H0: Investment management does not impact Cash Flow)

Embodied in financial (investment) management are the variables pertaining to both short-term

and long-term investments. The results of the statistical analyses indicate that both variables

statistically significantly influence Cash Flow. The aggregated impact of both short and long-

term investments on Cash Flow measured by PC4 is statistically significant at 99% confidence

level. We ones again reject the null hypothesis and conclude that financial (investment)

management statistically significantly impact Cash Flow.

Hypothesis 4 (H0: Risk management does not impact Cash Flow)

Risk management by insurance firms in Ghana is limited to their reinsurance of underwritten

losses with other insurers (reinsurers). Risk management (ReInsAsset_R) is statistically

significant at 1% level of significance (p < 0.01). Thus, risk management of firms is significant

in explaining Cash Flow. We thus reject the null hypothesis and conclude that risk management

statistically significantly impacts Cash Flow level of insurers.

87

Hypothesis 5 (H0: Underwriting activities do not impact Cash Flow)

Lastly, variables uniquely pertaining to insurers underwriting activities were also tested jointly

to find their impact on Cash Flow. The impact of underwriting activities measured by PC1 is

statistically significant at the 95 confidence level (p < 0.05). The study, therefore, rejects the

null hypothesis and conclude that underwriting activities do impact Cash Flow.

Key Findings:

a. Past Cash Flow data combined with observed variables related to insurers’ activities

improve the predictability of Cash Flow with minimal error.

b. The first two period-lagged Cash Flows consisting past t-1 and t-2 time periods

have significant abilities in determining future Cash Flow of insurers. Cash Flows

beyond past two periods were however irrelevant and contributes insignificantly

to future Cash Flow.

c. Core activities in capital, financial (investment) and risk management, as well as

activities in underwriting and firm-specific characteristics, are all significant in

determining future Cash Flows.

d. Period-lagged Cash Flows combined with latent industry variables in a FAAR

(DFM) model has a strong Cash Flow predictive ability.

e. Cash Flow financial data are relevant in forecasting future Cash Flow of insurance

firms as established in prior studies (Barth et al., 2001; Chotkunakitti, 2005;

Ebaid, 2011)

In conclusion, the regression models developed in this study are valid and adequately robust

with a reasonably high generalizability for forecasting future Cash Flows of insurance

companies for at least four future time periods.

88

5.2 Conclusions

This study investigated determinants of Cash flow in the insurance industry and validated a

model to forecast future Cash Flow of insurance firms so as to enable them to meet unexpected

future obligations and prevent insolvency. Cash Flow risks have been a major concern for many

firms and insurance firms which face unique underwriting risks not observed in other industries

are no exception. From the findings of this study, Cash Flow risks can be managed by

controlling capital ratio, short term and long term investments ratios, and industry-specific

variables such as reinsurance and net premium received ratios. Findings also revealed that

Ghanaian insurers’ in the non-life sector earn negative profits on underwriting. This result

implies that firms can generate favourable Cash Flows from other activities such as short-term

and long-term investments. The results also validated the acute forecasting ability of FAAR

(DFM) models in predicting future Cash Flows.

Findings regarding the forecasting ability of FAAR model also provide evidence that analysts

can effectively forecast future Cash Flow to assess both current and future financial health of

firms in order to protect the interest of stakeholders by preventing insolvencies and increase

insurance penetration.

5.3 Contribution to theory and knowledge

This study has successfully provided empirical evidence of the usefulness of Cash Flow

modelling to the insurance industry. This is further significant as it was conducted in the little-

researched context of Ghana, a Sub-Saharan African country so as to portray differences in

Cash Flow volatilities, industry characteristics and statutory regimes which are research issues

considered in this study.

This study has potential, therefore, to provide great contribution to the discourse on

determinants of Cash Flow, Cash Flow risks and future Cash Flow prediction as other studies

conducted in jurisdictions such as the United States and Europe

89

The conduct of this study also addresses the identified research problem by investigating

variables related to insurers’ activities that significantly impact Cash Flow and using that

information in a suitable model to predict future Cash Flow of insurance firms. The findings

provided empirical evidence that previous Cash Flow augmented with latent factors related to

insurance activities in a FAAR (DFM) model has significant ability to forecast future Cash

Flows.

5.4 Implications for Policy and Practice

Cash accounting convention to financial information reporting has been documented as one of

the important criteria for financial information disclosure to supplement the accrual-based

financial accounting. Detailed disclosure of Cash Flow data thus will enable users to better

assess the amount, timing and uncertainties of future Cash Flows. Findings in this study provide

empirical evidence to the applicability of financial data gathered from Cash Flow statements

in forecasting future Cash Flow. This will enable analysts to evaluate the ability of firms to

generate favourable Cash Flows to meet future obligations such as dividend payments and

payment for genuine insurance claims in the case of insurance firms. Also, findings regarding

determinants of Cash Flow in practice will help insurance companies make decisions that will

positively affect their future Cash Flows and also help in the formulation of policies to manage

these activities. The findings will serve as useful guidelines to both insurers and users of

reported financial information on particular variables to control in managing Cash Flow risks.

Regulators can thus rely on findings in this work to formulate policies, monitor compliance

and promote detailed and accurate disclosure of financial information relating to Cash Flow.

5.5 Recommendations

Based on the levels of significance the empirical study yielded, the research wishes to

recommend the following.

90

It is suggested that NIC enacts and effectively implements policies to monitor the pricing of

insurance products with serious penalties for defaulters so as to prevent undercutting of

premiums which is made evident in this study through the negative average underwriting gains

(UW_R1) of insurance firms. This will help prevent possible Cash Flow risks as well as future

insolvency of firms.

In order to improve the Cash Flow of insurance firms, it is also recommended that firms keep

the majority of their investments in short-term financial instruments to enable them to meet

unexpected future obligations such as insurance claims.

Also, regarding the forecastability of future Cash Flows using past Cash Flow data,

policymakers should ensure sufficient and accurate Cash Flow information is kept and

disclosed by insurers so as to aid careful prediction and analysis of firms’ financial health and

future ability to meet unexpected claims. Also, policymakers should devise methods and

strategies to encourage insurance firms to conduct internal forecasting and analysis of future

Cash Flow to enable them to manage any inherent Cash Flow risks. This will help protect

stakeholder interests and prevent insolvency of insurance firms.

It is also recommended that management of insurance companies take it upon themselves to

forecast future Cash Flows periodically so as to detect future cash flow risk and adopt strategies

to curb them before they do occur.

Future studies should, however, investigate the most effective means to manage key variables

identified to influence Cash Flow of insurance firms in Ghana through the application of

relevant financial instruments.

5.6 Limitations and Directions for Future Research

The scope of this study is restricted to non-life insurance companies within the Ghanaian

insurance industry. Results are thus specific to this industry and may not be generalizable to

91

other industries or sectors either within or outside Ghana. Consequently, future research can

imitate the methodologies employed in this study to include life insurers or firms in other

industries within or outside Ghana.

Also, insurance firms with limited and unavailable financial data were excluded from the

analysis. Specifically, only firms with feasible financial data for at least five years were

included in the analysis. Findings from this study may thus not be applicable to these firms.

The forecasting models developed assumed applicability of historical financial data in

forecasting future Cash Flows. These models were validated and are adequately robust in

forecasting future Cash Flows of non-life Ghanaian insurers for future time periods. However,

the assumptions of the applicability of past historical data stand to suggest that there remains a

fairly similar and stable economic climate in the future. Thus, drastic and substantial changes

in the business and economic environment may render findings in this research not

generalizable.

Also, since this study concentrated mainly on Cash Flow ratios, future studies can examine the

applicability of other predictive models using components such as earnings or accruals or a

combination of both to investigate the forecastability of Cash Flow in the Ghanaian context.

5.7 Chapter Conclusion

The last chapter of this study discussed the summary, conclusions and implications of findings

in this research. These findings were discussed within the context of relevant literature to

highlight the implications for both practice and policy. The chapter pointed to the research

problem identified as well as the various research questions formulated to create a link with

the findings.

This study expanded on previous studies conducted in other jurisdictions to predict future Cash

Flow of Ghanaian insurers. The study provided empirical evidence on the relevance of past

92

Cash Flows augmented with latent variables related to insurers’ activities in forecasting future

Cash Flow. The study also revealed the importance of individual predictors such as capital to

assets ratio, short term and long term investments to assets ratio, reinsurance to assets ratio and

net premium received to assets ratio as significant determinants of insurers’ Cash Flow.

Finally, this study to the best of the researcher’s knowledge serves as the pioneering research

applying the application of FAAR (DFM) model using Cash Flow data to forecast future Cash

Flow within the Ghanaian insurance industry. The study contributes to existing body of

knowledge by providing empirical evidence on the forecastability of Cash Flow of insurers in

order to meet future Cash Flow needs by properly formulating policies and managing inherent

Cash Flow risks.

93

REFERENCES

Abdullah, N. A., Halim, A., Ahmad, H., & Rus, R. M. (2008, May). Predicting corporate failure of

Malaysia’s listed companies: comparing multiple discriminant analysis, logistic regression and

the hazard model. International Research Journal of Finance and Economics, 15, 201-217.

Afza, T., & Adnan, S. M. (2007). Determinants of corporate cash holdings: A case study of Pakistan.

Singapore Economics Review and The University of Manchester (Brooks World Poverty

Institute), 164-165.

Akotey, O. J., & Abor, J. (2013). Risk management in the Ghanaian insurance industry. Qualitative

Research in Financial Markets, 5(1).

Al‐Attar, A., Hussain, S., & Zuo, L. Y. (2008). Earnings quality, bankruptcy risk and future cash flows.

Accounting and Business Research, 38(1), 5-20.

Alles, L., Lian, Y., & Xu, C. Y. (2012). The Determinants of Target Cash Holdings and Adjustment

speeds: An empirical analysis of Chinese Firms. Financial Markets and Corporate Governance

Conference.

Almeida, H., Campello, M., & Weisbach. (2004). The Cash-flow Sensitivity of Cash. Journal of

Finance, 59(4), 1777-1804.

Al-Najjar, B., & Belghitar, Y. (2011). Corporate cash holdings and dividend payments: Evidence from

Simultaneous analysis. Managerial and Decision Economics, 32(4), 231-241.

Alti, A. (2003). How Sensitive Is Investment to Cash-flow When Financing Is Frictionless? The Journal

of Finance, 58(2), 707-722.

Arthur, N., & Chuang, G. C. (2006). IAS7 alternative methods of disclosing cash flow from operations:

Evidence on the usefulness of direct method cash flow disclosures. Sydney: Working Paper,

University of Sydney.

Athukorala, S. L., & Reid, B. (2003). Accrual Budgeting and Accounting in Government and its

Relevance for Developing Member Countries. Manila: Asian Development.

Azmat, Q. (2014). Firm value and optimal cash level: Evidence from Pakistan. International Journal of

Emerging Markets, 9(4), 488-504.

Barth, M. E., Cram, D. P., & Nelson, K. K. (2001). Accruals and the prediction of future cash flows.

The Accounting Review, 76(1), 27-58.

Baskin, J. (1987). Corporate Liquidity in Games of Monopoly Power. The Review of Economics and

Statistics, 69(2), 312-319.

Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why Do U.S. Firms Hold so much more Cash than

they used to? The Journal of Finance, 64, 1985-2021.

Beck, D. F. (1994). Cash is king. The Health Care Manager, 13(1), 1-9.

Belviso, F., & Milani, F. (2006). Structural Factor-Augmented VARs (SFAVARs) and the Effects of

Monetary Policy. Journal of Macroeconomics.

Birt, J., Chalmers, K., Beal, D., Brooks, A., Byrne, S., & Oliver, J. (2008). Accounting: Business

reporting for decision making (2nd ed.). Milton: John Wiley and Sons.

Bodunrin, A. K. (2016). The Regulation of Cash Flow for Effective Economic Management and

Development of Businesses in Nigeria. Donnish Journal of Business and Finance Management

Research, 2(1), 001-004.

94

Born, P., Lin, H. J., & Wen, M. M. (2014). Cash-flow Risk Management in the Insurance Industry:

Adynamic Factor Approach.

Bowen, R. M., Burgstahler, D., & Daley, L. A. (1986). Evidence on the relationship between earnings

and various measures of cash flow. The Accounting Review, 61(4), 713-25.

Bryman, A., & Bell, E. (2003). Business research methods. Oxford: Oxford University Press.

Casey, C., & Bartczak, N. (1985). Using operating cash flow data to predict financial distress: Some

extensions. Journal of Accounting Research, 23(1), 384-401.

Caux, d. T. (2005). Cash Forecasting‖ Treasurer‘s Companion. Association of Corporate Treasurers.

Caux, d. T. (2013, March). "Cash Forecasting" Treasurer's Companion Association of Corporate

Treasurers. Retrieved from Simply CashFlow.com.

Chang, J., Khana, T., & Palepu, K. (2000). Analyst activity around the world. Working Paper.

Chang-Soo, K., Mauer, D. C., & Sherman, A. E. (1998, September). The determinants of corporate

liquidity: Theory and evidence. Journal of Financial and Quantitative Analysis, 33(3), 335-

359.

Chen, N., & Mahajan, A. (2010). Effects of macroeconomic conditions on corporate liquidity

international evidence. International Research Journal of Finance and Economics, 35, 112-

129.

Cheng, C. S., & Hollie, D. (2008). Do core and non-core cash flows from operations persist differently

in predicting future cash flows? Review of Quantitative Finance and Accounting, 31(1), 29-53.

Cheng, F. F., & Mohamad, S. (2008). Are cash flows relevant for stock pricing in Bursa Malaysia?

International Journal of Economics and Managment, 2(2), 353-367.

Chong, K. W. (2012). Prediction of operating cash flows using accrual-based and cash-based

accounting information among Malaysian industrial corporations. Lismore, NSW: DBA

thesis, Southern Cross University.

Chotkunakitti, P. (2005). Cash flows and accrual accounting in predicting future cash flow of Thai

listed companies. PhD thesis, Southern Cross University.

Collins, J., & Hussey, R. (2003). Business Research (2nd ed.). New York: Palgrave Macmillian.

Collinson, V. (2012). Leading by learning, learning by leading. Professional Development in Education,

38(2), 247-266.

Cummins, J. D., & Weiss, M. A. (2000). Analyzing Firm Performance in the Insurance Industry Using

Frontier Efficiency and Productivity Methods (Handbook of Insurance ed.). (G. Dionne, Ed.)

Norwell, MA: Kluwer Academic Publishers.

Cummins, J. D., & Xie, X. (2008). Mergers and Acquisitions in the US Property-Liability Insurance

Industry: Productivity and Efficiency Effects. Journal of Banking & Finance, 32(1), 30-55.

Cummins, J. D., Tennyson, S., & A, W. M. (1999). Consolidation and Efficiency in the U.S. Life

Insurance Industry. Journal of Banking & Finance, 23, 325-357.

Curriculum Research Development Division. (2005). Opportunity to learn English and Mathematics in

Ghanaian Primary Schools. Accra: Ghana Education Service.

Damodaran, A. (2009). Valuing financial services firm. Working paper, New York University.

Damodaran, A. (2013). Valuing financial service firms. Journal of Financial Perspectives, 1(1), 59-74.

95

Dechow, P. M., Richardson, S. A., & Sloan, R. G. (2008). The persistence and pricing of the cash

component of earnings. Journal of Accounting Research, 46(3), 537-566.

Dechow, P., & Dichev, L. (2002). The quality of accruals and earnings: The role of accrual estimation

errors. The Accounting Review 77 (Supplement), 35-39.

DeFond, M., & Hung, M. (2003). An empirical analysis of analysts Cash Flow Forecasts. Journal of

Accounting and Economics, 35(1), 73-100.

Deloof, M. (2003). Does Working capital Management Affect Profitability of Belgian Firms? Journal

of Business Finance and Accounting, 30(3), 573-587.

Diamond, J. (2002). Performance budgeting: Is accrual accounting required? Working Paper,

International Monetary Fund.

Dittmar, A., Mahrt-Smith, J., & Servaes, H. (2003). International corporate governance and corporate

cash holdings. The Journal of Financial and Quantitative Analysis, 38(1), 111-133.

D'Mello, R., Krishnaswami, S., & Larkin, P. J. (2008). Determinants of corporate cash holdings:

evidence from spin-offs. Journal of Banking and Finance, 32(7), 1209-1220.

Drobetz, W., & Gruninger, M. (2007). Corporate Cash Holdings: Evidence from Switzerland. Financial

Markets and Portfolio Management, 21(3), 293-324.

Drobetz, W., Grüninger, M. C., & Hirschvogl, S. (2010). Information asymmetry and the value of cash.

Journal of Banking and Finance, 34(9), 2168-2184.

Duchin, R. (2010). Cash Holdings and Corporate Diversification. The Journal of the American Finance

Association, 65(3), 955-992.

Earley, P., & Porritt, V. (2013). Evaluating the impact of professional development: the need for a

student-focused approach. Professional Development in Education, 40(1), 112-129.

Easterby, M., Thorpe, R., & Lowe, A. (1991). The Philosophy of Research Design: Management

Research an Introduction. London: Sage Publications.

Ebaid, I. E. (2011). Accruals and the prediction of future cash flows: Empirical evidence from a merging

market. Management Research Review, 34(7), 1-32.

Elahi, E. (2013). Risk management: the next source of competitive advantage. Foresight, 15(2), 117-

131.

Elliott, B., & Elliott, J. (2007). Financial Accounting and Reporting (11th ed.). Essex: Pearson

Education.

Elliott, D. J. (2014). Bank Liquidity Requirements: An Introduction and Overview. The Brookings

Institution.

Erickson, M., & Wang, S. W. (1999, April). Earnings management by acquiring firms in stock for stock

merges. Journal of Accounting and Economics, 27(2), 149-176.

Etsey, K. (2005). Causes of low academic performance of primary school pupils in the Shama Sub-

Metro of Shama Ahanta East Metropolitan Assembly (SAEMA) in Ghana. Regional

Conference on Education in West Africa, (pp. 1-34). Dakar.

Fairley, W. (1979). Investment Income and Profit Margins in Property-Liability Insurance: Theory and

Empirical Results. The Bell Journal of Economics, 10(1), 192-210.

Farshadfar, S., Ng, C., & Brimble, M. (2008). The relative ability of earnings and cashflow data in

forecasting future cash flows. Pacific Accounting Review, 20(3), 251-268.

96

FASB. (1978a). Financial Accounting in Non-business Organisation: An Exploratory Study of

Conceptual Issue. Norwalk: Financial Accounting Standards Board.

Faulkender, M., & Wang, R. (2006). Corporate Financial Policy and the Value of Cash. The Journal of

Finance, 61, 1957-1990.

Ferreira, M. A., & Vilela, A. S. (2004). Why Do Firms Hold Cash? Evidence from EMU Countries.

European Financial Management, 10, 295-319.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage Publications.

Foley, C. K., Hartzell, J. C., Titman, S., & Twite, G. (2007). Why do Firms hold so much Cash? A Tax-

based Explanation. Journal of Financial Economics, 86, 579-607.

Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2005). The Generalized Dynamic Factor Model.

Journal of the American Statistical Association, 100(471), 830-840.

Frost, D. (2012). From professional development to system change: teacher leadership and innovation.

Professional Development in Education, 38(2), 205-227.

Gao, H., Harford, J., & Li, K. (2013). Determinants of corporate cash policy. Journal of Financial

Economics, 109(3), 623-639.

Garcia-Teruel, P. J., & Martines-Solano, P. (2008). On the determinants of SME cash holdings:

Evidence from Spain. Journal of Business Finance and Accounting, 35(1-2), 127-149.

Geweke, J. F. (1977). The Dynamic Factor Analysis of Economic Time Series. Latent Variables in

Socioeconomic Models, 365-383.

Ghauri, P. N., & Grønhaug, K. (2005). Research Methods in Business Studies: A Practical Guide.

Financial Times Prentice Hall.

Giannone, D., Reichlin, L., & Sala, L. (2004). Monetary policy in real time. (M. R. Gertler, Ed.) NBER

Macroeconomics Annual. MIT Press,, 161-200.

Graham, J., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal

of Accounting and Economics, 40(1-3), 3-73.

Graham, J., Harvey, C., & Rajgopal, S. (2005). The economic implications of corporate financial

reporting. Journal of Accounting and Economics, 40(1-3), 3-73.

Guth, L., & Sepetys, K. (2001). Cash flow at risk for non-financial companies. Global Energy Business,

6, 12-14.

Habib, A. (2010). Prediction of operating cash flows: further evidence from Australia. Australian

Accounting Review, 20(2), 134.

IASB. (2001). Framework for the Preparation and Presentation of Financial Statement. London:

International Accounting Standards Board (IASB).

Iskander-Datta, M. E., & Jia, Y. (2012). Cross-country analysis of secular cash trends. Journal of

Banking and Finance, 36(3), 898-912.

Jang, S., Park, K., & Lee, J.-e. (2011). Estimating Cashflow-at-Risk(CFaR): A Comparables Approach

for Restaurant Firms. Cornell Hospitality Quarterly, 52(3), 232–240.

Jani, E., Hoesli, M., & Bender, A. (2004). Corporate cash holdings and agency conflicts. Geneva:

University of Geneva.

97

Jensen, M. (1986). Agency Costs of Free Cash Flow, Corporate Finance and Takeovers. The American

Economic Review, 76(2), 323-329.

Kadri, M. H., Ibrahim, M. K., & Aziz, R. A. (2010). Value relevance of book value and earnings:

Evidence from two different financial reporting regimes. Journal of FinancialReporting and

Accounting, 7(1), 1-16.

Keown, A. J., Martin, J. D., Petty, J. W., & Scott, D. F. (2005). Financial Management: Principles and

Application (10th ed.). Upper Saddle River: Pearson Education.

Keynes, J. M. (1936). The General Theory of Employment, Interest and Money. London: Macmillan.

Kim, J., Kim, H., & Woods, D. (2011). Determinants of Corporate cash-holding levels: An empirical

examination of the restaurant industry. International Journal of Hospitality Management,

30(3), 568-574.

Kim, M., & Kross, W. (2005). The ability of earnings to predict future operating cashflows has been

increasing – not decreasing. Journal of Accounting Research, 43(5), 753-780.

Knyazeva, E., Yuzvovich, L., Smorodina, E., Fomenko, V., & Katochikov, V. (2016). Cash Flow

Management at the Insurance Company Aimed at Provision of Financial Stability. American

Journal of Applied Sciences, 13(6), 794-803. doi:10.3844/ajassp.2016.794.803

Knyazeva, E., Yuzvovich, L., Smorodina, E., Fomenko, V., & Katochikov, V. (2016). Cash Flow

Management at the Insurance Company Aimed at Provision of Financial Stability. American

Journal of Applied Sciences, 13(6), 794-803.

Kothari, C. R. (2004). Research Methodology: Methods and Techniques. New Delhi: New Age

International.

Kusnadi, Y., & Wei, J. K. (2011). The determinants of corporate cash management policies: Evidence

from around the world. Journal of Corporate Finance, 17(3), 725-740.

Manning, M., & Munro, D. (2006). The Survey Researcher’s SPSS Cookbook. French’s Forest: Pearson

Education Australia.

McMurray, D. (2008). Qualitative Research Methods. Lismore: SouthernCross University.

McNicholas, M. (2002). Discussion of the quality of accruals and earnings: the role of estimation errors.

The Accounting Review, 77, 61-69.

Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal

of economics, 80(3), 413-435.

Ministry of Finance. (2016). 2016 Budget Estimates of Ministries, Departments and Agencies. Accra:

Government of Ghana.

Muhamad-Sori, Z., Abdul-Hamid, M. A., & Nassir, M. (2006). Forecasting financial problems in

emerging capital markets. Malaysia: Working Paper, University Putra Malaysia.

Myers, C. S., & Majluf, N. S. (198). Corporate Financing and Investment decisions when firms have

information that investors do not have. Journal of Financial Economics, 13(2), 187-221.

Myers, S. C. (1984). The capital structure puzzle. The journal of finance, 39(3), 574-592.

Neuman, W. L. (2006). Social Research Methods: Qualitative and Quantitative Approaches (7th ed.).

Whitewater: Pearson. University of Wisconsin.

NIC. (2005,2013,2015). Annual Report. National Insurance Commission.

98

NIC. (2013). Annual Report. National Insurance Commission.

NIC. (2016, May 17). Notice To The public. Retrieved September 27, 2016, from National Insurance

Commission: http://nicgh.org/news/notice-to-the-public/

Obinata, T. (2002). Concept and relevance of income. Tokyo: Working Paper, University of Tokyo.

Opler, T., Pinkowiz, L., Stulz, R., & Williamson, R. (1999). The determinants and implications of

corporate cash holdings. Journal of Financial Economics, 52(1), 3-46.

Ozkan, A., & Ozkan, N. (2004). Corporate cash holdings: An Empirical Investigations of UK

companies. Journal of Banking and Finance, 28(9), 2103-2134.

Palepu, K. G., Healy, P. M., & Bernard, V. L. (2000). Business Analysis and Valuation Using Financial

Statements. Cincinnati: South-Western College Publications.

Pedder, D., Storey, A., & Opfer, V. D. (n.d.). Synthesis report: schools and continuing professional

development (CPD) in England - state of the nation research project (T34718). Retrieved

February 13, 2012, from Training and Development Agency for Schools:

http://www.tda.gov.uk/cpd-leader/.../cpd_stateofthenation_report.pdf

Quirin, J. J., O'Bryan, D., Wilcox, W., & Berry, K. T. (1999). Forecasting cash flow from operations:

additional evidence. Mid - Atlantic Journal of Business, 35(2-3), 135-42.

Ramnath, S., Rock, S., & Shane, P. (2008). The Financial Analyst forecasting literature: a taxonomy

with suggestions for further research. International Journal of Forecasting, 24(1), 34-75.

Rangan, S. (1998). Earnings management and the performance of seasoned equity offerings. Journal of

Financial Economics, 50(1), 101-122.

Rochet, J.-C., & Villeneuve, S. (2011). Liquidity Management and Corporate Demand for Hedging and

Insurance. Journal of Financial Intermediation, 20, 303–323.

Ryu, K., & Jang, S. (2004). Performance measurement through cash flow ratios and traditional ratios:

A comparison of commercial and casino hotel companies. The Journal of Hospitality Financial

Management, 12(1), 15-25.

Sargent, T. J., & Sims, C. A. (1977). Business cycle modelling without pretending to have too much a

priori economic theory. New methods in business cycle research(1), 145-168.

Sauders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students (5th ed.).

Harlow: Pearson Education Limited.

Shukor, Z., Ibrahim, M., Kaur, J., & Nor, H. (2009). The value relevance of intangible non-current

assets during different economic conditions and accounting environments. Malaysian

Accounting Review, 8(2), 43-66.

Stock, J. H., & Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal

of Business and Economic Statistics, 20, 147-162.

Stock, J. H., & Watson, M. W. (2005). Implications of dynamic factor models for VAR analysis Working

Paper 11467.

Stock, J. H., & Watson, M. W. (2006). Forecasting with Many Predictors. Handbook of Economic

Forecasting, 515-554.

Studenmund, A. H., & Cassidy, H. J. (1987). Using Econometrics: A Practical Guide,. Boston: Little

Brown.

Tabachnick, B., & Fidell, L. S. (2007). Using Multivariate Statistics (4th ed.). Boston: Allynand Bacon.

99

Tahir, S. M., Alifiah, N. M., Arshad, U. M., & Saleem, F. (2016). Financial Theories with a Focus on

Corporate Cash Holding Behavior. International Journal of Economics and Financial Issues,

6(S3), 215-219.

Teoh, S. H., Welch, I., & Wong, T. J. (1998). Earnings management and the underperformance of

seasoned equity earnings. Journal of Financial Economics, 50(1), 63-99.

Tobin, J. (1956). The interest-elasticity of transactions demand for cash. The review of Economics and

Statistics, 241-247.

Torfason, A. B. (2013). Methods for Making Sense of Cash Flow in Banks. NFF Conference

Proceedings (p. 11). Reykjavik, Island: Gothenburg Research Institute (GRI).

Uyar, A., & Kuzey, C. (2014). Determinants of corporate cash holdings: Evidence from the emerging

market of Turkey. Applied Economics, 46(9), 1035-1048.

W., D., A., S., & H, Z. (2010). Corporate Governance and Expected Stock Returns: Evidence from

Germany. European Financial Management, 10(2), 267-293.

Waldron, M. A., & Jordan, C. E. (2010). The comparative predictive abilities of accrual earnings and

cash flows in periods of economic turbulence: the case of the bubble. The Journal of Applied

Business Research, 26`(1), 85-97.

Wasiuzzaman, S. (2014). Analysis of corporate cash holdings of firms in Malaysia. Journal of Asia

Business Studies, 8(2), 118 - 135.

Watson, D., & Head, T. (2004). Corporate Finance: Principles and Practice. London: Pearson

Education.

Watson, M. W. (2004). Comment on Giannone, Reichlin, and Sala,. NBER Macroeconomics Annual,

216-221.

Wen, M.-M., Lin, H. J., Born, P. H., Yang, C., & Wang, C. (2018). Cash Flow Risk Management in the

Property/Liability Insurance Industry: A Dynamic Factor Modeling Approach. North American

Actuarial Journal. doi:10.1080/10920277.2018.1423994

World Development Indicators. (2017, July 01). Retrieved April 17, 2017, from World Bank Group:

http://data.worldbank.org/data-catalog/world-development-indicators

Yang, S. S. (2000). Effects of the predictive ability of accruals and cash flows on earnings quality. PhD

Thesis, University of Houston.

Yoder, T. (2006). The incremental cash flow predictive ability of accruals models. Pennsylvania: PhD

thesis, Pennsylvania State University.

100

APPENDIX

Appendix A

Figure 5.1: Residual Plots for Cash Flow (CF)

Appendix B

Table 5.1: PCA Loading Matrix for Industry Observed Variables

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Unexplained

Cap_AT 0.0005 0.5451 0.3618 0.0759 0.2819 0.2010

Leverage 0.2328 -0.5385 0.1861 -0.1980 0.3923 0.1417

Size -0.1216 0.1667 -0.4624 -0.4384 0.5290 0.1473

IT_R 0.2922 0.2829 0.3420 -0.2084 -0.3603 0.3099

Sterm_R 0.3431 -0.0403 -0.1620 0.6303 0.0178 0.2836

Lterm_R -0.3100 0.2953 0.1196 0.3610 0.3454 0.2861

ReInsAsset_R -0.3927 -0.2304 0.2509 0.3650 0.2140 0.2597

NetPremRec_R 0.4534 -0.0593 0.0699 0.2317 0.4098 0.2865

UW_R1 -0.5176 0.2625 -0.1685 0.3426 -0.0096 0.2625

NPW_GPW 0.4936 0.3012 -0.1064 -0.0599 0.1534 0.2014 Source: Developed for this Study from PCA estimation

101

Appendix C

Table 5.2: Forecasting Values Using FAAR Model (2015Q1)

Panel A: Observed Values

2014Q4

Panel B: Cash Flows

of 2014Q4 and

Estimated PC

Panel C: Observed and

Forecasted Cash Flow

Variable Observed Value

Cap_AT 0.4636 lnCFt-1 16.65111 Observed lnCF

2015Q1 16.7133

Leverage 0.1206 lnCFt-2 16.5733 Forecasted lnCF

2015 1st quarter 16.6945

Size 15.2186

-1.52578 Difference in

GHC 1.0189

IT_R 0.0376

-1.03551

Sterm_R 0.4582 -0.69241

LTerm_R 0.4682 -0.79310

ReInsAsset_R 0.0424 0.98403

NetPremRec_R 0.0461

UW_R1 -0.1240

NPW_GPW 0.7030

growth 3.46

Interest rate 20.79

Unemployment 2.7

Inflation 17 Source: Developed for this Study

Table 5.3: Forecasting Values Using FAAR Model (2015Q2)

Source: Developed for this Study

Panel A: Observed Values

2015Q1

Panel B: Cash Flows of

2015Q1 and Estimated

PC

Panel C: Observed and

Forecasted Cash Flow

Variable Observed Value

Cap_AT 0.4276 lnCFt-1 16.75971 Observed lnCF

2015Q2 16.8209

Leverage 0.2858 lnCFt-2 16.65111

Forecasted lnCF

2015 2nd

quarter 16.6945

Size 15.3212

-1.95513 Difference in

GHC 1.1347

IT_R 0.0402

-0.63683

Sterm_R 0.5913

-0.03688

LTerm_R 0.3723

-0.68876

ReInsAsset_R 0.0600

-0.00271

NetPremRec_R 0.0377

UW_R1 0.0145

NPW_GPW 0.7018

growth 3.44

Interest rate 20.24

Unemployment 3.15

Inflation 8.64

102

Table 5.4: Forecasting Values Using FAAR Model (2015Q3)

Source: Developed for this Study

Table 5.5: Forecasting Values Using FAAR Model (2015Q4)

Source: Developed for this Study

Panel A: Observed Values 2015Q2 Panel B: Cash Flows of

2015Q2 and Estimated

PC

Panel C: Observed and

Forecasted Cash Flow Variable Observed Value

Cap_AT 0.5384 lnCFt-1 17.13253 Observed lnCF

2015Q3 17.2236

Leverage 0.2853 lnCFt-2 16.75971 Forecasted lnCF

2015 3rd quarter 17.0628

Size 16.4365

-2.19768 Difference in GHC 1.1744

IT_R 0.0625

-1.01744

Sterm_R 0.6197

-0.63832

LTerm_R 0.3165

0.192028

ReInsAsset_R 0.0828

0.286307

NetPremRec_R 0.0448

UW_R1 -0.1389

NPW_GPW 0.7351

growth 3.62

Interest rate 18.38

Unemployment 3.2

Inflation 9.75

Panel A: Observed Values

2014Q3

Panel B: Cash Flows of

2014Q4 and Estimated PC

Panel C: Observed and

Forecasted Cash Flow

Variable Observed

value

Cap_AT 0.5275 lnCFt-1 17.20935 Observed

lnCF

2015Q4

17.1753

Leverage 0.263118 lnCFt-2 17.13253 Forecasted

lnCF

2015Q$

17.0032

Size 16.4492

-2.19371 Difference

in GHC

1.1878

IT_R 0.0652

-0.9459

Sterm_R 0.6292

-0.85753

LTerm_R 0.2594

0.539197

ReInsAsset_R 0.0911

0.298156

NetPremRec_R 0.0361

UW_R1 0.0129

NPW_GPW 0.8927

growth 3.90

Interest rate 19.99

Unemployment 3.40

Inflation 8.3