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Investigating the role of economic-value framework and conventional financial metrics in explaining the total
shareholders returns of the UK:FTSE-All firms
Ali El-Jaber
Subject area: Finance and Economics Supervisor: Professor Chin-Bun Tse
Submitted: January 2014 Dissertation submitted to the University of Leicester in partial fulfilment of the requirements of the degree of Master of
Business Administration
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Table of Contents
Key to Abbreviations ............................................................................................................................ 4
Executive Summary .............................................................................................................................. 5
1.0 Introduction .................................................................................................................................... 6
1.1 Scope of the study ...................................................................................................................... 8
1.2 Research Questions .................................................................................................................... 9
2.0 Literature Review ......................................................................................................................... 11
2.1 Management Role .................................................................................................................... 11
2.2 Financial Statements ................................................................................................................ 12
2.3 EVA Accounting Treatments ..................................................................................................... 15
2.3 Basic EVA Framework ............................................................................................................... 16
2.4 WACC Assumptions and calculations ....................................................................................... 16
2.5 Adjusted EVA (S-EVA) calculations ........................................................................................... 18
2.6 EVA and Decision Making ......................................................................................................... 21
2.7 Driving value from EVA framework .......................................................................................... 21
2.8 Previous Empirical Research Overview .................................................................................... 23
3.0 Methodology ................................................................................................................................ 25
3.1 Financial Analysis ...................................................................................................................... 25
3.2 Dependent Variables ................................................................................................................ 25
3.3 Independent Variables ............................................................................................................. 26
3.4 Economic Analysis Assumptions ............................................................................................... 27
3.5 Statistical Analyses using Panel Data Analysis.......................................................................... 31
4. Analysis of Results .......................................................................................................................... 33
4.1 Data Collection ......................................................................................................................... 33
4.2 Data Analyses............................................................................................................................ 33
4.3 Analysis of MVA as a proxy of shareholder value creation ...................................................... 34
4.4 Descriptive Statistic for traditional accounting metrics ........................................................... 36
4.5 B-EVA Descriptive Statistics ...................................................................................................... 37
4.6 Adjusted EVA Descriptive Statistics (S-EVA) ............................................................................. 39
4.6.1 Descriptive Statistics of the WACC used in S-EVA calculations ............................................. 40
4.7 Testing the hypothesis against Panel Data Regression Analysis .............................................. 42
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5. Summary and conclusions of the research .................................................................................... 48
5.1 Summary ................................................................................................................................... 48
5.2 Theoretical Implications ........................................................................................................... 50
5.3 Practical Implications ................................................................................................................ 51
5.4 Limitations ................................................................................................................................ 52
5.5 Direction for Future Research .................................................................................................. 52
5.6 Reflections ................................................................................................................................ 53
References .......................................................................................................................................... 55
Appendix A – Dissertation Proposal ................................................................................................... 60
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Key to Abbreviations
B-EVA Basic Economic Value Added CAPM Capital Asset Pricing Model CE Capital Employed EBIT Earnings Before Interest & Tax EMV Equity Market Value EPS Earnings Per Share EVA Economic Value Added IC Invested Capital MVA Market Value Added NIBCL Non-Interest Bearing Current Liabilities NOPAT Net Operating Profit After-Tax R&D Research and Development RI Residual Income ROA Return on Assets ROCE Return on Capital Employed ROE Return on Equity S-EVA Simplified/Adjusted Economic Value Added WACC Average Weighted Cost of Capital
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Executive Summary Investors, shareholders and managers continuously use traditional financial ratios such as ROE,
ROCE, ROA and EPS to assess the financial performance of their organisation. However, published
accounting statements are subject to manipulations and distortions along with the assumption that
equity is a free source of capital. The economic value framework combines the fundamentals of
modern corporate finance theory where equity funding has a cost that needs to be recognised and
that earnings need to be higher than the cost of capital for shareholder value to be created.
This research investigates the value relevance of the economic value framework when evaluating
shareholder value returns by analysing 580 UK FTSE-ALL companies over a five year period
between 2008 and 2012. Panel data analyses were employed to explore the statistical associations
between shareholder value against traditional metrics. In addition, similar statistical analyses were
conducted to examine the association between shareholder value against two versions of economic
value calculations; basic-EVA (B-EVA) and adjusted-EVA (S-EVA). The results have revealed
statistically significant relationship between shareholders value against the adjusted-EVA (S-EVA).
This emphasises the significance of undertaking a certain number of accounting adjustments by re-
packaging the income statements and balance sheets. A secondary finding concluded a statistically
significant relationship between S-EVA and ROCE. The outcome of the analyses has
“conditionally” supported the claims made by proponents of EVA in explaining shareholder returns
when compared against other performance measures such as earning per share, and return on equity.
This conditionality is linked to the sensitivity of the EVA calculations which are associated with the
WACC estimations together with the selection and assumptions made to the accounting adjustments.
In the absence of such adjustments no statistically significant correlation was found between the B-
EVA and other accounting metrics suggesting no value relevance of using B-EVA as a performance
metrics.
In summary, it can be concluded that though traditional metrics may show rosy and positive outlook,
they may still fail to reflect the real financial performance of the company. It is therefore strongly
suggested to adopt the S-EVA alongside other traditional metrics to evaluate shareholder value
creation in the UK.
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1.0 Introduction
It has been universally accepted that maximising shareholder value is the primary management goal
and for shareholders’ wealth to increase, it has been argued that the invested returns in the business
must exceed shareholders’ expectations; otherwise share prices may inevitably fall (Bughin &
Copeland 1997). According to Rappaport (1992), when companies develop corporate plans, they
may also formulate alternative strategies. The selected strategy should be evaluated and tested
against maximising shareholder value returns (Crowther et al. 1998). Ballow et al (2004) have
contested that share prices are not a true reflection of the firm’s value as share prices are linked to
many micro and macro economic variables including the aggregate opinions of investors and
analysts which may overlook future growth opportunities. Taub (2003) observed that most financial
measurement tools across various industries concentrate on traditional accounting information
which is typically extracted from financial statements. However, Anand et al. (1999) noted that
traditional accounting measures are often under severe criticism as they often do not take into
consideration the cost of capital. Further, financial statements including income and balance sheets
are based upon accrual conventions and could be subject to many distortions and manipulations by
management. In addition, costs are reported in current prices while fixed assets are generally
reported using historical values in the balance sheet.
The growth in the e-commerce and globalisation over the last few decades have forced many
companies to invest more in intangible assets such as e-commerce, research & development,
knowledge transfer and brand equity and less so on physical assets making traditional accounting
practices less favourable for non-physical assets (Jusoh & Parnell 2008). These limitations prompted
many companies to review their financial results from economic value perspective through the
adoption of the Economic Value Added (EVA) concept which provides significant information
beyond the traditional accounting metrics e.g. Return on Assets (ROA), Return on Equity (ROE)
and Return on Capital Employed (ROCE) (Chen & Dodd 2001).
Another concept closely linked to EVA, is Market Value Added (MVA) which is defined as the
difference between the Company’s market capitalisation and the book value of shares (Thenmozhi
2000). If the market capitalisation is more than the book value of shares, the company has managed
to create shareholder value and if the market capitalisation is less than the book value of shares, the
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company has destroyed shareholder value. Many researchers including Thenmozhi (2000), and
Hillman & Keim (2001) have used this concept as a proxy for shareholder value. Market value
added (MVA) has the same meaning to the “market to book” ratio and the difference is only that
MVA is an absolute measure while “market to book” ratio is a relative measure (Thenmozhi 2000).
The economic value added (EVA) combines the fundamentals of modern corporate finance theory
where capital has a cost that needs to be recognised and earnings need to be higher than the cost of
capital for shareholder value to be created. In effect EVA refers to the excess net operating profit
after tax (NOPAT) over the expected rate of return by investors (Dierks & Ajay 1997).
Mathematically, it is expressed as depicted in Figure 1.1
Figure 1.1: Mathematical representation of EVA formula
The concept of EVA was first introduced by Stern Stewart & Company in 1991 which changed the
way investors and analysts evaluate on whether management was creating or destroying value for its
shareholders (Grant 2003 P. 10). Creating shareholder value therefore occurs when the market value
of a company is higher than the invested capital and destroying value occurs when the market value
of a company is lower than the invested capital (Shil 2009). In addition it has been argued that EVA
is a reflection of the true intrinsic value of share price (Worthington & West 2004) which provides
information to both management and investors on whether assets are managed efficiently to return
sufficient profits to meet shareholders’ expectations. Depreciation is another source of distortions
which may lead to higher economic value if management decides to employ older assets and defer
new investments causing lower cost of the invested capital. (Crowther et al. 1998)
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To overcome these distortions and other potential deficiencies of EVA, Stern Stewart suggested as
many as 164 adjustments that need to be recognised from financial statements. In practice however,
analysts and external investors may only be able to undertake a limited number of adjustments due
to the limited amount of information available (Crowther et al. 1998). This means that the type of
EVA adjustment is often company specific while the prioritisation of the EVA adjustments is
specific to the analyst decision. For example, some investors may apply “ R&D” and “deferred
taxes” adjustments to some companies and “lease cost” adjustments to others, (Grant 2003: 176).
Interestingly Stern Stewart has never revealed how these adjustments are determined as it is
proprietary information which is commercially offered to their clients (Keef & Rouch 2003)
1.1 Scope of the study
The main theme of this study is to evaluate the concept of economic value as a framework for
measuring shareholder value creation against other traditional accounting metrics. The relationship
and strength of correlation between these variable would provide invaluable source of information to
investors and management in order to support their investment decision making process. The other
aim of the study is to develop a simplified version of the economic value metric (S-EVA) by
exploring various accounting adjustments to reach a coherent and representative economic value
measure. However, the basic and most straightforward analysis form of economic value is to
determine the EVA values by not applying any financial adjustments to the basic information
contained within the financial statements. This basic economic value added metric (B-EVA) is
almost identical to the Residual Income concept (RI). As the research is founded upon the empirical
analysis of 540 UK FTSE ALL-Shares companies over a five year period between 2008 and 2012, it
is believed this will contribute to the body of knowledge on the credibility of EVA application in the
UK stock market. At the time of writing, the author is not aware of any comprehensive review of
the EVA framework on the UK market with the exception of the publication of “Top 200
performing firms by EVA/MVA by Stern Stewart” article by The Sunday Times on 27 September
1998 (cited in Froud et al. 2000).
The research method is developed upon a deductive approach (Figure 1.2) which examines the
relationship between theory and research where a series of hypothesis and research questions are
formulated (Bryman & Bell 2007 P. 155). In essence, the theory of economic value framework will
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be tested against certain hypothesis framework using empirical analysis to either accept or reject the
research questions.
Figure 1.2: Deductive Approach Flowchart (Qureshi, 2010 cited in Kanire 2012 P6)
1.2 Research Questions
Using various sources of information from previous related research on EVA and modern theories
of economics, the following research questions are formulated:
• What is the correlation between the proposed Simplified Economic Value metrics (S-EVA)
against Total Shareholder Returns ?
• What is the correlation between the proposed S-EVA metrics against traditional accounting
measures e.g. ROA,ROCE,ROE and EPS?
• What are the variables for creating S-EVA metric and what is the relationship between those
variables?
As a subset to the above questions, the research will also address the following questions:
• What is the correlation between shareholder value and traditional accounting metrics e.g.
ROA,ROCE,ROE and EPS?
• What is the relationship between Basic EVA, i.e. EVA without any adjustments, and
shareholder value creation?
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This dissertation is divided into five chapters, including this introduction. Chapter two provides a
literature review relating to the drivers of adopting the economic value framework against traditional
accounting metrics. Two approaches of EVA calculations are presented; Basic (B-EVA) and
Adjusted EVA (S-EVA). Chapter three describes the methodological approach taken to conduct the
analyses together with the assumptions made for the economic value estimations along with clear
definitions of the dependent and independent variables. The outcome of the statistical analyses using
panel data techniques including descriptive statistics for the B-EVA and S-EVA against shareholder
value are covered in details in chapter four. Chapter five summarises the main conclusions of the
dissertation with a discussion of the theoretical and practical implications of the results together with
the limitations and directions for future research.
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2.0 Literature Review
2.1 Management Role
Under the Agency theory which was developed in the 1970’s by a group of economists, the efficient
allocation of resources are best managed and controlled by corporate managers on behalf of their
shareholders (Lazonick & O’Sullivan 2000). Management primary job therefore, is to maximise the
market value of the firm. From societal perspective, maximising market value promotes capital to
flow to further investment opportunities with additional returns which would ultimately improve the
economic growth and standards of living (Goldberg 1999). In effect maximising shareholder value
will enhance the economy as whole. However, the problem with creating shareholder value may
generate inequalities of both income and wealth distribution (Lazonick & O’Sullivan 2000).
Furthermore, opponents of shareholder value have contested that managers who control the
allocation of resources and returns are pre-occupied in self-serving and may not adequately create
value for their shareholders (Lazonick & O’Sullivan 2000). This view is shared by Parka and Jang
(2013), who stated that managers, given additional resources, tend to expand the scale of their
operations by adopting projects which may lead to destruction of firm value. One way of controlling
this is to reduce the amount of free-cash-flow available to managers (Stewart 2002). If managers fail
to maintain or create value to their shareholders, then the free-cash flow should be distributed back
to shareholders who can then re-allocate their capital to alternative investments in order to secure
higher rate of returns (Lazonick & O’Sullivan 2000).
The sources of capital used to fund the operating activities is a combination of debt and/or equity
financing which incur costs in the form of either interest payments to lenders or as returns to equity
that would be acceptable to shareholder (Stewart 2002). Few researchers emphasise that share prices
define the shareholder value while many others state that value creation occurs when the return to
shareholder in the form of dividends and capital appreciation exceed the risk-adjusted rate of return
required in the stock market (Dalborg 1999). The market value of shares is influenced by many
factors including company, industry and country specific factors which Bao and Bao (2001)
reasonably concluded that different firms may have different determinants of value including life
cycle of the firm, age, sales growth, dividend payout etc while other scholars, such as Porter (1987)
noted that the structural characteristics of the industry influence the spread individual companies can
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achieve. The power of suppliers/buyers and barriers to entry and availability of substitutes and
rivalry amongst existing firms within an industry may contributes towards the profitability and value
creation capabilities.
Another popular measure of shareholder value creation suggested by Wool (1978) (cited in Shukla
2009), is the comparison between Market Value and Book-Value per share such that when the
market-value exceeds the book-value, shareholder value is created. The company market value
(equity market) is simply the share price multiplied by the number of shares (Fernandez 2004).
However, it is important to recognise that shareholder value added is the term used to identify the
difference between the wealth held by the shareholders at the end of a given year and the wealth
they held the previous year (Fernandez 2004). Hillman and Keim (2001), used the MVA to represent
the shareholder value creation as it captures the wealth creation for shareholders and the
performance of the company.
In finance literature, the most commonly used measure of profitability is the Return-on-Equity
(Stewart 2002). However, if the Return-on-Equity (ROE) is used as a shareholder return metric, then
projects that earn less than the cost of capital may be accepted and projects earning more than the
cost of capital may be rejected as the ROE does not consider the capital cost which is the
opportunity cost of capital of comparable risk (Goldberg 1999). Goldberg (1999) argued that the
goal of performance measurement metrics is to produce a cost-effective evaluation and
compensation system that would encourages managers to create value for shareholders. In the past,
many accounting measures have been investigated and proposed as metrics to determine firm value
including earnings, cash-flow, Book Value, RI/earnings and EVA (Bao & Bao 2001). Further, Bao
and Bao (2001) found that using traditional financial metrics to assess firms’ value were causing
variability across different industries and firm characteristics. They found that earnings and book-
value were determinants of value for large and small firms respectively. Goldberg (1999) on the
other hand asserted that earnings and growth are not an appropriate measure of value.
2.2 Financial Statements
The Chair of Securities and Exchange Commission Arthur Levitt (1993-2000) had condemned the
accounting rules as they could be twisted and manipulated by management to meet Wall Street’s
short-term expectations (Barr 1998). This manipulation stems from the application of accruals,
which recognise earnings when incurred rather than when cash is received (Grant 2003 P. 169). It is
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also not uncommon for managers to create hidden reserves to increase profits by using non-
recurring charges year on year (King & Langli 1998). Accounting distortions could also result from
treatment of deferred taxes, goodwill and inventories under LIFO and FIFO.
In addition of being historic in nature, perhaps one of the most outstanding errors made in the
financial statements is the treatment of equity capital as a free resource that does not reflect the
opportunity cost of the capital employed which could ultimately distort the bottom line figures
(Dearden 1972). As the cost of debt shows up in the interest expense it is difficult for managers to
recognise if value had actually been created/destroyed until the cost of equity capital is recognised
as a resource that has a cost (Tully & Hadjian 1993). In order to recognise the absence of cost of
equity component from financial statements, practitioners introduced the Residual Income (RI) as a
better proxy for economic profit which covers a charge for the capital employed (Balachandran &
Mohanram 2012). It is generally believed that the concept of residual income was developed as a
performance measure by the General Electric Company in the 1950s (Venanzi 2012 p 18). Residual
income therefore comprises accounting income less a capital charge, where the capital charge is
equal to the capital employed times the required rate of return (O'Hanlon & Peasnell 2002). It has
been used as a performance measure, and was the object of extensive debate in the management
accounting literature during the 1960s and 1970s. However, its prominence in financial accounting
was acknowledged following Ohlson’s (1995) seminal work, who recognised that the firm’s
economic value equals its accounting book value plus the present value of all of its expected future
residual incomes.
Under perfect capital market conditions i.e. in a world of no taxes and no bankruptcy costs,
corporate financing and investment decisions are completely separate (Modigliani and Miller 1958
cited in Mauer & Triantis 1994). However, empirical studies show that firm characteristics, such as
profitability, age, risk etc have a direct impact on the financing decisions. As leverage increases the
tax advantage of debt will be offset by an increased cost of debt reflecting greater risk and more
likelihood of financial distress. Modigliani and Miller (1961) (Cited in Goldberg 1999) further
stated that under certain condition, the market “equivalently” capitalises the value of four
alternatives; earnings, free-cash-flow (FCF), dividends or investment opportunities. The market
value of a firm is the future discounted FCF to present value where FCF is the net cash-flow
available after financing investments and paying lenders and investors. The discount rate used
considers the risk of the investments. It follows that in all equity firm (unlevered), FCF is equal to
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dividends and if earnings were not re-invested back in the business, then FCF would equals
earnings. Capital structure is determined by a trade off between the benefits of debt against cost of
debt in one hand and the benefits of equity against its cost (Harris & Raviv 1991). In larger debt
levels, for example, liquidation becomes more likely because of the increased probability of default.
This argument suggests that the value of corporate debt and capital structure are interlinked as the
debt value cannot be estimated without knowing the firm’s capital structure which ultimately affects
the potential for financial stress (Leland 1994).
Economic Value Added (EVA), on the other hand is a special case of residual income, marketed by
the consulting firm Stern Stewart and Co., which is based on measuring income and capital
employed to avoid some of the weaknesses of financial statements when reporting economic value
creation. Stern et al. (1995) defines EVA as the net operating profit after tax less a charge for the
capital employed to produce those profits. The difference between EVA and RI lies in the
adjustments required to the net assets and operating profits. Zimmerman (1997) identified three
major differences between EVA and RI; firstly, EVA is based on modern financial and economic
methods for more accurate measures of the cost of capital. Secondly, it allows for certain accounting
adjustments such as Research & Developments, Deferred taxes etc and finally, EVA could be used
as management compensation metric which measures changes in the intrinsic value of the firm and
it can be used for goal setting, capital budgeting, performance, incentive compensation, and
communication to investors.
The mathematical expression of EVA is;
EVA = NOPAT - Invested Capital x Cost of Capital Where NOPAT is the Net Operating Profits After Tax
In effect, EVA recognises that shareholders are expected to receive a return which compensates the
risk taken. In other words, if the NOPAT is higher than the capital charge this means the returns are
higher than the cost of capital which indicates that the company is creating value (Kaur & Narang
2008). EVA could either be positive, negative or neutral which means it is an absolute measure of
financial performance (Stern et al. 1995). However, Goldberg (1999) recognised that the cost of the
added complexity of EVA calculations over RI may exceed its benefits. Fernandez (2004) noted
that the link between EVA and shareholder wealth has prompted many leading companies to adopt
the EVA system following the improvements made from its implementation for many companies
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like AT&T, Coca-Cola, Quaker Oats etc. This is because, EVA provides an estimate of an
organisation’s economic profit, which is the value created over and above the expected return of
shareholders. Therefore, EVA reflects the profit of the organisation less the cost of financing its
capital, in other words, what shareholders make when the return from the capital employed is greater
than the cost of that capital (Stewart 1990). One way in which this amount can be calculated is by
re-packaging the financial statements and making adjustments to the income statement and balance-
sheet and deducting the opportunity cost of equity capital (Poll et al 2011). Value creation is the
difference between return on equity (ROE) and Cost of Equity (Ke ) which is the expected return
based on a risk premium over the risk-free return which Fernandez (2004) summed it up in the
following expression:
Created Shareholder Value = EMV x (ROE-Ke) Where EMV is the Equity Market Value ROE is the Return on Equity and Ke is the Cost of equity
ROE is calculated by dividing the Net Income by the share’s book-value and this is not the same as
Shareholder Returns (Fernandez 2004). It has been asserted by Turner and Morrell (2003) that the
cost of equity cannot be measured precisely. The equity cost which is in reality an opportunity cost
is the expected return by shareholders when purchasing a portfolio of shares in other companies of
comparable risk. If a firm cannot give its shareholders at least the return that they could earn by
investing in other portfolios, it will lose value. Thus, in an economist’s view, a company does not
begin to earn a profit until it can cover the opportunity cost of its equity capital. Financial theory
asserts that shareholders will be compensated for assuming higher risks by receiving higher returns
(Turner & Morrell 2003).
2.3 EVA Accounting Treatments The recommended accounting treatment suggested by Stewart (2003) is to separate the economic
profit calculations from all financing costs prior to subtracting them from each other. The economic
profit result is known as the net operating profit after taxes, or “NOPAT” for short. NOPAT is a
measure of pure operating results that is not influenced by temporary variations in capital structure
or interest rates and it is based on estimating the “unlevered” NOPAT. An incorrect focus on the
levered NOPAT rather than the unlevered NOPAT would lead to a favourable bias in the economic
profit calculations. This is because of the variability of capital structure amongst companies and the
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tax shield received from government. Once the NOPAT values are determined, the second step is to
subtract all financing costs which is represented by multiplying the firm’s overall financing cost i.e.
WACC by the Invested Capital (Worthington & West 2004). Furthermore, the details of the EVA
calculations could vary significantly and there are many methods that could be employed to
determine its value. For the purpose of this study two approaches are presented as prescribed by
Grant (2003). These are called Basic EVA (B-EVA) and Adjusted EVA (S-EVA).
2.3 Basic EVA Framework Grant (2003 p 61) stated that in the absence of any EVA accounting adjustments, the “unlevered”
NOPAT can be expressed in terms of its tax adjusted earnings before interest and tax (EBIT) i.e.
Basic EVA = NOPTA - WACC x IC NOPAT = EBIT (1-t) EBIT = Sales - Cost of Goods - Depreciation - SGA
Therefore,
Basic EVA = (Sales - Cost of Goods - Depreciation - SGA) x (1-t) - WACC x IC
Where; NOPAT is the net operating profit after tax WACC is the Weighted Average Cost of Capital EBIT is the Earnings before interest and tax SGA is the sales, general and administration costs
2.4 WACC Assumptions and calculations
For a company financed solely with debt and equity, the WACC is defined as follows:
𝑊𝐴𝐶𝐶 =𝐷
𝐷 + 𝐸 𝐾𝑑 (1 − 𝑇𝑚) +
𝐸𝐷 + 𝐸
𝐾𝑒
Where; (D) and (E) represents the debt and equity value respectively. Tm is the interest tax-shield on interest paid to lender Kd and Ke represent the Cost of Debt and Cost of Equity respectively
All of the above components are observable with the exception of the Cost of Equity (Ke) which is
simply the returns investors expect to receive from the company. The most popular model in
determining the cost of equity is the Capital Asset Pricing Model (CAPM), developed by Sharp et
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al. (1964), which is based upon finance theory contributions (Turner & Morrell 2003). CAPM is a
trade-off model between risk and expected return of listed companies and the market. In other
words, the model allows for a specific, market-based evaluation of risk for a company and its
individual business units which use the concept of ‘Beta’ (Stewart 2003).
Investors are risk averse and common shares have variability in volatility against the market as a
whole. Beta is therefore a measure of the volatility of a company’s share price against the overall
stock market (Turner & Morrell 2003). It reflects market risk as opposed to company specific risk
and cannot be diversified away. While market risk could be hedged with derivative instruments such
as options and futures, it cannot be eliminated as it is company specific. In addition, investors could
mitigate those risks by diversifying their investments into other stocks. The covariance between the
company’s return and the market return is known as Beta value and it is a measure of the systematic
risk of that company (Turner & Morrell 2003). This approach makes a clear distinction between the
systematic risk which is a market led risks that are attributed to factors common to all companies
such as September 11th events and company specific risks which are driven by the company’s
characteristics. If the Beta value is larger than one, this implies a risky instrument than the overall
market (Turner & Morrell 2003). The risk can therefore be expressed as:
Total Risk= Systematic (market) risk + Company specific risk
So the expected equity return is basically the return on risk-free asset plus a market risk-premium.
This is expressed as follows:
𝐶𝐴𝑃𝑀 (𝐾𝑒) = 𝑅𝑓 + 𝑀𝑅𝑃 𝑥 𝐵𝑒𝑡𝑎 (𝛽)
Where Ke is the Cost of Equity Rf is the Risk Free Rate MRP is the Market Premium Rate Beta is the company’s risk
The risk premium reflects the price paid by the stock market to all equity holders adjusted for a
company risk factor (beta). However, applying the above requires judgements and assumptions
which may lead to variations in the Cost of Equity values. This is because the exact methodology of
estimating Beta is not explicitly indicated for published values. In addition Turner and Morrell
(2003) reported that different sources of beta seem to make different assumptions with different
values emerging. The variability of these assumptions is driven by a number of factors including
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dividends, taxes and liquidity. Turner and Morrell (2003) also asserted that the frequency and
periods used for the beta calculations may differ across different models. The variations in Beta
values and the assumptions used could therefore introduce problems in the cost of capital (WACC)
calculations making them neither unique nor stable. It is also worth noting that WACC is linked to
government bonds and therefore the Cost of Capital would change from country to country.
Although applying the weighted average cost of capital is intuitive and relatively straightforward, it
has some drawbacks. Koller et al. (2010 p103) asserted that WACC model works best when the
debt-to-value ratio is relatively stable and when this is expected to change, it is recommended to use
other models such as the Adjusted Present Value (APV) model as WACC becomes more difficult to
apply in these circumstances. APV specifically forecasts and values any cash flows associated with
capital structure separately, rather than embedding their value in the cost of capital.
2.5 Adjusted EVA (S-EVA) calculations The aim of undertaking financial adjustments is to convert accruals to cash-based operating profits
and to remove the effects of financing decisions from operating results (Grant 2003 P. 169). Over
146 adjustments can be made varying in complexity which may require assumptions about future
performance that investors and directors may not agree with (Kaur & Narang 2008). However, in
practice, most analysts use between six to eight adjustments (Sparling & Turvey 2003). To drive
more value of the Adjusted EVA calculation, Kaur and Narang (2008) recommended simplicity and
consistency over time along with undertaking adjustments that could make a material impact on the
results. Worthington and West (2004) suggested to classify the EVA adjustments into R&D,
Deferred taxes, intangibles, depreciations, provisions for warranties and bad debts. Consequently, in
order to adjust the EVA to estimate the adjusted S-EVA values, the overall EVA formula remains
unchanged except for the details in calculating the various components of the EVA structure as
depicted below:
EVA = NOPAT - Invested Capital x Cost of Capital
Where NOPAT is the net operating profit after tax after adjusting for the followings
entries; (i) non-operating items (ii) non-recurring events and (iii) other economic adjustments to compute economic profits from accounting
profits etc. (iv) Etc.
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IC is the Invested Capital after adjusting for the followings: (i) Non-interest bearing liabilities (ii) Capitalised expenditure on R&D (iii) Deferred taxes (iv) Etc.
It is also noted that non-recurring income is excluded from NOPAT and are capitalised after tax and
non-recurring losses/expenses are taken as additions to capital (Kaur & Narang 2008). Furthermore,
Kaur and Narang (2008) suggested the following specific adjustments for calculating the Adjusted
EVA value:
NOPAT= (Profit after tax + non-recurring expenses+ Revenue expenditure
on R&D + Interest expenses + Goodwill written off + provision for
taxes) - non recurring income - R&D amoritization - cash operating
taxes
Invested Capital = Net fixed assets + Investments + Current Assets - (NIBCLs + Misc expenditure not written off + Intangible assets) + ( Cumulative non-recurring losses + capitalized expenditure on R&D + Gross Goodwill) - Revaluation Reserve - Cumulative Non-recurring gains.
Where NIBCL refers to non-interest bearing liabilities.
A brief description of some of the adjustments made to the financial statements is summarised
below:
2.5.1 Operating Lease Adjustments
Operating lease is viewed as a form of secured borrowing which is treated as rental expenses and the
related assets are not on the balance sheet. EVA proponents argue that the lease is really debt
incurred to purchase an asset and as such the interest portion of the lease payments is a capital
charge and should not be considered as part of NOPAT. So the present value of future lease
payments should be added back to capital and the interest payment of the lease should be added
back to NOPAT (Goldberg 1999).
2.5.2 Research and Development (R&D) Adjustments
All R&D costs are expensed in the Income Statement. However, R&D is an investment similar to
tangible assets and benefits the company over a longer period of time. The EVA adjustment calls for
20
capitalising such expenditure and amortise the amount over time. (Goldberg 1999). So EVA adjusts
for this and considers it as part of the Capital.
2.5.3 Adjustments to Deferred Taxes
As the deferred taxes remain on the balance sheet indefinitely, Goldberg (1999) suggested this
liability should be added back to NOPAT and subtracted from capital. NOPAT should be reduced
when the recorded book taxes exceed the actual taxes paid in an accounting period.
2.5.4 Adjustments to Warranty and Bad Debt
These expenses are adjusted back to underlying cash payments. Provisions for warranty expenses
and doubtful accounts on the balance sheet are added back to Invested Capital. Increases (decreases)
in these allowances during the period are added to (subtracted from) income to adjust it to the
NOPAT values (Goldberg 1999).
2.5.5 Adjustments to Goodwill
This is a permanent investment in the business where shareholders expect returns and so it should be
amortised. The gross goodwill adjustment cost is included in the Capital as it is viewed as a
permanent investment. Therefore amortization of goodwill is added back to NOPAT and cumulative
amortisation of goodwill is added back to the invested capital (Goldberg 1999). The reason for
adjusting it this way is to reflect the true cash-on-yield value (Prober 2000).
2.5.6 Adjustments to Interest
All interest expenses are added back to profit. The tax-benefit of interest is also removed and the
cash operating taxes for the company are adjusted accordingly. This is done as the tax-benefits of
interest are already considered in the cost-of capital (Goldberg 1999).
2.5.7 Adjustments to Non-Interest Bearing Current Liabilities (NIBCLE)
The financing costs associated with payments to suppliers and employees are already included in the
cost of goods and should therefore be excluded from Capital (Goldberg 1999)
21
2.5.8 Adjustments to Last In, First Out (LIFO)
Another possible adjustment relates to the LIFO reserve accounts. The increase (in inventory) is
added back to profit because it converts inventory from a LIFO to FIFO valuation, which is a more
accurate approximation of replacement costs. LIFO represents past holding gains and accordingly is
added back to the equity component to reflect the capital invested (Prober 2000).
Finally, judgment should be used to decide when and what to adjust in determining the Adjusted
EVA value. Stern et al (1995) set out three criteria when deciding which EVA adjustment to choose
from;
Materiality: Will the adjustment made have a material difference to EVA.
Manageability: Will the adjustments have an impact on future decisions?
Definitiveness: Could the adjustments be objectively determined?
2.6 EVA and Decision Making
According to Harris and Raviv (1991), implementing EVA model would assist managers to
determine areas of the business that have the highest contribution to success and by isolating certain
activities such as inventory management or capacity utilization, firms can judge the value of these
decisions on projects or business units. Thus management can drive more economic value, rather
than concentrating on reported numbers alone. By including the invested capital, firms are also able
to use EVA in evaluating the performance of individual divisions and managers who run the
company. In effect EVA helps to improve operating profits without locking up more capital in the
business which would help in liquidating investments that do not meet capital costs. Management
actions such as cost and capital reductions or decisions on the optimal capital structure represent the
types of value from adopting the EVA system.
2.7 Driving value from EVA framework
In order to realise more economic value from the EVA concept, the firm would need to increase its
operating income without increasing the capital and its associated cost. In addition, management
need to invest in projects that earn more than the cost of capital and divest from projects where
earnings are less than the cost of capital. It has therefore been argued by Stewart (2003) that the
NOPAT/Total Capital ratio is far more reliable measure than the Return on Equity because it
provides a much better insight on how productively management had been in managing its corporate
assets, regardless of its sources of finance. Shukla (2009) also asserted that higher EVA values were
22
caused by increases in asset productivity, and by controlling operational costs which results in
higher margins and lower cost of equity. Furthermore, EVA rises when operational efficiencies were
improved and the cost of equity is reduced. However, Fernandez (2004) noted that the decrease in
the cost of capital, which would lead to the increased EVA value, could be attributed to a drop in the
interest rates or a drop in the market premium rates which will have nothing to do with management
performance. Tully and Hadjian (1993) suggested in order to increase the EVA, the firm would need
to increase its revenues, reduce its operating costs, reduce the cost of capital and use less capital to
generate the same operating profits (e.g. business efficiency) which implies improvements in returns
on assets (ROA) and minimising business risks of the invested capital.
As mentioned previously, the total market value (MVA) of a firm is equal to the sum of the market
value of its equity and the market value of its debt leading to the concept of Market Value Added
(MVA) which is defined as the difference between total market value and the economic capital
(Reilly & Brown 1999 p 591). Reilly and Brown (1999 p 590) defines the economic capital as the
fixed assets plus the net working capital where the net working capital is the current assets less non-
interest-bearing liabilities. So from investors’ point of view, MVA is the best external performance
measure as changes to the MVA value could be used to assess if value had been created/destroyed.
The main limitations of the EVA is that in isolation, it is inadequate for assessing progress in
achieving strategic goals and objectives and it is also distorted by inflation and depreciation
variables which could change depending on macro-economic conditions, industry, asset age and
structure e.g. depreciable assets and un-depreciable assets (Stern et al 1995).
O’Byrne (1996) asserted that EVA can be used to calculate the Discounted Cash Flow (DCF).
However, the free-cash-flow (FCF), used in the DCF calculations, is poorly correlated with current
market value because it fails to match investment levels with the future periods. EVA, on the other
hand, recognises the future deferred expenditure and hence it is more highly correlated with current
market value. The DCF value of a company can be expressed in terms of EVA as:
Firm Value = Capital + PV of Future EVA
Goldberg (1999) asserted that EVA and DCF are mathematically equivalent except that EVA is
easier to communicate and explain to management.
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2.8 Previous Empirical Research Overview
The purpose of this section is to provide a literature review covering some the empirical research
conducted on examining the strength of correlation between EVA against traditional accounting
metrics. It is claimed that EVA provides a robust measure for shareholder value creation following
a number of US studies. However, other studies reported poor or no correlation between EVA and
shareholder returns (Ismail 2006). Stern Stewart (1989) (cited in Maditinos et al. 2006), for example
analysed the correlation between EVA and MVA for 613 US companies and found that when the
EVA was positive, the correlation with MVA was high and for negative EVA values the correlation
with MVA was poor and negative. The reason for this finding was attributed to the market value of
the firm which would always reflect the value of the net assets. Finegan (1991) analysed 450 firms
and found that EVA outperformed the other measures such as EPS, Capital Growth, ROC and
growth in cash-flow considerably with 61% correlation with MVA compared to 47% for ROC. Lehn
and Makhija (1996) conducted a research on 241 large US companies between 1987 to 1993 to
investigate the interrelationship between ROA,ROE ROS,TSR,EVA and MVA. EVA correlated
slightly better than the others against total shareholder returns. They concluded that EVA and MVA
were effective performance measures.
O’Byrne (1996) analysed 9 years of data between 1985-1993 using capitalised EVA i.e.
EVA/WACC, NOPAT and FCF relative to market value divided by invested capital. He found the
FCF did not explain changes in market value when divided by capital ratio while NOPAT explained
33% and EVA -31%. He concluded that EVA, unlike NOPAT is systematically linked to the market
value and that EVA is a powerful tool for understanding investors’ expectations. Another study by
Kleiman (1999) conducted on 71 companies who adopted EVA between 1987-1996 and compared
shareholder return with other companies in the same industry. The results illustrated better share
price returns than companies who did not adopt the EVA concept. Milunovich and Tsuei (1996)
investigated US based computer companies between 1990 to 1995 and found 42% correlation
between EVA and MVA. They also argued a weak correlation between FCF and MVA.
Biddle et al. (1999) on the other hand, conducted a research covering 600 companies for period
between 1984 to 1993 and found accounting earnings is highly associated with shareholder returns
than RI and EVA. They also found that earnings outperform EVA and that EVA components have a
marginal contribution to the financial information already available. Further studies by Dodd and
24
Chen (1996) covered data for 566 US companies over a 10 year period to investigate the correlation
between EVA and shareholder returns. They found the shareholder return were 20% correlated to
EVA, 25% to ROA, 19% to RI (Residual Income) and 7% to EPS. Interestingly, when multi-
regression analysis was used, the correlations for EVA and RI rose to 41% against shareholder
return. Fernandez (2002) analysed the relationship between shareholder value creation and the EVA
for 582 American companies covering period 1987 to 1997. He calculated the correlation between
the increase in MVA each year against EVA, NOPAT and WACC. For 296 companies, the
correlation between the increase in MVA each year and NOPAT was greater than the correlation
between the increase in MVA each year and the EVA. Kleiman (1999) found that companies who
adopted EVA concept increased their asset sales by 30%. Ismail (2006) analysed 252 firm year
observation of the UK market between 1990 to 1997. The analysis revealed low correlation
between EVA and share price returns compared to NOPAT and RI and hence rejecting the claims of
the value of EVA. However, Uyemura et al. (1996) demonstrated that EVA has a high correlation
with MVA and thereby share price returns. O’Byrne (1996) concluded that changes in EVA explain
more variations in the long-term performance of share price returns than changes in earnings.
25
3.0 Methodology
This section explains the methodology adopted for the study including the data sources, population
size, and the assumptions of the calculations made in developing both the EVA and MVA against
the traditional financial metrics. Details of the various assumptions made along with the statistical
methods used are also explained.
3.1 Financial Analysis
One of the objectives of this study is to investigate the correlation between shareholder returns
(Dependent Variable) against conventional financial metrics (Independent Variables) namely Return
on Investment, Return on Equity, Return on Assets and Earnings per share. All of these financial
ratios were extracted from Fame database for the entire population of 557 companies over a five
year period between 2008 and 2012. Stata package, Microsoft Excel, Sigma XL and Access
databases Software were used to analyse and calculate the descriptive statistical values such as
mean, median, variance, standard deviation, minimum, maximum and range of each metric for each
year.
The Generalised Least Square (GLS) Regression with correlated disturbance using heteroskedastic
correlation across panel data model was fitted to infer on whether there are any correlation
relationships between the dependant and independent variables.
3.2 Dependent Variables Measuring shareholder value creation, according to Stewart (2000) could be assessed by the firms
MVA capabilities, while others such as Boasson and Boasson (2006) argued that Tobins Q ratio is
the most appropriate metric of measuring value creation to its shareholders. MVA is determined by
the difference between the Market Capitalisation and its Book Value. A positive MVA indicates that
the company is creating value for its shareholders and a negative MVA implies destruction of
shareholder value. De Wet (2005) also used MVA as a proxy for shareholder value creation on a
comparative study conducted on the JSE Securities Exchange of South Africa to asses EVA against
traditional accounting metrics. As mentioned previously, others including Thenmozhi (2000), and
Hillman & Keim (2001) have used this concept as a proxy for shareholder value.
The Tobin’s Q Ratio, on the other hand, is estimated by dividing the market value of assets by the
replacement value of those assets. Baosson and Boasson (2006) explained that if the market value
26
reflects the asset value of a company, the value of Tobin's Q would be one. If Tobin's Q is greater
than one, then the market value is greater than the value of the company's assets. On the other hand,
if Tobin's Q is less than one, the market value is less than the recorded value of the assets of the
company. This suggests that the market may be undervaluing the company. From the above, it was
decided to select MVA as a proxy for shareholder returns and use that as the dependent variable
which demonstrates the value added to a particular equity share over its book-value at a given
period. This is measured by subtracting the book-value of shares from its market capitalisation.
𝑀𝑉𝐴(𝑖,𝑡) = 𝑀𝑎𝑟𝑘𝑒𝑡 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 (𝑖,𝑡) − 𝐵𝑜𝑜𝑘 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑆ℎ𝑎𝑟𝑒𝑠 (𝑖,𝑡)
Where (i,t) represents company and period (year) respectively.
3.3 Independent Variables Four performance Measures were considered as independent variables i.e. Return on Assets (ROA),
Return on Capital Employed (ROCE), Return on Equity (ROE) and Earnings Per Share (EPS). A
brief description of these is summarised below (Reilly & Brown 2000).
3.3.1 Return on Assets (ROA)
Return on Assets measures a company’s earnings in relation to all of the resources it has employed.
ROA describes the generated earnings from the invested capital (assets).
The assets of the company are comprised of both debt and equity. Both of these types of financing
are used to fund the operations of the company. The ROA figure gives investors an idea as to how
effectively the company is converting the money it has into net income. The ROA is expressed as:
𝑅𝑂𝐴 =𝐸𝐵𝐼𝑇𝐴𝑠𝑠𝑒𝑡𝑠
3.3.2 Return on Capital Employed (ROCE)
Return on Capital Employed (ROCE) is a measure of the returns that a company is realizing from its
capital (Watson and Head 2007 p 46). It is calculated by dividing the profit before interest and tax
by the difference between total assets and current liabilities. The resulting ratio represents the
efficiency with which capital is being utilized to generate revenue. The ROCE is expressed as:
𝑅𝑂𝐶𝐸 =𝐸𝐵𝐼𝑇
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 − 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
27
3.3.3 Return on Equity (ROE)
The ROE relates earnings to the book-value of equity and reports the profitability from the
investor’s perspective and it is expressed as:
𝑅𝑂𝐸 =𝐸𝐵𝐼𝑇
𝐵𝑜𝑜𝑘 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐸𝑞𝑢𝑖𝑡𝑦
3.3.4 Earnings per Share (EPS)
This is a commonly used metric by many managers/investors to assess the firm’s profitability
performance which relates the company's profit against each outstanding share. It is expresses as
follows
𝐸𝑃𝑆 =𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒
𝐶𝑜𝑚𝑚𝑜𝑛 𝑆ℎ𝑎𝑟𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔
3.4 Economic Analysis Assumptions
This section of the study is split into two parts;
3.4.1 Part 1: Develop a “Basic” EVA (B-EVA) and investigate any cross-sectional correlation with
Shareholder returns as measured by the MVA metric between 2008 and 2012.
3.4.2 Part 2: Develop a Simplified EVA (S-EVA) and investigate any cross-sectional correlation
with Shareholder returns as measured by the MVA metric between 2008 and 2012. However, in
order to ensure transparency, it is important to clarify and state the assumptions made for the B-
EVA and S-EVA calculations for parts 1 and 2 above
3.4.1 BASIC EVA Assumptions (B-EVA)
The unlevered EVA is estimated by applying the following formula:
𝐸𝑉𝐴(𝑐,𝑖) = 𝑈𝑛𝑙𝑒𝑣𝑒𝑟𝑒𝑑 𝑁𝑂𝑃𝐴𝑇(𝑐,𝑖) + 𝐼𝑛𝑣𝑒𝑠𝑡𝑒𝑑 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 (𝑐,𝑖) 𝑥 𝑊𝐴𝐶𝐶(𝐶,𝑖)
Where c, i represent the value for company (c) at a time period (t)
The B-EVA analyses was conducted without any accounting adjustments and so the unlevered
NOPAT can be expressed in terms of its tax-adjusted earnings before interest and taxes i.e. EBIT.
The Cost of Invested Capital is simply the product of the Invested Capital in the business by the
WACC cost. By applying the CAPM model developed by (Sharp et al 1964), the cost of equity (Ke)
could be estimated as follows:
28
𝐶𝐴𝑃𝑀 (𝐾𝑒) = 𝑅𝑓 + 𝑀𝑅𝑃 𝑥 𝐵𝑒𝑡𝑎 (𝛽)
Where Ke is the Cost of Equity Rf is the Risk Free Rate MRP is the Market Premium Rate Beta is the company’s risk
The assumptions made for the CAPM model were as follows:
Risk free rate (Rf ) = 3%. This figure was based on the UK Guilt 10 and 30 years yield which were
between 2.69% to 3.57% (Bloomberg 2013). The Risk Premium rate (MRP) was assumed at 5%.
This is because prior to April 2013, the UK Risk premium was 5.8% based on “AAA” credit rating
(Damodaran 2013). This findings match a survey conducted by Fernandez et al. (2013) where the
median for the applied MRP in the UK was 5% based on 171 respondents. It is worth noting that the
UK rating was downgraded to AA+ in April 2013 (BBC 2013). Gregory (2011) asserted that due to
a wider variability in the market movements, reasonable estimates of the cost of equity were
between 5.32% and 5.76% which translates into a risk premium between 4.18% and 4.62% when
incorporating a “Fama-French bias adjustment”. An empirical study conducted by Vivian (2007)
suggests the annual UK expected market equity premium was in the region of 3–4%. The evidence
suggests that future returns are expected to be even lower. The Beta values which refers to the
relevant company’s risk values were extracted from Fame database for each year of the analysis
period.
The WACC calculations are derived from the following formula:
𝑊𝐴𝐶𝐶 =𝐷
𝐷 + 𝐸 𝐾𝑑 (1 − 𝑇𝑚) +
𝐸𝐷 + 𝐸
𝐾𝑒
Where; (D) and (E) represents the debt and equity value respectively. Tm is the interest tax-shield on interest paid to lender Kd and Ke represent the Cost of Debt and Cost of Equity respectively
The historical corporate tax rates were extracted from the HMRC office (Anon. 2013). For the
analysis period, the corporate tax rates ranged between 28% to 24% as illustrated in Table 3.1.
29
Table 3.1 Extract of the main rates of Corporation Tax
Source: HMRC Office (CT Returns v 2.2 2013)
The last step in determining the Basic EVA is to re-package the ‘Balance sheet’ by year against each
company to estimate the equivalency of the firm’s capital Invested . This is done by summing the
Long Term debt with the Share holder’s equity to determine the financial capital of the business.
The NOPAT is determined by stripping out the tax provision from the ‘Operating Profit’ as
published in the income statement. The invested capital (IC) is determined by simply adding the
reported Long Term Debt to the Shareholders’ equity. If the NOPAT is less than the total cost of the
invested capital, the company is seen as a wealth destroyer as the operational profits are not large
enough to cover the cost of investments. If, on the other hand the NOPAT is larger than the total
cost of invested capital, the company is seen as a wealth creator. In order to quantify the rate of
wealth creating/destruction which would also aid in the panel analyses, the B-EVA figures were
capitalised by the Capital Employed for each company at each year of the analyses. This approach
was adopted by O’Byrne (1996) in a nine year data analyses where the EVA values and the NOPAT
relative to market value were capitalised by dividing these values by the invested capital along with
Grant (2003 P. 6) who examined the relationship between MVA and EVA divided by the invested
capital for 983 companies selected from the Stern Stewart database.
3.4.2 Adjusted EVA Analyses Assumptions (S-EVA)
As stated previously, the aim of adjusting the financial statements is to convert accruals to cash-
based operating profits and to remove the effects of financing decisions from operations. NOPAT
should therefore reflect the cash operating taxes that would normally be paid by an unlevered
company. This is because the benefits of leverage show up in the after-tax cost of debt component of
the WACC. From the EVA tax perspective, the income taxes are adjusted by changes in the deferred
income tax, and by adding back the various interest tax subsidies received by the company.
As there are many variations of adjustments to the NOPAT, it was decided to include the following
adjustments for the purpose of this study:
30
Adjustments made to the Net Operating Profits After Tax (NOPAT)
The adjustments made to the NOPAT estimations are discussed in this section which includes R&D,
Operating Lease, and Cash-Operating Taxes.
R&D adjustments to NOPAT
The R&D costs are expensed in the Income statements of the current period. However, these costs
may promote the long term profitability of the company and would therefore underestimate both, the
total earnings and assets for the current period. For the EVA calculations, the R&D costs should
therefore be capitalised over a period of time (Cheng 2011). The R&D expenses were therefore
capitalised and amortized over a four year period. To do this, the R&D expenses were extracted
from FAME for the period 2004 to 2012. The aggregated expenditure on R&D was amortized over a
four year period and the net change from previous year was then added back as an adjustment to the
NOPAT value. An example of the R&D adjustment for Smith Group PLC is shown in Figure 3.2
Figure 3.2: Example to illustrate the capitalisation of R&D over a four-year period for Smith Group plc
2012 2011 2010 2009 2008 2007 2006 2005 2004
Smiths Group PLC 68,000 69,200 66,700 52,700 52,300 108,400 108,400 143,600 0
R&D Expensed
over four years
0 0 0 0
35,900 35,900 35,900 35,900
27,100 27,100 27,100 27,100
27,100 27,100 27,100 27,100
13,075 13,075 13,075 13,075
13,175 13,175 13,175 13,175
16,675 16,675 16,675
17,300 17,300
17,000
Unamortised R&D 64,150 60,225 70,025 80,450 103,175 90,100 63,000 35,900 0
Net Change (added to NOPAT) 3,925 -9,800 -10,425 -22,725 13,075 27,100 27,100 35,900 0
Operating Lease Adjustments
Companies may own assets through operating lease. The costs of operating lease should be treated
as expenses in current period. When calculating EVA, the operating lease costs should be added
back to NOPAT (Cheng 2011). The implied interest on operating lease was assumed to be 6% which
was then added back to the EBIT values as an adjustment to NOPAT.
31
Adjustments to Cash-Operating Taxes
In order to adjust for the Cash Operating Taxes (COT), the following calculations were
implemented:
COT = Income Tax Expense + Tax on implied interest of operating lease
+ Changes in deferred taxes
Assumptions of the Adjustments made to the Invested Capital
It has to be recognised that for every income statement adjustment, a corresponding adjustment to
the balance sheet has to be made in order to arrive at the invested capital. In estimating the Invested
Capital, the Financing Approach described in Grant (2003 P. 170) was adopted where
IC = Total Equity – Total Debt
The Total Equity Equivalents is determined by adding:
Book Value of Common Equity + Minority Interest + Deferred Income Taxes
+ Unamoritizes R&D
The Total Debt Equivalents is calculated by adding:
Short-Term Loans + Bank Overdrafts + Other Short Term Loans + Long
Term Debt + Pension liabilities + Other provisions + PV of Operating lease
3.5 Statistical Analyses using Panel Data Analysis Panel data analysis is becoming more popular among social and behavioural science researchers. It
is a form of longitudinal data analysis where a cross-section of companies is surveyed over a given
period of time (Hoechle 2007). The process consists of regression analysis with both temporal and
spatial dimension. The temporal dimension includes periodic observations of a set of variables over
a particular time span. The spatial dimension includes a set of cross-sectional units of unrelated
observations e.g. countries, states, companies etc (Yafee 2003). The spatial dimension of the panel
data set in this study consists of 557 companies of which each company (i) has the same financial
and economic variables along with the share price information over a yearly temporal reference, (t)
over a five year period. This pooled dataset contains 2785 (i x t) observations for each variable of
the study. If the all the data for all the variables were available the dataset is called a balanced panel.
32
However, it was noted that some companies had missing values against some of these variables e.g.
share prices and in order to conduct the analyses on balanced panels, some of the companies were
omitted from the analyses.
The panel analyses equation could be expressed as
𝑌(𝑖,𝑡) = 𝛼 + 𝑏𝑥(𝑖,𝑡) + ∈(𝑖,𝑡)
Where Y is the dependent variable for company (i) and time (t). α and b are the coefficients. ∈(𝑖,𝑡) error present for company (i) and time (t) which could either be fixed or random effect errors.
Furthermore, the adopted dynamic panel model utilizes autocorrelation (AR) based on Durbin-
Watson test where common autocorrelation may be present across all panels rather than panel
specific autocorrelation. The main problem with panel models is the presence of outliers which
could have bias in the regression slopes. The use of heteroskedasticity covariance estimator with
ordinary least squares estimation can return errors that are robust along the predicted line (Green
2002 P. 210). Baltagi (2005 P. 4) lists many advantages to panel data modelling including
heterogeneity of the panel data which allows for individual-specific variables. In addition it provides
less collinearity amongst variables and more degrees of freedom (df) and higher efficiency. The
main disadvantages include missing data within variables making the analyses unbalanced and the
time span available for each individual means the asymptotic arguments is dependent on the number
of individuals.
In this study, two methods of statistical analyses have been used; the Generalized Estimating
Equations (GEE) and the Feasible Generalized Least Squares (FGLS). Biased statistical inference
may occur when the correlation of regression disturbances take place over the time period and
between subjects i.e. companies. Panel data analyses adjusts the standard errors of the coefficients
for possible dependence in the residuals and whilst most leading finance journals adjust for the
standard error estimates that are heteroskedasticity and autocorrelation consistent, cross-sectional or
“spatial” dependence is still largely ignored. Micro-econometric panel datasets are likely to exhibit
complete patterns of mutual dependence between the cross sectional units e.g. firms.
The GLS Regression with correlated disturbance using heteroskedastic correlation across panel data
was fitted to infer on whether there is any correlation between the dependant and independent
variables.
33
4. Analysis of Results
4.1 Data Collection
The study is based on gathering and analysing secondary data where the financial statements of the
UK FTSE ALL-Shares companies were extracted from Fame version 54.00 Database for all active
companies which covered a period of five years between 2008 and 2012. The Share price values for
the same companies covering the same period were also extracted along with the traditional
financial metrics and the Beta values. A total of 557 companies over a six year period made the
population for the analyses reach 3,342 company year observations. The full accounting data were
extracted from the Income Statements and Balance Sheets and exported to Excel spreadsheets. A
comprehensive database in Access was developed to store all the extracted fields. The advantage of
this approach was the ability to create various tables through relational queries. By using the
company name as the unique identifier, it was possible to create a table that combines many fields
across structured relational queries. As an example, current assets, EBIT and EPS could all be
integrated in one table by creating a relevant query in Access database. Other economic data were
also collected from other sources in order to facilitate the calculations of the Economic Value Added
(EVA) and MVA as indicated in the relevant parts of this section. Examples of these are the
corporate tax rates, Risk free rates etc.
4.2 Data Analyses All available entries within the imported financial statements including those contained within the
income and balance sheets were tabulated and classified into various headings and sub-heading from
year 1 (2012) to year 5 (Year 2008) using Access and Excel software. It was noted, however, that
some fields had missing values or denoted with “n.a”. As the major part of the analysis was based
on panel-data analyses using cross-sectional analyses, it was critically important to ensure the
dataset for each model is “balanced” as the FGLS analyses demands a “balanced” dataset.
In order to comply with the balanced datasets requirements, it was important to ensure that each
company had a full set of results within the analysis period. It was therefore necessary to trim the
dataset when either missing values or “n.a” values were present. This might explain the reasons of
the sample size variability throughout the analysis procedures. In addition to the above, descriptive
statistical values such as mean, median, variance, standard deviation, minimum, maximum and
range of each dependant and independent variables for the relevant data was reported using a
34
combination of Stata software and Excel spreadsheets which were powered by SigmaXL
functionalities.
The hypothetical assumptions were tested for statistical significance using cross-sectional panel
analysis methodology. In addition, descriptive analyses were undertaken by splitting the data into
yearly-time intervals to identify trending patterns over the analysis period. The order of this section
starts by descriptive statistics of the shareholder value proxy (MVA) followed by statistical analysis
of traditional accounting metrics. Both B-EVA and S-EVA are then analysed against MVA using
the FGLS cross-sectional panel data analysis.
As the MVA is an absolute value, it was decided to convert it to a relative measure by dividing it by
the capital employed (MVA/CE)
4.3 Analysis of MVA as a proxy of shareholder value creation The descriptive statistics for the calculated MVE/CE which was split into yearly intervals between
Yr5 (2008) and (Yr1) 2012 is shown in Table 4.1 below:
Table 4.1: Descriptive statistics for the MVE/CE ratio over a five year period
Descriptive Statistics MVACE_Yr1 MVACE_Yr2 MVACE_Yr3 MVACE_Yr4 MVACE_Yr5
Count 460 460 460 460 460
Mean 1.678297104 1.908629334 1.487718806 0.38392787 2.093860581
Stdev 5.197095535 4.959139294 4.361987014 3.946729692 5.749588195
Range 74.85128174 65.25536125 63.33430839 51.22719427 92.89636889
Minimum -24.325875 -25.0818281 -24.66175837 -27.82566526 -24.40093414
25th Percentile (Q1) -0.367111788 -0.30565569 -0.281631599 -0.595195453 -0.196061625
50th Percentile (Median) 0.335242641 0.63214433 0.421482491 -0.051359142 0.912020578
75th Percentile (Q3) 2.445594594 2.737539756 2.059567356 1.156072625 3.08036155
Maximum 50.52540674 40.17353315 38.67255002 23.40152901 68.49543475
95.0% CI Mean 1.2021 to 2.1545 1.4542 to 2.363 1.0881 to 1.8874 0.022307 to 0.74555 1.5671 to 2.6207
Skewness 2.463863923 1.644540226 1.96387295 -0.207183815 4.655567377
Kurtosis 22.10812215 13.75177795 19.5987771 15.20099006 46.46481031
The entire dataset of 558 companies was trimmed for any missing values, leaving 460 companies
nominated for the analyses. In order to visualise the trending patterns over the analysis period, a
box-plot chart was plotted to illustrate the year to year variations in the MVA/CE variable, as shown
in Figure 4.1. The histogram of the MVA/CE data was also plotted to validate the normal
distribution of the 95% confidence interval of the calcultaed values. Furthrmore, by using the MVA
35
as dependent variable and the Year as independend variable, it was decided to undertake a one-way
analysis of variance test (ANOVA) to examine the variability amongst the means againts the
variabilioty within each mean of the group
Figure 4.1 Box plot which illustrates the year to year variations in the MVA/CE along with frequency distribution of MVA/CE ratio
The ANOVA results for the test are shown in Table 4.2 below. Table 4.2: ANOVA test for the MVA/CE ratio
ANOVA MVA Sum of Squares Df Mean Square F Sig. Between Groups 804.089 4 201.022 8.498 .000 Within Groups 51309.516 2169 23.656 Total 52113.605 2173
The F-score value of 8.498 which is a ratio of the variance between groups to the variance within
groups shows that the mean values of the MVA/CE vary significantly (p=000 < 0.05) between 2008
and 2012. The results are depicted in Figure 4.2 below which illsutraes a significant drop of eight
folds from Yr5 (2008) to Yr4 (2009), followed by a steady increase in the MVA/CE ratio between
Yr3 (2010) to Yr1 (2012).
Figure 4.2: Chart illustrating the variability of the mean value of the MVE/CE ratio between 2008 and 2012
-20
020
4060
MVA
1 2 3 4 5
0
100
200
300
400
500
Freq
uenc
y
36
4.4 Descriptive Statistic for traditional accounting metrics The overall descriptive statistics for the traditional metrics (ROE, ROCE, ROA and EPS) are
depicted in Table 4.3.
Table 4.3: Descriptive statistics for the ROE, ROCE, ROA and EPS ratio over a five year period
The above data was then further stratified into yearly intervals and the average values for the
traditional metrics variables were plotted to identify trending patterns as depicted in Figure 4.3
which demonstrates the yearly average values for dependant and independent variables displayed in
both textual and graphical format. It is noted from Figure 4.3 that the EPS has been on a continuous
decline from 2008 to 2012 at an average rate of 33% per annum despite the increase in ROE from
2009 onwards. This could be attributed to the firm’s cash dividends policy where shares volume is
increased through share-split or issuing additional shares to generate additional cash-flow to the
business (Watson & Head 2007 P. 109).
Figure 4.3: Two-way chart/table illustrating the changes of ROCE,ROA, EPS and ROE between 2008 and 2012
Descriptive Statistics ROE ROCE ROA EPS
Count 2175 2175 2175 218
Mean 12.86388506 8.027227586 4.862243678 9.327674672
Stdev 30.9006338 21.36261846 16.25282621 80.78744168
Range 515.08 420.28 364.55 1003.693
Minimum -277.43 -182.63 -174.1 0
25th Percentile (Q1) 1.93 1.52 0.95 0.16875
50th Percentile (Median) 13.58 8.03 5.35 0.327
75th Percentile (Q3) 26.17 16.24 11.14 0.794
Maximum 237.65 237.65 190.45 1003.693
95.0% CI Mean 11.565 to 14.163 7.1289 to 8.9255 4.1788 to 5.5457 -1.4566 to 20.112
Skewness -1.05521119 -0.802623107 -1.68278666 10.4925836
Kurtosis 14.0640045 19.46940374 25.05537491 117.7378887
-5
0
5
10
15
20
25
-20
0
20
40
60
2012 2011 2010 2009 2008
ROE
ROCE
/RO
A/EP
S
Year
Traditional metrics over yearly interval
2012 2011 2010 2009 2008
ROE 14.336 15.489 13.728 -0.484 21.249
ROCE 9.542 10.078 9.196 -2.153 13.474
ROA 6.376 6.591 6.750 -4.245 8.839
EPS 9.328 20.584 25.305 36.290 51.363
37
However, research by Copeland (1979), suggests that share dilution could reduce the cash-flow as
trading volume is proportionately lower and transactions costs are proportionately higher. A quick
analysis of the total number of shares between 2008 and 2012 confirms share dilution (Figure 4.4)
by many companies supporting Watson and Head (2007) argument.
Figure 4.4: Chart illustrating the changes of the total number of shares between 2008 and 2012
The ROA, on the other hand has been stable between 2010 and 2012 following negative asset
efficiencies (under-utilised) in 2009. Furthermore, The ROCE and ROA were negative in 2009
which may explain the double-dip expectation in June 2010. A "double dip" refers to a recession
followed by a short term recovery which slides back into a second recession (Censky 2010).
4.5 B-EVA Descriptive Statistics Following the calculations of the B-EVA values, Table 4.4 illustrates the descriptive statistics for
the B-EVA outputs split in yearly intervals which is further supported by a box-plot chart to identify
the trending pattern of the calculated B-EVA over the analysis period. This illustrates a reduction of
191% in the B-EVA between 2008 and 2009 followed by a stable B-EVA increase of c.50% per
annum between 2010 and 2012. The data was further categorised into three bandings to illustrate the
B-EVA distribution over the analyses period such that positive B-EVA, neutral B-EVA, and
negative B-EVA bandings were identified (Table 4.5). This revealed a migration of about 40% of
the companies with positive B-EVA to negative B-EVA values between 2008 and 2009/2010 before
improvements of about 5% per annum between 2011 and 2012.
250
300
350
400
2012 2011 2010 2009 2008
No
Shar
es (m
illio
ns)
Year
Total number of shares in millions
Total number of shares in millions
38
Table 4.4 Descriptive statistics for the B-EVA values along with a Box-Plot to illustrate the year-to-year variations between 2008 and 2012
Table 4.5 B-EVA distribution over a five year period split into positive, neutral and negative B-EVA values
Descriptive Statistics B-EVA/CE_Yr1 B-EVA/CE_Yr2 B-EVA/CE_Yr3 B-EVA/CE_Yr4 B-EVA/CE_Yr5
Count 210 210 210 210 210
Mean 0.0303 0.0288 -0.0040 -0.0379 0.0354
Stdev 0.0779 0.08221 0.0945 0.1975 0.1065
Range 0.5458 0.7657 0.7069 1.8100 1.0186
Minimum -0.2541 -0.4472 -0.4500 -1.3786 -0.34410
Maximum 0.2916 0.318461271 0.256808757 0.431326616 0.674557127
95.0% CI Mean 0.0197 to 0.0409 0.01765 to 0.04002 -0.0169 to 0.0088 -0.0647to -0.011 0.0209 to 0.0499
Yr1 Yr2 Yr3 Yr4 Yr5 -1.4
-0.9
-0.4
0.1
0.6Median
25th
75th
Mean
Outliers
Extreme Outliers
2012 2011 2010 2009 2008
Positive B-EVA 145 137 112 111 150 Neutral B-EVA 0 0 0 0 0 Negative B-EVA 65 73 98 99 60
Total 210 210 210 210 210
0
50
100
150
200
250
2012 2011 2010 2009 2008
B-EV
ACE
B-EVA Distribution over 5 years
Positive B-EVA Nuetral B-EVA Negative B-EVA
39
The table below (Table 4.6) on the other hand demonstrates the highest five wealth creating (Green)
/destroying (Amber) companies sorted by latest year and based on the Basic EVA analyses.
Table 4.6 Highest five wealth creators/destroyers based on B-EVA calculation method.
Company Name EVA/CE_YR1 EVA/CE_YR2 EVA/CE_YR3 EVA/CE_YR4 EVA/CE_YR5
Rightmove PLC 1.75 1.27 1.30 -1.96 1.55
Howden Joinery Group PLC 1.06 2.94 -0.95 2.65 0.41
Micro Focus International PLC 0.90 0.33 0.30 0.35 0.40
WS Atkins PLC 0.66 3.02 -0.93 -2.09 -6.49
City Of London Invest.PLC 0.62 0.67 0.41 0.71 0.60
Dixons Retail PLC -0.25 -0.24 -0.01 -0.23 -0.24
Johnston Press PLC -0.27 0.00 -0.14 -0.34 0.04
De LA Rue PLC -0.61 1.30 9.58 0.85 0.35
Mothercare Plc. -0.84 -0.01 0.08 0.11 -0.03
Smiths News PLC -1.20 -0.36 -0.45 -0.96 -0.96
4.6 Adjusted EVA Descriptive Statistics (S-EVA) The descriptive statistics for the S-EVA analyses split into yearly intervals is depicted in Table 4.7 Table 4.7 Descriptive statistics for the S-EVA values to illustrate the year-to-year variations between 2008 and 2012
Descriptive Statistics S-EVA Yr1 S-EVA Yr2 S-EVA Yr3 S-EVA Yr4 S-EVA Yr5
Count 393 393 393 393 393
Mean 0.024161363 0.029653458 -0.043150727 -0.144916644 -
0.026671433
Stdev 0.104923994 0.10541076 0.359554234 0.511986531 0.22553721
Range 1.195269068 1.376054375 7.690431665 5.232107453 2.367563314
Minimum -0.578277413 -0.559550087 -4.442893204 -4.770863897 -
1.859898204
25th Percentile (Q1) -0.029397604 -0.028358154 -0.081553862 -0.144399423 -0.06541896 50th Percentile (Median) 0.013365908 0.015951095 -0.004131531 -0.020545222 0.008148348
75th Percentile (Q3) 0.067751991 0.068195758 0.05937187 0.041716729 0.056759506
Maximum 0.616991655 0.816504288 3.247538462 0.461243556 0.50766511
95.0% CI Mean 0.013756 to
0.034567 0.0192 to
0.040107 -0.078809 to -
0.0074925 -0.19569 to -
0.094141 -0.049039 to
-0.0043042
Skewness 0.767513797 1.587266575 -3.869460145 -5.399830844 -3.86302734
Kurtosis 7.878422925 12.83824107 78.74712652 35.80814492 22.84732535
The assumptions and calculation methodology for the WACC analyses were identical to those used
in the B-EVA calculations. However, the adjustments made had altered the WACC cost between the
B-EVA and S-EVA as the d/v (debt/value) had changed.
40
The above illustrates similar trending behaviour to the B-EVA. However upon closer examination, it
is apparent that the unlevered B-EVA overstates the EVA performance against the adjusted EVA
values (S-EVA). The gap seems to narrow between 2011 and 2012.
Figure 4.5: Chart illustrating the B-EVA against S-EVA values between 2008 and 2012
4.6.1 Descriptive Statistics of the WACC used in S-EVA calculations The descriptive statistics for the WACC used in the S-EVA analyses split into yearly intervals is depicted in Table 4.8 Table 4.8 Descriptive statistics for the WACC values used in the S-EVA methodology between 2008 and 2012
Descriptive Statistics WACC_yr1 WACC_yr2 WACC_yr3 WACC_yr4 WACC_yr5
Count 393 393 393 393 393
Mean 0.058180495 0.057184999 0.05905955 0.061748807 0.062195013
Stdev 0.023658094 0.025127608 0.030709066 0.040157665 0.035494757
Range 0.234108758 0.289033039 0.410359377 0.760058938 0.54729787
Minimum 0.006707729 -0.004182464 -0.012057869 -0.161132075 -0.111886228
25th Percentile (Q1) 0.042929514 0.042394415 0.04329772 0.044629493 0.04534737
50th Percentile (Median) 0.056191525 0.054972024 0.056399699 0.057344003 0.057360625
75th Percentile (Q3) 0.069391642 0.068884041 0.068415517 0.071230622 0.070598569
Maximum 0.240816487 0.284850575 0.398301508 0.598926863 0.435411642
95.0% CI Mean 0.0558 to 0.0605 0.0547 to 0.0597 0.0560 to 0.0621 0.0578 to 0.0657 0.0587 to 0.0657
Between Yr 2 (2010) and Yr3 (2011), the WACC cost was lower by c. 4% per annum against 2008
(Yr5) cost. The WACC cost was then increased by c.2% between 2011 and 2012. The decline of
market premium expectations of shareholders may explain the reduction in WACC costs over the
analyses period when taking into consideartion the macro-economic variables. The average WACC
values used in the B-EVA calculations were then compared against those used in the S-EVA
calculations (Figure 4.6) which demonstartes consistantly lower WACC costs for the S-EVA
-0.2
-0.15
-0.1
-0.05
0
0.05
Yr1 Yr2 Yr3 Yr4 Yr5EVA
Year
BEVA vs SEVA
SEVA
BEVA
41
calculations ranging between 10% to 15%. This is attributed to the adjustments made during the S-
EVA calculations which included R&D, Taxation and Lease adjustments.
Figure 4.6: WACC distribution used in the B-EVA and S-EVA calculations
The S-EVA based values were further stratified into three bands (Table 4.9) to illustrate its
distribution over the analyses period using positive i.e. larger than zero, neutral i.e. equal to zero,
and negative i.e. when the value is less than zero.
Table 4.9: S-EVA distribution of the number of companies over the analyses period stratified into positive, neutral and negative values.
2012 2011 2010 2009 2008
Positive S-EVA 237 241 209 193 265 Neutral S-EVA 22 18 11 12 8 Nehative S-EVA 200 200 239 254 186 Total 459 459 459 459 459
Figure 4.6: S-EVA distribution over five years stratified into positive, neutral and negative values
0.055
0.06
0.065
0.07
2012 2011 2010 2009 2008
Estimated WACC for B-EVA against S-EVA
WACC (S-EVA) WACC (B-EVA)
0
100
200
300
400
500
2012 2011 2010 2009 2008
S-EVA Distribution over five years
Positive S-EVA Neutral S-EVA Nehative S-EVA
42
Interestingly, the above pattern is similar to the B-EVA distribution where a dip in the positive S-
EVA occurred between 2008 and 2009 before increasing in 2010 and stabilising between 2011 and
2012. The table below is an extract of the highest five wealth creating (Green) /destroying (Amber)
companies sorted by the latest year and based on the (S-EVA) analyses. Table 4.6 Highest five wealth creators/destroyers based on B-EVA calculation method.
Company Name
S-EVA/CE_Yr1
S-EVA/CE_Yr2
S-EVA/CE_Yr3
S-EVA/CE_Yr4
S-EVA/CE_Yr5
Hargreaves Lansdown PLC 61.70% 81.65% 48.36% 46.12% -7.50%
Admiral Group PLC 51.41% 6.84% -59.69% -112.37% -104.35%
Smiths News PLC 47.66% 54.44% 324.75% -370.98% -185.99%
Next PLC 46.75% 39.88% 53.82% 26.24% 32.71%
WH Smith PLC 38.31% 31.53% 28.10% 21.21% 16.53%
Mothercare Plc. -57.83% 0.05% 5.93% 10.88% -5.54%
Centaur Media PLC -28.68% -5.52% -6.29% 0.87% 2.43%
Cairn Energy PLC -25.70% 22.94% -4.57% -9.60% -15.30%
Laird PLC -25.16% -8.12% -5.77% -4.11% -1.73%
Costain Group PLC -24.48% -18.48% -14.81% -26.38% -24.01%
4.7 Testing the hypothesis against Panel Data Regression Analysis The results and findings of the various statistical analyses are presented in this chapter along with
discussions against the research questions summarised in Section 1.2 above. In order to meet the
objectives of the study, the analyses was divided into four parts where Panel Data Analyses Model
was implemented using both the Generalized Estimating Equations (GEE) and the Feasible
Generalized Least Squares (FGLS) methods within the Stata SE10 software.
In Part one of the analyses, the traditional financial metrics against shareholder value metrics as
measured by MVA/CE were analysed using the Feasible Generalized Least Squares (FGLS) method
over a panel data of five year. In the second part of the analyses, a “basic” EVA (B-EVA)
calculation for each company was determined using specific information from the financial
statements. These “Basic EVA” values were regressed against the shareholder value metric
(MVA/CE) using the Feasible Generalized Least Squares (FGLS) method over a panel data of five
years. In the third part, the “simplified” EVA (S-EVA) value for each company was calculated by
undertaking various financial adjustments in order to improve the accuracy of the derived EVA
values. The “Simplified EVA” values were regressed against the shareholder return using the
43
Feasible Generalized Least Squares (FGLS) method over a panel data of five year. To assess the
predictive power of the traditional accounting metrics and EVA on the shareholder value, Panel
Data Regression Analysis was adopted. The explanatory variables used in the study include
shareholder value as the dependent variable represented in the MVA/CE which is a proxy measure
of shareholder value creation against both financial metrics and Economic Value metrics.
4.7.1 Relationship between shareholder-value against traditional accounting metrics
Having determined the historical values for the MVA/CE against the traditional accounting metrics
including ROE, ROCE, ROA and EPS a panel data analysis was conducted using the FGLS
configuration in the STATA application. If the p-value for the hypothesis test is p<0.05, then the
null hypothesis H0 is rejected and if the p-value for the hypothesis test is >= 0.05, then the null
hypothesis H0 is accepted.
1. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between
MVA/CE and ROE (Table 4.7)
Table 4.7: FGLS Regression: MAV/CE vs. Return on Equity
The null hypothesis assumes statistically insignificant correlation between MVA/CE and ROE. The
P-value of the FGLS regression test is 0.199 which is larger than 0.05. Although the correlation
coefficient equals to 0.5490 which is the linear correlation between the observed and model-
predicted values of the MVA/CE variable, the p-value is not statistically significant between
MVA/CE and ROE and therefore the Null Hypothesis (H0) is accepted i.e. there is no significant
relationship between MVA/CE and ROE for the period studies.
44
2. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between MVA /CE and ROCE (Table 4.8)
Table 4.8: FGLS Regression: MVA/CE vs. Return on Capital Employed
The null hypothesis assumes statistically insignificant correlation between MVA/CE and ROCE.
The P-value of the FGLS regression test is 0.505 which is larger than 0.05. Although the correlation
coefficient equals to 0.5725, the p-value is not statistically significant between MVA/CE and ROCE
and therefore the Null Hypothesis (H0) is accepted i.e. there is no significant relationship between
MVA/CE and ROCE for the period studies.
3. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between
MVA/CE and ROA (Table 4.9) Table 4.9: FGLS Regression: MVA/CE vs. Return on Assets
The null hypothesis assumes statistically insignificant correlation between MVA/CE and ROA. The
P-value of the FGLS regression test is 0.053 which is larger than 0.05. The correlation coefficient
45
equals to 0.5918, the p-value is not statistically significant between MVA/CE and ROA and
therefore the Null Hypothesis (H0) is accepted i.e. there is no significant relationship between
MVA/CE and ROA for the period studies.
4. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between
MVA/CE and EPS (Table 4.10).
The null hypothesis assumes statistically insignificant correlation between MVA/CE and EPS. The
P-value of the FGLS regression test is 0.151 which is larger than 0.05. Although the correlation
coefficient equals to 0.7549, the p-value is not statistically significant between MVA/CE and E|P|S
and therefore the Null Hypothesis (H0) is accepted i.e. there is no significant relationship between
MVA/CE and EPS for the period studied. Table 4.10: FGLS Regression: MVA/CE vs. Earnings Per Share
5. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between MVA/CE and B-EVA (Table 4.11)
Table 4.11: FGLS Regression: MVA/CE vs. B-EVA
46
The null hypothesis assumes statistically insignificant correlation between MVA/CE and B-EVA.
The P-value of the FGLS regression test is 0.787 which is larger than 0.05. Although the correlation
coefficient equals to 0.7567, the p-value is not statistically significant between MVA/CE and B-
EVA and therefore the Null Hypothesis (H0) is accepted i.e. there is no significant relationship
between MVA/CE and B-EVA for the period studied.
6. Panel Data analyses using heteroskedastic with cross-sectional panel correlation between
MVA/CE and S-EVA (Table 4.12).
The null hypothesis assumes statistically insignificant correlation between MVA/CE and S-EVA.
The P-value of the FGLS regression test is 0.038 which is less than 0.05 with a correlation
coefficient equals to 0.7360 indicting the p-value is statistically significant between MVA/CE and
S-EVA and therefore the Null Hypothesis (H0) is rejected i.e. there is statistically significant
relationship between MVA/CE and S-EVA for the period studied with a positive and strong
correlation of 0.7360. The relationship indicates that for a one unit change in the S-EVA there is a
corresponding probability of 73% increase of 46.5 units of the MVE/CE ratio. Table 4.12: FGLS Regression MVA/CE vs. S-EVA
4.7.2 Relationship between S-EVA and Traditional Accounting Metrics For completeness it was decided to investigate the correlation between the S-EVA against the ROE,
ROE,ROCE and EPS using the same statistical methodology i.e. cross-sectional time-series FGLS
regression applied on 353 companies over the analysis period i.e. 1765 observations (Table 4.13)
47
Table 4.13: FGLS Regression S-EVA against ROE,ROCE, ROA and EPS
In examining the correlation between S-EVA and traditional metrics, the P-value of the FGLS
regression test revealed values of 0.053, 0.170 and 0.052 for ROE, ROA and EPS respectively
which are larger than 0.05. However, the P-value was found to be 0.011 for ROCE which is smaller
than 0.05 at the 95% confidence interval indicating a statistically significant relationship between S-
EVA and ROCE. It is therefore concluded there is no significant relationship between S-EVA and
ROE,ROA and EPS. However, there is significant relationship between S-EVA and ROCE for the
period studied with a positive correlation of 0.5779. For clarity, the schematic below (Figure 4.7)
illustrates a visual flow-chart of the statistical analyses conducted for the purpose of this study.
Figure 4.7: Schematic illustrating the statistical analysis conducted for the purpose of this study. The next chapter provides detailed summary and concluding remarks covering the theoretical and
practical implications of the study.
ROE ROCE ROA EPS
S-EVA P-Value 0.053 0.011 0.170 0.052 AR-Correlation 0.614 0.5779 0.6150 0.6079
48
5. Summary and conclusions of the research
5.1 Summary
Shareholders and managers, in the main, employ traditional accounting metrics such as ROA,
ROCE, ROE and EPS to assess the firm’s performance and use these as investment decisions tools
for predicting future performance. However, conventional accounting statements do not take into
account the cost of the equity invested by shareholders, who have expectations to be rewarded if
they were to invest on similar projects carrying the same levels of risk. This major flaw has
prompted many analysts and researchers to consider the adoption of economic theory framework to
maximise shareholders’ value. The EVA framework reconciles the net operating profits against
both the cost of equity and cost of debt relevant to the invested capital which may lead to
behavioural changes of the entire company.
This study was founded on analysing 588 UK FTSE-ALL companies over a five year period
between 2008 and 2012. All financial data were extracted from Fame database. In order to progress
with this research, it was important to identify a suitable measure of shareholder value. As a proxy
of this measure, the concept of Market Value Added (MVA) was selected. As the MVA is an
absolute value, it was decided to convert it to a relative measure by dividing it by the invested
capital (CE). The development of the economic value added framework followed two approaches;
Basic EVA and Adjusted EVA (S-EVA). In the basic-EVA, the calculations were completed
without any financial adjustments to both, earnings and invested capital. As the EVA calculations
takes into account the weighted average cost of capital, certain assumptions were made to cover the
cost of equity. During the adjusted EVA (S-EVA) calculations, it was decided to undertake the
minimum amount of accounting adjustments in order to reach a statistically significant relationship
between the S-EVA and Shareholder value metric. The adopted statistical analyses methodology
was based on panel data analyses using balanced Feasible Generalized Least Squares (FGLS)
method.
49
The adjustments made in developing the S-EVA include, capitalisation of R&D, taxations, minority
interests and operational lease cost which was assumed as 6%. In addition certain assumptions were
made during the CAPM calculations.
The study has successfully addressed the research questions and revealed the following
observations:
• There was no statistically significant relationship between the shareholder value (MVACE)
against conventional accounting metrics e.g. ROA, ROE, ROCE and EPS. Despite a
reasonable correlation (AR) between 0.539 (ROE) and 0.755 for EPS, the p values ranged
between 0.053 for ROA to 0.505 for ROCE which were all larger than 0.05.
• The Basic-EVA analyses, conducted on 580 companies, revealed statistically insignificant
relationship against shareholder value (MVACE). Althouhg the AR value was high at
0.7567, the p value was found to be 0.787 which is larger than 0.05.
• The Simplified-EVA (S-EVA) approach, on the other hand, which included a handful of
adjustments revealed a statistically significant relationship against shareholder value at 95%
confidence interval level. These adjustments were conducted on both the NOPAT and IC
calculations. The auto-correlation (AR) was found to be 0.7360 and the p value was 0.038,
which is less than 0.05.
• It was also decided to analyse the S-EVA against conventional accounting metrics using the
same statistical analysis criteria (FGLS). The analyses had revealed no statistically
significant relationship with ROE, ROA and EPS where the p values ranged between 0.052
(EPS) to 0.170 for ROA. However, the analysis has revealed a statistically significant
relationship between S-EVA and ROCE at p value of 0.011 which is less than 0.05. The
strength of the correlation (AR) was found to be at 0.5779.
• It was also noted an adverse differntial of c. 6.5% between the WACC used in the S-EVA
calculations against those used for the B-EVA calculations. This is attributed to the
accunting adjustments made during the S-EVA calculation process. It was also noted that the
50
wieghted average cost of capital for the S-EVA based calculations has been declining from
2009 at an avereage rate of 5.5% per annum. This trend may explain the decline in the
market premium expectations of shareholders since 2008.
Interestingly, the shareholder value metric (MVA/CE) along with B-EVA and S-EVA followed
similar trending patterns where significant drop in these values were observed between 2008 and
2009 followed by improvements between 2009 and 2010. It was also observed that despite a steady
decrease in EPS metric of 33% per annum between 2008 and 2012, the ROE saw a significant
improvements between 2009 and 2010 followed by steady decline. Upon further investigation, it
was revealed this was attributed to steady increase in shares volume which led to dilution of
earnings between 2008 and 2012.
5.2 Theoretical Implications
In general, the published literature on EVA explains the calculation process which is categorised
into three components; NOPAT , WACC and Invested Capital calculations. These calculations may
entail up to 164 accounting adjustments to remove any accounting distortions (Sparling & Turvey
2003). Although the adjustments are generally explained, the published literature, in the main,
suggests a handful of adjustments but it does not state which ones are more important than others as
the selection of the correct adjustment is driven by company and industry specifics. Crowther et al.
(1998) noted that adjustments are time consuming and given the limited amount of information
available to investors, would make it complex and arbitrary. Three types of adjustments are
identified which include adjustments the economic operational result, the investments that generates
those results and adjustments to remove non-recurrent gains and losses (Kayo et al. 2005).
The invested capital calculations are determined by using the book-value of the asset base.
However, Poll et al (2011) suggested the use of market-value which would give a totally different
EVA figures. It is also noted that it is difficult to place a value in the services sector where
intellectual and human capital dominates the operational activities and the generated EVA value
may not be appropriate for all companies (Bontis et al 1999). Intellectual and human capital
including assets like knowledge, innovations, technology, skills, communications, brands, and
customer relations which are used to create wealth (Cheng 2011). The research examined
conventional accounting metrics along with economic value metrics to find out strength of
51
correlation against shareholder value. For this purpose, MVA was used as a proxy to shareholder
value, while two methodologies of economic value-added calculations were applied.
This research has confirmed the value relevance of including adjustments to the EVA calculations.
These adjustments which included R&D capitalization and taxation generated statistically
significant relationship between EVA and shareholder value. Our results support the argument that
S-EVA is more superior measure than other conventional metrics in explaining shareholder returns.
The outcome of the analyses has “conditionally” supported the claims made by proponents of EVA
in explaining shareholder returns including Lehn and Makhija (1996), Bao and Bao (1998) and
Worthington and West. (2004). Chen and Dodd (1997) reported that EVA provides better
information than traditional metrics and higher correlation with share returns. Similar findings were
observed by Finegan (1991) who illustrated a significant association between MVA and EVA when
compared against other performance measures such as earning per share, and return on equity. The
author believes that the reason for this “conditionality” is attributed to the sensitivity of the EVA
calculations in relation to the WACC estimations, selection and assumptions made to the accounting
adjustments that may cause poor correlation between EVA and other accounting metrics. This view
supports that of Biddle et al. (1999).
Contrary to the methodologies used by Grant (2003 p 61) the research has revealed that using EBIT
and capital employed to represent NOPAT and IC respectively to calculate the basic-EVA value
provides no value relevance to the EVA metrics. Further, the analyses has revealed a positive
correlation between ROCE and S-EVA which conform to the findings of Kaur and Narang (2008)
who asserted the superiority of economic value measures based on the correlation between ROCE
and the adjusted EVA values. With regards to the correlation between S-EVA against other
traditional metrics such as ROA, ROE and EPS, the research has empirically confirmed that these
metrics are unable to explain the S-EVA (adjusted EVA) values which conform to Chmelíková
(2008) findings.
5.3 Practical Implications
From the literature review, and despite the advantages of utilising EVA concept, it appears the EVA
framework is not widely used in the UK. The outcome of this study has concluded the viability of
52
considering the application of the S-EVA in measuring performance of the UK companies along
with other traditional metrics. In certain circumstances, S-EVA showed better explanatory power to
shareholder value than other metrics. However, as the netted shareholder value is assumed to belong
to the legal owners of the business, the reality is that there are a variety of other stakeholders who
have some claim of that business. This imposes a challenge to the economic value calculations
which does not take into considerations stakeholder interests (Crowther et al 1998). Adopting EVA
framework requires good preparation before gathering all relevant information. In addition, the
process demands environmental scanning at both internal and external levels to understand the
market behaviour and its risks.
5.4 Limitations
The study was limited to covers FTSE-All companies within a specific time-horizon. It is also based
on the reported data extracted from FAME without any verification to its integrity. Whilst many
companies adopt the IFRS accounting standards it is important to note that companies may have
“localised” conventions when reporting accounting figures. This may lead to variability in the
analyses and its conclusions. It was also observed that not all the raw data was available and as such
it was decided to trim and exclude some of the data from the analyses which may have introduced
some bias in the analyses. It was also critical to ensure that the panel data set was “balanced” in
order to conduct the correlated cross-sectional analyses resulting from further omissions of the
analysis.
The other limitations stem from the variability of the Beta values amongst different databases e.g.
Bloomberg, Fame etc where the regression value of R2 may be weak between the covariance to
variance ratio. In addition Fama and French (1992 cited in Young & O’Byrne 2000 P180) showed a
weak and almost non-existent relationship between the average share returns and Beta values.
5.5 Direction for Future Research
The research has investigated the links between economic value and shareholder value against
traditional accounting metrics by empirically testing the UK-FTSE All companies between 2008 and
2012. Based on the literature review, the author believes this field of study is under-researched in
the UK and as such there are many opportunities for further research. This could include a research
on the variability of economic value across different sectors within the FTSE companies or possibly
53
attempt to understand the impact of including/excluding companies with positive economic value
opposed to those with negative economic value. Other useful and powerful research would be based
on conducting a primary research methodology on a handful of UK companies who are adopting the
EVA framework and attempt to formulate a link to share price movements.
It is also recommended to explore the impact of selecting various accounting adjustments along with
conducting a full-scale sensitivity analyses to the cost of capital.
5.6 Reflections
The focus of this research was based on evaluating the value relevance of economic value
framework in explaining shareholder returns against traditional accounting metrics such as
ROE,ROA,ROCE and EPS by empirically testing the UK FTSE-All companies over a five year
period between 2008 and 2012. In practice, traditional metrics are widely used by shareholders and
management alike to evaluate the financial performance of companies. However, one of the major
flaws of accounting statements is while they take into consideration the cost of debt, they totally
ignore the cost of equity. This assumption implies a null-cost to shareholders, who have minimum
return expectations if they were to invest their cash in other companies sharing similar risks. The
other flaws of financial statements include accruals conventions, which recognise earnings when
incurred rather than when cash is received along with the presence of accounting distortions which
could be manipulated by management to show a rosy and positive outlook. The concept of EVA
addresses the above flaws quite eloquently by applying a basket of “user-defined” adjustments and
by taking into consideration the cost of capital. If the ratio of NOPTA to Invested Capital is larger
than the cost of capital, it is said that the company has created value and if the NOPTA to Invested
Capital ratio is smaller than the cost of capital, it is said the company has destroyed value.
However, the research has outlined that the existence/strength of correlation between EVA and
shareholder value was sensitive to the adjustments made and the WACC applied. In the first
scenario, no adjustments were made to the EVA (B-EVA) calculations which confirmed the lack of
explanatory power to shareholder value. This poor correlation has also extended to the other
traditional metrics. By undertaking a handful of accounting adjustments to NOPAT and IC along
with recasting the WACC calculations, the explanatory power to shareholder value became
statistically significant. Furthermore, the S-EVA was also found to be positively correlated to the
ROCE.
54
It is believed that investors would find the application of S-EVA in conjunction with other financial
metrics beneficial in supporting their investment decisions. Although the study has successfully
addressed the research questions within the constraints of the data and analyses assumptions, the
outcome could not be justifiably applied without further research in the UK market. This is due to
the limitation of accounting data available to external stakeholders opposed to internal management
causing information asymmetry. However, the author believes, the techniques could be applied at a
market-level to assess the overall trending direction of the economy at large. Companies may also
use the technique to benchmark their performance against others from within the same industry. The
major challenges that faced the research include the sheer volume of data entries where different
iterations were used to determine the optimum method of data-handling and data management. The
other challenge was related in selecting the optimum statistical analyses methodology to provide the
most robust and coherent conclusions.
Finally, the dissertation has provided the author with a huge opportunity to develop his skills-set in
research methodology and in understanding the flaws of current financial accounting practice and
how economic value framework could be applied successfully in measuring company performance.
Word Count: 15,684
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References 1. Anand, M., Ajay G. and Asha A. (1999). EVA : business performance measure of shareholder value. The
Management Accountant, May 1999, pp.351-256. 2. Anon. (2013). Rates of Tax – Appendix A CT Return Version 2.2. HMRC Office 2013 3. Balachandran, S, and Mohanram,P. (2012), Using Residual Income to Refine the Relationship between
Earnings Growth and Stock Returns, Review of Accounting Studies, (2012) 17:134–165 4. Ballow,J, Burgman,R and Molnar,M. (2004) "Managing for shareholder value: intangibles, future value
and investment decisions", Journal of Business Strategy, Vol. 25 Issue: 3, pp.26 – 34 5. Baltagi,B (2005), Econometric Analysis of Panel Data, Third Edition, John Wiley & Sons Ltd, England 6. Bao, B. H. & Bao, D. H. (1998). Usefulness of value Added and Abnormal Economic Earnings: An
Empirical examination. Journal of Business Finance and Accounting, 25(1-2), 251-265 7. Barr,S. (1998). MISREPORTING RESULTS13/01/2014Recent accounting frauds may point to deeper
problems. But it will take more than new accounting rules to solve them. CFO Magazine December 1998. 8. BBC (2013), Fitch downgrades UK credit rating to AA+, BBC Business News, http://www.bbc.co.uk/news/business-22219382.[ Last Accessed 20 September 2013]. 9. Biddle, C., Bowen, M. and Wallace, J. (1999).' Evidence on EVA'. Journal of Applied Corporate Finance,
12(2), 69-79. 10. Bloobmerg (2013). United Kingdom Government Bonds, Bloomberg Market,
http://www.bloomberg.com/markets/rates-bonds/government-bonds/uk. [Accessed 8 September 2013] 11. Boasson, E. & BOoasson, V. (2006) IT innovations in IT industries: Does it pay off? Information Systems
Education Journal, 4, 25-32 12. Bontis, N. (1999), “Managing organizational knowledge by diagnosing intellectual capital: framing and
advancing the state of the field”, International Journal of Technology Management, Vol. 18 Nos 5/6/7/8, pp. 433-62.
13. Bryman A and Bell E. (2007). Business Research Methods, Second Edition, Oxfor University Press. 14. Bughin,J and Copeland T. (1997). THE VIRTUOUS CYCLE OF SHAREHOLDER VALUE
CREATION, THE McKlNSEY QUARTERLY 1997 NUMBER 2 15. Censky,A. (2010).Double dip recession: What are the odds?. CNN Money. Available from
http://money.cnn.com/2010/06/09/news/economy/double_dip_recession. [Accessed 2 September 2013]. 16. Chen, S. and J. L. Dodd (1997), ‘Economic Value Added®: An Empirical Examination of a New
Corporate Performance Measure’, Journal of Managerial Issues, 9(3), pp. 318-333. 17. Chen, S. and J. L. Dodd (2001), ‘Operating Income, Residual Income and EVA™: Which Metric is More
Value Relevant?’, Journal of Managerial Issues, 13(1), pp. 65-86.
56
18. Cheng,L. (2011),Study on the Special Adjustment Methods for Calculating EVA, Proceedings of the 8th International Conference on Innovation & Management, November 30-December 2, 2011, Kitakyushu, Japan.
19. Chmelíková, G (2008). Economic Value Added versus Traditional Performance Metrics in the Czech
Food-Processing Sector, International Food and Agribusiness Management Review, Volume 11, Issue 4, 2008.
20. Copeland, T. E. (1979). Liquidity Changes Following Stock Splits The Journal of Finance, Volume 34:
Issue 1, P 115–141 March 1979. 21. Crowther ,D., Davies,M and Cooper,S. (1998). Evaluating Corporate Performance: a Critique of
Economic Value Added, Journal of Applied Accounting Research Vol 4 No 2 1998 pp 3-34. 22. Dalborg, Hans, (1999), Shareholder Value in Banking, session of institute International Detrudes
Bancaires, Malaysia. 23. Damodaran,A. (2013),Country Default Spreads and Risk Premiums, New York University Stern School of
Business, http://pages.stern.nyu.edu/~%20adamodar/New_Home_Page/datafile/ctryprem.html.[Last Accessed 10 September 2013]
24. De Wet, J. ,(2005). Eva Versus Traditional Accounting Measures of Performance as Drivers of
shareholder value – A Comparative Analysis. Meditari, Vol. 13, No. 2, pp. 1-16, Nov. 2005. 25. Dearden, J. (1972). How to make incentive plans work. Harvard Business Review, 50, 117-122) 26. Dierks, P and Ajay P. (1997). What Is EVA and How Can It Help Your Company.? Management
Accounting (November): 52-58. 27. Fernandez, P (2004), ‘Shareholder Value Creation of Microsoft and GE’, W.P. No.564, August, IESE,
Business School, University de Navarra, Barcelona. 28. Fernandez, P. (2002), A definition of shareholder value creation , Research Paper 448,Jaunuary 2002,
Research Division,University of Navarra, Barcelona. 29. Fernandez,P, Aguirreamalloa, J., and Avendaño, L. (2013), Market Risk Premium Used in 82 Countries in
2012: A Survey with 7,192 Answers (November 23, 2013). Available at SSRN: http://ssrn.com/abstract=2084213 or http://dx.doi.org/10.2139/ssrn.2084213 [Last accessed 6 January 2014].
30. Finegan, Y. P. (1991). Maximizing Shareholder value at the private company. Journal of Applied
Corporate Finance, Volume 4, Issue 1, 30-45 31. Froud J, Haslam C, Johal S and Williams K (2000). Shareholder Value and financialization consultancy
promises management moves, Economy and Society Volume 29 Number 1 February 2000: 80–110 32. Goldberg, R (1999). Economic value added: A better measure for performance and compensation?,
Journal of Corporate Accounting & Finance, Volume 11 Issue 1 P 55-67 33. Grant, J (2003). Foundations of Economic Value Added, Second Edition, John Wiley & Sons, Inc.,
Hoboken, New Jersey 34. Greene,W. (2003). ECONOMETRIC ANALYSIS, Fifth Edition, New Jersey, Pearson Education Inc.
57
35. Gregory,A. (2011) The Expected Cost of Equity and the Expected Risk Premium in the UK, Review of
Behavioral Finance, Vol. 3 Iss: 1, pp.1 – 26 36. Harris,M and Raviv,R. (1991). The Theory of Capital Structure, THE JOURNAL OF FINANCE . VOL.
XLVI, NO. 1 MARCH 1991 37. Hillman,A and Keim ,G (2001).SHAREHOLDER VALUE, STAKEHOLDER MANAGEMENT, AND
SOCIAL ISSUES: WHATS THE BOTTOM LINE?, Strategic Management Journal 22: 125-139 (2001) 38. Hoechle,D. (2007).Robust standard errors for panel regressions with cross-sectional dependence. The
Stata Journal (2007), 7, Number 3, pp. 281to312. 39. Ismail,A. (2006).Is economic value added more associated with stock return than accounting earnings?
The UK evidence, International Journal of Managerial Finance, Vol. 2 No. 4, 2006 pp. 343-353 40. Jusoh R. And Parnell,J. (2008).Competitive strategy and performance measurement in the Malaysian
context: An exploratory study, Management Decision, Vol. 46 Iss: 1, pp.5 – 31 41. Kanire,G (2012), Social Science Research Methodology ;Concept Methods and Computer Applications,
available at http://www.grin.com/en/e-book/203950/social-science-research-methodology-concepts-methods-and-computer-applications [Last Accessed 07 January 2014]
42. Kaur,M & Narang S.(2008), Economic Value Added Reporting and Corporate Performance: A Study of
Satyam Computer Services Ltd, The IUP Journal of Accounting Research and Audit Practices, IUP Publications, Vol VII (2) April 2008, pages 40-52.
43. Kayo,E., Basso, L. and Oliveira, S. (2005), The Major Accounting Adjustments to Calculate EVA. An
Application to Brazilian Firms (March 2, 2005) Available at SSRN: http://ssrn.com/abstract=677582 or http://dx.doi.org/10.2139/ssrn.677582
44. Keef, S & Roush, M (2003), The relationship between economic value added and stock market
performance: A theoretical analysis Agribusiness, 19 (2),2003, 245–253 45. King, R, and Langli, J. C. (1998). Accounting diversity and firm valuation. The International Journal of
Accounting, (December), 529-567. 46. Kleiman, R.T. (1999), Some new evidence on EVA companies, in: Journal of Applied Finance, Vol. 12,
No. 2, pp. 80-91. 47. Koller,T, Goedhart,M, and Wessels,D (2010). Valuation: measuring and managing the value of companies
Valuation: measuring and managing the value of companies, Fifth Edition, JohnWiley & Sons, Inc., Hoboken, New Jersey.
48. Lazonick,W and O'Sullivan, M (2000) Maximizing shareholder value: a new ideology for corporate
governance, Economy and Society, 29:1, 13-35 49. Lehn, K, and Makhija, A. (1996) EVA & MVA as performance measures and signals for strategic change,
Strategy & Leadership, Vol. 24 Iss: 3, pp.34 – 38 50. Leland, H. (1994). Corporate Debt Value, Bond Covenants, and Optimal Capitla Structure,THE
JOURNAL OF FINANCE . VOL. XLIX, NO. 4 September 1994
58
51. Maditinos D, Šević Z and Theriou,N (2006), Review of the Empirical Literature on Earnings and
Economic Value Added (EVA®) in Explaining Stock Market Returns. Which Performance Measure is More Value Relevant in the Athens Stock Exchange (ASE)? 5th Annual Conference of the Hellenic Finance and Accounting Association Thessaloniki, 15-16 December, 2006, University of Macedonia
52. Mauer,D and Triantis,A (1994). Interactions of Corporate Financing and Investment Decisions: A
Dynamic Framework THE JOURNAL OF FINANCE * VOL. XLIX, NO. 4 * SEPTEMBER 1994 53. Milunovich, S. and Tsuei, A. (1996), EVA® IN THE COMPUTER INDUSTRY. Journal of Applied
Corporate Finance, 9: 104–116. 54. O’Byrne, S. (1996). EVA and Market Value. Journal of Applied Corporate Finance, 9(1), 116-125. 55. O’Hanlon,J and Peasnell ,K.(2002), Residual Income and Value-Creation: The Missing Link, Review of
Accounting Studies, 7, 229–245, 2002 56. Ohlson, J. (1995). Earning, Book Values, and Dividends in Equity Valuation. Contemporary Accounting
Research, 11, 661-687 57. Parka,K,and Jang, S. (2013). Capital structure, free cash flow, diversification and firm performance: A
holistic analysis International Journal of Hospitality Management 33 (2013) 51–63 58. Poll,H, Booyse,J., Pienaar,J, Büchner,S. and Foot, J. (2011). An overview of the implementation of
Economic Value Added (EVA™) performance measures in South Africa, Southern African Business Review Volume 15 Number 3 2011
59. Porter, M.E., (1987). From competitive advantage to corporate strategy. Harvard Business, Review 65 (3),
43–59. 60. Prober L M (2000), “EVA: A Better Financial Reporting Tool”, Pennsylvania CPA Journal, Fall, Vol. 71,
No. 3. 61. Rappaport A (1992); CFOs and strategists: forging a common framework, Harvard Business Review
May/Jun 1992 pp 84-91 62. Reilly, F and Brown,K (2000).Investment Analysis and Portfolio Management, Sixth Edition, Dryden
Press, 2000 - Business & Economics 63. Shil, N. (2009). Performance Measures: An application of Economic Value Added . International Journal
of Business and Management, Vol 4, No.3 March 2009 64. Shukla H. (2009). Creating and Measuring Shareholder Value: A Study of Cadila Healthcare Limited,
Institute Of Management Technology, January 2009;13(1):66-72 65. Sparling, D. and Turvey, C. (2003), ‘Further Thoughts on the Relationship Between Economic Value
Added and Stock Market Performance’, Agribusiness Volume 19 Issue 2, pp. 255-267. 66. Stern, J., Stewart B. and Chew, D. (1995), The EVA Financial Management System, Journal of Applied
Corporate Finance, Vol. 8, No. 2, Summer 1995. 67. Stewart B (2002). How to Fix Accounting --- Measure and Report Economic Profit, Journal of Applied
Corporate Finance, S P R I N G 2 0 0 3 V O L U M E 1 5 . 3
59
68. Taub S. (2003). MVPs of MVA. CFO Mag. pp. 59-66. 69. Thenmozhi, M (2000),MARKET VALUE ADDED AND SHARE PRICE BEHAVIOUR AN
EMPIRICAL STUDY OF BSE SENSEX COMPANIES, Delhi Business Review ? Vol. 1, No. 1, Jan.2000 70. Tully, S and Hadjian, A (1993), The real key to creating wealth, Fortune, 9/20/93, Vol. 128 Issue 6. 71. Turner, S and Morrell, P (2003). An evaluation of airline beta values and their application in calculating
the cost of equity capital, Journal of Air Transport Management 9 (2003) 201–209 72. Uyemura, D.G., Kantor, C.C. & Petit, J.M. (1996). EVA for Banks: Value Creation, Risk Management,
and Profitability Measurement. Journal of Applied Corporate Finance, 9(2), 94-111. 73. Venanzi, D (2012), Financial Performance Measures and Value Creation: The State of the Art, Springer
Briefs in Business, 2012 74. Vivian, A. (2007), „The UK Equity Premium: 1901-2004‟, Journal of Business Finance and Accounting,
Vol. 34, 9&10, pp. 1496-1527. 75. Watson, D. and Head, A (2007). Corporate Finance: Principles & Practice. Forth Edition, Financial Times
Prentice Hall imprint in 2007 76. Worthington, A and West, T. (2004), Australian Evidence Concerning the Information Content of
Economic Value-Added. Australian Journal of Management 29(2):pp. 201-224. 77. Yafee, R. (2003),A Primer For Panel Data Analysis, Connect Information Technology at NYU - New
York University, Fall 2003 Edition 78. Young, D. and O'Byrne, S. (2000), EVA and Value-Based Management: A Pract ical Guide to
Implementation, McGrow Hills Company 79. Zimmerman, J (1997),EVA and Divisional Performance Measurement: Capture Synergies and Other
Issues, Journal of Applied Corporate Finance ,S U M M E R 1 9 9 7 V O L U M E 1 0 . 2
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Appendix A – Dissertation Proposal
Name: Ali El-Jaber
Student Number: 109023903
Ethical Approval No ae128-8c99
Word Count 1,869
Specialism Finance
Dissertation Supervisor Prof. Chin-Bun Tse.
Forum used to discuss proposal:
Face to face meeting on 18th December 2012 and Blackboard discussions between 24th April to 22nd June 2013
Title
"Investigating the role of economic-value framework and conventional financial metrics in explaining total shareholders returns of the UK:FTSE-All firms”
61
Introduction
Traditional accounting measures are often under severe criticism as they are influenced by accrual
conventions and do not take into consideration the cost of capital (Anand,Garg & Arora, 1999). The
economic value concept, however, provides management and shareholders with the opportunity to
recognise that the capital employed in the business needs to be charged at the rate of the weighted
average cost of capital (WACC) (Shil 2009). Creating shareholder value therefore occurs when the
market value of a company is higher than the invested capital and destroying value occurs when the
market value of a company is lower than the invested capital (Shil 2009).
Against this backdrop, this research project aims to evaluate on whether the economic-value concept
could be used as a framework to measure shareholder value creation against other traditional
accounting metrics for the UK FTSE-All listed companies.
The other aim is to develop and evaluate a "Simplified Economic Value (SEV)" metric model using
the fundamentals of Economic Value theory. The main research questions are therefore:
What is the correlation between the proposed SEV metrics against Total Shareholder Returns
(TSR)?
What is the correlation between the proposed SEV metrics against traditional accounting measures?
What are the variables for creating SEV metric and what is the mathematical relationship between
those variables?
The main driver for this research is to develop a robust and coherent metric to support investment
decisions when dealing with UK:FTSE-All equity market.
Relation to previous research (Theoretical Framework)
The premise of Economic Value is founded on the fact that shareholders are expected to receive a
return which compensate the risk taken. In other words, if the NOPAT (Net Operating Profits After
Tax) is higher than the capital charge this means the returns are higher than the cost of capital which
indicates that the company is creating value (Kaur & Narang 2008).
During the literature review process, it was astonishing to note the divergence of opinions over the
applicability of the economic value concept against traditional accounting metrics when evaluating
62
TSR. The following paragraphs capture some of the research conducted on Economic Value metrics
against other traditional measures:
The empirical study conducted by O’Hara et al. (2000) has proven that TSR is directly related to
earnings of the firm as well as to the dividends declared by the firm, whilst in a separate empirical
study of 613 US companies, over the period 1987-1988 (Stewart 1991cited in De Wet 2005) found
strong correlation between his version of economic value (EVA) and the market value-added
(MVA) suggesting the adoption of the EVA would enhance the market value of the company.
Lehn and Makhija (1996) using a sample of 241 US companies between 1987-1993 found that EVA
and MVA are correlated positively with stock returns. Furthermore, Uyemura et al. (1996:98)
analysed the 100 largest US banks for ten-year period between 1986 to 1995 and found 40%
correlation between EVA and MVA. They also found the range of correlations with ROA,ROE, EPS
and Net income to be between 6% for EPS and 13% for ROA. Milunovich and Tsuei (1996)
examined the US computer technology between 1990 to 1995 and found 42% correlation between
MVA and EVA and 29% between MVA and ROE. Further studies by Dodd and Chen (1996)
covered data for 566 US companies over a 10 year period to investigate the correlation between
EVA and shareholder returns. They found the shareholder return were 20% correlated to EVA, 25%
to ROA, 19% to RI (Residual Income) and 7% to EPS. Interestingly, when multi-regression analysis
was used, the correlations for EVA and RI rose to 41% to shareholder return. Hall and Geyser
(2004) concluded that EVA is the best appropriate measure for measuring value of shareholders
based on a research conducted on one organisation in South Africa. It has therefore been argued that
the economic value and shareholder returns have a trend to move together and the EV is therefore
far more superior that traditional accounting metrics. On the other hand, in a study conducted by
Kyriazis and Anastassis (2007) which covered 121 non-financial companies in Greece over a period
of eight years, between 1996 to 2003, it was concluded that EVA did not offer meaningful
explanatory power in relation to shareholder returns. Similarly Fernandez (2001) analysed 506
American companies to examine the correlation between EVA and MVA for the period 1987-1997
and found a higher correlation between the changes in NOPAT (Net Operating Profit After Tax) to
the changes in MVA (Market Value Added) for 60% of the sample and negative correlation
between EVA and MVA for the 40% of the sample. Sparling and Turvey (2003) used a time-series
analyses and found a weak correlation between EVA and shareholders return ranging between -0.03
to 0.16 based on two test methods. Their study was based on analysing 33 food companies from
Stern Stewart Fortune 1000 database.
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This research project should help us understand the strength of relationship between EV concepts
and TSR against conventional accounting metrics for the UK FTS-All companies. It is also hoped
by developing a simplified version of EV metric, investors will be able to identify shares that are
likely to yield better returns over time.
Proposed methods
It is proposed to conduct an empirical analysis on the UK FTSE-All companies listed under the
London Stock Exchange over a five year period i.e. between 2008 to 2012. The FTSE All-Share
Index represents around 605 companies and captures around 98% of the UK market capitalization
(FTSE Factsheet 2012). The financial data for these firms will be extracted from the Thomson
Reuters Datastream database available from Leicester University.
In order to meet the stated objectives of the research, it is proposed to first investigate the
correlations between shareholder returns and four traditional metrics, namely ROI, ROE, ROA and
EPS over the analysis period. The reason for selecting these metrics is because they represent the
profitability ratios of the companies under study and the information is readily available. It is
intended to use a combination of single and multiple regression model analyses using Stata and
SPSS software. The outcome of the analysis should confirm the null hypothesis i.e. no correlation
between shareholder returns and conventional metrics.
In the second part of the research, it is proposed to develop a simplified version of an economic
value metric under the name of “SEV” using the concepts and principles of “economic value”. In
developing the SEV metric, it is suggested to take into consideration the impact of other variables
such as the Capital Structure, Cost of Capital, Capital Invested along with any other adjustments
from the income statements, balance sheets and cashflow statements when deriving the Net
Operating Profit After Tax (NOPAT).
The SEV metric will then be regressed against shareholder returns for the period using a
combination of single and “Generalized Estimating Equations” (GEE) model techniques using Strata
and SPSS software. The development of the SEV metric will be iterative and it is aimed to optimise
the “highest” R-Squared value possible . It is hoped to be able to identify the most appropriate
adjustments that enhance the market value per equity share. The reason for including the “GEE”
model is that it is a form of panel data analysis which allows us to observe many unrelated subjects
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such as companies over a period of time. It is hoped that the outcome of this analysis is to reject the
second null hypothesis i.e. accept a relationship between SEV and total shareholder returns (TSR).
In the third part of the research a combination of single and multiple regression analysis will be
performed to evaluate the relationship between the newly proposed SVA metric against traditional
financial metrics. It is hoped that the outcome of this analyses would reject the third null hypothesis
i.e. accept a relationship between SVA and some/all of the traditional accounting metrics.
It is also important to note that the data will be tested against normality and skewness where data
transformation may be required during the above analysis.
Reflections
It is recognised that although developing a simplified version of EV metric would assist investors to
identify organisations with higher TSR, it is important to acknowledge that market value of shares is
influenced by a combination of both external events such as industry and economy along with other
internal factors such as resources and capabilities. Whilst the historical financial data for the FTSE-
All companies should be relatively straightforward to extract from Thomson Reuters Datastream,
the accounting entries in the reported financial statements may vary significantly amongst
companies within different industries or sectors. In addition, my preliminary research has identified
wide variations in share-price value for the FTSE-All companies ranging between £0.06 to £48 per
share. It is also expected to report wide variability in the economic value creation/ destruction
figures which could also be swinging between positive and negative values.
To address the above, two approaches will be considered as suggested by Sparling and Turvey
(2003): in the first method, it is proposed to measure the changes of EV (i.e. ∆EV) to changes in
TSR (i.e. ∆TSR) over the analysis period. In the second approach, it is proposed to convert the EV
into ratios by dividing it the Capital Employed (Sparlin & Turvey 2003). The other challenge of
applying the EV concept is associated with the significant amount of adjustments required to
calculate the NOPAT (Dillon & Owers 1997). It is therefore proposed to limit the number of
adjustments to six and adopt the approach for the NOPAT calculations as suggested by Wilson
(1997). On the statistical analysis methods, It is recognised there are a variety of regression models
used e.g. Kramer and Peters (2001) used cross-sectional–time series data analysis, while Stepwise
Regression Analysis was used by Thenmozhi (2000) to examine the correlation between Economic
Value against Share Price. Other such as Worthington and West (2001) used cross-sectional least
65
squares regression analysis on 110 Australian companies to investigate the EVA against other
conventional metrics. Chen and Dodd (1996) on the other hand, found dissimilar correlation results
between simple and multiple regressions models during their study between EVA and TSR.
In developing the SEV metric, it is proposed to undertake a combination of GEE, single and
multiple regression iterations to identify the optimum correlation to TSR.
This project is based on extracting and analysing secondary data with the aim of transferring and
sharing knowledge with others and as such it does not require engagement with ‘live’ respondents.
However, the production of knowledge demands integrity and rigour (O’Leary 2004, p. 5). It is
therefore my responsibility to ensure that the extracted data is authentic and free from fabrication or
falsification. The analysis will be performed without manipulation and without intentional
misunderstanding. Although the analyses will be conducted over a five year period i.e. between
2008 and 2012 we need to recognise the effect of the global financial crisis in 2007 on market
performance, which according to the financial theory should be severe for financially constrained
companies or those dependent on external sources of funding (Mitto 2011)
Finally, it is noted that the impact of this research on the political field is negligible.
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Timetable
I have allocated 4 months to complete the research study as depicted in the timetable below:
67
References
Anand,M. Garg,A. & Arora,A. ,1999. Economic value added: business performance measure of shareholder value . The Management Accountant, May 1999, pp 351-356
De Wet, J. ,2005. Eva Versus Traditional Accounting Measures of Performance as Drivers of Shareholder Value - A Comparative Analysis . Meditari, Vol. 13, No. 2, pp. 1-16, Nov. 2005
Dillon,R. and Owers,E. ,1997. EVA as a Financial Metric: Attributes, Utilization, and Relationship to NPV . FINANCIAL PRACTICE AND EDUCATION — SPRING / SUMMER 1997
Dodd, J. and Chen, S. ,1996. EVA : A New Panacea? . Business and Economic Review, July - September, pp.26 - 28.
Fernandez,P. ,2001. EVA, Economic Profit and Cash value added do Not measure sharehoder value creation . Working Paper, IESE Business School – University of Navarra 2001
FTSE FACTSHEET, ,2012. FTSE All-Share Index . FTES Group, 31 December 2012
Hall,J. and Geyser,J ,2004. The Financial Performance Of Farming Co-Operatives: Economic Value Added Vs Traditional Measures . Working Paper, Department of Agricultural Economics, Extension and Rural Development, University of Pretoria SA ,2004.
Ismail,I. ,2011. The ability of EVA ,Economic Value Added. attributes in predicting company performance . African Journal of Business Management Vol 5 ,12. pp 4993-5000 18 June 2011
Kaur,M. and Narang,S., 2008. Economic Value Added Reporting and Corporate Performance: Study of Satyam Computer Services. The Icfai Journal of Accounting Research, Vol VII,No. 2
Kramers,J. And Peters,R. ,2001. An Interindustry Analysis of Economic Value Added as a Proxy for Market Value Added . Journal of Applied Finance, Vol. 11 Issue 1, p41 March 2001
Kyriazis,D. and Anastassis,C. ,2007. The Validity of the Economic Value Added Approach: an Empirical Application . European Financial Management, Vol 13, No 1, 2007
Lehn,K. and Makhija A. ,1996. EVA & MVA as Performance Measures and Signals for Strategic Change . Strategy & Leadership Volume: 24 Issue: 3 June,1996
Milunovich, S. and A. Tsuei ,1996. EVA in the Computer Industry . Journal of Applied Corporate Finance, 9,2. pp. 104-115.
Mitto,U ,2011. Rethinking international finance: introduction and overview . International Journal of Management Finance, Volume 7 Issue 2, 2011
O Hara, H. Lazdowski,C. Moldovean,C. and Samuelsom S. , ,2000. Financial Indicators of Stock Pprice Performance . American Business Review, January 2000
O Leary,Z. ,2004. The Essential Guide to Doing Research . Sage Publications Ltd ,2004.
Shil, N.,2009. Performance Measures: An application of Economic Value Added . International Journal of Business and Management, Vol 4, No.3 March 2009
Sparling,D. and Tuvey,C. ,2003. Further Thoughts on the Relationship Between Economic Value Added and Stock Market Performance . Agribusiness, Vol. 19 ,2. 255–267 ,2003.
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Thenmozhi,M. ,2000. Market Value Added and Share Price Behaviour: An Empirical Study of BSE Sensex Companies . Delhi Business Review, Vol 1 No 1, Jan 2000
Wilson,J. ,1997. Economic Value Added; Use and abuse . UBS Global Research, Valuation Series, May 1997
Worthington,A. And West,T. ,2001. The Usefulness of Economic Value-Added ,EVA. and its Components in the Australian Context . Accounting, Accountability and Performance 7,1.:pp. 73-90 ,2001.