ifrm-term report final

8/9/2019 IFRM-Term Report Final

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Top down investment risk research on banking

sector through

Market risk metrics analysis

Prepared for;

Syed Adeel Hussain

Institute of Business Management

Prepared by;

Naureen Ahmed, Asma Ahmed

December 14, 2014


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Top down investment risk research of the banking sector

2

Contents

CHAPTER 1: INTRODUCTION ................................................................................................................. 6

CHAPTER 2: PROBLEM STATEMENT................................................................................................ 9

CHAPTER 3: LITERATURE REVIEW ............................................................................................ 11

CHAPTER 4: METHODOLOGY .................................................................................................. 24

CHAPTER 5: ANALYSIS AND DISCUSSION..................................................................... 30

CHAPTER 5: CONCLUSION............................................................................................. 41

TABLE OF FIGURES

Figure 1: Skewness and kurtosis comparison ............................................................................................. 34

Figure 2: Risk measures compared ............................................................................................................. 37


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Letter of Acknowledgement

First, we would like to present our gratitude to Almighty Allah, the merciful, Who provided us

with the strength and intellect for the completion of this research project. Then a sincere thanks

to our instructorMr. Adeel Hussain. Through exhibiting faith and confidence in us he provided

us with the opportunity to conduct the following research up till successful completion and with

the best of results.


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Executive Summary

When it comes to suitable investment avenues nearly every firm has been facing challenges

from top horizon risks impacting firms, arising from macroeconomic factors, geopolitical

changes to regulations at a local or regional level, or tax legislation changes. Therefore sound

risk management practices and procedure are essential to be devised. The purpose for the

conduct of this research study lies solely in the adoption of a sound risk identification strategy

for risk identification and present a through risk analysis using appropriate quantitative

technique.

The sector under consideration has been the banking sector of Pakistan covering about

thirteen banks listed at the Karachi Stock Exchange, the quantitative technique adopted is the

market risk metric analysis, incorporating significant statistical measures as indicators of risks

inherent in the economy. The fundamental focus has been on the market risk and investment risk

inherent in the bank equity trading and the strategic approach adopted has been the top-down risk

research. The significance of the study lies in providing a risk identification methodology that

will enable the readers to get a better understanding of the risks thereby enabling them to design

appropriate mitigation, measurement and control strategies as well as to identify the key factors

that elevate the particular risk type.


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CHAPTER 1: INTRODUCTION

Successful fund management business not only involves investments in asset classes that ensure

sound returns and profitability but is equally about the need for sound risk identification and

management practices. When it comes to suitable investment avenues nearly every firm has been

facing challenges from top horizon risks impacting firms, arising from macroeconomic factors,

geopolitical changes to regulations at a local or regional level, or tax legislation changes.

Investment risk arises from the promise of performance, which remains undelivered. A key

element of the overall investment risk framework is the clear identification, documentation and

communication of the clients risk appetite, for that purpose a through risk analysis is essential.

Firms are becoming independent by ensuring that investment risk is ring-fenced from bias and

conviction on the part of fund managers or founders. There is still scope for performance

improvement when applying risk budgeting, single portfolio views, risk metrics, performance

attribution, liquidity management and treatment of model risks. Since sound investment risk

management requires a clear identification and measurement of inherent risks of the asset class,

therefore the sole purpose for the conduct of this research is to gain an understanding regarding

the risk identification methodology, for which top down investment risk research approach has

been adopted incorporating the market risk metric for equity analysis as a quantitative support.

Among the many stock classes available, we concentrate on the banking sector since the banking

industry of the country is quiet growing not only locally rather international operations have

continued to grow all over the world and particularly in emerging countries. The first grew

mainly through cross-border operations and more recently also through the establishment of

branches and subsidiaries abroad. This has changed the business potential as well as the structure


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of risks not only faced and mitigated by the banking enterprise itself but also by the entities who

invest their capital not only for their regulatory requirements but also to earn suitable returns.

Banking crises have never been a strange episode in market economies. As long as banks are

major players in modern economies, a banking crisis would keep on emerging with its

accompanied multiple adverse consequences including out-put losses, monetary instability, and

other nonmonetary effects associated with information loss. Therefore a sound risk identification

and mitigation mechanism is essential for every banking institution. The causality between

macroeconomic conditions and financial instability goes together because declines in the value

of banks' portfolios can weaken the economy. In a world of forward looking economic agents

where everybody incorporates the eventual negative effects of a financial crash into his

economic decisions, not only financial crises, but also their likelihood of occurrence effects the

economy.

The incorporation of Market risk metrics application provides the means to establish periodic

measurements of basic risk factors, including key performance indicators (KPIs) and key risk

indicators (KRIs).For purposes of the Risk Management solution, the metrics application

facilitates the researcher in finding out key performance and risk indicators, identify their source,

and tie them to sector under consideration. The Metrics application provides the ability to

monitor quantitative metrics over time with respect to their impact on the organization as soon as

the risks are cataloged.


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Research question

The fundamental question to be answered under the conduct of this research is the verification of

risks inherent in the equity investment in the banking sector through the use of quantitative

techniques (market risk metric) that would provide a perspective on the various risks through its

significant indictors.

Signi fi cance of the study

The employment of top down approach for the purpose of equity investment risk analysis would

serve as tool for the understanding of significant risks inherent in the assets class as well as the

industry under consideration (banking industry).Through the use of top-down approach not only

the interaction among various risk types would be prevalent but it will also provide impetus

regarding the losses resulting through such interactions. The risk types can include broad

macroeconomic risks, industry risks, sector risk and the related asset class risk. Risk

identification will enable the readers to get a better understanding of the risks thereby enabling

them to design appropriate mitigation, measurement and control strategies as well as to identify

the key factors that elevate the particular risk type.

L imi tations of the study

Limitations exist in the use of the underlying quantitative technique, the historical data may not

always be the predictor of the future and the history cannot always repeat itself, the indicators


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CHAPTER 2: PROBLEM STATEMENT

Background of the problem

The stock market is fraught with volatility, uncertainty and inherent risks that had been a

significant threat for the equity investors across the global markets, therefore risk identification

and measurement is the fundamental requirement for the equity investors pertaining to the

prevailing economic circumstances. The banking sector in particular is fraught with risks that are

quiet wide in their perspective and pertain to a variety of disciplines. The disciplines might

belong to the wider macro economic variables as well as the micro variables related to the

general industry considered.Banks are risky because their portfolio returns have their affects on

the strength of the economy which represents a non-diversifiable aggregate risk. Macroeconomic

risks affect business cycles because all agents suffer the effects of banking failures and

incorporate the endogenously determined probability of a crisis into their economic decisions.

Unforeseen interest-rate rise tends to breed banking sector problems. Furthermore, the country-

specific interest-rate spread is counter-cyclical because financial crises are less likely during

booms. All this leads to the conclusion of adopting a risk research strategy that help identify the

risks and also the interactions among various risks measures to gain a numerical perspective as

well.

Problem statement

The problem of this study lies in finding out the risks inherent in the equity investment of the

banking industry through the use of the top down risk research approach that will help identify


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market risks covering all the relevant areas that include broader economy, industry, particular

institution and the asset class under consideration. The model adopted would be the market risk

metric analysis.


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CHAPTER 3: LITERATURE REVIEW

Since the Investors depend heavily in equity investments to maximize their returns, the

perception of risk in equity investing have become increasingly important (DAngelo,

n.d.).Violent market moves have surprised many institutional investors and brought risk

management to the forefront (Caboodle, 2009). Some investors argue that current risk

management practices failed when they were needed most, and with multi-sigma events

extending across formerly uncorrelated asset classes.Institutional investors need to manage the

total risk of their investments, which means protecting themselves not only from asset liability

deficits, declines in broad asset classes and, non-fulfillment of investor obligations but also from

the risk of managers underperforming their benchmarks .To assess future risks, it is essential to

measure and monitor risk both at the aggregate level and at the factor level.

A risk management framework should be aligned with the investment objectives as well as the

investment horizon (Caboodle, 2009). The framework must tackle multiple aspects of risk along

with measuring, monitoring and managing exposures to economic and fundamental drivers of

risk and return across asset classes to avoid over exposures to any one risk factor. Finally, it

should also manage risk for normal times but must also recognize and aim to be prepared for

extreme events. Financial institution primarily banks are exposed to various kinds of risk due to

the nature of their business (Grundke, 2007). The responsibility of the risk management division

is to identify all these risks and find out the dependence between various risk factors. Two

theoretically sound approaches used in this regard are the top-downand bottom upapproaches.

Using a "top down" approach requires the identification of key business risks that can be

compliance, business, strategic or major operational risks (Managemen, 2011). These risks can
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be driven by several factors that might include major incidents or losses, regulatory compliance

requirements or systemic issues in operations of the business.

As these risks are cataloged then the risks can be monitored over time through periodic review,

loss tracking, implementing metric or other indicators. In the end, by going through this process,

the organization has implemented a consistent, continuous practice to address major risks in the

organization.

Every individual wants to invest for returns and productivity of ones funds (Dr. Taqadus Bashir,

2013). Any potential investor invests in shares of a company for return in form of dividend and

price appreciation in the market price of his holding. There were lot of options for investment

like equity, mutual bonds, company funds, gold/silver, bank deposits, real estate and life

insurance etc. But people prefer them according to their choices which were most appropriate

and suitable for them. Asset class evaluation is based on deciding what risks explain required

rates of return (Eric Girard, 2005). Portfolio theory suggests that only systematic risks can be

associated with a premium in financial markets however it is not evident now since it is not

evident on how to measure risk. While investing, many different approaches have been proposed

for pricing local financial or real assets. The degree of integration with the world financial

ECONOMY RISK

Macroeconomic analysis

SECTOR RISK

Microeconomic analysis

INDUSTRY RISK

co-relation with theeconomy

ASSETCLASS/ISSUER RISK

Business model/productrisk


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market will determine what risks explain risk premiums in capital markets and a country asset

pricing model should use a multifactor framework with local and common risk attributes.

Risk Management is a discipline that covers many avenues and techniques (Managemen, 2011).

Every organization faces different risks and perhaps had differing business goals therefore their

procedures for risk management do vary. Risk management is not an exact science, thus for

implementing a comprehensive risk management program had always been a remarkable

challenge but once implemented a sustainable, consistent and thorough risk management

program can be a tremendous advantage for any organization.The cycle of cost growth, fee

competition, squeezed margins and the need for greater

Scale and acceleration trends had challenged asset managers to innovate to safeguard sustainable

profits (Anon., 2013).As change is constant, the need for proportionate risk management in the

form of appropriate governance, risk appetite, embedded procedures and effective use of risk

management frameworks/key risk indicators has became greater in the current business climate.

Given the tsunami of new directives and regulatory measures at global, regional and local levels,

the gap between risk management and regulatory management is narrowing. The recognition of

the investment risk as being ring-fenced from bias and conviction had became a badge of honor

however general views regarding the management of certain strands of investment risk, e.g., risk

budgeting, single portfolio views, advanced risk metrics, sensitivity analyses and management of

model risks still varies among potent risk researchers.

Stock market risk is the tendency of stock prices to decline due to the change in value of the

market risk factors (Sinha, 2013). The stock value is directly related to the market value of those


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investments held by the stock market. Though banking and financial services sector funds have

accelerated on generating superior risk adjusted returns, they become victim to the risk of

portfolio concentration as a single stock accounts for equity portfolio in some gear. The market

value of those investments fluctuates depending on the financial performance of the issuers and

general economic, political, tax and market conditions.

Standard market risk factors include stock prices, interest rates, foreign exchange rates, and

commodity prices. Banks play a vital role in flourishing the economic activity and boasting

growth in the wide economy therefore their tendency to get affected from the financial crisis is

quiet evident. Banking and financial services sector funds have proved to be more volatile than

the pure diversified equity funds which make some of them a high risk proposition. Since the

banking industry is controlled by the central banks of the country their chances of being

adversely affected by inflation, interest rate, and money supply are more evident and so does the

a high instability in their share prices that reduces the real investors interest.

In complex systems, no single risk identification method can realistically identify all risks (The

board of the international organization of securities commission, 2014). Instead, risk

identification frameworks consist of various risk identification methods which can then be

combined into an overall approach for the identification and monitoring of risk. The use of top

down risk research approach for the identification of risks requires to be identified at the macro

level that includes the broader economic risks then moving down to the individual asset class

level risks that incorporate the industry as well as the company represented by the asset class.

Direct and individual risk management approach aims to identify all the risks to which the

company is exposed to, determination of the level of each risk, the risk appetite of the investor


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and the surety of risk monitoring and management (Methods Commission, n.d.). Global and

indirect risk management however aims to develop a security policy based on evaluating risks, to

identify certain elements that can lead to risks, classify these elements by order of importance

and determine a policy and security goals.

The asset class volatility forecasting beyond horizons of ten or fifteen trading days is important

for risk management (Peter F. Christoffersen, 1998).No single relevant risk horizon for risk

management, instead risk horizon will generally vary by asset class, industry, position in the firm

and motivation along with the rest of other things. Thought must be given to the relevant horizon

on an application-by-application basis. The fact is quiet well known that short-horizon asset

return volatility fluctuates and can be highly forecasted , a phenomenon that forms the base of

the modern risk management paradigms, however not much information exists about the

forecasting capability of long-horizon volatility, and the speed and pattern with which it decays

as the horizon lengthens. To assess long-horizon volatility forecast capability, it is necessary to

have a measure of long horizon volatility.

TheMarket Risk Metrics approach incorporates significant statistical measures to quantify the

risks that investment managers may need for evaluating the market risk inherent in their

portfolios or when making decisions on asset selection, portfolio allocation and portfolio

optimization. The correlation coefficient for instance is an important tool for quantitatively

assessing the linear relationship between two instruments. Other significant measures include

mean, standard deviation and coefficient of determination. Standard deviation is a common

statistical measure of portfolio volatility that measures how much a portfolios total return varies

from its mean or average. The more a portfolios returns fluctuate from month to month, the
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higher its standard deviation and the greater its volatility. Correlationmeasures how a portfolios

asset classes move in relation to each other in response to market events (MFS, n.d.).It ranges

from +1 to -1. The closer two assets are to a +1 correlation, the more likely they are to move in

the same direction Anegative correlation indicates two assets moving in opposite directions. R2

(R-Squared) calculated by squaring the correlation coefficient is that part of a portfolios

volatility that can be explained by movements in its benchmark or market. An R2of 100% shows

that all movements of a portfolio are completely explained by movements in the benchmark or

market. A low R2 indicates that little of the portfolios movement can be explained by

benchmark or market movements.Alpha which is the intercept of the Security market line

measures a portfolios risk-adjusted performance against that of its benchmark a positivealpha

indicates relative outperformance and vice versa. It measures the firm specific/industry risk.Beta

measures the volatility of security or portfolio to market movements and covers the broader

economic (systematic risk) .A beta less than 1.0 indicates likely lower volatility than the market.

Systemic risk is the risk of disruption to financial services that is caused by an impairment of all

or parts of the financial system and has the potential to have serious negative consequences for

the real economy (The board of the international organization of securities commission, 2014).

Factors that are essential to identify systematic risk include size of the market, degree of

interconnectedness or interdependence among market participants, lack of

substitutes/concentration due to one or a few market participants providing a product or activity,

leverage, Typology and structure of assets and liabilitiesheld in the balance sheet or off the

balance sheet: some low quality assets (downgraded or non-investment grade assets, unprofitable

loans) or highly concentrated or immediately redeemable liabilities may endanger the

profitability of the financial institution concerned , liquidity risk arising from not being able to
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sell a security at its fair value, as a result either of a liquidity discount or the complete absence of

a market or buyers or of being unable to obtain funding. In addition, market liquidity risk and

funding liquidity risk can exacerbate each other, transparency about interconnections and in

information about markets or products to assess the market price, potential return, and risk

exposure and behavior of the participants can result in mispricing of assets and an accumulation

of risk in the financial system. Incorporating the "top down" approach, the organization

identifies key business risks and catalogs those risks into a central repository (Managemen,

2011). These risks are the firm specific non systematic risks can be related to compliance,

business, strategic or major operational risks and represent the executive level risks that are "top

of mind" for the organization. The risks could be driven by several factors such as major

incidents or losses, regulatory compliance requirements or systemic issues in operations. Once

these risks are cataloged they are monitored over time. This process can be then institutionalized

to keep major risks on the radar screen. Over time, these may become inactive through

mitigation by various controls and does not warrant monitoring, or the risk becomes outdated

and not pertinent to the organization. By going through this process, the organization has

implemented a consistent, repeatable practice to address major risks in the organization. The

concept of systematic, non diversifiable risk or beta was first discussed under the frame work of

capital asset pricing model (CAPM), presented by Sharpe (Nawazish Mirza, n.d.). The CAPM

framework is very simple under ideal conditions and stipulates that systematic risk attributes its

sensitivity to macroeconomic factors is reflected in the slope () while non-systematic risk, the

unexpected component due to unexpected events that are relevant only to the security, is

reflected in the intercept (). The expected return on an asset depends only on its systematic risk.


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No matter how much total risk an asset has, only the systematic portion is relevant in

determining the expected return on that asset.

Historically, the banking sector has not been a preferred choice in the capital market since the

investors like to invest in deposits and saving accounts more than they would like to go for the

stocks of a bank or a financial institution due to the higher risk involved in stock markets

(Nawazish Mirza, n.d.). Thats why the banking sector has less representation in stock markets as

compared to other sectors. However, the absence of public equity also increases the risk of a

bank. The major chunk of assets and liabilities in a bank are of a financial nature. They are

subject to interest rate changes and respond quickly to the volatility in the economy. The

sleeping nature of banking stocks makes them an alien in the financial markets and their

sensitivity to economic events makes them more volatile as compared to other industries.

However banking stocks could be possible candidates for inclusion in a diversified portfolio but

the problem arises as to how these stocks respond to the stock markets and what level of

systematic risk they are exposed to in different markets, given certain economic circumstances.

Systematic risk usually covers the broad macroeconomic factors that have the tendency to impact

the stocks under study, the basic indicators include are the rate of inflation, economic growth,

interest rates and market concentration (Sudirman, n.d.).Policy interest rates defined by the

central banks have positive effect on the profitability of the bank, because higher the interest

rate, interest rate will be utilized by the bank as an alternative placement of funds with higher

yields.Economic growthis an important variable that determines profitability because economic

growth can affect the supply and demand for banking services. It has been argued that economic

growth may result in increased business activity and increased business performance of the

borrower which will lead to increased demand for bank credit that encourages banks increase


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interest rates which have implications on improving profitability. The impact of inflation on

profitability depends on whether inflation is anticipated or not by the bank. If inflation is

anticipated, then when the bank adjusts interest rates, resulting in increasing interest rate margin,

which means an increase in profitability and vice verse. The value of the banks' portfolios may

fall because of a weak economic performance of the banks' borrowers due to risks that cannot be

diversified. A slowdown of the economy bankrupts a higher proportion of borrowers compared

to normal times and the downturn will be severe enough that the banks themselves are in distress

and cannot fully repay their creditors. This reasoning along with the greater volatility of

emerging market economies can account for the higher vulnerability of these economies to

waves of banking failure (Oviedo, 2013).The causality between macroeconomic conditions and

financial instability also goes the other way around because declines in the value of banks'

portfolios can weaken the economy for instance the effect of a negative term-of-trade shock

would be a rise in the cross border bankruptcies that will ultimately raise the riskiness of the

banking sector.

Macroeconomic conditions trigger banking crises because the likelihood of a crisis is

incorporated into the decisions of every economic agent, that how both macroeconomic risk and

financial fragility effect business cycles. Out of the business-cycle paradigm, both declining

aggregate productivity and rising interest rates are capable of bringing about a banking crisis.

However, not every recession, no matter how deep, causes a crisis; it certainly does when the

down-turn is both deep and unexpected.The banking sector in Pakistan has witnessed drastic

changes over a period of 64 years since countrys independence in 1947 (FBR, n.d.). Since late

2007, Pakistan faced a difficult macroeconomic environment, not as such due to the global crisis


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but rather due to the gradual build up of macroeconomic imbalances. The Global Financial Crisis

(GFC) had an indirect impact in Pakistan indeed there was a decline in exports due to recession

in economies which are Pakistans major trading partners, and there was pressure on capital

flows where strained liquidity positioning global financial markets impacted foreign portfolio

investment considerable decline in foreign direct investment due to weak economic

fundamentals, high inflation, security concerns and above all, the mounting fiscal deficit

breaching previous records in the countrys economic history, all hada role to play in keeping

the process of economic recovery in Pakistan weak at best. The leading evidence of these various

pressures on domestic firms and industries is that their loan repayment capacity has been

compromised, with a consequent rise of non-performing loans (NPLs) on the banks balance

sheets.

The risks contained in the bank's principal activities, i.e., those involving its own balance sheet

and its basic business of lending and borrowing, are not all borne by the bank itself (Santomero,

n.d.).The institution will usually eliminate or mitigate the financial risk associated with a

transaction through proper business practices and in other cases it will shift the risk to other

parties through a combination of pricing and product design. The banking industry recognizes

that an institution need not engage in business in a manner that imposes risk upon it; nor should

it absorb risk that can be efficiently transferred to other

Participants; rather, it should only manage risks at the firm level that are more efficiently

managed there than by the market itself or by their owners in their own portfolios. According to

standard economic theory, managers of value maximizing firms ought to maximize expected

profit without regard to the variability around its expected value thats why firms opt for the


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increased the business potential of international banks, but has also changed the risk profile of

banks balance sheets in terms of country, market and liquidity risks, although it is difficult to

determine whether those risks, on the whole, have increased compared to those which

international banks used to incur with cross-border lending to emerging countries. As a

consequence, risk management of banks international activities has also changed. The main

difficulty confronting any empirical investigation into the roles that banks play in business cycle

fluctuations involves identification of credit supply shocks. Most economic disturbances that

affect the supply of credit likely have independent effects on real variables as well; for example,

an unanticipated change in the stance of monetary policy may change the interest rate on, or

quantity of, bank loans, but at the same time, that change may also affect spending and

production through its influence on expectations and interest rates. (Divisions of Research &

Statistics and Monetary Affairs, Washington, n.d.). There has been much discussion of the

RAROC and VaR methodologies as an approach to capture total risk management. Yet,

frequently, the decisions to accept risk and the pricing of the risky position are separated from

risk analysis. If aggregate risk is to be controlled, these parts of the process need to be integrated

better within the banking firm. Both aggregate risk methodologies presume that the time

dimensions of all risks can be viewed as equivalent. Finally, operating such a complex

management system requires a significant knowledge of the risks considered and the approaches

used to measure them.


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price of KSE-100 index over similar look-back period. The stock and index prices will be used to

calculate discrete returns on which selected market risk metrics will be applied to identify the

risks focused banks are subjected to and in doing that, gauge performance of banking sector

under consideration. Market risk metrics results will therefore be the main primary data that will

be subjected to data classification and analysis so that general theme pertaining to risks

Pakistans banking sector is exposed to could be identified. Market risk metrics that will be used

to assess risks facing banking sector mainly include mean, standard deviation, skewness and,

kurtosis of stock returns, minimum and maximum returns and, maximum draw down.

Covariance, Correlation, performance, firm, co-movement and systematic risk; known as relative

indicators, will also be calculated with the independent variable in the formula being KSE-100

index returns, and dependent variable being returns on sample stocks respectively.

Secondary data will mainly include findings reported in prior studies and journal articles

relevant to the research study under consideration; in an attempt to unravel historical trends and

statistics regarding risks in the banking industry through macroeconomic and microeconomic

analysis and deliver comprehensive verdict. In-depth and comprehensive secondary data

collection will be facilitated bytree search referencing; using which references of the journal

articles already utilized for the study can also be referred in the current study. The use of this

research method is justified considering its ability to identify misjudgments in the primary data

through comparing it against secondary data to intercept major discrepancies that could then be

manually assessed to determine any factor of irrationality. Additionally, use of secondary data in

isolation from primary data would not be practical as banking industry is dynamic and hence

current figures and recent statistics; stock prices, should be used to arrive at a more accurate

findings.


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Sample Size

The sample size for this research study includes fifty per cent of the commercial banks

listed on the Karachi Stock Exchange as also mentioned above. Fifty percent of the commercial

banks make up twelve and a half rounded to thirteen banks of twenty five listed commercial

banks and so this research study is focused on thirteen listed commercial banks comprising banks

belonging to all three top tier (Big-5 group), middle tier and third tier. The banks include Allied,

Bank Al-Falah, Habib, Habib Metropolitan, Bank Al-Habib, Askari, United, NIB, National Bank

of Pakistan, Faysal, KASB, JS and Samba. The look back or historical period used for the study

starts from 2nd

January 2012 and continues until 31stOctober 2014, from which period daily

stock quotations will be used for returns calculation. This makes the total observations 701 in

number implying 702 trading days. Including all the tiers ensures fair representation of

Pakistans banking industry which is important for unbiased and hence reliable risk investment

research. The look back period is long enough to capture variations both upside as well as

downside and hence meaningful for the study under consideration.

Data Collection and Analysis

For primary data collection; where primary data comprises stock and KSE-100 index

prices or quotations for data collection purpose as prices are required for calculation of discrete

returns upon which market risk metrics will be applied to gather actual primary data, credible

websites will be used to download daily quotations for the banks selected for the study and KSE-

100. The number of observations will be 701 in correspondence to the number of trading days.

For the prices, http://www.ksestocks.com/QuotationsData will be used. The daily quotations of


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market represented by composite KSE-100 index. However, for six market risk metrics which

incorporate index discrete returns as independent variable in their function making this approach

effectively invalid, maxima and minima in itself would highlight highest and lowest risk points;

or risks in their best and worst form, pertaining banking sector, in that disclosing sectors

riskiness and attractiveness for investors. The above discussed analysis technique reflects

thematic analysis that will lead towards identification of central theme representing overall

riskiness of Pakistans commercial banking sector.

This method for data collection, classification and analysis is in line with current research

topic as it comprehensively unveils risks Pakistans commercial banking sector is subjected to,

through exploring the sector in light of highest/best and lowest/worst performance on risk

indicators. The significance of this method also manifests in its ability to point out if an investor

should consider Pakistans banking sector for making equity investments, whilst also

highlighting worthy commercial banksstocks for risk takers and risk-aversive investors alike,

from risk investment research perspective.

L iterature search

For literature search, Google search engine was primarily used along with various other

research databases. Google Scholar, ProQuest and EBSCO databases were accessed to view and

download relevant research studies which are also referred in the ongoing research study. Google

was used to search and navigate through to relevant peer-reviewed journal articles as well as

meta-analysis concerning current research topic. These are used in literature review of the study

as secondary data or sources. Search Tree technique was also adopted, as mentioned above, in

an attempt to undertake comprehensive data gathering through reviewing and using even those

research articles in the study that are referenced in the researches attained from databases


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indicated above. Apart from this extensive data mining, the main source of secondary data

remains Google, ProQuest and EBSCO because of humungous quantity of data on relevant

variables as well as accessibility of the articles and studies therein.


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CHAPTER 5: ANALYSIS AND DISCUSSION

This section will focus on the analysis part of the research study, analyzing the figures

and results generated by application of market risk metrics on discrete returns calculated for all

the thirteen stocks and selected index composite for a total of 702 trading days, making returns

equal 701 (n-1). As explained in chapter 3 in detail, analysis will primarily start with

classification of outcomes on selected market risk metrics for all thirteen stocks into minima and

maxima, to compare the results against indexs performance. Comparing with market

performance represented by KSE-100s performance will highlight the riskiness of banking

sectors stocks. The similar treatment could not be undertaken for market risk relative indicators,

which could be transformed into maxima and minima but could not be compared against any

benchmark. The analysis for these indicators will comprise maxima and minima investigation

and interpretation which in itself effectively highlights banking sectors risks. This thematic

analysis will be complemented by macro and microeconomic analysis to ensure identification of

predominant trend in Pakistans banking sector with respect to risk; as to whether the sector

offers highly risky or less risky stocks or even an amalgamation of both of them. At this point, it

is important to reiterate that this chapter will mainly focus on last two components of top-down

risk investment research model; that is correlation with economy and product and model risks.

For the first two components however, secondary data was more important and so it is covered in

chapter two; Literature Review, of the study to a significant extent.


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Mean

Highest Mean Discrete Return Lowest Mean Discrete Return Index Mean Discrete Return

SBL: .326% NBP: .080% KSE-100: .145%

Mean refers to the average return paid by or earned over the stock. Highest mean daily

discrete stock return among 13 stocks representing commercial banking sector is .326% whilst

lowest mean is .080%. Both the highest and the lowest mean discrete returns are positive which

indicates favorable fluctuation in stock prices or returns from investment perspective. The higher

the price fluctuation facing an equity security, however, the higher is the risk factor harbored by

that particular stock. This is explained by risk-return trade-off theory as per which risk and return

are directly proportional making an equity security that posits high return subjected to higher

risk, manifesting in increased price fluctuations. Accordingly, Samba Bank Limited having

symbol SBL earning .326% on average is most risky stock in the sample and, National Bank of

Pakistan (NBP) giving an average return of .080% to its holder/investor; that is on any given day

the stock price is expected to increase by .080% on average, exhibits least risk among all the

stocks comprising study sample. Comparing stocks maximum and minimum return against

index mean return of .145%, SBL appears to be outperforming benchmark boasting more than

two times the indexs mean return as opposed to which, NBP is underperforming. This highlights

SBL as most suitable for risk-taker and NBP as most suitable for risk-averse investor.


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Standard Deviation

Highest Standard Deviation Lowest Standard Deviation Index Standard Deviation

SBL: 4.526% HBL: 1.683% .836%

Standard deviation refers to the volatility/variability of discrete daily return from mean

return for any stock or index, for that matter. The spread between discrete daily and mean return

is more stringent risk measure; accounting for total risk, implying volatility of return. The

highest standard deviation among that of sample stocks is set at 4.526% which is projected by

SBL and the lowest standard deviation is exhibited by HBL at 1.683%. Index standard deviation

(SD) appears to be .836%. Interpreting the figures for investment purposes, SBL stock could be

classified as aggressive investment/security exhibiting not only higher return but also volatility.

In contrast, HBL exhibits significantly lower volatility of 1.683% as compared to that of SBL but

despite that, HBLs stock turns out to be two times more risky; positing greater systematic and

unsystematic risk, than KSE-100 index which stands at .836% with respect to expectation

regarding upside or downside movement from the average index return of .145%. Thus, in

comparison to SBL which features variance that is 5.4 times index SD, HBL could be classified

as moderate whilst SBL as aggressive. With the lowest variance featured in the sample being

twice the indexs variance, riskiness borne by Pakistans commercial banking sector is well

starting develop, however definite shape and risk position of the sector could only be discovered

once all the metrics have been analyzed. It is also because of the fact that variance assigns

greater weight to outliers resulting in lopsided interpretations.


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Skewness

Highest Skewness Lowest Skewness Index Skewness

NIB: 5.0245 BAHL: -3.0281 .283

Skewness assesses the tendency of data to be asymmetrical around it means; the

concentration of large number of discrete returns towards either right or left tail making

respective tail fatter, with fat right tail signifying profits and fat left tail being associated with

losses. Fat left tail event makes the distribution of the variable negatively skewed, whilst vice

versa scenario, fat right tail event, renders distribution positively skewed. The highest skewness

among sample banks of 5.0245 is exhibited by NIB and the lowest skewness of -3.0281 is faced

by BAHL. Index skewness stands at -.283. Interpreting the figures, returns on NIB denote fat

right tail indicating higher frequency of profit; higher positive than negative returns, whereas

BAHLs returns form fat left tail indicating higher incidences of loss. Returns on index itself are

negatively skewed denoting more negative than positive returns and hence losses. Using the

figures to determine riskiness of commercial banking sector, BAHL raises red flag for risk-

aversive investors, denoting huge 10.89 times more frequent negative returns than KSE-100

which itself posits negative skewness. NIB, on the other hand, delivers higher positive than

negative returns while also outperforming benchmark index by more than 17.75x (5.0254/.283),

in terms of frequency of positive returns, appealing to risk appetite of risk-taking and risk-averse

investors alike. Accordingly, it can be said that banking sector has the potential to deliver higher

number of positive than negative returns, reducing loss potential while building investor

confidence.


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Kurtosis

Figure 1: Skewness and kurtosis comparison

Highest Kurtosis Lowest Kurtosis Index Kurtosis

NIB: 67.8799 UBL: .9143 2.255

The above graph compares and subsequently summarizes risk commercial banking sector

is exposed to with respect to skewness and kurtosis. However, for a more meaningful analysis,

highest and lowest points in kurtosis will be discussed, conforming to treatment of prior risk

metrics focused earlier to facilitate risk investment research. The highest kurtosis of 67.8799 is

exhibited by NIB making the distribution leptokurtic (Kurtosis > 3) whereas lowest kurtosis of

.9143 is exhibited by UBL making the distribution platykurtic. The benchmark index is subjected

to the kurtosis of 2.255 indicating even platykurtic distribution; dispersal of volatility risk

throughout distribution of returns. High kurtosis of NIB indicates concentration of volatility risk

in fat tail events meaning overall risk of the stock is driven by extreme values or frequent large

-10.000

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

Skewness

Kurtosis


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magnitude of returns towards tails, making it suitable for risk-takers, particularly when the

skewness of NIB is also the highest among sample banks. Preference by risk-takers is embedded

in that whilst kurtosis is higher and so risk of tail events, skewness is also higher (positive) which

means positive returns outweigh negative returns for NIB, thereby hinting on significant

likelihood of tail events to incline towards right tail making investment potentially worthwhile.

On the other hand, UBL appeals conservative investors more through its platykurtic distribution

of returns whereby overall risk resides in a predictable band exposing investors to moderate

amount of risk, as opposed to massive risk that characterizes stocks denoting leptokurtic

distribution of returns. Returns of benchmark index; denoting market, are also distributed in

platykurtic fashion, however, being less conservative than UBL with its relatively higher kurtosis

coupled with negative skewness highlights lower risk level of Pakistans commercial banking

sector, with respect to kurtosis. The deduction is reinforced by profit potential of stocks with

NIB boasting kurtosis significantly higher than index.

Maximum Draw Down

Risk Metrics Highest Lowest Index

Maximum Return NIB: 50.794% BAHL: 4.988% 2.856%

Minimum Return UBL: -5.966% NBP: -22.933% -4.456%

Maximum Draw Down NIB: 62.460% UBL: 10.966% 7.312%

The above chart summarizes three major market risk metrics; maximum and minimum

return and maximum draw down. Over the look-back period of 702 trading days, NIB appears to


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have earned highest maximum return of 50.794% among the sample banks whereas lowest

minimum return has been recorded by NBP which was -22.933%. This statistic confirms NIB as

aggressive; which is what previous metric (skewness and kurtosis) also indicate. The most

important risk metric in this scenario is MDD however, as it is derived using maximum and

minimum returns, they are discussed together to present better picture. Its importance reflects in

its ability to make traders/investors bring their trading/investing activities to halt, particularly due

to strong drawdown usually triggered by market sell-off and leverage conditions; prevalent

conditions across Pakistan. NIB projects highest MDD implying its investors need to have

greater risk appetite than that of any other sample banks investor. This is in conformance with

risk-return trade-off theory highlighted earlier. On the other hand, UBL provides its investors

relatively safer haven exposing them to significantly low financial risk, as implied by its lowest

MDD among sample banks; 5.7 times below NIBs MDD. Despite appealing conservative

investors though, UBL remains unable to track up to indexs MDD which is 3.654% below

UBLs MDD. Nevertheless, UBLs lowest MDD among sample banks does further its investor

confidence, which is also reinforced by its lowest kurtosis. This highlights commercial banking

sector as an amalgamation of aggressive and moderate investments catering to almost all risk

appetites appropriately.


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Covari ance and Correlation

Figure 2: Risk measures compared

Metrics Highest Lowest

Covariance NIB: .0001 HMB: .00001

Correlation UBL: .5238 HMB: .0747

The graph above the table compares risk of sample commercial banks as per certain

absolute and all selected relative risk indicators, presenting bigger picture however, all the

absolute risk metrics have been and relative risk metrics will be analyzed separately to facilitate

effective risk investment research. As the above table indicates, NIB is subjected to highest

covariance as opposed to HMB which enjoys lowest covariance. To understand the risk banking

sector is exposed to, it is important to interpret the above figures. NIB bank is most risky with

respect to covariance because among all the sample banks, its returns are most aligned with

market returns represented by returns on benchmark index. HMB however appears to be least

-0.20000

0.00000

0.20000

0.40000

0.60000

0.80000

1.00000

1.20000

1.400001.60000

1.80000

RsikMeasures

Banks

Covariance

Correlation

Coefficient of Determination

Firm Risk

Comovement Risk

Performance Risk

Standard Deviation

Minimum Draw down


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risky as despite the movement of its returns being in tandem with market returns, it depicts least

covariance or alignment with market among sample banks. The overall risk of the commercial

banking sector as dictated by Covariance is nevertheless higher due to lack of diversification,

implied by positive covariance throughout the sample. Diversification mitigates risk and

diversified portfolio; preferred by investors, comprises stocks that have negative or low

covariance with respect to each other. Focusing on correlation, it is similar to covariance except

that its deductions are purely derived from sign convention whilst covariance primarily reflects

degree of correlation. Looking at correlation statistics, it confirms the observations covariance

triggered. With both the stocks that projects highest and lowest correlation with market being

positively correlated, it can be safely deduced that commercial banking stocks moves in lockstep

with the market. This is important to be factored into portfolio formation and analysis to mitigate

risk.

Coeff icient of Determination and F irm Risk

Risk Metrics Highest Lowest

Coefficient of Determination UBL: 27.436% HMB: .558%

Firm Risk HMB: 99.442% UBL: 72.564%

Coefficient of determination or correlation-squared (systematic risk) and Firm risk

(unsystematic risk) are complements of each other, because of which they are discussed together.

Highest systematic risk of UBL canbe interpreted as the changes/variations/movements in

UBLs price are explainable by the changes/movements in benchmarkindex, more than that of


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any other stock. HMBs prices are least explainable by movements in benchmark index making

it less prone to market/systematic risk. However, despite UBLs performance being most in line

with market performance, it and hence the entire sector is devoid of substantial market risk as r-

squared value is not significant (between 85%-100%). 27.436% systematic risk or only 27.436%

movements in UBLs stock price being explained by broader market movements is akin to lower

correlation between the security and index. Focusing on unsystematic or firm risk though, it is

significant for the entire sector, with highest firm risk among the sample banks being 99.44% for

HMBs and lowest being 72.564% facing UBL. This calls for significant caution for the

investors when investing in the banking sector. The security that faces highest systemic risk is

exposed to lowest firm unsystematic risk. But with the systematic risk profile on the whole being

low, firm risk becomes more important to be assessed. As per these two metrics, returns on

commercial banking stock comprise an element of uncertainty; depending strongly on

managerial performance in any given tenure/period. This makes the sector more preferable for

risk-takers as hedge fund managers and less so for risk-averse investors as mutual funds.

Co-movement and Performance Risk

Risk Metrics Highest Lowest

Co-movement Risk JSBL: 1.6456 HMB: .1567

Performance Risk SBL: .0023 NIB: -.001192

Co-movement risk (beta) figures are only meaningful if systematic risk is higher; that is

greater than 50%, because it measures ability of investment to respond to market swings.


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However, in our study where even highest systematic is around half of 50%, the beta or co-

movement risk figures stands baseless and, hence will be ignored. Focusing on performance

risk/alpha subsequently which comprehends ability of the stock to earn extra returns when

market is stationary, primarily due to organizational and managerial performance, SBL appears

to be most favorably positioned having outperformed the market by modest .0023%, whereas

NIB appears to be least so having underperformed by .001192%. This reiterates earlier deduction

derived from firm risk statistics, which also projected management as critical factor for

concerned equities performance. In the light of this metric, commercial banking sector turns out

to be risky with the propensity to underperform even when market sentiments are bullish. This

makes the sector less attractive for risk-averse investors.


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CHAPTER 5: CONCLUSION

Current research study undertaken to explore commercial banking sector with respect to selected

market risk metrics is essential to understand the position of equity securities offered by listed

commercial banks incorporated in Pakistan. Only market risk metrics are used for this research

study despite the banking sector being exposed to five risk classes/types including liquidity and

credit risk. This is primarily because market risk metric interfaces credit ad liquidity risk. When

market is falling, it becomes challenging for investors to dispose/sell-off their

asset/equity/security, implying positive relationship between market risk and liquidity risk.

Credit risk is not present in equity market which further endorses use of market risk metrics for

this research study as appropriate and viable. Risk investment research is particularly important

for investors, analysts and even the banks itself for the purpose of informed decision-making.

Discrete daily returns have been used as it gives fair representation of volatility and hence risk.

702 trading days have been used as look-back period which has both, positive and negative

aspects. Reliance on historical data spanning past two years could highlight volatility such From

the above analysis, it appears that commercial banking sector is not conservative, rather equity

securities pertaining commercial banking sector ranges between aggressive and moderate, as

none of the sample stocks could be termed as appropriate for mutual fund managers or risk-

averse investors if decision is to be taken with relevance to the outcomes on elected market risk

metrics. The stocks posit tendency to benefit their investors from upside movement when the

market is bullish, however during bearish markets, significant downside risk prevails. Standard

deviation which denotes any securitys total risk; representing both systematic as well as

unsystematic risk, indicates lowest standard deviation among that of sample commercial banks


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as twice the benchmark indexs variance. This relation, a lso stated earlier, essentially reflects

high overall volatility of Pakistans commercial banking sector making it significantly risky

comprising mainly aggressive equity securities. Skewness of the index which itself is inclined

towards higher negative returns being outweighed by commercial banking sectors stocks

confirms to the elevated degree of risk commercial banking sector is exposed to. The coefficient

of determination however signifies explained variation as low, implying performance of

commercial banking securities is not mainly dependent on the performance of the market. This

result attained on coefficient of determination metric is of pivotal importance in this study as it

implies performance of equity commercial banking performance is also dictated by factors other

than market movements. This finding is further corroborated by the results attained on firm and

performance risk which indicates organizational and managerial performance impacts

movements of stocks pertaining commercial banking sector by substantial magnitude. This

makes it important for prospective investors to closely follow developments in commercial

banking organizations; they are considering for making investment into, in addition to analyzing

the sector and individual banking companies in juxtaposition with the wider market represented

by relevant benchmark index. On the basis of this nevertheless, the sector appears to be

rewarding for risk-takers who depicts greater risk appetite and tolerance levels than risk-averse

investors projecting low risk appetite and tolerance level. For risk-averse investors, use of

diversification could make sector attractive which is important for formulation of a well-

balanced portfolio with constituent assets being negatively correlated or having low correlation

between them. This will lead to the mitigation of risk inherent in highly risky commercial

banking sector, but even with the use of diversification it is essential to revisit and revise

portfolio periodically to ensure portfolio continuously corresponds to investors risk appetite.


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Annexure

KSE-100

IndexReturns

Allied Bank(ABL)

StockReturns

Askari Bank(AKBL)

StockReturns

11282.01 53.54 10.11

11402.04 0.011 56.21 0.050 10.25 0.014

11361.97 -0.004 54.02 -0.039 10.01 -0.023

11187.88 -0.015 57.21 0.059 10.01 0.000

11125.35 -0.006 55.19 -0.035 10 -0.001

11040.31 -0.008 54.12 -0.019 9.94 -0.006

10933.18 -0.010 57.84 0.069 10.03 0.009

10930.49 0.000 57.79 -0.001 10.12 0.009

10909.12 -0.002 54.6 -0.055 10.06 -0.006

11014.46 0.010 55.41 0.015 10.2 0.014

11112.65 0.009 55.12 -0.005 10.27 0.007

11305.16 0.017 56.31 0.022 10.3 0.003

11547.72 0.021 56.06 -0.004 10.44 0.014

11515.59 -0.003 57 0.017 10.36 -0.008

11774.68 0.022 58.86 0.033 10.3 -0.006

12037.66 0.022 61.03 0.037 10.7 0.039

11991.38 -0.004 60.6 -0.007 10.77 0.00711949.75 -0.003 61.14 0.009 10.64 -0.012

11883.92 -0.006 61.04 -0.002 10.66 0.002

11960.22 0.006 61.03 0.000 10.67 0.001

11883.01 -0.006 60.47 -0.009 10.35 -0.030

11874.89 -0.001 60.51 0.001 10.25 -0.010

11930.55 0.005 60.9 0.006 10.35 0.010

11929.78 0.000 61 0.002 10.25 -0.010

11982.62 0.004 60.6 -0.007 10.32 0.007

12136.92 0.013 61.37 0.013 11.06 0.07212284.62 0.012 62.03 0.011 12.06 0.090

12263.25 -0.002 62.51 0.008 11.96 -0.008

12213.24 -0.004 62.12 -0.006 11.25 -0.059

12231.6 0.002 62 -0.002 11.11 -0.012

12250 0.002 60.63 -0.022 11.01 -0.009

12261.85 0.001 61.02 0.006 11.48 0.043


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12311.04 0.004 61.08 0.001 11.53 0.004

12404.24 0.008 61.67 0.010 11.66 0.011

12495.68 0.007 62 0.005 11.89 0.020

12517.9 0.002 61.09 -0.015 11.8 -0.008

12544.45 0.002 61.01 -0.001 12.16 0.031

12603.67 0.005 61.97 0.016 12.68 0.043

12515.92 -0.007 61.55 -0.007 12.62 -0.005

12706.52 0.015 62.47 0.015 12.73 0.009

12743.66 0.003 63.53 0.017 13.05 0.025

12739.22 0.000 63.32 -0.003 12.85 -0.015

12877.88 0.011 64.81 0.024 12.7 -0.012

12941.38 0.005 65.48 0.010 12.31 -0.031

13088.97 0.011 65.99 0.008 12.56 0.020

13278.31 0.014 66.22 0.003 12.97 0.033

13324.34 0.003 69.17 0.045 13.14 0.01313244.95 -0.006 68.49 -0.010 12.92 -0.017

13271.39 0.002 68.9 0.006 13.4 0.037

13352.74 0.006 69.08 0.003 13.52 0.009

13382.54 0.002 69 -0.001 13.95 0.032

13283.65 -0.007 68.89 -0.002 13.63 -0.023

13360.67 0.006 69.25 0.005 13.82 0.014

13451.07 0.007 69.89 0.009 14.23 0.030

13297.12 -0.011 69.13 -0.011 13.79 -0.031

13077.72 -0.016 67.91 -0.018 13.15 -0.04613303.33 0.017 60.51 -0.109 13.31 0.012

13293.12 -0.001 59.5 -0.017 11.59 -0.129

13273.29 -0.001 60.01 0.009 11.81 0.019

13286.73 0.001 59.54 -0.008 12.39 0.049

13449.73 0.012 60.28 0.012 12.95 0.045

13575.41 0.009 62.21 0.032 13.69 0.057

13559.1 -0.001 62.85 0.010 14.01 0.023

13761.76 0.015 64.08 0.020 14.64 0.045

13663.32 -0.007 64 -0.001 14.32 -0.022

13691.08 0.002 64.01 0.000 14.47 0.010

13945.3 0.019 65.76 0.027 14.88 0.028

13831.47 -0.008 65.25 -0.008 14.64 -0.016

13875.53 0.003 65 -0.004 13.77 -0.059

13864.68 -0.001 64.8 -0.003 14.26 0.036

13903.12 0.003 64.76 -0.001 14.51 0.018


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13816.96 -0.006 65 0.004 14.65 0.010

13693.74 -0.009 64.5 -0.008 14.89 0.016

13799.43 0.008 65.2 0.011 14.55 -0.023

13770.7 -0.002 63.24 -0.030 14.04 -0.035

13764.22 0.000 63.28 0.001 14.31 0.019

13937.95 0.013 63.62 0.005 14.59 0.020

13929.47 -0.001 64.24 0.010 14.64 0.003

13936.48 0.001 65.53 0.020 14.47 -0.012

14083.44 0.011 68.8 0.050 15.16 0.048

14132.59 0.003 70.49 0.025 15.39 0.015

14217.74 0.006 69.83 -0.009 15.99 0.039

14066.09 -0.011 69.08 -0.011 15.56 -0.027

14042.77 -0.002 67.75 -0.019 15.24 -0.021

13990.38 -0.004 67.68 -0.001 15.65 0.027

14142.52 0.011 70 0.034 15.73 0.00514419.92 0.020 72.66 0.038 15.57 -0.010

14612.28 0.013 73.39 0.010 15.36 -0.013

14617.97 0.000 70.97 -0.033 15.27 -0.006

14513.96 -0.007 70.6 -0.005 15.02 -0.016

14613.59 0.007 70.03 -0.008 15.36 0.023

14420.19 -0.013 68.54 -0.021 15.39 0.002

14230.49 -0.013 66.81 -0.025 15.1 -0.019

14228.77 0.000 69.32 0.038 15.18 0.005

14313.67 0.006 68.61 -0.010 15.26 0.00514081.07 -0.016 67.01 -0.023 14.78 -0.031

14063.08 -0.001 67 0.000 14.77 -0.001

13857.78 -0.015 66.01 -0.015 14.38 -0.026

13875.74 0.001 65.98 0.000 14.15 -0.016

14142.08 0.019 66.49 0.008 15.11 0.068

14032.82 -0.008 65.95 -0.008 14.96 -0.010

13936.92 -0.007 65.31 -0.010 14.98 0.001

13925.06 -0.001 65.01 -0.005 15.05 0.005

14031.51 0.008 64.22 -0.012 14.8 -0.017

14071.85 0.003 63.99 -0.004 14.71 -0.006

13871.76 -0.014 62.91 -0.017 14.72 0.001

13786.62 -0.006 64.09 0.019 14.62 -0.007

13876.97 0.007 64.39 0.005 14.67 0.003

13757.92 -0.009 64.25 -0.002 14.54 -0.009

13708.23 -0.004 64.3 0.001 14.85 0.021


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13745.73 0.003 63.55 -0.012 14.44 -0.028

13717.3 -0.002 63.16 -0.006 14.16 -0.019

13558.7 -0.012 63 -0.003 14.05 -0.008

13601.46 0.003 63.68 0.011 13.98 -0.005

13429.56 -0.013 63.99 0.005 13.58 -0.029

13368.89 -0.005 63.6 -0.006 13.49 -0.007

13656.2 0.021 63.96 0.006 13.79 0.022

13665.8 0.001 64.01 0.001 13.83 0.003

13754.13 0.006 64.01 0.000 13.75 -0.006

13682.99 -0.005 63.5 -0.008 13.52 -0.017

13667.18 -0.001 64.82 0.021 13.26 -0.019

13600.6 -0.005 64.55 -0.004 13.07 -0.014

13730.82 0.010 64.55 0.000 13.56 0.037

13642.2 -0.006 64.49 -0.001 13.34 -0.016

13656.04 0.001 64.28 -0.003 13.4 0.00413799.12 0.010 64.07 -0.003 13.59 0.014

13805.42 0.000 63.98 -0.001 13.61 0.001

13801.41 0.000 64.18 0.003 13.57 -0.003

14142.92 0.025 64.92 0.012 14.16 0.043

14200.79 0.004 64.37 -0.008 13.96 -0.014

14178.1 -0.002 64.65 0.004 13.89 -0.005

14170.91 -0.001 64.07 -0.009 13.7 -0.014

14310.18 0.010 64.25 0.003 13.69 -0.001

14379.54 0.005 64.61 0.006 13.75 0.00414374.34 0.000 64.64 0.000 13.8 0.004

14380.46 0.000 65 0.006 14.78 0.071

14401.74 0.001 65.79 0.012 14.64 -0.009

14332.29 -0.005 65.51 -0.004 14.46 -0.012

14384.58 0.004 65.93 0.006 14.34 -0.008

14445.28 0.004 67.02 0.017 15 0.046

14596.59 0.010 68.74 0.026 15.04 0.003

14568.21 -0.002 68.26 -0.007 14.82 -0.015

14564.49 0.000 68.97 0.010 15.72 0.061

14527.25 -0.003 69 0.000 15.54 -0.011

14512.07 -0.001 70 0.014 15.36 -0.012

14564.68 0.004 70.11 0.002 15.5 0.009

14553.29 -0.001 70.52 0.006 15.59 0.006

14526.41 -0.002 70 -0.007 15.46 -0.008

14511.54 -0.001 70.01 0.000 15.68 0.014


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14577 0.005 70.52 0.007 15.52 -0.010

14716.86 0.010 70.5 0.000 15.56 0.003

14730.67 0.001 70 -0.007 15.43 -0.008

14676.43 -0.004 70 0.000 15.25 -0.012

14673.77 0.000 70.61 0.009 14.88 -0.024

14672.24 0.000 72.19 0.022 14.72 -0.011

14744.14 0.005 73.04 0.012 15.01 0.020

14759.59 0.001 73.96 0.013 14.98 -0.002

14761.49 0.000 71.13 -0.038 14.77 -0.014

14911.97 0.010 71.66 0.007 14.55 -0.015

14970.93 0.004 71.71 0.001 15.52 0.067

15000.08 0.002 72.2 0.007 15.37 -0.010

15080.55 0.005 72.98 0.011 16.12 0.049

15039.18 -0.003 74 0.014 15.73 -0.024

15171.66 0.009 73.57 -0.006 15.82 0.00615234.48 0.004 72.92 -0.009 15.74 -0.005

15151.31 -0.005 72.37 -0.008 15.5 -0.015

15253.71 0.007 72.48 0.002 15.88 0.025

15391.58 0.009 72.25 -0.003 15.89 0.001

15428.49 0.002 71.52 -0.010 15.81 -0.005

15388.13 -0.003 70 -0.021 15.68 -0.008

15293.39 -0.006 67.88 -0.030 15.42 -0.017

15188.53 -0.007 68 0.002 15.5 0.005

15253.96 0.004 67.5 -0.007 15.79 0.01915240.19 -0.001 67 -0.007 15.6 -0.012

15214.02 -0.002 68.31 0.020 15.57 -0.002

15278.48 0.004 68 -0.005 15.51 -0.004

15306.51 0.002 69.27 0.019 15.26 -0.016

15449.61 0.009 68.3 -0.014 15.31 0.003

15398.68 -0.003 67.69 -0.009 15.22 -0.006

15517.19 0.008 68.11 0.006 15.03 -0.012

15588.66 0.005 67.81 -0.004 15.16 0.009

15452.64 -0.009 67 -0.012 15 -0.011

15375.52 -0.005 66 -0.015 15.03 0.002

15373.46 0.000 66.04 0.001 15.42 0.026

15399.42 0.002 64.23 -0.027 15.26 -0.010

15357.59 -0.003 63.56 -0.010 15.3 0.003

15444.82 0.006 63.9 0.005 15.28 -0.001

15559.94 0.007 63.89 0.000 14.97 -0.020


48/125


48

15648.29 0.006 64.56 0.010 15.41 0.029

15712.21 0.004 65.01 0.007 15.47 0.004

15788.96 0.005 67.82 0.043 15.31 -0.010

15754.39 -0.002 68.4 0.009 15.22 -0.006

15652.01 -0.006 70.1 0.025 15.47 0.016

15688.24 0.002 69.83 -0.004 15.58 0.007

15753.82 0.004 68.21 -0.023 15.67 0.006

15845.3 0.006 69 0.012 15.86 0.012

15694.21 -0.010 68.6 -0.006 15.74 -0.008

15746.9 0.003 68.02 -0.008 15.73 -0.001

15674.3 -0.005 69.17 0.017 15.66 -0.004

15654.62 -0.001 71.19 0.029 15.62 -0.003

15679.19 0.002 71.42 0.003 15.52 -0.006

15792.75 0.007 70.93 -0.007 16.51 0.064

15848.63 0.004 69 -0.027 16.83 0.01915853.84 0.000 68.5 -0.007 16.55 -0.017

15865.53 0.001 68.28 -0.003 16.39 -0.010

15812.72 -0.003 69.99 0.025 16.45 0.004

15795.93 -0.001 69.13 -0.012 16.64 0.012

15910.11 0.007 69.5 0.005 16.57 -0.004

15962.37 0.003 69.9 0.006 16.84 0.016

16101.55 0.009 69.2 -0.010 16.75 -0.005

16156.36 0.003 68.91 -0.004 16.95 0.012

16051.14 -0.007 69.67 0.011 16.86 -0.00516218.01 0.010 69.49 -0.003 16.8 -0.004

16243.27 0.002 68.36 -0.016 16.62 -0.011

16213.68 -0.002 68.5 0.002 16.58 -0.002

16129.72 -0.005 69 0.007 16.37 -0.013

16120.52 -0.001 68.95 -0.001 16.31 -0.004

16143.07 0.001 68.61 -0.005 16.3 -0.001

16197.74 0.003 67.53 -0.016 16.36 0.004

16251.38 0.003 68.5 0.014 16.2 -0.010

16251.79 0.000 69 0.007 16.25 0.003

16233.19 -0.001 68.67 -0.005 16.27 0.001

16251.01 0.001 69 0.005 16.54 0.017

16237.59 -0.001 69.94 0.014 16.78 0.015

16270.48 0.002 70.03 0.001 16.72 -0.004

16364.77 0.006 70.51 0.007 16.63 -0.005

16424.03 0.004 71.65 0.016 16.48 -0.009


49/125


49

16527.08 0.006 71.77 0.002 16.56 0.005

16573.86 0.003 72 0.003 16.61 0.003

16537.98 -0.002 73.58 0.022 16.53 -0.005

16650.15 0.007 75 0.019 16.47 -0.004

16675.7 0.002 75.18 0.002 16.63 0.010

16824.55 0.009 75.3 0.002 16.87 0.014

16807.91 -0.001 74.74 -0.007 16.77 -0.006

16787.54 -0.001 75.05 0.004 17.07 0.018

16701.69 -0.005 74.01 -0.014 16.87 -0.012

16744.6 0.003 74.01 0.000 16.84 -0.002

16806.58 0.004 74.5 0.007 16.82 -0.001

16845.09 0.002 74.51 0.000 16.79 -0.002

16801.02 -0.003 74 -0.007 16.74 -0.003

16858.68 0.003 74 0.000 16.83 0.005

16869.83 0.001 73.95 -0.001 16.74 -0.00516908.02 0.002 73.63 -0.004 16.75 0.001

16865.34 -0.003 73.5 -0.002 16.63 -0.007

16891.94 0.002 73.49 0.000 16.5 -0.008

16927.34 0.002 73.49 0.000 16.5 0.000

16892.32 -0.002 72.15 -0.018 16.75 0.015

16943.19 0.003 71 -0.016 17.41 0.039

16905.33 -0.002 71 0.000 17.22 -0.011

16794.87 -0.007 72.5 0.021 17 -0.013

16489.99 -0.018 71 -0.021 17.06 0.00416588.54 0.006 71.5 0.007 17.25 0.011

16648.84 0.004 71.58 0.001 17.63 0.022

16502.65 -0.009 70.71 -0.012 17.84 0.012

16645.76 0.009 71.5 0.011 18.84 0.056

16742.22 0.006 70.97 -0.007 18.85 0.001

16529.92 -0.013 70.33 -0.009 18.67 -0.010

16634.71 0.006 70 -0.005 18.69 0.001

16633.18 0.000 71.5 0.021 18.79 0.005

16107.89 -0.032 73.05 0.022 17.82 -0.052

16181.47 0.005 74.98 0.026 17.79 -0.002

16291.09 0.007 75.54 0.007 18.12 0.019

16601.77 0.019 74.83 -0.009 18.72 0.033

16640.81 0.002 75.15 0.004 18.71 -0.001

16894.09 0.015 73.49 -0.022 18.79 0.004

16908.67 0.001 75 0.021 18.57 -0.012


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50

17056.36 0.009 74.02 -0.013 18.51 -0.003

17004.99 -0.003 74 0.000 18.43 -0.004

17172.04 0.010 73.9 -0.001 18.64 0.011

17205.27 0.002 73.88 0.000 18.21 -0.023

17242.74 0.002 73.75 -0.002 18.29 0.004

17266.23 0.001 73.5 -0.003 18.44 0.008

17288.07 0.001 73.33 -0.002 18.67 0.012

17408.52 0.007 73.5 0.002 18.53 -0.007

17383.32 -0.001 73.8 0.004 18.7 0.009

17477.94 0.005 73.5 -0.004 19.69 0.053

17548.54 0.004 71.97 -0.021 19.45 -0.012

17611.4 0.004 69.97 -0.028 19.5 0.003

17696.45 0.005 69.76 -0.003 19.15 -0.018

17765.82 0.004 68.51 -0.018 19.12 -0.002

17797.22 0.002 68.65 0.002 19.08 -0.00217865.61 0.004 69.01 0.005 19.03 -0.003

17817.71 -0.003 69.75 0.011 18.62 -0.022

17947.07 0.007 69.06 -0.010 18.8 0.010

17921.02 -0.001 68.43 -0.009 18.98 0.010

18074.27 0.009 68.97 0.008 18.77 -0.011

18020.5 -0.003 68.5 -0.007 18.69 -0.004

17894.9 -0.007 68.4 -0.001 18.63 -0.003

18080.91 0.010 68.55 0.002 18.88 0.013

18173.67 0.005 68.94 0.006 18.74 -0.00718185.19 0.001 68.77 -0.002 19.42 0.036

18126.77 -0.003 68.01 -0.011 19.53 0.006

18053.32 -0.004 67.74 -0.004 19.33 -0.010

18000.45 -0.003 66.89 -0.013 19.26 -0.004

17992.91 0.000 68.15 0.019 19.27 0.001

17964.18 -0.002 68 -0.002 19.28 0.001

17522.56 -0.025 68.13 0.002 18.74 -0.028

17872.85 0.020 68 -0.002 18.88 0.007

17760.44 -0.006 66 -0.029 18.7 -0.010

17740.69 -0.001 67.07 0.016 18.6 -0.005

17664.83 -0.004 59.47 -0.113 18.54 -0.003

17492 -0.010 59.75 0.005 18.3 -0.013

17693.37 0.012 60 0.004 18.05 -0.014

17753.97 0.003 59.02 -0.016 18.13 0.004

17913.62 0.009 59 0.000 18.38 0.014


51/125


51

17963.12 0.003 58.93 -0.001 18.39 0.001

17961.91 0.000 58.6 -0.006 18.61 0.012

17872.15 -0.005 58.95 0.006 18.66 0.003

17926.14 0.003 59.6 0.011 18.67 0.001

17947.76 0.001 59.4 -0.003 18.66 -0.001

18043.31 0.005 58.43 -0.016 19.16 0.027

18272.11 0.013 57.97 -0.008 19.28 0.006

18345.74 0.004 57.16 -0.014 19.05 -0.012

18575.88 0.013 58 0.015 19.04 -0.001

18613.44 0.002 57.64 -0.006 19.24 0.011

18636.03 0.001 58 0.006 19.13 -0.006

18653.06 0.001 57.56 -0.008 18.77 -0.019

18713.61 0.003 57.67 0.002 18.96 0.010

18723.35 0.001 56.55 -0.019 19.08 0.006

18764.55 0.002 55.93 -0.011 19.1 0.00118714.28 -0.003 56 0.001 19.08 -0.001

18524.5 -0.010 56.14 0.003 18.77 -0.016

18361.87 -0.009 55.49 -0.012 18.72 -0.003

18394.12 0.002 56 0.009 18.71 -0.001

18614.36 0.012 55.63 -0.007 17.71 -0.053

18631.21 0.001 56.69 0.019 16.71 -0.056

18605.55 -0.001 56.54 -0.003 15.71 -0.060

18647.29 0.002 56.6 0.001 15.3 -0.026

18779.66 0.007 56 -0.011 15.75 0.02918885.61 0.006 55.01 -0.018 16.75 0.063

18917.71 0.002 55.89 0.016 16.74 -0.001

18822.85 -0.005 58.68 0.050 17.47 0.044

18982.42 0.008 60.02 0.023 18.45 0.056

19034.53 0.003 58.44 -0.026 18.4 -0.003

19226.63 0.010 58.45 0.000 18.2 -0.011

19256.7 0.002 58.95 0.009 17.34 -0.047

19262.74 0.000 59.4 0.008 16.91 -0.025

19472.55 0.011 61.05 0.028 16.83 -0.005

19661.46 0.010 63.68 0.043 16.68 -0.009

19916.27 0.013 66.5 0.044 17.07 0.023

20244.82 0.016 67.29 0.012 17.4 0.019

20474.62 0.011 68.66 0.020 17.59 0.011

20566.69 0.004 69.9 0.018 17.23 -0.020

20416.6 -0.007 69 -0.013 17.2 -0.002


52/125


52

20537.03 0.006 69.83 0.012 16.84 -0.021

20814.14 0.013 69.77 -0.001 16.95 0.007

21168 0.017 69.99 0.003 17.1 0.009

21458.9 0.014 69.75 -0.003 16.88 -0.013

21342.65 -0.005 69.45 -0.004 16.71 -0.010

21283.77 -0.003 68.85 -0.009 15.82 -0.053

20958.86 -0.015 68.77 -0.001 15.64 -0.011

21501.72 0.026 68.38 -0.006 15.47 -0.011

21441.12 -0.003 66.43 -0.029 15.28 -0.012

21590.66 0.007 68.96 0.038 15.55 0.018

21823.05 0.011 70.12 0.017 16.41 0.055

22080.85 0.012 70.2 0.001 16.23 -0.011

22274.51 0.009 72 0.026 15.87 -0.022

22092.42 -0.008 69.56 -0.034 16.1 0.014

22276.7 0.008 70.99 0.021 16.71 0.03822358.96 0.004 70.21 -0.011 16.73 0.001

22150.74 -0.009 72.41 0.031 16.59 -0.008

22209.07 0.003 73.53 0.015 16.56 -0.002

22324.57 0.005 72.33 -0.016 16.49 -0.004

22757.72 0.019 71.29 -0.014 16.53 0.002

22541.64 -0.009 70.68 -0.009 16.9 0.022

22216.46 -0.014 71.43 0.011 16.75 -0.009

21919.63 -0.013 71 -0.006 16.53 -0.013

22135.72 0.010 68.55 -0.035 16.46 -0.00422015.04 -0.005 69.65 0.016 16.69 0.014

21698.35 -0.014 69.3 -0.005 16.12 -0.034

21048.08 -0.030 68.58 -0.010 15.5 -0.038

21110.34 0.003 68.54 -0.001 15.55 0.003

21002.57 -0.005 70 0.021 15.33 -0.014

21015.34 0.001 71 0.014 15.35 0.001

21005.69 0.000 71.5 0.007 15.22 -0.008

21363.16 0.017 73.53 0.028 15.55 0.022

21644.17 0.013 76.46 0.040 15.59 0.003

21802.86 0.007 76.58 0.002 15.57 -0.001

21966.96 0.008 78.65 0.027 15.73 0.010

22178.34 0.010 80 0.017 15.85 0.008

22365.72 0.008 78.64 -0.017 15.48 -0.023

22721.22 0.016 79 0.005 16.1 0.040

22984.94 0.012 81.39 0.030 15.82 -0.017


53/125


53

22747.13 -0.010 81.5 0.001 15.51 -0.020

23037.32 0.013 81 -0.006 15.69 0.012

23172.35 0.006 78.69 -0.029 15.95 0.017

23160.89 0.000 79 0.004 15.94 -0.001

22994.72 -0.007 78.52 -0.006 15.73 -0.013

23114.97 0.005 77.81 -0.009 15.91 0.011

23428.93 0.014 80.88 0.039 16.66 0.047

23657.81 0.010 80.56 -0.004 16.33 -0.020

23683.27 0.001 80.14 -0.005 15.55 -0.048

23776.22 0.004 78.7 -0.018 14.68 -0.056

23573.68 -0.009 78.03 -0.009 14.82 0.010

23497.07 -0.003 78.03 0.000 14.98 0.011

23315.15 -0.008 78 0.000 14.93 -0.003

23284.81 -0.001 76 -0.026 14.86 -0.005

23312.78 0.001 77.29 0.017 14.82 -0.00323091.87 -0.009 78.48 0.015 14.68 -0.009

22701.3 -0.017 77.79 -0.009 14.22 -0.031

22621.93 -0.003 78.39 0.008 14.23 0.001

23237.19 0.027 79.62 0.016 14.44 0.015

23437.99 0.009 78.8 -0.010 15.09 0.045

23613.2 0.007 78.5 -0.004 15.05 -0.003

23687.89 0.003 79.57 0.014 15.1 0.003

23673.3 -0.001 78.24 -0.017 15.16 0.004

23600.23 -0.003 77.73 -0.007 15.53 0.02423487.23 -0.005 78.25 0.007 15.46 -0.005

23015.27 -0.020 77.5 -0.010 15.29 -0.011

22714.32 -0.013 77 -0.006 15.48 0.012

22714.68 0.000 76.66 -0.004 15.84 0.023

22922.24 0.009 76.95 0.004 15.85 0.001

22523.71 -0.017 77.47 0.007 15.92 0.004

22236.33 -0.013 76.47 -0.013 13.42 -0.157

22214.73 -0.001 75.5 -0.013 13.06 -0.027

22160.85 -0.002 76 0.007 13.19 0.010

21724.68 -0.020 77.68 0.022 13.24 0.004

21808.48 0.004 78.55 0.011 13.46 0.017

21875.83 0.003 77.47 -0.014 13.27 -0.014

22451.46 0.026 77.5 0.000 13.59 0.024

22765.87 0.014 77.75 0.003 13.67 0.006

22838.84 0.003 77.28 -0.006 13.78 0.008


54/125


54

22992.17 0.007 77 -0.004 13.89 0.008

23231.68 0.010 80.43 0.045 14.49 0.043

23222.21 0.000 78.31 -0.026 14.19 -0.021

23168.04 -0.002 79.25 0.012 14 -0.013

23242.68 0.003 83.21 0.050 14.2 0.014

23066.5 -0.008 85.11 0.023 14.02 -0.013

22930.06 -0.006 86.03 0.011 13.63 -0.028

23456.98 0.023 85.25 -0.009 14.29 0.048

23595.61 0.006 84.26 -0.012 13.78 -0.036

23639.97 0.002 82.25 -0.024 13.83 0.004

23088.49 -0.023 78.4 -0.047 13.59 -0.017

23060.9 -0.001 77.17 -0.016 13.56 -0.002

22780.82 -0.012 78.59 0.018 13.42 -0.010

22387.31 -0.017 78.49 -0.001 13.31 -0.008

21832.68 -0.025 76.9 -0.020 12.79 -0.03921980.42 0.007 76.75 -0.002 12.86 0.005

22189.67 0.010 77.83 0.014 12.76 -0.008

22152.35 -0.002 76.6 -0.016 12.66 -0.008

22085.96 -0.003 74.91 -0.022 12.6 -0.005

21864.85 -0.010 77.15 0.030 12.21 -0.031

22080.47 0.010 79 0.024 12.05 -0.013

21657.22 -0.019 79.5 0.006 11.68 -0.031

21674.98 0.001 80 0.006 11.54 -0.012

21775.39 0.005 81.5 0.019 11.7 0.01421599.78 -0.008 80.98 -0.006 12.01 0.026

21754.95 0.007 81 0.000 11.38 -0.052

22230.43 0.022 83.15 0.027 11.3 -0.007

22347.29 0.005 83.5 0.004 11.29 -0.001

22360.85 0.001 84.98 0.018 11.14 -0.013

22445.59 0.004 85.18 0.002 10.98 -0.014

22353.2 -0.004 82 -0.037 10.93 -0.005

22276.65 -0.003 84.99 0.036 11.01 0.007

22310.6 0.002 84.79 -0.002 10.93 -0.007

22775.85 0.021 84.25 -0.006 10.88 -0.005

22649.09 -0.006 83.94 -0.004 10.82 -0.006

22377.83 -0.012 84.75 0.010 10.76 -0.006

22790.7 0.018 84.96 0.002 10.85 0.008

23165.21 0.016 85.4 0.005 10.84 -0.001

23220.21 0.002 84.55 -0.010 10.85 0.001


55/125


55

23367.15 0.006 85 0.005 11.34 0.04

ifrm-term report final

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