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This study investigates the impact bank’s specific factors and macroeconomic factors on bank’s profitability, which is measured by return on average assets (ROAA) in the
UK banking industry over the period 1999-2006 with aim to indicate the strengthen of risk management in banks. The results show that the impact of loan loss reserves has a negative impact on profit and statistically significant. This implies that higher credit risks result in lower profits. The result for liquidity is mixed and not significant, indicates that conclusion about the impact of liquidity remains questionable and further research is needed. Capital strength was one of the main determinants of UK banks performance providing support to the argument that well capitalized banks face lower costs of going bankrupt, which reduces their cost of funding. Finally, we observe macroeconomic variables, that inflation, interest rate and GDPGR have insignificant impact on performance. The prove of the importance of internal factors provide further implication on the strength of risk management practice in banks.
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For supporting me through this endeavor, my sincere thanks and appreciation go to:
My supervisor, Stephen Diacon, thanks for providing me guidance and support on a number of practical difficulties during dissertation. I never would have been able to complete this dissertation without the guidance and support.
My parents, thanks for their endless love, unconditional support and incessant attention.
My friends and dear BB, thanks for their encouragement and consistent emotion support.
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Acknowledgement
Abstract
I
II
Contents III
V List of Tables
Chapter one – Introduction 7
Chapter two-Review on bank performance studies
2.1. The role of banks
2.2. Studies on Banks Performance and Its Determinants
2.2.1. Studies on internal determinants
2.2.2. Studies on external determinants
2.2.2.1. Market Characteristics
2.2.2.2. Macroeconomic Control variables
2.2.3. Single country study VS Cross-countries study
Chapter Three –Background of UK Banking Industry
Chapter Four –Data and Methodology
4.1. Research Aim & Objectives
4.2. The Research Approach
4.3. Research Method
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4.4. Data
4.4.1. Data Source: BankScope
4.4.2 Sample Mechanism
4.4.3 Data Management
4.5. Design of Empirical model
4.5.1 Variable Selection
4.5.1.1.1 Identification and Measurement of dependent Variables
4.5.1.1.2. Identification and Measurement of Independent Variables
4.6. Limitations of methodology
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4.6.1 Limitations of ratio analysis
4.6.2 Limitations of data source
4.6.3 Limitations of sample selection
Chapter Five –Findings
5.1. Descriptive statistics of variables
5.2. Regression Models Comparison
5.2.1. Liquidity
5.2.2. Credit Risk
5.2.3. Capital Strength
5.2.4. Macroeconomic Factors
5.2.5. Time effect
5.3. Structure Reverse Causality
5.4. Future suggestion
Chapter 6 Discussion
6.1. Risks in Banks
6.2. Risk Management in Banks
6.3. Suggestion on risk management practices
6.3.1. Risk management framework
6.3.2. Integration of risk management
6.3.3. Managing credit risk
6.3.4. Managing interest rate risk
6.3.5. Managing liquidity risk
Chapter 7 Conclusion
Appendix
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Table 1 Definitions, notation of the variables
Table 2 Independent Variables Correlations
Table 3 Descriptive Statistics of Variables
Table 4 Estimation for Fixed Effect Model
Appendix 2
Appendix 2
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Chapter 1 Introduction
1. During the last two decades the banking sector has experienced worldwide major transformations in its operating environment. In the UK, banking system has witnessed a substantial growth and change in recent years and its total assets have expanded rapidly since 1990. Major trends in the UK banking sector over the last years include the conversion of building societies into banks, the consolidation of the
UK banking industry and the entrance of non-financial firms into the financial services market. Following the Building Societies Act 1986 a number of building societies converted into banks, especially between 1994 and 1997. In addition, the remaining building societies witnessed an increase in their commercial freedom in
1997 with the Building societies act 1997. These changes enhanced the scope for increased competition and wider choices for consumers. Furthermore, according to
McCauley and White (1997) and White (1998), the UK experienced more merger and acquisition activity in its banking sector (in value terms) between 1991 and 1996 than any other European country. Finally, more recently, new players such as supermarkets, insurance companies and football clubs were allowed to enter the retail financial markets in Britain and are now offering a range of financial services such as credit cards, unit trusts etc. It is reasonable to assume that all the above changes posed great challenges to UK banks as the environment in which they operated changed rapidly, which consequently affected their performance.
Meanwhile, rapid expanding of the banking industry addresses the crucial role that banks play in the operation of most economies. Recent research, as surveyed by
Levine (1997), shows that the efficacy of financial intermediation can affect economic
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growth while at the same time bank insolvencies can result in systemic crises which have adverse consequences for the economy as a whole. This asserted the important role that bank remains in financing economic activity and contribute to the stability of the financial system (MPRA). Therefore, the determinants of bank performance have attracted the interest of academic research as well as of bank management, financial markets and bank supervisors.
Previous studies (Short, 1979; Bourtke, 1989; Molyneux and Thornton, 1992;
Demirguc-Kunt and Huizinga, 2000) on bank profitability are usually expressed as a function of internal and external determinants. The internal determinants refer to the factors that originate from bank accounts (balance sheets and/or profit and loss accounts) and therefore could be termed micro or bank-specific determinants of profitability. The external determinants are variables that are not related to bank management but reflect the economic and legal environment that affects the operation and performance of financial institutions. A number of explanatory variables have been proposed for both categories, according to the nature and purpose of each study.
Studies dealing with internal determinants employ variables such as size, capital, credit risk or costs etc. Size is introduced to account for existing economies or diseconomies of scale in the market. Akhavein et al. (1997) and Smirlock (1985) find a positive and significant relationship between size and bank profitability. Demirguc-
Kunt and Maksimovic (1998) suggest that the extent to which various financial, legal and other factors (e.g. corruption) affect bank profitability is closely linked to firm size. In addition, as Short (1979) argues, size is closely related to the capital adequacy of a bank since relatively large banks tend to raise less expensive capital and, hence,
8

appear more profitable. Using similar arguments, Haslem (1968), Short (1979),
Bourke (1989), Molyneux and Thornton (1992) Bikker and Hu (2002) and Goddard et al. (2004), all link bank size to capital ratios, which they claim to be positively related to size, meaning that as size increases – especially in the case of small to medium-sized banks – profitability rises.
In terms of risks, poor asset quality and low levels of liquidity are the two major causes of bank failures. During periods of increased uncertainty, financial institutions may decide to diversify their portfolios and/or raise their liquid holdings in order to reduce their risk. In this respect, risk can be divided into credit and liquidity risk. In addition, Molyneux and Thornton (1992), among others, find a negative and significant relationship between the level of liquidity and profitability. In contrast,
Bourke (1989) reports an opposite result, while the effect of credit risk on profitability appears clearly negative (Miller and Noulas, 1997).
Turning to the external determinants, several factors have been suggested as impacting on profitability and these factors can further distinguish between control variables that describe the macroeconomic environment, such as inflation, interest rates and cyclical output, and variables that represent market characteristics. The latter refer to market concentration, industry size and ownership status.
Most of these studies conclude that internal factors explain a large proportion of banks profitability; nevertheless external factors have also an impact on the performance.
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Based on the reviews of previous studies, this work aims to test whether previous results are applicable to the UK banking industry. It is also considered that given the fact of increased competition in the UK banking industry, it is worthwhile to identify the main determinants of banks profitability hence draw the conclusion on the implication of effective risk management practices.
The rest of the paper is structured as follows: chapter 2 reviews the previous studies on profitability of banks and summaries the main determinants and relevant findings.
Chapter 3 outlines the background of UK banking industry with aim to provide research ground. Details about the methodology adopted in assist in achieving research objectives are included in chapter 4. It is comprised by the approach adopted to examine the effect of main determinants on banks profitability, the type of data used and the techniques employed to collect the data, the sampling mechanism including sample size, the methods utilized to manage and analyse the data, and the process of constructing empirical model with identification and measurement of its components. This followed by chapter 5, which presents the empirical results, which includes the summaries of individual variable, the detailed report on the strength of relationship between tested determinants and bank profitability, and limitation regarding to the generated findings. Based on previous studies and empirical findings from this work, chapter 6 provides a detailed discussion on implications of tested key determinants of bank profitability with emphasis on risk management practice in banks. It reviews the principal risks that banks expose and offer suggestion on effective risk management practices. Finally, chapter 7 gives conclusion remark on the reviewed literature, the empirical findings with future research suggestion based on evaluation on the limitations of this work.
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Chapter 2 Review on bank performance studies
2.1. The role of banks
A financial intermediary is defined as an institution that acts as a middleman in capital markets (Beck, 2001). It achieves this by matching supply and demand in the capital market. Therefore, a financial intermediary is an intermediary institution between lenders and borrowers. A financial intermediary provides market transparency in its role. Such intermediaries are facilitators of risk transfer, which are well positioned to deal with complex financial instruments and markets (Allen and Santomero, 1997).
Risk management is therefore a key activity of intermediaries. In contrast, the traditional theory about intermediaries provides little explanation about why institutions should perform a risk management function. At the same time, financial intermediaries reduce participation costs, that is; the costs involved in learning about using markets as well as participating in them regularly. Of course, this is an important explanation of the changes that have taken place.
Heffernan (1996) defines banks (as a special financial intermediary) as intermediaries between depositors and borrowers participating in the economy. Banks are distinguished from other types of financial firms because they provide deposit and loan products. To compliment this definition, Bossone (2001) suggests that banks are special intermediaries since they thy have a unique capacity to finance production by lending their own debt to agents that are willing to accept it. In turn, the banks use this as money. As such, banks manage liabilities but also lend money and thereby create bank assets. In general, the intermediation of banks results in them offering payment
11

services to customers. Banking can also be defined as supply transaction and portfolio management services according to Fama (1980). However, Kareken (1985) paid more attention to the role of banks in managing the payment system. On the other hand, it can also be said that the banks’ twofold role of backup sources of liquidity for all enterprises in the economy of transmission belt for monetary policy (Corrigan, 1982).
At the same time, there is a special feature of banks’ in that they act as delegated monitors of borrowers, on the behest of the ultimate lenders, where monitoring is costly.
Essentially, banks produce a net social benefit by exploiting scale economies in processing the information involved in monitoring and enforcing contracts with borrowers. Banks reduce the delegation costs through a sufficient diversification of their loan portfolio. Fama (1985) points to the specialness of banks as deriving from integrating credit and liquidity provision functions. By having borrowers hold deposits with them, banks can observe cash-flow movements and gain private information on borrowers, which they then feed into the processing of new loans.
Further more, Bossone (2001a) concluded two key features of banks, one is to issue debt claims on themselves that are accepted as money by the public, and the other is to inject money into the economy by lending out claims on their own debt. Thus, banks create money in the form of claims on their own debt and inject in the system by lending, which is to economize the use of outside money with their own deposit liabilities. As concluded by Heffernan (1996), with a lot of cost-intensive local branches, bank provides a bundle of different services while most other intermediaries only concentrate on one or few specific business. For example, a bank provides credit to firms and private customers, sells stocks and mutual funds and pays interest for
12

saving deposits and distributes the money it receives from the central bank by providing its customers with cash. Integrating information-intensive lending and payment services distinguishes banks from other intermediaries, according to
Goodfriend (1991). In short, banks are in the risk management business - they assess, assume and manage risk. The risks faced by banks include liquidity risk, interest risk, credit risk, etc. The traditional focus of risk management in banking was the management of interest rate risk and liquidity risk, with a bank’s credit risk usually managed by a separate department or division (Heffernan, 1996).
Besides the function roles banks played, as financial intermediaries, banks play a crucial role in the operation of most economies. Levine (1997) conducted survey and the result revealed that the efficacy of financial intermediation can affect economic growth. Crucially, financial intermediation affects the net return to savings and the gross return to investment (Demirgiic-Kunt, 1999). A number of authors mention that the efficiency of financial intermediation affects country’s economic growth (e.g
Rajan and Zingales, 1998; Levin, 1997, 1998) while at the same time bank insolvencies can result in systemic crises which have adverse consequences for the economy as a whole with losses that arise in many cases 10-20% of GDP and occasionally as much as 40-55% of GDP (Caprio and Klingebiel, 2003).
In specific, the UK banking sector makes a significant contribution to the UK economy, accounting for an estimated 3.7% of the UK's Gross Domestic Product which is more than half of that generated by the financial sector as a whole (British
Bankers Association, 2004). In terms of the contribution to labour market, the UK banking industry provides jobs for over 1.6% of UK employees and 40% of financial services employees (Maslakovic and McKenzie, 2002).
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2.2. Studies on Banks Performance and Its Determinants
As addressed in the preceding section, the role of bank remains central in financing economic activity and its effectiveness could exert positive impact on overall economy as a sound and profitable banking sector is better able to withstand negative shocks and contribute to the stability of the financial system (Athanasoglou et al,
2005). Therefore, the determinants of bank performance have attracted the interest of academic research as well as of bank management, financial markets and bank supervisors since the knowledge of the internal and external determinants of banks profits and margins is essential for various parties.
During the last two decades the banking sector has experienced worldwide major transformations in its operating environment. Both external and domestic factors have affected its structure and performance. Correspondingly, in the literature, bank profitability is usually expressed as a function of internal and external determinants.
The internal determinants refers to the factors originate from bank accounts (balance sheets and/or profit and loss accounts) and therefore could be termed micro or bankspecific determinants of profitability. The external determinants are variables that are not related to bank management but reflect the economic and legal environment that affects the operation and performance of financial institutions. A number of explanatory variables have been proposed for both categories, according to the nature and purpose of each study.
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2.2.1. Studies on internal determinants
Studies dealing with internal determinants employ variables such as size, capital, risk management and expenses management. Size is introduced to account for existing economies or diseconomies of scale in the market. Akhavein et al. (1997) and
Smirlock (1985) find a positive and significant relationship between size and bank profitability. Demirguc-Kunt and Maksimovic (1998) suggest that the extent to which various financial, legal and other factors (e.g. corruption) affect bank profitability is closely linked to firm size. In addition, as Short (1979) argues, size is closely related to the capital adequacy of a bank since relatively large banks tend to raise less expensive capital and, hence, appear more profitable. Taking the similar approach,
Haslem (1968), Short (1979), Bourke (1989), Molyneux and Thornton (1992) Bikker and Hu (2002) and Goddard et al. (2004), all link bank size to capital ratios, which they claim to be positively related to size, results indicated that as size increases – especially in the case of small to medium-sized banks – profitability rises. However, many other researchers suggest that little cost saving can be achieved by increasing the size of a banking firm (Berger et al., 1987), which suggests that eventually very large banks could face scale inefficiencies.
Other internal factors, such as credit or liquidity are considered as bank specific factors, which closely related to bank management, especially the risk management.
The need for risk management in the banking sector is inherent in the nature of the banking business. Poor asset quality and low levels of liquidity are the two major causes of bank failures and represented as the key risk sources in terms of credit and liquidity risk and attracted great attention from researchers to examine the their impact on bank profitability.
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As Golin (2001) mentions “it is critical that a bank guard carefully against liquidity risk-the risk that it will not have sufficient current assets such as cash and quickly saleable securities to satisfy current obligations e.g those of depositors – especially during times of economic stress”. Without the required liquidity and funding to meet obligations, a bank may fail. However, liquid assets are usually associated with lower rates of return. In terms of liquidity measurement, the ratio of liquid assets to customer plus short term funding and the ratio of liquidity asset to total deposit and borrowing are the most common ratio used in research as a measure of liquidity. The higher this percentage the more liquid the bank is and less vulnerable to a classic run on the bank.
Referring to previous studies, the results concerning liquidity are mixed. Molyneux and Thorton (1992) among others, find a negative and significant relationship between the level of liquidity and profitability. In consistent with their results, Guru et al (1999) also find a negative relationship between liquidity and bank profitability.
However, Bourke (1989) and Kosmidou and Pasiouras (2005) found a significant positive relationship between liquidity and bank profits. Therefore conclusion about the impact of liquidity on bank performance remains ambiguous and further research is required.
In terms of credit risk, it is defined by Heffernan (1996) as the risk that an asset or a loan becomes irrecoverable in the case of outright default, or the risk of delay in the servicing of the loan. In either case, the present value of the asset declines, thereby undermining the solvency of a bank. Credit risk is critical since the default of a small number of important customers can generate large losses, which can lead to
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insolvency (Bessis, 2002). Credit risk is by far the most significant risk faced by banks and the success of their business depends on accurate measurement and efficient management of this risk to a greater extent than any other risk (Giesecke,
2004). Increases in credit risk will raise the marginal cost of debt and equity, which in turn increases the cost of funds for the bank (Basel Committee, 1999).
To measure credit risk, there are a number of ratios employed by researchers. The ratio of Loan Loss Reserves to Gross Loans (LOSRES) is a measure of bank’s asset quality that indicates how much of the total portfolio has been provided for but not charged off. Indicator shows that the higher the ratio the poorer the quality and therefore the higher the risk of the loan portfolio will be. In addition, Loan loss provisioning as a share of net interest income (LOSRENI) is another measure of credit quality, which indicates high credit quality by showing low figures. In the studies of cross countries analysis, it also could reflect the difference in provisioning regulations (Demirgiic-Kunt, 1999). To measure the impact of loan activities on bank risk, Brewer (1989) uses the ratio of bank loans to assets (LTA). The reason to do so is because bank loans are relatively illiquid and subject to higher default risk than other bank assets, implying a positive relationship between LTA and the risk measures. In contrast, relative improvements in credit risk management strategies might suggest that LTA is negatively related to bank risk measures (Altunbas, 2005).
In terms of empirical results from previous studies, Bourke (1989) reports the effect of credit risk on profitability appears clearly negative This result may be explained by taking into account the fact that the more financial institutions are exposed to highrisk loans, the higher is the accumulation of unpaid loans, implying that these loan
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losses have produced lower returns to many commercial banks (Miller and Noulas,
1997).
Interest rate risk is the risk of declines of earnings due to the movement of interest rates. It is known that interest rate changes periodically. When rates of not locked in up tot maturity, there is an interest rate risk. Mark (1983) found that large banks are well hedged against interest rate fluctuations. When market rates change, their revenues and costs adjust equally quickly, leaving net current operating earnings largely unaffected while for others may experience mismatched balanced sheet, causing their earnings to fluctuate violently when interest rates change.
As showed in previous studies results, capital strength is one of the main determinants of performance of UK banks as the relatively high significant coefficient of the ratio equity to assets shows. Our finding is consistent with previous studies (e.g Berger,
1995); Demirguc-Kunt and Huizinga, 1999; Ben Nacuer, 2003; Kosmidou and
Pasiouras 2005; Pasiouras et al. 2005) and indicates that well capitalised UK banks face lower costs of going bankrupt, which reduces their cost of funding or that they have lower needs for external funding which results in higher profitability. Demirguc-
Kunt and Maksimovic (1998) stated that bank’s capital is the ultimate line of defense against the risk of bank’s technical insolvency. This becomes obvious considering that if the bank will face a serious asset quality problem and loan loss reserves will be insufficient to allow all bad loans to be written of against them, the excess will have to be written off against shareholder’s equity. Therefore capital strength, is linked to bank’s soundness and safety. The ratio of equity to total assets (EQAS), which is considered one of the basic ratios to measure capital strength (Golin, 2001). It is
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expected that the higher the equity to assets ratio, the lower the need to external funding and therefore the higher the profitability of the bank. In addition, wellcapitalized banks face lower costs of going bankrupt which reduces their costs of funding.
Apart from the main bank specific determinants, bank expenses are also considered as a very important determinant of profitability, closely related to the notion of efficient management. There has been an extensive literature based on the idea that an expenses-related variable should be included in the cost part of a standard microeconomic profit function. For example, Bourke (1989) and Molyneux and
Thornton (1992) find a positive relationship between better-quality management and profitability.
2.2.2. Studies on external determinants
Turning to the external determinants, several factors have been suggested as impacting on profitability and these factors can further distinguish between control variables that describe the macroeconomic environment, such as inflation, interest rates and cyclical output, and variables that represent market characteristics. The latter refer to market concentration, industry size and ownership status (Athanasoglou et al,
2005).
2.2.2.1. Market Characteristics
The structural effects on bank profitability is based on the Market-Power (MP) and the Efficient-Structure (ES) hypotheses. The MP hypothesis (also known as the
Structure-Conduct-Performance (SCP)), argues that increased market power yields
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monopoly profits. Within the MP hypothesis there is the Relative-Market-Power
(RMP) hypothesis. The RMP hypothesis suggests that the only firms that are in a position to exercise market power and earn non-competitive profits are those with large market shares and differentiated products (see Berger, 1995a). Similarly, within the ES hypothesis is the X-efficiency version (ESX). The ESX hypothesis suggests that increased managerial and scale efficiency leads to higher concentration and therefore higher profits (Athanasoglou, 2005). Smirlock (1985), Berger and Hannan
(1989) and Berger (1995a) investigated the profit-structure relationship in banking and as a result, provided tests of the two hypotheses. The RMP hypothesis is verified to some degree because there is evidence to suggest that superior management and increased market share, particularly in the case of small to medium sized banks, raise profits. In sharp contrast, in relation to the ESX hypothesis, weak evidence is found.
Managerial efficiency can lead to market share gains, as well as managerial efficiency raising profits, and therefore increased concentration, so that a positive relationship between concentration and profits maybe a spurious result due to correlations with other variables (Berger, 1995a). Therefore, controlling for other facors, the role of concentration should be negligible. However, it is possible to argue that instead increased concentration reflects increasing deviations from competitive market structures, which lead to monopolistic profits, rather than it being the result of managerial efficiency. Therefore, concentration should be positively related to bank profitability (Bourke, 1989; and Molyneux and Thornton, 1992).
One of the issues to arise from this is whether the ownership status of a bank is related to its profitability. However, there is little evidence found thus far to support the theory that privately owned institutions will return relatively higher economic profits.
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Short (1979) carried out a study on cross country evidence of a strong negative relationship between government ownership and bank profitability. Barth et al (2004) claim that government ownership of banks is negatively correlated with bank efficiency. Bourke (1989) and Molyneux and Thornton (1992), however, report that ownership status is irrelevant for explaining profitability.
2.2.2.2. Macroeconomic Control variables
Some of the recent literature emphasises the importance of changes in macroeconomic conditions on bank performance. The common variables used include inflation rate, interest rate and/or growth rate of money supply. Revell (1979) He notes that the effect of inflation on bank profitability depends on whether the wages of banks’ and other operating expenses increase at faster rate than inflation. The question that is of more concern is how mature an economy is so that future inflation can be accurately forecasted and therefore banks’ can appropriately manage the operating costs incurred to them. In this line of thought, Perry (1992) suggests that the extent to which inflation affects bank profitability depends really on whether inflation expectations are fully anticipated. If the bank fully anticipates the inflation rate, then this implies that it can accordingly adjust its interest rates in order to increase their revenues faster than their costs and thus acquire higher economic profits. Most studies
(see for example, Bourke, 1989; and Molyneux and Thornton, 1992) show a positive relationship between inflation or long-term interest rate and profitability.
Demirguc-Kunt and Huizinga (2000) and Bikker and Hu (2002) attempted to identify possible cyclical movements in bank profitability or in other words, the extent to which bank profits are correlated with the business cycle. They suggest that such
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correlations do exist although the variables employed in their studies are not direct measures of the business cycle. Demirguc-Kunt and Huizinga (2000) used the annual growth rate of GDP and GNP per capita to identify this type of a relationship. Bikker and Hu (2002) used a range of macroeconomic variables (such as GDP, unemployment rate and interest rate differential).
Overall, the literature does offer a comprehensive account of the effect of internal and industry-specific determinants on bank profitability, but however the effect of the macroeconomic environment is not adequately dealt with in the literature. The time of the panels employed in empirical studies is often too small to properly capture the effect of control variables related to the macroeconomic environment, and in particular the business cycle variable. At the same time, there are some overlaps between variables in the sense that some of them essentially proxy the same profitability determinant. The empirical results vary significantly, since both datasets and environments differ. On the other hand, some common elements allow for a further categorisation of the determinants.
2.2.3. Single country study VS Cross-countries study
Previous studies on bank performance undertaken research on both single country and cross countries’ banking systems.
Studies in single country mainly concern the banking system in the US (e.g. Berger et al., 1987 and Neely and Wheelock, 1997) or the emerging market economies (e.g.
Barajas et al., 1999). Examples of single countries studies have examined US (Berger,
1995; Angbazo, 1997), Greece (Mamatzakis and Remoundos, 2003; Kosmidou and
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Pasiouras, 2005), Australia (Pasiouras et al. 2005), Malaysia (Guru et al., 1999),
Colombia (Barajas et al., 1999), Brazil (Afanasieff et al., 2002) and Tunisia (Ben
Naceur, 2003).
Molyneux and Thorton (1992) examined the European banking sector and were among the first that examined the determinants of banks’ profitability in several countries. Other cross countries studies includes Short (1979), Bourke (1989),
Molyneux and Thornton (1992) and Demirguc-Kunt and Huizinga (2000). A more recent study in this group is Bikker and Hu (2002), though it is different in scope; its emphasis is on the bank profitability, business cycle relationship. All of the above studies examine combinations of internal and external determinants of bank profitability. Other panel country studies are those of Abreu and Mendes (2001) and
Staikouras and Wood (2003) who also examined the European markets, Hassan and
Bashir (2003) who examined a sample of Islamic banks from 21 countries and
Demirguc-Kunt and Huizinga (1999) who considered a comprehensive set of bank characteristics, macroeconomic conditions, taxation, regulations, financial structure and legal indicators to examine the determinants of bank interest margins and profitability in over 80 countries.
At a general level, evidence suggests that legal tradition, accounting conventions, regulatory structures, property rights, culture and religion explain cross-border variations in financial development and economic growth (Beck et al., 2003a,b; Beck and Levine, 2004; La Porta et al., 1997, 1998; Levine, 2003, 2004; Levine et al.,
2000). Most of these studies conclude that internal factors explain a large proportion of banks profitability; nevertheless external factors have also an impact on the
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performance. However, the relations between bank’s characteristics or external factors and profits and margins are not constant across countries or different periods within the same country. Therefore, further research is required. In addition given the differences in the banking sectors among countries, it is worthwhile to observe if the previous results are applicable to other locations.
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Chapter 3 Background of UK banking industry
3. In the UK banking system, major changes have occurred in that there is substantial growth in its total assets, which have expanded rapidly since the early 1990’s. Of course, the sector consists of both domestic and foreign banks, where total assets summed £4, 234bn during 2003. That is, more than three times the total in 1990 at £1,
266bn. The assets of UK owned banks represent 48% of the total assets of the UK banking sector over the last years include the conversion of building societies into banks, the consolidation of the UK banking industry and the entrance of non-financial firms into the financial services market. These changes increased the scope for more and more competition and wider choices of financial products for consumers.
Furthermore, according to McCauley and White (1997) and White (1998), the UK experienced more merger and acquisition activity in its banking sector (in value terms) between 1991 and 1996 than any other European country. More recently, new competitors such as supermarkets and insurance companies were allowed to enter the retail financial markets in the UK and are now offering a range of financial services such as credit cards, unit trusts etc.
Therefore, these changes can be assumed to pose large challenges for UK banks, and this affected their performance. The UK banking sector makes a significant contribution to the UK economy, and accounts for an estimated 3.7% of the UK's
GDP and this is more than half of that generated by the financial sector as a whole
(British Bankers Association, 2004). At the same time, the UK banking industry
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provides employment for over 1.6% of UK employees and 40% of financial services employees (Maslakovic and McKenzie, 2002).
Until recently, banking was a traditional sector in the UK and remained unchanged in many ways for about 200 years. The activities of these banks were bounded and legislation confined retail banks to certain types of businesses. Cross sector competition was, therefore, limited with retail banks focused on financing industry and providing money transmission facilities. At the same time, building societies were restricted to savings and mortgages. The retail banking sector has been dominated by the big 4, where administration was organised to capture scientific management styles into a functional hierarchy by region and with customer contact maintained through a local branch network. The local bank and the local bank manager have been important actors in their community, embedded in a powerful local network. From the 1950s to the 1980s, a career in a major UK retail bank was regarded as a respectable, traditional occupation with considerable status that tended to be a “job for life.” The stability that has traditionally been associated with the UK banking sector faced challenges when the major retail banks forged high levels of cooperation and were therefore accused of being an oligopoly. As a result, restrictions were less tight to allow for increased competition in the sector, which eventually led to the deregulation of the banking sector. Post-deregulation led to conditions of hyper-competition and consequently, traditional lines of separation within the sector were broken down and competition widened to a broader product range ( Pasiouras,2003).
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Chapter 4 Data & Methodology
4. This chapter provides details about the methodology adopted in assist in achieving research objectives. It includes the approach adopted to examine the effect of main determinants on banks profitability, the type of data used and the techniques employed to collect the data, the sampling mechanism including sample size, the methods utilized to manage and analyse the data, and the process of constructing empirical model with identification and measurement of its components.
4.1. Research Aim & Objectives
The main aims and objectives of this research are:
1. What are the main determinants of banks profitability
2. To what extent these determinants exert impact on banks profitability
3. What are the implication on risk management
4. Further improvement suggestion based on discussion of findings.
4.2. The Research Approach
In terms of investigative study there are two common approaches to business and social research: (1) Deductive approach – develops theories and hypotheses followed
27

by a research strategy to test the hypotheses; and (2) Inductive approach – finds data and develops theories as a result of the data analysis (Saunders et al, 2003). The deductive approach introduces a high level of objectiveness in research through external observation insofar as the choice of questions and subsequent phrasings are not subjective. In contrast, the inductive approach provides a high level of subjectiveness and a number of theoretical possibilities based on the context of the individual research situation (Saunders et al, 2003).
This study will examine the previous findings in the literature, and apply the results in current practical settings. Therefore, a deductive approach was adopted by constructing an empirical model and hypothesising its collinear relationship between target determinants and its dependent variable: profitability of banks.
4.3. Research Method
The methodology of carrying out this research is based on the objectives of the dissertation and the availability of relevant information.
To comply with the objective of this research, the paper is primarily based on quantitative research, which constructed an econometric model to identify and measure the determinants of banks profitability. In specific, multiple regression analysis is adopted to measure the effect of determinants on banks profitability. The use off multiple regressions considers the simultaneous relationships (Cooper, 2005) amongst the multiple numbers of independent and dependant variables found across the regression model, therefore suited to the nature of the study.
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The significance of the impact of the independent variables on dependent variables is, at the same time, highlighted in using multiple regressions. Multiple regressions are further utilised to examine the associative relationships between variables in terms of the relative importance of the independent variables and predicted values of the dependent variables (Saunders et al, 2003) in the constructed model.
For the initial construction of the decomposed model an exploratory study was carried out through a search of the available literature to identify the exact components of the model. Further literature search was conducted to find other factors which could potentially and clearly affect. By summarizing previous studies, liquidity, capital strength, credit risk, inflation rate, interest rate and GDP growth are selected to be included explanatory variables in the model. Time effect has also been taken into consideration as additional explanatory variables in order to capture the impact from time difference.
As mentioned in the preceding section, due to time and data availability constraints, the aim of this research is to focus on the internal determinants and external factors, which are macroeconomic variables impact on bank profitability, therefore, market structure factors, e.g concentration ratio is excluded from constructed econometric model. However, discussion on the impact market structure factors will be included with aim to generate more reliable findings.
In order to enhance the reliability of the model, the study also employs experiment to detect endogeneity problem in regression model. This concept generally stats that
29

economists often models behaviour as simultaneous equations systems in which economically endogenous variables are determined by each other and some additional economically exogenous variables. The simultaneity gives rise to empirical models with variables that do not satisfy the zero-conditional-mean assumption. It can be caused by omitted covariates, or by measurement errors in the covariates.
If endogenous factors as an explanatory variable exist in the regression model, it may create problems for accurately understanding relationships of determinants and bank profitability because of the possibility of biased results.
Due to time constrains and the complexity to perform endogeneity test, this study chose to uses experiment (Appendix 3), which involve testing different instrumental variables I to compare the outcome of instrumental variables regression models instead of running Stata test on endogeneity.
Six instrumental variable regressions (Appendix 3) were constructed in relation to suspected endogenous variables, such as liquidity or liquidity with other variables. It also takes time effect into consideration and introduced lagged instrumental variable
(e.g l2ir) and difference variable (e.g dliq). Different settings of instrumental variables in regression aimed to provide attempt to identify endogenous variable.
Based on the outcomes of all regression models, joint findings is provided to give more consistent result.
I Instrumental variable is defined as variable that can be used in structural equation models to produce a consistent estimator of a structural parameter when the explanatory variables are correlated with the error terms. (Wikipedia)
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4.4. Data
Due to time constraint and data availability, the study is based on secondary quantitative data, which obtained from public database. Researcher stated that the advantage of using secondary data includes the higher quality data compared with primary data collected by researchers themselves (Stewart and Kamins, 1993); the feasibility to conduct longitudinal studies, which is the case in this study; and the permanence of data, which means secondary data generally provide a source of data that is both permanent and available in a form that may be checked relatively easily by others, i.e more open to public scrutiny. Therefore, enhance the reliability of the data (Denscombe, 1998).
4.4.1. Data Source: BankScope
The primary secondary data source for this paper is BankScope, which is a comprehensive database of 11,000 world banks, provided by Bureau Van Dijk
Electronic Publishing. It combines data from 7 sources with software for searching and analysis and it has up to 8 years of detailed financial and ownership information for the banks. Each bank report contains detailed consolidated balance sheet and income statement, these data supplied by Fitch Ratings, Reuters, Capital Intelligence,
Financial Times, Dow Jones, etc. The data is provided in varying degrees of standardization and detail so one can search and analyze banks across borders. “As reported” information is also provided so the individual banks can be analyzed in detail (BankScope Brochure, 2005).
Descriptive information is also provided and includes: address, contact numbers, bank history etc. a profile report provides a concise summary of each bank and contains 4
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statement items and 10 ratios. These ratios will be utilised when assessing the performance of credit risk in banks in following section.
BankScope is updated weekly on the Internet and 24 times a year on CD-ROM. In terms of analytical function, BankScope has integrated analysis software to provide instant peer group analyses, graphics and statistical analysis.
BankScope can benefit many areas of academic research and development. It includes its comprehensive coverage, which contains information on both public and private banks worldwide, integrating the highly regarded Fitch Ratings database with other reputable data sources. It has standard and “as reported” data which allow user to choose which data format to use according to their research projects. In addition, it offers flexible analysis options, and also includes simplified search options for less experienced users. Apart from these main benefits, it also possess advantage of reliable customer service and consultancy, academic research has made widespread use of the bank financial statements provided by BankScope. Therefore, the reliability of this data source is acceptable.
Despite the wide coverage and minor reporting error in the individual reporting unite, even if BankScope homogenizes the financial information into a global format and classifies firms in terms of specialization with the accounting uniformity is guaranteed, and reduce discrepancies of financial statements, it is still important to recognize the potential drawbacks of using BankSocpe. The first drawback is due to the nature of BankScope, as BankScope is maintained for commercial reasons, one of its main limitations is the almost total omission of rural and very small banks. The
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BankScope database could only be regarded as a sample of individual country but does not cover the entire population of banks in a country. It is, therefore, essential to examine how good and representative these samples are for different countries. It is well known that banking market structures in many economies are heterogeneous and segmented. Further more, Fitch Rating only collects data from banks that publish independent financial reports. Hence, it may omit some branches and subsidiaries of foreign banks. Thirdly, the data for banks from less developed and transition countries require substantial editing before a reliable sample can be constructed. Careful review of these data is needed to avoid double counting of institutions, to choose the most appropriate accounting standards, and to exclude non-bank financial institutions of various kinds.
Kaushik Bhattacharya (2003) conducted detailed research on the quality of
BankScope database by comparing results based on it to those obtained from the population-level data for India disseminated by the Reserve Bank of India. Despite good coverage and minor reporting errors in the individual reported unties, strong evidence of selectivity bias in BankScope data for India is found. It is shown that this selectivity bias affects estimates of all summary statistical measures and could result in misunderstanding of users.
Therefore, these drawbacks present challenge for data selection in this paper and requires careful consideration when analyzing obtained data. To minimize the distortion of collected data and enhance the validity research result, qualitative information is needed to complement primary quantitative research result.
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4.4.2 Sampling Mechanism
There are two sampling categories generally used in business and social research: (1)
Probability sampling – requires making inferences from the sample about the total population to answer research questions; and (2) Random sampling - requires making subjective judgments from the limited sample to answer research questions. Each category has several techniques of sampling.
Given the little time and resources available, it is considered that the simple random sampling, which involves selecting the sample at random from the sampling frame, in this case, from BankScope database is more appropriate. Random sampling possess the advantage of preventing selection bias, therefore the selected sample can be said to be representative of the whole population. However, the selection that simple random sampling provides is more evenly dispersed throughout the population for samples of more than a few hundred cases. The first few hundred cases selected using simple random sampling normally consist of bunches of the technique’s random nature it is therefore possible that the chance occurrence of such patterns will result in certain parts of a population being over- or under- represented. (Sander, 2003)
For testing the research hypothesis, a convenience sampling technique was employed for a sample size of 123 banks restricted in the regional area of UK to avoid cross countries effect on research objectives. In addition to cross sector data, the study also takes time series into consideration, and obtained accounting data of 123 UK banks in the period time of 1999 to 2006.
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The 123 banks are randomly selected from BankScope database according to certain criteria. Banks should meet the following two conditions in order to be selected into the sample. First they had to be characterized as public and private banks in the UK to allow wider availability of data. Second, they should have annual report for at least one year between 1999 and 2006 in Bankscope Database. The time period was selected considering that it offers recent time series observations and it avoids major structural changes in banking sector external environment which may affect our research on internal determinants.
In terms of data value, the process of screening rare data was conducted to eliminate data with problems of missing value for main variables, and the exists of outlier. It is believed that filtered data could help to mitigate result bias and generate more reliable findings.
However, as certain variables still possess missing value for certain years, the study adopted unbalanced panel data analysis.
Panel data analysis is an increasingly popular form of longitudinal data analysis among social and behavioral science researchers. Panel data analysis is a method of studying a particular subject within multiple sites, periodically observed over a defined time frame. With repeated observations of enough cross-sections, panel analysis permits the researcher to study the dynamics of change with short time series.
The combination of time series with cross-sections can enhance the quality and quantity of data in ways that would be impossible using only one of these two dimensions (Yaffee, 2003).
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The above procedure yielded an unbalanced panel data of 123 UK banks over the period 1999 to 2006, consisting of 361 observations.
4.4.3 Data Management
Prior to input data into regression model, data screening process was conducted to enhance the reliability and accuracy of data. The main aim of data screening is to spot illogical relationship or non sense figure in secondary data, identify outlier which could cause biased result.
Once data has been verified, STATA package was used to construct regression model to establish the collinear relationship between the independent variables and their impact on the dependant variables. It is also utilized to computer statistical figures include R²to assess the explanatory power of the overall models, standardised coefficients (represented by ß) to illustrate the amount that the Y (dependant) variables change by in relation to each unit change of the X (independent) variables when the effects of all of the other X variables remain constant, which are all assessed by the significance found by the P-value
The model assumptions (Appendix 1) were detailing that the errors are independent, normally distributed with a mean of the errors as zero and the variance for each treatment group was the same. As there was a fixed number of sampling units used in the experiment blocking was applied and therefore, reduced the variability not controlled for in the experiment in order to optimise the accuracy in the efficiency of
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detecting difference in treatment means (if present). The FStatistics were used to find probable significant, i.e. an effect present.
4.5. Design of Empirical model
The literature generally, in so far as it is discussed, comes to the conclusion that the appropriate functional form for testing is a linear function although there are dissenting opinions. Short (1979) investigated this question and concluded that linear functions produced as good results as any other functional form. The Davidson,
Godfrey, MacKinnon (1985) specification test was also applied with results that supported the use of the linear function. Accordingly, in order to test for the empirical relevance of the hypotheses regarding to the causes of bank profitability, the following model has been developed.
Y it
= α it
+ β X it
+ u it
Where i= the no. of banks cross sections and periods t=1996, 1997……2006
Y= profitability of banks,
X=independent variables which represent liquidity, credit, capital, macroeconomic factors.
By using the model and comparing the coefficiency of each explanatory variable, it will generate the finding that which factor is more significant in relation to banks profitability and the finding will correspond to the theoretical evidence.
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In terms of regression analysis, as panel data is adopted in this study, corresponded regression model is selected from fixed effect and random effect regression. Fixed effects regression is the model to use when researcher want to control for omitted variables that differ between cases but are constant over time. It allows using the changes in the variables over time to estimate the effects of the independent variables on dependent variable. On the other hand, between effect regression with between effects is the model to use when want to control for omitted variables that change over time but are constant between cases. It allows using the variation between cases to estimate the effect of the omitted independent variables on dependent variable. In comparison, if we have reason to believe that some omitted variables may be constant over time but vary between cases, and others may be fixed between cases but vary over time, then we can include both types by using random effects. Stata’s randomeffects estimator is a weighted average of fixed and between effects.
The general accepted way of choosing between fixed and random effects is running a
Hausman II test (Appendix 4). Statistically, fixed effects are always a reasonable thing to do with panel data (they always give consistent results) but they may not be the most efficient model to run. Random effects will give better P-values as they are a more efficient estimator, so random effects regression should be adopted if it is statistically justifiable to do so. Based on Hausman test result, the model is estimated through fixed effect regression.
II The Hausman test checks a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results. It tests the null hypothesis that the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consistent fixed effect estimator. If they are (insignificant P-value, Prob>chi 2 larger than .05) then it is safe to use random effects and vice visa.
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4.5.1 Variable Selection and Measurement
As previously mentioned, the empirical part of this paper attempts to examine the main determinants of profits of the UK banks. Three bank’s characteristics are used as internal determinants of performance and three factors are selected as macroeconomic factors. Time effect is also taken into consideration as additional explanatory variables. The variables chosen to measure the performance of banks along with those chosen as proxies of the in Table 1 (Appendix 2) and discussed below. In addition, correlations between the independent variables are presented in Table 2 (Appendix 2).
Referring to previous studies, the use of ratio in measuring credit, liquidity and profitability performance is common in the literature of finance and accounting practices. Bird and McHuge (1977), Leve and Sunder (1979), and Chen and Shimerda
(1991) used ratio in measuring bank performance. Ross (1991), Spindler Etal (1991),
Sabi (1996), Hempel and Sionpson (1998), Samad (1999) and Samad and Hassan
(2000) used ratio index in measuring commercial bank performance. The greatest advantage for using ratio index in measuring bank performance is that it compensates bank disparities created by bank size. (Athanasoglou et al, 2005). In line with earlier studies that examined the determinants of banks’ profitability, accounting ratios will be used as measurement of individual variables. In specific, the dependent variable Y, profitability of banks, is measured by ROE; for independent variable, by reviewing previous studies, main internal determinants are proposed to be included in the model are liquidity, credit, capital strength, which X1 is liquidity, measured by liquidity assets/Tot Dep & borrows ratio; X2 is credit, measured by impaired loans to gross
39

loan ratio; X3 is capital, measured by equity/tot asset ratio. All the ratios could be obtained from BankScope.
In terms of external determinants, two sets of variables have been considered in this study, indicating financial structure and macroeconomic conditions. The three macroeconomic variables used are GDP growth (GDPGR), Interest rate (IR) and inflation (INF). GDPGR is a measure of the total economic activity and is expected to have an impact on numerous factors related to the supply and demand for loans and deposits. A positive relation is expected between the performance of the banks and this variable. Inflation may affect both the costs and revenues of any organization including the banks. Perry (1992) points out that the effect of inflation on bank performance depends on whether the inflation is anticipated or unanticipated.
In addition, since the dataset across different years, in order to capture the time difference impact on bank profitability, time variables are created and included in the regression model.
4.5.1.1.1 Performance Measures
Performance of banks is measured by the ratio of the return on average assets
(ROAA), calculated as net profit after tax divided by average total assets. This is probably the most important single ratio in comparing the efficiency and operating performance of banks as it indicates the returns generated from the assets that bank owns. Average assets are being used in this study, in order to capture any differences that occurred in assets during the fiscal year.
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4.5.1.1.2. Identification and Measurement of Independent Variables
In order to select the determinants as explanatory variables in the model, previous studies have been reviewed and literature suggests that liquidity, credit risk and capital strength exert strong impact on banks profitability as internal determinants, therefore, they will be adopted in the constructed model.
The three variables that are used as internal determinants of performance are the ratio of liquid assets to deposit and borrowing, the ratio of Loan loss provisioning as a share of net interest income, the ratio of equity to total assets. They represent efficiency in capital strength, liquidity, asset quality accordingly. Three sets of external variables are selected to indicate macroeconomic condition.
Liquidity
As Golin (2001) mentions “it is critical that a bank guard carefully against liquidity risk-the risk that it will not have sufficient current assets such as cash and quickly saleable securities to satisfy current obligations e.g those of depositors – especially during times of economic stress”. Without the required liquidity and funding to meet obligations, a bank may fail. However, liquid assets are usually associated with lower rates of return. The ratio of liquid assets to deposit and borrowings (LIQ) is used in this study as a measure of liquidity. The higher this percentage the more liquid the bank is and less vulnerable to a classic run on the bank.
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Credit risk
The ratio Loan Loss Reserves to Net Interest Revenue (LOSRNI) is a measure of bank’s asset quality that indicates how much of the total portfolio has been provided for but not charged off. The higher the ratio the poorer the quality and therefore the higher the risk of the loan portfolio will be.
Capital strength
The ratio of equity to total assets (EQAS), which is considered as one of the basic ratios for capital strength (Golin, 2001), is used in this study as a measure of capital strength. It is believed that capital strength, is linked to bank’s soundness and safety
This becomes obvious considering that if the bank will face a serious asset quality problem and loan loss reserves will be insufficient to allow all bad loans to be written of against them, the excess will have to be written off against shareholder’s equity. It is expected that the higher the equity to assets ratio, the lower the need to external funding and therefore the higher the profitability of the bank. In addition, wellcapitalized banks face lower costs of going bankrupt which reduces their costs of funding.
External Factors
For the external variables, annual growth rate for inflation and GDP are selected with interest rate as explanatory variables in the model.
Referring to previous studies, GDP growth should exert positive impact on bank profitability and this provides support to the argument of the association between
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economic growth and the financial sector performance, as proved by previous studies of Kosmidou an Pasiouras (2005) and Hassan and Bashir (2003).
In terms of inflation impact on ROAA, previous studies (e.g Claessens et al., 1998;
Demirguc-Kunt and Huizinga, 1999) showed a positive result while some studies show low significance of the coefficient in the regression and offer the explanation that possibly because banks obtain higher earnings from float or because there are delays in crediting customer (Demirguc-Kunt, 1999).
The effect of interest rates on bank profits has been examined by Samuelson (1945). It is shown that under general conditions, bank profits increase with rising interest rates.
“The banking system as a whole is immeasurably helped rather than hindered by an increase in interest rates… and commercial banks would profit more than savings banks” (Samuelson 1945). Short (1979) also found a positive relationship between nominal interest rates and return on capital. In addition, Flannery (1983) concluded that the reported profits by banks generally fluctuate little when market rates change.
Taking all these explanatory variables into consideration, the extended equation to reflect the variables is formulated as follows:
ROAAit = α it + a1t LIQit + a2t LORESNI+ a2t EQUAS + a3t INF + a4t GDPGR + a5t IR+ a6t YR + uit
4.6 Limitations of methodology
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The unrealistic assumptions about the statistical techniques employed in this project such as, as linear relationship among the variables investigated, normal distributions of data may have affected the reliability and validity of the various tests (Appendix 1).
The multiple regressions utilised are required due to the nature of this project, but also possess a major conceptual limitation, which is that the mechanisms governing the various phenomena are not statistically identified.
In specific, limitations of methodology can grouped as following
4.6.1 Limitations of ratio analysis
This limitation is resulted from the nature of ratio analysis. Financial ratio is an expression of the relationship between two items selected from the income statement or the balance sheet. Even if ratio analysis helps to evaluate the weak and strong points in the financial and managerial performance, while it does not reveal the amount of its components and the quality of its components. Consequently, it could mislead the research results if there is an improvement on the ratio figure, but comes from and increase or decrease from the individual components.
Furthermore, there are a number of ratios in terms of measurement for individual variables. The ones are selected in the model might possess certain bias as they can not fully represent the accurate measurement for the tested variable. This is due to the data availability and the nature of ratio analysis.
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4.6.2 Limitations of data source
As this study is mainly based on quantitative studies, and all the data are secondary data which obtained directly from BankScope database, therefore it may have potential bias from the data source as the limitation outlined in the preceding section about BankScope database
4.6.3 Limitations of sample selection
There are 123 UK banks are selected to constitute tested sample, this relatively small size of sample may cast doubt on its representatives and generate bias result. Even the paper conducted data filter process to mitigate sample bias, due to time constrains, it does not studies on the operating condition of selected banks. This means some banks may in the process of merger or acquisition, or even closure, which would have impact on reported accounting data. It is inevitable and this bias could affect the reliability and accuracy of findings.
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Chapter 5 Findings
5. This chapter provides detail about the results in reference to the research aim and objectives listed in section 4.1. It includes the summaries of individual variable (see table 2), the comparison of regression model outcome, and the analysis of the strength of relationship between tested determinants and bank profitability, measured by
ROAA.
5.1. Descriptive statistics of variables
Table 3 present descriptive statistics for the main variables involved in the regression model. Key figures, including mean, standard deviation, min and max value are reported. This is generated to give overall description about data used in the model and served as data screening tool to spot unreasonable figure.
Table 3 Descriptive Statistics
Variable
2.46
31.17
-10.62
26.94
0 283.39
36.04
-273.27
172.49
11.51
0.17
87.74
0.53
1.56
3.2
0.64
1.8
3.8
0.54
3.75
6
According to the table, most variables comprise 378 observations except the variable of liquidity ratio, which missed 18 observations. This is due to missing reported figure and exclusion for outlier. During data screening process, result shows that the min value for liquidity ratio is -4, which is considered to be inappropriate as there should
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not be negative figure for liquidity ratio. Therefore, the study excluded that observation as outlier. The 360 observations resulted in an average ratio of 23.18, with standard deviation 31.16. After excluding the outlier, the min value is 0 while the max value is 283.39.
As the table shows, variables of liquidity and credit risk present larger standard deviation with 31.17 and 36.04 respectively compared with other variables. It revealed that the quality of loan and liquidity position in banks have more significant variance than other variables.
The variables of external factor measurement present small standard deviation, this implies that macroeconomics in the UK during the period of 1999 to 2006 remains reasonable stable.
In addition to descriptive statistics, a correlation matrix for independent variables was also conducted to detect multicolliearity problem in regression model. As result shows in table 2 (Appendix 2), there is no problem of multicolliearity, thus enhanced the reliability for regression analysis.
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5.2. Regression Models Comparison
As stated in preceding section (4.5), Hausman test (Appendix 4) was performed to make the decision that fixed effect regression model will be used in this study. Table
4 reported the empirical estimation for the model.
Table 4 Fixed effect model.
Independent variable Dependent variable:
ROAA
LIQ -.020143
( 0.001
)
EQAS .091844
( 0.0000
)
LORESNI -.0057258
( 0.045
)
INF .1748704
(0.296)
GDPGR -.0113998
( 0.935 )
IR -.0482618
(0.752)
Yr 1
Yr 2
Yr 4
Yr 5
Yr 6
.5299922
(0.200)
.5473935
(0.170)
-.222223
(0.559)
.1259549
(0.742)
.2579522
(0.466)
Yr 7
Yr 8
.3247182
(0.354)
.4195161
(0.232)
R ²
(0.936)
0.1927
Prob>F 0.0009
123 banks, period 1999-2006, no. of observation=360
P-values in parentheses
Significant at 5% level.
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The overall model is very statistical significant (P-value = 0.0009) with R²of 19.27%, which means the model explained 19.27% variance in dependent variable ROAA.
This outcome is not very satisfactory as the model still left around 70% variance unexplained. In addition, the result shows that liquidity exerts negative impact on bank profitability, which means the more liquidity the bank, the lower the profit it has, which is not considered as perfect logical relationship, and contradicted to some previous studies.
The result cast certain doubt on the reliability of the regression model and it is reasonable to assume the possible exist of endogeneity problem in regression model.
This concept generally stats that economists often models behaviour as simultaneous equations systems in which economically endogenous variables are determined by each other and some additional economically exogenous variables. The simultaneity gives rise to empirical models with variables that do not satisfy the zero-conditionalmean assumption.
If endogenous factors as an explanatory variable exist in the regression model, it may create problems for accurately understanding relationships of determinants and bank profitability because of the possibility of biased results.
Due to time constrains and the complexity to perform endogeneity test, this study chose to uses experiment (Appendix 3), which involve testing different instrumental variables (section 4.3) to compare the outcome of instrumental variables regression models instead of running Stata test on endogeneity.
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Six instrumental variable regressions (Appendix 3) were constructed in relation to suspected endogenous variables, such as liquidity or liquidity with other variables. It also takes time effect into consideration and introduced lagged instrumental variable
(e.g l2ir) and difference variable (e.g dliq). Different settings of instrumental variables in regression aimed to provide attempt to identify endogenous variable. Since the results are different in response to different instrumental variable, it is reasonable to believe that the original fixed effect model dose possess the problem of endogeneity, therefore, the model is not sufficient to generate reliable findings, consideration on result generated by instrumental variable regression is needed.
Amongst six instrumental variable regression models, model 3 and 4 have insignificant result with very low R²value, therefore they are excluded from consideration. Table 5 reports the estimation for other four instrumental variable models in comparison with fixed effect model. By comparing the R²value and significant level for overall model, regression model 5, which instrumented variable liquidity with other variables and differenced variable of liquidity, equity to asset and
GDP growth ranked as top estimator. Alongside with other 4 regression modes, the final five models presented in Table 5 will provide joint findings on estimation of each variable effect on bank profitability.
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Independent variable
Fixed Effect
Model
Table 5 Regression Models Result
Instrumental
Variable
Regression 1
Instrumental
Variable
Regression 2
Instrumental
Variable
Regression 5
Instrumental
Variable
Regression 6
LIQ
EQUAS
LORESNI
INF
GDPGR
IR
R ²
Prob>F
-0.020143 -0.0122769 -0.013411 -0.0059644 -0.0104618
(0.001) (0.476) (0.416) (0.382) (0.226)
0.091844 0.1334216 0.1170456 0.0656354 0.0943565
(0.000) (0.115) (0.144) (0.002) (0.001)
-0.0057258 0.0010409 -0.0092352 -0.0064158 -0.0068254
(0.045) (0.940) (0.353) (0.019) (0.014)
0.1748704 -0.0827851 -0.0960154 0.0480353 0.0419016
(0.296) (0.708) (0.650) (0.779) (0.805)
-0.0113998 na
(0.935) (0.876) (0.358)
-0.0482618 na
(0.752) (0.417) (0.667)
0.1927 0.1632 0.1362 0.2059 0.1499
0.0000 0.0289 0.0054 0.0000 0.0000
In total model 5 explains 20.59 percent of the variability of return on bank average asset overt time, which indicated by R². However, F value of 0.0000 indicates strong statistical significance, which enhanced the reliability and validity of the model.
5.2.1. Liquidity
By comparing the five models, four of them showed negative effect of liquidity however none of them are statistical significant.
Fixed effect model shows that the ratio of liquid assets to total deposit and borrowing is negatively related to ROAA and statistically significant. The low coefficient indicates that liquidity has little impact on profitability.
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Referring to previous studies, the results concerning liquidity are mixed. Molyneux and Thorton (1992) and Guru et al (1999) find also a negative relationship between liquidity and bank profitability. However, Bourke (1989) and Kosmidou and
Pasiouras (2005) found a significant positive relationship between liquidity and bank profits. Therefore conclusion about the impact of UK bank’s liquidity on their performance remain ambiguous and further research is required.
5.2.2. Credit Risk
Loan loss provisioning as a share of net interest income is a direct measure of differences in credit quality, the higher the ratio, the poorer the quality of loan portfolio.
Amongst five regression models, three of them report that the ratio of loan loss reserves to net interests revenue has a negative impact on ROAA with statistical significance. This implies that higher credit risks results in lower profit, which in line with our expectation. However, the coefficient is nearly to zero, implies the little impact that credit risk has on bank profitability in our model. In comparison, previous studies, Pasiouras (2005) showed a less strong significant for the relationship of
ROAA and LOSRESNI, and he suggested that the reason due to that loan loss reserves is the cumulative stock of loans loss reserves that changes according to the amount of new loan provisions added each year. Provisions are subtracted from operating profit before provisions, taxes and extraordinary items to arrive at operating profit before taxes and extraordinary items and consequently after sub.
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5.2.3. Capital Strength
As expected, all the models showed positive relationship between capital strength and profitability, three of them are associated with strong statistical significance. The coefficient of the ratio EQAS is relatively high compared with other variables, showing that an increase in capital strength will result in increased profitability. This is in line with our expectation as a bank with a sound capital position is able to pursue business opportunities more effectively and has more time and flexibility to deal with problems arising from unexpected losses, thus achieving increased profitability. As showed in previous studies results, capital strength is one of the main determinants of performance of UK banks as the relatively high significant coefficient of the ratio equity to assets shows. Our finding is consistent with previous studies (e.g Berger,
1995); Demirguc-Kunt and Huizinga, 1999; Ben Nacuer, 2003; Kosmidou and
Pasiouras 2005; Pasiouras et al. 2005) and indicates that well capitalised UK banks face lower costs of going bankrupt, which reduces their cost of funding or that they have lower needs for external funding which results in higher profitability.
5.2.4. Macroeconomic Factors
Turning to the effects of macroeconomic variables, all the models came with insignificant results which indicated that in the model of this study, macroeconomic factors have little impact on profitability of banks.
However, referring to previous studies, GDP growth should exert positive impact on bank profitability and this provides support to the argument of the association between economic growth and the financial sector performance, as proved by previous studies of Kosmidou an Pasiouras (2005) and Hassan and Bashir (2003).
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In terms of inflation impact on ROAA, previous studies (e.g Claessens et al., 1998;
Demirguc-Kunt and Huizinga, 1999) showed a positive result and this implies that during the period of the study, inflations was anticipated which gave banks the opportunity to adjust the interest rates accordingly, resulting in revenues that increased faster than costs, with a positive impact on profitability. However, some studies show low significance of the coefficient in the regression and offer the explanation that possibly because banks obtain higher earnings from float or because there are delays in crediting customer (Demirguc-Kunt, 1999). In addition, with inflation, bank costs also tend to rise, a larger number of transactions may lead to higher labor costs as shown by Hanson and Rocha (1986). However, on net the regression result from previous studies suggest that the impact of inflation on profitability, although not very significant is positive throughout.
The impact of interest rate is again not significant in the above models. Referring to previous studies, the effect of interest rates on bank profits has been examined by
Samuelson (1945). It is shown that under general conditions, bank profits increase with rising interest rates. “The banking system as a whole is immeasurably helped rather than hindered by an increase in interest rates… and commercial banks would profit more than savings banks” (Samuelson 1945). Short (1979) also found a positive relationship between nominal interest rates and return on capital. (Bourke, 1989) In addition, Flannery (1983) concluded that the reported profits by banks generally fluctuate little when market rates change. Contrary to the conventional wisdom, bank failures from negative cash flows are unlikely even if market rates risk sharply.
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5.2.5. Time effect
Since the dataset across different years, in order to capture the time difference impact on bank profitability, time variables are created and included in the regression model.
According to the result, all of the models showed insignificant result for time variable and it appears that time variable in our model has no discernable impact on profitability, and the coefficients are statistically insignificant. It may due to the time period selected in this study is relatively short and stable thus not showing significant impact in the model.
5.3. Structure Reverse Causality
The results to this point establish a strong and consistent correlation between capital, liquidity, credit risk, inflation and GDP growth and their impact on bank profitability.
These correlations in general suggest that banks that possess low liquidity and credit risk with strong capital strength are expected to have better performance in terms of return on average asset. As is always the case, it needs to point out that previous studies on determinants of bank profitability mainly focus on the correlationship between various factors and bank profitability, and bank performance is assumed to be passively reacting to these impacts. However, it is difficult to rule out reverse causality, which means perhaps banks with better performance (i.e, higher ROE) are capable to install sound risk management system which may exert positive impact on its liquidity and credit performance rather than the other way around.
The above suggests that to clarify further the role of capital strength, credit risk and liquidity in terms of how their impact on bank profitability, there is a need for further research to address issues relating to the control of residual confounding and reverse
55

causality in the association between various determinants and bank profitability.
5.4. Future suggestion
The limitations of the model and methodology suggest directions to pursue for future research. If the reverse-causation hypothesis is correct, the estimated model is failed in matching the observed determinants-profitability correlations and the findings will be subject to invalidity in relation to the information it provides and how to model the real case.
Due to time constraints and data available, the model in this study does not include market variable, such as industry concentration ratio, however, previous studies showed evidence that this omitted variable has impact on bank profitability. Therefore when interpreting results generated from the model, it needs to take omitted variables into consideration as exclusion of these variables could lead to biased results. It is suggested that future research could include a wider range of variables to spot potential influential factors. In addition, future research could cover a longer or different time period and cross countries to fully reveal the impact of determinants and capture countries differences to uncover underling difference from financial system, regulation factors.
The application of the statistical cost accounting method to examine the differences in the determinants of profitability between different groups of banks, such as low and high profit, small and large and domestic and foreign banks could also reveal some useful insights.
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Chapter 6 Discussion
Based on previous studies and empirical findings from this work, this chapter provides a detailed discussion on implications of tested key determinants of bank profitability with emphasis on risk management practice in banks.
Referring to the literature, banks profitability is determined by internal factors in terms of bank-specific determinants and external factors that reflect the macroeconomic factors and financial market environment. The results from both previous studies and this paper showed that compared with internal factors, external factors have less impact on bank profitability. In contrast, internal factors, such as credit risk, liquidity and capital strength showed close relationship with bank performance. These bank-specific factors are related to bank management and therefore the findings imply that strengthen risk management practices in bank could enhance the efficiency of banks, hence profitability. There has been an extensive literature based on this idea. For example, Bourke (1989) and Molyneux and Thornton
(1992) find a positive relationship between better-quality management and profitability.
Before the discussion of how banks could strengthen their risk management practices, it needs to briefly review the key risks source in banks.
6.1. Risks in Banks
As tested in our model, credit risks and liquidity risks impact on profitability of banks.
Apart from the two tested risks, banks are facing multiple sources of risks, especially
57

since the 70s, the wave of deregulation drastically widened the range of products and services offered by banks. Consequently, banks entered new business fields and faced new risks. Risks increased because of new competition, product innovations, increased market volatility, and the liberalization of financial environment, which removed the entry barriers and stimulated the competition in banking industry.
Solvenc y
Foreign
Exchan ge
Credit
Banking risks
Market Liquidit y
Interest
Rate
Figure 1 (Source: Joel Bessis, 2002)
Figure 1 shows the principal banking risks. In response to the findings from this study, this chapter focuses on liquidity, credit and interest rate risks and risk management practice, definition of other risks are included in Appendix 5.
Credit risk is defined by Heffernan (1996) as the risk that an asset or a loan becomes irrecoverable in the case of outright default, or the risk of delay in the servicing of the loan. In either case, the present value of the asset declines, thereby undermining the solvency of a bank. Credit risk is critical since the default of a small number of important customers can generate large losses, which can lead to insolvency (Bessis
2002). Credit risk is by far the most significant risk faced by banks and the success of
58

their business depends on accurate measurement and efficient management of this risk to a greater extent than any other risk (Giesecke, 2004). Increases in credit risk will raise the marginal cost of debt and equity, which in turn increases the cost of funds for the bank (Basel Committee, 1999).
Liquidity risk is generated by the difference between the sizes of assets and liabilities, and the discrepancies between their maturities (Joel). It refers to that short-term asset values are not sufficient to match short-term liabilities or unexpected outflows. From this standpoint, liquidity is the safety cushion which helps to gain time under difficult conditions. (Bessis 2002) Liquidity risk also means having difficulties in raising funds and the inability to manage unplanned decreases or changes in funding sources.
Interest rate risk is the risk of declines of earnings due to the movement of interest rates. It is known that interest rate changes periodically. When rates of not locked in up tot maturity, there is an interest rate risk. Flannery (1983) found that large banks are well hedged against interest rate fluctuations. When market rates change, their revenues and costs adjust equally quickly, leaving net current operating earnings largely unaffected while for others may experience mismatched balanced sheet, causing their earnings to fluctuate violently when interest rates change.
Referring to previous studies, Molyneux and Thornton (1992) among others, find a negative and significant relationship between the level of liquidity and profitability. In contrast, Bourke (1989) reports an opposite result, while the effect of credit risk on profitability appears clearly negative ( Miller and Noulas, 1997 ) . This result may be explained by taking into account the fact that the more financial institutions are
59

exposed to high-risk loans, the higher is the accumulation of unpaid loans, implying that these loan losses have produced lower returns to many commercial banks.
6.2. Risk Management in Banks
The need for risk management in the banking sector is inherent in the nature of the banking business. Poor asset quality and low levels of liquidity are the two major causes of bank failures. During periods of increased uncertainty, financial institutions may decide to diversify their portfolios and/or raise their liquid holdings in order to reduce their risk.
In terms of credit risk management, the goal is to maximize a bank’s risk-adjusted rate of return by maintaining credit risk exposure within acceptable parameters and the maximization of shareholder value. Banks need to manage the credit risk inherent in the entire portfolio as well as the risk in individual credits or transactions. Banks should also consider the relationship between credit risk and other risks, for example, the relationship between credit risk, interest risk, liquidity risk, and market risk. The effective management on credit risk is a critical component of a comprehensive approach to risk management and essential to long-term success of any banking organization.
According to Bessis (2002), liquidity management addresses the issue to make sure that predictable deficits can be funded under normal conditions, without incurring the abnormal costs associated with the emergency funding. It is the continuous process of raising new funds, in case of a deficit, or investing excess resources when there are excesses of funds.
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Regarding to interest rate risk management, The Basle Committee (1999) outlines four basic elements which expected to construct a sound interest rate risk management.
It involves: 1) Appropriate board and senior management oversight; 2) Adequate risk management policies and procedures; 3) Appropriate risk measurement, monitoring and control functions; and 4) comprehensive internal controls and independent audits.
In practice, instead of managing specific risk, a comprehensive risk management system is essential in banks. Risk management provides banks with a better view of the future and the ability to define the business policy accordingly. Without risk management, there would be no visibility on possible outcomes, and on the possible fluctuations of profitability, nor any way to control the uncertainty over expected earnings. As Joel stated that the importance of risk management stems from the fact that, without it, strategy implementation would be limited to commercial guidelines, with no view of their impact on the risk-reward trade off of the bank.
According to the risk management guidelines for commercial banks & DFIs, outlined by State Pakistan Bank, a sound risk management system encompasses all the activities that affect its risk profile. It involves identification, measurement, monitoring and control of risks. In specific, in every financial institution, risk management activities broadly take place simultaneously at following different hierarchy levels.
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1. Strategic level: It encompasses risk management functions performed by senior management and BOD, such as formulating strategy and policies for managing risks and establish adequate systems and controls to ensure that acceptable risks remains.
2. Macro level: It encompasses risk management within a business area or across business lines.
3. Micro level: This is risk management activities performed by individuals who take risk on organisation’s behalf such as front office and loan origination functions.
6.3. Suggestion on risk management practices
Basle (1998) conducted interviews on banks to understand their views on the enhancement to risk management in terms of how to perform effective assessment of risk.
These include efforts to monitor credit and market risk on an integrated basis for the whole bank (although this is hampered by data limitations); the use of risk management models to assess the adequacy of spreads and assign capital; and the use of stress tests to assess the sensitivity of their exposure (especially for off-balance sheet items) to potential changes in market and credit conditions.
In specific, State Pakistan Bank offers comprehensive guidelines for risk management practice in banks. The essences of efficient risk management practices are outlined as following:
6.3.1. Risk management framework
It is essential to set up an effective risk management framework at first place, which should includes clearly defined risk management policies and procedures covering risk identification, acceptance, measurement, monitoring, reporting and control. The
62

framework should have a mechanism to ensure an ongoing review of system, policies and procedures for risk management and most important, there should be an effective management information system that ensures flow of information from operational level to top management.
6.3.2. Integration of risk management
The concept of risk management has developed from traditional risk management which focuses on individual risk to integration risk management which emphasis on the integrated effect or multiple risk sources. Integration risk management is based on the standpoint that risk must not be viewed and assessed in isolation, this is not only because a single transaction might have a number of risks but also one type of risk can trigger other risks. It is essential that the risk management process should recognize and reflect risk interactions in all business activities and assess and manage risk in a structural way across the organization.
To apply the above suggestion, detailed suggestion on credit, liquidity and interest risks are listed as following:
6.3.3. Managing credit risk
Although the main source of credit risk stems from loans, it needs to apply careful identification on multiple risk sources, in this case, it needs to notice that credit risk could stem from activities both on and off balance sheet. In addition to direct accounting loss, credit risk should be viewed in the context of economic exposures, which encompasses opportunity costs, transaction costs. This clear identification of
63

credit risk source would allow banks to uncover potential risk and apply more effective measurement and management.
It also needs to take integration risk management approach in assessing credit risk.
Credit risk not necessarily occurs in isolation, the same sources that endanger credit risk for the banks may also expose it to other risk. For example, a bad portfolio may attract liquidity problem.
6.3.4. Managing interest rate risk
Particular attention needs to pay to interest rate risk assessment is the measurement of interest rate risk as interest rate changes periodically and accurate and timely measurement is necessary for proper risk management. Banks may adopt multiple risk measurement techniques to capture interest rate risk, the management also should have an integrated view of overall market risk across products and business lines. In designing interest rate risk measurement systems, banks should ensure that the degree of detail about the nature of their interest sensitive position commensurate with the complexity and risk inherent in those position.
6.3.5. Managing liquidity risk
Similar to other risks, liquidity risk management requires integrated view because financial risks are not mutually exclusive and liquidity risk often triggered by consequence of other risks such as credit risk, market risk etc. for example, a bank increasing its credit risk through asset concentration may be increasing its liquidity risk as well. Similarly a large loan default or changes in interest rate can adversely impact a bank’s liquidity position. In addition, a liquidity risk management involves not only analyzing banks on and off balance sheet positions to forecast future cash
64

flow but also how the funding requirement would be met. The later involves identifying the funding market the bank has access. In all, sound liquidity risk management employed in measuring, monitoring and controlling liquidity risk is critical to the viability of any institution. Banks should have a thorough understanding of the factors that could give risk to liquidity risk and put in place mitigating controls.
In conclusion, suggestion on effect risk management system should involve a well designed risk management framework to identify measure, monitor and control its risks exposures. An integrated view towards risk management is also required, which involves accurate identification on multiple risk sources and understanding both existing as well as future risks that the bank can be exposed to.
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Chapter 7 Conclusion
7. The present study aims to identify the main internal determinants of banks profitability and to what extent these determinants exert impact on banks profitability.
In doing so, previous studies (Short, 1979; Bourtke, 1989; Molyneux and Thornton,
1992; Demirguc-Kunt and Huizinga, 2000) on bank profitability have been reviewed and it is summarised that the profitability of bank is usually expressed as a function of internal and external determinants. The internal determinants refers to the factors originate from bank accounts (balance sheets and/or profit and loss accounts) and therefore could be termed micro or bank-specific determinants of profitability. The external determinants are variables that are not related to bank management but reflect the economic and legal environment that affects the operation and performance of financial institutions. Empirical results from previous studies conclude that internal factors explain a large proportion of banks profitability; nevertheless external factors have also an impact on the performance.
A number of explanatory variables have been proposed for both categories, according to the nature and purpose of each study. Studies dealing with internal determinants employ variables such as size, capital, credit risk or costs etc while for external determinants, several factors have been suggested as impacting on profitability and these factors can further distinguish between control variables that describe the macroeconomic environment, such as inflation, interest rates and cyclical output, and variables that represent market characteristics. The latter refer to market concentration, industry size and ownership status.
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Based on the review on previous studies, the present studies investigates the impact of proposed determinants- credit risk, liquidity risk, capital, and macroeconomic conditions on bank’s profitability in the UK banking industry over the period of 1999 to 2006. To comply with the objective of this research, the paper is primarily based on quantitative research method, which obtained data from BankScope to construct an econometric model to identify and measure the determinants of banks profitability. In specific, multiple regression analysis is adopted to measure the effect of determinants on banks profitability.
For testing the research hypothesis, a sample size of 123 UK banks in the period time of 1999 to 2006 generated an unbalanced panel data set of 378 observations, which provided the basis for the econometric analysis.
The empirical findings on the impact of bank profitability in the UK in our sample suggest the following conclusions. First, negative and positive effect of liquidity on bank profitability has been found, with weak significant coefficient. This is in consistent with previous studies as the results concerning liquidity are mixed.
Therefore, the conclusion about the impact of UK bank’s liquidity on their performance remains ambiguous and further research is required. Second, the ratio of loan loss reserves to net interests revenue has a negative impact on ROAA with statistical significance. This implies that higher credit risks results in lower profit, which in line with my expectation. Third, as expected, the result showed a positive relationship between capital strength and profitability with strong statistical significance. The coefficient of the ratio EQAS is relatively high compared with other variables, showing that an increase in capital strength will result in increased
67

profitability. This is in line with my expectation as a bank with a sound capital position is able to pursue business opportunities more effectively and has more time and flexibility to deal with problems arising from unexpected losses, thus achieving increased profitability. Lastly, macroeconomic factors have little impact on profitability of banks in this model.
As the findings shows that liquidity and credit risks do have negative impact on bank profitability, and it provides further implication on the effective risk management practices in banks. Therefore, further discussion on this issue is included in Chapter 6.
In particular, it briefly introduced the principal risks in banks and corresponded risk management practices adopted by banks. It also highlights the key considerations of risk management practices faced by banks in the changing environment. In particular, a sound risk management system requires the set up of an effective risk management framework at first place, which should includes clearly defined risk management policies and procedures covering risk identification, acceptance, measurement, monitoring, reporting and control. The framework should have a mechanism to ensure an ongoing review of system, policies and procedures for risk management and most important, there should be an effective management information system that ensures flow of information from operational level to top management. In addition, integration risk management approach which emphasis on the integrated effect or multiple risk sources is also needed. Integration risk management is based on the standpoint that risk must not be viewed and assessed in isolation, this is not only because a single transaction might have a number of risks but also one type of risk can trigger other risks. It is essential that the risk management process should recognize
68

and reflect risk interactions in all business activities and assess and manage risk in a structural way across the organization.
In response to the main determinants examined in this work, suggestions on credit, liquidity and interest rate risks are detailed with application of key principles of risk management practices, which listed above.
7.1. Limitations and future research suggestion
As stated in section 4, the multiple regressions analysis employed are required due to the nature of this project, but also possess a major conceptual limitation, which is that the mechanisms governing the various phenomena are not statistically identified. The unrealistic assumptions about the statistical techniques employed in this project cast further doubt on the reliability of the generated findings. The limitations of the model and methodology suggest directions to pursue for future research. If the reversecausation hypothesis is correct, the estimated model is failed in matching the observed determinants-profitability correlations and the findings will be subject to invalidity in relation to the information it provides and how to model the real case.
Due to time constraints and data available, the model in this study does not include market variable, such as industry concentration ratio, however, previous studies showed evidence that this omitted variable has impact on bank profitability. Therefore when interpreting results generated from the model, it needs to take omitted variables into consideration as exclusion of these variables could lead to biased results. It is suggested that future research could include a wider range of variables to spot potential influential factors. In addition, future research could cover a longer or
69

different time period and cross countries to fully reveal the impact of determinants and capture countries differences to uncover underling difference from financial system, regulation factors.
70

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Appendix 1
Assumptions for statistical techniques:
Hypothesis test
1. Hypothesis test is valid only if the data are normally distributed.
2. No decision is actually taken and therefore it is difficult to justify a particular significant level. In this project we use a5% level. The drawback of the method is a lack of the objectivity because the analysis and interpretation involved personal experience, knowledge and opinion.
Regression
1. Linearity of the relationship between dependent and independent variable
2. Constant variance of error term.
3. Independence of error term.
Normality of error term distribution
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Appendix 2 (Cont.)
Table2
Independent Variables Correlations
LIQ GDPGR IR
1.0000
LOESNI
EQUAS 0.3864 -0.1671 1.0000
INF -0.1672 0.0951 -0.1311 1.0000
GDPGR 0.1151 -0.0687 0.0520 0.2412 1.0000
IR 0.0635 0.0502 -0.0226 0.2725 0.428 1.0000
The correlation matrix shows that there is no problems of Multicolliearity since none of the correlation coefficients are more than 0.75.
Table3
Descriptive Statistics of variables
Variable Obs Mean Std.Dev Min Max
ROAA 378 1.02 2.46 -10.62 26.94
LOESNI 378 9.72 36.04 -273.27 172.49
EQUAS 378 10.001 11.51 0.17 87.74
INF 378 2.75 0.53 1.56 3.2
GDPGR 378 2.67 0.64 1.8 3.8
IR 378 4.68 0.54 3.75 6

Appendix 2 (Cont.)
Table 4
Estimation for Fixed effect model.
Independent variable Dependent variable:
ROAA
LIQ -.020143
( 0.001
)
EQAS .091844
( 0.0000
)
LORESNI -.0057258
( 0.045
)
INF .1748704
(0.296)
GDPGR -.0113998
( 0.935 )
IR -.0482618
(0.752)
Yr 1
Yr 2
.5299922
(0.200)
.5473935
(0.170)
Yr 4
Yr 5
Yr 6
Yr 7
Yr 8
-.222223
(0.559)
.1259549
(0.742)
.2579522
(0.466)
.3247182
(0.354)
.4195161
(0.232)
R ²
(0.936)
0.1927
Prob>F 0.0009
123 banks, period 1999-2006, no. of observation=360
P-values in parentheses
Significant at 5% level.
81

Appendix 3
Experiments for instrumental variable regression
Instrumental Variable Regression 1
Independent variable
Dependent variable:
ROAA
LIQ
EQAS
LORESNI
INF
GDPGR
IR
Yr 1
Yr 2
-0.0122769
(0.476)
0.1334216
(0.115)
0.0010409
(0.940)
-0.0827851
(0.708) na na na na
Dropped Yr 3
Yr 4 -0.8108687
(0.114)
Yr 5 -0.7057743
(0.181)
Yr 6 -0.3830154
(0.452)
Yr 7 -0.1956978
(0.692)
Yr 8 -0.5952829
(0.242)
Cons 0.058609
(0.936)
R ² 0.1632
Prob>F 0.0289
123 banks, period 1999-2006, no of observation=167, Significant at 5% level
Instrumented: LIQ LOESNI EQUAS
Instruments: yr1 yr2 yr4 yr5 yr6 yr7 yr8 inf
LIQ L2.LOESNI L2.EQUAS L2.ROAA
gdpgr
Instrumental Variable Regression 2
Independent variable
Dependent variable: ROAA
LIQ
EQAS
LORESNI
INF
GDPGR
IR
Yr 1
Yr 2 na na na na
-0.013411
(0.416)
0.1170456
(0.144)
-0.0092352
(0.353)
-0.0960154
(0.650)
Yr 3
Yr 4
Yr 5
Yr 6
0.7861369
(0.117)
-0.135938
(0.762)
Dropped
0.181
0.3375841
(0.447)
Yr 7
Yr 8
0.5586869
(0.200)
0.1069441
(0.809)
Cons 0.1427341
(0.852)
R ² 0.1362
Prob>F 0.0054
123 banks, period 1999-2006, no of observation=167, Significant at 5% level
Instrumented: LIQ LOESNI EQUAS
Instruments: yr1 yr2 yr3 yr4 yr5 yr6 yr7 yr8 inf LIQ L2.LOESNI L2.EQUAS
82

Appendix 3 (Cont.)
Instrumental Variable Regression 3
Independent variable
Dependent variable:
ROAA
LIQ
EQAS
LORESNI
INF
GDPGR
IR
Yr 1
Yr 2
Yr 3
Yr 4
Yr 5
Yr 6
-0.5664497
(0.181)
-0.6942384
(0.104)
-0.1691771
(0.749)
-0.197718
(0.596)
Yr 7
Yr 8
-0.145849
(0.717)
-0.0865629
(0.830)
Cons
R ²
0.7687506
(0.291)
0.0033
0.000
123 banks, period 1999-2006, no of observation=167, Significant at 5% level
-0.0110855
(0.692) na
-0.0076748
(0.114)
0.2478374
(0.148)
-0.016356
(0.904) na
-0.0240632
(0.956)
Dropped
Instrumented: LIQ
Instruments: yr1 yr3 yr4 yr5 yr6 yr7 yr8 gdpgr LOESNI inf ir
Instrumental Variable Regression 4
Independent variable Dependent variable:
ROAA
LIQ
EQAS
LORESNI
INF
GDPGR
IR
Yr 1
Yr 2
0.067491
(0.245)
-0.0593215
(0.553)
-0.0042807
(0.378)
-0.0837334
(0.771)
0.1185981
(0.686) na na
Dropped
Yr 3
Yr 4
Yr 5
Yr 6
Dropped
-1.18837
(0.065)
-1.428762
(0.100)
-1.29445
(0.156)
Yr 7
Yr 8
-0.7807202
(0.314)
-1.227351
(0.114)
Cons 0.4487454
(0.658)
R ² 0.0078
Prob>F 0.1019
123 banks, period 1999-2006, no of observation=166, Significant at 5% level
Instrumented: LIQ
Instruments: yr2 yr3 yr4 yr5 yr6 yr7 yr8 gdpgr LOESNI EQUAS inf l2ir
83

Appendix 3 (Cont.)
Instrumental Variable Regression 5
Independent variable
Dependent variable:
ROAA
LIQ -0.0059644
(0.382)
EQAS 0.0656354
(0.002)
LORESNI -0.0064158
(0.019)
INF 0.0480353
(0.779)
GDPGR -0.0262449
(0.876)
IR -0.1323581
(0.417)
Yr 1 Dropped
Yr 2 0.203668
(0.592)
Yr 3 Dropped
Yr 4 -0.8695267
(0.020)
Yr 5
Yr 6
Dropped
0.181
-0.2809939
(0.448)
Yr 7 -0.1846172
(0.597)
Yr 8 -0.3257886
Cons
(0.353)
1.367979
(0.056)
R ² 0.2059
Prob>F 0.0000
123 banks, period 1999-2006, no of observation=260, Significant at 5% level
Instrumented: LIQ
Instruments: LIQ LOESNI EQUAS yr1 yr2 yr4 yr5 yr6 yr7 yr8 inf gdpgr ir dliq
dequas
Instrumental Variable Regression 6
First-differenced IV regression
Independent variable
Dependent variable: ROAA
LIQ
EQAS
LORESNI
INF
GDPGR
IR
Yr 1
Yr 2
Yr 3
Yr 4
Yr 5
Yr 6
-0.0104618
(0.226)
0.0943565
(0.001)
-0.0068254
(0.014)
0.0419016
(0.805)
0.1506698
(0.358)
-0.0662626
(0.667)
Dropped
0.0040054
(0.992)
Dropped
-0.7073625
(0.023)
-0.3019448
(0.401)
-0.1141019
Yr 7
Yr 8
(0.727)
0.1456255
(0.485)
Dropped
-0.0583204 Cons
(0.675)
R ² 0.1499
Prob>F 0.0000
123 banks, period 1999-2006, no of observation=260, Significant at 5% level
Instrumented: LIQ
Instruments: LIQ LOESNI EQUAS yr1 yr2 yr4 yr5 yr6 yr7 yr8 inf gdpgr ir dliq
dequas
84

Appendix 3 (Cont.)
Independent variable
LIQ
Fixed Effect Model Instrumental
Variable
Regression
1
Instrumental
Variable
Regression 2
Instrumental
Variable
Regression 5
-0.020143 -0.012277 -0.013411 -0.0059644
EQAS
(0.001) (0.476) (0.416) (0.382)
0.091844 0.1334216 0.1170456 0.0656354
LORESNI
INF
(0.000) (0.115) (0.144) (0.002)
-0.0057258 0.0010409 -0.0092352 -0.0064158
(0.045) (0.940) (0.353) (0.019)
0.1748704 -0.082785 -0.0960154 0.0480353
GDPGR
IR
Yr 1
Yr 2
(0.296) (0.708) (0.650) (0.779)
-0.0113998 na na -0.0262449
(0.935) (0.876)
-0.0482618 na na -0.1323581
(0.752) (0.417)
0.5299922 na na Dropped
(0.200)
0.5473935 na na 0.203668
Yr 3
Yr 4
Yr 5
Yr 6
Yr 7
Yr 8
Cons
R ²
Prob>F
(0.170) (0.592)
Dropped Dropped 0.7861369 Dropped
(0.117)
-0.222223 -0.810869 -0.135938 -0.8695267
(0.559) (0.114) (0.762) (0.020)
0.1259549 -0.705774 Dropped Dropped
(0.742) (0.181) 0.181 0.181
0.2579522 -0.383015 0.3375841 -0.2809939
(0.466) (0.452) (0.447) (0.448)
0.3247182 -0.195698 0.5586869 -0.1846172
(0.354) (0.692) (0.200) (0.597)
0.4195161 -0.595283 0.1069441 -0.3257886
(0.232) (0.242) (0.809) (0.353)
0.058609 0.058609 0.1427341 1.367979
(0.936) (0.936) (0.852) (0.056)
0.1927 0.1632 0.1362 0.2059
0.0000 0.0289 0.0054 0.0000
85

Appendix 4
Model Selection- Hausman Test
. hausman fixed random
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fixed random Difference S.E.
-------------+----------------------------------------------------------------
LIQ | -.020143 -.0091289 -.011014 .0044997
LOESNI | -.0057258 .0012314 -.0069572 .0010769
EQUAS | .091844 .1210061 -.0291621 .0169886
yr1 | .5299922 .7435443 -.2135521 .
yr2 | .5473935 .7086968 -.1613033 .
yr4 | -.222223 -.1361885 -.0860345 .
yr5 | .1259549 -.0507107 .1766656 .
yr6 | .2579522 .4414702 -.183518 .
yr7 | .3247182 .5440482 -.21933 .
yr8 | .4195161 .6523303 -.2328142 .
inf | .1748704 .2051808 -.0303104 .
gdpgr | -.0113998 .0047861 -.016186 .
ir | -.0482618 -.1649548 .116693 .
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 48.52
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
Since the P-value is significant enough, the fixed effects model will be utilized.
86

Appendix 5
Principal risks in banks
Credit risk : it is the risk that customers default, that is fail to comply with their obligation to service debt.
Interest risk : it is the risk of declines of earnings due to the movement of interest rates. It is known that interest rate changes periodically.
Market risk : it is the risk of adverse deviations of the market-to-market value of the trading portfolio during the period required to liquidate the transactions.
Foreign exchange risk : the currency risk is that of observing losses due to changes in exchange rates.
Solvency risk : it is the risk of being unable to cover losses, generated by all types of risks, with the available capital.
Operational risk : it is the risk of malfunctioning of the information system, of reporting systems, and of the internal risk monitoring rules.
87