2.7 Risk Management and Performance
2.7.1 Measuring the Performance of Banks
In the context of a bank, performance means a capacity to generate sustainable profitability (European Central Bank, 2010). The profitability is very crucial for banks to maintain their on-going activities and for their shareholders to gain fair returns (Oral and Yolalan, 1990). The assessment of bank performance has received much attention and has been well researched over the past years (Seiford and Zhu, 1999; Maghyereh and Awartani, 2014). The performance of a commercial bank is often described with the help of efficiency analysis (Ahmed, 2008). Diverse methods are used to measure the performance of banks and some common methods include financial ratio analysis, CAMELS analysis, the parametric and the non- parametric analysis techniques (Ahmed, 2008; Kumbirai and Webb, 2010). Table 2.1 indicates a brief summary of diverse empirical studies have been undertaken by different researchers in order to measure the performance of banks in different countries. All these studies have applied diverse methodologies, considered various variables and taken different sample sizes and types of banks.
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Table 2.1: Performance measurement techniques applied in the different studies Sr. # Author(s) and Date Population of Study
Performance Measurement Tool
1 Yue (1992) Missouri Banks Data Envelopment
Analysis
2 Miller and Noulas (1996) US Banks Data Envelopment Analysis
3 Yeh (1996) Taiwan Banks Data Envelopment
Analysis 4 Ayadi, Adebayo and
Omolehinwa (1998)
Nigerian Banks Data Envelopment Analysis
5 Avkiran (1999) Australian Banks Data Envelopment
Analysis
6 Pastor (1999) Spanish Banks Data Envelopment
Analysis
7 Samad and Hassan (1999) Malaysian Islamic Bank Financial Ratio Analysis
8 Altunbas et al. (2000) Japanese Banks Stochastic Frontier Analysis 9 Jackson and Fethi (2000) Turkish Commercial Banks Data Envelopment
Analysis 10 Iqbal (2001) Islamic Banks of Different
Countries
Financial Ratio Analysis
11 Noulas (2001) Greek Banks Data Envelopment
Analysis 12 Grigorian and Manolc
(2002)
Seventeen Transition Countries
Data Envelopment Analysis
13 Jemric and Vujcic (2002) Croatian Banks Data Envelopment Analysis
14 O’Donnell and Van der Westhuizen (2002)
South African Banks Stochastic Frontier Analysis 15 Casu and Molyneux
(2003)
European Banks Data Envelopment Analysis
16 Sathye (2003) Indian Banks Data Envelopment
Analysis 17 Ataullah, Cockerill and Le
(2004)
Indian and Pakistani Banks Data Envelopment Analysis
18 Fan and Shaffer, 2004 US Banks Stochastic
Frontier Analysis 19 Girardone, Molyneux and
Gardener (2004)
Italian Banks Stochastic
Frontier Analysis 20 Ho and Zhu (2004) Taiwan Commercial Banks Data Envelopment
Analysis
21 Samad (2004) Bahrain Commercial Banks Financial Ratio Analysis
22 Jaffry et al. (2005) Indian Sub-continent Banks Data Envelopment Analysis
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Sr. # Author(s) and Date Population of Study
Performance Measurement Tool
23 Beck, Cull and Jerome (2005)
Nigerian Banks Financial Ratio Analysis (Return on Assets)
24 Ataullah and Le (2006) Indian Banks Data Envelopment Analysis
25 Das and Ghosh (2006) Indian Commercial Banks Data Envelopment Analysis
26 Tarawneh (2006) Omani Commercial banks Financial Ratio Analysis (Return on Assets)
27 Ahmed (2008) Pakistani Commercial
Banks
Data Envelopment Analysis
28 Debnath and Shankar (2008)
Indian Banks Data Envelopment
Analysis
29 Pasiouras (2008a) Greek Banks Data Envelopment
Analysis 30 Pasiouras (2008b) Commercial Banks of
Different Countries
Data Envelopment Analysis
31 Singla (2008) Indian Banks Financial Ratio
Analysis
32 Sufian and Majid (2008) Malaysian Islamic Banks Data Envelopment Analysis
33 Kiyota (2009) Commercial Banks of Sub- Saharan African Countries
Stochastic Frontier Analysis 34 Naceur and Kandil (2009) Egyptian Banks Financial Ratio
Analysis 35 Al-Tamimi (2010) UAE Islamic and
Conventional National Banks Financial Ratio Analysis (Return on Assets and Return on Equity) 36 Banker, Chang and Lee
(2010)
Korean Banks Data Envelopment
Analysis
37 Burki and Niazi (2010) Pakistani Banks Data Envelopment Analysis
38 Hsiao et al. (2010) Taiwanese Commercial Banks
Data Envelopment Analysis
39 Karim, Chan and Hassan (2010)
Commercial banks of Malaysia and Singapore
Stochastic Frontier Analysis 40 Kumbirai and Webb
(2010)
South African Commercial Bank
Financial Ratio Analysis
41 Sangmi and Nazir (2010) Indian Commercial Banks CAMEL Analysis 42 Sarkar and Sensarma
(2010)
Indian State-owned Banks Stochastic Frontier Analysis 43 Aebi, Sabato and Schmid
(2011)
US Banks Financial Ratio
Analysis
44 Ahmed (2011) Jordanian Commercial
Banks
Financial Ratio Analysis (Return on Assets)
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Sr. # Author(s) and Date Population of Study
Performance Measurement Tool
45 Al-Khouri (2011) Gulf countries Financial Ratio
Analysis (Return on Assets)
46 Ariffin and Kassim (2011) Islamic Banks of Malaysia Financial Ratio Analysis (Return on Assets and Return on Equity)
47 Dincer et al. (2011) Turkish Banks CAMEL Analysis
48 Jaffar and Manarvi (2011) Islamic and Conventional Banks of Pakistan
CAMEL Analysis 49 Kao et al. (2011) Taiwan Financial Holding
Companies
Data Envelopment Analysis
50 Reddy and Prasad (2011) Regional Rural Banks of India
CAMEL Analysis 51 Shar, Shah and Jamali
(2011)
Pakistani Banks CAMEL Analysis
52 Siddiqui and Shoaib (2011) Pakistani Commercial Banks Financial Ratio Analysis (Return on Equity)
53 Abbas, Tahir and Rahman (2012) Pakistani Commercial Banks Financial Ratio Analysis (Return on Assets, Return on Capital and Return on Equity) 54 Afza and Yusuf (2012) Pakistani Banks Stochastic
Frontier Analysis 55 Ariffin (2012) Islamic Banks of Malaysia Financial Ratio
Analysis (Return on Assets and Return on Equity) 56 Choong, Thim and Kyzy
(2012)
Islamic Commercial Banks in Malaysia
Financial Ratio Analysis (Return on Assets and Return on Equity) 57 Chortareas, Giradone and
Ventouri (2012)
EU Commercial Banks Data Envelopment Analysis
58 Garza-Garcia (2012) Commercial Banks of Mexico
Data Envelopment Analysis
59 Glass, Kenjegalieva and Thomas (2012)
Kazakh banks Stochastic
Frontier Analysis 60 Jhamb and Prasad (2012) Indian Banks Financial Ratio
Analysis 61 Kolapo, Ayeni and Oke
(2012)
Nigerian Commercial Banks Financial Ratio Analysis
62 Kouser and Saba (2012) Pakistani Conventional and Islamic Banks
CAMEL Analysis
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Sr. # Author(s) and Date Population of Study
Performance Measurement Tool
63 Nawaz et al. (2012) Nigerian Banks Financial Ratio Analysis
64 Poudel (2012) Nepalee Commercial Banks Financial Ratio Analysis (Return on Assets)
65 Sharma, Sharma and Barua (2012)
Indian Banks Data Envelopment
Analysis 66 Afriyie and Akotey (2013) Commercial Banks of
Ghana
Financial Ratio Analysis
67 Barth, Caprio and Levine (2013)
Commercial Banks of Different Countries
Data Envelopment Analysis
68 Bokpin (2013) Commercial Banks of
Ghana
Stochastic Frontier Analysis 69 Jha, Hui and Sun (2013) Commercial Banks of Nepal Data Envelopment
Analysis
70 Lee and Chih (2013a and b) Chinese banks Data Envelopment Analysis
71 Najjar (2013) Bahraini Banks Financial Ratio
Analysis
72 Oluwafemi et al. (2013) Nigerian Banks Financial Ratio Analysis (Return on Assets and Return on Equity) 73 Soltani et. al (2013) Iran Public and Private
Banks
CAMEL Analysis 74 Tabari, Ahmadi and
Emami (2013) Commercial Banks of Iran Financial Ratio Analysis (Return on Equity)
75 Alzorqan (2014) Jordan Banks Financial Ratio
Analysis 76 Fernando and Nimal
(2014)
Sri Lankan Banks Data Envelopment Analysis
77 Maghyereh and Awartani (2014)
Gulf Cooperation Countries banks
Data Envelopment Analysis
Table 2.1 shows that the adaption of conventional ratios is one of the most popular methods to measure the performance of banks. The financial ratio analysis has been used in a large number of research studies (Samad and Hassan, 1999; Beck, Cull and Jerome, 2005; Singla, 2008; Naceur and Kandil, 2009; Kumbirai and Webb, 2010; Alzorqan, 2014). The most common ratios to measure the performance of banks are return on assets and return on equity (Al-Tamimi, 2010; Aebi, Sabato and Schmid, 2011; Ariffin and Kassim, 2011; Abbas, Tahir and Rahman, 2012; Choong, Thim and Kyzy, 2012; Oluwafemi et al., 2013). However,
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several researchers have also used some other ratios including return on capital, cost to income ratio, net interest margin, profit expense ratio as a performance measurement technique for banks (Samad 2004; Kumbirai and Webb, 2010; Abbas, Tahir and Rahman 2012).
The CAMEL model analysis has been undertaken by several researchers in order to evaluate the performance of banks in different countries (Sangmi and Nazir, 2010; Dincer et al., 2011; Jaffar and Manarvi, 2011, Reddy and Prasad 2011; Shar, Shah and Jamali 2011; Kouser and Saba 2012; Rozzani and Rahman 2013 and Soltani et. al, 2013). The CAMEL model first has been introduced and applied by American regulatory agencies in 1980s as a framework of rating for on-site examinations of banks (Soltani et. al 2013). The CAMEL bank rating has been adopted by the management of banks to measure the financial health and performance of banks (Rozzani and Rahman, 2013). The CAMEL model is used by the World Bank, African Development Bank, Asian Development Bank, Federal Reserve Bank in U.S and several banking regularity bodies of different countries to measure the performance of banking institutions (Sangmi and Nazir, 2010; Soltani et. al 2013).
Initially the CAMEL model has been comprised of five components and another component added in the CAMEL model in the late 1990s to consider the market risk (Soltani et. al 2013). As a result of this extension, it is also known as CAMELS model. The key parameters of CAMELS model are (capital, asset quality, management, earnings, liquidity and sensitivity). Each of these performance evolution parameters is scored from 1 to 5. The score 1 shows the strongest rating and the score 5 indicates the weakest rating. A compound rating, varying from the fundamentally strong bank (1) to fundamentally weak bank (5), has been assigned as a compendium of the parameter ratings to indicate banks financial soundness. Different financial ratios regarding capital adequacy, assets quality, management soundness, earnings and profitability, liquidity and sensitivity to market risk are involved in CAMELS analysis to measure the performance and soundness of banks. The SBP also conducts regular on-site inspections on the basis of CAMELS framework to strive for the soundness and stability of the financial
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system and safeguard the interest of stakeholders in Pakistan (Jaffar and Manarvi, 2011).
The use of financial ratio analysis has certain limitations. For instance, each ratio only indicates single aspect of bank activities which is already a complex organization to be studied (Burki and Niazi, 2010). A large number of ratios make analysis process too complex to interpret due to large number of financial indicators thereby often lead to complicated and contradictory results and may produce an unsuitable method to measure the general performance (Berger and Humphrey, 1997; Wozniewska, 2008; Kiyota, 2009).
In order to address these limitations, efficiency analyses are often undertaken to measure the performance of banks (Ahmed, 2008; Wozniewska, 2008). Chien and Danw (2004) and Wozniewska (2008) propose that it is valuable to supplement performance measurement analysis with more effective approaches such as parametric and non-parametric techniques. This argument is further supported in other studies (Sathye, 2003; Atullah and Le, 2006; Das and Ghosh, 2006) advocating that both approaches provide more conclusive estimates of the latent performance of banks. Table 2.1 also indicates that both parametric and non- parametric techniques have also been used extensively to measure the performance of banks.
The parametric technique has been developed by Aigner, Lovell and Schmidt (1977). The most extensively adopted parametric approach is Stochastic Frontier Analysis (SFA) (O’Donnell and Van der Westhuizen, 2002). The SFA approach measures the efficiency of banks by using cost efficiency, profit efficiency and alternative profit efficiency (Laeven, 1999). According to Girardone, Molyneux and Gardener (2004), the cost efficiency provides an estimate of how close the cost of a bank is with a best-practice (benchmark) bank’s cost for generating the same amount of output under similar conditions. The profit efficiency estimates how close a firm is to generating the maximum potential profit within a specified level of input and output prices. In contrast with the cost efficiency function, the profit efficiency function permits for revenues consideration which can be produced by changing the inputs as well as outputs. The
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alternative profit efficiency estimates how close a firm is in producing maximal profits inclined with the firm’s outputs levels instead of output prices.
Charnes, Cooper and Rhodes (1978) have introduced a non-parametric programming that is Data Envelopment Analysis (DEA). It is the most widely non- parametric technique which is used to measure the performance of banks (Laeven, 1999). Several authors advocate that the DEA might be favoured over SFA (Yue, 1992; Jackson and Fethi, 2000; Casu and Molyneux, 2003; Sathye, 2003; Pasiouras, 2008a). DEA is one of the most extensive and popular methods to measure the performance of firms offering analogous services and using same set of resources (Oral and Yolalan, 1990; Grigorian and Manole, 2002). Cooper, Seiford and Tone (2000) highlight that DEA is very useful method to providing new intuition into entities and activities which have already been measured by some other techniques.
DEA measures the efficiency of banks through the ratio of weight sum of outputs to weighted sum of inputs (Yue, 1992; Miller and Noulas, 1996; Barth, Caprio and Levine, 2013; Jha, Hui and Sun, 2013). A detailed explanation of this performance measurement is reported in Chapter Three (see Section, 3.9.2.1)
In Pakistan, the performance and efficiency of banks are generally evaluated by using financial ratios. The SBP measured the performance of banking sector in its different assessment reports by using CAMELS analysis approach. Several studies (Atullah, Cockerill and Le, 2004; Jaffry et al., 2005; Ahmed, 2008; Burki and Niazi, 2010) have used DEA to estimate the effects of regulations, privatizations, economic reforms, financial liberalization, mergers and acquisitions on the performance but none of these studies has yet explored to observe the impact of risk management on the performance of banks in Pakistan.