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BENCHMARKING PERFORMANCE INDICATORS: EVIDENCE FROM BANKING SECTOR OF PAKISTAN

CHAUDHARY ANEES BAJWAA

Institute of Management Sciences, IQRA University, Pakistan

IMTIAZ ARIF

Institute of Management Sciences, IQRA University, Pakistan

WASIM UD DIN

Institute of Management Sciences, COMSATS Institute of Information Technology, Islamabad

*Corresponding Author:
CHAUDHARY ANEES BAJWAA
Institute of Management Sciences
IQRA University, Pakistan
Tel: 00923332224300
E-mail: as.bajwaa@gmail.com

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Abstract

Purpose: The aim is to provide insight regarding performance evaluation of twenty-four different banks in Pakistan with relation to the stock market. Design/methodology/approach: The methodology is based on Grey Relation Analysis (GRA) by using six-power analyses which has been used previously in the field of industry and commerce for evaluating the comparative performance of banks. Furthermore, the GRA is based on TOPSIS technique to rank the banks. Findings: The findings reveal that benchmarking performance indicators are essentially locating the representative indicator from the existing ratios most commonly used in financial analysis to assess business operational performance. The GRA is linked with Technique for Order Preferences by the Similarity to Ideal Solution (TOPSIS) technique may result in incompleteness in the combination of ratios, and requires adjustment when other issues for analysis are involved. Overall, the study also demonstrates that a bank with a high ROA leads to a high financial performance. This paper conducted a review of literature and used six-power analyses to aggregate financial ratios appropriate for the TOPSIS technique. Future research could set up a specific model for the preliminary selection of financial ratios with a new to make studies of this kind more complete. Limitations: The current study is limited to twenty-four listed banks of Pakistan Stock Exchange (PSX) during period from 2013 to 2015 and also limited to secondary data instead of primary data. Originality/Value: This paper presented a new technique for performance evaluation - GRA. The major contribution of this paper is to use 24 banks in order to investigate the performance evaluation of listed banks of PSX during period from 2013 to 2015. Furthermore, GRA could avoid the waste of resources due to the uncertainty of relations among the ratios when using them for analysis.

Keywords

Performance; Stock Market; Indicators; Outcomes; Financial Performance; Specific Model

Introduction

A firm’s owner could conduct internal/specific measurement techniques at systematic intervals and the management of the firm can also gain an improved understanding regarding efficient use of their means. The outcome of such a performance assessment can help the owner of a business with regards to the situation in terms of resources allocation for the future. The professionals and businesses can create their objectives, evolving methods for achieving stated objectives and a performance evaluation of system is known as benchmarking [1]. Hence, effective benchmarking performance evaluation for businesses is absolutely contributing to operational management. In the banking industry, the performance evaluation of commercial banks has achieved a better consideration in the past years [2]. Mohapatra [3] said that the process of benchmarking is to understand, identify and adapt the unresolved practices and process of businesses anywhere in the world in order to help and improve their performance in relation to the performance of other businesses. In order words, the author has defined benchmarking as a secret process of relating the performance of one business with its rivals, along with the alteration in rationality that is mainly required by the new-fangled level of directness and originality. The past work done by Ho [4] and Ho and Wu [5] is a source of inspiration and that are mainly concerned with the performance of banks and their evaluation that mainly emphasizes on operational performance. Furthermore, researchers have said that the performance of a certain stock has a direct effect on sound decision making regarding investors which generally goes unnoticed. Therefore, Ho and Wu [5] attempted to present an idea regarding the performance evaluation of three major banks of Australia. The assessment of the operative performance of businesses is a major apprehension for governments, industries and academies alike. In addition to this, the operative performance of a business not only serves as a basis for improvements and as criteria for noticing problematic issues within the business, but also as a policy determinant for government to take appropriate measures.

Firms’ managements conduct their own analysis and measurements at regular intervals of time and such management are enhancing in order to understand the successfulness of enterprise in general. The outcomes of such performance evaluation serve as a reference point in allocating the resources of enterprise in future. Although Benchmarking is unquestionably an essential tool for consultants and enterprises for establishing their objectives, other approaches should be developed alongside, to measure the achievement of objectives and the performance of the system [1]. Therefore, effective benchmarking performance evaluation for an enterprise can contributes regarding basic level to the performance evaluation of banks; mostly commercial banks has been receiving greater consideration over previous numerous years [2]. The past work done by many researchers have said that the performance evaluation of banks mainly focuses on operative performance. The evaluating of the operative performance of firms is a major issue for academics, governments, and industries. Operative performance is not only serves as a foundation for a firm’s enhancement and standards for noticing enigmatic issues but also serves as policy determinants for the states in taking appropriate measures. However, selecting a practical technique for effective performance evaluation is not a simple task. There is extensive past work done by numerous researchers that discusses diverse research methods that are mainly applied to a firm’s performance evaluation such as the financial statement analysis [4-8] and the GRA [4,5,9].

Seiford and Zhu [2] who claimed that operative benchmarking performance evaluation for businesses absolutely contributes to effective management. The performance evaluation of banks shows that commercial banks have attained a good consideration during past years. It is clear from the above-mentioned views of scholars that without benchmarking, it is difficult for organizations to compete either on national or international level. According to Mohapatra [3] benchmarking can be different in different organizations such as manufacturing and services industry. In the manufacturing industry, it is fairly defined through productivity which is the ratio of output to input while in service industry, the enigmatic issues rise because of output is not visible and physical. But the services industry provides extra enigmatic issues in measuring productivity and performance related matters. In short, the current study is used GRA in order to investigate the ranking based benchmarking of banking industry of Pakistan during the period of 2013 to 2015. This paper is divide in five parts i.e. introduction, literature review, the data and methodology, results and discussion and conclusions.

Literature Review

Different scholars across the globe have investigated the optimal capital structure from different perspectives. The pioneer work done by the two scholars Ho and Wu [5], who studied the benchmarking performance indicators of three major banks of Australia, gives us a good insight. The main goal of their work was to create a performance evaluation of banks with the performance of the stock market being taken into attention. The methodology is based on the Financial Statement Analysis (FSA) and the Grey Relation Analysis (GRA) which is a concept taken from the industry and has been increasingly applied in the field of commerce. The main objective of using the GRA technique is to lessen the number of financial indicators by choosing demonstrative indicators from the FSA. They used only year 2000 for FSA and GRA. There are 59 indicators of three banks of Australia for analysis. In GRA, the indicator reduced to 23 by applying clustering after vector normalization which leads to reducing the rate of benchmarking performance indicator to 61.02 percent. The results of the GRA test are the same when compared the results of the FSA test. Overall, their work yielded the hypothetically expected outcomes of the three banks. Please rephrase, because this passage is extremely unclear!

Ho [4] measured the operation performance of the banking sector by used GRA. The objective of his study is based on a new methodology of performance evaluation known as GRA which is an idea taken from the of industry and progressively used in the field of commerce to evaluate the relative performance of three investment Taiwanese trust firms that are restructured into banks. The goal of GRA is based on the belief to reduce the number of monetary indicators by picking the representative indicators from the FSA. They used only year 1997 for FSA and GRA. There are 38 indicators of three banks for analysis. In GRA, the indicator reduced to 17 by applying clustering after vector normalization which leads to reducing the rate of benchmarking performance indicator to 55.03 percent. The findings reveal that size of sample and unidentified the distribution of secondary data. In short, GRA tool are successfully used in evaluating the performance of banking industry. Furthermore, similar results are attained when comparing the result of GRA with FSA.

Xiong [10] used GRA to evaluate the financial condition of the listed firms. The methodology is based on Grey Relational Analysis (GRA) technique that is used to evaluate the financial situation of the listed companies. The finding reveals that GRA is flexible and are avoiding the demerits of evaluating financial condition from only in one feature in the earlier period. Peker and Baki [11] measured the performance evaluation in Turkish Insurance sector with by using the GRA. The objective of their work is based on ranking of the financial performance of three leading firms that are functioning in the insurance sector. The methodology is based on GRA that are measuring firm’s performance by liquidity, financial leverage and profitability ratios. It is concluded that a firm with higher liquidity ratios may have a higher financial performance.

Dogan [12] measured banking performances in Turkey using the GRA. The aim of his study used GRA measurement and comparison of monetary performances of ten banks listed on Istanbul Stock Exchange (ISE) during period from 2005 to 2011. The second objective of his work was to lessen the number of financial ratios that are used to find the performance of banks, and by doing so, obviously identifying that financial ratios are more important in measuring the performance. The result reveals that Akbank is on first number and Yapı Kredi Bank is on the tenth number in financial performance. In addition to this, the study also finds that a bank with a higher ROA ratio could also have a higher financial performance. There is another study done by Dogan [13] who studied the comparison of the financial performance of banks in Turkey. The objective of his works is used GRA technique for measurement and comparison of financial performances of banks during period from 2012 to 2014. The second objective of the study was to lessen the number of financial ratios, which determine participation of bank performance, and by doing so, identify which financial ratios are essential in performance measurement. The result reveals that Albaraka Turk is on the first number and Bank Asya is on the tenth number in financial performance. Furthermore, the study also finds that a participation bank with a high ROA leads to a high financial performance.

In short, the literature examines that the significance of benchmarking performance indicators for banking industry on an international level. However, no specific study has been conducted in the context of Pakistan regarding benchmarking performance indicators of banking. This study tries to fill the gap in academic research as a combination of forty-eight financial ratios by using FSA and GRA method during period from 2013 to 2015. The ranking of 24 banks will be performed which is based on the Technique for Order Preferences by the Similarity to Ideal Solution (TOPSIS) in GRA after vector normalization and to investigate the proposed model, ranking of banking industry and will also compare the FSA with GRA.

Data and Methodology

The population of the current study is based on banking industry of Pakistan while sample size is the listed 24 banks of Pakistan Stock Exchange. The software used in current study is MS-Excel and Turbo Pascal 8. The current study is an applied research in terms of objectives and it is a descriptive in terms of methodology. The methodology of current paper is GRA which is linked with TOPSIS [5]. The concept of GAR was initially given by Deng, and was grounded on the theory of grey relation space. The essential meaning of the word greyness means the information that is half-finished or unfamiliar. Hence, an element from half-finished message is measured as a grey element. Grey relation means the measurement of varying links between two systems or between two factors that happen in a system in some period [14], and GRA is a research technique used to capture the links among elements when the movements of their growth have either heterogeneity or homogeneity. If two elements develop in a consistent trend, the two elements have a high level of relation. If two elements are developing and unable to follow a consistent trend will lead to lower level of relation. Deng, proposed that the following modeling is considered in GRA.

Let, X={xj| j=1,2,…..;n} selected as a factors in a grey relations in sequential order.

Where,

x1 ϵ X=Reference Order

x1 ϵ X ( j ≠ 1)=Comparative Order

Then x1(i) and xj (i) (I=1, 2,…, m; j=2, 3,…, n) would be the values of x1 and xj at point i. If γ(x1,xj) are the real numbers, then it is calculated in equation 1.

image (1)

The mean value of x1(i) and xj (i) necessity to meet 4-axioms of the grey relation:

Axiom No. 1: The Norm Interval [0<γ(x1,xj)≤1]

γ(x1,xj)=1↔ x1= xj, (i.e. whole relation)

γ(x1,xj)=0 ↔x1=xj, (i.e. whole non-relation)

Axiom No. 2: The Duality Symmetric

x,yX

γ (x,y)=γ(y,x)X=(x,y)

γ (x1,xj)=1X1=Xj,

Axiom No. 3: The Wholeness

xj,xiX

γ(xj,xi)= γ(y,x)X=(x,y)

Axiom No. 4: The Approachability

γ[(i) and xj (i)] get higher along with |x1(i) and xj(i)| get lesser

If 4-axioms are fulfilled, then g(x1(i) and xj (i)) is called as grey relation coefficient of xj to x1 at point i. The order of financial indicators in a cluster is based on the magnitude of the grey relational coefficient. Deng has presented the mathematical equation that satisfies the 4-axioms of grey relation which is given as under:

image (2)

γ[x1(i) and xj (i)]=The grey relation coefficient of xj to x1 at point i,

image Distinguished Coefficient,

image The above function is reducing its numerical value to product of maximum & minimum then taken magnitude of x1(i) and xj(i)

It is getting large, to affect its loss-authenticity and to heighten the remarkable difference among correlation coefficients. In short, GRA is conception which is borrowed from the industries and progressively applied in field of commerce for evaluating the relative performance of major banks. The aim of using the GRA tool is to lessen the number of financial indicators by choosing some representative indicators from the financial statement analysis [5]. The TOPSIS was initially developed by Hwang and Yoon. The aim of TOPSIS is to determine a solution which is closest to the positive ideal solution (A+) and furthest from the negative ideal solution (A-). In addition, the A+ is the most effective or least costly value among a set of feasible solutions while A- is a value of least effectiveness and highest cost. TOPSIS is used as the ranking method in GRA in the previous studies. The merit of such technique is fairly simple that yield an extremely consistent preference of order [4,5].

The TOPSIS technique is calculating the net performance score of separate bank. After accompanying the TOPSIS technique, the researchers determine the results of ranking regarding three major banks of Australia in terms of profitability, leverage, asset utilization, liquidity, growth, stock performance and total performance. There are six steps which are used in TOPSIS. In first step, the vectors are normalized by the formula given in equation 3. Furthermore, the GRA method follows TOPSIS which is based on normalization of vector and focusing by using original value of ratio and then taking square root of the summation of unique indicator values [4,5].

image (3)

Where,

i=The ith bank

j=The jth financial ratio

rij=The value of performance regarding financial ratios after normalization of vectors for magnitude and direction

xij=The unique performance value of financial ratios

m=The no. of banks

In second step, clustering is performed on the bases of the grouping and significant values, and remove those financial ratios that are not significant which are used in FSA [4,5]. In short, many studies perform GRA by using TOPSIS analysis to execute benchmarking performance indicator and ignored the step of clustering of financial indicators [15-18]. Hence, the indictors used in FSA and GRA are forty-eight and both analysis in current study used same indicators. In third step, the following formulas are used for Positive Ideal Solution (A+) and Negative Ideal Solution (A-).

image (4)

image (5)

Where,

J={j=1, 2, ...., k | k is the efficiency}

J'={j=1, 2, …., k | k is the cost}

In fourth step, distance was calculated from each solution (bank) to the Positive Ideal Solution (S+) and the Negative Ideal Solution (S-) by following formulas shown in equation 6 and 7, we get;

image (6)

image (7)

S+ i is the smallest distance from the ideal solution (bank); S- i is the furthermost distance from the worst solution (bank). In second last step, the Proximity is calculated of each Solution (Bank) to Ideal Solution (C* i) by the following formula shown in equation 8, we get;

image(8)

The last step is based on the value of C* i from previous step and rank the performance among solutions (banks). In this way, the ranking results of all 24 banks in terms of profitability, asset utilization, leverage, liquidity, growth, stock performance and net performance are performed in Techniques under Order Preferences by the Similarity to Ideal Solution (TOPSIS) technique [19-26].

In short, many studies perform GRA by using TOPSIS analysis to execute benchmarking performance indicator and ignored the step of clustering of financial indicators [15-18]. Hence, the indictors used in FSA and GRA are forty-eight and both analysis in current study used same indicators. The current study focused on the forty-eight financial ratios during period of 2013 to 2015 regarding twenty-four banks by using twelve profitability ratios, two assets utilization ratios, nine leverage ratios, seven liquidity ratios, ten growth ratios and eight stock performance ratios and net performance by using TOPSIS technique are shown in Appendix Table 1.

Results and Discussions

The ranking of twenty-four banks in the year 2013 to 2015 under TOPSIS analysis is shown in Appendix Tables 2-4. The TOPSIS technique is used to fine the relative performance of the banks under scrutiny. For TOPSIS, the selected indicators should have the shortest distance from the ideal solution and farthest from the worst. The ideal solution is the one that enjoys the largest efficiency indicator and the smallest cost indicators among each of the substitute banks. The worst solution is the one that enjoys the smallest efficiency indicator and the largest cost indicator among each of the substitute-bank.

It is clear form Appendix Table 2 that the highest net performance is for National Investment Bank (NIB), and lowest net performance is for Bank Alfalah Limited (BAFL) in the year 2013. It is a time to exert safety measure to improve overall performance based on profitability, asset utilization, liquidity, leverage, growth, stock performance. The ranking result of top eight banks gives the road map for lower banks from 9 to 24 to improve their financial performance in future. Furthermore, the banks if get edge in any ratio among forty-eight indicators; that bank will get also edge in overall performance. So, banks must consider the FAS and GRA by critically examining the financial ratios to compare their result with their own result.

It is clear form Appendix Table 3 that the highest net performance is for Allied Bank Limited (ABL) and lowest net performance is for National Investment Bank (NIB) in the year 2014. It is a time to exert safety measure to improve overall performance based on profitability, asset utilization, liquidity, leverage, growth, stock performance. The ranking result of top eight banks gives the road map for lower banks from 9 to 24 to improve their financial performance in future. Furthermore, the banks if get edge in any ratio among forty-eight indicators; that bank will get also edge in overall performance. So, banks must consider the FAS and GRA by critically examining the financial ratios to compare their result with their own result. It is clear form Appendix Table 5 that the highest net performance is for United Bank Limited (UBL) and lowest net performance is for SILK Bank Limited (SILK) in the year 2015. It is a time to exert safety measure to improve overall performance based on profitability, asset utilization, liquidity, leverage, growth, stock performance. The ranking result of top eight banks gives the road map for lower banks from 9 to 24 to improve their financial performance in future. Furthermore, the banks if get edge in any ratio among forty-eight indicators; that bank will get also edge in overall performance. So, banks must consider the FAS and GRA by critically examining the financial ratios to compare their result with their own result.

In order to comparing banks from 2013 to 2015, the NIB comes 1st in total performance in the year 2013 while comes 24th in the year 2014 and 23rd in the year 2015. It is due to less total assets and income as compared to other banks. The lower the net income the lower will be its net performance on the basis of TOPSIS. The BIPL comes 2nd in total performance during 2013, comes 21st in total performance in 2014 and comes 4th in total performance in 2015. It is due to fluctuation in total asset and net income that varies for BIPL as compared to other banks. The list of total assets and net income of 24 banks during period from 2013 to 2015 is shown in Appendix Tables 5 and 6, respectively.

Conclusion

In today’s world, economic growth of a country depends on its financial sector especially banking industry working in that country. This study is conducted to investigate the benchmarking performance indicators. The objective of the current study is to construct a performance evaluation of listed banks of Pakistan Stock Exchange taken into consideration during period from 2013 to 2015. The methodology is based on GRA in order to use TOPSIS technique which is focusing on forty-eight indicators regarding twenty-four banks. The overall result shows the rating of twenty-four banks in year 2013 to 2015 and shows rankings which are varying by using six indicators such as profitability, asset utilization, leverage, liquidity, growth and stock performance. Overall, GRA gives different results based on proximity value to positive ideal solution in the year 2013 to 2015 by examining the overall performance of twenty-four banks to rank the banks as well. Furthermore, the banks if get edge in any ratio among forty-eight indicators that bank will get also edge in overall performance. It is highly recommended for banking sector of Pakistan that is to identify the weaknesses and treats to avoid and improve the profitability, asset utilization, leverage, liquidity, growth and stock performance, also striving to expand their inside operational efficiency and also productivity in organizing both human and monetary capital. So, paying attention to the benchmarking as an important factor for measuring financial performance on the basis of ranking.

References

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