A STOCHASTIC SETTING TO BANK FINANCIAL PERFORMANCE FOR REFINING EFFICIENCY ESTIMATES

• Author: Wai‐Peng Wong,Loo‐Hay Lee,Chee‐Wooi Hooy,Qiang Deng,Ming-Lang Tseng
• Published date: 01 October 2014
• DOI: http://doi.org/10.1002/isaf.1357
• Date: 01 October 2014

A STOCHASTIC SETTING TO BANK FINANCIAL PERFORMANCE

FOR REFINING EFFICIENCY ESTIMATES

WAI-PENG WONG,

a

*QIANG DENG,

a

MING-LANG TSENG,

b

LOO-HAY LEE

c

AND CHEE-WOOI HOOY

a

a

School of Management, Universiti Sains Malaysia, Minden, Penang, Malaysia

b

Department of Business Administration, Lunghwa University of Science & Technology, Taiwan

c

Department of Industrial & Systems Engineering, National University of Singapore

SUMMARY

This study contributes to develop a framework to measure the financial performance of banks in a stochastic

setting. The framework comprises several steps, the first of which is the development of a financial performance

measurement model to evaluate a bank’sfinancial performance using a set of factors from the CAMEL (Capital

adequacy, Assets, Management Capability, Earning and Liquidity) system. Second, the stochastic setting of the

efficiency measurement is handled using the data collection budget allocation approach, whereby Monte Carlo

simulations are used to analyse additional generated data and a genetic algorithm is used to refine the accuracy

of the efficiency estimates. The results show that the accuracy of the model is greatly improved using the proposed

approach. In contrast to the conventional deterministic model, the proposed framework is more useful to managers

in determining the bank’s future financial operations to improve the overall financial soundness of the bank.

Copyright © 2014 John Wiley & Sons, Ltd.

Keywords: Monte Carlo simulation; bank financial performance; genetic algorithm (GA)

1. INTRODUCTION

The banking industry is the main channel for monetary transmission and the main source of funds for

businesses (Andersen & Tarp, 2003; Fase & Abma, 2003; Hemmati, Dalghandi, & Nazari, 2013).

Undoubtedly, efficient banks serve as the hub for overall development for economic and financial

growth. Evaluating the financial performance of banks is thus important. Knowing the performance

of market competitors would enable banks to keep abreast of their competitors. This information allows

managers to choose appropriate strategies andsubsequently allows policymakers to formulate appropriate

policies and regulations to support the financial development of the banking industry.

CAMEL (Capital adequacy, Assets, Management Capability, Earningand Liquidity) has been a widely

used measure of financial performance.Some of the publishedliteratureon CAMEL includes Ongore and

Kusa (2013); Abdullah Al Mamun (2013); Costea (2013); Dang (2011) and Baral (2005). CAMEL

consists of sets of ratios(quotients) of various items from the financialstatements. The Commercial Bank

Examination Manual produced by the Board of Governors of the Federal Reserve System in USA had

described five composite rating levels for CAMEL rating from 1 (sound in every respect) to 5 (critically

weak, rendering to the probability of failure in the near term) (Siems & Barr, 1998).

Though the sets of quotients in CAMEL provide the insights to the various aspects of the perfor-

mance, managers sometimes find it difficult to gauge and benchmark the overall financial performance;

* Correspondence to: Wai Peng Wong,School of Management, Universiti Sains Malaysia, Penang, Malaysia. E-mail: wongwp@usm.my

Copyright © 2014 John Wiley & Sons, Ltd.

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT

Intell. Sys. Acc. Fin. Mgmt. 21, 225–245 (2014)

Published online 11 July 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/isaf.1357

Wirnkar and Tanko (2008) revealed the inability of each factor in CAMEL to capture the holistic per-

formance of a bank. In that respect, managers prefer the use of a single indicator to represent financial

performance. In addition, the existing framework of CAMEL does not incorporate uncertainties or ran-

domness of the data used in the evaluation. The evaluation of a bank’s performance with CAMEL’sratio

method under these circumstances (i.e. data uncertainties) can create aninaccurate efficiency with impli-

cations on firms’and industries’performance evaluation (Halkos& Tzeremes, 2012). Kloptchenko et al.

(2004) further iterated that intelligent techniques combining data and text mining, rather than the stand-

alone CAMEL ratio, should be used to analyse financial reports.

Given the debates above on the inadequacy of CAMEL to depict the overall performance of banks

and inaccuracies of efficiency when uncertainties or randomness of the data exists, this paper tries to

fill this gap by applying a stochastic data envelopment analysis (DEA) technique to solve this problem.

This paper proposes a DEA financial performance model under CAMEL for banks; the model is able to

combine multiple ratios into a single efficiency score without having to define the complex interactions

or trade-offs among the ratios. For a stochastic setting, the model is enhanced with Monte Carlo

simulations and a genetic algorithm (GA) to further refine the model’s accuracy to prevent managers

from making a wrong judgemen t on the efficiency scores.

The remainder of the paper is structured as follows. Section 2 will give a brief review of banking

financial performance. This will be followed by a discussion of the development of the financial

performance model in Section 3. Subsequently, an application of the model to the financial performance

of banks in the ASEAN (Association of Southeast Asian Nations) region will be given in Section 4.

Finally, conclusions and highlights of future research directions are presented in Section 5.

2. LITERATURE REVIEW

Over the past decades, numerous studies on banki ng financial performance have been written, and new

methods and new results have been developed and introduced to add into the current literatures. Based

on the literature surveys by Berger and Humphrey (1997); DeYoung (1998); Berger (2007); Cook and

Seiford (2009); Hays, Lurgio, and Gilbert (2009); Fethi and Pasiouras (2010) and Paradi, Yang, and Zhu

(2011), the common techniques used in measuring bank efficiency are ratio analysis and frontier analysis.

Ratio analysis (Noulas, Glaveli, & Kiriakopoulos, 2008) is a standard technique us ed in bank studies to

estimate efficiency;it measures the relationshipbetween twovariables which are chosento provide insights

into different aspects of the bank’s operations; for example, risk management, capital adequacy,profitability

and asset quality. This technique has attracted many analysts owing to it is simple to use and easy to under-

stand. However, the main weakness in ratio analysis is that each ratio measures only one partof the activ-

ities. It is unable to express a whole bank’s performancethat has diversenatures and information. Moreover,

an unlimited number of ratios may be created from bank statement data and the results would be confusing.

Ratio measurement does provide some usefulmeasuring information fora bank on certain aspects, whileit

is not suitable for making targets to help inefficient banks to improve (Barnes, 1987; Smith, 1990;

Fernandez-Castro & Smith, 1994). This traditional method cannot tolerate a modern bank’sefficiency mea-

surement(i.e. measuring with multipleinputs and outputs);thus, the frontier methodshave been developed

to focus on this issue (i.e. measuring the efficiency with multiple inputs/outputs).

Frontier analysis is an important technique for the benchmarking process and it measures a bank’s

efficiency by its distance to the efficient frontier (Farrell, 1957a, 1957b; Battese, 1992). Bank efficiency

measures with frontier analysis mainly focus on two approaches: the parametric stochastic frontier

approach (SFA) and non-parametric DEA.

226 W.P. WONG ET AL.

Copyright © 2014 John Wiley & Sons, Ltd. Intell. Sys. Acc. Fin. Mgmt., 21, 225–245 (2014)

DOI: 10.1002/isaf