Effects of Financial Holding Company Act on bank efficiency and productivity in Taiwan

ARTICLE IN PRESS Neurocomputing 72 (2009) 3490–3506

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Effects of Financial Holding Company Act on bank efficiency and productivity in Taiwan Chei-Chang Chiou  Department of Accounting, National Changhua University of Education, Changhua 500, Taiwan, ROC

a r t i c l e in f o

a b s t r a c t

Available online 11 June 2009

Taiwan’s banking industry has experienced greatly structural changes since the implementing of Financial Holding Company Act in July 2001. The paper investigates whether Taiwan’s commercial banks establishing or joining in financial holding companies (FHCs) could promote their efficiency and productivity, as well as the determinants of efficiency and productivity changes of commercial banks. The paper applies a data envelopment analysis approach for calculating bank efficiency and a Malmquist total factor productivity index for measuring productivity change of banks. The results show that except for pure technical efficiency, other efficiencies and productivity of commercial banks do not improved because of their establishment of or joining in FHCs; on the contrary, it is just because of their better efficiency that they can firstly establish or join in FHCs. The results also reveal that, in the aspect of determinants of bank efficiency, bank size and overdue ratio have significant negative relations to technical efficiency of establishing or joining in FHCs’ banks, while equity-to-total asset ratio and loanto-deposit ratio have significant positive relations to their technical efficiency; moreover, bank size and overdue ratio have significant negative relations to technical efficiency of banks with no established FHCs, while business diversification and loan-to-deposit ratio have significant negative relations to scale efficiency for these banks. Finally, in the aspect of determinants of bank productivity changes, overdue ratio has a significant negative relation to productivity growth of establishing or joining in FHCs’ banks, while business diversification has significant negative relations to both technological growth and productivity growth for banks with established and no established FHCs; furthermore, equity-to-total asset ratio and loan-to-deposit ratio have slightly significant negative relations to technical efficiency growth of banks with no established FHCs, while business diversification has a slightly significant positive contribution to their technical efficiency growth. & 2009 Elsevier B.V. All rights reserved.

Keywords: Efficiency Productivity change Financial holding companies

1. Introduction On national economy, commercial banks normally serve as the main intermediary institution of finance and currency, its operation has great influences on the society and people’s livelihood of a country. The main purpose of evaluating the operating efficiency of commercial banks is to highlight the status of operational performance so that managers or regulators can improve that performance [28]. As for bank stockholders, depositors, investors and bank managers and employees and so on, evaluating operating efficiency and supervising financial situation of commercial banks are very important. Therefore, there are many literatures on evaluating operating efficiency of commercial banks for different countries. However, because of different financial rules, financial environment, culture of each country and even differences on scales, resources, operational modes, ownership and service objects of commercial banks themselves, analysis results of literatures on

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operating efficiency for commercial banks are also different. Taking Taiwan for example, its financial environment and rules are extremely different from that of great countries of Europe and America; for example, small banks of America always go bankrupt in fierce competition, but there are many small commercial banks or credit cooperatives in Taiwan, if they close down, it must bring about social disturbance, so the ROC government will take measures to prevent this situation. Therefore, study of operating efficiency for commercial banks in Taiwan is also regarded, such as Chen [26,27], Chen and Yeh [28], Lin [78–80]. In Taiwan, financial environment endured three important reforms. First, in 1991, the ROC government announced Commercial Bank Establishment Promotion Decree in order to open up the bank market further. Government then invited domestic and foreign investors to participate in Taiwan’s banking industry and set up new, privately owned commercial banks since 1992 [26]. Second, Asian financial crisis in 1997 and a small scale financial crisis happened in Taiwan in 1998 affected the operation of Taiwan enterprises, and even some enterprises were closed down, so operating efficiency of many commercial banks were extremely influenced, which was completely cooled till 2000. Third, after

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financial deregulation, excessive opening of new banks and station of foreign banks caused fierce competition of commercial banks, so small commercial banks were difficult to survive, and then the government passed the Bank Mergers and Acquisitions Act and Financial Holding Company Act in December 2000 and July 2001, respectively, to encourage merging of financial institution and opening cross-business operation in order to achieve the goal of integration and macro-scale operation of financial industry of Taiwan. As for operating efficiency of commercial banks of Taiwan, only the first two important reforms were discussed in the past literatures. Chen [26] and Chen and Yen [28] compared operating efficiency of publicly owned banks and privately owned banks after financial deregulation. Chen and Yen [28] and Chan and Liu [22] compared operating efficiency and productivity of new and old banks. Chan and Liu [22] also discussed the influences of Asian financial crisis on efficiency and productivity of banks. While in the third important reform period, only the influences of mergers on cost efficiency of banks and credit cooperatives were discussed [78–80]. However, the influences of Financial Holding Company Act on operating efficiency and productivity of commercial banks are still not discussed in any literature; while the establishment or joining in financial holding companies (FHCs) of commercial banks aims at seeking for a greater business scope and resource share in an attempt to realize the optimum of capital, cost reduction and cross sales for achieving a better operating efficiency, whether the establishment of or jointing in FHCs for commercial banks really can improve its operating efficiency and productivity is still under discussing. The purpose of this paper is to investigate the influences of Financial Holding Company Act implemented in 2001 on commercial bank performance and the determinants of performance of banks in Taiwan during 1999–2004. In order to discuss the influences of performance of Taiwan banking after the implementation of Financial Holding Company Act, the study period from the end of 2001 and the beginning of 2002 that the most banks established or joined in FHCs is considered as standard in the paper to divide 1999–2001 (before establishing or joining in FHCs) and 2002–2004 (after establishing or joining in FHCs) as two subsample periods, and the study samples are divided into nine banks establishing or joining in FHCs (in which only two banks joined in FHCs latter, and other seven banks established FHCs taking the bank as the main body) and 17 banks with no established FHCs. The empirical study is done in two steps. The first step is to compare performances of nine banks established or joined in FHCs and 17 banks with no FHCs established in two sub-sample periods, to test whether commercial banks perform well in operation because of the establishment of or joining in FHCs or not, or just because of their better operational performances that it is helpful for establishing or joining in FHCs. The second step is to explore the determinants of performance of banks established or joined in FHCs and banks with no FHCs. This paper applies a data envelopment analysis (DEA) approach to calculate bank efficiency and a Malmquist total factor productivity (TFP) index to calculate productivity changes of banks for measuring that performance of commercial banks has the advantages for considering several inputs and several outputs at the same time, and can also rank banks with respect to each other according to their own efficiency scores. However, due to efficiency calculated by DEA does not consider that production frontier will change with time, so it cannot reflect cross-period performance of each bank. Therefore, the TFP index is employed in this paper to measure cross-period performance of banks before and after the implementation of Financial Holding Company Act. In the aspect of regression model of determinants of bank efficiency, most literatures applied an ordinary least square (OLS)

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model in the past. But Coelli et al. [31] pointed out that efficiency scores were censored data, i.e. efficiency scores of banks between 0 and 1, it caused that efficiency scores of banks were censored at 0 and 1. Casu and Molyneux [19] and Grigorian and Manole [56] applied a censored Tobit regression model to solve this problem. This method is also employed in this paper. In the aspect of regression model of determinants of bank productivity changes, there is not censored for the TFP index, so the OLS model is employed in the paper referring to Chen [26]. Moreover, the artificial intelligence approaches, such as neural networks, have been increasingly used for this domain problem. Many research studies have compared the predictive power of conventional statistical forecasting methods to that of neural networks (see [90]). Therefore, this study will also compare the predictive ability of conventional statistical techniques used in the paper (i.e. Tobit and OLS regressions) to that of neural networks. This study selects Taiwan as a case study, and it differs from past studies for several reasons. First, the essential contents of mergers and the establishment of FHCs are different. The mergers look like two small banks combined into one big bank, with no significant change concerning their business scope. However, the FHCs allow the resources of individual financial institutions to be consolidated, and permit cross sector financial mergers, such as between banks and securities and insurance companies, under a holding company. Under the FHCs, banks will be able to diversify and extend their business scope instead of competing against homogenous products [74]. The FHCs’ structure is attractive in the United States and Japan due to the expanded non-bank powers and geographic advantage [10,106,120]. Many previous studies have evaluated the effect of mergers on bank efficiency and productivity [1,3,13,32,57,70,73,78–80,96–98,103,113]; however, relatively few research studies have evaluated the impact of the establishment of FHCs on bank efficiency and productivity [39,50,71,114,120]. Second, many research studies have explored the effect of the establishment of FHCs on bank efficiency and productivity in developed countries such as US, EU, and Japan [39,50,71,114,120]. However, relatively few studies have evaluated the effect of the establishment of FHCs in developing countries. Taiwan’s experience in this case can function as a guide for other developing countries, which are also in a quandary concerning the effects of the implementation of the Financial Holding Company Act and the establishment of FHCs on bank efficiency and productivity. Third, the majority of research studies on the efficiency of FHCs take the US as their study object; however, the legal systems of America and Taiwan have enormous differences. The American legal system adopts a common law system (adopting unwritten laws which emphasize former precedents and convenience) while Taiwan follows a civil law system (adopting written law which emphasizes legal stability and integrity); the difference in legal systems can help to differentiate the labor-management relation of banks after banks establish or join in FHCs and further differentiate bank operating efficiency (i.e. the relation between inputs and outputs) and productivity [105]. Taiwan’s result in this case can serve as a guide for other countries which follow a civil law system. Fourth, this article further compares the conventional statistical forecasting methods (i.e. Tobit and OLS regressions) with neural networks to bolster the results of this article; this has never been done in past studies investigating bank efficiency and productivity.

2. Literature review In the past literatures, there are two frontier approaches for evaluating operating efficiency of commercial banks: parametric frontier and non-parametric frontier. These methods primarily

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differ in how much restriction is imposed on the specification of the frontier and the distributional assumptions imposed on the random error and inefficiency [14,118]. Parametric frontier approaches, such as stochastic frontier approach (SFA) are specifying functional forms for the cost, profit, or production relationship among inputs, outputs, and environmental factors, and allows for random error. SFA posits a composed error model where inefficiencies are assumed to follow an asymmetric distribution, usually the half-normal, while random errors follow a symmetric distribution, usually the standard normal [14]. The half-normal assumption for the distribution of inefficiencies is relatively inflexible and presumes that most firms are clustered near full efficiency [14]. In practice, however, other distributions may be more appropriate [52]. Besides, the parametric approaches commit the sin of imposing a particular functional form that presupposes the specification of the frontier [14,39]. If the functional form is misspecified, measured efficiency may be confounded with the specification errors [14]. Related literatures included Elyasiani and Mehdian [40], Berger and Humphrey [12], Field et al. [47], and Kaparakis et al. [69]. Non-parametric frontier approaches, such as DEA are linear programming techniques where the set of frontier observations are those for which no other decision making unit or linear combination of units has as much or more of every output (given inputs) or as little or less of every input (given outputs) [14]. Compared to SFA, DEA is a better way to organize and analyze data since it allows efficiency to change over time and requires no prior assumption on the specification of the frontier [118]. However, a key drawback to DEA is that it does not allow for random error owing to luck, data problems, or other measurement errors [39]. If random error exists, measured efficiency may be confounded with these random deviations from the true efficiency frontier [14]. Studies using DEA to investigate bank efficiency are stated as follows. Mukherjee et al. [86] explored productivity growth for a group of 201 large US commercial banks over the initial postderegulation period from 1984 to 1990. Chan and Liu [22] examined bank efficiency and productivity growth for Taiwan commercial banks over the post-deregulation period from 1992 to 2000. They also used regression models to discuss the determinants of productivity growth of banks. Chou et al. [29], Grabowski et al. [51], and Isik [63] compared bank efficiency and productivity growth before and after financial deregulation. Casu and Molyneux [19] investigated whether there has been an improvement in and convergence of productive efficiency across European banking markets since the creation of the Single Internal Market. Miller and Noulas [84], Mukherjee et al. [86], and Chou et al. [29] pointed out that when the asset size of bank enlarged, bank efficiency or productivity growth was higher; but Christopoulos et al. [30], Esho [41], and Isik [63] had contrary empirical results. Both Chen [26] and Chen and Yen [28] pointed out that technical efficiency of publicly owned banks in Taiwan was lower than that of privately owned banks, while Isik [64] indicated that productivity growth of private banks in Turkey was higher than that of state banks. Sherman and Gold [102], Parkan [91], and Oral and Yolalan [89] analyzed operating efficiency of different branches of each bank. Numerous researches have been conducted using the parametric and non-parametric approaches to evaluate bank and credit cooperative mergers efficiency. Rhoades [97], Peristiani [92], and Akhavein et al. [3] analyzed the cost or profit efficiency of US commercial and savings bank mergers. Rose [98] studied 84 large US bank holding companies undergoing interstate bank mergers. Vander Vennet [113] employed the stochastic frontier cost function to study the effects of EU credit institution mergers

on bank cost efficiency and economies of scale. Lang and Welzel [73] utilized a frontier cost function with a time-variable stochastic efficiency for all Bavarian cooperative banks. Lin [78–80] used the stochastic frontier cost function to evaluate the effects of Taiwan commercial bank and credit cooperative mergers on bank efficiency. However, conclusions of these past studies exploring the effects of mergers on operating efficiency are inconsistent. This is mainly due to the varying modes used in these mergers, and since each merger mode has its own effect on operating efficiency [80]. In the aspect of FHCs, several studies have investigated abnormal returns on the announcements of bank holding company acquisitions [34,49,58,67]. However, relative few research studies have evaluated the effect of establishing of or joining in FHCs on bank efficiency. Kohers et al. [71] inspected the influence of bank efficiencies on the market assessment of bank holding company mergers. Using profit and cost X-efficiencies as proxies for pre-merger performance, the findings of the study indicated that the merger announcement period abnormal returns of bidder banks are significantly negatively associated with both the target banks’ profit X-efficiency and cost X-efficiency. Furthermore, the bidder bank holding companies’ abnormal returns are positively related to the difference between the average peer cost X-efficiency score and the corresponding target’s score. Eisenbeis et al. [39] searched the properties of the X-inefficiencies in US bank holding companies derived from both stochastic and linear programming frontiers. The major contribution of the paper is to examine the time-series properties of the X-efficiency of a group of large US bank holding companies and to explore the ‘‘informativeness’’ of the efficiency scores. Stiroh [106] probed the improved performance of US bank holding companies from 1991 to 1997. Analysis of cost and profit functions suggests that the gains were primarily due to productivity growth and changes in scale economies. Nevertheless, these literatures did not compare the efficiency of banks affiliated with bank holding companies and independent banks. Benston [9] and Saunders and Walter [100] have concluded that universal banking would be beneficial for the United States. They argue that a move to universal banking would enhance the static and dynamic efficiency of the financial services sector, without increasing the risks to financial system stability. Grabowski et al. [50] compared the relative performance of US bank holding company and branch banking organizational forms. Performance was measured by constructing non-parametric frontiers from which measures of overall, allocative, technical, pure technical, and scale efficiency can be derived. The results indicated that branch banking is a more efficient organizational form than the bank holding company. While the results of Newman and Shrieves [87] indicated that multibank holding companies outperform both one-bank holding companies and individual banks. Vander Vennet [114] compared the cost and profit efficiency of European financial conglomerates and universal banks to that of their specialized competitors and nonuniversal banks. He found that conglomerates are more revenue efficiency than their specialized competitors and that the degree of both cost and profit efficiency is higher in universal banks than in non-universal banks. Yamori et al. [120] investigated whether Japanese banks affiliated with bank holding companies are more efficient and profitable than independent banks. Unlike Newman and Shrieves [87], Benston [9], Saunders and Walter [100] and Vander Vennet [114], their results suggested that banks affiliated with bank holding companies are not more cost-efficiency than are independent banks. They debated that the brief history of Japanese bank holding companies was the main reason. This paper offers three differences and contributions to the large and growing literature on the efficiency of financial

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institutions or bank holding companies. First, previous studies have evaluated the performance of bank holding companies in developed countries such as US, EU, and Japan. This paper employs the DEA and the Malmquist TFP index approach to explore the differences in operational performance between commercial banks before and after establishing or joining FHCs in Taiwan, which can be as a guideline for other developing countries. Second, relatively few literatures discuss the determinants of performance for both banks established or joined in FHCs and banks with no FHCs. This study further investigates the reasons that urge bank efficiency or inefficiency. Third, this article further compares the conventional statistical forecasting methods (i.e. Tobit and OLS regressions) with neural networks to bolster the results of this article; this has never been done in past studies investigating bank efficiency and productivity.

3. Theoretical models and econometric approaches 3.1. Data envelopment analysis and Malmquist total factor productivity index This paper applies DEA to calculate efficiency and productivity change indexes. DEA belongs to a kind of frontier production function which applying mathematical programming to measure relative efficiency score of each unit, the greatest advantage of this approach is that it is extremely applicable for organization efficiency evaluation of several inputs and outputs without preset functional relation between inputs and outputs as well as weighting. Moreover, DEA allows efficiency to change over time without assumptions on the frontier shape and it is a better way to organize and analyze data when compared with parametric frontier approaches [14,118]. In addition, if the functional form is misspecified, the efficiency measurement would be incorrect because the characteristic requirement in the explicit functional form for the parametric approaches. When a local approximation such as the translog is specified, poor banking data approximations which are not near the mean scale or product mix are provided [14,83,85]. In this sense, DEA is a leading approach to analyze bank performance based on literature review [118]. Two DEA methods, CCR and BCC models, were applied in this paper. The CCR model was first published by Charnes et al. [25] who employed a mathematical planning model to measure the efficiency frontier based on the concept of Pareto optimum [26]. The BCC model was developed by Banker et al. [8] to measure pure technical efficiency and scale efficiency. The CCR model assumes that the decision making unit (DMU) produces under constant returns to scale (CRS) for obtaining technical efficiency; while BCC model assumes that DMU produces under variable returns to scale (VRS), which can then divide technical efficiency into pure technical efficiency and scale efficiency. The base idea of DEA is to identify the most efficiency DMU among all DMUs. The most efficient DMU is called a Pareto-optimal unit and is considered the benchmark against which all other DMUs are compared [28,53,54,68,107]. Therefore, efficiency measured by DEA is a concept of relative efficiency. For example, a bank has reached Pareto-optimal efficiency, and the efficiency score is 1, which indicating no output will increase if no input increased, or no input will reduce if no output reduced. Other banks not reaching Pareto-optimal efficiency are compared with banks reaching Pareto-optimal efficiency, so efficiency score will be o1, and when efficiency is worse, its efficiency score will be smaller but not o0 [55]. Technical efficiency can be divided into pure technical efficiency and scale efficiency. Pure technical efficiency indicates whether the bank has efficiency on management as compared

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with the best bank in the same period, so it is also called as managerial efficiency. Scale efficiency reflects the difference between output scale and the most optimal scale of a bank, scale efficiency equaling to 1 indicates that the bank is operating under the most optimal scale; scale efficiency o1 indicates that the scale of the bank has no efficiency. According to Charnes et al. [25] (CCR model), the methodology of calculating DEA scores can be formulating as a fractional linear programming problem. In the case of banks, the technical efficiency of a particular bank is obtained as the maximum of a ratio of weighted outputs to weighted inputs subject to the condition that the similar ratios for every bank be r1. Let yrk denotes the r-th output of the k-th bank and xjk denotes the j-th input of the k-th bank. If a bank uses m inputs to produce s outputs, the technical efficiency score of k-th bank, hk, is a solution from the fractional linear programming problem (CCR model) [25,26,28,47,54,56,68]: Ps ur yrk max hk ¼ Pr¼1 ; r ¼ 1; . . . ; s; j ¼ 1; . . . ; m m ur ;vj j¼1 vj xjk Ps ur yri  1; i ¼ 1; . . . ; k; . . . ; n s:t: Pr¼1 m j¼1 vj xji u r ; vj  0

(1)

where ur and vj represent the weights associated with each output and input. The above model can be restructured as a linear programming problem: max ur ;vj

s:t:

hk ¼

s X

ur yrk

r¼1 s X

ur yri 

r¼1 m X

m X

vj xji  0

j¼1

vj xjk ¼ 1

(2)

j¼1

Given that the number of constraint equations (n+s+m+1) is larger than that of variables (s+m) in the original linear programming problem, this problem can be transformed into a dual problem: 0 1 m s X X 0 A @ Skj þ Skr min TE ¼ y   j¼1

s:t:

yxkj 

n X

r¼1

xij li  Skj ¼ 0;

i ¼ 1; . . . ; k; . . . ; n;

j ¼ 1; . . . ; m

i¼1 n X

yri li  S0kr ¼ ykr ;

r ¼ 1; . . . ; s

i¼1

li ; Skj ; S0kr  0 8i; j; r

(3)

where y represents the maximum proportion of input levels which can be used to obtain current output levels for the particular bank. 40 is a small non-Archimedean quantity. Skj and S0kr are the input slacks and the output slacks, respectively. As Banker et al. [8] noted the above model can be added an P equation ni¼1 li ¼ 1 in the constraint equations to make the BCC model [8,26,28,46,60,107]. The pure technical efficiency score can be calculated from the BCC model, and then the scale efficiency score is equal to the technical efficiency score divide by the pure technical efficiency score [26,28,43]. The Malmquist TFP index was employed to measure the productivity change of individual bank in different periods. The Malmquist index was introduced by Caves et al. [20,21] who named it the (output-based) Malmquist productivity index after Sten Malmquist, who calculated constructing quantity indexes as ratios of distance functions in 1953 (see [82]). Distance

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functions applied multiple-output and multiple-input technology for input and output quantities. Therefore, in contrast to the Tornqvist index, Malmquist index is a primal index of productivity change that does not require cost or revenue shares to aggregate inputs and outputs but it can determine total factor productivity growth in a multiple-output setting [45]. Caves et al. [20,21] indicated that in certain circumstances, the Tornqvist index is the geometric mean of two Malmquist output productivity indexes. The conditions including technical efficiency, allocative efficiency, that the underlying technology must be translog, and that all second-order terms must be identical over time. In contrast, the Malmquist index requires any assumptions neither in efficiency nor in functional form [45]. Fa¨re et al. [45] proposed that to identify productivity change based on Malmquist index, the production technology St models in different time periods (t ¼ 1,y,T) could transform inputs, M xt 2 RN þ , into outputs, yt 2 Rþ , St ¼ fðxt ; yt Þ : xt can produce yt g

(4)

Therefore, the output distance function according to Stephard [101] is defined at t as Dt0 ðxt ; yt Þ

¼ inffy : ðxt ; yt =yÞ 2 St g

(5)

The reciprocal of the maximum propositional expansion of the output vector yt, under inputs xt is illustrated in the above function. However, the Malmquist index required two different period of time information in order to determine distance functions: Dt0 ðxtþ1 ; ytþ1 Þ ¼ inffy : ðxtþ1 ; ytþ1 =yÞ 2 St g

(6)

The maximal propositional change in outputs was measured by distance function which allows (xt+1,yt+1) possible in related to the technology at t. Likewise, measurement the maximal proportional output change is required to make (xt,yt) possible in related to the technology at t+1 when defining the Malmquist productivity index. Dtþ1 0 ðxt ; yt Þ is defined when technology at t+1. Malmquist productivity index was illustrated by Caves et al. [20,21] and listed as follows: Mt ¼

Dt0 ðxtþ1 ; ytþ1 Þ Dt0 ðxt ; yt Þ

(7)

In this formulation, technology in period t is the reference technology. Alternatively, one could define a period-(t+1)-based Malmquist index as M tþ1 ¼

Dtþ1 0 ðxtþ1 ; ytþ1 Þ

(8)

Dtþ1 0 ðxt ; yt Þ

In stead of using an arbitrary benchmark, researchers measure the output-based Malmquist productivity change index after specified the geometric mean in Eqs. (7) and (8) [31,42,44, 45,61,75]: " TFP0 ðxtþ1 ; ytþ1 ; xt ; yt Þ ¼

Dt0 ðxtþ1 ; ytþ1 Þ Dtþ1 ðxtþ1 ; ytþ1 Þ  0 tþ1 Dt0 ðxt ; yt Þ D0 ðxt ; yt Þ

#1=2 (9)

The above equation represents the productivity of the production point ðxtþ1 ; ytþ1 Þ relative to the production point ðxt ; yt Þ. A value 41 will indicate positive TFP growth from period t to period t+1, and vice versa. In the assumption of CRS, the above index can be broken down into technological change (TECH) and technical efficiency change (EFFCH) indexes [42,44,61]. The equation can be

written as TFP 0 ðxtþ1 ; ytþ1 ; xt ; yt Þ ¼

" #1=2 Dtþ1 Dt0 ðxtþ1 ; ytþ1 Þ Dt0 ðxt ; yt Þ 0 ðxtþ1 ; ytþ1 Þ   Dt0 ðxt ; yt Þ Dtþ1 Dtþ1 0 ðxtþ1 ; ytþ1 Þ 0 ðxt ; yt Þ

(10)

The TECH index is the ratio inside the brackets which measure the shift in frontier technology between period t and t+1 [28,42,44,45]. TECH is larger than one, indicating that the production technology of bank is progressive within two periods, and vice versa. Some reasons such as employing high-tech personnel, the change of market structure (e.g. bank merged), and the change of government policy (e.g. deregulation), may lead to the technological change. The EFFCH index is the ratio outside the brackets which represent the catching-up in efficiency [28,42,44,45]. EFFCH is larger than one which means the technical efficiency improves within two periods, and vice versa. In the assumption of VRS, the EFFCH index can further be decomposing into pure technical efficiency change (PECH) and scale efficiency change (SECH) components [28,42,44,45]. The equations can be represented as PECHðVRSÞ ¼

Dtþ1 0 ðxtþ1 ; ytþ1 jVRSÞ Dt0 ðxt ; yt jVRSÞ

(11)

Dtþ1 0 ðxtþ1 ; ytþ1 jCRSÞ SECHðVRSÞ ¼

Dtþ1 0 ðxtþ1 ; ytþ1 jVRSÞ Dt0 ðxt ; yt jCRSÞ Dt0 ðxt ; yt jVRSÞ

(12)

The PECH index represents that whether bank managers manage input resources more efficiency from period t to period t+1. If PECH is larger than one, indicating that the pure technical efficiency improves within the two periods, and vice versa. The SECH index measures the degree of bank’s production scale achieving the long-term optimal production scale. 3.2. Input and output definition To calculate bank efficiency and Malmquist TFP index, it has to measure inputs and outputs of banks. This study uses the intermediation approach to measure inputs and outputs. The intermediation approach views banks as financial intermediaries where deposits are treated as an input since a bank’s main business is to borrow funds from deposits to lend to others [12,28]. In accordance with this approach and referring to Miller and Noulas [84], Chen [26], Chen and Yen [28], and Chan and Liu [22], this study specifies four outputs: the provision of loan services (including business and individual loans), investment (including short- and long-term investment), interest revenue, and non-interest revenue (including transaction fees, securities investment revenue, and other business revenues). The former two outputs are the main activities of banks and the latter two outputs are revenue sources of banks [26]. These four outputs need four input operating resources, namely staff, fix asset, bank deposits (including current deposits, savings deposits, time deposits, check deposits, and other deposits), and salary expense. To eliminate the effects of inflation, all data are deflated with the consumer price index deflator to convert monetary values into constant 2001 dollars. 3.3. Bank efficiency and productivity changes regression model The study applies a censored Tobit regression model to investigate the determinants of bank technical efficiency (TE) and its decomposition items pure technical efficiency (PTE) and

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scale efficiency (SE), it is mainly because that TE, PTE and SE are between 0 and 1, so it is censored at 0 and 1 [19,56]. Model setting is TEit ðPTEit ; SEit Þ ¼ a0 þ a1 FHTIMEit þ a2 SIZEit þ a3 EQTAit þ a4 NPLit þ a5 DIV it þ a6 LDRit þ a7 ROEit þ a8 BMIit þ eit

(13)

In addition, an OLS regression model is used for exploring the determinants of total factor productivity change of banks, in which total factor productivity change can be divided into technological change (TECHCH) and technical efficiency change (EFFCH). While technical efficiency change also can be divided into pure technical efficiency change (PECH) and scale efficiency change (SECH). TFPit ðTECHCHit ; EFFCHit ; PECHit ; SECHit Þ ¼ b0 þ b1 FHTIMEit þ b2 SIZEit þ b3 EQTAit þ b4 NPLit þ b5 DIV it þ b6 LDRit þ b7 ROEit þ b8 BMIit þ it

(14)

Independent variable used in the article mainly refers to Miller and Noulas [83], Berger and DeYoung [11], Chen and Yen [28], Mukherjee et al. [86], Isik and Hassan [65], Christopoulos et al. [30], and Chan and Liu [22] literatures discussing the determinants of bank efficiency and productivity changes. Independent variable includes: time of banks establishing or joining in FHCs (FHTIME), bank size (SIZE), equity-to-total asset (EQTA) ratio, overdue ratio (NPL), business diversification (DIV), loan-todeposit ratio (LDR) and return on equity (ROE). Why and how these variables are related to bank efficiency and productivity change is explained as follows. Prior studies have found that bank size (SIZE) variable is related to bank efficiency or productivity change [29,41,65,81 ,84,86,94]. However, the results of these studies are inconsistent. Chou et al. [29], Miller and Noulas [84], and Mukherjee et al. [86] found that the relationships between bank size and bank efficiency or productivity change are positive. The reason is when bank size is greater, it has sufficient capital and management capability for effective resource management and is helpful for resource loss and reducing operational cost. While Christopoulos et al. [30], Esho [41], and Isik [65] pointed out that bank size is negatively related to bank efficiency. The reason is when bank size is small the specialized management is advantageous to the adaptation environment of competition. When bank size is greater, because of excessive scale expansion, it will cause decreasing returns to scale and scale diseconomies of bank, so that bank efficiency reduces. Equity-to-total asset ratio is also proved to associated with bank efficiency or productivity change [22,29,41,69,86]. Some researches have showed that the higher equity-to-total asset ratio is, the higher bank efficiency or productivity is [41,69]. It is because the higher bank capital is from stockholder equity, the bank has more capability in preventing insolvency of bank arising from severe loss of risky assets, and the stockholders have more rights to supervise the bank managers to improve operational performance of bank, so that the operating efficiency and productivity of bank can be improved [41,69]. However, other researches have discovered that the relation of equity-to-total asset ratio and bank efficiency or productivity is negative [22,29,86]. Since the higher bank capital is from stockholder equity, the lower the financial leverage of bank is. It may result in the reduction of return on equity of bank. To increase return on equity, bank manager may select high-risk output set, and higher risk may lessen bank efficiency and productivity [22]. Earlier literatures have revealed that overdue ratio has a negative effect upon bank efficiency or productivity change [22,69]. The reason they explained is the higher the overdue

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ratio, the poorer the bank loan quality, and the higher the operational risk of the bank, and which may bring about bad influences on operating efficiency of the bank [22,69]. Business diversification (DIV) is also related to bank efficiency or productivity change [22,86]. Mukherjee et al. [86] and Chan and Liu [22] both verified that business diversification has negative influence on bank efficiency or productivity change. Their explanation is the higher business diversification level of the bank, which may cause the bank more nonprofessional, and the higher the information asymmetry will cause difficulty in supervision and management, so that the efficiency is lower [22,86]. Previous studies also supported that loan-to-deposit ratio influences bank efficiency or productivity change [15,29,30,86]. Some studies demonstrated the higher loan-to-deposit ratio can benefit bank efficiency or productivity changes [15,30]. They pointed out when loan is of competitive advantage, and the bank is actively promoting loan business, it can improve its operating efficiency. While other researches showed that the relationship between loan-to-deposit ratio and bank efficiency or productivity changes is negative [29,86]. Their reason is the loan has no competitive advantage in output item, or loan itself is a risky asset with poorer liquidity of the bank, so the higher the loanto-deposit ratio, the higher the bad debts risk of the bank, so that the efficiency is poorer [86]. Return on equity (ROE) may be related to bank efficiency or productivity change. Xie [119] specified that the maximized TFP of a company is related to the maximized profit. Sufian and Majid [108] showed that bank profitability has a significantly positive impact on bank efficiency. While Chan and Liu [22] indicated that in order to increase return on equity, bank manager may select high-risk output set, and higher risk may lessen bank efficiency and productivity. Additionally, the macroeconomic situation may affect bank efficiency and productivity change [62]. This study also incorporates the macroeconomic situation as an independent variable and uses business monitoring indicators (BMI) as its proxy variable. Business monitoring indicators, compiled by the Cabinet’s Council for Economic Planning and Development of the Executive Yuan of the ROC, represent the prospects for various economic environments in Taiwan. Time of banks establishing or joining in FHCs (FHTIME) is a dummy variable, mainly used for testing whether there is any difference in operational performance of nine commercial banks before and after establishing or joining in FHCs. If sample period is 1999–2001, it is set as 0; if sample period is 2002–2004, it is set as 1. Bank size (SIZE) is measured by total assets of banks. Equity-to-total asset ratio is computed by dividing the stockholders’ equity by the total assets of banks. Overdue ratio (NPL) is measured through dividing the total of overdue loan and receivable on demand by total loan. Business diversification degree of banks (DIV) is calculated through Herfindahl index [86], totaling squares of proportions of each operational revenue sub-item taking up the operational revenue in bank income statement. Loan-to-deposit ratio is computed by dividing total loans by total deposits of banks. Return on equity (ROE) is computed by dividing net income by the stockholders’ equity of banks. The ROE variable is used to measure the profitability of banks. Business monitoring indicators are composed of nine individual economic indicators, including monetary aggregate M1B, direct and indirect finance, stock price index, industrial production index, non-agricultural employment, exports, imports of machineries & electrical equipments, manufacturing sales, and sales index of wholesale, retail and food services. The total score of BMI is computed by summing points of the nine individual economic indicators.

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3.4. Data analysis Data of this study were gathered from the financial database of Taiwan Economic Journal Co. Ltd. and the annual report of Market Observation Post System set up by Taiwan Stock Exchange. Sample period is divided into two sub-sample period 1999–2001 and 2002–2004. Sample banks are 26 commercial banks of the state with (or joining in) FHCs or without (or no joining in) FHCs.

Table 1 shows the mean of banks’ inputs and outputs. Inputs or outputs data approximately increase with time indicating the expanding of scale and business during sample periods. In addition, comparing banks with or joining in FHCs and banks without FHCs, whether input or output data, mean values of banks establishing or joining in FHCs are all higher than banks with no established FHCs, this seems that the scale and business scope of banks establishing or joining in FHCs are greater than that of banks with no established FHCs.

Table 1 Banks’ inputs and outputs mean. Year

Banks establishing or joining in FHCs Samples

Outputs

Inputs

Loan

Investment

Interest revenue

Non-interest revenue

Staff

Fix asset

Deposits

Salary expense

266.99 287.91 294.34 313.88 337.47 381.26 283.08 344.2 313.64

57.19 56.3 68.62 70.3 116.77 140.91 60.7 109.33 85.01

22.07 24.63 23.18 19.72 17.19 18.63 23.29 18.51 20.91

3.81 3.13 3.92 3.38 5.81 6.09 3.62 5.1 4.36

2311 2482 2597 2877 3409 3766 2463 3351 2907

8.13 8.81 9.37 10.31 11.62 11.66 8.77 11.2 9.98

280.82 311.34 336.81 369.88 418.22 465.37 309.65 417.82 363.74

2.49 2.63 2.92 3.27 3.59 4 2.68 3.62 3.15

Fix asset

Deposits

Salary expense

254.01 257.71 272 273.74 287.19 305.09 261.24 288.67 274.96

2.03 2.02 2.02 2.02 2.13 2.31 2.02 2.15 2.09

1999 2000 2001 2002 2003 2004 1999–2001 mean 2002–2004 mean 1999–2004 mean

9 9 9 9 9 9 – – –

Year

Banks with no established FHCs Samples

Outputs Loan

1999 2000 2001 2002 2003 2004 1999–2001 mean 2002–2004 mean 1999–2004 mean

17 17 17 17 17 17 – – –

225.15 230.64 232.04 225.69 237.63 253.19 229.27 238.84 234.06

Inputs Investment 28.05 24.09 25.69 23.17 36.31 43.51 25.94 34.33 30.14

Interest revenue

Non-interest revenue

Staff

17.69 17.85 16.32 12.9 10.48 10.34 17.29 11.24 14.26

2.08 1.43 1.64 1.13 1.81 1.88 1.72 1.61 1.66

2022 2053 2072 2128 2307 2521 2049 2318 2184

5.61 5.67 5.82 5.86 6 6 5.7 5.95 5.83

Note: All data are deflated with the consumer price index deflator to convert monetary values into constant 2001 dollars. All dollars are in billion NTD.

Table 2 The mean values of banks efficiency scores and Malmquist total factor productivity indexes. Situations

Sample periods

TE

PTE

SE

TFP

TECHCH

EFFCH

PECH

SECH

Banks establishing or joining in FHCs

1999 2000 2001 Sub-mean 2002 2003 2004 Sub-mean Mean

0.914 0.913 0.869 0.899 0.900 0.878 0.846 0.875 0.887

0.952 0.951 0.950 0.951 0.984 0.979 0.959 0.974 0.963

0.961 0.960 0.917 0.946 0.914 0.897 0.885 0.899 0.922

– 0.903 1.057 0.98 0.872 0.972 1.020 0.955 0.965

– 0.900 1.108 1.004 0.847 0.982 1.062 0.964 0.980

– 1.004 0.954 0.979 1.042 0.987 0.967 0.999 0.991

– 0.999 0.999 0.999 1.045 0.995 0.979 1.006 1.003

– 1.004 0.955 0.980 0.998 0.990 0.988 0.992 0.987

Banks with no established FHCs

1999 2000 2001 Sub-mean 2002 2003 2004 Sub-mean Mean

0.790 0.803 0.774 0.789 0.829 0.780 0.783 0.797 0.793

0.890 0.908 0.897 0.898 0.932 0.903 0.885 0.907 0.903

0.892 0.890 0.868 0.883 0.892 0.868 0.886 0.882 0.883

– 0.920 0.975 0.948 0.855 0.905 1.033 0.931 0.937

– 0.900 1.019 0.960 0.793 0.963 1.033 0.930 0.942

– 1.023 0.957 0.99 1.096 0.949 0.999 1.015 1.005

– 1.025 0.986 1.006 1.051 0.968 0.978 0.999 1.002

– 1.001 0.971 0.986 1.041 0.978 1.021 1.013 1.002

Note: FHCs ¼ financial holding companies; TE ¼ technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency; TFP ¼ total factor productivity change; TECHCH ¼ technological change; EFFCH ¼ technical efficiency change; PECH ¼ pure technical efficiency change; and SECH ¼ scale efficiency change.

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4. Empirical results analysis and discussion 4.1. Efficiency and productivity changes comparison The first purpose of this study is to compare performances of banks establishing or joining in FHCs and banks with no established FHCs in two sub-sample periods, to test whether commercial banks perform well in operation because of the establishment of or joining in FHCs or not, or just because of their better operational performances that it is helpful for establishing or joining in FHCs. Table 2 indicates the mean values of efficiency scores and productivity change indexes of banks establishing or joining in FHCs and banks with no established FHCs during sample periods. As for efficiency, whether technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE), average scores of banks establishing or joining in FHCs are higher than that of banks with no established FHCs during the whole sample period (from 1999 to 2004), which indicates that operating efficiency performance of banks establishing or joining in FHCs is nearly better than that of banks with no established FHCs. Additionally, the study uses nonparametric Mann–Whitney test to verify whether mean values of efficiency of two sets of banks during sample period have significant difference or not as shown in Table 3. Table 3 shows that SE in 1999 and TE in 1999 and 2000, banks establishing or joining in FHCs are significantly or slightly significantly greater than that of banks with no established FHCs, the results are indicated in the former sub-sample period, because scale of banks establishing or joining in FHCs is greater, they have more sufficient capital and management capability to face the competitive environment, and also is helpful for reducing operational cost, so they can improve their SE and TE [29,84]. In addition, PTE of banks establishing or joining in FHCs is slightly significantly greater than that of banks with no established FHCs in 2003 and 2004, the result may mean that banks establishing or joining in FHCs after establishing or joining in FHCs for one year are better than banks with no established FHCs in resource management based on adjustment of FHCs structure, therefore their PTE can be improved. As for comparison of two sub-sample periods, it can be seen in Table 3 that during 1999–2001 sub-sample period, PTE, SE and TE of banks establishing or joining in FHCs are significantly better than that banks with no established FHCs, which indicates that it is helpful for banks establishing or joining in FHCs firstly because of their better operational performances; secondly, during 2002–2004 sub-sample period, PTE and TE of banks establishing

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or joining in FHCs are significantly higher than that of banks with no established FHCs, and significance of PTE is greater than that in the former sub-sample period, but SE has no significant difference, the result shows that source management of banks is better after they establish or join in FHCs, so that their PTE is higher; but it may because scale of banks establishing or joining in FHCs is excessively expanded or banks with no established FHCs strive for expanding because of FHCs competitiveness, so that SE of banks establishing or joining in FHCs is not significantly higher than that of banks with no established FHCs as compared. As seen from productivity change, it is known from Table 2 that average productivity changes (TFP) from 1999 to 2004 of all sample banks are declining, but average decline rate of banks with no established FHCs is 6.3%, which is higher than 3.5% of that of banks establishing or joining in FHCs, and then it is known from the decomposition item that it is because technology (TECHCH) of banks with no established FHCs declines more quickly; but as for technical efficiency change (EFFCH), average value of technical efficiency change of banks establishing or joining in FHCs declines by 0.9%, while that of banks with no established FHCs increases by 0.5%, which because mean value of scale efficiency change (SECH) of banks establishing or joining in FHCs declines by 1.3%, while that of SECH of banks with no established FHCs increases by 0.2%. The result is consistent with efficiency analysis result in the above paragraph, which indicates that banks with no established FHCs strive for expanding business and operational scale for competing with banks with FHCs because of their smaller operational scale, and then the SECH is gradually increasing; while SECH of banks establishing or joining in FHCs is decreasing because of excessive scale expanding. In addition, as shown in Table 3, TECHCH and TFP in 2001 of banks establishing or joining in FHCs are significantly or slightly significantly higher than that of banks with no established FHCs, banks establishing or joining in FHCs almost established or joined in FHCs during the end of 2001 and the beginning of 2002, so the result may mean that these banks desire to strengthening their new technology or new products in convenience for enhancing its capability in establishing or joining in FHCs. But wholly speaking, as seen from Table 3 on comparison of commercial banks before and after establishing or joining in FHCs, commercial banks do not make their efficiency and productivity better because establishing or joining in FHCs. 4.2. Determinants of bank efficiency and productivity change The second purpose of this paper is to verify the determinants of efficiency and productivity change of banks establishing or

Table 3 Efficiency and productivity comparison between banks with and without established FHCs. Situations

Sample periods TE

PTE

Comparison between banks with and without established FHCs in different periods 1999 2000 2001 2002 2003 2004

2.04 1.94 1.52 1.54 1.39 1.57

Comparison in two sub-sample periods

1999–2001 2002–2004

3.30 1.98 2.13 2.64

Comparison between before and after establishing FHCs

1999–2004

0.53

1.30 1.01 1.09 1.59 1.71 1.75

0.78

SE 1.82 1.34 0.92 0.92 0.63 0.80 2.24 0.94 1.03

TFP

TECHCH EFFCH PECH SECH

– – 0.92 0.00  2.29 1.75 0.54 0.51 0.73 0.08 0.13 0.21 0.86 0.39

1.17 0.47

0.72

0.97

– – – 0.98 1.36 0.76 0.41 0.80 0.30 0.81 0.22 0.81 0.38 0.88 0.16 0.03 0.28 0.66 0.53 0.53 0.31 0.32 0.69 0.77 0.96

0.11

0.48

Note: FHCs ¼ financial holding companies; TE ¼ technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency; TFP ¼ total factor productivity change; TECHCH ¼ technological change; EFFCH ¼ technical efficiency change; PECH ¼ pure technical efficiency change; and SECH ¼ scale efficiency change. The numbers in the tabulations are Z values by conducting Mann–Whitney tests.  po0.1.  po0.05.  po0.01.

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Table 4 Determinants of bank efficiency. Determinants

Constant FHTIME SIZE EQTA NPL DIV LDR ROE BMI

Banks establishing or jointing in FHCs

Banks with no established FHCs

TE

PTE

SE

TE

PTE

SE

0.361 (0.301) 0.072 (0.062) 0.0001 (0.00007) 0.044 (0.015) 0.027 (0.009) 0.361 (0.287) 0.004 (0.001) 0.001 (0.002) 0.001 (0.003)

0.013 (0.374) 0.114 (0.063) 0.001 (0.0003) 0.050 (0.020) 0.064 (0.014) 0.140 (0.327) 0.00005 (0.002) 0.003 (0.003) 0.0006 (0.005)

0.637 (0.252) 0.008 (0.046) 0.0002 (0.00006) 0.028 (0.013) 0.010 (0.008) 0.215 (0.220) 0.003 (0.0007) 0.0006 (0.001) 0.002 (0.002)

1.075 (0.238) – 0.0002 (0.00004) 0.001 (0.008) 0.015 (0.003) 0.110 (0.172) 0.0003 (0.002) 0.0002 (0.0004) 0.003 (0.002)

0.058 (0.284) – 0.0005 (0.00009) 0.022 (0.011) 0.007 (0.006) 0.201 (0.188) 0.005 (0.002) 0.0002 (0.0015) 0.001 (0.003)

1.621 (0.152) – 0.0003 (0.00003) 0.007 (0.007) 0.016 (0.003) 0.223 (0.091) 0.003 (0.001) 0.0003 (0.0006) 0.003 (0.002)

Note: FHCs ¼ financial holding companies; TE ¼ technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency; FHTIME ¼ time of banks establishing or joining in FHCs; SIZE ¼ bank size; EQTA ¼ equity-to-total asset ratio; NPL ¼ overdue ratio; DIV ¼ business diversification; LDR ¼ loan-to-deposit ratio; ROE ¼ return on equity; and BMI ¼ business monitoring indicators.  po0.1.  po0.05.  po0.01.

joining in FHCs and banks with no established FHCs. Table 4 shows the Tobit regression results of efficiency determinants of banks. The time of banks establishing or joining in FHCs (FHTIME) variable does not have a statistically significant relation to technical efficiency (TE) and scale efficiency (SE), which is consistent with the results as shown in Table 3, indicating that operating and scale efficiencies of commercial banks do not improve because of establishing or joining in FHCs; instead, it is helpful for establishing or joining in FHCs because of their better operating and scale efficiencies. However, FHTIME variable is marginally significantly related to pure technical efficiency (PTE), meaning that the PTE of commercial banks during the 2002–2004 sub-sample period is higher than that during the 1999–2001 subsample period. The result indicates that source management of banks is better after they establish or join in FHCs, so that their PTE is higher, which is in accord with the results as shown in Table 3. The finding also supports the result of Bosworth et al. [17], that the larger bank holding companies exhibit greater pure technical efficiency. Commercial banks whether with established or with no established FHCs, bank size (SIZE) variable and TE have a significant negative relation, but as seen from its decomposition item, bank size has a significant positive relation to PTE, while has a significant negative relation to SE; because significance of SE is higher, which causes symbol of TE is also negative. As for PTE, in the past literatures, when bank size is greater, it has sufficient capital and management capability for effective resource management and is helpful for resource loss and reducing operational cost, therefore, it can improve its PTE [29,84]. But when bank size is greater, because it has too many branches, so it will restrict the output obtained from the same inputs [81,94], or because of excessive scale expansion, it will cause decreasing returns to scale and scale diseconomies of bank, so that the SE reduces [41,65]. These results suggest that a bank manager must strategically manage the operating scale since when the bank size is greater, the PTE can be improved by effective resource management due to sufficient management capability, while the SE may be worsened by excessive scale expansion [13]. The TE of banks may finally be reduced when the bank size is too great. The findings are also supported by the results of the comparison between banks with and without established FHCs in two sub-sample periods in Table 3, and the higher PTE after banks establish of or join in FHCs in Table 4. In regression results of establishing or joining in FHCs banks, the equity-to-total asset ratio variable presents significant

positive relationship with PTE, SE and TE, the results may because the higher bank capital is from stockholder equity, the bank has more capability in preventing insolvency of bank arising from severe loss of risky assets, or reducing high financial risks caused by reduction of high financial leverage, and the stockholders have more rights to supervise the bank managers to improve operational performance of bank, so that the operating efficiency of bank can be improved [19,41,69]. While in the regression results of banks with no established FHCs, the variable only has a significant positive relation to PTE. The findings imply that high equity capital and low financial leverage will constitute a better financial strategy for banks since it can improve bank operating efficiency. Overdue ratio (NPL) is an important index measuring bank loan quality. In regression results of banks with established FHCs, overdue ratio has significant negative relationship with PTE and TE; while in regression results of banks with no established FHCs, overdue ratio has significant negative relationship with SE and TE. The results show that the higher the overdue ratio, the poorer the bank loan quality, and the higher the operational risk of the bank, and which may bring about bad influences on operating efficiency of the bank [22,69]. The findings indicate that bank managers have to strategically manage loans; that is, they must first take account of the customer’s credit (i.e. loan quality) when they endeavor to improve loan performance because rising nonperforming loans can result in a decrease in a bank’s technical efficiency for banks with or without established FHCs. In regression results of banks with no established FHCs, business diversification level (DIV) of the bank has a significant negative relation to SE, the possible reason may be the higher business diversification level of the bank, so that the business becomes busy, which may cause the bank more non-professional, and the higher the information asymmetry will cause difficulty in supervision and management, so that the efficiency is lower [4,86]. The result means that specialization can benefit bank scale efficiency; thus a bank manager should not operate too many operational revenue items. In regression results of banks with no established FHCs, the loan-to-deposit ratio variable is different in regression of banks with established FHCs, which may be related with different supporting argumentations in demonstration literatures in the past. Due to loan is output item, some literatures point out that when loan is of competitive advantage, and the bank is actively promoting loan business, it can improve its operating efficiency [15,30]; in addition, deposit is input item, if the loan-to-deposit

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ratio is higher, it indicates that the bank can effectively manage deposit resource, so that loan quantity can be improved under the existing deposit, and then the PTE can be improved. However, some literatures point out that, if the loan has no competitive advantage in output item, or loan itself is a risky asset with poorer liquidity of the bank, so the higher the loan-to-deposit ratio, the higher the bad debts risk of the bank, so that the efficiency is poorer [86]. As stated earlier, these findings indicate that bank managers have to strategically manage loans since great loan quantities can improve bank operating efficiency, while too many non-performing loans, due to great loan quantities, will reduce its operating efficiency. However, the business monitoring indicators do not statistically significantly affect bank efficiency. The result seems to contradict our intuition that the macroeconomic situation may affect bank efficiency. The possible reason is that bank managers can strategically manage the input operating resources and output activities of banks to adapt to the change of economic environments. Table 5 shows the regression results of productivity change determinants of banks. It is roughly in agreement with the result in Table 4, the time of banks establishing or joining in FHCs (FHTIME) variable is not statistically significant at the 10% level, indicating that productivity does not growth because of commercial banks establishing or joining in FHCs. In regression results of banks establishing or joining in FHCs, overdue ratio (NPL) has a significant negative relation to productivity change (TFP), the result shows that the higher the overdue ratio of bank, the poorer the loan quality of bank, the higher the bad debts risk, loan has no greater competitive advantage in output items, so that it has a negative effect on productivity growth [22]. The finding is consistent with the results of bank efficiency in Table 4. Therefore, it is very important for bank managers to strategically manage loan quantities and loan quality of their banks. When they endeavor to increase loan quantities under performance pressure,

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the loan quality can be disregarded and the overdue ratio can thus be raised; consequently bank efficiency and productivity growth will be reduced. Business diversification degree (DIV) of banks has significant negative relations to technological change (TECHCH) and TFP. It shows that, the higher the bank business diversification level, the more the business, which will cause difficulties in supervision and management, and more difficult to introduce new technology and cause negative effect on technological growth, so that its productivity is lower [4,86]. The findings appear once more attest to the advantage of specialization for banks; that is, a bank manager cannot operate too many operational revenue items since the activity will result in a decrease in bank scale efficiency and productivity. In regression results of banks with no established FHCs, the equity-to-total asset ratio has slightly significant negative relations to scale efficiency change (SECH) and technical efficiency change (EFFCH), but has slightly positive relation to TECHCH, therefore it can cancel out its influences on TFP. The results are partially in agreement with Mukherjee et al. [86] and Chan and Liu [22]. The results may mean that, when the equity-to-total asset ratio is higher, banks obtaining higher rate of return through financial leverage will be greatly restricted, and it is more difficult to generate scale economy, therefore, it will cause negative effects on scale efficiency growth, and negative influence on technical efficiency growth; but if stockholders equity is higher, the stockholders can effectively supervise the managers to introduce new technology so as to make it positively contribute to its technological growth. Bank business diversification degree (DIV) has slightly significant positive relations to pure technical efficiency change (PECH) and EFFCH, while has significant negative relations to TECHCH and thus results in a negative influence on TFP. Chan and Liu [22] points out that if bank business becomes more diversified, the generated economies of scope can reduce deposit and

Table 5 Determinants of bank productivity change. Determinants

Banks establishing or joining in FHCs TFP

TECHCH

EFFCH

PECH

SECH

Constant FHTIME SIZE EQTA NPL DIV LDR ROE BMI

1.274 (0.217)

1.359 (0.244)

0.897 (0.242)

1.118 (0.115)

0.016 (0.066) 0.00008 (0.00008) 0.001 (0.003) 0.029 (0.014) 0.593 (0.270) 0.001 (0.001) 0.003 (0.002) 0.004 (0.004)

0.047 (0.067) 0.00004 (0.00008) 0.001 (0.003) 0.019 (0.013) 0.588 (0.328) 0.0004 (0.001) 0.002 (0.003) 0.004 (0.003)

0.042 (0.070) 0.00003 (0.00008) 0.00005 (0.002) 0.012 (0.014) 0.013 (0.293) 0.0002 (0.0004) 0.002 (0.002) 0.0003 (0.003)

0.007 (0.044) 0.00001 (0.00002) 0.0007 (0.0008) 0.001 (0.005) 0.129 (0.148) 0.00004 (0.0002) 0.0001 (0.001) 0.002 (0.001)

0.779 (0.179) 0.035 (0.042) 0.00002 (0.00008) 0.0007 (0.002) 0.011 (0.014) 0.140 (0.190) 0.0002 (0.0004) 0.002 (0.002) 0.002 (0.003)

Determinants

Banks with no established FHCs TECHCH

EFFCH

PECH

SECH

TFP Constant SIZE EQTA NPL DIV LDR ROE BMI

1.251 (0.176) 0.000005 (0.00004) 0.005 (0.007) 0.003 (0.003) 0.196 (0.107) 0.001 (0.002) 0.001 (0.0004) 0.0002 (0.002)

1.090 (0.173)

1.258 (0.228)

0.999 (0.148)

0.00006 (0.00004) 0.009 (0.005) 0.003 (0.003) 0.400 (0.102) 0.001 (0.002) 0.0008 (0.0006) 0.002 (0.001)

0.00007 (0.00005) 0.017 (0.009) 0.006 (0.004) 0.234 (0.129) 0.004 (0.002) 0.00006 (0.0009) 0.002 (0.002)

0.00002 (0.00002) 0.005 (0.005) 0.004 (0.003) 0.232 (0.089) 0.001 (0.001) 0.0002 (0.0003) 0.0004 (0.0009)

1.238 (0.168) 0.00005 (0.00005) 0.013 (0.007) 0.003 (0.003) 0.008 (0.081) 0.002 (0.002) 0.0002 (0.0008) 0.001 (0.001)

Note: FHCs ¼ financial holding companies; TFP ¼ total factor productivity change; TECHCH ¼ technological change; EFFCH ¼ technical efficiency change; PECH ¼ pure technical efficiency change; SECH ¼ scale efficiency change; FHTIME ¼ time of banks establishing or joining in FHCs; SIZE ¼ bank size; EQTA ¼ equity-to-total asset ratio; NPL ¼ overdue ratio; DIV ¼ business diversification; LDR ¼ loan-to-deposit ratio; ROE ¼ return on equity; and BMI ¼ business monitoring indicators.  po0.1.  po0.05.  po0.01.

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operational cost, therefore, it can generate positive influence on pure technical efficiency growth, and then cause the growth of technical efficiency. But if so, it may cause difficulties in supervision and management, so it is more difficult to introduce new technology, and then causing negative influence on technological growth, therefore its productivity becomes lower [4,86]. The findings are consistent with the former results of this study, indicating that specialization can benefit bank operating performance. The loan-to-deposit ratio has a slightly significant negative relation to EFFCH, it indicates that if loan in output item has no competitive advantage, or loan itself is the risky asset with poor liquidity of the bank, if the loan-to-deposit ratio is higher, bad debts risk of the bank may be higher, so that it will generate a negative effect on technical efficiency growth [22,86]. Finally, the return on equity (ROE) has a significant positive relation to TFP, which indicating that the bank has more profit, and its productivity growth will be high, the result is in agreement with the opinion of Xie [119] that the maximized TFP of a company is related to the maximized profit. The finding implies that the higher the return on equity, the higher the bank productivity growth will be, so bank managers can strategically improve return on equity and productivity growth together. 4.3. Comparing conventional statistical forecasting methods with neural networks Majority of discussion bank efficiency researches adopt a twostage approach, DEA (TFP) and Tobit regression (OLS regression), to investigate the bank efficiency (productivity) and efficiency (productivity) determinants. That is, adopting the DEA (TFP) method in the first stage to estimate bank efficiency (productivity), and the Tobit regression (OLS regression) model in the second stage to estimate efficiency (productivity) determinants [7,19,23,24,33,56,57,61,66,95,108,117]. Relative few studies employ artificial intelligence (or neural network) approaches to predict bank efficiency and productivity determinants. Accordingly, this study uses conventional linear regression forecasting techniques, such as Tobit and OLS regression models, to predict determinants of bank efficiency and productivity change. In an empirical study, a regression method can be thought of decomposing the variability of the dependent variable into one component which can be modeled as a function of independent variables chosen, and another which is purely random or possibly explained by omitted variables. The intrinsic functional relationship between the dependent and independent variables can in turn be subdivided into its linear and nonlinear characteristics. In most social sciences, it is standard procedure to use a linear regression technique to model most empirical relationships [5]. A linear regression can be represented as yt ¼ a0 þ

n X

bi xit þ t

(15)

i¼1

where yt is the output (e.g. bank efficiency, productivity change) which is a function of all inputs xit (i ¼ 1,2,y,n) (e.g. the independent variables in this study), bi is the model parameter. However, real-world financial data and its underlying economic processes are often nonlinear in nature. As a result, conventional linear regression models may not be adequate to study and predict their behavior [5]. At this time, nonlinear regression techniques may be more suitable for the work of prediction. Artificial intelligence approaches, which allow a nonlinear characteristic and not assume underlying distributions, have become applicable to modeling and forecasting a host of economic and financial relationships. Neural networks, in parti-

cular, have been widely used in the areas of prediction and classification [5,90]. Besides, taking bank data as an example, prior literature Angelidis and Lyroudi [6] examined the productivity of the 100 larger Italians banks for the period 2001–2002 and revealed that the use of the natural logarithms of the nominal values of the inputs and outputs variables and neural networks reduces the errors of the prediction of TFP change indices. The result of the paper may indicate that the relationships between financial variables and bank efficiency or productivity are probably nonlinear. Tam and Kiang [109,110] have applied neural network models to predict bank failures. Using bank bankruptcy data, they compare neural network models to statistical techniques such as logistic regression and linear discriminant analysis. Their results also show that neural networks are generally more accurate and robust for evaluating bank status. This study, therefore, further compares the predictive ability of Tobit and OLS regression models with neural networks. The performance comparison results may provide a future guideline for the application of neural network models to predict bank performance and efficiency. The major advantage of neural networks is their flexible nonlinear modeling capability [121], and with no assumption regarding underlying distributions [38]. The problem with using neural networks, however, is that they do not generate the usual regression coefficient estimates and t-statistics. It can be difficult to evaluate the performance of the models. To overcome this problem, this study, referring to Eakins et al. [38], uses the neural network models to predict future bank efficiency and productivity change. The accuracies of the forecasts from these models are evaluated using the correlation coefficients between the actual and forecast values [38,111,116]. Single hidden layer feed-forward neural networks are the most widely used form for financial forecasting, due to their ability to correctly classify and predict the dependent variable [111,121]. Back-propagation is by far the most popular neural network training algorithm that has been used to perform learning on feed-forward neural networks [111]. The typical back-propagation neural networks consist of three layers: the input, the hidden, and the output layer. It can be represented in regression terms as [121] yt ¼ a0 þ

m X j¼1

aj f b0j þ

n X

!

bij xit

þ t

(16)

i¼1

where yt is the output (e.g. bank efficiency, productivity change) which is a function of all inputs xit (i ¼ 1,2,y,n) (e.g. the independent variables in this study), that has been rescaled through a series of hidden layer transfer functions; aj (j ¼ 0,1,2, y,m) and bij (i ¼ 1,2,y,n; j ¼ 0,1,2,y,m) are the model parameters, often called the connection weights; n is the number of input nodes; and m is the number of hidden nodes. The logistic function is used as the hidden layer transfer function in this study. Fig. 1 shows a typical single hidden layer feed-forward neural network with one output which is used in this study. In order to evaluate the performance of the models, the data sets in this study are divided into two subsets: training and testing data. Considering the limited sample number (for regression models), the training data include the data from the year 1999 through 2003, and the testing data are the data in 2004. In each model, the training data are used to test the model’s ability to predict bank efficiency and productivity change for the training years and the testing year. The correlation coefficients between the actual and predictive values will be used to evaluate the predictive accuracies of these models. In order to examine the predictive ability of the Tobit and OLS regression models, the constant term and regression coefficients from these regressions for training data are used to forecast the

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bank efficiency and productivity change for the training years (from 1999 through 2003) and the testing year (year 2004). The forecasts of bank efficiency and productivity change then are evaluated against the actual bank efficiency and productivity

x1

xn

Input layer

Hidden layer

Output layer

Fig. 1. A three-layer feed-forward neural network used for prediction.

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change values. The correlation coefficients between actual and predictive values are reported in Table 6. The same sequence of tests is employed to examine the predictive ability of the neural network model. The correlation coefficients between actual and predictive values are also reported in Table 6. Panel A of Table 6 shows the results of comparing the predictive ability of Tobit regression with that of neural networks. For the banks which established or joined in FHCs, the results show that the correlation coefficients between actual and predictive values for the same training year data (1999–2003) and testing year data (1999–2003) using Tobit regression are 0.666 for TE; 0.618 for PE; and 0.799 for SE; while the correlation coefficients using neural networks are 0.569 for TE; 0.581 for PE; and 0.779 for SE. The Tobit regression model results are likely to be better than the neural networks results. The outcomes are in accord with the discoveries of Lee and Jung [76] and Warner and Misra [115] which compared the performance of logistic regression and neural networks. However, for the banks which had not established FHCs, the correlation coefficients using Tobit regression are 0.597 for TE; 0.367 for PE; and 0.763 for SE; while the correlation coefficients using neural networks are 0.600 for TE; 0.388 for PE; and 0.718 for SE. The neural network model results are slightly better than the Tobit regression results. The consequences are agreeable to the findings of Eakins et al. [38],

Table 6 Results of comparing the predictive ability of conventional regressions and neural networks. Panel A: A comparison of Tobit regression with neural networks Results of Tobit regression predicting bank efficiency Training year data

Testing year data

Correlation coefficients Banks with FHCs

1999–2003 1999–2003

1999–2003 2004

Banks without FHCs

TE

PE

SE

TE

PE

SE

0.666 0.578

0.618 0.567

0.799 0.502

0.597 0.233

0.367 0.301

0.763 0.700

SE

Results of neural networks predicting bank efficiency Training year data

Testing year data

Correlation coefficients Banks with FHCs

1999–2003 1999–2003

1999–2003 2004

Banks without FHCs

TE

PE

SE

TE

PE

0.569 0.556

0.581 0.434

0.779 0.801

0.600 0.455

0.388 0.148

0.718 0.674

Panel B: A comparison of OLS regression with neural networks Results of OLS regression predicting bank productivity change Training year data

Testing year data

Correlation coefficients Banks with FHCs

1999–2003 1999–2003

1999–2003 2004

Banks without FHCs

TFP

TECHCH

EFFCH

PECH

SECH

TFP

TECHCH

EFFCH

PECH

SECH

0.604 0.173

0.650 0.486

0.337 0.090

0.332 0.097

0.277 0.364

0.434 0.314

0.675 0.259

0.408 0.238

0.364 0.076

0.333 0.219

Results of neural networks predicting bank productivity change Training year data

Testing year data

Correlation coefficients Banks with FHCs

1999–2003 1999–2003

1999–2003 2004

Banks without FHCs

TFP

TECHCH

EFFCH

PECH

SECH

TFP

TECHCH

EFFCH

PECH

SECH

0.518 0.929

0.330 0.677

0.182 0.111

0.248 0.017

0.169 0.365

0.373 0.316

0.673 0.360

0.241 0.114

0.163 0.205

0.074 0.277

Note: FHCs ¼ financial holding companies; TE ¼ technical efficiency; PTE ¼ pure technical efficiency; SE ¼ scale efficiency; TFP ¼ total factor productivity change; TECHCH ¼ technological change; EFFCH ¼ technical efficiency change; PECH ¼ pure technical efficiency change; and SECH ¼ scale efficiency change.

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Fletcher and Goss [48], Olson and Mossman [88], Salchenberger et al. [99], Tam and Kiang [109,110], and Zhang et al. [122] which focused on the comparison of neural networks to more traditional statistical techniques namely Tobit regression, logistic regression and logit regression. Because the estimates are probably biased upward for Tobit regression using the same estimating and testing data [38], this study next conducts out-of-sample tests. That is, a Tobit regression and neural networks on the training year data (1999–2003) are estimated and tested on the testing year data (2004). The results show, for the banks which established or joined in FHCs, that the correlation coefficients between actual and forecasting values using Tobit regression are 0.578 for TE; 0.567 for PE; and 0.502 for SE; while the correlation coefficients using neural networks are 0.556 for TE; 0.434 for PE; and 0.801 for SE. For the banks which had not established FHCs, the correlation coefficients using Tobit regression are 0.233 for TE; 0.301 for PE; and 0.700 for SE; while the correlation coefficients using neural networks are 0.455 for TE; 0.148 for PE; and 0.674 for SE. These results show that the accuracies of forecasting bank efficiency from Tobit regression and neural networks are confused. The conclusions accord with the results of Boritz and Kennedy [16], Desai et al. [35], Spear and Leis [104], and Limsombunchai et al. [77] which compared the performance of neural networks and conventional regression techniques, such as logit, probit, and logistic. In addition, panel B of Table 6 shows the results of comparing the predictive ability of OLS regression with that of neural networks. For the banks which established or joined in FHCs, the results show that the correlation coefficients between actual and predictive values for the same of training year data and testing year data using OLS regression are 0.604 for TFP; 0.650 for TECHCH; 0.337 for EFFCH; 0.332 for PECH; and 0.277 for SECH; while the correlation coefficients using neural networks are 0.518 for TFP; 0.330 for TECHCH; 0.182 for EFFCH; 0.248 for PECH; and 0.169 for SECH. For the banks which had not established FHCs, the correlation coefficients using OLS regression are 0.434 for TFP; 0.675 for TECHCH; 0.408 for EFFCH; 0.364 for PECH; and 0.333 for SECH; while the correlation coefficients using neural networks are 0.373 for TFP; 0.673 for TECHCH; 0.241 for EFFCH; 0.163 for PECH; and 0.074 for SECH. The OLS regression model results exhibit greater accuracy in predicting bank productivity change than the neural networks results. These results clearly support earlier findings of Angelidis and Lyroudi [6] who concluded when inputs and outputs are used as nominal values the performance for predicting TFP of OLS regression outperformed that of neural networks. Much of the research on comparing neural networks with conventional statistical techniques, which focused on accounting and finance problems, also indicated that linear regression outperformed neural networks or both approaches were comparable [18,36,59,72,104]. However, if an OLS regression and neural networks on the training year data (1999–2003) are estimated and tested on the

testing year data (2004), the results show little difference from forecasts performed with the in-sample (1999–2003) tests of the models. The results show, for the banks which established or joined in FHCs, that the correlation coefficients between actual and forecasting values using OLS regression are 0.173 for TFP; 0.486 for TECHCH; 0.090 for EFFCH; 0.097 for PECH; and 0.364 for SECH; while the correlation coefficients using neural networks are 0.929 for TFP; 0.677 for TECHCH; 0.111 for EFFCH; 0.017 for PECH; and 0.365 for SECH. For the banks which had not established FHCs, the correlation coefficients using OLS regression are 0.314 for TFP; 0.259 for TECHCH; 0.238 for EFFCH; 0.076 for PECH; and 0.219 for SECH; while the correlation coefficients using neural networks are 0.316 for TFP; 0.360 for TECHCH; 0.114 for EFFCH; 0.205 for PECH; and 0.277 for SECH. The neural network model results are likely to be slightly better than the OLS regression results. The findings are consistent with the results of Angelidis and Lyroudi [6] which reported when inputs and outputs are used as the natural logarithms of nominal values the performance of neural networks in predicting TFP outperformed that of OLS regression. Most previous studies which compared the performance of linear regression and neural networks in the prediction of finance and business problems indicated that neural networks produced significantly or slightly superior forecasts relative to linear regression [5,37,88,93,112,116]. In summary, the findings from this section indicate that conventional statistical forecasting methods such as Tobit and OLS regressions used in this study and artificial intelligence method such as neural networks, are comparable (see Table 7 for summary). The results may support the robustness of the empirical results of the determinants of bank efficiency and productivity change in Section 4.2. Nevertheless, they seem to be inconsistent with most prior literatures which showed that neural network outperformed conventional linear regression techniques in the prediction of finance and economic problems. The possible explanation is as follows. First, Adya and Collopy [2] found that neural networks produced much better forecasts for certain kinds of data. In particular, neural networks were found to both effectively implemented and validated for bankruptcy or financial distress data, while time series application were generally less successful. Thus, the different kinds of data sets in this study relative to prior researches may lead to the different results of this study from prior studies. Second, this paper examines the determinants of bank efficiency and productivity. Nearly all previous studies used the linear regression approaches such as Tobit and OLS regressions to investigate the determinants of bank efficiency and productivity. It may represent that the relations between financial variables and bank efficiency or productivity quite near to linear. However, the relations must further be confirmed in the future study. Third, seldom research adopts neural networks to examine the problem of the prediction of bank efficiency and productivity. The possible reason is neural networks do not create the usual regression coefficient

Table 7 Summary of performance comparison of conventional regression techniques with neural networks. Performance comparison

Banks with FHCs 1999–2003

Banks without FHCs 2004

1999–2003

2004

Panel A: Bank efficiency as the predicted variable Better performance Tobit regression

Comparable

Neural networks

Comparable

Panel B: Bank productivity as the predicted variable Better performance OLS regression

Neural networks

OLS regression

Neural networks

Note: 1999–2003 indicates that the training year data is 1999–2003 and the testing year data is 1999–2003. 2004 indicates that the training year data is 1999–2003 and the testing year data is 2004.

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estimates and t-statistics, while prior relational studies need the information for policy decisions and managerial suggestions. The following will discuss the empirical results of the determinants of bank efficiency for 1999–2003 from Tobit regression, OLS regression, and neural networks. In TE aspect, the empirical results show that the important determinants of technical efficiency for banks in FHCs include bank size, equity-to-total asset ratio, overdue ratio, business diversification, and loan-to-deposit ratio, while the significant determinants of technical efficiency for independent banks contain bank size and overdue ratio. In PTE aspect, the outcomes appear that the considerable determinants of pure technical efficiency for banks in FHCs consist of bank size, overdue ratio, and loan-to-deposit ratio, while the remarkable determinants of pure technical efficiency for independent banks comprise bank size, equityto-total asset ratio, and loan-to-deposit ratio. In SE aspect, the consequences present that the primary determinants of scale efficiency for banks in FHCs involve the time of banks establishing or joining in FHCs, bank size, equity-to-total asset ratio, business diversification, and loan-to-deposit ratio, while the influential determinants of scale efficiency for independent banks encompass bank size, equity-to-total asset ratio, overdue ratio, business diversification, and loan-to-deposit ratio. Even though neural networks cannot produce the usual regression coefficient estimates and t-statistics, they can reveal the values of relative importance of input variables. After deleting insignificant input variables, the important input financial variables for technical efficiency of banks in FHCs include the time of banks establishing or joining in FHCs, bank size, equity-to-total asset ratio, overdue ratio, and loan-to-deposit ratio, which are coincident with the results of Tobit regression except the time of banks establishing or joining in FHCs variable. While the significant input financial variables for technical efficiency of independent banks consist of bank size, overdue ratio, business diversification, and loan-to-deposit ratio, which are similar to the conclusions of Tobit regression excluding business diversification and loan-to-deposit ratio variables. For PTE, the considerable input financial variables for pure technical efficiency of banks in FHCs encompass the time of banks establishing or joining in FHCs, bank size, and overdue ratio, which are similar to the products of Tobit regression aside from the time of banks establishing or joining in FHCs, bank size and loan-to-deposit ratio variables. While the critical input financial variables for pure technical efficiency of independent banks include bank size, equity-to-total asset ratio, and loan-to-deposit ratio, which are consistent with the results of Tobit regression. For SE, the important input financial variables for scale efficiency of banks in FHCs cover bank size, business diversification, loan-todeposit ratio, and return on equity, which are similar to the findings of Tobit regression omitting the time of banks establishing or joining in FHCs and return on equity variables. While the vital input financial variables for scale efficiency of independent banks comprise bank size, equity-to-total asset ratio, overdue ratio, business diversification, loan-to-deposit ratio, and return on equity, which are coherent with the outcomes of Tobit regression leaving from the return on equity variable. Moreover, the empirical results of the determinants of bank productivity change for 1999–2003 from Tobit regression, OLS regression, and neural networks are discussed as follows. In FTP perspective, the results exhibit that the remarkable determinants of productivity change for banks in FHCs include overdue ratio, return on equity, and business monitoring indicators, while the sole determinant of productivity change for independent banks is business monitoring indicators. In TECHCH perspective, the consequences indicate the only determinant of technological change for banks in FHCs is business monitoring indicators, while

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the major determinants of technological change for independent banks contain equity-to-total asset ratio, business diversification, and business monitoring indicators. In EFFCH perspective, the outcomes demonstrate that there is no any financial variable significant influence on technical efficiency change of banks in FHCs, while the equity-to-total asset ratio and overdue ratio variables significantly influence technical efficiency change of independent banks. In PECH perspective, the empirical results show that the exclusive significant determinant of pure technical efficiency change for banks in FHCs and independent banks is business diversification. In SECH perspective, the conclusions display that there is no any financial variable significant influence on scale efficiency change for banks in FHCs and independent banks. On the other hand, the results of neural networks show the considerable input financial variables for productivity change of banks in FHCs include overdue ratio, return on equity, and business monitoring indicators, which are in accordance with the findings of OLS regression except the return on equity variable. While the influential input financial variables for productivity change of independent banks encompass return on equity and business monitoring indicators, which are similar to the consequences of OLS regression outside of the return on equity variable. For TECHCH, the important input financial variables for technological change of banks in FHCs involve business diversification and business monitoring indicators, which are parallel to the results of OLS regression except the business diversification variable. While the main input financial variables of for technological change of independent banks cover equity-to-total asset ratio, business diversification, return on equity and business monitoring indicators, which are conformable to the outcomes of OLS regression excluding the return on equity variable. For EFFCH, PECH, and SECH, the conclusions of banks in FHCs reveal the same as the findings of OLS regression. However, for the independent banks, the comparison results show that neural networks are slightly different from OLS. When the EFFCH is regarded as the dependent (output) variable, the neural network model shows the influential financial variables comprise equity-to-total asset ratio, overdue ratio, and business diversification. When the SECH is regarded as the dependent (output) variable, the neural network model signifies the equity-to-total asset ratio variable is the only important input financial variable.

5. Conclusion The article aims at investigating whether establishing or joining in FHCs of commercial banks can improve their operating efficiency and productivity, and also the determinants of efficiency and productivity changes of commercial banks are discussed. The investigations have important implications for the current banking policy (e.g. the implementation of Financial Holding Company Act) implemented by the government in Taiwan, which encourages banks to consolidate. Taiwan’s experience in this case can also be served as a guide for other developing countries (such as Korea, Southeast Asian countries) which are in a quandary over the effects of the implementation of Financial Holding Company Act and the establishment of FHCs on bank efficiency and productivity. The first result suggests that except for pure technical efficiency, other efficiencies (i.e. technical and scale efficiencies) of commercial banks have become better or productivity has grown not due to the establishment or joining in FHCs, but because of its better efficiency in the capability of establishing or joining in FHCs. The advantages of establishing or joining in FHCs for commercial banks include improving their resource

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management due to sufficient management capability in FHCs, and further increase a bank’s pure technical efficiency. However, the establishment of FHCs may reduce the bank scale efficiency by the excessive scale expansion. Secondly, it is found that a larger scale of the bank can improve the pure technical efficiency for banks with and without established FHCs; however, a scale too large will reduce bank scale and technical efficiencies of the banks with and without established FHCs. Additionally, it is concluded that overly high overdue ratio can decrease technical efficiency of the banks with and without established FHCs. While a higher equity capital can improve pure technical efficiency and technical efficiency of the banks with established and (or) without established FHCs. Moreover, a higher loan-to-deposit ratio can benefit bank scale efficiency and technical efficiency of the banks with established FHCs; however, a higher loan-to-deposit ratio can increase the bank’s pure technical efficiency, but reduce the bank scale efficiency for banks without established FHCs. Furthermore, the result points out that a higher diversification degree in banking business can decrease scale efficiency of banks without established FHCs. Thirdly, the findings reveal that an overly high overdue ratio will lower the productivity of the banks with established FHCs. In addition, a higher diversification degree in banking business can hinder the technical progress, and result in a decrease in the productivity of the banks with established FHCs. Moreover, the results indicate that a higher equity capital will impede the growth of scale and technical efficiencies, but can improve technical advances of the banks without established FHCs. Furthermore, a higher diversification degree in banking business can benefit the growth of pure technical and technical efficiency, but also hinder technical progress and lower productivity of banks without established FHCs. Finally, a strong relationship between return on equity and productivity growth indicates that a bank manager can strategically improve return on equity and productivity growth together. The findings of this research provide several specific managerial and strategic insights:

(1) The pure technical efficiency of banks can be improved under sufficient resource management capability produced through the establishment of FHCs. This supports Taiwanese government’s policy to increase the competitiveness of domestic banks through bank consolidation. However, the decrease of scale efficiency for banks establishing or joining in FHCs comparing to banks with no established FHCs indicates that a small consumer market in Taiwan can result in limited source of revenue for FHCs, while too many employees and branches for FHCs can increase the operating cost. Therefore, Taiwanese government must further deregulate the limited loans and securities/bond investment, or further open international customer market for commercial banks. (2) The contrary effect of bank scale on bank pure technical efficiency and scale efficiency indicates that a bank manager must strategically manage bank operating scale to improve its operating efficiency. (3) A positive relationship between equity-to-total asset ratio and bank efficiency implies that high equity capital and low financial leverage can benefit bank efficiency. Therefore, in order to improve bank efficiency, a financial strategy of high equity capital and low financial leverage is a better strategy for commercial banks. (4) The results of negative relationships between overdue ratio and bank efficiency (or productivity growth) as well as a positive relationship between loan-to-deposit ratio and bank

efficiency suggest that a bank manager has to strategically manage loan quantity and loan quality. Because greater loan quantities can improve bank efficiency, while too many nonperforming loans due to large loan quantities will reduce bank efficiency and productivity growth rate. (5) The negative effects of business diversification on scale efficiency and productivity growth indicate that specialization is a better strategy for a bank manager; that is, a bank manager cannot operate too many operational revenue items.

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Chei-Chang Chiou completed his M.C.S. in Business Education at National Changhua University of Education, and Ph.D. in Accounting at National Chengchi University in Taiwan. Currently, he is an Professor of Department of Accounting at National Changhua University of Education. His research interest includes operating efficiency and productivity, capital market, corporate governance, business education, concept mapping, and supply chain management and inventory model.