Bank efficiency and shareholder value in Asia Pacific

Accepted Manuscript Title: Bank Efficiency and Shareholder Value in Asia Pacific Author: Xiaoqing Maggie Fu Yongjia Rebecca Lin Philip Molyneux PII: DOI: Reference:

S1042-4431(14)00108-5 http://dx.doi.org/doi:10.1016/j.intfin.2014.08.004 INTFIN 726

To appear in:

Int. Fin. Markets, Inst. and Money

Received date: Accepted date:

14-7-2014 14-8-2014

Please cite this article as: Fu, X.M., Lin, Y.R., Molyneux, P.,Bank Efficiency and Shareholder Value in Asia Pacific, Journal of International Financial Markets, Institutions and Money (2014), http://dx.doi.org/10.1016/j.intfin.2014.08.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Highlights (for review)

HIGHLIGHTS

We investigate bank shareholder value and efficiency in Asia Pacific Shareholder value is positively linked to cost and profit efficiency Bank size, credit losses, and market risk influence bank performance

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A negative relationship exists between market risk and value creation

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1) Title Page (WITH Author Details)

Bank Efficiency and Shareholder Value in Asia Pacific

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Xiaoqing (Maggie) Fu1, Yongjia (Rebecca) Lin2 and Philip Molyneux3*

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Abstract

This paper uses dynamic panel estimation approaches to investigate the relationship

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between shareholder value and efficiency for a large sample of commercial banks in 14 Asia-Pacific economies between 2003 and 2010. In general, the results indicate

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that shareholder value is positively linked to improvements in both cost and profit efficiency, and the influence varies over time. The findings also suggest that bank size, credit losses, and market risk significantly influence bank performance. These

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results are robust to various model specifications.

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JEL classification: D24, G21, G32

Keywords: bank efficiency; shareholder value; risk-taking; banks in Asia Pacific

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*The authors are grateful for funding from the University of Macau. We highly appreciate the comments from FINEST 2013 Winter Workshop, 2014 Global Finance Conference, 2014 Asian Finance Association Annual Conference, especially those from Giorgio Gobbi, Iftekhar Hasan, and Amine Tarazi. All errors are our responsibility.

1

Faculty of Business Administration, University of Macau, Taipa, Macau, China (Email: [email protected]). 2 Faculty of Business Administration, University of Macau, Taipa, Macau, China (Email: [email protected]). 3 Corresponding author: Bangor Business School, Bangor University, UK (Email: [email protected]).

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1. Introduction

The global banking industry has been transformed over the last two decades. Forces driving this transformation include technological innovation, structural deregulation, prudential reregulation, internationalization, and changes in corporate behavior, such as growing disintermediation and increased emphasis on shareholder value (Berger et al,

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2010). The global financial crisis of 2008-2009 also accentuated these pressures and

illustrated that bank performance can have dramatic effects on capital allocation,

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company growth, and economic development – namely via increased capital and funding costs. It is well known that capital costs are linked to sovereign and other risks

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(see IMF, 2011; BIS, 2011 & 2013). Post-crisis, regulators in the developed world have forced banks to raise massive amounts of new capital and these firms are struggling to

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achieve returns in excess of the cost of capital (ECB, 2012). The big, internationally active banks are being asked to hold even more capital and liquidity under Basel III. In such an environment, many banks are finding it too costly and therefore difficult to

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issue new capital and the only way they can boost capital is to refrain from capital costly activity – so they are cutting lending, selling or shrinking capital costly

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investment banking and other businesses (Economist, 2013). This is related to

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shareholder value creation that focuses on generating returns in excess of the cost of capital to create value for owners (namely, shareholder value creation). In a world characterised by increasing capital costs it may be difficult for banks (particularly from the developed world) to ‗add value‘. A major motivation of this paper, therefore, is to investigate whether banks from Asia Pacific, (a region less affected by the global 20089 crisis) are creating value for their shareholders and whether operational efficiency influences value creation.

An accepted financial axiom is that the objective of management is to maximise the shareholders‘ wealth by the efficient allocation of resources, like minimising costs and maximising profits. Although there is a rich body of literature on bank performance, only a handful of papers associate bank efficiency with shareholder value.1 The majority of these studies utilize stock returns to measure shareholder value in testing such relationships for banks in the US and Europe (e.g., Adenso-Diaz and Gascon, 1997;

1

Appendix 1 provides a brief summary of these studies.

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Eisenbeis et al., 1999; Becalli et al., 2006; Erdem and Erdem, 2008; Pasiouras et al, 2008; Liadaki and Gaganis, 2010). Only one paper, a study of European banks, employs Tobin‘s Q as a measure for shareholder value (De Jonghe and Vander Vennet, 2008). Two studies (Fiordelisi, 2007 and Fiordelisi and Molyneux, 2010) adopt Economic Value Added (EVA) as a measure of shareholder value creation to test such

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relationships for European banks.

Focusing on studies that address banks in the Asia-Pacific region, Chu and Lim (1998)

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evaluate the relationship between efficiency and stock returns for a panel of six

Singapore-listed banks between 1992 and 1996. The results show that changes in stock

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prices positively reflect variation in profit- rather than cost-efficiency. Kirkwood and Nahm (2006) examine the effects of changes in profit efficiency on stock returns for

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Australian banks from 1995 to 2002; their findings suggest that changes in profit efficiency are significantly and positively reflected in bank stock returns, particularly for regional banks. Sufian and Majid (2006) analyze the relationship between efficiency

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and stock returns in Malaysian banking over 2002-2003 and find that both cost and profit efficiencies are positively linked to stock prices. In addition, stock prices react

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more to improvements in profit efficiency than to improvements in cost efficiency.

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Majid and Sufian (2008) investigate whether the stock performance of Chinese listed banks are related to their efficiency during the 1997-2006 period, and their findings indicate that changes in technical efficiency are statistically significant in determining banks‘ stock price returns, whereas scale efficiency does not explain variation in the same returns.

The recent global financial turmoil was the worst economic crisis in over 60 years, but most Asia-Pacific countries weathered it quite successfully with a rapid and comprehensive policy response; maintaining a substantial cushion of official reserves; and generally robust corporate balance sheets and banking systems (IMF, 2008). Thus, we argue that this region offers a particularly interesting environment in which to study the relationship between bank efficiency and shareholder value. To our knowledge, there is only one cross-country study in this field. Ioannidis et al. (2008) examine the relationship between changes in bank efficiency and stock price returns for a sample of Asian and Latin American listed banks over the 2000-2006 period. The results indicate a positive relationship between changes in profit efficiency and stock returns, whereas 3 Page 4 of 38

there is no link between changes in cost efficiency and stock returns. The main limitation of this study is that it uses only simple stock returns as a measure of shareholder value, and shareholder value might be overstated because the valuation does not take into account the replacement cost of assets; additionally, it only estimates the impact of bank efficiency on stock returns but does not consider other factors that may influence shareholder value (such as bank‘s risk-taking and firm growth). Another

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limitation is that it employs a random-effects panel-data model that does not capture the time dynamics of the expected influences; thus, it cannot estimate ―how quick actions

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pay off‖ (Fiordelisi and Molyneux, 2010).

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Overall, the empirical literature on the relationship between bank efficiency and shareholder value is somewhat limited. To fill this knowledge gap, this paper examines

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the impact of bank efficiency on shareholder value for 14 Asia-Pacific economies from 2003 to 2010. This paper contributes to the literature in the following ways. First, both market-based and accounting-based measures are employed to measure shareholder

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value for the first time. On the one hand, Tobin‘s Q is used to measure market-based shareholder value instead of stock return for listed banks as it reflects the discounted

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value of current as well as future potential earnings (De Jonghe and Vander Vennet,

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2008). On the other hand, we use EVA to measure accounting-based shareholder value instead of traditional financial ratios for both listed and non-listed banks; this is regarded as a preferred indicator of changes in shareholder wealth because it accounts for the cost of capital (Stewart, 1994). Second, we employ system Generalized Method of Moments (GMM) estimators to capture both short-term and medium-term relationships between efficiency and shareholder value, if any. This method may also address possible endogeneity issues and the autoregressive process in the data concerning the dependent variable. We also provide a series of sensitivity analyses using different model specifications. Third, in addition to the efficiency indicator, we also incorporate other bank-specific variables and environmental factors into our dynamic panel data model. Finally, the sample period covers the recent global financial crisis of 2008-2009,2 which enables us to examine whether there are any significant change in shareholder value during this special period. In general, the paper aims to 2

BIS (2010) identifies the pre-crisis period from January 2003 to June 2007 and the acute-crisis as July 2007 to March 2009. Since quarterly data are not available, we consider 2003-2007 and 2008-2009 as the pre-crisis and the crisis periods respectively.

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provide a comprehensive assessment of the relationship between bank efficiency and shareholder value in the Asia-Pacific region.

The remainder of the paper is organized as follows. Sections 2 and 3 outline the methodology and data, respectively. Section 4 discusses the empirical results. A final

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section concludes.

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2. Methodology

Following Fiordelisi and Molyneux (2010), we employ a model utilizing the system

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GMM estimators to investigate whether the changes in cost and/or profit efficiency can influence shareholder value for banks in 14 Asia-Pacific economies. Our model has the

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following general form:

(1)

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Shareholder Value = f (Efficiency Change, Bank Controls, Macro Controls)

Traditionally, accounting profits are used to discriminate between the drivers of bank

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performance, but accounting profits only capture past behavior. Thus, this study adopts

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a forward-looking and market-based performance measure - Tobin‘s Q - to assess shareholder value for listed banks, which is defined as the ratio of the market value of a firm to the replacement cost of its assets. Tobin‘s Q is used to estimated shareholders‘ risk-adjusted required return by incorporating both the cash flow expectations of investors and the required returns they use to discount anticipated cash flow (e.g., Allen and Rai, 1996; Cornett et al., 2007; Ferreira and Matos, 2008). The calculation for Tobin‘s Q is as follows:

Tobin’s Q = (Market Value of Equity+Book Value of Debt)/Book Value of Total Assets (2)

In addition, we calculate EVA to measure shareholder value creation for both listed and non-listed banks. Stewart (1991) defines EVA as current-period after-tax economic earnings net of the charge to cover the costs of capital. Therefore, a firm‘s value is understood to be increasing only when the firm‘s after-tax operating profit is greater than its cost of capital. The procedure for estimating EVA for each bank follows

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Stewart‘s methodology: 3 EVA = Net Operating Profits after Tax – Invested Capital * Cost of Capital

(3)

Following Fiordelisi (2007), the cost of capital for listed banks is calculated using the

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Capital Asset Pricing Model (CAPM): K  r f   (rm  r f )

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K

denotes the cost of capital; rf denotes the annual free risk return, which is

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where

(4)

measure by interest rate of ten-year US bonds; rm denotes the annual market return; rm denotes market risk premium;4 and  denotes the sensitivity of the excess asset returns

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rf

to the excess market returns. For non-listed banks, the cost of capital is the mean of the

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cost of capital of all listed banks in the country.

Turning to the independent variables, both cost and profit efficiency changes over two consecutive years are included.5 Cost and profit efficiency levels are estimated using the

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parametric stochastic frontier approach (SFA). 6 As proposed in previous studies (Athanasoglou et al. 2008; Brissimis et al. 2008; Fiordelisi and Molyneux, 2010; Montgomery et al., 2014), several control variables are incorporated in the model to recognize

that

banks'

risk-taking

activities,

market

structure

features,

and

macroeconomic environment can influence shareholder value.7 Credit risk exposure is defined as the ratio of loan loss reserves over gross loans and is used to measure output quality and management's strategy for high-risk investment. Market risk exposure is 3

Following Heffernan and Fu (2010), EVA is normalized by factor inputs to minimize possible heteroskedasticity and scale effects in the model and to ensure its comparability with Tobin‘s Q. 4 As indicated in Grabowski (2009), cost of capital estimates derived from typical CAPM models may be biased downwards in crisis periods, such estimates maybe subject to ―significant estimation and data input problems‖ (p. 32). For example, T-bond yields are a typical benchmark used in the CAPM model to estimate the cost of capital. However, these were temporarily very low for several months around the crisis period, boosting EVA estimates for this period. Therefore, we adjust the CAPM model by using the market risk premium (MRP) developed by Fernandez et al. (2011). They do not provide the MRP for Sri Lanka so we use the average MRP for India and Pakistan as a proxy. 5 As mentioned in various studies (e.g., Beccalli et al., 2006; Fiordelisi and Molyneux, 2010), the focus on efficiency changes should be between two consecutive years instead of on efficiency levels in a single year when estimating the relationship between shareholder value and bank efficiency. 6 Please refer to Appendix 2 for details. 7 See Fiordelisi and Molyneux (2010) for a review of this literature.

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measured as the ratio of the total amount of investments in securities to total assets. Liquidity risk exposure is measured as the ratio of total loans to total deposits. Financial leverage is an indicator of capital risk exposure, which is measured by the ratio of total liabilities over total equity. Bank asset size is defined as the natural logarithm of total assets and accounts for market structure features. In addition, three dummy variables for industrialized (RGN1), newly industrialized (RGN2), and developing economies

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(RGN3) are employed to control for different levels of economic development in the Asia-Pacific region. Finally, global financial crisis (CRISIS) is a dummy variable with a

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value of one for the years 2008-2009 and zero otherwise; it is employed to control for

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(Table 1 inserted here)

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macroeconomic conditions.8 The variable definitions are summarized in Table 1.

3. Data

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Our study focuses on commercial banks in Asia-Pacific economies from 2003 to 2010. Bank and stock price information, converted to US dollars, are obtained from the

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Bankscope database of Bureau van Dijk and supplemented by Datastream of Thompson

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Financial Limited; macroeconomic information is obtained from the updated version of the World Bank database on financial development structure developed by Barth, et al. (2012) and from the International Monetary Fund (IMF). We exclude banks that have the following features: (1) missing, negative or zero values for inputs/outputs (in the efficiency estimates), (2) missing values for environment variables, and (3) missing values for total cost and net income. Thus, the final sample consists of an unbalanced panel from 14 Asia-Pacific countries, comprising 688 banks and 3901 observations (see Appendix 3). Furthermore, the subsample for listed banks includes 1745 observations from a total of 274 banks (see Appendix 4). Regarding the sample distribution in terms of the number of banks, the industrialized, newly industrialized, and developing economies make up 38%, 10%, and 52% of the subsample of listed banks, respectively. For the entire sample, including both listed and non-listed banks, the ratios are 26%, 16%, and 58%. Furthermore, the subsample of listed banks consists mainly of Japanese (34%), Indian (17%), and Indonesian (10%) banks, whereas the entire sample of both 8

We also include real GDP growth (RGDP) as a control variable. However, because the correlation coefficient between RGDP and CRSIS is over 0.4, we drop RGDP to avoid multicollinearity problems.

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listed and non-listed banks mainly includes Japanese (23%), Chinese (17%), and Indian (11%) banks. Table 2 presents descriptive statistics of variables used to analyze shareholder value.9 Table 3 shows the average shareholder value and bank efficiency changes by year, by country, and by region. On average, the Tobin‘s Q value is greater than 1, which implies

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that the market value of equity is greater than the book value of equity for listed banks in Asia-Pacific economies. In particular, listed banks in the developing economies

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display the highest shareholder value (Tobin‘s Q = 1.05), whereas those in the

industrialized economies offer the lowest return to their shareholders (Tobin‘s Q = 1.00).

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In addition, listed banks in Japan display the lowest shareholder value (Tobin‘s Q = 0.99). This finding is not surprising because Japan has suffered through an economic

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depression for over a decade. However, the mean cost and profit efficiency changes are 1.19% and 0.64% over the sample period, respectively, suggesting that cost efficiency improvements have been on average greater than those for profit efficiency. Specifically,

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listed banks in the two industrialized economies show substantial improvements in both cost (2.86%) and profit (1.58%) efficiency between 2003 and 2010. Conversely, listed

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banks in the developing economies demonstrate a decrease in profit efficiency (-0.55%)

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and a much lower improvement in cost efficiency (0.14%).

(Tables 2 & 3 inserted here)

The time trend suggests that listed Asia-Pacific banks were affected by the recent financial crisis. The average Tobin‘s Q dropped from its highest level (1.04) to its lowest level (0.99) between 2007 and 2008. Furthermore, the largest decline in both cost (-5.57%) and profit (-8.96%) efficiency are recorded during the crisis period. However, all four indicators show that listed bank performance improved in 2010, lending support to the argument that—in contrast to the banking systems of the U.S. and Europe—AsiaPacific banks have emerged from the global turmoil in a comparatively strong position.

9

Appendices 5 and 6 present Pearson correlation coefficients of variables used to analyze the full sample of banks and the subsample of listed banks, respectively. The figures show that the magnitude of the estimated coefficients is less than 10% in the majority of cases and suggests that our models do not suffer from multicollinearity problems.

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For both listed and non-listed banks, the average economic value added (EVA) is negative, which indicates that, on average, Asia-Pacific banks destroyed shareholder value over the study period, which is consistent with findings in Europe (Fiordelisi, 2007) and in China (Heffernan and Fu, 2010).

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However, banks in newly

industrialized and developing economies created value for their shareholders. This positive influence was counteracted by the shareholder value destruction that occurred

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in industrialized economies. Over time, EVA increased between 2003 and 2008, fell in 2009 due to the global turmoil, and then rebounded and reached its peak in 2010. In

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general, the results for the full sample are also confirmed if one just considers listed

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banks. 11

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4. Empirical results

Table 4 presents the empirical results obtained by estimating models with Tobin‘s Q as the dependent variable. Model (1) includes cost efficiency changes (CEC) as

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independent variables, whereas profit efficiency changes (PEC) are incorporated in Model (2). The results show that both cost and profit efficiency enhancements have a

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positive influence on shareholder value in general. These results are consistent with the

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findings of Fiordelisi and Molyneux (2010) on European banks, yet only partially supportive of the previous literature on Asia-Pacific banks (Chu and Lim, 1998; Kirkwood and Nahm, 2006, Sufian and Majid, 2006; and Ioannidis et al., 2008) that typically only find a positive link between profit efficiency changes and stock returns. Specifically, the performance measure is found to be positively influenced by 1-year lagged profit efficiency improvements and the 2- and 3-year lagged cost efficiency benefits, which suggest that improvements in cost efficiency may take more time to be incorporated into shareholder value. This finding is not surprising because profit maximization is superior to cost minimization, as it represents the economic goals of bank managers and owners more completely (Berger and Mester, 2003). It also lends support to the ―time-dynamics‖ assumption developed by Fiordelisi and Molyneux 10

Fiordelisi (2007) find that shareholder value destruction was widespread in the European banking market during the 2000-2002 period. He argues that it was most likely the result of the widespread fall in stock market values over this period. Heffernan and Fu (2010) also find similar evidence of the inability of Chinese banks to create value between 1999 and 2006. 11 A series of mean-difference t-tests are performed for both Tobin‘s Q and EVA between different regions/periods. The results suggest that all the differences are significant at the 10% significance level, although there is no significant difference for EVA between the crisis (2008-2009) and non-crisis periods.

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(2010), namely that banks must put actions into place to increase shareholder value. Some actions may generate an immediate increase in value, whereas other actions require some time to have an influence on value.

In addition, three other bank-specific variables are found to have a significant influence on Tobin‘s Q, including credit risk exposure (CR), market risk exposure (MR), and bank

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asset size (SIZE). The results show (in some detail) that 2-year lagged credit losses lead

to lower shareholder value, whereas the 1-year lagged market risk exposure shows a

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significantly positive link with shareholder value. This finding is inconsistent with that of Fiordelisi and Molyneux (2010) and suggests that shareholders may take a negative

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view about a bank‘s credit risk exposure simultaneously with a relatively positive view on its market risk exposure. In addition, there is a significantly positive/negative

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relationship between the level/1-year lagged bank asset size and Tobin‘s Q, respectively, which implies that shareholders may focus more on the synergy effects brought about by increased asset size in the short-run, whereas over time asset expansion/consolidation

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may have a negative influence on shareholder value. Fiordelisi and Molyneux (2010) also find a significantly positive link between asset size and shareholder value creation.

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Finally, the coefficients on the regional dummy variables (RGN) demonstrate that banks in the newly industrialized and developing economies are able to produce higher

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shareholder value than their counterparts in the industrialized Asia-Pacific countries.12 The crisis dummy (CRISIS) is negatively and significantly related to Tobin‘s Q, implying that shareholder value is lower during financial turmoil.

(Table 4 inserted here)

Moving to the full sample with both listed and non-listed banks, Table 5 demonstrates the results obtained from estimating our models by using EVA as the dependent variable. The results are similar to those in the Tobin‘s Q analyses. The key difference is that there is a negative link between the 1-year lagged cost efficiency changes (CEC) and shareholder value creation, although EVA is found to be positively influenced by the 3year lagged cost efficiency improvement. In addition, the coefficient on credit risk exposure (CR) is no longer significant; and market risk exposure (MR) shows a 12

We also re-estimate our model by including RGN1 and RGN2/RGN3. The finding shows that RGN1 is significantly and negatively associated with Tobin‘s Q. The results are available upon request.

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significantly negative link with shareholder value creation.

(Table 5 inserted here)

We undertake a variety of robustness checks on our main models. First, we use the market-to-book ratio (MB) and as an alternative measure of shareholder value for listed

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banks. In addition, return on average equity (ROAE) is adopted as an alternative

indicator of shareholder value creation for both listed and non-listed banks. The MB

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ratio is the ratio of the market value of equity divided by the book value of equity and

measures what a bank's worth is at present compared to the amount of capital invested

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into it by current and past shareholders. ROAE is a popular accounting indicator of return on shareholder‘s equity; it is computed as the ratio of net income over average

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equity. Tables 6 and 7 demonstrate that the major findings are consistent with those discussed above - both cost and profit enhancements are reflected in shareholder value, although the influence varies over time. For listed banks, bank asset size, the crisis

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dummy variable and two regional dummy variables are found to be statistically significant and with coefficients that have the same signs that are reported in Table 4.

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By contrast, both credit and market risk exposures are not significant, whereas financial

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leverage displays a significantly positive effect on shareholder value. For the full sample, the influences of credit and capital risk exposures on bank performance become significant, whereas bank asset size and market risk exposure no longer matter.

(Tables 6 & 7 inserted here)

Banks from Japan and India are prevalent in our sample of listed banks, while banks from Japan and China are prevalent in our sample of listed and non-listed banks. Therefore, we re-estimate models with these removed. The empirical results are presented in Tables 8 and 9. Again, the findings are in line with the key results analyzed above. That is, both cost and profit enhancements have significant influence on shareholder value, and the influence varies over time.

(Tables 8 & 9 inserted here)

Finally, some banking frontier studies apply separate frontiers for individual countries 11 Page 12 of 38

due to the perceived difficulty of adequately capturing country differences in bank regulatory arrangements, competition, and management competence when a single frontier is applied to a collection of different countries. We thus re-estimate bank efficiency using a single frontier for each country. Following some of the previous studies, we also include an interaction term (with lnE) in estimating bank efficiency. Meanwhile, the price of labor is also added to the model as the third input factor. The

with those reported above.

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(Tables 10 & 11 inserted here)

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findings using these different approaches (shown in Tables 10 and 11) are consistent

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5. Conclusions

This study investigates the impact of cost and profit efficiency changes on shareholder value for a large sample of commercial banks in 14 Asia-Pacific economies during the

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2003-2010. Employing a dynamic panel data model, it is the first study to examine the determinants of market-based and accounting-based bank shareholder value (measured

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using both Tobin‘s Q and EVA, respectively) as a linear function of various bank-

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specific and macroeconomic factors. The primary results are consistent with the previous literature, which suggests that both cost and profit efficiency enhancements are positively related to bank shareholder value in general. Furthermore, we find that cost efficiency benefits may take more time to be reflected in shareholder value because shareholders are more interested in profit maximization than cost minimization. Such finding also supports the ―time-dynamics‖ assumption developed by Fiordelisi and Molyneux (2010).

Focusing on listed banks, we find a significantly negative/positive relationship between credit risk losses/market risk exposure and bank shareholder value as measured by Tobin‘s Q, which implies that shareholders may take a negative view of banks‘ credit risk exposure but a relatively positive view of their market risk exposure. The results also show a significantly positive/negative relationship between the level/1-year lagged bank asset size and Tobin‘s Q, which indicates that shareholders may give more value to the synergy effects derived from consolidation/expansion at the early stage. However, more negative effects of consolidation/expansion may appear and disappoint 12 Page 13 of 38

shareholders at a later stage. For both listed and non-listed banks, we find a significantly negative link between market risk exposure and shareholder value creation as measured by EVA, suggesting that excessive risk-taking in financial markets may destroy shareholder value. In addition, the results show a significantly negative relationship between the 2-year lagged bank asset size and shareholder value creation, which again

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confirms the diminishing synergy effects mentioned above.

The robustness checks confirm that our findings are consistent using alternative

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shareholder value indicators, different sampling strategies, and various efficiency estimations. Overall, results from this paper suggest that banks need to place greater

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emphasis on both cost and profit efficiency in order to boost their performance in the

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future so as to reward their owners.

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Table(s)

Table 1: Variable Definitions and Sources Variable

Definition

Data Sources

Dependent Variable: Tobin’s Q is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets.

Bankscope, Datastream

Economic value added (EVA)

Economic value added is calculated as the difference between net operating profits after tax and a capital charge over the same period.

Estimated by the authors

Market-to-book ratio (MB)

Market-to-book ratio is the ratio of the market value of equity divided by the book value of equity.

Bankscope, Datastream

Return on average equity (ROAE)

Return on average equity is the ratio of net income divided by average equity

Bankscope

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Tobin’s Q (Q)

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Independent Variable:

Profit efficiency change is the percentage change of profit efficiency. Profit efficiency is estimated using the stochastic frontier approach.

Estimated by the authors

Cost efficiency change (CEC)

Cost efficiency change is the percentage change of cost efficiency. Cost efficiency is estimated using the stochastic frontier approach.

Estimated by the authors

Credit risk (CR)

Credit risk is measured by the ratio of loan loss reserves over gross loans.

BankScope

Market risk (MR)

Market risk is measured by the total amount of security investments to total assets ratio.

BankScope

Liquidity risk (LIQ)

Liquidity risk is measured by total loans to total deposits ratio.

BankScope

Financial leverage (LEV)

Financial leverage is measured by the ratio of the book value of total liabilities to the book value of total equity.

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Profit efficiency change (PEC)

BankScope

Bank size (SIZE)

Bank size is defined as the natural logarithm of total assets in thousands of USD.

BankScope

Global financial crisis (CRISIS)

CRISIS is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise.

Compiled by the authors

Region-1 (RGN1)

Region-1 is a dummy variable that takes a value of one for banks in the industrialized countries in Asia Pacific (Australia and Japan) and zero otherwise.

As defined by IMF

Region-2 (RGN2)

Region-2 is a dummy variable that takes a value of one for banks in the newly industrialized economies in Asia Pacific (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise.

As defined by IMF

Region-3 (RGN3)

Region-3 is a dummy variable that takes a value of one for banks in the developing countries/regions in Asia Pacific (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise.

As defined by IMF

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Listed banks Observations

Mean

Std. Dev.

Min

Tobin's Q (Q)

1745 1745

1.0251 1.3149

0.1571 1.0373

0.7996 0.0084

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Market to book ratio (MB) Economic Value Added (EVA) Return on average equity (ROAE) %

Max

Listed and non-listed banks Observations

Mean

Std. Dev.

Min

Max

3901

-3241

440163

-6467434

7211641

3901

0.1055

0.0334

0.0120

0.1919

6.1988 15.1536

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Variable

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Table 2: Descriptive Statistics 2003-2010

1515 1515

1.1908 0.6429

9.6539 16.1584

-39.8454 -92.8785

80.8735 191.7533

3206 3206

0.8808 1.0566

9.3970 18.7162

-66.5116 -92.8785

135.7970 292.8967

Credit risk (CR) Bank size (SIZE)

1745 1745

0.0312 16.1757

0.0441 1.6966

0 10.108

0.8015 21.2627

3901 3901

0.0297 15.6009

0.0489 1.9569

0 8.6387

0.9186 21.3976

Financial leverage (LEV) Market risk (MR)

1745 1745

16.1787 0.2409

16.9699 0.0996

0.7281 0

638.3231 0.8405

3901 3901

16.3057 0.2256

18.1768 0.1259

0.0386 -0.0006

638.3231 0.9814

Liquidity risk (LIQ) Global financial crisis (CRISIS)

1745 1745

0.7398 0.2602

0.3215 0.4389

0.0015 0

6.8649 1

3901 3901

0.7441 0.2556

0.5744 0.4362

0.0015 0

15.1081 1

Region-1 (RGN1) Region-2 (RGN2)

1745 1745

0.3765 0.1037

0.4846 0.305

0 0

1 1

3901 3901

0.2556 0.1610

0.4362 0.3676

0 0

1 1

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Cost efficiency change (CEC) % Profit efficiency change (PEC) %

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Region-3 (RGN3) 1745 0.5198 0.4998 0 1 3901 0.5834 0.4931 0 1 Source: Bankscope, World Bank database, and Datastream. Notes: All financial variables are measured in thousands of constant 2003 USD. Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Market to book ratio (MB) is the ratio of the market value of equity divided by the book value of equity. Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Return on average equity (ROAE) is the ratio of net income divided by average equity. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measured by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured as the ratio of the total amount of investment securities to total assets. Liquidity risk (LIQ) is measured by the ratio of total loans to total deposits. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the 2008-2009 period and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise.

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Listed banks MB Obs.

Q

PEC

CEC

Obs.

EVA

Listed and non-listed banks ROAE Obs. PEC

CEC Panel A: mean by year 2003 183 1.0307 1.3483 421 -64985 6.4409 2004 194 1.0422 1.3225 184 6.7943 -4.0588 451 -24889 11.0607 396 6.5388 -2.5903 2005 221 1.0317 1.4984 208 2.2362 -0.0711 511 -8543 10.3311 434 0.9511 0.0865 2006 231 1.0319 1.5128 226 -3.8885 6.282 541 -6279 8.6999 486 -3.522 4.2761 2007 236 1.0426 1.594 229 -1.7273 5.4179 535 -241 10.871 498 -0.4579 3.8039 2008 216 0.9933 0.9283 212 -8.9623 6.6262 494 3489 9.8223 467 -7.7397 5.1754 2009 238 1.0129 1.1492 232 8.0792 -5.5714 503 -682 6.3955 485 11.3512 -5.4307 2010 226 1.0176 1.1524 224 2.4945 -0.9236 445 72925 10.4554 440 0.9866 0.1285 Panel B: mean by country Australia 59 1.0597 2.2007 52 3.0672 -3.359 106 12701 12.9889 83 1.6704 -2.7092 China 76 1.0246 1.7 69 0.4299 -0.1832 677 7364 14.9666 525 0.1833 -0.2317 Hong Kong 45 1.0831 2.0874 40 2.2769 2.0402 189 281009 12.0008 153 0.9807 0.9641 India 290 1.0257 1.3235 257 -2.3058 0.8035 445 -2022 15.1928 380 -1.8947 0.7239 Indonesia 175 1.1288 1.9614 151 2.1073 -0.06 401 17001 15.4178 333 3.573 -0.0824 Japan 598 0.9889 0.9074 512 1.427 3.4904 891 -42750 0.7548 744 1.8933 3.8209 Korea 50 1.0053 1.1947 43 5.7816 -0.2736 122 -87595 11.4316 103 2.9666 -0.406 Malaysia 24 1.0837 2.1589 21 -0.1118 0.3676 188 58498 13.5068 158 0.2292 0.3863 Pakistan 122 1.053 1.6637 109 -4.0675 0.3786 167 -645 6.588 139 -3.8781 0.3378 Philippines 81 1.0131 1.2206 70 0.5621 -1.5294 169 4837 8.4854 137 2.3562 -0.8437 Singapore 22 1.0367 1.4378 20 1.9935 1.536 60 52770 10.3686 48 8.8005 -1.2344 Sri Lanka 53 1.0024 1.008 45 3.3614 -0.1087 83 3418 11.0412 69 2.2608 0.0131 Taiwan 64 0.999 1.0548 51 2.8744 -0.3577 257 -148763 0.2053 210 1.6423 -0.5204 Thailand 86 1.0077 1.2317 75 0.768 -0.1654 146 -42136 5.3504 124 0.6063 1.2062 Panel C: mean by region Region-1 657 0.9953 1.0235 564 1.5782 2.8589 997 -36854 2.0581 827 1.8709 3.1655 Region-2 181 1.0262 1.3967 154 3.4165 0.5346 628 11717 6.9071 514 2.3792 -0.1223 Region-3 907 1.0465 1.5097 797 -0.5548 0.1373 2276 7356 13.1101 1865 0.3309 0.1442 Average 1745 1.0251 1.3149 1515 0.6429 1.1908 3901 -3241 9.2867 3206 0.8808 1.0566 Notes: Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Market-to-book ratio (MB) is the ratio of the market value of equity divided by the book value of equity. Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Return on average equity (ROAE) is the ratio of net income divided by average equity. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Cost and profit efficiencies are estimated using the Battese and Coelli (1995) model of a stochastic frontier function.

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Table 3: Sample Means of Key Variables

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Table 4: The Relationship between Shareholder Value and Bank Efficiency for Listed Banks Dependent Variable: Tobin’s Q Model (2) Standard error Coefficient Standard error Qt-1 0.0760 0.6429*** 0.0716 Qt-2 0.0866 0.1090 0.0886 Qt-3 0.0440 -0.0897* 0.0477 CECt-1, t 0.0002 CECt-2, t-1 0.0001 CECt-3, t-2 0.0002 PECt-1, t 0.0007*** 0.0002 PECt-2, t-1 0.0000 0.0001 PECt-3, t-2 -0.0002 0.0001 CR 0.3176 0.4071 0.2538 0.4055 CRt-1 0.0929 0.2723 0.1045 0.2704 CRt-2 -0.6148* 0.3616 -0.6118* 0.3697 CRt-3 0.25 0.1641 0.2811* 0.1545 SIZE 0.1592*** 0.0309 0.1681*** 0.0284 SIZEt-1 -0.2069*** 0.0349 -0.2118*** 0.0343 SIZEt-2 0.0338 0.0222 0.0222 0.0251 SIZEt-3 0.0176 0.0178 0.0236 0.0204 LEV 0.0006 0.0008 0.0011 0.0007 LEVt-1 -0.0007 0.0010 -0.0012 0.0012 LEVt-2 0.0001 0.0007 -0.0001 0.0008 LEVt-3 -0.0008 0.0006 -0.0006 0.0004 MR -0.1051 0.1144 -0.1021 0.1130 MRt-1 0.1797* 0.1004 0.1546 0.0970 MRt-2 -0.0446 0.0622 -0.0427 0.0607 MRt-3 -0.0067 0.0516 -0.0073 0.0450 LIQ -0.0177 0.0676 0.023 0.0707 LIQt-1 0.0321 0.0639 0.0008 0.0641 LIQt-2 0.0225 0.0468 0.0121 0.0464 LIQt-3 -0.0339 0.0346 -0.0336 0.0318 CRISIS -0.0124** 0.0053 -0.0090* 0.0048 RGN2 0.0252*** 0.0065 0.0190*** 0.0060 RGN3 0.0248*** 0.0076 0.0185*** 0.0063 Constant 0.2801*** 0.0960 0.2956*** 0.1034 Observation 887 887 F test (1) 36.24*** 44.00*** Hansen test (2nd step: p-value) 0.13 0.156 AR(1) test 0 0 AR(2) test 0.68 0.554 Notes: This table presents the results of the system GMM estimations with Tobin’s Q as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Model (1) Coefficient 0.6351*** 0.0955 -0.0852* -0.0002 0.0003* 0.0004*

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Table 5: The Relationship between Shareholder Value and Bank Efficiency for Listed and NonListed Banks Dependent Variable: Economic Value Added (EVA) Model (2) Standard error Coefficient Standard error EVAt-1 0.0786 0.5738*** 0.0850 EVAt-2 0.0570 0.0270 0.0507 EVAt-3 0.0377 0.1168*** 0.0371 CECt-1, t 0.0009 CECt-2, t-1 0.0007 CECt-3, t-2 0.0008 PECt-1, t 0.0008* 0.0005 PECt-2, t-1 0.0008*** 0.0003 PECt-3, t-2 0.0003 0.0004 CR -0.1176 0.9004 0.1661 1.0132 CRt-1 -0.2576 0.9879 -0.3835 1.0995 CRt-2 0.1169 0.4538 -0.0774 0.5267 CRt-3 -0.1658 0.2018 -0.1893 0.1792 SIZE -0.0065 0.0601 -0.0015 0.0487 SIZEt-1 0.0312 0.0627 0.0470 0.0549 SIZEt-2 -0.0606 0.0388 -0.0800* 0.0438 SIZEt-3 0.0389 0.0282 0.0409 0.0305 LEV 0.0021 0.0019 0.0011 0.0011 LEVt-1 -0.0003 0.0008 0.0002 0.0005 LEVt-2 0.0002 0.0006 0.0001 0.0008 LEVt-3 0.0000 0.0005 0.0000 0.0004 MR -0.4414* 0.2342 -0.3826** 0.1853 MRt-1 0.2206 0.1970 0.1731 0.2156 MRt-2 0.0443 0.1045 0.1130 0.1123 MRt-3 -0.0535 0.0754 -0.0678 0.0749 LIQ -0.1260 0.1216 -0.1243 0.0882 LIQt-1 0.0697 0.0632 0.0798 0.0513 LIQt-2 -0.0054 0.0233 -0.0093 0.0295 LIQt-3 -0.0103 0.0146 -0.0178 0.0176 CRISIS -0.0118 0.0128 -0.0040 0.0120 RGN2 -0.0337 0.0307 -0.0301 0.0212 RGN3 0.0365 0.0280 0.0432** 0.0212 Constant 0.0205 0.1955 -0.0429 0.1598 Observation 1814 1814 F test (1) 14.94*** 15.60*** Hansen test (2nd step: p-value) 0.125 0.120 AR(1) test 0 0 AR(2) test 0.130 0.167 Notes: This table presents the results of the system GMM estimations with Economic Value Added as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Model (1) Coefficient 0.5639*** 0.0131 0.1264*** -0.0019** -0.0007 0.0014*

5 Page 25 of 38

Table 6: The Relationship between Shareholder Value and Bank Efficiency for Listed Banks (Robustness Check 1) Dependent Variable: Market-to-Book Ratio (MB) Model (2) Standard error Coefficient Standard error MBt-1 0.0597 0.5315*** 0.0559 MBt-2 0.0546 0.1395** 0.0565 MBt-3 0.0469 0.0655 0.0454 CECt-1, t 0.0018 CECt-2, t-1 0.0014 CECt-3, t-2 0.0022 PECt-1, t 0.0077*** 0.0018 PECt-2, t-1 0.0009 0.0013 PECt-3, t-2 -0.0006 0.0012 CR -0.789 3.1604 -1.0966 3.8949 CRt-1 -1.0386 3.5435 -0.9905 4.0562 CRt-2 0.9274 2.4288 1.3575 2.4907 CRt-3 0.0013 1.5635 -0.0212 1.6798 SIZE 1.3038*** 0.2297 1.4042*** 0.2419 SIZEt-1 -1.7681*** 0.2490 -1.8076*** 0.2819 SIZEt-2 0.3874** 0.1762 0.3167 0.1944 SIZEt-3 0.1008 0.2006 0.0916 0.2081 LEV 0.0333*** 0.0056 0.0351*** 0.0061 LEVt-1 -0.0177 0.0144 -0.0188 0.0147 LEVt-2 -0.0123 0.0086 -0.0137 0.0088 LEVt-3 -0.0021 0.0072 -0.0006 0.0069 MR 0.3158 0.9680 0.3784 0.9606 MRt-1 0.3445 0.9459 0.042 0.9125 MRt-2 0.0952 0.5788 0.1779 0.5354 MRt-3 -0.3852 0.5134 -0.3531 0.5216 LIQ -0.0285 0.4525 0.4144 0.4513 LIQt-1 0.0829 0.5312 -0.2223 0.5245 LIQt-2 -0.0041 0.5669 -0.107 0.5638 LIQt-3 -0.0199 0.4394 -0.0235 0.4222 CRISIS -0.2543*** 0.0619 -0.2279*** 0.0602 RGN2 0.3274*** 0.0745 0.2941*** 0.0696 RGN3 0.3359*** 0.0802 0.3035*** 0.0701 Constant -0.3931 0.5074 -0.1395 0.5282 Observation 887 887 F test (1) 310.49*** 47.67*** Hansen test (2nd step: p-value) 0.151 0.162 AR(1) test 0 0.001 AR(2) test 0.195 0.175 Notes: This table presents the results of the system GMM estimations with market-to-book ratio as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Market-to-book ratio (MB) is the ratio of the market value of equity divided by the book value of equity. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measured by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Model (1) Coefficient 0.5297*** 0.1222** 0.0666 0.0055*** 0.0023 -0.0004

6 Page 26 of 38

Table 7: The Relationship between Shareholder Value and Bank Efficiency for Listed and NonListed Banks (Robustness Check 2)

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Dependent Variable: Return on Average Equity (ROAE) Model (1) Model (2) Coefficient Standard error Coefficient Standard error ROAEt-1 0.4315*** 0.0678 0.4315*** 0.0710 ROAEt-2 0.0396 0.0356 0.0367 0.0356 ROAEt-3 0.0606* 0.0340 0.0641* 0.0374 CECt-1, t 0.0887*** 0.0329 CECt-2, t-1 -0.0992*** 0.0329 CECt-3, t-2 -0.0576 0.0604 PECt-1, t 0.0030 0.0212 PECt-2, t-1 0.0372*** 0.0109 PECt-3, t-2 0.0355* 0.0214 CR -168.9060** 73.3919 -157.6505** 74.2791 CRt-1 66.7651 65.5130 60.3688 63.3913 CRt-2 39.9841** 19.5824 35.4399* 19.9447 CRt-3 -3.7945 11.6917 -1.5441 9.7291 SIZE 2.7698 2.7417 -0.0764 2.5373 SIZEt-1 -1.1526 3.5449 1.9987 3.1809 SIZEt-2 -2.1013 1.7557 -1.9753 1.6427 SIZEt-3 1.0691 1.2473 0.7126 1.1932 LEV -0.2719* 0.1639 -0.1668 0.1150 LEVt-1 0.5521*** 0.1910 0.5115*** 0.1802 LEVt-2 -0.0542 0.0711 -0.0674 0.0875 LEVt-3 0.0258 0.0530 0.0345 0.0574 MR -9.7697 11.3594 -12.7486 15.2240 MRt-1 6.7768 10.8580 9.1024 11.6213 MRt-2 0.8051 5.8726 3.1669 5.9479 MRt-3 -1.0069 3.9310 -2.9120 4.5487 LIQ -1.6998 2.2201 -0.8001 2.4148 LIQt-1 0.8554 0.9969 0.5605 0.8905 LIQt-2 0.6989 0.6807 0.6960 0.8469 LIQt-3 0.0454 0.6922 -0.1702 0.7422 CRISIS -0.1364 0.6491 -0.9515* 0.4927 RGN2 2.3527** 1.0885 2.8369** 1.1264 RGN3 6.7379*** 1.5051 8.0202*** 1.6068 Constant -10.3327 8.3214 -13.0028* 6.9180 Observation 1810 1810 F test (1) 40.65*** 33.74*** Hansen test (2nd step: p-value) 0.201 0.321 AR(1) test 0 0 AR(2) test 0.222 0.183 Notes: This table presents the results of the system GMM estimations with return on average equity (ROAE) as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Return on average equity (ROAE), which is the ratio of net income divided by average equity. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measured by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

7 Page 27 of 38

Table 8: The Relationship between Shareholder Value and Bank Efficiency for Listed Banks (Robustness Check 3) Dependent Variable: Tobin’s Q Model (2) Standard error Coefficient Standard error Qt-1 0.1067 0.5803*** 0.1160 Qt-2 0.1006 0.1699 0.1072 Qt-3 0.0726 -0.1565** 0.0733 CECt-1, t 0.0010 CECt-2, t-1 0.0007 CECt-3, t-2 0.0006 PECt-1, t 0.0009*** 0.0002 PECt-2, t-1 -0.0000 0.0002 PECt-3, t-2 -0.0004** 0.0002 CR -0.1679 0.4334 0.1649 0.5074 CRt-1 0.2056 0.3491 0.0156 0.4212 CRt-2 -0.3149 0.4319 -0.4709 0.4077 CRt-3 0.0161 0.2465 0.0577 0.2622 SIZE 0.1271** 0.0506 0.1330*** 0.0489 SIZEt-1 -0.1876*** 0.0622 -0.1826*** 0.0619 SIZEt-2 0.0551* 0.0283 0.0378 0.0279 SIZEt-3 0.0046 0.0221 0.0100 0.0254 LEV 0.0015 0.0010 0.0016 0.0010 LEVt-1 -0.0012 0.0015 -0.0019* 0.0011 LEVt-2 -0.0011 0.0012 -0.0014 0.0010 LEVt-3 -0.0007 0.0011 0.0001 0.0012 MR -0.0331 0.1035 -0.0451 0.1257 MRt-1 0.0629 0.1350 0.0222 0.1736 MRt-2 0.0069 0.0782 0.0078 0.1226 MRt-3 -0.0103 0.0851 0.0198 0.0755 LIQ -0.0143 0.0661 0.0297 0.0492 LIQt-1 0.0291 0.0971 -0.0041 0.0753 LIQt-2 -0.0033 0.0689 -0.0165 0.0494 LIQt-3 -0.0328 0.0405 -0.0215 0.0354 CRISIS -0.0376*** 0.0089 -0.0350*** 0.0076 RGN2 -0.0278* 0.0154 -0.0058 0.0148 RGN3 -0.0169 0.0198 0.0002 0.0181 Constant 0.5157*** 0.1328 0.4949*** 0.1165 Observation 437 437 F test (1) 28.83*** 19.35*** Hansen test (2nd step: p-value) 1 1 AR(1) test 0.005 0.010 AR(2) test 0.927 0.836 Notes: This table presents the results of the system GMM estimations with Tobin’s Q as the dependent variable. Banks in Japan and India are excluded to lessen heterogenerity problems in the sample. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Model (1) Coefficient 0.6318*** 0.1015 -0.1509** -0.0021** -0.0002 0.0013**

8 Page 28 of 38

Table 9: The Relationship between Shareholder Value and Bank Efficiency for Listed and NonListed Banks (Robustness Check 4) Dependent Variable: Economic Value Added (EVA) Model (2) Standard error Coefficient Standard error EVAt-1 0.1105 0.6108*** 0.1247 EVAt-2 0.0672 -0.0156 0.0760 EVAt-3 0.0396 0.1601*** 0.0491 CECt-1, t 0.0018 CECt-2, t-1 0.0022 CECt-3, t-2 0.0019 PECt-1, t 0.0006 0.0007 PECt-2, t-1 0.0011*** 0.0004 PECt-3, t-2 0.0004 0.0006 CR -0.4473 0.8357 -0.1559 0.7955 CRt-1 0.1557 1.0317 -0.0179 0.8733 CRt-2 0.4815 0.4394 0.2185 0.4228 CRt-3 -0.1932 0.2397 -0.2241 0.1709 SIZE -0.0392 0.0659 -0.0293 0.0662 SIZEt-1 0.0918 0.0608 0.0783 0.0613 SIZEt-2 -0.0388 0.0441 -0.0469 0.0563 SIZEt-3 0.0048 0.0283 0.0156 0.0292 LEV 0.0003 0.0022 -0.0003 0.0024 LEVt-1 0.0010 0.0017 0.0008 0.0019 LEVt-2 -0.0008 0.0015 -0.0012 0.0015 LEVt-3 0.0001 0.0005 0.0002 0.0004 MR -0.4299** 0.2031 -0.5014** 0.2147 MRt-1 0.1863 0.2034 0.1767 0.1898 MRt-2 0.1173 0.1281 0.1423 0.1333 MRt-3 -0.1342 0.1055 -0.1752* 0.0949 LIQ -0.1255 0.0937 -0.1911** 0.0808 LIQt-1 0.0874 0.0662 0.1046* 0.0620 LIQt-2 -0.0100 0.0249 0.0006 0.0318 LIQt-3 -0.0186 0.0214 -0.0217 0.0242 CRISIS 0.0002 0.0150 -0.0077 0.0181 RGN2 -0.0481 0.0378 -0.0327 0.0335 RGN3 0.0421 0.0553 0.0551* 0.0312 Constant -0.2117 0.1960 -0.1370 0.1461 Observation 1168 1168 F test (1) 12.51*** 11.48*** Hansen test (2nd step: p-value) 0.129 0.128 AR(1) test 0 0 AR(2) test 0.193 0.139 Notes: This table presents the results of the system GMM estimations with Economic Value Added as the dependent variable. Banks in Japan and China are dropped to lessen heterogeneity problems in the sample. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Model (1) Coefficient 0.6348*** -0.0255 0.1533*** -0.0042** -0.0066*** 0.0018

9 Page 29 of 38

Table 10: The Relationship between Shareholder Value and Bank Efficiency for Listed Banks (Robustness check 5)

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Dependent Variable: Tobin’s Q Model (1) Model (2) Coefficient Standard error Coefficient Standard error Qt-1 0.5622*** -0.0915 0.5963*** -0.1187 Qt-2 0.1481 -0.1014 0.1626* -0.0974 Qt-3 -0.0983* -0.0533 -0.0818* -0.0444 CECt-1, t -5.2362*** -1.8824 CECt-2, t-1 9.9304*** -3.5972 CECt-3, t-2 -4.6870*** -1.713 PECt-1, t -0.0546 -0.0351 PECt-2, t-1 0.1027* -0.0588 PECt-3, t-2 -0.0438* -0.0232 CR -0.213 -0.6194 -0.2281 -0.5054 CRt-1 0.7252 -0.5999 0.816 -0.6499 CRt-2 -1.0220** -0.495 -1.2035** -0.5799 CRt-3 0.3521 -0.3412 0.3371 -0.3381 SIZE 0.1742*** -0.0429 0.1615*** -0.0434 SIZEt-1 -0.2090*** -0.034 -0.2258*** -0.038 SIZEt-2 0.0129 -0.0336 0.0155 -0.0358 SIZEt-3 0.0299 -0.0258 0.0516* -0.0267 LEV -0.0002 -0.002 -0.0003 -0.002 LEVt-1 0.0005 -0.0018 0.0001 -0.0017 LEVt-2 -0.0005 -0.0012 -0.0003 -0.0013 LEVt-3 -0.001 -0.0009 -0.001 -0.0011 MR -0.1168 -0.1289 -0.1344 -0.119 MRt-1 0.1651 -0.1176 0.1569 -0.1126 MRt-2 0.0313 -0.1086 0.0403 -0.1088 MRt-3 -0.0099 -0.0799 -0.0256 -0.0771 LIQ -0.0156 -0.0978 0.02 -0.0742 LIQt-1 -0.0007 -0.0927 -0.0536 -0.0963 LIQt-2 0.0098 -0.0848 0.0372 -0.0712 LIQt-3 0.0168 -0.0449 -0.0023 -0.0373 CRISIS -0.0153** -0.0075 -0.0160** -0.0078 RGN2 0.0245 -0.0217 0.0015 -0.0161 RGN3 0.017 -0.0271 0.005 -0.0204 Constant 0.2364 -0.1739 0.3028** -0.136 Observation 457 457 F test (1) 13.34*** 11.40*** Hansen test (2nd step: p-value) 0.189 0.303 AR(1) test 0 0.001 AR(2) test 0.45 0.316 Notes: This table presents the results of the system GMM estimations with Tobin’s Q as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Table 11: The Relationship between Shareholder Value and Bank Efficiency for Listed and Non-Listed Banks (Robustness check 6)

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Dependent Variable: Economic Value Added (EVA) Model (1) Model (2) Coefficient Standard error Coefficient Standard error EVAt-1 0.5354*** -0.0603 0.5131*** -0.0685 EVAt-2 0.2041*** -0.0724 0.1734** -0.0698 EVAt-3 0.0954 -0.0583 0.1062** -0.0525 CECt-1, t -0.061 -0.1066 CECt-2, t-1 0.3079* -0.1859 CECt-3, t-2 -0.2344* -0.134 PECt-1, t 0.0484* -0.0279 PECt-2, t-1 -0.0804** -0.0403 PECt-3, t-2 0.0330** -0.0156 CR -0.3676 -0.9594 -0.7406 -1.0248 CRt-1 0.0121 -1.26 0.2406 -1.172 CRt-2 0.8939 -0.7628 0.8213 -0.7159 CRt-3 -0.4138 -0.4537 -0.4764 -0.4759 SIZE -0.0294 -0.0487 -0.0336 -0.049 SIZEt-1 0.063 -0.056 0.0606 -0.062 SIZEt-2 -0.0584 -0.0459 -0.0559 -0.0449 SIZEt-3 0.0405 -0.0251 0.0420* -0.0238 LEV -0.0046 -0.0032 -0.0059* -0.0032 LEVt-1 0.0019 -0.0013 0.0025 -0.0015 LEVt-2 0.0002 -0.0013 0.0006 -0.0013 LEVt-3 0.0009 -0.0011 0.0014 -0.0014 MR -0.2265 -0.1709 -0.1319 -0.1965 MRt-1 0.1352 -0.1596 0.0897 -0.167 MRt-2 -0.0231 -0.1174 -0.018 -0.1265 MRt-3 -0.2167* -0.1199 -0.1855 -0.1148 LIQ -0.0943 -0.0935 -0.0791 -0.0917 LIQt-1 0.036 -0.03 0.0281 -0.0291 LIQt-2 0.0152 -0.0201 0.0115 -0.0191 LIQt-3 -0.0199 -0.0207 -0.0183 -0.0216 CRISIS 0.0046 -0.0143 0.0053 -0.0143 RGN2 -0.0049 -0.0365 -0.0289 -0.0346 RGN3 0.0414 -0.0415 0.0247 -0.0391 Constant -0.1326 -0.1183 -0.0962 -0.1452 Observation 847 847 F test (1) 33.93*** 30.26*** Hansen test (2nd step: p-value) 0.69 0.754 AR(1) test 0.004 0.007 AR(2) test 0.565 0.535 Notes: This table presents the results of the system GMM estimations with Economic Value Added as the dependent variable. The robust standard errors corrected for heteroskedasticity are applied. Significant F statistic (1) confirms the joint significance of all independent variables. The Hansen statistics are insignificant, suggesting joint validity of the instruments in all six system GMM models. Arellano–Bond test for AR (1) in first differences rejects the null of no first-order serial correlation, but the test for AR (2) does not reject the null that there is no second-order serial correlation. Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measure by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. Region-1 (RGN1) is a dummy variable that takes a value of one for the industrialized economies (Australia and Japan) and zero otherwise. Region-2 (RGN2) is a dummy variable that takes a value of one for the newly industrialized economies (Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise. Region-3 (RGN3) is a dummy variable that takes a value of one for the developing economies (China, India, Indonesia, Malaysia, Pakistan, Philippines, Sri Lanka, and Thailand) and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Robust standard errors are in parentheses.

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Table(s)

Appendix 1: Summary of Extant Literature on Bank Efficiency and Shareholder Value Country/ Region

Paper

Period

Shareholder Value

Bank Efficiency

Panel A: Stock returns/Tobin’s Q a) US/Europe stock return

1994

four measures of partial efficiency

stock return

EU

2000

cost

stock return

Turkish

1998-2004

cost efficiency

EU

1997-2004

x-efficiency

Greece

2000-2005

profit efficiency

EU

2002-2006

cost and profit

Chu and Lim(1998)

Singapore

1992-1996

cost and profit efficiency

stock return

Kirkwood and Nahm(2006)

Australia

1995-2002

cost and profit efficiency

stock return

Sufian and Majid (2006)

Malaysia

2002-2003

x- and p-efficiency

stock return

China

1997-2006

technical efficiency

stock return

Asia and Latin America

2000-2006

cost and profit efficiency

stock return

Erdem and Erdem (2008) De Jonghe and Vander Vennet (2008) Pasiouras et al. (2008) Liadaki and Gaganis (2010)

Spain

Panel B: EVA EU

Fiordelisi and Molyneux (2010)

EU

1997-2002 1998-2005

cost, profit, shareholder value efficiency cost, revenue, profit, shareholder value efficiency

stock return stock return

EVA EVA

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Ioannidis et al. (2008)

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Majid and Sufian (2008)

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b) Asia Pacific

stock return

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Becalli et al.(2006)

1986-1991

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Adenso-Diaz and Gascon(1997)

US

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Eisenbeis et al. (1999)

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Appendix 2: Estimation of Cost and Profit Efficiency Using Stochastic Frontier Approach

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The efficiency estimates in this study have been obtained using the parametric stochastic frontier approach (SFA) first proposed by Aigner, Lovell, and Schmidt (1977) and Meeusen and van den Broeck (1977). Specifically, the study employs the Battese and Coelli (1995) model of a stochastic frontier function. This model estimates efficiency in a single-step for panel data and assumes that non-negative technical inefficiencies are a function of environmental variables, including bank-specific and country-specific variables that are independently distributed as truncations of normal distribution with a constant variance but with means that are a linear function of observable variables. Under the single-step method, this study estimates a global frontier with accounting for banking environment variables, instead of country-specific frontiers, because it increases the number of available observations.

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Cost efficiency measures the extent to which a bank's costs approach the costs for a "best practice" or the least cost under the same assumption. It is measured by estimating a cost function where the dependent variable is the sum of each bank's total costs, and the independent variables include prices of inputs, quantities of variable outputs, differences in the economic environment, random error, and inefficiency. The translog function to estimate the cost frontier takes the following form:1

where TC it : the total cost of bank i at time t; : the pth output of bank i at time t (p=1, 2, 3); y pit : the mth input prices of bank i at time t (m=1,2); : the time trend; t : the natural logarithm of total equity; ln E REG1 : a dummy variable that takes a value of one for Industrialized Asia (including Australia and Japan) and zero otherwise; REG2 : a dummy variable that takes a value of one for Newly Industrialized Economies (including Hong Kong, South Korea, Singapore, and Taiwan) and zero otherwise; : the random error of bank i at time t with i.i.d normal distribution, N (0,  v2 ) ; and vit : the non-negative inefficiency of bank i at time t, which is assumed to be obtained by u it truncation (at zero) of the N (mit ,  v2 ) distribution. wmit

In this one-step model, following Lozano-Vivas and Pasiouras (2010 & 2013) and Radić et al. (2012), we also include some environmental variables to model the inefficiency distribution, including the real GDP growth rate, inflation rate, 3- bank concentration ratio, the minimum regulatory capital-to-assets ratio, and activity restrictions. This approach allows us to account for heterogeneity across banks and still benchmark different banks against an identical frontier (Bos et al, 2008). 1

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Standard symmetry restrictions apply to this function (i.e. pq   qp ;  mn   nm ) , which is consistent with several recent studies.2 Moreover, total cost and input price terms are normalized by w2 , which imposes linear homogeneity to ensure that the cost-minimizing bundle does not change if all input prices are multiplied by the same positive scalar. Thus, only changes in the ratios of the input prices affect the allocation of inputs.

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Based on the intermediation approach, we specify three outputs and two input prices. The output variables include total net loans (y1), other earning assets (y2), and non-interest income (y3) which are commonly used in the previous litearture.3 Due to lack of data on personnel expenses for most of the sample banks, we follow Hasan and Marton (2003), Soedarmon et al (2011), Sun and Chang (2011) and Jiang et al. (2009, 2013) and only employ two variables as input prices. Price of purchased funds (w1) is measured by the ratio of interest expenses to deposits and short-term funding. Price of physical capital (w2) is measured by the ratio of non-interest expenses to total fixed assets.

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Furthermore, the study estimates alternative profit efficiency. Like cost efficiency, profit efficiency measures the extent to which a bank's profits fall below the profit of the best practices bank under identical assumptions. Berger and Mester (1997) note that when there are significant interfirm differences in product quality, outputs that are not completely variable, output markets that are not perfectly competitive, or imperfectly constructed proxies for output prices, an alternative specification for the profit function may produce better results. Thus, alternative profit efficiency is estimated. Following Fiordelisi (2007) and Uchida and Satake (2009), net income replaces total cost, and the sign of the inefficient term is changed (i.e.,  uit ) in the translog function of profit efficiency, while input prices and outputs remain identical. Because a few banks in the sample have losses rather than profits, common modification is employed (i.e., Liadaki and Gaganis, 2010; Lozano-Vivas and Pasiouras, 2010; Radić et al., 2012).4

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Individual bank (in)efficiency scores are calculated from the estimated frontiers as cost efficiency (CEF) = exp(u) and profit efficiency (PE) = exp(−u), using the FRONTIER 4.1 program developed by Coelli. CEF takes a value between one and infinity, whereas PE is between zero and one. To make efficiency scores comparable, following Liadaki and Gaganis (2010), the index of cost efficiency is calculated as CE=1/CEF. Thus, both cost and profit efficiency scores will be between 0 and 1, with values closer to 1 indicating higher levels of efficiency.

2

See among others, Fu and Heffernan (2007 & 2009), and Fiordelisi and Molyneux (2010). See among others, Stiroh (2000). 4 In order to calculate the natural logarithm, we find the maximum losses among banks and then we add the absolute value of these losses plus 1 to all banks. 3

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Appendix 3: Number of Listed and Non-Listed Banks in the Sample 2004

2005

2006

2007

2008

2009

2010

Total

Industrialized Economies Australia 11 Japan 117

8 121

13 119

16 118

16 115

18 87

14 115

10 99

106 891

Sub-total

129

132

134

131

105

129

109

997

29 18 9 36

27 16 8 31

23 14 8 31

24 12 8 33

76

6 27

61

74

95

92

82

58 48 24 15 14 10 18

53 55 51 24 16 21 12 16

72 56 54 26 21 26 12 17

98 58 55 25 23 25 12 19

109 58 56 25 24 20 11 19

232

248

284

421 451 Total Source: BankScope (Bureau Van Dijk)

511

Sub-total

Developing Economies China 45 India Indonesia Malaysia Pakistan Philippines Sri Lanka Thailand

21 9 7 34

189 122 60 257

77

71

628

104 56 53 24 25 21 11 19

99 54 50 21 23 21 10 19

97 50 34 19 20 21 5 19

677 445 401 188 167 169 83 146

315

322

313

297

265

2276

541

535

494

503

445

3901

Ac ce pt e

d

Sub-total

M

Singapore Taiwan

an

5 27

29 19 9 38

cr

Newly Industrialized Economies Hong Kong 12 24 Korea 17 17

us

128

ip t

2003

4 Page 35 of 38

Appendix 4: Number of Listed Banks in the Sample 2003

2004

2005

2006

2007

2008

2009

2010

Total

Industrialized Economies 6

6

7

9

9

8

7

7

59

Japan

74

76

81

79

76

56

79

77

598

80

82

88

88

85

64

86

84

657

Sub-total

Newly Industrialized Economies 4

6

6

6

6

5

6

Korea

6

7

8

8

7

6

4

Singapore

2

3

3

3

3

3

3

Taiwan

5

5

10

10

9

8

17

21

27

27

25

22

Developing Economies

45

4

50

2

22

8

9

64

21

21

181

us

Sub-total

6

cr

Hong Kong

ip t

Australia

4

4

6

8

11

India

30

30

34

38

39

Indonesia

19

19

21

25

Malaysia

3

3

3

3

Pakistan

6

8

12

12

Philippines

9

11

11

11

Sri Lanka

5

6

8

Thailand

10

10

11

11

11

11

11

11

86

86

91

106

126

131

121

907

183

194

221

236

216

238

226

1745

Total

15

76

39

40

40

290

24

19

175

24

3

3

3

3

24

22

22

19

122

M

24

10

10

9

10

81

7

7

8

4

53

116

130

8

231

Ac ce pt e

Source: BankScope (Bureau Van Dijk)

14

21

d

Sub-total

14

an

China

5 Page 36 of 38

ip t cr

CEC

PEC

CR

1.000 -0.044* 0.019 0.078*** -0.015 0.285*** -0.067*** 0.099*** -0.164***

1.000 -0.463*** -0.058** 0.020 0.056** 0.031 0.014 -0.063**

1.000 -0.045* 0.025 -0.002 -0.018 -0.060** -0.028

M

an

MB

d

Q MB CEC PEC CR SIZE LEV MR LIQ CRISIS

Q 1.000 0.596*** -0.026 0.041 0.243*** -0.111*** -0.074*** 0.041 0.365*** -0.084***

us

Appendix 5: Pearson Correlation Coefficient of Variables Used to Analyze the Sample of Listed Banks

1.000 -0.347*** -0.088*** 0.077*** 0.180*** 0.010

SIZE

LEV

MR

LIQ

CRISIS

1.000 0.102*** 0.054** -0.035 0.006

1.000 -0.034 -0.021 -0.029

1.000 -0.287*** -0.030

1.000 0.037

1.000

Ac c

ep te

Notes: Tobin’s Q (Q) is the ratio of the market value of equity plus the book value of liabilities divided by the book value of assets. Market to book ratio (MB) is the ratio of the market value of equity divided by the book value of assets. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measured by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

6 Page 37 of 38

ip t cr

EVA 1.000

ROAE

EVA ROAE

0.325***

1.000

CEC

-0.030*

-0.031*

1.000

PEC

0.018

-0.073***

-0.404***

1.000

CR

-0.074***

-0.121***

-0.023

0.042**

1.000

SIZE

0.195***

0.047***

0.031*

-0.000

an

PEC

CR

-0.253***

1.000

LEV

0.086***

-0.010

0.052***

-0.012

-0.059***

0.194***

1.000

MR

0.101***

0.006

0.048***

-0.039**

0.015

0.215***

0.042**

1.000

LIQ

-0.085***

-0.023

0.022

-0.002

0.188***

-0.018

-0.042**

-0.294***

M

CEC

us

Appendix 6: Pearson Correlation Coefficient of Variables Used to Analyze the Sample of Listed and Non-Listed Banks SIZE

LEV

MR

LIQ

CRISIS

1.000

Ac c

ep te

d

-0.010 -0.063*** -0.077*** 0.032* -0.009 0.027 -0.043** -0.015 0.022 1.000 CRISIS Notes: Economic Value Added (EVA) is calculated as the difference between net operating profits after tax and a capital charge over the same period. Return on average equity (ROAE) is the ratio of net income divided by average equity. Profit efficiency change (PEC) is the percentage change of profit efficiency. Cost efficiency change (CEC) is the percentage change of cost efficiency. Credit risk (CR) is measured by the ratio of loan loss reserves over gross loans. Bank asset size (SIZE) is defined as the natural logarithm of total assets in thousands of USD. Financial leverage (LEV) is measured by the ratio of the book value of total liabilities to the book value of total equity. Market risk (MR) is measured by the total amount of security investments to total assets ratio. Liquidity risk (LIQ) is measured by total loans to total deposits ratio. Global financial crisis (CRISIS) is a dummy variable that takes a value of one for the years 2008-09 and zero otherwise. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

7 Page 38 of 38