the relationship between bank efficiency and stock … asia and latin america 4 the relationship...

The relationship between bank efficiency

and stock returns: evidence

from Asia and Latin America

Christos Ioannidis1, Philip Molyneux

2, Fotios Pasiouras

1*

1School of Management, University of Bath, UK

2 Bangor Business School, Bangor University, UK

University of Bath

School of Management, Working Paper Series

2008.10

This working paper is produced for discussion purposes only. The papers are expected to be

published in due course, in revised form and should not be quoted without the author’s

permission.

* © Copyright: The Authors, November 2008. Author for correspondence, Tel: +44 (0) 1225 384297; Emails:

[email protected] , [email protected] , [email protected]

2

University of Bath School of Management

Working Paper Series

School of Management

Claverton Down

Bath

BA2 7AY

United Kingdom

Tel: +44 1225 826742

Fax: +44 1225 826473

http://www.bath.ac.uk/management/research/papers.htm

2008

2008.01 Ana Lozano-Vivas

& Fotios Pasiouras

The impact of non-traditional activities on the

estimation of bank efficiency: international

evidence

2008.02 G.C. Parry, M.

James-Moore, A.P.

Graves and O.

Altinok

Legal aspects of electronic signatures

2008.03 Glenn Parry,

Andrew Graves &

Mike James-Moore

Lean New Product Introduction: a UK

Aerospace Perspective

2008.04 G. C. Parry, A. J.

Towler & A. Graves

Travel Policy: a case study to deliver cost

savings to MOD business air travel

2008.05 Glenn Parry A theoretical approach to construction project

progress drawing on complexity and

metaphysics

2008.06 Manthos D. Delis,

Philip Molyneux &

Fotios Pasiouras

Regulations and productivity growth in banking

2008.07

Richard John

Fairchild

The Airbus-Boeing Launch Aid Dispute-

a Game-theoretic Analysis.

2008.08 Philip Cooper,

Laurence

Etherington, Stuart

Bell, Andrew

Farmer & Julian

Williams

Socio-Economic Scenarios of European

Development and Integrated Management of the

Marine Environment

3

2008.09

Maria-Eleni K.

Agoraki , Manthos

D. Delis &Fotios

Pasiouras

Regulations, competition and bank risk-taking in

transition countries

2008.10

Christos Ioannidis,

Philip Molyneux &

Fotios Pasiouras

The relationship between bank efficiency

and stock returns: evidence


4

The relationship between bank efficiency and stock returns: evidence


Christos Ioannidis1, Philip Molyneux

2, Fotios Pasiouras

1*

1School of Management, University of Bath, UK

2 Bangor Business School, Bangor University, UK

Abstract

This paper examines the relationship between bank efficiency change and stock price

returns. We first estimate the cost and profit efficiency of a sample of Asian and Latin

American listed banks over the period 2000-2006 while controlling for cross-country

differences such as regulations and the macroeconomic environment. We then regress

the annual efficiency changes on stock returns. The results indicate a positive and

robust relationship between profit efficiency changes and stock returns. However, we

find no evidence that cost efficiency changes are reflected in stock returns. We also

find that profit efficiency better explains bank stock returns compared to traditional

accounting profits measures (ROE). Overall, we conclude that profit efficiency

measures include useful information for shareholders wishing to explain bank stock

returns.

Keywords: Asia, Banking, Efficiency, Latin America, Stock returns

* © Copyright: The Authors, November 2008. Author for correspondence, Tel: +44 (0) 1225 384297;

Emails: [email protected] , [email protected] , [email protected]

5

1. Introduction

Over the last forty years, the relationship between stock returns and publicly available

information has attracted considerable attention in the accounting and finance

literature. Starting with the seminal work of Ball and Brown (1968) these studies find

that earnings reflect some of the information in stock prices, however, this constitutes

only a small proportion of price movements (Kothari, 2001; Chen and Zhang, 2007).

The present study examines the relationship between efficiency change and

stock returns in 19 Asian and Latin American banking sectors. The motivation for our

study is twofold. First, as Chen and Zhang (2007) highlight, the majority of the

existing studies on accounting information and stock returns focus on earnings which

are applicable only under special economic settings and fail to consider the role of

balance sheet data (Patel, 1989). It is therefore not surprising that recent research has

shifted towards the use of additional data such as accruals (e.g. Sloan, 1996; DeFond

and Park, 2001), revenues (e.g. Jegadeesh and Linvat, 2006), economic value added

(Biddle et al., 1997) and efficiency (Alam and Sickles, 1998), to understand how they

affect stock prices and returns1. Kothari (2001) points out that, overall, the results

from such studies indicate that performance measures that have evolved voluntarily in

an unregulated environment are more likely to be incrementally informative than

those mandated by regulation. The efficiency measures used in the present study

fulfill the aforementioned criteria and have several advantages over traditional

accounting ratios as performance measures, and have also been shown in the past to

better explain stock returns2.

1Sloan (1996), and DeFond and Park (2001) examine the price impact and profitability of trading

strategies based on total accruals and discretionary accruals and reveal that financial statement

information beyond earnings has significant value implications. Jegadeesh and Linvat (2006) show that

stock price reactions to earnings announcement data is significantly related to contemporaneous as well

as past revenue surprises. Alam and Sickles (1998) examine the U.S. airline industry and report a

significant positive relationship between stock market returns and changes in technical efficiency. In

contrast, Biddle et al. (1997) examine whether economic value added (EVA) performance measures

correlates more highly with stock returns and prices than historical cost accounting earnings and find

that earnings generally outperform EVA. 2 Berger and Humphrey (1997) and Bauer et al. (1998) emphasise that efficient frontier approaches

seem to be superior compared to the use of traditional financial ratios. Thanassoulis et al. (1996) point

out that the advantage of efficiency frontier techniques is that they take into account simultaneously all

inputs and all outputs of a firm, in contrast to ratios where one input (e.g. total assets) is related to one

output (e.g. profits) each time. In a similar manner, Berger and Humphrey (1997) argue that frontier

approaches offer an overall objective numerical score and ranking, an efficiency proxy to comply with

the economic optimization mechanism. Beccalli et al. (2006) find that a model that includes efficiency

estimates obtained through data envelopment analysis explains a much higher variability in stock prices

than a model developed with traditional accounting ratios.

6

Second, Cooper et al. (2003) and Beccalli et al. (2006) point out that the

literature on accounting information and stock returns typically excludes banking

institutions due to their high leverage and other distinguishing characteristics of the

industry (e.g. regulations). In an attempt to close this gap, studies from the banking

literature have recently investigated the relationship between bank efficiency, and

stock returns3. However, despite the substantial number of studies on bank efficiency,

this specific strand of the literature remains rather limited with only a handful of

country-specific studies covering Australia (Kirkwood and Nahm, 2006), Greece

(Pasiouras et al., 2008a), Malaysia (Sufian and Majid, 2006), Spain (Adenso-Diaz and

Gascon, 1997), Singapore (Chu and Lim, 1998; Sufian and Majid, 2007), and the US

(Eisnbeis et al., 1999). Furthermore, in a cross-country setting, Beccalli et al. (2006)

provide evidence from the five principal banking sectors (i.e. France, Germany, Italy,

Spain, UK), Liadaki and Gaganis (2008) investigate the EU-15, while Fernandez et al.

(2002) examine fifteen countries from the European economic area as well as US,

Canada, and Japan. Generally, these studies (that typically focus on developed

banking systems) indicate that efficiency measures derived from various frontier

estimation procedures are related to bank stock price performance.

The aim of this paper is to extend the aforementioned literature by drawing on

a sample across 19 developed and developing Asian and Latin American countries,

the majority of which have not been examined in the past. As Myring (2006) points

out, differences in the environment such as the quality of accounting standards (Ali

and Hwang, 2000), the relative importance of a country’s equity market to the

economy, macro corporate governance mechanisms (Ball et al., 2000) and the

availability of earnigns forecast may impact the performance-returns relation.

Therefore, we evaluate the bank returns – efficiency relationship by estimating a

common frontier while accounting for environmental differences across countries. As

such, we avoid the limitations of using a small sample, a drawback that is common to

the three earlier studies that provide country-specific evidence from emerging

markets4. Another important aspect of our study is that we examine both cost and

3 In addition to the bank efficiency-stock return studies, Cooper et al. (2003) examine the predictability

in the cross-section of bank stock returns using information contained in individual variables such as

income from derivative usage, previous loan commitments, interest raw swap activity, loan-loss

reserve, earnings and leverage. 4 Chu and Lim (1998) and Sufian and Majid (2007) examine 6 Singaporean banks over 1992-1996, and

1993-2003 respectively. Sufian and Majid (2006) examine 9 Malaysian banks over 2002-2003.

7

profit efficiency in contrast to most of the previous studies that focus on the

relationship between technical and/or cost efficiency and stock returns5,6. Profit

efficiency is a wider concept as it combines both costs and revenues in the

measurement of efficiency and as Maudos et al. (2002) argue it provides a more

important source of information than the partial view offered by analyzing cost

efficiency. Furthermore, from a shareholder’s perspective, we would expect

shareholders to be more sensitive to the profits of a bank rather than its costs, because

the dividends that they receive depend on the bank’s earning. Thus, profit efficiency

changes may be reflected better in stock returns, compared to other measures such as

cost and technical efficiency changes.

The rest of the paper is structured as follows. Section 2 presents the data and

methodology. Section 3 discusses the empirical results and section 4 concludes the

study.

2. Data and Methodology

2.1. Data

Our starting point consisted of the population of commercial banks and bank holding

companies that are listed on the stock exchanges of Asia and Latin America, and

appear in the Bankscope database7. After excluding banks due to: (i) missing or zero

values for inputs/outputs, and (ii) missing values in the case of the country-specific

control variables, we obtained a sample of 260 banks operating in 19 countries

between 2000 and 2006. The dataset is unbalanced and consists of 1,629 yearly

observations. Table 1 presents the distribution of the sample by year and country.

Obviously, both the efficiency estimates and the second stage regressions could be influenced by the

small number of observations. 5 Technical efficiency (TE) indicates whether a bank uses the minimum quantity of inputs to produce

given quantity of outputs. Allocative efficiency (AE) refers to the ability of a bank to use the optimum

mix of inputs given their respective prices. Cost efficiency, which is the product of TE and AE, shows

the ability of a bank to provide services without wasting resources as a result of technical or allocative

efficiency. 6 Chu and Lim (1998) in Singapore, Sufian and Majid (2006) in Malaysia, and Pasiouras et al. (2008a)

in Greece estimate profit-oriented measures of efficiency by defining expenses as inputs and profits as

outcomes. However, these studies do not use information on input and/or output prices which are

required for the estimation of cost and profit efficiency (Coelli et al., 2005). Thus, one can relatively

easily argue that these studies focus on technical efficiency from a different perspective rather than

profit efficiency. One study that actually examines profit efficiency is the one of Kirkwood and Nahm

(2006) in Australia. Finally, Liadaki and Gaganis (2008) examine the relationship between cost and

profit efficiency and stock returns in the EU-15. 7 Information on whether banks were listed or not on a stock exchange was also obtained by

Bankscope.

8

[Insert Table 1 Around Here]

We collected information from various sources. Bank-specific data were obtained

from Bankscope database of Bureau van Dijk and were converted to US dollars. As in

Altunbas et al. (2001) and Lozano-Vivas and Pasiouras (2008) among others, we

express the data in real terms using country GDP deflators. Information on bank

regulations and supervision was obtained by the World Bank (WB) database

developed by Barth et al. (2001) and updated by Barth et al. (2006, 2007)8. Data for

concentration were collected from the updated version of the WB database on

financial development and structure (Beck et al., 2006b). Data for the macroeconomic

conditions and financial development indicators were obtained from the Global

Market Information Database (GMID). The International Monetary Fund (IMF) list

served as the basis for classifying countries into developed and developing.

2.2. Methodology

2.2.1. Efficiency estimates

We use the Battese and Coelli (1995) model that allows the estimation of efficiency in

a single stage, while controlling for cross-country differences. In its general form, the

cost model can be written as follows:

tititititi vupqCC ,,,,, );,(ln ++= β , ;,...,2,1 Ni = Tt ,...,2,1= (1)

where: tiC , is the total cost of bank i at time t; tiq , is a vector of outputs; tip , denotes a

vector of values of input prices associated with a suitable functional form; β is a

vector of unknown scalar parameters to be estimated; sv ti, are random errors,

8 This WB database is available for only three points in time. Version I was released in 2001 and has

information for 117 countries (Barth et al., 2001b). For most of the countries, information corresponds

to 1999, while for others information is either from 1998 or 2000. Version II describes the regulatory

environment at the end of 2002 in 152 countries (Barth et al., 2006). Version III describes the situation

in 142 countries in 2005/06 (Barth et al., 2007). Consequently, we had to work under the assumption

that the scores of our regulatory variables remain constant within short windows of time. More

precisely, we used information from Version I for bank observations from the period 1999-2000, from

Version II for bank observations from the period 2001-2003, and from Version III for bank

observations from 2004-2006. Other studies that have used this database across a number of years have

obviously worked under a similar assumption (e.g. Demirguc-Kunt and Detragiache, 2002; Demirguck-

Kunt et al., 2004; Fernandez and Gonzalez, 2005; Beck et al., 2006c; Pasiouras et al., 2008b; Lozano-

Vivas and Pasiouras, 2008).

9

assumed to be i.i.d. and have ),0( 2

vN σ ; su ti , are the non-negative inefficiency effects

in the model which are assumed to be independently (but not identically) distributed,

such that tiu , is obtained by truncation (at zero) of the ),( 2

, utimN σ distribution where

the mean is defined by:

δtiti zm ,, = (2)

where tiz , is a )1( xM vector of observable explanatory variables that influence the

inefficiency of bank i at time t; and δ is an )1(Mx vector of coefficients to be

estimated (which would generally be expected to include an intercept parameter). The

parameters of equations (1) and (2) are estimated in one-step using maximum

likelihood9.

The specification of the profit frontier model is the same as that of the cost

frontier (equation (1)) with profit before tax (PBT) replacing total costs as the

dependent variable. However, the sign of the inefficiency term now becomes negative

(-uit). Thus, as in most previous studies, we estimate an alternative profit frontier,

which ignores output prices10. Furthermore, since a number of banks in the sample

exhibit negative profits (i.e. losses), the dependent variable in the profit model is

transformed to ( )( )1lnmin ++ PBTPBT , where min)(PBT is the minimum absolute

value of PBT over all banks in the sample.

For the selection of the inputs and outputs, we follow the intermediation

approach. We use three outputs namely loans (Q1), other earning assets (Q2), and

non-interest income (Q3), and two input prices that account for the cost of borrowed

funds (W1), and the cost of non-financial inputs (W2)11. A time trend (T=1 for 2000,

9 See Battese and Coelli (1995) and Coelli et al. (2005), for further details.

10 Berger and Mester (1997) argue that alternative profit efficiency may provide useful information and

be preferred when one or more of the following conditions are applicable: (a) there are substantial

unmeasured differences in the quality of banking services; (b) outputs are not completely variable; (c)

output markets are not perfectly competitive; (d) output prices are not accurately measured. Based on

these arguments, Kasman and Yildirim (2006) point out that in international comparisons with a

diverse group of countries and competition levels it seems more appropriate to estimate an alternative

rather than a standard profit function. Furthermore, DeYoung and Hasan (1998) point out that output

quantities tend to vary across banks to a greater extent than output prices, thus explaining a larger

portion of the variation in profits in regression analysis. 11Non-interest income is used as an output to account for the fact that a large proportion of total income

is generated by off-balance-sheet and other non-traditional activities. The cost of borrowed funds is

calculated as the ratio of interest expenses to total deposits. The cost of non-financial inputs is

calculated as non-interest expenses to total assets. Ideally, we would include three input prices, one for

labour (e.g. personnel expenses to total), one for borrowed funds (interest expenses to deposits) and

10

T=2 for 2001,…, T=7 for 2006) is included in the function to account for changes in

technology over time. As in Lensink et al. (2008) the trend is included with both T

and T2 terms. Following Berger and Mester (1997) among others, we specify equity as

a fixed input to control for differences in bank capitalization. We impose linear

homogeneity restrictions by normalizing the dependent variables and input prices by

the second input price W2. Using the multi-product translog specification gives our

empirical cost frontier model as follows12:

( )( ) ( ) ( ) ( ) ( )

titi vu

TEQTW

WTQTQTQTT

W

WEQEQEQEEQ

EQW

WQ

W

WQ

W

WQ

W

W

QQQQQQQQ

QW

WQQQ

W

TC

,,

2726252423

2

2221

20191817

2

16

15141312

2

11

2

109

2

876

2

543210

)ln(2

1ln)3ln()2ln()1ln(

2

1lnln3ln)ln(2ln)ln(1ln)ln(ln

2

1

)ln(2

1ln)3ln(

2

1ln)2ln(

2

1ln)1ln(

2

1ln

2

1

))3(ln(2

1)3ln()2ln())2(ln(

2

1)3ln()1ln()2ln()1ln(

))1(ln(2

1

2

1ln)3ln()2ln()1ln(

2ln

++

+

++++++

++++

++

+

+

+

+

+++++

+

++++=

βββββββ

βββββ

βββββ

βββββ

ββββββ

(3)

To control for cross-country differences and bank-specific risk we define itm in

Equation (2) as:

JAPANDEVELCLAIMSGDPGRINF

CONCACTRSMDISCIPOFFPRCAPITRQLLRmit

1110987

6543210

δδδδδ

δδδδδδδ

+++++

++++++= (4)

Where LLR is calculated as loan loss reserves to gross loans and controls for bank-

risk. CAPITRQ, OFFPR, MDISCIP and RESTR are variables that control for the main

one for physical capital (non-interest expenses other than personnel expenses to fixed assets) as in most

previous studies. However, due to missing values for more than 500 observations in the case of

personnel expenses, we restricted our analysis to the two input prices discussed above. Our approach is

similar to the one of Hasan and Marton (2003), Bonin et al. (2005a,b), Fries and Taci (2005),

Staikouras et al. (2008), Berger et al. (2009). 12 For brevity of space, we present only the cost function here. As mentioned before, for the estimation

of profit efficiency, we replace total costs by profit before taxes, and change the sign of the inefficiency

term (-uit).

11

regulatory conditions in the various banking sectors13. CAPITRQ is a measure of

capital requirements that accounts for both initial and overall capital stringency14.

OFFPR is a measure of the power of the supervisory agencies indicating the extent to

which they can take specific actions against bank management and directors,

shareholders, and bank auditors. MDISCIP is an indicator of market discipline and

shows the degree to which banks are forced to disclose accurate information to the

public and whether there are incentives to increase market discipline. RESTR is a

proxy for the level of restrictions on banks’ activities. It is determined by considering

whether securities, insurance, real estate activities, and ownership of non-financial

firms are unrestricted, permitted, restricted, or prohibited. CONC is the concentration

in the banking sector, as measured by the proportion of total assets held by the three

largest banks in the country. INF is the annual rate of inflation and GDPGR is the real

GDP growth, both capturing macroeconomic conditions. CLAIMS measures the

activity in the banking sector and it is calculated by dividing bank claims to the

private sector with GDP. DEVEL is as a dummy variable that takes the value of one

for developed countries and zero for developing countries. Finally, JAPAN is as a

dummy variable that takes the value of one for Japan and zero for other countries15.

The individual bank (in)efficiency scores are calculated from the estimated

frontiers as CEkt= exp(ui) and PEFkt = exp(-ui), the former taking a value between one

13 Recent studies that have used these variables in the efficiency and productivity literature include

Pasiouras (2008), Pasiouras et al. (2008b), Delis et al. (2008), and Lozano-Vivas and Pasiouras (2008). 14 For the construction of CAPITRQ, OFFPR, and MDISCIP, we use the summation of the 0/1

quantified answers as in Gonzalez and Fernandez (2005), Barth et al. (2001b, 2004b, 2007), Pasiouras

et al. (2006, 2008b), Pasiouras (2008), Fonseca and Gonzalez (2008), Lozano-Vivas and Pasiouras

(2008), Delis et al. (2008). An alternative would be to use the principal component as in Beck et al.

(2006a). Barth et al. (2004a) have followed both approaches. They mention that on the one hand the

drawback of using the summation for the construction of the index is that it assigns equal weight to

each of the questions whereas on the other hand the disadvantage of the first principal component is

that it is less transparent how a change in the response to a question changes the index. While they only

report the empirical reports using the principal component indexes, they mention that “we have

confirmed all this paper’s conclusions using both methods” (p. 218), implying that there are no

differences in the results. Further information on the calculation of these variables is available in the

Appendix. 15 We include this dummy variable in the analysis in an attempt to capture any distinguishing

characteristics of the Japanese banking sector and account somehow for the large number of Japanese

banks in our sample. For instance, Japan is the world’s second-biggest economy and a highly advanced

developed country with a large financial market. It is also being considered as the largest bank-based

economy (Liu and Tone, 2008) and more than 100 Japanese banks are constantly being listed in the

1,000 largest world banks accounting for 10% of the Top 1000 Tier 1 capital and total assets in 2007

(The Banker, 2004, 2007). However, at the same time, Japan’s banking industry was one of the most

distressed in the region in recent years and has been subject to significant restructuring. After several

years of poor performance, Japanese banks recorded profits in 2004, however their average return of

capital (5.2% in 2004 for the 113 banks included in the world top 1,000) was well below the Asian

(12.1%) and Latin American (23.9%) corresponding figures (The Banker, 2004).

12

and infinity and the latter between zero and one. In both cases, scores closer to 1

indicate higher efficiency. To make our results comparable, we calculate the index of

cost efficiency as follows: CEFkt= 1/ CEkt. Hence, both cost and profit efficiency

scores take values between 0 and 1 with values closer to 1 indicating a higher level of

efficiency.

2.2.2. Efficiency change and stock returns

To test whether cost and profit efficiency changes are incorporated into stock

valuation and they subsequently result in higher returns for the banks’ shareholders,

we undertake the estimation of the simple linear model:

( ) ( )∑ ∑ +++=J

j

K

k

titiktijti hprfdcfdmr ,,,0,12 γββ

tiitih ,, ωε += (5)

Where ,12 i tr m denotes the annual returns of bank (i) over the time period16 (t).

Changes in cost efficiency per bank over the same period are given by ,( )i td cf (d is

the first difference operator), and profit efficiency changes are shown as ,( )i td prf .

The stochastic component tih , contains the unobserved bank specific effect

( )εσε ,0~ IIDi that is assumed to be time invariant and the idiosyncratic

disturbance ( )ωσω ,0~, IIDti .

The role of the bank-specific stochastic component in the error structure

cannot be determined a-priori; it will be established depending on the method of

estimation. Allowing for unknown correlation between this component and the

regressor requires the estimation of such parameter; to obtain consistent estimates of

the parameter vector ( , )j kβ γ , this is normally referred as fixed effects estimation.

Alternatively the assumption of zero correlation between the firm-specific unobserved

element and the regressor reduces the number of parameters to be estimated thus

16 We use a 12-months window that covers the same period as the fiscal year. As in Chu and Lim

(1998) we use stock prices from the first and the last day of the window that we examine, and calculate

the returns as [(Pt-Pt-1)/Pt-1]. Stock prices have been adjusted based on the stock split/dividend ratio and

as other bank-level data are expressed in real terms using GDP deflators.

13

increasing the efficiency of the estimated coefficients; in this case the appropriate

method is what is known as random effects.

By and large, inference in this context requires robust estimators of the

variance matrix as the usual least squares estimators rely heavily on the very strong

assumptions of time invariance and independence of the conditional variances of both

error components and zero conditional co-variances. To establish the appropriate

estimation method the test statistic proposed by Hausman (1978) is used. Under the

null hypothesis both fixed and random effects estimators are consistent with the

random effect being the efficient choice. Under the alternative the fixed effects is

inefficient but it remains consistent whilst the random effects estimator becomes

inconsistent. Rejection of the null hypothesis advises against the use of the random

effects estimator.

If improvements in cost and profit efficiency are reflected in stock returns, we

expect that there will be a positive association between these changes and the

dependent variable. In addition, to establish whether the same variables carry

information that cannot be fully reflected in other published accounting data, we

incorporate in the model another variable that indicates financial performance, namely

the change in bank’s return on equity (ROE). By augmenting the previous model:

Under the null hypothesis that ROE incorporates all the relevant information and

therefore efficiency improvements do not convey independent information it is

expected that:

( ) ( )∑ ∑ ∑ ++++=J

j

K

k

L

l

titiktiktijti hroedprfdcfdmr ,,,,0, )(12 δγββ (6)

0 ,j k j kβ γ= = ∀

3. Results

3.1. Efficiency scores

Table 2 presents the cost and profit efficiency estimates by year and geographical

region. It is evident that there are marked differences in their evolution and range and

in addition between the groups of Asian banks on the one hand and Latin American in

the other.

14


By and large, the measures of cost efficiency are far more predictable in all

cases as measured by the coefficient of variation (s.d/mean). This measure of

volatility does not exceed 10% in any case and shows remarkable stability over time.

In addition, the steady mean figures, in the region of 92% with similar median figures

(95%) indicate a near symmetric distribution. Both Asian and Latin American banks

exhibit very similar distributions as both the differences between the first and second

moments are not significantly different from zero, over the whole period of the

sample. From our sample, it appears that all the banks are performing very near the

limit of maximum cost efficiency implying that local managers are mindful of

expenses when allocating resources to the production of the bank’s different outputs.

Thus, the average bank in the sample should reduce its costs by only 7.24% to match

the best practice bank. The similarity between the means and standard deviations are

indicative of a common production technology that is present in the banking sector in

the region under study. The adoption of such technology may limit managerial

discretion in choosing a somewhat idiosyncratic combination of resources reflecting

individual preferences. The transmission of technology across countries for the

making of similar products is a well established consequence of globalisation and

from this, preliminary, assembled evidence it appears that this process is not limited

to manufacturing but it is also present in the production of financial products and

services.

Turning to the profit efficiency the average figure is equal to 0.7025 meaning

that the average bank in sample should improve its profits by 29.75% to match the

best practise bank. A correlation analysis of the cost and profit efficiency scores,

confirms the results of previous studies that these scores do not move in parallel as

one could expect17. One explanation is that the profitability of the bank depends both

upon the costs that are incurred in the production of the services and products and the

yield of the assets and liabilities that they manage. The management of assets and

liabilities is probably the most demanding task that the managing team is facing. Their

17 Our finding is consistent with Berger and Mester (1997), Rogers (1998), Pasiouras et al., (2008b). In

our case, the Pearson’s coefficient is as low as 0.096. As in Rogers (1998), we also examine the

relationship between the rankings of the banks, rather than the efficiency scores. In this case, the

Spearman’s coefficient is equal to 0.306, indicating that there are important differences in the ranking

of banks between these two measures of efficiency.

15

actions are limited by ‘domestic’ regulatory practices and other institutional factors. It

is in this metric that the ‘quality’ of managerial teams is distinguished and their

relative performance is judged by the shareholders whose judgement will be reflected

on the value of equity. In our sample, we observe significant differences between

banks from the two geographical blocks. The Asian banks exhibit profit efficiency at

least twice the level exhibited by Latin American banks. The mean efficiency score

for Asian banks is 0.75 and 0.35 for the Latin America banks. In addition to such

discrepancies at the mean, there is significant difference in the coefficient of variation.

Unlike the performance of cost efficiency where there is no significant difference

between the moments of the two distributions, in this case there is a marked increase

in the coefficient of variation in both distributions. Regarding profit efficiency, the

relevant magnitudes are at least twice as large compared to the cost efficiency

averaging 24% and 43% for Asian and Latin American banks respectively. Such

diversity of performance between the geographical blocks and between the banks may

be informative predictors of volatile series such as bank stock returns and so we

expect their inclusion will add significant value to the explanation of bank stock

return variation.

3.2. Regression results

Panel A in Table 3 presents the second stage regression results of the model discussed

in section 2. The exclusion restrictions 0k lγ δ= = were not rejected in either fixed or

random effects model the p-values of the test statistics being 0.11 and 0.12

respectively. The exclusion restriction regarding the contribution of the variable

denoting improvements in profit efficiency was decidedly rejected in both cases with

p-value equal to 0.0025.


As our sample contains extreme return observations and these may exercise undue

influence on the estimated coefficients we reformulate the model by transforming the

dependent variable into binary values, assuming the value of 1 if ,12 0i tr m > and zero

otherwise. The resulting logistic panel regression is therefore free form all scale

16

factors of the dependent variable and allows for robustness in the reported results. In

this case, we are examining whether changes in efficiency categorise the bank into

one belonging to a set of banks experiencing positive stock returns independently of

their size. The results of the logistic regression are shown in Panel B. As in the

previous specification, we established that profit efficiency improvements are

incorporated into stock returns and their information content makes a modest but

independent contribution that is not captured by changes of ROE.

To conclude, our results show that profit efficiency changes are reflected in

stock returns, although this is not the case for cost efficiency changes. These results

are consistent with the ones of Liadaki and Gaganis (2008) for the EU-15 banking

sector. One potential explanation is that rational shareholders are supposed to be

interested in their eventual wealth, which is the result of dividend payments and

capital gains18 (Board and Day, 1989). Consequently, because dividends will be paid

on the basis of profits, stock returns are more sensitive to profits efficiency rather than

cost efficiency. Furthermore, while cost efficiency offers an indication as for the

capability of managers, it will not necessarily result in improvements in profits, as

these are subject to revenue efficiency as well. This is particularly important because

it is the capability in generating profits rather than solely managing costs that allows

companies to implement their strategies and grow.

As for our observation that profit efficiency appears to be more important than

traditional indicators of profitability (i.e. ROE) this can be potentially attributed to

“quality of earnings” and the “persistency of earnings” explanations. As Chan et al.

(2006) point out, there have been growing concerns about the quality of earnings, or

the extent to which reported earnings reflect operating fundamentals. Furthermore,

Nichols and Wahlen (2004) demonstrate that stock returns react more strongly to

changes in profits that are likely to recur than to changes likely to be transitory. As a

positive )( prfd indicates that the bank is capable of operating efficiently in terms of

generating profits there is an increased likelihood that it will continue to generate

earnings in the future. In addition, efficiency is an indicator of quality that may allow

banks to improve their profits, at least relative to competitors, even under adverse

18 Obviously, not all investors intend to keep the stocks in the long-term and benefit from dividend

payments. However, this group of investors will also have an interest on the stream of expected future

dividends since the stock price should reflect the present value of future dividends. Thus, positive

changes in the expectations about the dividends will result in positive stock price changes, allowing

them to earn profits on sale (Board and Day, 1989).

17

environmental circumstances. In other words, because profit efficiency changes take

into account input and output considerations simultaneously via economic

optimization mechanisms (we would argue) that they provide more information about

the quality and the persistency of earnings than changes in ROE. Furthermore,

Destefanis and Sena (2007) mentions that ratios such as ROE under-represent the

firm’s value because of the investment myopia problem, while this is not the case for

efficiency scores. Destefanis and Sena (2007) also note that if managers engage in

myopic behaviour, long-term investment should be expected to decrease resulting in

lower efficiency. Additionally, due to the separation between management and

ownership, managers may have incentives to invest in projects granting power and

prestige, but not resulting in an improvement in productivity (Shleifer and Vishny,

1997). Such behaviour is likely to be instantly reflected in a reduction in efficiency,

justifying the interest of shareholders in such indicators of performance (Destefanis

and Sena, 2007).

4. Conclusions

The relation between stock returns and publicly available information has traditionally

attracted the attention of researchers in accounting and finance. While the majority of

the literature focuses on earnings, some recent studies examine other firm attributes

such as accruals, revenue surprises, economic value added, and efficiency. Motivated

by the limited research in banking, the present study examines the relationship

between efficiency change and stock returns in 19 Asian and Latin American banking

sectors.

We first use the stochastic frontier analysis to obtain estimates on the cost and

profit efficiency of banks during 2000-2006, while accounting for environmental

differences. Then, we regress annual efficiency changes on annual stock returns. The

results indicate a positive and robust relationship between profit efficiency changes

and stock returns. However, we find no evidence that cost efficiency changes are

reflected in stock returns. Furthermore, we observe that the change in the return on

equity does not provide incremental information.

There are a number of potential explanations for these findings. First,

shareholders are interested in profits rather than costs due to the relationship of the

former with dividends that influences both the future dividend payments and

18

subsequent movements in stock prices. Second, it is likely that profit efficiency

measures are indicators of the “quality of earnings” and the “persistency of earnings”

while traditional profitability ratios are not. Finally, efficiency measures may be able

to provide information that is not biased by the investment myopia problem or agency

problems.

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Table 1 - Sample composition by year and country

2000 2001 2002 2003 2004 2005 2006 Total

Argentina 4 4 4 5 5 5 5 32

Brazil 6 6 6 7 7 7 7 46

Chile 4 4 4 4 4 4 4 28

China 10 10 10 10 10 10 10 70

Colombia 2 2 2 2 2 2 2 14

Hong Kong 9 10 10 10 10 10 9 68

India 4 24 21 23 25 22 25 144

Indonesia 19 20 20 21 21 21 18 140

Japan 84 88 91 92 92 95 95 637

Kazakhstan 2 2 2 2 2 2 2 14

Korea 4 7 9 9 9 11 11 60

Malaysia 8 8 11 10 11 13 9 70

Mexico 2 3 2 2 2 2 2 15

Pakistan 4 4 4 4 4 4 4 28

Peru 5 5 5 5 5 5 5 35

Philippines 11 11 11 11 9 9 9 71

Singapore 4 4 3 4 4 3 3 25

Thailand 13 13 13 13 13 13 13 91

Venezuela 5 6 6 6 6 6 6 41

Total 200 231 234 240 241 244 239 1,629

25

Table 2- Descriptive statistics of Cost and Profit efficiency estimates

Panel A: Cost efficiency 2000 2001 2002 2003 2004 2005 2006 2000-06

Latin America

Average 0.9054 0.9144 0.9125 0.9162 0.9255 0.9203 0.9082 0.9148

St.dev 0.0721 0.0540 0.0508 0.0509 0.0386 0.0399 0.0536 0.0517

Median 0.9327 0.9354 0.9209 0.9305 0.9334 0.9327 0.9286 0.9305

Min. 0.6482 0.7562 0.7698 0.7402 0.7975 0.8329 0.7416 0.6482

Max. 0.9774 0.9798 0.9763 0.9692 0.9732 0.9742 0.9684 0.9798

Coefficient of Variation 0.0796 0.0591 0.0556 0.0555 0.0417 0.0434 0.0591 0.0565

No. obs 28 30 29 31 31 31 31 211

Asia

Average 0.9349 0.9203 0.9253 0.9290 0.9359 0.9360 0.9255 0.9295

St.dev 0.0866 0.0894 0.0801 0.0744 0.0641 0.0435 0.0792 0.0749

Median 0.9543 0.9541 0.9517 0.9526 0.9525 0.9542 0.9541 0.9534

Min. 0.1163 0.0949 0.1419 0.2023 0.1941 0.7161 0.0804 0.0804

Max. 1.0000 1.0000 1.0000 1.0000 1.0000 0.9881 0.9837 1.0000


No. obs 172 201 205 209 210 213 208 1418

Total

Average 0.9307 0.9195 0.9237 0.9273 0.9345 0.9340 0.9233 0.9276

St.dev 0.0852 0.0856 0.0771 0.0719 0.0615 0.0433 0.0765 0.0725

Median 0.9522 0.9527 0.9506 0.9512 0.9505 0.9515 0.9522 0.9514

Min. 0.1163 0.0949 0.1419 0.2023 0.1941 0.7161 0.0804 0.0804

Max. 1.0000 1.0000 1.0000 1.0000 1.0000 0.9881 0.9837 1.0000


No. obs 200 231 234 240 241 244 239 1629

Panel B: Profit efficiency 2000 2001 2002 2003 2004 2005 2006 2000-06

Latin America

Average 0.2921 0.2979 0.3255 0.3720 0.4305 0.4259 0.4796 0.3768

St.dev 0.1038 0.1026 0.1235 0.1591 0.1785 0.1565 0.1943 0.1628

Median 0.2889 0.2854 0.3019 0.3417 0.4075 0.4388 0.4764 0.3429

Min. 0.0975 0.1352 0.1193 0.1330 0.1488 0.1461 0.1477 0.0975

Max. 0.5408 0.5055 0.5966 0.7263 0.7437 0.7348 0.8884 0.8884


No. obs 28 30 29 31 31 31 31 211

Asia

Average 0.7397 0.7356 0.7472 0.7512 0.7484 0.7585 0.7737 0.7510

St.dev 0.2017 0.1946 0.1932 0.1824 0.1864 0.1720 0.1682 0.1853

Median 0.8445 0.8222 0.8347 0.8149 0.8164 0.8320 0.8507 0.8302

Min. 0.0924 0.1260 0.0000 0.1151 0.0417 0.1900 0.2040 0.0000

Max. 0.9614 0.9608 0.9823 0.9734 0.9709 0.9705 0.9658 0.9823


No. obs 172 201 205 209 210 213 208 1418

Total

Average 0.6770 0.6788 0.6950 0.7022 0.7075 0.7163 0.7355 0.7025

St.dev 0.2463 0.2367 0.2321 0.2200 0.2135 0.2028 0.1979 0.2216

Median 0.7365 0.7430 0.7601 0.7669 0.7731 0.7718 0.8228 0.7702

Min. 0.0924 0.1260 0.0000 0.1151 0.0417 0.1461 0.1477 0.0000

Max. 0.9614 0.9608 0.9823 0.9734 0.9709 0.9705 0.9658 0.9823


No. obs 200 231 234 240 241 244 239 1629

26

Table 3 – Regression results

Panel A: Linear Regression (Dependent Variable = Bank stock return over twelve months,

,12 i tr m )

Fixed Effects Random Effects Random Effects Fixed Effects

d(cf) 0.00359 (0.997) 0.643 (1.07) - -

d(prf) 0.35 (3.03) 0.354 (3.22) 0.2269 (3.03) 0.219 (2.64)

d(roe) -0.559 (1.93) -0.472 (1.88) - -

R-sqr 0.0135 0.0145 0.0072 0.008

Panel B: Logistic Regression (Dependent Variable: dpos =1 if Bank stock return over twelve

months : ,12 i tr m >0, 0 otherwise)

Fixed Effects Random Effects Random Effects Fixed Effects

d(cf) 0.023 (0.74) 0.037 (1.63) - -

d(prf) 0.009 (1.98) 0.010 (2.69) 0.00842 (2.32) 0.00845 (2.04)

d(roe) -0.007 (0.02) -0.008 (1.10) -

Notes: d(cf) = cost efficiency change, d(prf) = profit efficiency change; d(roe) = roe change; t-

statistics are in parentheses.

27

Appendix - Information on regulatory variables Variable Category Description

CAPITRQ Capital

requirements

This variable is determined by adding 1 if the answer is yes to questions 1-6 and 0 otherwise, while the opposite occurs in the case of

questions 7 and 8 (i.e. yes=0, no =1). (1) Is the minimum required capital asset ratio risk-weighted in line with Basle guidelines? (2) Does

the ratio vary with market risk? (3-5) Before minimum capital adequacy is determined, which of the following are deducted from the book

value of capital: (a) market value of loan losses not realized in accounting books? (b) unrealized losses in securities portfolios? (c)

unrealized foreign exchange losses? (6) Are the sources of funds to be used as capital verified by the regulatory/supervisory authorities?

(7) Can the initial or subsequent injections of capital be done with assets other than cash or government securities? (8) Can initial

disbursement of capital be done with borrowed funds?

MDISCIP Market

discipline

This variable is determined by adding 1 if the answer is yes to questions 1-7 and 0 otherwise, while the opposite occurs in the case of

questions 8 and 9 (i.e. yes=0, no =1). (1) Is subordinated debt allowable (or required) as part of capital? (2) Are financial institutions

required to produce consolidated accounts covering all bank and any non-bank financial subsidiaries? (3) Are off-balance sheet items

disclosed to public? (4) Must banks disclose their risk management procedures to public? (5) Are directors legally liable for

erroneous/misleading information? (6) Do regulations require credit ratings for commercial banks? Is there a compulsory external audit by

a certified/licensed auditor? (8) Does accrued, though unpaid interest/principal enter the income statement while loan is non-performing?

(9) Is there an explicit deposit insurance protection system?

OFFPR Official

disciplinary

power

This variable is determined by adding 1 if the answer is yes and 0 otherwise, for each one of the following fourteen questions: (1) Does the supervisory agency have the right to meet with external auditors to discuss their report without the approval of the bank? (2) Are auditors required by law to communicate directly to the supervisory agency any presumed involvement of bank directors or senior

managers in illicit activities, fraud, or insider abuse? (3) Can supervisors take legal action against external auditors for negligence? (4) Can the supervisory authorities force a bank to change its internal organizational structure? (5) Are off-balance sheet items disclosed to

supervisors? (6) Can the supervisory agency order the bank's directors or management to constitute provisions to cover actual or potential

losses? (7) Can the supervisory agency suspend director’s decision to distribute dividends? (8) Can the supervisory agency suspend

director’s decision to distribute bonuses? (9) Can the supervisory agency suspend director’s decision to distribute management fees? (10)

Can the supervisory agency supersede bank shareholder rights and declare bank insolvent? (11) Does banking law allow supervisory

agency or any other government agency (other than court) to suspend some or all ownership rights of a problem bank? (12) Regarding

bank restructuring and reorganization, can the supervisory agency or any other government agency (other than court) supersede

shareholder rights? (13) Regarding bank restructuring & reorganization, can supervisory agency or any other government agency (other

than court) remove and replace management? (14) Regarding bank restructuring & reorganization, can supervisory agency or any other

government agency (other than court) remove and replace directors?

RESTR Restrictions on

banks activities

The score for this variable is determined on the basis of the level of regulatory restrictiveness for bank participation in: (1) securities

activities (2) insurance activities (3) real estate activities (4) bank ownership of non-financial firms. These activities can be unrestricted,

permitted, restricted or prohibited that are assigned the values of 1, 2, 3 or 4 respectively. We use an overall index by calculating the

average value over the four categories.

Note: The individual questions and answers were obtained from the World Bank database developed by Barth et al. (2001b, 2006, 2007)

the relationship between bank efficiency and stock … asia and latin america 4 the relationship...

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