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ournal of Advanced Studies in Finance

Biannually Volume V Issue 2(10) Winter 2014 ISSN 2068 – 8393 Journal DOI http://dx.doi.org/10.14505/jasf 129

Journal of Advanced Studies in Finance


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Summer 2014 Volume V Issue 2(10) Editor in Chief Laura ŞTEFĂNESCU Spiru Haret University and Association for Sustainable Education Research and Science, Romania

Co-Editor Rajmund MIRDALA Technical University of Kosice, Slovak Republic

Contents: 1

Rosaria Rita Canale University of Naples Parthenope, Italy Francesco P. Esposito Allied Irish Bank, Group Market Risk Management

Zhonglin HUANG

The effect of international soccer games on exchange

2 rates using evidence from Turkey Ender DEMIR, Chi Keung Marco LAU

Renata Karkowska Faculty of Management, University of Warsaw, Poland Kosta Josifidis University of Novi Sad, Serbia Ivan Kitov Russian Academy of Sciences, Russia Piotr Misztal The Jan Kochanowski University in Kielce, Faculty of Management and Administration, Poland

3

Linkages in corporate social responsibility indices and major financial market indices. An ARMA-APARCH approach Li - Lun LIU, John Francis DIAZ

…157

Esentur IVAGOV

4

Capital market efficiency. An empirical test of the weakform in the nigerian capital market Barine Michael NWIDOBIE Julius Babatunde ADESINA

ASERS Publishing http://www.asers.eu/asers-publishing ISSN 2068-8393 Journal's Issue DOI: http://dx.doi.org/10.14505/jasf.v5.1(9).0

Andreea Pascucci University of Bologna, Italy Rachel Price-Kreitz Ecole de Management de Strasbourg, France Daniel Stavarek Silesian University, Czech Republic Laura Ungureanu Spiru Haret University and Association for Sustainable Education Research and Science, Romania

…145

Ka Wai Terence FUNG

Lean Hooi Hooi Universiti Sains Malaysia, Malaysia Terence Hung United International College, Hong Kong

…133

Mei CAI

Editorial Advisory Board Mădălina Constantinescu Spiru Haret University and Association for Sustainable Education Research and Science, Romania

Analysis of non performing loan and capital adequacy ratio among Chinese banks in the post-reform period in China

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…164


Call for Papers

Volume VI, Issue 1(11), Summer 2015


The Journal aims to publish empirical or theoretical articles which make significant contributions in all areas of finance, such as: asset pricing, corporate finance, banking and market microstructure, but also newly developing fields such as law and finance, behavioral finance and experimental finance. The Journal will serve as a focal point of communication and debates for its contributors for the better dissemination of information and knowledge on a global scale. The primary aim of the Journal has been and remains the provision of a forum for the dissemination of a variety of international issues, empirical research and other matters of interest to researchers and practitioners in a diversity of subjects linked to the broad theme of finance. The Editor in Chief would like to invite submissions for the 6th Volume, Issue 1(11), Summer 2015 of the Journal of Advanced Studies in Finance (JASF). Journal of Advanced Studies in Finance is indexed in EconLit, RePEC, CABELL's Directories, EBSCO, ProQuest, CEEOL databases. All papers will first be considered by the Editors for general relevance, originality and significance. If accepted for review, papers will then be subject to double blind peer review. Deadline for Submission: Expected Publication Date: Web: E-mails:

30th May, 2015 July, 2015 www.asers.eu/journals/jasf/ [email protected] [email protected]

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Volume V Issue 2(10) Winter 2014

DOI: http://dx.doi.org/10.14505/jasf.v5.2(10).01

ANALYSIS OF NON PERFORMING LOAN AND CAPITAL ADEQUACY RATIO AMONG CHINESE BANKS IN THE POSTREFORM PERIOD IN CHINA Mei CAI University of Adelaide, Australia [email protected] Zhonglin HUANG EHLE Institute Japanese Language School, Japan [email protected] Suggested Citation: Cai, M., Huang, Z. (2014). Analysis of non performing loan and capital adequacy ratio among Chinese banks in the postreform period in China, Journal of Advanced Studies in Finance, (Volume V, Winter), 1(10):133-144. Doi:10.14505/jasf.v5.2(10).01. Available from: http://www.asers.eu/journals/jasf/curent-issue. Article’s History: Received July, 2013; Revised September, 2014; Accepted December, 2014. 2014. ASERS Publishing. All rights reserved.

Abstract: In this paper, we use the panel data to analyze the correlation among the Return on Assets (ROA), NonPerforming Loan (NPL), Capital Adequacy Ratio (CAR), and other important factor that influence the internal and external environment such as deposit, total assets, GDP and Interest Rate. Our results indicate that NPL has no effect on ROA and the impact of CAR is negative. Keywords: return on assets (ROA), non-performing loan (NPL), capital adequacy ratio (CAR). JEL Classification: G17, G21, G28. 1. Introduction With the reconstruction of the Peoples’ Republic of China, and the opening up of the Chinese economy, the development of Chinese banks can be divided into three stages:  The banks system before 1948;  1949-1952, new banking system set up for the new government;  1953-1978, economy planning in China. The most important period in Chinese Banking History was between 1953-1978, when the only central bank was the Peoples’ Bank of China, which served as both a central bank and commercial bank, and it controlled almost 93% financial assets (Li, 2008). In order to meet the rising demand of the financial market, the government is committed to the reform of Chinese banking system. One of the key reforms is separating People’s Bank of China’s duties, and setting up the four major nationalized banks to conduct commercial banking. The People’s bank of China was a central bank, and the Chinese government began to use banks as a tool to boost the economy, not just for Marco-Control. Since 1978, the Chinese economy has developed quickly, and the financial market has become more complex; the four major national banks were used by the government to adjust the Macro-Control because it cause a large NPL and low CAR in especially large nationalized banks. In this period, the two major problems of Chinese banks are that the lower the NPL, the higher the CAR, and how the four banks implemented joint-stock system reform. Historically, CAR and NPL would have a significant impact on profitability, that is because the government will pay more money to them. After reform, banks needed

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Journal of Advanced Studies in Finance to survive by themselves. In the market, a comparative higher profitability is their major skill to live, thus, the profitability should be a new serious problem. Why do we research bank NPL now? The reason is straightforward: The “financial pollution” may be harmful to economic growth and social welfare. After the economic crisis in 2008, China used quantitative easing to stimulate consumption and encourage investment. It caused bank loans to increase by 17.5 trillion CNY (2.667 trillion USD) in the past two years, compared with just more than 4.5 trillion CNY in 2008. Much of the new lending found its way into real estate and local government infrastructure projects. Analysts and even some Chinese bankers say the credit binge has helped fuel inflation and many of the loans will not be repaid. (Jamil Anderlini 2011). If so, a major banking crisis is possible in the future, that is, large amount of NPL breaks the capital chain among banks, and low CAR leads banks to give credit to many industries without assessment. Since the government issued more money to solve this problem, a negative effect such as high inflation rate and low real interest rate will take place, and force the people losing trust to bank and government. As the result, a bankrupt storm may happen in China. However, some people think that the financial crisis was an exaggeration, and they believe that the reforming among Chinese banks would be positive in the future. (Yuanhua Wen 2013). They think that stronger supervision mechanisms and an interest of competition environment will force Chinese banks to improve themselves to overcome the problem they faced. Hence, we must regard the CAR and NPL as important factors in bank operation. In the future, the CAR and NPL should be placed and reconsidered the level of priority instead focus on aspects of profitability; and what direction banks should steer in is the focus of this paper.

2. Literature review 2.1 Profitability One of the common measures of profitability in finance is excess returns i.e. the difference between individual stock returns and risk-free rate (See Fama and French, 1993; Lettau and Ludvigson, 2001; Fung et al..2014a; Fung et al. 2014b). However, Flamin et al. (2009) argued that the determinants of bank profitability have focused on the returns on bank assets. To measure bank profitability, we use the return on assets (ROA) to define the banks’ after tax profit over total assets. (Flamin et al., 2009) Qin (2008) claimed that the interest income that comes from the interest-bearing assets, is the main revenue. Another revenue origin is the non-interest income, because the bank will be the agent of investing, and selling financial product. (Qin, 2008) In fact, on using the revenue cannot define the profitability of any bank. Consequently, Qin (2008) also claimed that although the income can be an important index to measure the earning performance, it can also be more directly to represent the amount of earning, that is, among banks of similar scale, the income can be comparable. Meanwhile, in order to find a more adaptable index to define the profitability, Qin (2008) claimed that the Return of Assets (ROA) is the most important method to measure the profitability. It is calculated by the net income and total assets in the equation below: ROA = Net Income / Total Assets * 100% Normally, the people will use the Average ROA to define the profitability instead of the Average Net ROA. That is because the the Average Net ROA will be impacted by the Capital Adequacy Ratio (CAR) to make the result larger than the normal situation. On the contrary, the Average ROA includes the cost of fund and assets to make the result more exactly. (Qin, 2008) Since this report will try to deduce the correlation between the profitability and CAR, using the Average Net ROA will facilitate finding out the correlation between these two variables. The equation of the Average Net ROA is that: ANROA = Net Income/ Average Assets * 100% Therefore, we use the ANROA as the Dependent Variable to continue the further research. 2.2. Capital adequacy ratio Previous research documents that CAR is main elements repress banks risk ， high levels CAR will decrease banks ROA. Based on the definition from Basel Accord, capital adequacy ratios (CARs) are a measure of the amount of a bank's core capital expressed as a percentage of its risk-weighted asset, and in order to keep normal operations in a bank, Basel Accord required CARs can not be under 10%. (Mei & Liu, 2010) The Capital

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Volume V Issue 2(10) Winter 2014 adequacy ratio management of commercial banks is the core of risk asset management. Then, as Wei constructed a model that reflects the correlation between capital adequacy ratio and risk assets and analyzed the specific correlation between them, from those results, the change of commercial banks＇capital adequacy ratio will impact the commercial bank capital structure and then influence its risk assets. (Wei et al..2011). In relative terms, from An’s research (An 2010), the government introduces policies about CAR and set low CAR to cause the larger capital stock and decrease bank operation. It results in decreasing banks ROA. As Qin (2008) claimed that a strong leverage effect existing in the operation mode of the bank, thus, to control the risk, the monitor institution put forward a strict request to keep the CAR of the bank. Therefore, it is obvious that the CAR will have a strong effect to the profitability as basic restraining factors. (Qin, 2008) 2.3 Ratio of non-performing loan The large non-performing loan and low profitability Bonin and Huang (2001) offered some modest proposals for dealing with the bad loans of Chinese banks. Chinese banks suffer from serious financial fragility problem by high proportions of NPLs and low capitaladequacy ratios. A key policy introduced by the Chinese government to reduce financial risks was the establishment of four asset management companies (AMCs) to deal with bad loans. NPL is not only a number, the change of NPL is related to the variation of the whole economy and directly related to about the enterpriser’s operating1. Up to 2001 the four nationalized banks have total business loan of 7000 billion CNY, and 1760 billion CNY accounted for NPL, 600billion in the NPL was the actual loss. The NPL of China was ten times higher than that in the US in the same period. According to Xu’s (2005) research, big amount of NPL in banks cause the low profitability, and the banks which pay more attention to controlling the NPL will get more positive effect and increase the profitability. From Li’s research, (Li 2008) big NPL in China banks were caused by the socialistic system. After the reform as far, NPL is not the main point for banking system, profitability is closely highly related to other elements like service quality, financial product quality. 2.4. The bank deposit The Bank Deposit can directly decide the loan limit to a certain extent. As Goldstein and Pauzner (2005) claimed, the increasing of bank deposit will make the bank operating flexible, that is, through the bank deposit, the loan limit will be larger, and investment will be more diverse. (Goldstein and Pauzner, 2005) On the contrary, Goldstein and Pauzner (2005) also claimed that the increasing of bank deposit will lead to the liabilities increasing, that is, the ratio of the owners’ equity will corresponding decrease. Thus, it will have a negative impact on the profitability. (Goldstein and Pauzner, 2005) What’s more, Tian (2007) claimed that the surplus of the bank deposit in China will lead to the decreasing of liquidity and the operation profitability. (Tian, 2007) On the contrary, Graham, Li and Qiu (2008) claimed that, to most of the commercial banks in China, the interest revenue is the majority of the profit, and the interest revenue is brought by the repaid bank loan, thus, the more loan the bank has lent, the more revenue the bank will get. (Graham, Li and Qiu, 2008) 2.5. The total assets The Total Assets include many factors such as the liquidity, the size, the distribution structure. As Kashyap and Rajan (2002) claimed that the liquidity will cause this bank will operate more flexibly, and it can be a comparative advantage to the same size banks. Meanwhile, if the cardinal number of the total assets is larger than the same liquidity bank, there will be an absolute advantage in the financial market. (Kashyap and Rajan, 2002) But Shen, Shen, Xu and Bai (2009) argue that many Chinese economics encouraged the establishment of small and medium sized banks because these banks can give a low demand of credit and interest rate to stimulate the small and medium enterprises to grow. (Shen et al., 2009). As Qin (2008) claimed that the large scale bank can not only gain the strength of deposit or loan business, but also gain more non-interest revenue from the credit card fees, security bonds, insurance entrust business and some other financial products. (Qin, 2008) As Qin (2008) claimed that the scale of the bank determines the revenue, but it does not mean good things will take place, because the large scale seems to bring a huge cost. Meanwhile, the risk controlling and 1

Another popular measure of corporate risk is Value at Risk (VaR). For applications to Chinese enterprises, see Veiga et al. (2008) and Fung and Wan (2013).

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Journal of Advanced Studies in Finance management will be more difficult to implement. Thus, the large operational scale may not bring a high profitability to a bank. (Qin, 2008). 2.6. Gross domestic product Gross domestic product is the main element for banks profitability. In the macroeconomic part, according to Lhacer&Nakane’s(2002) research about the Brazil banks, only macroeconomic variables are the main elements that influence the bank profitability. It can be used in the developing country. In China, GDP and interest rate are two main targets for researcher. (Yao & Dong, 2005) Yao & Dong (2005) found the resuscitating of the whole macroeconomic environment would impact the profitability of a bank, that is, the increase in GDP cause more profit earning. In Cao’s research, (Cao, 2010) a sharply raising of GDP will give an positive impact to bank profitability, however, the monetary policies which issued by government, has insignificant effect to bank profitability. In Gao’ research (Gao, 2010), the inflation rate has insignificant positive correlation to bank profitability, thus, Gao use nominal GDP in the research. 2.7. The interest rate As the only external factor, the interest rate will make an direct impact to the bank deposit and loan, and indirect effect to the Profitability. As Flamin et al. (2009) claimed that the interest rate is fully anticipated raises profits as banks can appropriately adjust costs in order to increase revenues. High bank profitability can reduce financial intermediation if the high returns imply that interest rates on loans - for the same maturity - are higher than in other parts of the world. Thus, there was a positive relation between profitability and interest rates. 3. Objectives of the study What is the orientation of the Chinese government to implement the reform of Banking system? The NPL and CAR are the major factors, which have affected Chinese banking system. Is that any other factor can control the orientation of them? Our target is focusing on analyzing the data with 95 banks between 2008 to 2010. Meanwhile, in this paper, we posit Return on Assets as Dependent Variable to identify the Profitability, and research the relationship with these variables, which are NPL, CAR, Deposit, Total Assets, GDP and Interest Rates. Then, we can use the research results as the basic to discuss how these factors impact the profitability, and put forward some recommendations to the Chinese Banking System. 4. Statement of hypotheses We can get CAR is the main measure be use in the world to reflect banks profitability. And, normally, higher CAR proves that the banks have more enough assert for the loan. In this research, we posit the Hypothesis 1: H1: The CAR has a positive correlation to the Return on Assets Null Hypothesis (H0): β0 ≤ 0, CAR has an insignificant positive correlation to ROA in 95% confident level; Alternative Hypothesis (H1): β0 ＞ 0, CAR has a significant positive correlation to ROA in 95% confident level. NPL use to be the important problem for the China banks, both government and banks did much for the reform. In the new period, increase services and others will be more effective for banks increase the profitability. In this research, we posit the Hypothesis 2: H2: The RONPL has NEGATIVE correlation to the Return on Assets Null Hypothesis (H0): β0 ≥ 0, RONPL has an insignificant positive correlation to Profitability in 95% confident level; Alternative Hypothesis (H1): β0＜ 0, NPL has a significant correlation to Profitability in 95% confident level. It is obvious that the risk which the bank will confront, that is, like the liabilities, risk exposure and leverage may have a certain probability to impact the bank gain the profit. On the contrary, the bank

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Volume V Issue 2(10) Winter 2014 must rely on the loan, which rely on the deposit, to earn the profit. For this consequence, we posit the Hypothesis 3: H3: The Deposit has a positive correlation to the Return on Assets It is obvious that the total assets of the bank will expand the bank operation. Besides interest revenue, large bank have more chances to earn profits from the non-interest income, because the bank will be the agent of investing, selling and financial product seller. On the contrary, the large size of the bank will force to undertake a huge cost and liabilities, because most of the bank assets are deposit, which are liabilities in credit courses of the balance sheets. Another problems are risk controlling and management. For this consequence, we posit the Hypothesis 4: H4: The Total Assets has a negative correlation to the Return on Assets We got in China rising in GDP is the standard for the whole economy, it will be reflecting in the ROA. In this research, we posit the Hypothesis 3. H5: The GDP has a positive correlation to the Return on Assets It is obvious that all the literatures are reported the IR has a positive correlation to Profitability; however, we still need to find out how the interest rate will impact the ROA in this estimation. Thus, based on the theory above, we posit the hypothesis H6: H6: The Interest Rate has a positive correlation to the Return on Assets 5. Methodology As Flamin et al..(2009) claimed that Athanasoglou (2006) applied a dynamic panel data model to study the performance of Greek banks over the period 1985–2001, and found some profit persistence. Meanwhile, for estimating, to propose the general linear model is the most effective. (Flamin et al.., 2009) Since we have 95 cross section observations, 3 years period and 6 independent variables in total, it is obvious that the panel data should be the most adaptable method for us to apply in further estimating and reaching a conclusion. For this consequence, we use the following equation: ROAit = Ci + αRONPLit + βCARit +δINTASit +γINDDit + θGDPit +λIRit + εit (i = 1, 2, 3 …… 100, t =2 008, 2009, 2010) The dependent variable is ROA, the Return on Assets, which we use for defining the profitability. The RONPL, CAR, INTAS, INDD, GDP, and IR represent the Ratio of the Non-Performing Loan, Capital Adequacy Ratio, Total Asset with logged, Deposit with logged, the Gross Domestic Product, and Interest Rate, to measure the correlation with Return on Assets as independent variables. The “i” and “t” are representing the ‘Name of the Banks’ and ‘the time’ in this panel data model. The “C” is the intercept and the ε is the error in the regression. The “α, β, δ, γ, θ, and λ” are the coefficients of the independent variables. Meanwhile, from our data, it is obvious that some of the variables we use ratio to define such as “Return on Assets” and “Ratio of Non-Performing Loan”, the others are numerical, especially the “Total Assets” and “Deposit”, nearly all of them are over ten thousands; thus, we define them with Natural Logarithm to make the coefficients identical. However, there is also a large difference between these logarithmic data. Thus, these data may exist the heteroscedasticity. To estimate the panel data, there are three methods, which are Least Squares, Two Stage Least Squares, and Generalized Method of Moment/Dynamic Panel Data, as the basic. Because our data has nearly 100 cross-section observations and 3 period observations for each, thus, before we start to estimate, it is necessary for us to apply the Redundant Fixed Effect - Likelihood Ratio to judge whether the model can be applied both Random Effect and Fixed Effect, or only one effect to estimate. To the Redundant fixed Effect - Likelihood Ratio, as Bai (2009) claimed that the Redundant fixed EffectLikelihood Ratio is used for testing this panel data can apply both random and fixed effect or only one of them. Based on the Eviews system, the data we entered will be identified, that is, the system will output the adaptable effects, and we need to compare the probabilities of the Chi-square value and the statistical value of the data. If the P-value is greater than 0.05, we should accept the alternative hypothesis and only apply that effect. On the contrary, we can apply either one effect, or even both effects. (Bai, 2009)

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Journal of Advanced Studies in Finance Thus, from the result of the Likelihood Ratio, we will apply Least Square for basic, and use Ordinary, Random and Fixed Effect for finding the most comparatively adaptable result. However, only applying these three methods is not enough, the problem of heteroscedasticity will not be solved, that is easy to say, we still need to apply another method to exclude this impact factor. As Bai (2009) claimed that if the linear trend estimation has heteroscedasticity, the Least Square can be applied into the model. However, to exclude the effect of heteroscedasticity, the most effective way is to apply the White Test for weighting, and output a comparatively accurate result. Thus, as we mention that the heteroscedasticity may exist into the estimation, we will apply the White Test to compare with the result from the estimation with each effects. However, to apply the effect will be an important factor. Bai (2009) claimed that the Fixed Effect can be divided into 3 modes, the Cross-Section Fixed Effect, Period Fixed Effect, and Cross-Section and Period Fix Effect. The effect of Cross-Section Fixed Effect is that the intercept will be significantly different in Time Series, but it will be insignificantly different in each cross-section. The Period Fixed Effect is that the intercept will be significantly different in each Cross-Section, but it will be insignificantly different in Time Series. The CrossSection and Period Fixed Effect can be renamed as Dual Fixed Effect, that is, no matter the Period and CrossSection, the intercept will be significantly different. The Random Effect means that the intercept of the Period and Cross-Section will be regarded as the Random Variables. Assume these two variables can be fitted into the Normal Distribution; the degree of freedom will be saved when estimating the model. (Bai, 2009) That is, different effects will give a different result to impact the further study. Bai (2009) claimed that the Hausman Test is a method to judge whether the Random Effect is adaptable or not, in other words, when the Chi-square is greater than Chi-square difference, and the Probability is over 0.05, the alternative hypothesis, which states that unobservable effect has a correlation to variables, should be accepted, and apply the fixed effect for further research. On the contrary, the null hypothesis, which is the unobservable effect has no correlation to variables, should be accepted, and apply the random effect for further research. (Bai, 2009) Thus, we will use the Hausman Test of Random Effect to compare which one effect will be adaptable for our result. For this consequence, we will output 3 result groups. According to the Hausman Test, we can pick a most adaptable result to put forward as our further recommendation and discussion. 6. Result and analysis We will first present the results of the redundant likelihood test to check if pooled OLS or panel data is appropriate. Then, the random effect and fixed effect models will be estimated. We control for cross-sectional differences only since there are only three time periods. At last, the Hausman test will be used for judging which specific model is the most adaptable and persuasive for further recommendation. Table.6.1. Summary of likelihood ratio

Effect Test Cross-Section Fixed Cross-Section Chi-Square

Statistic 12.692534 531.586031

d.f.

Prod -91,143 91

0.0000 0.0000

Table 6.1 reports the results of redundant likelihood test. The null hypothesis is that there is no individual effect that pooled OLS is the appropriate model. It is obvious that the value of F-statistics is 12.69 and the Probability is approaching to 0. Meanwhile, the F value of Cross-Section Chi-square is 531, and the Probability is also 0. Thus, we should reject the null hypothesis, that is, we should only apply the Cross-Section fixed Effect or Random Effect for estimating. Table.6.2. Summary of Ordinary Least Square

Ordinary Least Square Estimation Result VARIABLES Dependent Variable: ROA INDEPENDENT VARIABLES RONPL (White)* CAR*

COEF

T-STAT

0.007994

0.466540 9.937101 -2.813030

-0.025052*

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Volume V Issue 2(10) Winter 2014 (White)* Control Variables

-2.289054

Indd (White)* Intas (White)* GDP (White)* IR (White)*

0.000546 -0.000672 7.02E-08 0.000584

C (White)*

0.315986 2.370250 -0.394902 -2.549345 1.214045 29.41539 1.076777 11.37105

0.009170

1.649145 3.960487

R-square = 0.045762 DW Stat = 0.822782 Prob(F-stat) = 0.086718 Note: The ‘*’ indicates the value of variable is greater than 1.96, the 5% significant level.

Table.6.2 shows the R-square is about 0.05, it means that the degree of Fitted is in a low level, and the model itself can comparatively difficult to reflect the relationship between the Independent Variables and Control Variables with the Dependent Variables. The Result of DW Test is about 0.82, it means that the residual of each variable has strong positive autocorrelation. Under Ordinary Least Square without White correction, the result shows that only the t-stat with absolute value of CAR is greater than 1.96, the critical value of the 5% significant level. The absolute value of t-stat of other variables included RONPL, INDD, INTAS, GDP and IR are lower than 1.96, the critical value of 5% significant level, that is, under OLS without White Test, these variables are insignificant. From the coefficient sign, the CAR and INTAS have negative correlation to the ROA, the dependent variable, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we reject the null hypothesis of H1, the Hypothesis of CAR, and accept the Null Hypothesis of INDD, INTAS, GDP and IR. We also reject the null hypothesis of RONPL, because we assume the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. With heteroskedasticity adjustment, all the t-stat with absolute value of these variables included RONPL, CAR, INDD, INTAS, GDP and IR are greater than 1.96, the critical value of 5% significant level, that is, under OLS with White Test, these variables are significant. From the coefficient sign, the CAR and INTAS have negative correlation to the ROA, the dependent variable, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we cannot reject the null hypothesis of H1, H3, H4, H5, and H6, the Hypothesis of CAR, INDD, INTAS, GDP and IR. However, we must reject the null hypothesis of RONPL, that is because we assume the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. Furthermore, the result can reflect that most of the variables have heteroskedasticity. Table.6.3. Summary of Cross-Section Random Effect under Least Square

Panel Least Square Cross-Section Random Effect Estimation Result VARIABLES Dependent Variable: ROA INDEPENDENT VARIABLES RONPL (White s.e)*

COEF.

CAR*

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T-STAT

0.024501

1.328494 4.467858

-0.019032

-2.376342

Journal of Advanced Studies in Finance (White s.e)* CONTROL VARIABLES Indd (White s.e)* Intas (White s.e)* GDP* (White s.e)* IR* (White s.e)* C (White s.e)* R-square = 0.099806 DW Stat = 1.765920 Prob(F-stat) = 0.000370

-2.929486 0.004083 -0.004116 6.99E-08 0.000880 0.006014

1.713074 2.929486 -1.733336 -3.355908 2.637603 13.70240 3.642611 11.83839 1.248398 3.311140

Note: The ‘*’ indicates the value of variable is greater than 1.96, the 5% significant level. From these result of Table.6.3, it is obvious that the R-square isabout 0.1, it means that the degree of Fitted is in a low level, and the model itself can comparatively difficult to reflect the relationship between the Independent Variables and Control Variables with the Dependent Variables. The Result of DW Test is about 1.77, it means that the residual of each variable has nearly no autocorrelation. The Probability of F-Statistics can also reflect the estimation model has a comparatively strong persuasion. Under Cross-Section Random Effect without White Test, the result of t-stat shows that the t-stat with absolute value of CAR, GDP and IR are greater than 1.96, The absolute value of t-stat rest of the variables included RONPL, INDD, and INTAS are lower than 1.96, that is, under Panel Least Square of Cross Section Random Effect, these variables are insignificant. From the coefficient sign, the CAR and INTAS have negative correlation with the ROA, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we can reject the null hypothesis of H1, H5 and H6, the Hypothesis of CAR, GDP and IR, and accept the Null Hypothesis of INDD and INTAS. We also reject the null a of RONPL, that is because we assume the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. However, with heterskedasticity adjustment, all the t-stat with absolute value of these variables included RONPL, CAR, INDD, INTAS, GDP and IR are greater than 1.96, that is, under OLS with White correction, these variables are significant. From the coefficient sign, the CAR and INTAS have negative correlation to the ROA, the dependent variable, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we can reject the null hypothesis of H1, H3, H4, H5, and H6, the Hypothesis of CAR, INDD, INTAS, GDP and IR. However, we must reject the null hypothesis of RONPL, that is because we assume the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. Furthermore, the result can reflect that most of the variables have heteroskedasticity. Table.6.4. Summary of Cross-Section Fixed Effect under Least Square

Ordinary Least Square Estimation Result VARIABLES Dependent Variable: ROA INDEPENDENT VARIABLES RONPL (White s.e)*

COEF.

0.040369

1.655648 3.427049

CAR (White s.e) CONTROL VARIABLES

-0.013807

-1.386542 -1.244791

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T-STAT

Volume V Issue 2(10) Winter 2014 Indd* (White s.e)* Intas* (White s.e)* GDP (White s.e) IR* (White s.e)* C (White s.e)*

0.012300 -0.011588 4.60E-08 0.001032 0.002556

2.631212 9.738081 -2.293163 -16.84842 1.054160 1.685016 3.784791 36.51573 -0.122307 -0.167231

R-square = 0.894874 DW Stat = 2.513337 Prob(F-stat) = 0.000000 Note: The ‘*’ indicates the value of variable is greater than 1.96, the 5% significant level.

Table 6.4 is the result of fixed effect model. The R-square is about 0.895, it means that the degree of Fitted is in a high level, and the model itself can comparatively clear to reflect the relationship between the Independent Variables and Control Variables with the Dependent Variables. The Result of DW Test is about 2.51, it means that the residual of each variable has nearly no autocorrelation. The Probability of F-Statistics can also reflect the estimation model has a comparatively strong persuasion. Under Cross-Section Fixed Effect without White Test, the result of t-stat shows that the t-stat with absolute value of INDD, INTAS and IR is greater than 1.96. The absolute value of t-stat rest of the variables included RONPL, CAR, and GDP are lower than 1.96, the critical value of 5% significant level, that is, under Panel Least Square of Cross Section Fixed Effect without White Test, these variables are insignificant. From the coefficient sign, the CAR and INTAS have negative correlation to the ROA, the dependent variable, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we can only accept the Alternative Hypothesis of H3, H4 and H6 - the Hypothesis of INDD, INTAS and IR, and accept the Null Hypothesis of CAR and GDP. However, we must reject both null and alternative hypothesis of RONPL, that is because we assume that the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. However, with heterskedasticity adjustment, beside CAR, the t-stat with absolute value of these variables included RONPL, INDD, INTAS, GDP and IR are greater than 1.96, that is, Cross-Section Fixed Effect with White correction, these variables are significant. From the coefficient sign, the CAR and INTAS have negative correlation with the ROA, the dependent variable, and the rest of the variables included RONPL, INDD, GDP, IR have positive correlation. Therefore, from this result, we can reject the null hypothesis of H3, H4, H5, and H6, the Hypothesis of INDD, INTAS, GDP and IR. We also reject the null hypothesis of RONPL, that is because we assume the RONPL has negative correlation of ROA, but we find a positive correlation between these two variables from the result. Furthermore, the result can reflect that most of the variables have heteroskedasticity. Table.6.5. Summary of Hausman Test

Test Summary

Chi-Square Stat

Chi-Square.d.f

Prod

Cross-Section Random

6.253672

6

0.3954

From Table.6.5, it is obvious that the Chi-square is 6.253677, which is over Chi-square difference. Meanwhile, the Probability is 0.3954, which is greater than 0.05, the significant level value, thus, in the Least Square model, the Hausman test reject the Alternative Hypothesis and accept the Null Hypothesis, that is, to our research, the Random Effect may be more adaptable than Fixed Effect. Thus, the result of Cross-Section Random Effect with White Test under Panel Least Square will be applied for further study.

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Journal of Advanced Studies in Finance 7. Limitation and discussion 7.1 Limitation Since we have 95 cross section observations, 3 years period and 6 variables in total, the result has some limitations from many aspects. A 3 years for period may not definitively and accurately describe the change for each cross section, especially in 2008, during the Financial Crisis was taken place and affected many countries and regions, more or less, the data of these banks in this year may have been impacted by the crisis and caused waves compared with the normal value. The Capital Adequacy Ratio and GDP have insignificant correlations with the ROA. The CAR was limited about 8% by the policies, and the GDP included many intervening factors; thus, in this research, CAR has a weak negative correlation and GDP has a weak positive correlation to the ROA. Finally, from the result, the Total Assets has a significantly negative correlation to the ROA. However, the large scale of a bank will not only create more chance to earn profit, but also increase risks, costs, and the difficulties in management implementation. In fact, it is believed that the Total Assets have a significant correlation to the Profitability, but this correlation will be impacted by several existing factors, therefore, it will not be a definitively positive or negative correlation to the profitability. 7.2 Discussion The CAR has a negative correlation with bank profitability, it proves that higher CAR will decrease the profitability, but decrease the risks. In the long term, lower risks and a stable profitability rate is the best way for banks to survive. After the European debt crisis, New Basel Concordat (Jiwei Y 2011) introduced a new policy about CAR control to reduce bank risks. This research is only based on three years of data, it cannot predict the long term situation. Base on the long term theory, to the equilibrium external economic in recent days, and keeping a comparatively high CAR will be helpful for raising banks profitability. The RONPL has a positive correlation with banks profitability. It’s hard to believe high RONPL causes high profitability, but it’s reasonable in the short term. Firstly, our data are collected from 2008 to 2010. During this period both the central and the local government introduced polices to help banks reduce NPL and raise profitability. For example, in Shanghai Pudong 2010, local government collected 212.5 billion CNY for Shanghai Banks charge-off, and encouraged Shanghai Banks to lend more loans. (Cai Jing 2013) For these Banks, if they control the NPL within reasonable range, the higher NPL will create an increase in bank subsidy. Hence, higher RONPL will cause higher profitability in this estimation model. 8. Conclusions and recommendation 8.1 Conclusion Since 1990s, the Non-Performing Loan was a serious problem to affect the profitability, that is, the risk control was not enough during that period. However, this research is about how the earning situation and nonperforming loan were improved by the adjustment and new system, like the restricted to the Capital Adequacy Ratio and Allowance of NPL, after the Financial Crisis and Bank Reforming in China. As the main idea of the research, the certification process for testing the correlation between CAR and RONPL with ROA are include the Likelihood Ratio Test, three effects of Least Square and the Hausman Test. According to the result after test, and the limitation analysis, it is obvious that the RONPL and CAR have significant correlation to the ROA, which we use for defining the Bank Profitability. However, they are controversially because the White Test reflected that they have heteroscedasticity. The RONPL has positive correlation to ROA, and significant under White Test, the CAR is definitely on the contrary of RONPL. To the other factors, the Deposit and Total Assets have have heteroscedasticity, that is, they will be significant under White Test, and the TAS has negative correlation to ROA. The GDP and Interest Rate have definitely significant positive correlation to the ROA, that is because these two variables are comparatively simple. In General, these variables are important to judge what factors happen to impact the profitability, and helpful to adjust the internal policies of a bank in a certain period. 8.2 Recommendation According to the result, for further researching and developing, there are two aspects need to be improved. For increasing the methods to gain profit, the bank party can try to research some new instruments or products to permeate into every domain. That is because the Finance industry can be the top of the industry pyramid, thus, only enhance the cooperation with the enterprises and find out some new cooperation chance will

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Volume V Issue 2(10) Winter 2014 bring the potential space to improve and expand, thus, the bank will exist in such this heavily competitive environment, and increase the profitability. According to the policies, which are the limitation of Capital Adequacy Ratio and the Allowance of NonPerforming Loan, it is necessary to adjust some credit extension policies and management. That is, to decrease the lost, the bank can reduce the risk exposure in the same credit level; adjust the discount of bank acceptance, and the ratio of the guarantee. For example, the estate industry has a foam, thus, if the customer use their estate for guarantee or use the loan for investing the estate industry, the bank must give a restrict to prevent the risk. With these two methods, compared with the same industry, the bank should adapt to the external environment as the rule, because they cannot give some strong effect to the whole industry, only the adjustment in the details can help the bank to avoid some unnecessary risk and increase more chances for the business. As a banker, it is necessary to concentrate on the details, because it will bring or take away some chance, and determine success or failure. As a member who exists in the certain economy environment, it is necessary to revere every factors of the comparative market. Reference [1] Ai, J. (2011, March). Prospect of China bank crisis dismissed, The Financial Times. [2] An, X. (2011, May). The impact of The Capital Adequacy of Basel on the commercial bank-Empirical research on the commercial banks that are listed company in the A-share market. People’s University Economic Forum, (5): 125-136. [3] Bai, Z.L. (2009). The Panel Data Econometric Analysis [J]. Panel Data Analysis, 1(11): 13-16. [4] Bonin, J.P., Huang, Y.P. (2001). Dealing with the Bad. Loans of the Chinese Banks. Journal of Asian Economics, 12(2): 197-214. doi:org/10.1016/S1049-0078(01)00082-3 [5] Cai, J. (2013). The Cooperation between Shanghai Pudong governemt and Shanghai Bank, CAIJING.COM http://finance.caijing.com.cn/2013-11-18/113576453.html [6] Cao, J. (2010). The elements control Business Banks probfitability changing. Economic forum, (6): 159-162. [7] Liangyong, M., Yong, L. (2010). Analysis on Capital Supervision Reform of Basel Accord m and its Effect. Financial Theory and Practice, 12: 8-10, doi: 10.3969/j.issn.1003-4625.2010.12.002 [8] Fama, E., French, K. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33: 3-56. [9] Flamini, V., Schumacher, L., McDonald, C.A. (2009). The determinants of commercial bank profitability in Sub-Saharan Africa, International Monetary Fund, 10: 3-15. [10] Fung, K.W.T., Demir, E., Zhou, L. (2014). Capital Asset Pricing Model and Stochastic Volatility: A Case study of India. MPRA working paper. [11] Fung, K.W.T., Lau, C.K.M., Chan, K.H. (2014). The Conditional CAPM, Cross-Section Returns and Stochastic Volatility. Economic Modeling, 38: 316-327. [12] Fung, K.W.T, Wan, W. (2013). The Impact of Merger and Acquisition on Value at Risk (VaR): A Case Study of China Eastern Airline, International Research Journal of Finance and Economics, 110: 121-127. [13] Goldstein, I., Pauzner, A. (2005). Demand–deposit contracts and the probability of bank runs, Journal of Finance, 60(3): 1293-1327. [14] Kashyap, A.K., Rajan, R., Stein, J.C. (2002). Banks as liquidity providers: An explanation for the coexistence of lending and deposit‐taking, The Journal of Finance, 57(1): 33-73. [15] Kishan, R.P., Opiela, T.P. (2000). Bank size, bank capital, and the bank lending channel. Journal of Money, Credit and Banking, 32(1): 121-141. [16] Lhacer, A. Nakane, M. (2002). The Determinants of Bank Interest Spreads in Brazil. Banco Centrail di Brazil, Working papers. [17] Li, Z.H. (2008). Shanghai, Development of China commercial banks industry, Vol. 1st, Shanghai People′s Publishing House.

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Journal of Advanced Studies in Finance [18] Lettau, M., Ludvigson, S. (2001). Resurrecting (C) CAPM: A Cross-Sectional Test When Risk Premia are Time-Varying. Journal of Political Economy, 109: 1238-1287. [19] Moyer, S.E. (1990). Capital adequacy ratio regulations and accounting choices in commercial banks, Journal of Accounting and Economics, 13(2): 123-154. [20] Qin, X. (2008). The Estimation Model and Management of the Commercial Bank. The 21st Century Business Herald. [21] Shen, Y., Shen, M., Xu, Z., Bai, Y. (2009). Bank size and small-and medium-sized enterprise (SME) lending: Evidence from China, World Development, 37(4): 800-811. [22] Tian, S.H. (2007). The Impact Factors of Liquidity surplus of Chinese Commercial Banks and the potential risk analysis, Theory and Practice of Finance, 4: 8-13. [23] Veiga, B.D., Chan, F., MaAleer, M. (2008). Evaluating the impact of market reforms on Value-at-Risk forecasts of Chinese A and B shares, Pacific-Basin Finance Journal, 16(4): 453-475. [24] Wen, Y.H. (2013 March). National Business Daily, Sina Finance. [25] Wei, X.Q, Zhang, N., Cong, H.Y. (2011). Research of relationship between capital adequacy ratio and risk assets of chinese commercial bank, Journal of Financial Development Research, 12: 4-5. doi:10.3969/j.issn.1674-2265.2011.12.015 [26] Xu, W.H. (2009). Analysis of new Basel Accords and CAR management in China business banks, financial community. [27] Yang, J.W. (2011). Research on Effect of New Supervision of Bank Capital Adequacy, Journal of Financial Development Research. [28] Yao, Y. Dong, L. (2005). China banks profitability analisis, Naikai Economic Research, 2: 72-83. [29] Zeng, S.H. (2012). Bank Non-Performing Loans (NPLS): A Dynamic Model and Analysis in China. Modern Economy, 2012, 3, 100-110 doi:10.4236/me.2012.31014 Published *** http://www.ft.com/cms/s/0/e2aa6306-4a78-11e0-82ab-00144feab49a.html?ftcamp=rss *** http://finance.sina.com.cn/money/bank/yhpl/20130313/012414808961.shtml

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THE EFFECT OF INTERNATIONAL SOCCER GAMES ON EXCHANGE RATES USING EVIDENCE FROM TURKEY Ender DEMIR Faculty of Tourism, Istanbul Medeniyet University, Turkey [email protected] Chi Keung Marco LAU Newcastle Business School, Northumbria University, United Kingdom [email protected] Ka Wai Terence FUNG Division of Business and Management Beijing Normal University, Hong Kong Baptist University, United International College, China [email protected] Suggested Citation: Demir, E., Lau, C.K.M., Fung, K.W.T. (2014). The effect of international soccer games on exchange rates using evidence from Turkey, Journal of Advanced Studies in Finance, (Volume V, Winter), 1(10):145-156. Doi:10.14505/jasf.v5.2(10).02. Available from: http://www.asers.eu/journals/jasf/curent-issue. Article’s History: Received August, 2013; Revised November, 2014; Accepted December, 2014. 2014. ASERS Publishing. All rights reserved.

Abstract: This paper examines the impact of international soccer games of three major soccer clubs of Turkey (Besiktas, Fenerbahce, and Galatasaray), as well as the Turkish national team on Turkish Lira (TL) against the U.S. dollar and TL against Euro during the period between 2003 and 2010. We find that draws and losses do not affect the exchange rates. However, when the effect of wins for each team is analyzed, it is found that a win effect exists only for the Turkish national team. Early studies found that TL tends to depreciate after a Turkish soccer club win over foreign rivals. However, a depressing effect after a win is rare in the literature. This study provides evidence that local currency actually appreciates after a win. Keywords: soccer, exchange rate, behavioral finance, event study. JEL Clasiffication: G02, G10, G11, F31. 1. Introduction There is a large literature of sport sentiments on market returns. The idea is that although the results of sporting events are supposed to be economically neutral, psychological evidences suggest that these events would affect the mood of investors. Edmans et al. (2007) is the most important study in this line of research. They used an international soccer sample comprising matches of 39 different countries for the period from 1973 to 2004. Losses of national soccer teams led to a strong negative stock market reaction and the loss effect increased with the importance of games. This loss effect was not due to a reduction in trading volume. In addition to soccer, Edmans et al. (2007) also explored the effect of other sports on stock returns. There was a significant but small loss effect for international basketball, rugby, and cricket. There was no win effect for any sports. Most of the related studies focus on market or firm returns. For instance, Kaplanski and Levy (2010) focus on how soccer games affect U.S. market rather than the markets of the two teams that play. It is found that the World Cup effect is significantly negative and it does not depend on the game results. As the aggregate effect is not related to the game result, the investors could exploit this predictable effect. Berument et al..(2006) and Berument et al. (2009) investigate the effect of European Cup game wins for three major Turkish soccer teams 145

Journal of Advanced Studies in Finance (Besiktas, Fenerbahce, and Galatasaray) on Borsa Istanbul (formerly named as the Istanbul Stock Exchange). Both studies show that stock market returns increase following the wins of Besiktas, whose fans are known for their higher degree of fanaticism. Mills et al. (2013) use evidence of 2008 Olympics to show how news would affect sentiment and eventually market returns. This study investigates a different financial outcome - namely exchange rates. We consider the performance of three major soccer clubs of Turkey in European Cup games, as well as the performance of the Turkish national team as the mood variable. The impact of game results on the TL relative to two major currencies (USD and Euro) will be examined. Exchange rate movement is vital to Turkey. A stable currency is a pre-requisite joining the European Union (EU). For a developing country like Turkey, many investors are holding foreign exchange as a form of asset. Clearly, exchange rate fluctuation has significant wealth effect. Grauwe and Schnabl (2008) found a significant impact of exchange rate stability on low inflation as well as a highly significant positive impact of exchange stability on real growth using 1994-2004 macroeconomic data of Eastern and Central European countries in the European Monetary Union. Aghion et. al. (2009) offers empirical evidence that real exchange rate volatility can have a significant impact on productivity growth. The magnitude depends on the domestic credit market restrictions. It contributes to the literature by testing the impact of soccer results on exchange rates rather than the stock market. We found a significant win effect under various model specifications. Our results are different from the existing literature in two important aspects. First, the individual team effect cannot be found. Instead, only national team win matters. Second, the existing literature related to sport sentiment and exchange rate suggest that, after a win, the TL would depreciate. Such a negative (depressing) win effect is not found in the literature using other financial variables. However, in this study, we use a sample of more stable economic period, including national team dummy, an appreciating effect is found. What are the underlying mechanisms through which international football games would lead to systematic variation of exchange rates? There are two rationales. First, foreign currency is sometimes used as a substitute of savings in unstable financial markets. Hyperinflation renders real interest rate negative; therefore, individual investors hold foreign currency as a protection of wealth (Eker et al. 2007). Changes of investor’s sentiment will lead to changes of loanable fund (savings) demand, indirectly affecting exchange rates. Psychological evidences indicate that supporters feel more confident about their decision making after a home team win (Ibarra, 1999). After a win, the individual investors may overestimate their performance increasing their demand for assets. A second rationale for the linkage of sports events and foreign exchange rate is the connection between stock market index and foreign exchange rate. International transactions are settled in foreign exchange, which increases the transaction exposure of investment and eventually the expected returns (Ma and Kao, 1990). At last, the equilibrium relative stock price will be affected by exchange rate fluctuation. The third justification is related to internet football gambling activities. According to the Statista Database, the global gambling market includes lotteries, casino, gambling machines, bingo, etc. The sport gambling makes up about 13 per cent of the global betting market in 20122. While the gross value of global sports betting market is hard to be estimated, some estimates put the value of the sports betting industry at between 700 billion U.S. dollars and 1,000 billion U.S. dollars in 2012, not to mention the fact that illegal sports betting could be worth 500 billion U.S. dollars. What is the interaction between results of sporting event, online gambling and exchange rates? Soccer is the most dominant sport in Turkey. Since sport betting was under restrictions, most of the (illegal) gambling was online3. Most of these companies accept Euro or US dollar as the transaction currency4. Assuming that the sport sentiment hypothesis is true, Turkish is more likely to bid Turkish team to win. Suppose that one bets 1 TL for a Turkish team win (initially sell 1 TL change for US dollar of 0.5 USD for example) and the odds is 1:12. If Turkish team does win, that person (Turkish) can get 12 USD and he will sell it for TL, therefore the net effect is TL appreciating against USD. The rest of the paper proceeds as follows. The next section is a literature review. Third section describes the data and methodology. Section 4 reports the findings following by a discussion in the last section.

http://www.statista.com/topics/1740/sports-betting/ Despite the ban on private internet gambling for decades, the Turkish government eventually decided to privatize Spor Toto, the only legal sports betting organization, in June 2013. Most of the sport betting was involved in illegal internet companies, the official figure of total gambling value is lacking. However, we can get a rough estimate from the gross income of Spor Toto - $24 billion in 2012. 4 This is the reason using exchange rates relative to USD and Euro in this study. 2 3

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Volume V Issue 2(10) Winter 2014 2. Background 2.1 Impact on stock markets Previous studies have tested the impact of a variety of mood variables on stock markets, including weather, temperature, daylight saving time, and air pollution (Saunders, 1993; Hirshleifer and Shumway, 2003; Cao and Wei, 2005; Yuan et al. 2006; Chang et al. 2008; Levy and Yagil, 2011). If the stock markets are efficient, these economically neutral variables should have no impact; however, the literature mostly find supporting evidences for the association between mood variables and stock returns. One strand of the event study literature focuses on the impact of sports (particularly international game results) on stock markets (Ashton et al. 2003 and 2011; Edmans et al..2007; Berument et al. 2009; Demir and Danis, 2011; Demir and Rigoni, 2014). As well documented in the event study literature, there exists a linkage between sports events sentiment and stock market index (Berument et al. 2006; Edmans et al. 2007). It is believed that a national team win in international matches could translate into a boost in the investors' confidence, which at last increases trading activities in the stock exchange. For instance, Hirt et al..(1992) and Kerr et al. (2005) document that sports have an important impact on the mood of people. A win of a team may lead to a positive mood change on supporters (in case of national team wins, the mood change can be observed for majority of a country), but a loss may lead to a negative mood change. The changes in emotions and mood following the matches affect the investment decisions. Hakim and McAleer (2010) investigate international interactions across asset markets. By VARMAGARCH, they find international interactions across stock, bond and foreign exchange markets from Australia, Japan, Singapore New Zealand and USA. Ashton et al..(2003) find that there is a strong relation between the performance of England’s national soccer team and the FTSE 100. There is also evidence from less-developed country. Mishra and Smyth (2010) show that a loss of the Indian national cricket team leads to a decrease in the Indian stock market. There are also a limited number of studies, which show that stock market returns are not affected by sports performance (Boyle and Water, 2003; Gerlach, 2011). Klein et al. (2009) question the findings of Ashton et al. (2003) and reject the link between sports performance and stock market return by detecting mistakes in the empirical set-up. Ashton et al. (2011) later reassess their initial findings and again document a link between international soccer results and stock market prices. Ehrmann and Jansen (2012), by using minute-by-minute trading data of 15 international stock exchanges and the FIFA 2010 World Cup games, show that the number of transactions and the volume of traded stocks decreased and these impacts were even stronger in the stock markets of two teams that play. Some studies examine the impact of sport events on firm returns. For instance, Chang et al. (2012) demonstrated that smaller firms have larger sports sentiment effect due to larger share of domestic investors. 2.2 Impact on exchange rates Although the impact of soccer on the levels of various stock markets has been widely researched, to our knowledge, there are a limited number of studies examining the impact of soccer results on other financial or economic environments. For example, Berument and Yucel (2005) document the importance of soccer on the mood of workers by finding a positive relationship between monthly industrial growth rates with the wins of Fenerbahce in European Cup games. Eker et al. (2007) consider the impact of soccer on exchange rates. They find that the wins of three major clubs of Turkey in the UEFA Champions League is statistically related to Turkish exchange rate depreciation while losses and ties do not affect the exchange rate significantly. The impact of a win is found to be far greater for Galatasaray and Fenerbahce compared to Besiktas and according to Eker et al. (2007), this is a reflection of the higher socioeconomic status of the supporters of those two teams. Ning (2010) examines the connection between equity market and foreign exchange market by copulas. The author explores different copulas to model the underlying dependence structure. She finds that there is significant symmetric upper and lower tail dependence between these two financial markets. In similar fashion of Eker et al. (2007), we examine the impact of soccer game results on stock exchange rates. However, this paper extends Eker et al. (2007) to different dimensions. We use a data of a more stable economic period when TL is used as a saving instrument. The impact of game results are tested on TL/Euro in addition to TL/USD. Moreover, we consider the impact of not only the games of three major soccer clubs of Turkey but also the games of the Turkish national team on the foreign exchange market. The impact of the performance of the Turkish national team on supporters is deemed to be more straightforward and it will affect the mood of all people (even if they are not directly interested in soccer) in the same direction. Eker et al. (2007) found a that a win of Turkish team in international soccer games is followed by a depreciation of TL. The

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Journal of Advanced Studies in Finance rationale is that foreign exchange is an investment asset to economic agents. After a win of their team, the investors would be more confident and demand for more assets (foreign exchange), depreciating TL eventually. Nonetheless, section one also outlines a hypothesis that a win can be followed with appreciation of domestic currency. More importantly, a negative effect of a win is very rare in the sport sentiment literature. As the TL is becoming more stable in recent years, the role of foreign currency as an investment asset is declining. Therefore, we try to re-examine the impact of international soccer games using a more relevant data sample, namely Turkish national team results. What is the advantage including national team results? Soccer is the most popular and dominant sport in Turkey as in many other countries. The majority of fans supports one of the three big clubs in Turkey and associate themselves strongly with their chosen team. Only international games are considered because such games affect not only the supporters of the club team playing, but also the supporters of other clubs since games against foreign rivals are seen as a reflection of national pride. The effect of domestic games can be eliminated or minimized as a win (a loss) of one soccer club will positively (negatively) affect its supporters and negatively (positively) affect the supporters of the other teams (Eker et al..2007). More importantly, the choice of Turkey as the reference country can avoid spurious correlation. When there are multiple sports events on the same day (for instance, soccer and basketball), it will be difficult to disentangle the effects of each event. However, soccer is the most dominant sports. When Turkish clubs participate in European Cup games, daily routines simply stop in the country (Abazov, 2009). Fans want to see the their teams’ win against European clubs as success in European Cups has been an important objective and moreover European Countries are considered as the main historical rivals. This is reflected in the famous slogan of the 1990s (which is still popular), ‘Europe Europe! Hear our voice; hear the uproar of the marching Turks’ (Gokalp, 2006). Wins in these European Cup games are considered a symbol of national pride and boost the morale of people in Turkey. Away wins against foreign rivals are often expressed as a conquest of that city or country in the Turkish media (Gokalp, 2006). This study will examine the games played at home and away. 3. Data and methodology The data include the daily (closing) TL/USD and TL/Euro exchange rate from 2003 to 2010 and game results of three Turkish soccer clubs and the Turkish National team (TR). Game results are collected from www.mackolik.com and they are crosschecked from various sources. Exchange rate data are acquired from the website of the Central Bank of Republic of Turkey. Most of the European Cup games are played on Tuesday, Wednesday, or Thursday; however, the games of the national team are more homogenously distributed in a week. The effect of a game result is observed on the first trading day just after the game. Thus, if a game is played on the weekend, the effect will be observed on Monday. Likewise, the impact of weekday games is observed on the next trading day. Games that intersect with each other (soccer clubs played on the same day 23 times) are excluded from the dataset as they can lead to a mixed-mood effect on supporters. In addition, preparation games are not included in the dataset5. Moreover, if there are more than two days of holiday (except weekends) after the game day, the observation is excluded from the sample. To be precise, assume that a game is played on Sunday and the stock market is closed from Monday to Wednesday; in this case, the game is not included in the study. Based on these selection criteria, the dataset has 177 games in total. Table 1 contains the statistics for the international team and national soccer games. The highest win percentage is observed for the Turkish national team (49.1), followed by Besiktas (46.8). Overall, win, draw, and loss percentages of the whole sample are 45.8, 18.6, and 35.6, respectively. Table 1. Contingency table of international soccer match results of three major turkish soccer clubs and national team

Win Draw

5

BJK

FB

GS

TR

TOTAL

22 (46.8%) 6 (12.8%)

17 (42.5%) 8 (20.0%)

15 (42.9%) 6 (17.1%)

27 (49.1%) 13 (23.6%)

81 (45.8%) 33 (18.6%)

As a matter of fact, early studies always exclude unimportant matches.

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Loss TOTAL

BJK

FB

GS

TR

TOTAL

19 (40.4%)

15 (37.5%)

14 (40.0%)

15 (27.3%)

63 (35.6%)

47

40

35

55

177

Note: The values in parenthesis represent the percentages The efficient market hypothesis (weak form) argues that returns on traded assets should be unpredictable because the market price already subsumed all past information available. Similarly, exchange rates should not be affected by the investors' emotions triggered by sports event results. The alternative hypothesis is that the game results do matter. The following methodology is implemented in line with Eker et al. (2007), Berument et al. (2009), and Kaplanski and Levy (2010) and run the following regression: ∑ (1)

where Et is the daily percentage exchange rate change and Et-1 is the daily lag value of daily percentage exchange rate change (to adjust for serial correlation)6. Win, draw, and loss are dummies indicating the game results. Away and neutral variables are introduced to control for the impact of game venue (Demir & Danis, 2011). Away is equal to 1 if the game is an away game and zero in case of a home or neutral game; and neutral is equal to 1 if the game is played on a neutral ground and 0 otherwise. Dummy variables for each day of the week are included to control for the day of the week effect (Aydogan and Booth, 2003; Berument et al. 2004; Berument et al. 2007; Ke et al. 2007). Dit, i = 1,2,3,4, are dummy variables for the days of the week: Monday, Tuesday, Wednesday, and Thursday, respectively. To avoid dummy trap, Friday is excluded. Best10 and Worst10 are dummy variables for the 10 days with the highest and lowest exchange rates during the studied period (Kaplanski and Levy, 2010). These variables will help us to guarantee that no single extraordinary day will distort the results. As a second step, wins of the three major teams of Turkey are included. It is intended to test the hypothesis that national team effect should be stronger than the effect of the three big Turkish teams; a Turkish national team win dummy is appended to the model. ∑

(2)

where winFB, winGS, winBJK, and winTR represent the victory of Fenerbahce, Galatasaray, Besiktas, and the Turkish National Team against their foreign rivals, respectively. Equations (1) and (2) are estimated by conventional least square method using TL/Euro and TL/USD exchange rates as dependent variables. The result for TL/USD exchange rates was presented in Table 2; while the result for TL/Euro exchange rates was presented in Table 3. Columns 1- 4 reported the results for Equation (1), and column 5 reported the results for Equation (2). Since the estimation method is OLS, the error is assumed to be homoskedastic. However, serial correlation will render statistical inferences invalid. We include a lagged value of daily exchange rate to capture temporal dependence which is essentially an autoregressive model of order one. The estimates can be biased downward if the exchange rates exhibit time-varying volatilities7. As the exchange rate return of TL/USD and TL/EURO exhibited significant ARCH effects we adopted the same procedure as Edmans et al. (2007) to correct the bias. First, a GARCH (1,1) model is estimated using equation (2). Then, the estimated conditional volatilities will be used to normalize the exchange rates to have zero mean and standardized variance8. Basically, we use the residuals of the model specified in equation (2) to model the There is evidence that the ISE daily returns exhibit temporal persistence (Sensoy, 2012). We found significant ARCH(12) effect for TL/USD and TL/EURO exchange rate returns; Lagrange Multiplier LM test for ARCH effects distributed as a χ2 with N degree of freedom: For TL/USD The X2 statistics is 63.585, and that of TL/EURO is 90.66, which indicates significant ARCH effects in the time series data itself. 8 For the detailed implementation, see Edmans et al. (2007) and Kaplanski and Levy (2010). 6 7

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Journal of Advanced Studies in Finance volatility of the error term as a GARCH(1,1) process: σt2=ω + αεt-12 + β σt-12, where σt2 is the return volatility on day t. The index returns is then normalized using the following transformational function: EtN = a + bEt/( ˆt ) 2

where ˆt is the estimated volatility of the GARCH (1,1) process, and a and b are selected so that the mean and variance of the normalized returns are identical to those of the raw returns. The normalized returns, EtN are then used in the model specification (2), the empirical result of this adjustment, which takes conditional heteroskedasticity into account is presented in Table 4. 2

3. Findings The OLS regression estimates are presented in Table 2 for TL/USD and in Table 3 for TL/Euro. Different model specifications are examined to check for robustness. The first column of the tables show that the coefficient estimates of a win are -0.2102 and -0.1963 (both statistically significant at the 10% level) for TL/USD and TL/Euro, respectively; whereas the coefficients of draws and losses are not statistically significant9. Although the efficient market hypothesis argues that change in the foreign exchange rates is not predictable, these findings indicated a win effect on the exchange rates (both for USD and for Euro). As indicated in column 2, when only the win dummy is included in the model, the win effect has similar magnitude (-0.2228) but a higher level of significance. The result is robust enough that even if draw and loss are considered separately (columns 3 and 4), the coefficients remain insignificant. The findings of model 2, which explores the win effect of club and national team effect, are presented in column 5. Model (2) is different from Model (1) in two important aspects. 1. Only win effect is considered. 2. We break down winning matches to individual teams and national team. While the win effect is negative for all teams considered in model 1, it is only statistically significant for Turkish national team. The coefficient estimates for win of the Turkish national team are -0.3807 and -0.3983 for TL/USD and TL/Euro, respectively. The magnitude is evidently larger than those of Model (1), indicating a stronger sentiment effect. A consistent and significant win effect is found on both TL/USD and TL/Euro exchange rates; however, draws and losses do not affect the exchange rates. When the effect of wins for each team is analyzed, it is found that a win effect exists only for the Turkish national team and the individual team effect is not found. Considering other variables, a game venue effect is found only for away games. The coefficient estimate for away games is statistically significant and positive for wins on both TL/USD and TL/Euro. The day of the week effect is also considered in the model. From Tables 2 and 3, it can be seen that the coefficient of the Monday and Tuesday dummy variables are positive and statistically significant for TL/USD. This result is in line with Aydogan and Booth (2003) and Eker et al..(2007). Considering the TL/Euro exchange rate, in addition to Monday and Tuesday, the coefficient estimate of Wednesday is also positive and statistically significant. The dummy for Thursday is not statistically significant. The coefficient estimate for the lag exchange rate variable (Et-1) is positive for both TL/Euro and TL/USD exchange rate; however, it is only statistically significant for the TL/USD exchange rate. The outliers (Worst10 and Best10) are statistically significant and have a strong impact on exchange rates10. Our findings suggest that football sentiment is able to explain 22% of short run fluctuation of TL / Dollar exchange rate return (coefficient of variation), and 20 % of short run fluctuation of TL / Euro exchange rate. The explanatory power of our model is higher than that of the FIFA World Cup Effect on U.S. stock market (Kaplanski and Levy; 2010). The models in Table 2 and Table 3 are jointly significant as indicated by the high F statistics. We also tested for the presence of multicollinearity. Multicollinearity is not an issue in our model since the mean Variance Inflation Factor (VIF) is 1.25, which is far below the cut off value of 10. Nonetheless, we found significant ARCH effect for TL/USD and TL/EURO exchange rate returns. Lagrange Multiplier LM test for ARCH effects distributed as a χ2 with degree of freedom equal to number of lags11. The critical value (5%) is 21.026. From the last rows of Table 2 and Table 3, the LM statistics is around 180, clearly rejecting the null hypothesis.

Since the foreign currency appears as the denominator, a negative coefficient means local currency appreciating. To further check for robustness, an AR(2) model was also estimated. However, the coefficient is not significant. 11 The optimal number of lag is 12 by AIC. 9

10

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Volume V Issue 2(10) Winter 2014 Table 2. The Impact of International Soccer Matches on TL / Dollar Exchange Rate (OLS)

Win Draw Loss

Model (1) OLS -0.2102* (-1.91) 0.1579 (0.96) -0.0187 (-0.14)

Win only OLS -0.2228** (-2.18)

Draw only OLS

Loss only OLS

Model (2) OLS

0.2154 (1.39) 0.0261 (0.21)

WinFB

-0.1769 (-0.86) WinBJK -0.2312 (-1.27) WinGS -0.0082 (-0.04) WinTR -0.3807** (-2.17) Away 0.1494 0.1754* 0.0653 0.0953 0.1838* (1.13) (1.76) (0.66) (0.88) (1.84) Neutral 0.0085 0.0215 -0.1024 -0.0929 0.1004 (0.03) (0.08) (-0.39) (-0.34) (0.36) Et-1 0.0490** 0.0492** 0.0490** 0.0491** 0.0489** (2.46) (2.47) (2.46) (2.47) (2.46) Monday 0.1284** 0.1284** 0.1321** 0.1324** 0.1377** (2.17) (2.17) (2.24) (2.24) (2.31) Tuesday 0.0934 0.0926 0.1021* 0.1017* 0.0980* (1.57) (1.56) (1.72) (1.72) (1.65) Wednesday 0.0787 0.0774 0.0845 0.0832 0.0808 (1.33) (1.31) (1.43) (1.40) (1.36) Thursday 0.0153 0.0194 0.0071 0.0121 0.0222 (0.26) (0.33) (0.12) (0.20) (0.37) Worst10 -3.8396*** -3.8274*** -3.8311*** -3.8135*** -3.8277*** (-14.49) (-14.46) (-14.45) (-14.39) (-14.46) Best10 4.8726*** 4.8822*** 4.8816*** 4.8958*** 4.8836*** (18.39) (18.45) (18.43) (18.48) (18.46) Constant -0.0664 -0.0655 -0.0748* -0.0743* -0.0711* (-1.56) (-1.55) (-1.77) (-1.76) (-1.67) N 1,991 1,991 1,991 1,991 1,991 R2 0.22 0.22 0.22 0.22 0.22 F statistics 46.97 56.27 55.94 55.7 43.46 D-W statistics 2.062 2.061 2.063 2.062 2.058 ARCH(N) 183.88 182.13 188.35 186.32 179.25 Notes: Values in parenthesis are t-values and. ***, **, and * represent 1%, 5%, and 10% significance levels, respectively; ARCH(N) is the Lagrange Multiplier LM test for ARCH effects and distributed as a χ2 with N degree of freedom.

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Journal of Advanced Studies in Finance Table 3. The Impact of International Soccer Matches on TL / Euro Exchange Rate (OLS)

Win Draw Loss

Model (1) OLS -0.1963* (-1.84) 0.0308 (0.19) -0.0314 (-0.24)

Win only OLS -0.1923* (-1.94)

Draw only OLS

Loss only OLS

Model (2) OLS

0.0887 (0.59) 0.0316 (0.26)

WinFB

-0.0381 (-0.19) WinBJK -0.1045 (-0.59) WinGS -0.1628 (-0.77) WinTR -0.3983** (-2.35) Away 0.186 0.1769* 0.1007 0.104 0.1876* (1.45) (1.84) (1.05) (0.99) (1.95) Neutral 0.0904 0.079 -0.019 -0.0234 0.1814 (0.33) (0.30) (-0.07) (-0.09) (0.68) Et-1 0.0113 0.0114 0.0113 0.0114 0.0113 (0.56) (0.56) (0.56) (0.57) (0.56) Monday 0.1175** 0.1181** 0.1212** 0.1217** 0.1285** (2.06) (2.07) (2.12) (2.13) (2.23) Tuesday 0.112* 0.1126* 0.1204** 0.1207** 0.117** (1.95) (1.96) (2.10) (2.11) (2.04) Wednesday 0.1127** 0.1123* 0.118** 0.1171** 0.1149** (1.97) (1.96) (2.06) (2.05) (2.00) Thursday 0.0876 0.0884 0.0795 0.0818 0.0874 (1.53) (1.55) (1.39) (1.43) (1.52) Worst10 -3.529*** -3.528*** -3.523*** -3.5237*** -3.5279*** (-13.81) (-13.82) (-13.79) (-13.79) (-13.82) Best10 4.332*** 4.333*** 4.338*** 4.3435*** 4.334*** (16.94) (16.97) (16.97) (17.00) (16.98) Constant -0.0762* -0.077* -0.0843** -0.0844** -0.0816** (-1.85) (-1.87) (2.07) (-2.07) (-1.98) N 1,991 1,991 1,991 1,991 1,991 Adjusted- R2 0.1949 0.1957 0.1942 0.1941 0.1953 F statistics 41.15 49.41 48.99 48.96 38.21 DW statistics 1.966 1.966 1.967 1.969 1.969 ARCH(N) 179.142 178.714 183.513 182.887 179.571 Notes: Values in parenthesis are t-values and. ***, **, and * represent 1%, 5%, and 10% significance levels, respectively; ARCH(N) is the Lagrange Multiplier LM test for ARCH effects and distributed as a χ2 with N degree of freedom. The existence of ARCH effect is common for high-frequency financial data as observed by many researchers. This means heteroskedasticity may occur in the exchange rate return model, and the consequences are that the OLS point estimates remain unbiased and consistent, but their standard errors will be inconsistent, as will hypothesis test statistics and confidence intervals. Since the estimates can be biased

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Volume V Issue 2(10) Winter 2014 downward if the exchange rates exhibit time-varying volatilities, we proceed to estimate model (2) adjusting for GARCH effect as outlined in last section. The results are reported in Table 4. Columns 2 and 3 in Table 4 replicate the results of model 2 for both TL/USD and TL/Euro exchange rates adjusting for time-varying volatility, respectively. The findings are in line with OLS estimates, namely significant national team victory, weekday, Worst10 and Best10, and away venue effects. The win coefficient of TL/Euro is actually larger in magnitude. Table 4. The impact of international soccer matches on foreign exchange rates with GARCH adjustment TL/USD: Model (2) TL/EURO: Model (2) GARCH (1,1) GARCH (1,1) -0.2190 0.02005 WinFB (-0.93) (0.09) -0.1899 -0.13470 WinBJK (-0.91) (-0.65) -0.0273 -0.06279 WinGS (-0.11) (-0.25) -0.3435* -0.43307** WinTR (-1.71) (-2.17) 0.1893* 0.26597** Away (1.66) (2.34) 0.0120 0.09947 Neutral (0.04) (0.31) 0.0569*** 0.03203 Et-1 (2.66) (1.50) 0.1528** 0.12683* Monday (2.24) (1.87) 0.1292* 0.10629 Tuesday (1.90) (1.57) 0.1051 0.10238 Wednesday (1.55) (1.51) 0.0485 0.06458 Thursday (0.71) (0.95) -2.4291*** -3.04433*** Worst10 (-8.03) (-10.11) 3.5155*** 3.28990*** Best10 (11.63) (10.93) -0.0917* -0.08534* Constant (-1.88) (-1.76) N 1,991 1,911 2 Adjusted R 0.099 0.11 F statistics 16.74 18.12 Notes: Values in parenthesis are t-values and. ***, **, and * represent 1%, 5%, and 10% significance levels, respectively. 4. Discussion Motivated by the findings supporting the impact of soccer results on national mood, this study examines the effect of performance of three major soccer clubs of Turkey in European Cup games, as well as the performance of the Turkish national team on the TL/USD and TL/Euro exchange rates. This strand of literature is very limited and Eker et al..(2007) is one of those. Deviating from the existing literature, this study focuses on national team effect in Turkey where soccer is the most popular sports, and victories over foreign rivals are considered as a symbol of national pride and can boost the morale of people. The most important finding is that TL will appreciate against US dollar once the Turkish national team won the game; in particularly the point

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Journal of Advanced Studies in Finance estimate suggested that the TL will be appreciated by 34.3 basis point (against US dollar) upon the Turkish national team won the game. The findings of this study are important because under the efficient market hypothesis, economically neutral events like soccer matches should not have impact on financial asset returns. The study provides psychological evidence that individual emotional change is a contributing factor to the fluctuation of exchange rates. In fact, Akhtar et al.. (2011) contend that consumer sentiment index possesses information that influences Australian dollar against 13 foreign currencies. The results of this study are different from Eker et al. (2007) in an important aspect - the negative win effect disappears. Moreover, the team effect documented by Eker et al. (2007) no longer exists. There are a couple of reasons. The first rationale is straightforward – national sentiment towards international sports simply escalates to a higher level. The performance of the Turkish national team affects all the population in Turkey and triggers the mood of a larger number of people. A national victory over a foreign rival increases people’s confidence in the TL and appreciates its value against the USD and the Euro. There is a win effect only for the Turkish national team; that is to say, the individual team effect is not found. This result is contrary to the finding of Eker et al. (2007). The second reason is related to different methodology – this paper uses a different sample period. Eker et al. (2007) work with data spanning from 1987 to 2003, while this study considers a period from 2003 to 2010 in which a more stable economic conditions along with a lower and less volatile exchange rate exists. Therefore, the use of foreign currency as a proxy for saving behavior decreased and the TL has improved its reputation in relation to the USD and the Euro. The TL is now used as a saving instrument. A win of the Turkish national team boosts people’s confidence in the TL and its value appreciates relative to foreign currency. There are at least two limitation of this study. It is limited by the scope of sports events. Robustness can be checked by applying the test to other sports (see Edmans et al. 2007). However, as mentioned, soccer is the dominant sports event in Turkey, rendering other sports activities irrelevant in this case. An application of a similar test to countries with more popular sports events would be interesting. Another limitation of such sports event study is related to the scope of the test as it is limited to market returns or exchange rate analysis. An interesting extension can be a test using firm level data to check for the existence of individual investors' sentiment in a microeconomic platform (Chang et al.. 2012; Fung et al.. 2014). With this approach, it is possible to analyze the effects of sports results by using firm-level analysis rather than using aggregate stock market indices. The firm-level data allows examining how sports games affect investor mood on stock returns across firms with different characteristics. Further studies may transfer the mood variable proxied by the performance of soccer teams in international games into other finance and economic-related measures and test whether the impact of soccer is also observed in other contexts. References [1] Abazov, R. (2009). Culture and Customs of Turkey, USA, Greenwood Publishing. [2] Aghion, P., Bachetta, P., Rancière, R., and Rogoff, K. (2009). Exchange rate volatility and productivity growth: The role of financial development, Journal of Monetary Economics, 56(4): 494-513, http://dx.doi.org/ 10.1016/j.jmoneco.2009.03.015 [3] Akhtar, S.M., Faff, R.W., and Oliver, B.R. (2011). The Asymmetric Impact of Consumer Sentiment Announcements on Australian Foreign Exchange Rates, Australian Journal of Management, 36(3): 387-403, http://dx.doi.org/10.1177/0312896211410723 [4] Ashton, J.K., Gerrard, B., and Hudson, R. (2003). Economic impact of national sporting success: evidence from the London stock exchange, Applied Economics Letters, 10(12): 783–785, http://dx.doi.org/ 10.1080/1350485032000126712 [5] Ashton, J.K., Gerrard, B., and Hudson, R. (2011). Do national soccer results really impact on the stock market?, Applied Economics Letters, 43(26): 3709-3717, http://dx.doi.org/10.1080/00036841003689762 [6] Aydogan, K., and Booth, G.G. (2003). Calendar anomalies in the Turkish foreign exchange markets, Applied Financial Economics, 13(5): 353-360, http://dx.doi.org/10.1080/09603100210129457 [7] Berument, M.H., Inamlik A., and Kiymaz, H. (2004). The Day Of The Week Effect On Stock Market Volatility: The Case of Istanbul Stock Exchange, Iktisat İşletme ve Finans, 19: 91-102. [in Turkish]

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LINKAGES IN CORPORATE SOCIAL RESPONSIBILITY INDICES AND MAJOR FINANCIAL MARKET INDICES. AN ARMA-APARCH APPROACH Li-Lun LIU Department of Business Administration and Department of Accounting College of Business, Chung Yuan Christian University, Chung-li City, Taiwan [email protected] John Francis DIAZ Department of Finance and Department of Accounting College of Business, Chung Yuan Christian University, Chung-li City, Taiwan [email protected] Esentur IVAGOV College of Business, Chung Yuan Christian University, Chung-li City, Taiwan [email protected] Suggested Citation: Liu, L.-L, Diaz, J.F., Ivanov, E. (2014). Linkages in Corporate social social responsability indices and major financial market indices. An ARMA-APARCH approach, Journal of Advanced Studies in Finance, (Volume V, Winter), 2(10):157-163. Doi:10.14505/jasf.v5.2(10).03. Available from: http://www.asers.eu/journals/jasf/curent-issue. Article’s History: Received August, 2014; Revised November, 2014; Accepted December, 2014. 2014. ASERS Publishing. All rights reserved.

Abstract This research utilizes the Autoregressive Moving Average – Asymmetric Power Autoregressive Conditional Heteroscedasticity (ARMA-APARCH) in studying return and volatility relations among the three main Thomson Reuters Corporate Social Responsibility (CSR) Indices, and their major stock market indices counterparts. Using data from the post-Subprime Mortgage Crisis period, this study finds that both indices are not immune to negative shocks caused because coefficients of asymmetric volatility phenomenon is positive in all Thomson Reuters CSR and stock market indices. The lagged returns coefficient of the Wilshire Large Cap Total Market Index (WLCTM) has a negative effect on the future returns of the Thomson Reuters CRI US Large Cap ESG (TRESGUS). However, this is not the case with the Dow Jones Developed Markets Index (DJDM) and the Thomson Reuters Developed Markets ESG (TRESGDX) in which the lagged stock market index returns coefficient has positive effect on the CSR index returns. In terms of their volatility linkages, positive bilateral relations exist between the lagged volatility coefficients of WLCTM and TRESGUS, which suggests that the previous day’s heightened volatility in both indices will create a similar degree of fluctuations the next day on both the CSR and stock indices. However, only a unilateral lagged volatility effect is observed on the positive influence of the DJDM on TRESGDX. Keywords: CSR investing, stock markets, return and volatility linkages, ARMA-APARCH models. JEL Clasiffication: G11, M14 1. Introduction Investments in socially responsible companies have become more profitable over the recent years and find positive feedback from investors. The term Socially Responsible Investment (SRI) refers to the investments in business organizations with socially responsible activities. These are enterprises that act in accordance to saving the environment and uplifting the conditions of community. As Kramer and Porter (2007) put, doing

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Journal of Advanced Studies in Finance business today with the thinking of saving tomorrow’s needs. SRIs have become more appealing to the discerning investors as the number of business and ethical issues being exposed increased over the years. Most SRI funds take into account both positive and negative screening policies to follow certain social-screening criteria identifying companies with enhanced social performance associated with community, diversity, employee relations, environment, human rights, and products. Husted and De Jesus Salazar (2006) argue that more social output will be achieved by investing in socially responsible enterprises. SRIs have taken another leap on catering to a broader market of investors with the creation of the socalled Corporate Social Responsibility (CSR) indices. The first of its kind was launched in 1999 by Dow Jones, naming it Dow Jones Sustainability Index (ticker: DJSI); and the next was in 2000 by Calvert Investment Fund, naming it Calvert Social Index (ticker: CSI). These indices were launched to develop an established benchmark for companies involved in CSR activities, and to represent investments focusing on the environment, social and governance (ESG) criteria. The launching of various funds investing into CSR portfolios also creates a growing interest of investors in the underlying companies of CSR indices. CSR indices provide greater motivation to enterprises to include the ESG aspects to their strategic business activities. Thus, individual enterprises are becoming eager to be part of these indices to market the reputation of their companies, further boosting their stock performance. The study of Kempf and Osthoff (2007) showed that investing in companies with CSR activities leads to abnormal returns of 8.7% a year. This strong impact of SRI through CSR companies inspired this paper to study the relatively new launched set of CSR indices by the Thomson Reuters Company. These indices track the performance of major global benchmarks through enterprises that have substantially higher than average ESG ratings. Thomson Reuters CSR indices also have a comprehensive benchmarking system that CSR investors can use to compare return performance with major stock indices. This study will particularly focus on the return and volatility performance of three Thomson Reuters CSR indices, namely, Thomson Reuters CRI US Large Cap ESG (ticker: TRESGUS) Thomson Reuters Developed Markets ESG (ticker: TRESGDX) Thomson Reuters CRI Europe ESG (ticker: TRESGEU); and their major stock market index counterparts, namely, Wilshire Large Cap Total Market Index (ticker: WLCTM) Dow Jones Developed Markets Index (ticker: DJDM) Financial Times and the London Stock Exchange100 Index (ticker: FTSE); respectively. This research is interested in determining the spillover effects of return and volatility between CSR indices and major stock market indices. This phenomenon is already proven by Ackert and Tian 2000; Elton et al. (2005) using exchange-traded fund’s (ETF) ability to affect asset al.location and change a tracked security’s underlying volatility. The asymmetric volatility phenomenon will be also tried to capture in this study. We will also determine if CSR indices also have the presence of asymmetric volatility like most other investment indices do, where negative shocks create a leverage effect or have a greater impact than positive innovations (Chen 2011). The research will investigate whether there is a relationship between previous returns and future returns, as well as relationship between previous stock price volatilities and future volatilities of major stock market indices and CSR indices. This study has enough reason to believe that these relationships exist, because the component stocks of the CSR indices are also part of the composition of the major stock market indices. This paper proves this possible relationship by applying the autoregressive moving average (ARMA) models combined with the asymmetric power autoregressive conditional heteroscedasticity (APARCH) models. GARCH models and its extensions are very useful in modelling the dynamic behaviour of investment instruments based on the survey made by Bollerslev et al. (1992); and the APARCH model of Ding et al. (1993) can also capture these tendencies as well as the presence of time-varying volatility. Findings of the study will also support the argument that SRIs are a reliable approach in building a portfolio, and an effective screening of portfolio to check SRI ratings of securities and facilitate to better performance of a portfolio as a whole. The study is also motivated by the fact that no empirical study yet has delved into this kind of relationship among CSR indices and major stock market benchmark indices. The paper examines the presence of unilateral and bilateral-return influences and asymmetric-volatility effects, and provides empirical evidences on whether these major stock indices as a consequence of one-way or two-way returns influence create a positive effect or a negative effect on CSR indices. This transmission effect of major stock indices to other indices have been established in the literature because of their ability to signal potential market trends that impacts allocations of investors (Luo et al., 2011; Zhou et al., 2012). CSR indices may also have that same effect, because of the growing interests of investors into SRIs. This makes our findings offer more economic importance to the investing community, because it can be a strategic basis for both traders and fund managers in devising CSR investment strategies in relation to major stock market indices. Also, since this area is relatively new, our conclusions can also provide potential avenues for further research for academicians and researchers.

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Volume V Issue 2(10) Winter 2014 The paper is structured as follows. Section 2 describes related studies, Section 3 defines the data and methodology of ARMA-APARCH models; Section 4 interprets the empirical findings; and Section 5 presents the conclusion. 2. Related literature This section gives an overview of related studies that proved the existence of interdependencies through spillover effects on returns and volatilities among various investment markets. A recent study of Yang and Kun (2014) found that returns of triple-listed China stocks and their indices in the Hong Kong Stock Exchange, Shanghai Stock Exchange and New York Stock Exchange are co-integrated across the three markets, which indicates zero chance of arbitraging.Wahab (2012) showed the interdependencies among advanced nations and showed co-movements among the stock returns of Germany, France, UK and the US. On the other hand, Luo et al..(2011)demonstrated that the markets of Hong Kong, Singapore, Thailand, Korea and Taiwan are affected by changes in the US, Japan and Australian stock exchanges. Also, a similar basket of stocks called exchangetraded funds (ETFs), which normally has the same but miniature allocation of the major stock index that they are mimicking also creates a positive and negative effect towards their benchmark indices. For example, the GARCH and EGARCH models of Chen and Huang (2010) established in the literature the existence of spillover and leverage effects between stock indices and ETFs. Majority of literature in CSR investing focus on the relationship between SRI funds and mainstream portfolios or market indices, and SRI performances have been mixed so far. Bauer et al. (2005) for instance showed that the returns of SRI funds actually tend to either underperform due to the requirements for SRI screening fees; or even have no significant performance differentials. Bello (2005) examined the performance of socially responsible mutual funds with the conventional funds, and findings show no significant differences. A recent study of De and Clayman (2014) showed that asset managers with high ESG ratings experienced higher gains in their portfolios over managers with low ESG ratings, and the benefit on CSR investing strengthens when markets are more volatile. Closer set of studies in this paper are the researcher of Chen (2011) on ethical ETFs and Chen and Diaz (2012) on faith ETFs. The authors discovered that both ethical and faith ETFs have positive relationship with their tracked major stock market indices; and lagged ethical and faith ETFs have both one-way and two-way influences on their underlying stock benchmark returns. From the above literature, we can provide a good understanding that spillover effects of returns and volatilities exists among investment instruments and the performance of SRI funds. These studies provide evidence that major stock market indices and CSR indices may also have this type of relationship and are worth exploring in the literature. 3. Data and methodology Collected data on major stock market and CSR indices focused on the period at the end of subprime mortgage crisis, from July 2009 to October 2014; and sourced from the Quandl.com database and the Thomson Reuters Corporate Responsibility Index website, respectively. Our paper focused after the recession, because according to De and Clayman (2014), more people became more concerned on volatility and a way to mitigate risks is through investments in socially responsible enterprises and SRI funds. The returns of both the major stock market indices and CSR indices were calculated as the logarithm of closing prices, and are represented as follows:

 I  Rim,t  ln  t  *100,  I t 1   P  Rie,t  ln  i ,t  *100,  Pi ,t 1  Rim,t Rie,t

(1) (2)

where and are the stock index returns and CSR returns at time t, respectively; I is the price stock index; and P is the price of CSR index. 3.1 The ARMA model Box and Jenkins (1970) created time-series models that capture short-range correlations. The predictors are previous observations represented by the autoregressive (AR) function, and previous residual errors are modelled by the moving average (MA) process.The basic ARMA (r, s) model can be written as:

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yt  1 yt 1  ...  r yt r   t  1 t 1  ...   s  t s

(3)

and the general ARMA (r,s) can be represented as: r

s

i 1

j 1

yt  0  i yt 1   t   j  j 1

(4)

where r represents the order of the AR(r) part, i its parameters, s the order of the MA(s) part, j its parameters and t nnormally and identically distributed noise. ARMA models are flexible and able to describe the serial dependencies of time-series in terms of the number of parameters of the AR and MA components. 3.2 The APARCH model Ding et al. (1993) included a power term in the traditional autoregressive conditional heteroscedasticity (ARCH) to represent the periods of relative tranquillity and volatility by magnifying the outliers in the time-series. This represents the asymmetric power in the APARCH model, which represents the effect of negative and positive innovations on the structure on the data. The APARCH (p,q) model can be written as: q

p

i 1

j 1

 t   0   i (  t i   i  t i )    j t1 where

 0 > 0, δ ≥ 0,  j ≥ 0,  i ≥ 0

and -1