DOES FINANCIAL LEVERAGE AFFECT TO ABILITY AND EFFICIENCY OF FAMA AND FRENCH THREE FACTORS MODEL? THE CASE OF SET100 IN THAILAND

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1 DOES FINANCIAL LEVERAGE AFFECT TO ABILITY AND EFFICIENCY OF FAMA AND FRENCH THREE FACTORS MODEL? THE CASE OF SET100 IN THAILAND by Tawanrat Prajuntasen Doctor of Business Administration Program, School of Business, University of the Thai Chamber of Commerce, Bangkok, 10400, Thailand ABSTRACT The objective of this research was to measure and to compare the ability and efficiency of rational pricing in explanation the average return of stock of high financial leverage firms and low financial leverage firms in Thailand when using Fama and French three factors model in SET100 of the Stock Exchange of Thailand for the period 2006 to The company was used in this research consisted of 100 listed companies and were divided into 6 groups; BH, BM, BL, SH, SM, and SL. B and S was mean the size effect which measure from market capitalization of each company while H, M and L was mean the value effect which measure from book to market value. These can improves the efficiency of the explanation and capture the risk and return in SET100 of The Stock Exchange of Thailand in SH, BH,BM,SL groups. Finally, we expect to contribute financial leverage that effect the ability and efficiency of Fama and French three factors model can explain risk with of rational pricing in stock return of low leverage financial firms better than high financial leverage firms. KEYWORDS Financial Leverage, Asset Pricing, Factor Models INTRODUCTION The investors who want to invest in stock exchange should decide carefully to make maximum wealth. Making wealth, the investors have to estimate all important factors that effect to return from investment in the future. Considering these important factors needs a lot of information for using in estimation, stipulation and offering suitable price in stock trading. Using the model is very important to assess stock price and help the investors to plan and decide in investment correctly and effectively. Pricing Model is very popular and apply broadcast in the present. It has developed and improved continually. It was started with Sharpe (1964), Lintner (1965) and Mossin (1966) who presented Capital Asset Pricing Model: CAPM which show how to be related between the average return of stock and market risk factors. The other researchers did not agree because there are other factors, more than one factor. After that, Ross (1976) developed the model that peruses many factors for assessing the return of stock. The model is called Arbitrage Pricing Theory: APT. Although APT Model is more efficiency than CAPM, APT Model lacks of general in using. The different elements of model depend on conditional economic and position business in each country so APT Model is not very famous as it should be. Fama and French (1992) developed the pricing model that was combined these factors; market (following CAPM Model), size and Book to Market Ratio: BE/ME Ratio to use in forecast and explain the average return of stock. Then, Fama and French studied for correct and efficiency of model in many times. The all results of the studying could confirm the model of Fama and French to be able to explain average return of stock in stock exchange better than CAPM model. Fama and French (1992) find that the main prediction of the CAPM, a linear cross-sectional relationship between mean excess returns and exposures to the market factor, is violated for the US stock market. Exposures to two other factors, a size based factor and a book-to-market-based factor, often called a value factor, explain a significant part of the cross-sectional dispersion in mean returns. If stocks are priced rationally, then systematic differences in average returns should be due to differences in risk. Thus, given rational pricing, the market, size and value exposures

2 must proxy for sensitivity to pervasive risk factors in returns. Fama and French (1993) confirm that portfolios constructed to mimic risk factors related to market, size, and value all help to explain the random returns to well-diversified stock portfolios. Fama and French (1995) attempt to provide a deeper economic foundation for their three- factor pricing model by relating the random return factors to earnings shocks. They claim that the behaviour of stock returns in relation to market, size and value factors is consistent with the behaviour of earnings. They admit that their findings are weak, especially relating to the value factor, but attribute this to the measurement error problems in earnings data. There is a burgeoning research literature contradicting, confirming, criticizing, and extending the Fama-French model, see for example the discussion and references in Davis, Fama and French (2000). In Thailand, using Fama and French Three Factors Model in explanation return of stock in the Stock Exchange of Thailand is not studied clearly and propagates. This study is measured and compared the ability and efficiency of rational pricing in explanation the average return of stock of high financial leverage firms and low financial leverage firms in Thailand when using Fama and French three factors model in SET100 of the Stock Exchange of Thailand for the period 2006 to Finally, we expect to contribute that financial leverage effect the ability and efficiency of Fama and French three factors model that it can explain risk with of rational pricing in stock return of low leverage financial firms better than high financial leverage firms. LITERATURE REVIEW This section begins with overview of the Fama French model, which is to show important it is to have an accurate model to predict rate of return of stocks before moving on to method to compare accuracy. An introduction of Capital Asset Pricing Model(CAPM) and the MM propositions are presented along with the Fama and French model. Finally, an introduction of financial leverage is cutted point the low leverage financial firms and high financial leverage firms. The Capital Asset Pricing Model Capital Asset Pricing Model is the most widely recognized explanation of stock prices and expected return. It is described that Systematic Risk is the main factor to expected return. As in all financial theories, a number of assumptions were made in the development of the CAPM. The assumptions of CAPM consist of wealth. 1) All investors focus on a single holding period, and seek to maximize the expected utility of their terminal 2) All investors can borrow or lend an unlimited amount at a given risk free rate of interest. 3) Investors have homogeneous expectations. 4) All assets are perfectly divisible and perfectly liquid. 5) There are no transactions costs. 6) There are no taxes. 7) All investors are price takers. 8) The quantities of all assets are given and fixed (Brigham and Ehrhardt 2005). The CAPM equation could be represented as: (Sharpe,1964; Lintner, 1965; Mossin;1966) E(Ri ) = Rf + [E(Rm ) - Rf ] βi + εi Where E(Ri) is the expected return of any asset i, Rf is the risk free rate, E(RM) is the expected return of market, βi is the sensitivity of the asset returns to market returns and εi is the residual term of any asset i Due to complex relationship between risk and return, Ross (1976) proposed a model called Arbitage Pricing Theory: APT. This model can add many factors to model however it is based on complex statistical especially multicolinearlity. APT is much more discussion in academic but rarely to find in practical. Fama and French Three Factors Model Fama and French (1992) found that the cross section of average stock returns for the period for US stocks is not fully explained by the CAPM beta and that stock risks are multidimensional. Fama and French (1993)

3 presented Fama and French Three Factors Model. Both of them thought macro economical factors effect to return on stocks such as effect to growth rate of firm, debt, sales, profits, etc. These factors will effect to return on securities. They tested the hypothesis that factors affect to expected security market line. The assets are risk, there are three factors: 1. Market risk premium 2. Return of Small Size minus Return of Big Size: SMB. 3. Return of High BE/ME Ratio minus Return of Low BE/ME Ratio): HML. The Fama and French Three Factors Model equation is E(Ri ) = Rf + [E(Rm ) - Rf ] bi + sie(smb) + hie(hml) + εi Where E(Ri) is the expected return of any asset i, Rf is the risk free rate, E(RM) is the expected return of market, βi is the sensitivity of the asset returns to market returns, E(SMB) is the expected return of small size minus return of big size, si is the sensitivity of the asset returns to return of SMB, E(HML)is the expected return of high BE/ME ratio minus return of low BE/ME ratio, hi is the sensitivity of the asset returns to return of HML, and εi is the residual term of any asset i Fama and French (1996) define the patterns in average returns that are not explained by the CAPM. These are related to firm characteristics such as size, earnings/price, cash flow/price, book-to market equity, past sales growth, long-term past return, and short-term past return. They also conclude that Fama and French Three Factors Model can capture many of the CAPM average-return anomalies except for continuation of short-term returns. Fama and French (1998) studied by using value and growth of international evidence for the period , the value stocks have higher returns than growth stocks in markets around the world. For the period 1975 through 1995, the difference between the average returns on global portfolios of high and low book-to-market stocks is 7.68 percent per year, and value stocks outperform growth stocks in twelve of thirteen major markets. An international capital asset pricing model cannot explain the value premium, but a two-factor model that includes a risk factor for relative distress captures the value premium in international returns. A general asset pricing model consistent with the MM propositions The Modigliani and Miller (M&M) theorems on capital structuring have, inarguably, laid down the foundations for modern corporate finance. There are several principles that underlie these theorems and two of these, which are most relevant to this paper, may, very simply, be reiterated as follows: 1. In the absence of taxes, there are no benefits, in terms of value creation, to increasing leverage. 2. In the presence of taxes, such benefits, by way of interest tax shield, do accrue when leverage is introduced and/or increased. An outcome of the above, whose proof can be found in almost any academic finance text [see, for instance, chapter 16 of Ross et al (1998) or chapter 18 of Brealey and Myers (1996)], is that the value added to a firm by taking on a debt of, let us say, D, is Δ V = DT (1a) where ΔV is the incremental value added and T is the tax rate. It, thus, follows that the value, V, of the levered L firm becomes: ΔV =V L u + DT (1b) where V is the value of the unlevered firm. Simply stated, therefore, the value of the levered firm is that of its u unlevered counterpart, plus the present value of the interest tax shield, which is DT. We will now implement the above to illustrate how debt and equity are coupled to each other when a firm decides to take on debt to buy back its shares - or alternatively, when it issues shares to pay down debt. The approach used here will be simplistic and numerical in nature, with intent to illustrate how a firm s financial statement [income statement and balance sheet] is affected when the amount of debt changes. For the sake of simplicity, and for the time being, it shall be assumed that the cost of debt remains constant throughout i.e. the firm experiences no default risk as it

4 raises its leverage. Including these effects is quite trivial, but shall not be pursued at this point. A companion paper, instead, focuses on it (Cohen, 2004). Financial Leverage Financial leverage is the degree to which a company uses fixed items, such as debt and preferred equity. A high degree of financial leverage implies high interest payments. As a result, earnings per share are negatively influenced by interest charges. The higher interest payments due to increased financial leverage, the lower Earnings per Share, EPS. Financial risk is the risk to the shareholders caused by an increase in debt and preferred equities in the firm s capital structure. When a firm increases preferred equities and debt, interest charges increase, and EPS are reduced. As a result, risk to shareholder return increases. A firm should take into account its optimal capital structure when making financing decisions to make sure any increases in preferred equity and debt increase the value of the firm. Degree of Financial leverage is usually defined as: Financial Leverage = Operating Income = EBIT Net Income EBIT - interest charges = % EPS %EBIT Operating leverage is an attempt to estimate the percentage change in operating income (earnings before interest and taxes or EBIT) for a one percent change in revenue. Financial leverage tries to estimate the percentage change in net income for a one percent change in operating income. The product of the two is called Total leverage, and estimates the percentage change in net income for a one percent change in revenue. This is the percentage change in EPS divided by the percentage change in EBIT. This is the "degree of financial leverage" (DFL). It is a measure of the sensitivity of EPS to changes in EBIT as a result of changes in debt. It can be seen as a sort of elasticity. RESEARCH Connor and Sehgal (2001) empirically examined the Fama-French three factor model of stock returns for India. It was found that cross-sectional mean returns were explained by market, size and book-to-market factors, not only by the market factor. It also found that it is not link between common risks factors in earnings and in stock returns. Chawarit (1996) studied about comparing CAPM and APT model for explaining predictable the return of stock in the Stock Exchange of Thailand in There are 2 parts of times for studying; they are before and after economical crisis. The research was found APT model is better than CAPM model to explain the return of stock in the Stock Exchange of Thailand for both times. Chanthirakul (1998) tested Arbitrage Pricing Theory by principal component analysis in the Stock Exchange of Thailand. It was found there are 2 factors that could describe return of stock in the stock exchange but these factors could not specify what the causes were or where they were from. The research also found that quantity of factors that explain return on security will change when sample change. Manatsanan 2007) empirically examined the Fama-French three factor model in the Stock Exchange of Thailand(SET) in Energy Sector since January 2003 to December It showed that the size factor(the average returns that has small size minus big size :SMB) and value factor (the average returns that has BE/ME ratio minus low BE/ME ratio: HML) in this study isn t in line with the hypothesis of Fama and French because this study found that the security that has big size and low BE/ME ratio will gave high stock returns. While the hypothesis of Fama French showed that the security that should gave high stock returns is the security that has small size and high BE/ME ratio. Rathawan (2007) tested the effect of size of the firm and book to market value on the rate of return based on the Fama-French three factor model in the Stock Exchange of Thailand(SET) in Property Development

5 Sector since January 2003 to December The result of this study indicated that size factor(smb) and value factor (HML) do not follow the hypothesis of Fama French. Nopbhanon 2009) tested the efficiency of Fama and French Three Factors Model in The Stock Exchange of Thailand over the period July 2002 to May The company was used in this research consisted of 421 companies. And they found the size effect and the book to market ratio effect into the Capital Asset Pricing Model following the Fama and French can improves the efficiency of the explanation and capture the risk and return in The Stock Exchange Of Thailand in SH, BH, BM, SL groups, It was concluded that The Fama and French Three Factors Model can explain risk in stock return better than the traditional only one factor: Capital Asset Pricing Model in 4 groups (SH, BH, BM, SL) Michael Drew (2003) compared the explanatory power of a single index model with the multifactor asset-pricing model of Fama and French (1996) for Hong Kong, Korea, Malaysia and the Philippines. Their findings suggest that the CAPM beta alone is not sufficient to describe the cross-section of expected returns. They also find that the absolute pricing errors of the CAPM are quite large when compared with the multifactor model of Fama and French (1996). Their findings show that firm size and book-to-market equity help explain the variation in average stock returns in a meaningful manner. Recent studies that test the relation between leverage and stock returns report contradicting results. Dimitrov and Jain (2005) report negative relation between leverage and stock returns. They study changes in leverage levels and show that they are negatively related to contemporaneous and future adjusted returns. They calculate contemporaneous and future returns as raw and risk adjusted returns. Their main focus is to examine the change in leverage as a result of the economic performance and not due to growth, mergers and acquisitions or sudden changes in capital structure for some other reasons, and hence they make a distinction between financing for operating performances or for the reasons such as growth. Sivaprasad et al (2007) investigated whether capital structure is value-relevant for the equity investor. They integrate the MM framework into an investment approach by estimating abnormal returns in excess of the market return on leverage portfolios in the time-series for different risk classes. In this study, we adopt the explicit valuation model of MM by estimating returns in excess of the risk-free rate. Additionally we also undertake robustness tests to examine if returns can be explained by firm leverage even if portfolios are constructed to mimic other risk factors related to size, market-to-book, market risk and momentum to capture variation in returns is in the time-series regressions. George et al (2006) find a negative relation between returns and book leverage. They explain that firms that suffer most (least) in financial distress maintain low (high) leverage. Thus, the return premium to low leverage firms relative high leverage firms appear to be a form of compensation for the financial distress costs. The negative relationship between returns and leverage could also be due to the market s pricing of the firm s ability to raise funds if need be. RESEARCH METHODOLOGY The section of this research is to introduce how the data used in the research Fama and French three factors model in SET100 of the Stock Exchange of Thailand Data The populations of the research is the closing price at the ended of month of SET100 (registered in the Stock Exchange of Thailand). The source of all our data is from database of Settrade of Thailand. This study uses SET100 securities that are trading now and recorded the data about market to book value. This study does not use the securities whose market to book value is minus. The data are used over the period 2006 to 2010 for this study. All securities in this study would be listed by market equity or market value. It was cut the securities into 2 group are (1) the low leverage financial firms and (2) high financial leverage firms base on degree of financial leverage (DFL). These are compare the ability and efficiency of Fama and French three factors model.

6 The Logic of Fama and French Model The model explains expected return in a regression model in accordance with market risk E(Ri ) = Rf + [E(Rm ) - Rf ] bi + sie(smb) + hie(hml) + εi Or R i,t - Rf,t = Rf + [(R m,t - Rf,t ) - Rf ] bi + sie(smb) + hie(hml) + εi Where E(Ri) is the expected return of any asset i, Rf is the risk free rate, E(RM) is the expected return of market, βi is the sensitivity of the asset returns to market returns, E(SMB) is the expected return of small size minus return of big size, si is the sensitivity of the asset returns to return of SMB, E(HML)is the expected return of high BE/ME ratio minus return of low BE/ME ratio, hi is the sensitivity of the asset returns to return of HML, and εi is the residual term of any asset i By adding factors, the Fama French model allowed investors to better specify their risk to the market, size and value risk. From the equation above, the variable in this research consist of 1) The rate of return on portfolio i ; R i,t = ln (P i,t - P i,t -1 / P i,t -1 ) Where, R i,t = The rate of return on portfolio i at time t P i,t = The price index of portfolio i at time t P i,t -1 = The price index of portfolio i at time t-1 2) The rate of return on market ; R m,t = ln (P m,t - Pm m,t -1 / P m,t -1 ) Where, R m,t = The rate of return on market i at time t P m,t = The price index of market i at time t P m,t -1 = The price index of market i at time t-1 3) Risk free rate use the yield of government bond 1 year. This study uses all securities that are trading now and recorded the data about market to book value. There are 100 securities that are used for this study. All securities in this study would be listed by market equity or market value. The market equity of remaining securities was calculated median which was cutting point. All companies were divided into 2 parts; companies have market value more than cutting point are big company (B) while companies have market value less than and equal cutting point are small company (S). In addition to bring the Book to Market ratio of each securities in year t-1 divide to be 3 groups according to BE/ME resulted was first group 30 percentage of whole securities has BE/ME highest (called High: H group) second group 40 percentage of whole securities has BE/ME in medium (called Medium: M group) and the last group 30 percentage of whole securities has BE/ME lowest (called Low:L group). Third step organize securities to be 6 groups according to cross of securities group that invent in first and second step is S/L, S/M, S/H, B/L, B/M, B/H for example group S/L consist of the securities that exist in small group including the securities group has BE/ME at the lowest or B/H group consist of securities that exist in large securities group including the securities group has BE/ME highest, etc. For this study given the Weight Average Monthly Return of each group equally according to method of Fama and French (1996) Obtain of Variable SMB and HML SMB (Small minus Big) represent the risk factor diverge of rate return which involve with size effect, SMB will different in each month among average return rate of small sample group (S/L, S/M and S/H) with the average return rate of 3 large groups (B/L, B/M, B/H). SMB = Small minus Big = Average Returns of Small Size minus Big Size

7 = 1/3 (S/H + S/M + S/L) - 1/3 (B/H + B/M + B/L) HML (High minus Low) represent the risk factor of return rate that involve with ratio Book to Market Value Effect. HML each year had differ between average return rate of 2 portfolios that has BE/ME high (S/H and B/H) with average return rate of 2 portfolios has BE/ME low (S/L and B/L) Model Testing Method HML = High Minus Low = Average Returns of High BE/ME Ratio minus Low BE/ME ratio = 1/2 (S/H + B/H) 1/2 (S/L + B/L) The appropriate testing of Fama and French Three Factors Model with set of data in the Stock Exchange of Thailand use Standard Multivariate Regression Framework to test the appropriate of Fama French model and appropriate of each variable in the model that market factor, size factor and value factor. The amount of the study is the yearly historical data from January 2006 to December The followings variables used in this study are: ER = f (RM,SMB,HML) Which can be said in linear equation as: ER = a0+a1rm+a2smb+a3hml where ER = The rate of return on stocks RM = The rate of return on market SMB = The average return on size factor HML = The average return on book-to-market factor a0 = Constant a1, a2, a3 = Coefficients To present the appropriate of each factor in Fama and French Three Factor Model, it was used Ordinary Least method(ols) is applied to investigate the assumption of the fundamental regression s analysis.

8 Conceptual Frameworks Independent variables Dependent variables Market Factor The risk free rate of return: Government bond 1 year R f,t Market Factor The rate of return on the market portfolio: Closing Price of SET100 R m,t R i,t : The expected return of portfolio: Closing Price Size Factor The expected return of small size minus return of big size portfolio SMB t of Securities of portfolio Value Factor The expected return of high BE/ME ratio minus return of HML t low BE/ME ratio portfolio

9 CONCLUSION This conceptual research was to measure and to compare the ability and efficiency of rational pricing in explanation the average return of stock of high financial leverage firms and low financial leverage firms in SET100 of the Stock Exchange of Thailand over the period 2006 to 2010 when using Fama and French three factors model. In this model to achieve more accurate prospecting predicted return of portfolio, we focus on financial leverage as well as historical and estimating data completely. Finally, we expect to contribute financial leverage that effect the ability and efficiency of Fama and French three factors model can explain risk with of rational pricing in stock return of low leverage financial firms better than high financial leverage firms. In other word, the results in this paper suggest reasonable differences in the ability and efficiency of rational pricing in the average return of stock in Thailand when using Fama and French three factors model that it depend on financial leverage of firm. REFERENCES Brigham, E. F., and C. M. Ehrhardt. Financial Management: Theory and Practice, 11 th Edition. South-Western, (2005) Chawarit, W. The Comparison of CAPM and APT in describe return of stock predicting Thesis in master degree of the Faculty of Economics Chulalongkorn University, (2000) Connor, G., and S. Sehgal. Tests of the Fama and French Model in India. Working Paper,(2001) Fama, E. F., and K. R. French. Multifactor Explanations of Asset Pricing Anomalies. Journal of Finance 51, (1996): Fama, E. F., and K. R. French. The Capital Asset Pricing Model: Theory and Evidence. Working Paper, (2004) Fama, E. F., and K. R. French. The Cross-Section of Expected Stock Returns. Journal of Finance 47, (1992): Janteeratikun, N. The Testing of CAPM by factor analysis method: A Case study of the Stock Exchange of Thailand Thesis in master degree of the Faculty of Commerce and Accountancy Chulalongkorn University, (2000) Lintner, J. Security Prices, Risk and Maximal Gains from Diversification. Journal of Finance 20, (1965): Ross, S. The Arbitrage Pricing Theory of Capital Asset Pricing. Journal of Economic Theory 13, (1976): Sharpe, W. F. Capital Asset Prices: A Theory of market Equilibrium under Condition of Risk. Journal of Finance 19, (1964):