Pengaruh Model Tiga Faktor Terhadap Return Saham

Sumber :	Akuntabilitas : Jurnal Ilmiah Akuntansi
Penerbit :	Jurusan Akuntansi Universitas Pancasila
Tahun Terbit Artikel:	2007
Volume :	7
No :	1
Halaman :	79-84
Kata Kunci :	Return on investments; Property business; Real estate business
Abstrak :	This study used the sample of twenty six companies engaged in property and real estate activities which are listed in Indonesia Stock Exchange during the period of 2002-2006. The purposes of this study are to know and analyze the level of beta, firm size and book to market effect to the return share of companies engaged in property and real estate activities which are listed in Indonesia Stock Exchange during the period of 2002-2006. Secondary data was analyzed by using the three factor model of Farma and French (1996). With return share as dependent variable and beta, firm size and book to market as in dependent variables. The result of this study shows a statement that Beta (Xl), 5MB (X2) and HML (X3) simultaneously have a significant effect to return share with contribution of beta variable, 5MB and HML that reach 55,5 percent in explaining the level of return share. Beta and HML variable have a positive and significant effect to return share. On the other hand, 5MB has negative effect and insignificant to return share.
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Detecting Premium Portfolios in Higher-order Moments CAPM

Tepmony Sim

E-mail: tepmonysim_rupp@yahoo.com

M.sc. “QEM ”, Ca ’ Thesis

Foscari University of Venice

June 2010,

Executive Summary

Since the first introduction by Sharpe (1964), the Capital Asset Pricing Model (CAPM) has then been one of the most popular models for asset pricing, and is a cornerstone of financial economics. However, the CAPM suffers from several restrictive hypotheses such as the normality of return distributions. It has been criticized by many empirical evidences and this has led for further considerations on the extensions within the model. An extension which we regard in this thesis is to insert higher-order moments other than variance into capital asset pricing relation. The resulting model is the Higher- order Moments CAPM. This approach is initially proposed by Rubinstein (1973) and sequentially developed by Kraus and Litzenberger (1976), Fang and Lai (1997), Athayde and Flôres (1997 and 2000), and Jurczenko and Maillet (2006b).

In this dissertation, the Higher-order Moment CAPM which takes into account up to the fourth moment is considered. In Four-moment CAPM, to arrive at the four- moment CAPM fundamental relation, two specific portfolios, besides the riskless asset and the market portfolio, are assumed to exist. Our main purpose is to confirm their existence and show that when they exist, they are not unique. From the validity of four- moment CAPM fundamental relation, the roles of the third moment (skewness) and the fourth moment (kurtosis) can be investigated. To detect the two specific portfolios above as well as to investigate the role of skewness and kurtosis in current financial data, the Seemingly Unrelated Regression (SUR) method by Zellner (1962) is carried out.  It is also our interest to confirm the validity of Four-moment CAPM in another way. Following Fang and Lai (1997), Hwang and Satchell (1999) and Galagedera et al (2004), we can use the Cubic Market Model as a proxy. Our purpose of doing so is to compare the performance of the two models with the in-hand data.

Before we can go through the empirical part, some theoretical foundations are provided. In this part, we basically follow the works by Jurczenko and Maillet (2006b). Several notations used to represent and to compute the higher-order moments are also given. Moreover, a generalization of the univariate higher-order C-(co)moments to multivariate higher-order C-(co)moments is introduced as well. The systematic risk, systematic skewness and systematic kurtosis can be calculated by several means. They can be obtained from the cubic market model, the four-moment CAPM fundamental relation, or by their own definitions. Besides the C-moments these risk factors are also calculated by L-moments. Finally, a comparison of these calculations can thus be made.  Detecting Premium Portfolios in Higher-order Moments CAPM*

Abstract

In Four-moment CAPM, the roles of skewness and kurtosis can be investigated under the validity of the so-called four-moment CAPM fundamental relation. This relation assumes that, besides the riskless asset and the market portfolio, another two specific portfolios exist. We are to show that these portfolios exist but they are not unique. We also confirm the validity of the four-moment CAPM in another direction. Following Fang and Lai (1997), Hwang and Satchell (1999) and Galagedera et al (2004), we use the Cubic Market Model as a proxy of Four-moment CAPM. In the theoretical framework, various notations to represent and to compute the higher-order C-moments of assets ’ returns are introduced. The generalization of the univariate C-(co)moments to the multivariate C-(co)moments is also provided. Besides C-moments, the systematic risk, the systematic skewness, and the systematic kurtosis are also calculated by using L-(co)moments.

Keywords: CAPM, Higher-order Moments, Kurtosis, L-moments, Premium Portfolios,  Skewness, SUR.

JEL Classification: C01, C10, G11, G12.

Introduction

Since it was first introduced by Sharpe (1964), Lintner (1965) and Mossin (1966), the Capital Asset Pricing Model (CAPM) has then been one of the most popular models for asset pricing, and is a keystone of financial economics. This particular theoretical framework relates the risk-return trade-off to a simple mean-variance relationship and/or to a quadratic utility function. However, the empirical evidence shows that the normality hypothesis, which it bases on, has to be rejected for many financial data. A quadratic utility function for an investor, furthermore, implies an increasing risk aversion. Instead, it is more reasonable to assume that risk aversion decreases with an increase in wealth. Due to several inadequacies revealed by empirical tests, CAPM has been considered for further extensions by taking into account more factors additional to mean and variance.  Amongst these extensions, multifactor CAPM is included. The most prominent one of multifactor framework is size effect of Banz (1981). He finds that the size of a firm and the return on its common stock are inversely related.

Later on, Fama and French (1992) suggest three-factor model, which includes the capital size and book-to-market value into classical CAPM. The findings of Fama-French in their three-factor model suggest that small cap and value portfolios have higher expected returns -- and arguably higher expected risk -- than those of large cap and growth portfolios. Carhart (1997), who aims to study the persistence of mutual fund returns, then proposes four-factor model, which is an extension of Fama-French three-factor model by adding a new factor, one-year momentum in stock returns. In short term at least, the results do not support the existence of skilled or informed mutual fund portfolio managers.  Another appropriate approach, which we regard as the center of our interests, is to insert higher-order moments than variance in a pricing relation. The main feature of these models is to obtain, for any risky asset, a linear equilibrium relation between the expected rate of return and higher-order moments systematic risk measures.

In this dissertation, we consider some extensions of the traditional mean- variance framework that account for higher-order moment conditions and a more variegated structure of the risk premium concept. In particular, we examine the roles of skewness and kurtosis in pricing the recent financial data. Skewness characterizes the degree of asymmetry of a distribution around its mean. Negative (positive) skewness indicates a distribution with an asymmetric tail extending towards more negative (positive) values. Kurtosis characterizes the relative peakedness or flatness of a distribution compared with the normal distribution. In standard definition, kurtosis higher (lower) than three indicates a distribution which is more peaked (flatter) than a normal one. Similarly to the so-called systematic risk or beta, it is possible to test for a systematic skewness and systematic kurtosis. Systematic skewness and kurtosis are also known as co-skewness and co-kurtosis (Christie-David and Chaudry, 2001). Provided that the market has a positive skewness of returns, investors will prefer an asset with positive coskewness. Cokurtosis measures the likelihood that extreme returns jointly occur in a given asset and in the market; and thus investors prefer small co-kurtosis. The common characteristic of the models accounting for co-skewness and co-kurtosis is that they incorporate higher moments in the asset pricing framework. In the literature, two main approaches have been investigated: three- moment and four-moment CAPM. The theoretical higher-order moments CAPM is initially proposed by Rubinstein (1973), and, subsequently, developed by Ingersoll (1975), Kraus and Litzenberger (1976), Athayde and Flôres (1997 and 2000), and Jurcenzko and Maillet (2001 and 2006b). Other authors empirically study the validity of the higher-order moments CAPM such as, often, three- moment and four-moment CAPM. For three-moment CAPM, Barone-Adesi (1985) proposes a quadratic model to test the three-moment CAPM, while Harvey and Siddique (2000) find that the systematic skewness requires an average annual risk premium of 3.6% for US stocks. They also find that portfolios with high systematic skewness are composed of winner stocks (momentum effect).

Harvey (2000) shows that skewness, coskewness and kurtosis are priced in the individual emerging markets but not in developed markets. He observes that volatility and returns in emerging markets are significantly positively related. But the significance of the volatility coefficient disappears when co-skewness, skewness, and kurtosis are considered. Harvey ’s explanation for this phenomenon is the low degree of integration of the emerging markets. When accounting up to the fourth moment, Berényi (2002), Christie-David and Chaudry (2001), Chung, Johnson and Schill (2006), Fang and Lai (1997), Hwang and Satchell (1999), Galagedera, Henry and Silvapulle (2002) propose the use of the Cubic Market Model as a test for coskewness and cokurtosis. Berényi (2002) applies the four-moment CAPM to mutual fund and hedge fund data, and he then shows that volatility is an insufficient measure of risk for hedge funds and for medium risk averse agents.

Christie-David and Chaudry (2001) employ the four-moment CAPM on the future markets, where they find that systematic skewness and systematic kurtosis increase the explanatory power of the return generating process of future markets. Fang and Lai (1997), in purpose to corporate the effect of kurtosis, apply the four-moment CAPM on New York Stock Exchange (NYSE). They find that the expected rate of return is not only related to the systematic variance but also to the systematic skewness and systematic kurtosis. Hwang and Satchell (1999) investigate co-skewness and co-kurtosis in emerging markets. They show that systematic kurtosis is better than systematic skewness in explaining emerging market returns.

Following Jurzenko and Maillet (2006b), in Four-moment CAPM, we attempt to detect the two premium portfolios Z1m and Z2m introduced in the so- called Four-moment CAPM Fundamental Relation. These two portfolios are defined as such that: Z1m possesses zero-covariance and zero cokurtosis and has unitary coskewness with market portfolio, and Z2m  possesses zero-covariance and zero coskewness and has unitary cokurtosis with market portfolio. We are to show that these portfolios always exist, however, they are not unique. We propose some methods to elicit the appropriate ones. It is straightforward that when we can find these two premium portfolios, the effect of skewness and kurtosis can be examined. Besides, we also wish to test the validity of the four-moment CAPM in another way. We depart from testing the mean-variance CAPM, then the three-moment CAPM and finally the four-moment CAPM by  using, respectively, the linear market model, the quadratic market model and the cubic market model as the proxies.

In the theoretical framework, various notations for presenting and computing the portfolio returns are introduced. Moreover, a generalization of scalar C-moments of returns to multivariate case is also provided. In addition to conventional moments (C-moments) used in traditional way in higher-order moments CAPM, we also introduce robust moments -- called linear moments (L-moments). The main advantage of L-moments over C-moments is that L-moments, being linear functions of the data, suffer less from the effects of sampling variability: L-moments are more robust than C-moments to outliers in the data and enable more secure inferences to be made from small sample about an underlying probability distribution (see, for instance, Hosking, 1990; Hosking and Wallis, 1987; Ulrych et al, 2000). L-moments sometimes yield more efficient parameter estimates than the maximum likelihood estimates (see Hosking, 1990). Furthermore, L-moments exhibit some specifically fascinating features for financial applications. For example, they have abilities to reduce the so-called Hamburger moment problem (see Jondeau and Rockinger, 2003a; and Jurczenko and Maillet, 2006a); and they are also coherent shape measures of risk (see Artzner, Delbaen, Eber and Heath, 1999).

To obtain the estimations of all wanted parameters, we carry out throughout the thesis the so-called Seemingly Unrelated Regression (SUR) method by Zellner (1962). As pointed out by the author, it is only under special conditions that classical least-squares applied equation-by-equation yields efficient coefficient estimators. For conditions generally encountered, SURE are at least asymptotically more efficient than single-equation least-squares estimators.

The organization of the thesis is as follows. In Chapter 2, we discuss the literature and selected research papers of interest as well as the central theory behind CAPM. In Chapter 3, we present the theoretical framework used for the analysis. Chapter 4 presents the econometric methods used. We devote Chapter 5 for discussing the properties of the data material and for presenting the results obtaining by using econometric methods in previous part. The last part, Chapter 6, the conclusions are made; and the rests are for references and appendices.

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Application of the Fama and French Three-Factor-Model to the Greek Stock Market

Georgios Maris

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

in the

Department of Accounting & Finance

UNIVERSITY OF MACEDONIA

Autumn 2009

ABSTRACT

This thesis investigates the robustness of the Fama and French Three-Factor- Model on the Greek stock market for the period July 1999 to June 2009. It continues the out of sample tests of the model conducted by Malin and Veeraraghavan (2004). The test follows the time series regression approach of Black, Jensen and Scholes (1972). Monthly portfolio returns are regressed on three factors (market, size, BE/ME ratio). We document negative excess market returns probably because of the two major bear market rallies that took place in the beginning and in the ending of the sample period. We also find a big firm effect in contrast to Fama and French (1996) and Malin and Veeraraghavan (2004) who observe small firm effects. Finally, we observe a value effect that is consistent with Fama and French (1996) findings in the U.S. market. However, the portfolios constructed under this model have insignificant market, size and value premia, a finding that seriously questions the validity of the model in the Greek market. In addition, diagnostic tests of the model reveal serious flaws that should be addressed before reaching safe conclusions. Further testing across sub-periods should be conducted in order to check parameter stability because there are many indications that structural breaks have taken place during the sample period. For the time being we suggest the model not to be used for making investment decisions in the Greek stock market.

INTRODUCTION

An investor is faced with a choice from among an enormous number of assets. When one considers the number of possible assets and the various possible proportions in which each can be held, the decision process seems overwhelming. Fortunately, there are basic principles underlying rational portfolio choice that help decision makers to structure their problems in such a way, that they are left with a manageable number of alternatives (Elton et al: 2007).

All decision problems have common elements. They all involve the delineation of alternatives, the selection of criteria for choosing among those alternatives and the solution of the problem. Furthermore, individual solutions can often be aggregated to describe equilibrium conditions that prevail in the marketplace. Under certainty, these problems become simple. One has to define investor’s opportunity set and indifference curves in order to reach a solution. Constrained optimization is a sufficient tool to solve investment problems under certainty. However, uncertainty does exist in the real world. Asset pricing theory tries to understand the prices or values of claims to uncertain payments. To value an asset in an uncertain world, we have to account not only for the delay (time value of money) but also for the risk of its payments. Corrections for risk are very important determinants of many assets’ values (Cochrane: 2005). The existence of risk means that the investor can no longer associate a single number or payoff with investment in any asset. The payoff must be described by a set of outcomes and each of their associated probability of occurrence, called a return distribution. The two most frequently employed attributes of such a distribution are expected return and standard deviation.

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Financial integration in the EMU: The Fama and French Factors in the Euro zone

H.W.C. Vreedenburgh

ERASMUS UNIVERSITY ROTTERDAM

ERASMUS SCHOOL OF ECONOMICS

MSc Economics & Business

Master Specialisation Financial Economics

July 2010

ABSTRACT

Integration in the EMU stock markets has some major implications for investors, their international portfolio diversification possibilities and the way they should price stocks. This paper will add an insight and provide evidence in the discussion whether or not the EMU stock market is can regarded as an integrated market, and what this means for the way stocks should be priced. This paper’s main contribution is providing evidence of EMU stock market integration by using a non-correlation method and asset pricing models, and what asset pricing model is able to price the stocks best in the integrated EMU zone. Using the principal component analysis, we have shown that there is an increasing degree of integration in the EMU zone. Although the rate of the smaller EMY countries is higher, the larger EMU countries were already quite integrated.This article also uses local, EMU and combined (EMU and local) factor models to see which model is able to price EMU stocks as a way of testing integration. We show that the Fama and French three factor model is better at pricing stocks for individual countries better than CAPM. This article shows that the EMU factors are doing quite well on pricing stocks, especially in the larger countries, although local factors still have an impact. Considering different time frames, we see that the local factors have lost much of their additional explanatory power in the post-2001 period. Finally, this paper shows that an EMU factor model is able to price all EMU stocks better than countries individually.

Keywords:  Asset pricing, Portfolio Choice, International Financial Markets, Financial Aspects of Economic Integration

JEL: F36, G11, G12, G15

Introduction

After the completion of the European Economic and Monetary Union (EMU), with the signing of the Maastricht Treaty in 1992, and eventually the introduction of the euro in 1999, Europe is supposed to have seen a remarkable economic integration ever since. As the euro was only introduced relatively recently, there are still limited academic studies on what impact the EMU (and the introduction of the euro) has on the EMU stock market, the integration of the EMU stock markets and its impact on stock pricing alone.

Integration in the EMU equity markets has some major implications for investors, their international portfolio diversification possibilities and the way they should price stocks.

This paper will add an insight and provide evidence in the discussion whether or not the EMU stock market can be regarded as an integrated market, and what this means for the way stocks should be priced.

This paper’s main contribution is providing evidence of EMU stock market integration by using a non- correlation method and asset pricing models, and to show what asset pricing model is best to price the stocks in the integrated EMU zone.

 The structural changes in the financial markets of the EMU zone have resulted in a changing approach to the use of EMU stocks in international portfolio management. An integrated European market could have a major impact on the way investors price stocks and how to achieve a well-diversified portfolio.  Although the size of the EMU equity market is - compared to the United States- not that big in terms of global market value, it has attracted a large number of non-EMU investors for its diversification benefits.

These investors have looked for opportunities to reduce portfolio risk by investing in stocks across different national markets where low correlations in return exist, while keeping the expecting return at the same level.

However, the assumed integration process of the EMU zone could potentially limit these benefits, as correlations between the EMU countries will rise. This could result in new optimizations in the commonly used mean-variance frontier in modern portfolio theory (Markowitz, 1952) for investors in the EMU zone.

On the other hand, the integration will lead to new opportunities and policies. The integration of the EMU stock market could result in one big investment area instead of several different ones, resulting in better risk sharing benefits, improvements in allocation efficiency and a reduction in economic volatility (Baele et al., 2004).

The creation of the EMU made it also possible for investors to buy EMU stocks without any limitations, as it is supposed to be a single market. Often, (institutional) investors were often restricted (for a  certain amount) to a certain country (or currency). This limitation could be removed if the EMU appears to be actually one single market. This could result in more investments in the EMU zone. It could also limit the question which EMU country is a better option, as the EMU zone will appear as one investment opportunity, and shifts the question to which industry in the EMU is a better investment.

In this paper we assume that the integration in the EMU market means that every stock within the EMU countries is subject to same (financial) circumstances and sensitive to the same (financial) shocks, regardless of the country in which they are traded. There should be no market frictions within the EMU stock markets and EMU countries.

This means that every investor in the EMU has the same opportunity set, the same limitations, same costs and risks when investing in stocks. We consider this as a fair expectation of an integrated market, however, we will look for evidence to support this assumption.

If this is the case (which we expect), then it is interesting to know if the stocks could be priced by the same risk factors, which could indicate if the market is really integrated. Do national risk factors still add something to the pricing of EMU stocks? Or is one EMU risk factor able to price all EMU stocks?

If we think about risk factors, it is a logical step to come to the Capital Asset Pricing Model (CAPM). At present, the CAPM is a model which probably is the most widely used model to price assets in the financial market. Even in the corporate world the CAPM is present, as it is the foundation to calculate the cost of equity. Hence it has a major impact in calculating the Weighted Average Cost of Capital (WACC), as the cost of equity is directly related to CAPM (investors want compensation for being exposed to none diversifiable risk) (Arzac, 2005).

The CAPM is presumed that in a case of a fully integrated market, with the assumption that purchasing power parity holds, CAPM should be able to price all assets (Grauer et al., 1976).

From the ‘basic’ CAPM - a one factor model - the multifactor extension by Fama and French is the most widely used (1992, 1993, 1995, 1996, 1998); the Fama and French Three Factor Model (3FM). They showed in their papers that the two variables (risk factors) ‘size’ and ‘value’ add to the explanatory power of the model. So it is interesting to see how the CAPM and 3FM perform in an integrated EMU market.

In this paper we will compare the two models and see if they are able to price the EMU countries and the EMU zone as a whole. As both models are based on the same principle, it is easy to compare them and it is interesting which one significantly performs better at pricing the European market.

This paper could also contribute to the methodological discussion on which asset pricing models perform better. Although most academic papers provide evidence that the 3FM performs better than CAPM, most research has been focused on the United States (US) and on European countries individually (the United Kingdom in particular). Limited articles are written about the EMU as a whole or on the EMU countries together. This is mostly because of the lack of data, different currencies before the euro and the small number of stocks in many European countries.

The creation of the EMU created potentially a new data area in which different theories could be tested, besides the UK, Japan and the US. The empirical results in this paper could contribute to the discussion if the models are able to explain the returns of stocks and possibly add support for (one of) the models. At first, we will look (a) if there is evidence for the stock markets of the 12 initial EMU countries (which do not contain current EMU members Cyprus, Malta, Slovenia and Slovakia) to be integrated.  We will use the principal component analysis (PCA) for the EMU zone in order to see if the equity markets in the EMU equities are correlated with the first principal component.

By doing this we want to find evidence which supports our assumption that the EMU zone is integrated and is subject to (some of) the same financial circumstances. Also we want to see what the impact of the EMU is on the integration in the EMU stock markets.

Secondly, we will construct the CAPM and the Fama and French three factor model (3FM) for the 12 EMU countries and for the EMU zone as a whole. We will compare the results in order to see if the CAPM is better a pricing EMU stocks then 3FM (b) when using national factors and (c) when using EMU factors.

We also add national factors to the EMU CAPM and 3FM to see (d) if the addition of these factors to an EMU 3FM has any significant impact. We can look if the EMU risk factors are able explain the returns, which could be evidence for EMU integration in the stock markets. We will look (e) if there is evidence that the EMU got more integrated after 2001 by looking at the impact of national factors in the asset pricing models.

Finally, we will look (f) for evidence if the EMU factors are able to price the EMU zone as a whole. We will test the PCA for the period January 1992 until December 2009, while the CAPM and 3FM will be tested for the period of July 1993 until June 2009 by using the adjusted R2 and – only for the CAPM and 3FM - the pricing error α (Jensen, 1968).

This paper is structured as follows. Section 2 will provide background information and a review of prior research. Section 3 describes the data employed. Section 4 defines the methodology used. Section 5 shows how the risk factors are constructed. Section 6 presents the descriptive statistics used and the results. Finally, section 7 will conclude the paper.

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An Empirical Study of the Presidential Elections Effect On Stock Market in Taiwan, South Korea, Singapore, Philippine, and Indonesia

by

LING-FANG LIU

A dissertation presented in part consideration for the

degree of MA Finance and Investment

The University of Nottingham

2007

Abstract

The behavior of stock market around election periods has been investigated for several decades but the presidential elections held in Asian countries have not been analyzed in the previous studies. The main objective of this study is to examine the return pattern around presidential election period in the stock markets of Taiwan, South Korea, Singapore, the Philippines, and Indonesia during the sample period 1996-2005. It has been found that stock markets generate positive abnormal returns fifteen-day period before and after the presidential elections, and that the magnitude of abnormal return is greatest in the presidential elections held in less-free countries when an incumbent loses. In addition, other financial and political factors have been found to play an important role in influencing the return pattern around presidential elections. This dissertation may be of interest to investors and financial analysts, especially those who intend to put money into Asian stock market during the coming South Korea 2007 and Taiwan 2008 presidential elections.

Introduction

One of the most fundamental theories in financial economics is the theory of market efficiency. In an efficient market all available information will be embodied in the stock price implying that investors cannot earn abnormal returns. Although the theory of market efficiency has been widely known in the real world, the various studies that investigate capital market efficiency have detected a large number of market anomalies that question the view on the efficiency of the markets. Some of the most profound market anomalies that have been found are as following. The P/E effect: Basu (1977) discovered that portfolios of low P/E ratio stocks have higher returns than do high P/E portfolios; The size effect: Banz (1981) found that the average annual returns are consistently higher for small than in large firms’ portfolio; The Fama and French three factor model: Fama and French (1993) found that the group with the highest book to market ratio outperformed those with the lower ratio; Seasonalities: Fama (1965), Bonin and Moses (1974) detected that stocks exhibit significant lower returns over the period between Fridays close and Mondays close. In addition, returns are much higher during the month of January than in any other month.

Besides these anomalies which are related to firms’ characteristic and special trading time, a large number of studies have discovered the pattern of common stock returns over the presidential elections.

Allvine and O’Neill (1980) presented strong evidence in support of the relation between stock market returns and the presidential election cycle. They found that stock market had a rising trend over the two years prior to the United State’s presidential elections. Also, Huang (1985), Smith (1992), and Johnson et al. (1999)  investigated that stock returns were significantly higher in the second half of the presidential term. However, these researches were constrained on the relationship between U.S. stock market and presidential elections.

In 2000, Pantzalis, Stangeland, and Turtle are the first researchers to examine the behaviour of stock market indices around political election dates in an international scale. Their findings displayed that positive abnormal returns lead up to the election week, and that the positive returns are shown to be a function of country degree of political, economic, and press freedom and a function of election timing and the success of the incumbent in being re-election.

Objectives

Although the study mentioned above have gathered a large number of election samples to examine the market behaviour around election dates, the presidential elections held by Asian countries in recent years were not included in the sample pool. As a result of the lack of empirical testing on the effect of Asian presidential elections on stock markets, this dissertation will concentrate on the stock markets of five Asian countries- Taiwan, South Korea, Singapore, the Philippines, and Indonesia that hold presidential elections in the period of 1996-2005 to examine their return patterns around elections. By analyzing these countries market behaviour, the following questions can be addressed:

1.   Are there abnormal returns during pre-election and post-election period in Taiwanese, South Korean, Singaporean, Philippines, and Indonesia stock markets?

2.    Does the political and press freedom rankings difference among these countries influence the level of abnormal returns during election period?

3.   Does the outcome of elections (incumbent win or lose) influence the level of abnormal returns during election period?

4.   Are there any other potential financial or political factors leading different level of abnormal return among these countries? Structure

The rest of the chapters within the dissertation are organized as follows. Chapter 2 gives a brief overview of the political background and stock market development of the five sample countries. Chapter 3 reviews previous empirical studies that analyze the stock market return pattern around elections and provides possible theories to explain the return pattern. Chapter 4 describes the data and methodology adopted in this dissertation. Chapter 5 presents the empirical results of the dissertation and analyzes the findings with literature reviewed. Chapter 6 summarizes the conclusions, limitations of the study and recommendations on future research directions.

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An Empirical Study of Serial Correlation in Stock Returns Cause-Effect Relationship for Excess Returns from Momentum Trading in the Norwegian Market

Maximilian Brodin and Øyvind Abusdal

Supervisor: Per Östberg

Master Thesis in Financial Economics

NORGES HANDELSHØYSKOLE

This thesis was written as a part of the Master of Science in Economics and Business

Administration program - Major in Financial Economics. 

Abstract

This paper documents the maximum theoretical excess return on the market to 3.8% monthly from momentum trading in Norway and estimates the economical excess return to be marginally higher than 1% per month when accounting for microstructure influences. We find that the excess returns of various momentum strategies are not explained by systematic risk or exposure to other factors such as size or book-to-market value. We uncover a positive correlation between types of investor and the degree of momentum in the market. Studying business cycles has provided evidence of reversals following bust periods which are in-line with behavioral theories of overreaction.

Introduction

Can historic observations of a publically traded company’s performance be used to predict their future performance? That question is the essence of this paper and there are several ways of answering it; for example one could look at various performance measures such as earnings or stock prices. We have chosen to work with the latter, or more specifically, we are examining whether there is a tendency for stock returns to trend in the same direction and thereby establish whether there is momentum in the stock market. We test whether or not it is possible to earn abnormal returns on the Oslo Stock Exchange by forming winner and loser portfolios on the basis of past stock returns.

Empirical evidence from vast research in several markets document this anomaly known as momentum. A recent London Business School research with 108 years of data covering about 85% of the world equity market capitalization concluded that “The momentum effect, both in the UK and globally, has been pervasive and persistent” (Dimson, Marsh and Staunton, 2008). Rouwenhorst (1998) finds in a study of 12 European countries including Norway in the period from 1978 to 1996 that an internationally diversified momentum portfolio earns about 1% excess return on the market per month.

Much of the research on momentum has been dedicated to trying to explain the excess return earned from following such a strategy by adjusting for various factors such as the size effect, book-to-market ratios and market risk. During the last 25 years, attempting to explain investor behavior has also gained a lot of attention in trying to explain the momentum effect.

Jegadeesh and Titman (1993) find that excess returns from following momentum strategies are not due to systematic risk or to delayed stock price reactions to common factors such as the January effect. Jegadeesh and Titman (2001) also present evidence which supports the prediction of behavioral finance models that suggest that the momentum effect is due to overreactions in the market. Grinblatt and Keloharju (2000) analyze different investor groups and find that the degree of momentum behavior seems to be strongly correlated to the degree of sophistication of the investor types.

Kloster-Jensen (2005) finds that a momentum strategy on the Oslo Stock Exchange (OSE) yields significant positive returns, but this is due to a large extent by compensation for taking on added systematic risk. Hence, he concluded that there is no momentum effect in the Norwegian market. Conversely, Myklebust (2007) examines sixteen different time-strategies for momentum trading on the Oslo Stock Exchange and finds that all strategies yielded positive excess returns, which could not be explained by market risk or the size effect.

Up until now OSE momentum research has been limited to using data samples of stocks that have been traded during the whole sample periods. This has narrowed the data sets to about 70 stocks which can be compared to the actual number of almost 600 stocks that have been listed during the last eleven years, which is the time period we examine. Our approach is different; and by analyzing a dataset of 598 stocks we can provide evidence of the maximum theoretical excess return that can be earned from a momentum strategy on the OSE. This is accomplished by 16 different time-strategies that are comprised of a forming period (ranking period of the stocks) and a holding period. These strategies are evaluated by accounting for risk exposure, or more precisely systematic risk (CAPM) and the size effect using a two- factor regression model.

The total data set is then screened based on a set of rules that provides us with 123 stocks suitable for evaluating the maximum economic excess return that can be earned (i.e. a dataset that gives us the opportunity to test the momentum strategy when accounting for microstructure influences such as transaction costs). In this part of the study we explore one time-strategy, which we call “the best strategy portfolio”.

As with many of our predecessors, we attempt to explain excess return by accounting for various factors; here we expand the model to include a third factor: book-to market ratio, using the Fama and French three factor model.

We also probe areas that have not been explored in earlier momentum research for the Norwegian stock market. We test for seasonality by deducting and secluding January returns. Through descriptive studies of the dataset we highlight any under or over-representation among sectors in the momentum portfolios and provide intuitive explanations to why some sectors are biased towards either the loser portfolio or the winner portfolio. Moreover, we examine the momentum returns throughout business cycles to identify any variations in good times and bad times.

Finally, we expand the discussion of momentum explanations by building on Grinblatt and Keloharju’s 2000 research on the behavior of different investor types. We find that there has been a development over time in the type of investors that are active on the Oslo Stock Exchange and we examine whether this could be correlated to an increase (or a decrease) in the momentum effect over time.

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An Empirical Investigation of the Multi-factor and Three- factor Pricing Model in Chinese Stock Market

by

Chengjian Su

Abstract

We propose a Multi-factor (including macro-economic variable, microeconomic variable and market variable) and a Three Factor (including intrinsic value, technical factor, and liquidity) asset pricing models, and carries on the empirical study of China’s stock market. It reports that market return essentially affects on individual stock return, and β is significantly positive ranging from 0.41 to 0.53. EPS exerts the strongest  positive influence on stock price, with the coefficient close to 1; while GDP growth rate, money supply, deposit  interest rate, inflation rate, saving amount, and loan amount exert significant negative influence. The result demonstrates that we can effectively find out the key factors of stock pricing by the Multi-factor model, while the Three Factor Model can well price them.

Key words: Multifactor Model, Three- factor Model, Technical factor, Liquidity

Chengjian Su is from the School of Business at Shantou University. Shantou City, Guangdong Province, ZipCode:515063,China.

Introduction

Capital Asset Pricing Model is the core of modern finance theory. It was first proposed by  Sharpe (1963, 1964), and perfected by Mossin (1966) and Lintner (1965, 1969). The first test of  CAPM was carried out by Lintner (1965). Miller and Scholes (1972) reinvestigated CAPM by  employing the ten year data (1954-1963) of 631 stocks on New York Stock Exchange. Their  results did not support CAPM. Roll (1977) made his famous criticism: there were fundamental  problem while using a proxy for the market portfolio, such as Standard & Pool 500 index.

Because the CAPM model was empirically suspected, Ross (1976) advanced his  Arbitrage Pricing Theory. Advocates of APT pointed out that compared with CAPM, APT had two advantages: first, it had less hypothesis limitation about investor’s risk and return  preference; second, it could be examined by empirical study. Roll and Ross (1980) conducted  an empirical study by using the stock data that ranged from 1962 to 1972, discovering that at  least three or four factors influenced the combined stock return. Chen, Roll and Ross (1986)  supposed several macro-economic variables as the systematical factors that affect the market  income of stock. And they proved their hypothesis.

MacKinlay (1995) carried out an empirical study and put forward a new model. He  discovered that it was very difficult to prove that the deviation of CAPM was caused by lacking  of risk factor; but it was easy to prove the deviation that caused by non-risk factors. Meanwhile, he also found that Multi-factor model could not explain the deviation of CAPM.

Fama and French (1992) studied the data of American stock market from 1962 to 1989,  focusing on the relation of stock return and market BETA, company size, finance lever, B/M  ratio, E/P ratio, cash flow, sales increase, long-term return and short-term return. They found that market BETA, finance lever and E/P ratio can hardly explain stock return, while the unity  of the two factors company size and B/M ratio could nearly achieve.

After conducting an empirical study on the data of American stock market that ranged  from 1963 to 1993, Fama and French (1996) put forward the famous Three Factor Model. They  thought that stock return could be interpreted by the three factors including market risk  premium, company size premium and B/M premium.

In recent years, the burgeoning Asian stock markets have attracted the research interest  worldwide. Chui and Wei (1998) firstly verify the Fama and French Three Factor Model in  Asian stock markets. They studied the relation between stock return and β coefficient, B/M, and  company size in the stock markets of Hong Kong, South Korea, Malaysia, Taiwan, and  Thailand. They found that the average stock return had little to do with the β coefficient, but  was strongly related to B/M ratio and company size.

Drew Naughton and Veeraraghavan (2002) applied the Fama and French Three Factor  Model to Shanghai A stock market. They found that B/M ratio was untenable in Shanghai A stock market, but the β coefficient and company were relevant for stock return. Risk could not  well explain this phenomenon, whereas investors’ irrational behaviors did.

The domestic Chinese scholars, such as Chen Langnan, Qu Wenzhou (2000), Chen  Xinyuan, Zhang Tianyu, Chen Donghua (2001), Fang Longzhen, Wang Haitao (2003), Wang  Chengwei, Wu Chongfeng(2003), Jia Quan, Chen Zhangwu (2003), Dan Yaowen (2004), Wu  Shinong and Xu Nianxing (2004), mainly focus on the validity of the Fama and French Three  Factor Model in China’s stock market and other micro-factors that determine stock returns. They leave the empirical study of Multi-factor model that takes macro-factor into consideration  blank. Su Dongwei, Mai yuanxun (2004) conducted an empirical study of turnover ratio and  expected return. They found that asset of less liquidity had high expected return.

Some scholars have carried out empirical test on the relationship between Chinese stock  market return and the macro-economic indexes. Zhao Xingqiu (1999) studied the relation  between Chinese stock market return and inflation and total industrial output growth. He found  that the inflation and stock return was significantly negatively correlated; the relation between  total industrial output growth and stock return was not simply positively correlative; the  expected output growth caused stock return to fluctuate to the opposite direction while the non-expected output growth caused stock return go to the same direction. These findings portrayed  the statistical feature of the undulation of China’s stock market. However, after controlling the  effect of output growth, the relation of inflation and stock return disappeared, which showed  that the effect of inflation on stock return came from the relationships of output and stock return, output and inflation. This just kept the same with the view of Fama.

Shang Pengyue and Li Shenghong (2002) studied the cointegration relation of Shanghai and Stock Exchange index and macroscopical economic indicator. Basing on eh cointegration analysis of Multi-factor, he established a predictive model of the two by using error revise model. Its result showed that, from January 1995 to September 2000, Shanghai Stock Exchange index was sensitive to the changes of long-term interest rate, short-term interest rate and money supply, but did not have a long time balanced relation with GDP, investment in fixed assets and national price index. This has some guidance function to the study of securities market of China.

Through empirical study, Yan Yanyang, Li Zhi, Xu Junping (2004) discovered that  co-integration relation existed between Shanghai and Shenzhen stock market index and some  macro-economic factors; share index could reflect the whole trend and level of economy  development of China to some extent, but as having weak relation with GDP, they still could not make “barometer” of economy development of China. This means that present development  of Shanghai and Shenzhen stock markets is not mature enough. Stock market is interfered  heavily by the noisy, non-economic factors like main banker control, administrative  interference, excessive speculation and information asymmetry.

In sum, viewed from domestic and foreign scholars’ studies on the CAPM, APT, the  Fama and French Three Factor Model, and macro-economic factor (Chen-Roll-Ross) model, we  can find that these asset pricing models take into consideration market equilibrium, the micro-economic conditions (the Fama and French Three Factor Model and multi-micro-economic  variable model), and macro-economic variables (Chen-Roll-Ross model), respectively. This  paper starts from analyzing the drives that cause the change of stock price, and then establishes  the econometrics model of asset pricing incorporating micro-economic and macro-economic variable, conducts empirical study on China’s stock market, finally draws an innovative  conclusion.

The rest part of this paper is organized as follows. Part II first establishes the regression  model which reflects macro-economic factor, micro-economic factor, and market factor within  the APT framework. Then it carries out empirical study on verifying the factors that affect  stock return, such as macro factors (e.g. time limit structure of interest rate, consumption, petroleum price, money supply and stock market policy), micro factors (e.g. book market value  ratio (B/M), size effect ratio (SIZE), capital stock structure, earning per share (EPS), investment  surplus stock price ratio (EP), cash flow to stock price ratio, net assets growth per share, sale  proceeds growth per share), technical factors (e.g. Composite Index of Shanghai Stock  Exchange and Component Index of Shenzhen Stock Exchange), and liquidity factor (e.g. the  turnover rate). Part III set up the empirical regression model that portrays technical factor,  inherent value and liquidity, and carries out empirical study. Finally, Part IV analyses and  draws the conclusion.

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