Risk and Return in Asian Emerging Markets

A Practitioner's Guide

N. Cakici, K. Topyan

• Publisher: Palgrave Macmillan
• Year: 2014
• Print ISBN: 9781349472062, 9781137360885
• eBook ISBN: 9781137359070
• Topic: Economia
• Subtopic: Contabilità

Risk and Return in Asian Emerging Markets offers readers a firm insight into the risk and return characteristics of leading Asian emerging market participants by comparing and contrasting behavioral model variables with predictive forecasting methods.

Chapter 1

Introduction

The Asian emerging markets¹ covered in this book, namely, China, India, Indonesia, Korea, Malaysia, Philippines, Thailand, and Taiwan, are getting increasingly important in the new world order. In today’s financial world, it is important to understand the characteristics of emerging market economies as their significance is expected to increase with time. It is, of course, quite possible to expand or modify the list of Asian emerging markets included in this book, and hopefully, in the future, researchers will evaluate other emerging market countries and help to obtain a more complete picture. Our work is just an attempt to compile a set of selected Asian emerging markets under one title.

This book uses the popular portfolio method and cross-sectional Fama-MacBeth regressions (see Fama and MacBeth, 1973) and reports the risk and return characteristics of the listed Asian emerging market countries. The evaluated parameters will help researchers to understand the relative importance of certain parameters in forecasting and determining trading strategies for the listed countries. We expect that evaluating and comparing major behavioral model variables with predictive powers for the eight Asian emerging markets and in eight different categories included in the book will specially help applied finance academics, as the subject is an important research topic around the world for literally thousands of academicians and graduate students studying behavioral financial models, stock markets, emerging markets, and Asian markets. Within this context, it is important to have a better understanding of the risk and return structure of the included Asian emerging markets. We also hope that compiling the eight highlighted Asian emerging market countries in one book will be useful for finance practitioners operating in a variety of fields, as well as others interested in trading strategies dealing with Asian emerging markets and its foundations and implementations, since the book may be used as a risk-and-return guide for Asian emerging markets.

With this in mind, the chapters in this book have been labeled using the firm-level return predictors, not the countries involved. This way, we believe that a researcher will obtain a more complete country-specific information in a comparative setting. A specific chapter covering a firm-level predictor for an included country will provide the researcher with the desired information in tables, together with the other seven emerging market countries. This design will not only present the information for a selected firm-level predictor for a country, but it will also show the same predictor’s values as well as the statistical significance levels for all other included countries, making the researcher instantly aware of the similarities and differences of the used firm-level predictor’s effectiveness in other Asian emerging market countries. We believe that organizing the chapters by the included Asian emerging market countries’ names would have produced an undesired disconnect as well as isolated and less integrated results.

Stock return predictability has been widely studied by researchers. Although risk-based asset pricing models, such as the capital asset pricing model or the arbitrage pricing model, will tie the predictability patterns to economically meaningful risk factors, empirical studies show that these models are generally not very effective in explaining return predictability patterns dealing with tendencies such as momentum and reversals.² As market efficiency implies unpredictability of stock returns, it is well documented that stock returns can often be predicted by certain firm-level return predictors such as size, momentum,³ and book-to-market ratios.⁴ Using the portfolio method and Fama-Macbeth regressions, researchers often find statistically significant relationships between stock returns and firm-level predictors. In the portfolio method, researchers utilize the value-weighted and/or equal-weighted average monthly returns of quintile portfolios sorted on the basis of the predictor in question and analyze them to obtain meaningful return predictors. In the cross-sectional regression method, researchers regress stock returns on firm-level return predictors and analyze the regression results.

Relationships between stock returns and firm-level return predictors are visible in many markets around the world and are not just relevant or valid for a few selected set of markets with specific features. Following Jegadeesh and Titman’s (1993) study for the United States, other studies such as those of Fama and French (1998); Rouwenhorst (1998, 1999); Chan, Hameed, and Tong (2000); Grundy and Martin (2001); Wu (2011); Titman, Wei, and Xie (2004); and Pincus, Rajgopal, and Venkatachalam (2007) showed that anomalies identified in the US market also exist in many markets outside the United States. With that in mind, we decided to evaluate firm-level return predictors in eight Asian emerging market countries to help practitioners as well as other applied finance professionals to understand better the popular firm-level return predictors. Within this context, we provide a comprehensive analysis of the effectiveness of highlighted return predictors, including momentum, in selected Asian emerging market economies and test the validity of the results in an emerging market environment.

Researchers studied and tested several firm-level variables in an attempt to explain the stock returns. Here is a short list: Litzenberger and Ramaswamy (1979) highlighted the dividend yield; Ball (1978) noted the predictive power of earnings-to-price ratio; Banz (1981) documented the size effect relating market capitalization to stock returns; Rosenberg, Reid, and Lanstein (1985) studied the book- to-market ratio; Bhandari (1988) used financial leverage; and recently, Jegadeesh and Titman (1993) suggested momentum as the return predictor. Firm-level predictors may be incorporated into return models either indirectly or directly, using the portfolio approach or the multiattribute approach. The indirect approach extracts the signals from the difference between returns on two portfolios (one with the highest values of the chosen attribute and one with the lowest values of the chosen attribute, steps set as quintiles, etc.). The direct approach uses the firm-level attributes as explanatory variables in explaining stock returns for a set period—in general, monthly. In both methods, a time-series regression covering the entire period will be necessary to obtain the testable power of the attributes. The relative effectiveness of those approaches is highly debatable but it may depend on the attribute(s) in question: Daniel and Titman (1997) promote the portfolio approach,⁵ whereas Fama and French (1992), van Rensburg and Robertson (2003), Cohen and Polk (1995), and Davis, Fama, and French (2000) prefer the cross-sectional approach.⁶

Our firm-level return predictors are (1) market capitalization, (2) price, (3) stock’s beta, (4) stock’s total volatility, (5) stock’s idiosyncratic volatility, (6) short-term reversal, (7) momentum, and (8) book-to-market ratio. Each of the listed firm-level return predictors is defined and explained in detail, together with its characteristics and theoretical structure, as well as the steps of obtaining its values. Each of the listed firm-level predictors is evaluated in a separate chapter. It should be noted that a subgroup of attributes may be highlighted as the risk-related ones, such as total volatility, idiosyncratic volatility, and beta, and another subgroup as the cheapness-related ones, such as book-to-market ratio.

Many of the firm-specific attributes included in this book are used as proxies of unnamed sources of risk as referred by Fama and French (1992), when they are statistically and economically significant. Therefore, it is very important to evaluate these attributes in different settings and see when and where they become powerful proxies and when and where they should not be considered as valuable proxies. As one can imagine, literature documenting the explanatory power of proxies used in this book is not scarce as are the conflicting results presented by the same literature. We do not intend to compare and contrast those results in an attempt to uncover inconsistencies or evaluate the relative reliability or popularity of the different attributes in this book. Our goal is to make the reader familiar with the risk-and-return in Asian emerging markets using the eight most commonly used attributes in the eight Asian emerging market economies. As expected, some of these attributes will work in one market but not in the other; some of them will be statistically and economically significant in one market but not in the other. After all, one may use the results to get more familiar with Asian emerging markets or to compare the relative effectiveness of certain attributes across those markets. Finally, it is important to note that the analysis evaluates one long period of time for all the included countries. This is necessary to make the analysis comparable across the countries but has a well-known shortcoming: within a selected country, certain time segments could be special and need to be evaluated separately to obtain information attributable to this specific period. In order to implement this, one has to study a country in detail and determine the highlighted time segments that should be evaluated in isolation. We never intended to do this in the present book simply because, first, we prefer to highlight the firm-level predictors, not the countries, and second, to make the effectiveness of those predictors comparable, we used the longest possible common period available. This, however,...

Table of contents

• Cover
• Title
• Chapter 1  Introduction
• Chapter 2  Market Capitalization
• Chapter 3  Price Level
• Chapter 4  Beta
• Chapter 5  Total Volatility
• Chapter 6  Idiosyncratic Volatility
• Chapter 7  Short-Term Reversal
• Chapter 8  Momentum
• Chapter 9  Book-to-Market Ratio
• Chapter 10  Multiple Regressions
• Appendix
• Index