Accounting
Dissertation: Relationships between Stock Returns and Interest Rates in South-East Asia (Part 2 of 5)

Chapter 2: LITERATURE REVIEW

2.1 Chapter Overview

In this chapter, the discussions will be concentrated on available literature on methods used to predict stock market returns. A top-down approach will be used to present the findings and discussion. Firstly, a review on the available methods used to predict stock market return will be briefly articulated. This is to provide suitable context for the readers on the many methods or philosophies employed to predict stock returns. Secondly, the efficiencies of the financial market in the South East Asia countries investigated in this study. This is crucial as if the financial market is truly efficient, investors simply cannot gain abnormal returns from any form of research. After that, the existing literatures related to studies on stock markets in countries located in South East Asia will be presented. Then, the discussion will be focusing on using economic variables to predict stock market returns. Some interesting points or empirical evidences related to this area will be articulated. Later, the discussion will zoom into the use of interest rates to predict stock market returns. To do this, theories related to interest rates and its impacts to stock market will be outlined. Then, empirical evidences on the relationships between stock market returns to interest rates will also be compiled and presented. All of these are crucial for readers to gain references points and greater understandings on the available literature related to the topic to be further discussed in this dissertation.

2.2 Methods Used to Predict Stock Returns

The field of stock market prediction is a highly interesting, and indeed, controversial one. There are many different school of thoughts or ideology on the real and useful ways to predict stock market returns in the future. Indeed, according to the Efficient Market Hypothesis, stock market prediction is simply impossible – where no investors can yield above average returns through stock market analysis or whatever other methods to time the market (Malkiel, 2007; Fox, 2009). Under such school of thoughts, the market is efficient, and investors simply cannot yield higher than market averages returns over the long time through fundamental or technical analysis (Reilly & Brown, 2003).

However, there are a few reasons pointing to the impracticality or the invalidity of such theory in the real world. Firstly, the existence of many legendary investors indicates that there are ways to beat the markets. Besides, as many of the legendary investors able to do so in the long run, it indicates that market is indeed not efficient as argued by the Efficient Market Hypotheses. Indeed, books on how legendary investors can beat the market are abundance. Some of the famous boos include: Zweig (1997), Templeton & Phillips (2008), Lynch & Rothchild (1993), Hagstrom (2005), Greenblatt (2006), Fisher, Chou & Hoffmans (2007), and Fisher (1984). Secondly, as argued by Greenblatt (2006), daily observations on the stock prices indicate that the market is not efficient. For example, the prices of a stock can move up and down in a very fast manner, often more than 20% (and worst, in certain times more than 30%) in a month, but the real fundamental of the company has never really change that much. Indeed, it is irrational to believe that the fundamentals of any stable company can change at such a magnitude in just a month. The volatility and changes in stock market prices for a company is just purely due to the change of investors’ perceptions on that particular stock (Templeton & Phillips, 2008). Thus, the notion of Efficient Market Hypothesis, which the stock prices are efficient in reflecting all information available in the market place, is simply not true. Thirdly, the rise of behavioral finance inform us that people are simply not completely rationales, and the decision making process of human beings are simply not rational and logic as stipulated in the assumptions under the Efficient Market Hypotheses. Thus, as the market participants are not rational, and are found to be often suffering from various emotional or psychological biases, the prices on securities are simply not efficient, due to crowd behaviors or psychological biases of market participants (Greenblatt, 2006). As such, in this dissertation, it is believed that there are chances to be exploited from stock market analysis and research to yield extraordinary performance in the market – in contrast of what is predicted or asserted by the theory of Efficient Market Hypotheses.

Financial market practitioners largely understand the weaknesses and invalidity of the Efficient Market Hypotheses and had been using two main approaches to stock market prediction – fundamental analysis and technical analysis. Generally speaking, fundamental analysis is about researching the company profitability, financial statements, industry structure, economy, and other aspects related to business nature of a particular company or industry (Reilly & Brown, 2003). In contrast, technical analysis is often associated with those techniques used to predict future stock market prices through price and volume actions of a particular stock or the broad stock market index (Bodie, Kane & Marcus, 2007). However, to discuss in-depth on the various methods is not the objective of this dissertation. Indeed, the discussions of this dissertation will be concentrated on using economic variables to predict stock market returns – which such method can be regarded as one subset of the fundamental analysis widely carried out by researchers (Bodie, Kane & Marcus, 2007). As clearly stipulated in the first chapter, this dissertation will focus solely on the usage of economic variables to predict stock market returns. To do so, the historical data can be used to survey the relationships between the economic variables as well as stock returns. The statistical relationships, as well as the use of statistical concepts, such as Pearson Correlation Coefficient and Granger Causality can be used for this purpose. As will be discussed in greater details in the following few sections, such methods in investigating the relationships between fundamental variables (be it company specific fundamentals or the economy related variables) to stock market returns are common ways employed by scholars.

2.3 Market Efficiencies in South East Asia

On major advances of finance theory and literature in 1970s is the emergence of Efficient Market Hypothesis (EMH). As discussed by White et al. (2003), EMH is a theory asserting that the market prices of assets are efficient when the asset prices fully reflect all the available information in the marketplace. This suggests that access to or knowledge concerning the information will not allow investors to reap extra profits from the information, as the information should had already being reflected in the pricing of these assets. Secondly, EMH also imply that all the information will be reflected and incorporated into the pricings of securities instantly and accurately once the information is known. Technically speaking, there are three forms of EMH, namely, the strong form, the semi-strong form and the weak form. EMH is classified into three forms due to the idea that information can be classified into three different sets. As discussed by Bodie, Kane & Marcus (2007), the strong form is that all information, which includes privately held (or more frequently known as insider information) will be reflected in stock prices. Then, the semi-strong form of EMH is about the idea that all publicly available information (except insider information) will be fully reflected in the stock prices. Lastly, the weak form of EMH is essentially about information about past securities prices will be reflected in current stock prices. As EMH can be classified into three different forms, researchers tend to conduct studies according to the different forms of EMH to test about the validity of these different forms of EMH in real life financial market. In the following sections, the empirical studies concerning the different forms of EMH in the context of South East Asia will be discussed.

2.3.1 Empirical Studies on Strong Forms of EMH

According to a research performed by Chu and Song (2010), it is found that insider ownership and industrial competition will lead to information asymmetry in Malaysian manufacturing firms. According to the findings, insiders often are found to be able to capitalize on information asymmetry to protect their personal interest in the stock market. Thus, the findings presented by Chu and Song (2010) are contradictory to the assertion of Strong Form of Efficient Market Hypothesis. It suggests that stock market in Malaysia cannot be considered as an efficient market. Then, in another study conducted by Ma, Pagna and Chu (2009), it is found that information leakage about Merger and Acquisition (M&A) deals are statistically significant in some of the South East Asia countries, namely, Indonesia, Malaysia, Singapore and Thailand. Abnormal returns can be reaped from the announcement of M&A news, even in a time frame of two days before and after the news announcement.

2.3.2 Empirical Studies on Semi-Strong Forms of EMH

However, contradictorily, Hussin, Ahmed and Ying (2010) had reported interesting results indicating that there are some degrees of empirical evidences supporting the semi-strong form of efficient market hypothesis in Malaysia from dividend and earning announcement. According to the research findings, it is discovered that increasing dividend announcement is likely to earn positive abnormal returns. Indeed, the abnormal returns likely to happen fast when the news of increasing dividend is announced. Thus, the author concluded that there are some empirical evidences supporting existences of semi-strong form efficiencies in Malaysia.

2.3.3 Empirical Studies on Weak Forms of EMH

Nonetheless, there are many studies found that technical trading rules can be employed to time the market in certain countries in South East Asia (which indicate that the stock market in the region is not efficient). In a recent study conducted by Azizan, Mohammed and Phooi M’ng (2011), it is found that application of one of the technical indicators, namely, Adjustable Bands Z-test-Statistics (ABZ) able to yield abnormal returns in the futures in Malaysia stock index futures and Singapore stock index futures. The availability of such empirical findings suggested that future market in Malaysia and Singapore may not be efficient, as the findings of Azizan, Mohammed and Phooi M’ng (2011) found that application of technical trading indicators enable traders to yield profitability of 316 index points for Malaysia stock index futures, as compared to the money losing buy-and-hold strategy of -562 index points. Indeed, this research is also supported by the study carried out by Lai, Balachandher and Nor (2003). According to these researches, it is found that technical trading rules, such as the famous variance ratio text and moving average rides can be employed to trade profitably in Malaysia stock market, even after transaction costs into consideration. In a similar vein, a study conducted by Wong, Manzur, and Chews (2003) found that two of the widely available technical indicators, namely, moving averages (MA) and Relative Strength Index (RSI) can be employed to generate substantial positive returns in stock market in Singapore and Malaysia. The authors further explained that this could be the reasons that many firms under Singapore Stock Exchange have their own trading team to exploit such inefficiencies for superior profits. Nevertheless, a study conducted by Kung and Wong (2009) found that the profitability of technical trading rules has been decreasing in the recent years in Singapore stock exchange. Their findings suggest that certain technical trading rules that are known to most people can no longer beat the buy and hold strategy in the recent years. His studies provide limited evidences that perhaps the market efficiencies in Singapore had improved in the recent years. However, another study found that technical trading rules may still be profitable. Jarrett (2010) conducted a study specially to investigate the validity of Efficient Market Hypothesis and daily variation in the following countries – Singapore, Malaysia, Korea and Indonesia. The author found that even weak form of efficiencies did not present in the stock markets in these countries mentioned above. In the study, a wide range of trading rules, which are widely available to common investors, are tested. It is found that in the short term, the stock market of all of the four countries demonstrated predictable properties. This suggests that weak form of Efficient Market Hypothesis indeed does not hold in the financial markets in Singapore, Malaysia, Korea and Indonesia.

The, from another dimensions, the existences of seasonality of calendar effects may indicate inefficiencies of a stock market (Lim, Ho & Dollery, 2010). There are several types of seasonality frequently investigated by scholars. As articulated by Eng (1998), for example, turn-of-the-year effect means that stock returns are usually much higher during the last trading day of December as well as during the first trading day of January. Then, day-of-the-week effect suggests that stock returns are usually higher on Friday while lower on Monday. Last but not least, holiday effects is concerning the assertion that stock prices tend to increase more before holidays (Eng, 1998). In this context, a study conducted by Muhammad & Rahman (2010) found that Malaysia market is not truly efficient, as empirical evidences on Monday effects are recorded in their research. Similar research had also been conducted by Lim, Ho & Dollery (2010). The authors provide further evidences on the existence of Monday effects in Malaysia. Specifically, the researchers found that stock returns on Monday are typically the lowest in a week, with a mean return of -0.09%. In contrast, stock returns tend to be the highest on Wednesday, with a mean return of 0.07%, followed by Friday, with a mean return of 0.04%. Not only is that, the researchers also found that stock returns on Monday is highly dependent on the stock returns on the previous Friday. When the stock returns on previous Friday is negative, the median of stock returns on Monday is 0.26%; while when the stock returns on previous Friday is positive, the median of stock returns on the following Monday is 0.02%. They findings provide further empirical evidences that the stock market in Malaysia is not efficient. Evidences of seasonality, day of the week, and month of the year effects were also found to exists by Yakob, Beal & Delpachitra (2005) in ten of the Asia Pacific countries, namely in Australia, China, Japan, India, Hong Kong, Indonesia, Malaysia, South Korea, Singapore and Taiwan. Then, the study of Ahmad and Hussain (2001) also found that there were empirical evidences of Chinese New Year effects (which are perceived as similar to the January effects in United State) in Malaysia. Then, in order to test the existence of seasonality effects even after many literatures on seasonality or calendar effects are published, Chen, Chen and Chang performed updating studies on examining seasonality effects in Singapore. It is suggested that there are empirical evidences supporting the existence of monthly seasonality in the latest years (1990 to 2007).

Lim (2008) conducted an interesting study on the efficiency of Malaysian market from another perspective. He found that the different sectors tend to exhibit different degree of efficiencies in Malaysia stock market. Specifically, the sector of tin and mining is found to be the most efficient sector, while the properties sector is found to deviate most significantly from random walk theory. Besides, Lim (2008) also asserted that market inefficiencies were highest during market crash or financial crisis. It is explained that during market crashes or financial crisis, investors tend to lose confidence – causing the market to be highly turbulent. The authors concluded that it is possible that occurrence of market crashes and financial crisis is the contributors of market inefficiencies in Malaysia.

2.3.4 Concluding Remark on Empirical Studies on EMH

Overall, it is found that most of the study found evidences that the stock markets in the region are largely not efficient. The availability of literature arguing that certain techniques can be used to reaped abnormal returns in the market suggest that the notion of Efficient Market Hypothesis may not apply in South East Asia. Although there are some studies finding limited amount of evidences supporting the assertion that the stock markets are somehow roughly efficient; these literatures are rare.  Thus, it is reasonable to conclude that the stock markets in South east Asia is not efficient, and investors can reap abnormal returns from diligent analysis or adoption of certain investment or trading rules that are found to able to beat the market.

2.4 Studies on Stock Returns in South East Asia

There are some studies on the context of stock returns in South East Asia. In this section, the limited month of studies on stock returns in South East Asia is presented. Firstly, there are sufficiently good number of studies on investigating the linkages between the different stock markets around the region or around the world. Indeed, many of the existing literature on stock markets in the South East Asia region found that there are statistically significant correlations between stock returns among the different stock markets of the countries in South East Asia. For example, by utilizing a GARCH-M Model, Valadkhani, Chancharat & Havie (2009) found that stock market returns in Singapore, Malaysia and Indonesia tend to influenced stock market returns in Thailand. Then, the researchers found that the changes of stock markets outside the region had no significant immediate influence against stock market in Thailand. From another perspective, Pattarathammas & Khanthavit (2009) found that all the three following factors: (a) world factors, (b) regional factors, and (c) idiosyncratic factors have different impacts towards stock returns on countries such as Japan, Hong Kong and Singapore. For example, in Singapore, all of these three factors are found to have statistically significant influence against Singapore stock market. Specifically, regional factors have faster and more influential influence against Singapore stock market, as compared to the world factors. In a similar vein, Wasiuzzaman and Li (2009) found that the stock returns of the four countries as follow: Malaysia, Singapore, Japan and United States have financial market linkages and co-movements. By adoption of Granger causality test, it is shown that most of the stock markets are affecting each others.

Besides studies on correlations between stock markets, there are also studies performed to investigate usefulness of multi-factor model in explaining stock returns in countries such as Indonesia and Singapore. For this, Naughton and Veeraraghavan (2005) performed a study to investigate the validity of Fama and French three factors model in explaining the stock returns in the countries mentioned above. It is found that there are statistically significant risk premia in Singapore and Indonesia. The two key factors employed in the multifactor model, namely, the size and book to market equity factor has different level of significance across the different countries. To explain, Fama and French three factor model is a popular multifactor models often studied by scholars. Besides, beta as one of the factor included in the multiple regression equation, two other factors included are: size factor (as proxy by market capitalization) and value factor (as proxy by book value to market value ratio). The two factors are include because, firstly, smaller size firms are perceived as more risky, and hence, demanding higher risk premium to compensate for the higher business risks associated with the size of the firm. Secondly, it is observed that companies traded with a low market value to book value tend to be undervalued, and hence, can be included to explain the value premium associated with these undervalued firms (Bodie, Kane & Marcus, 2007).

In the context of relationships between macroeconomic indicators to stock returns, several previous literatures found are presented as follow. Firstly, Majid (2010) performed a study to investigate the relationships between real stock returns to inflationary trends in the Malaysia stock market. However, the author found not statistically significant relationships between real stock returns to inflationary trends in Malaysia. Apart from that, Majid & Yusof (2009) also investigated the relationships between Islamic stock returns in Malaysia to interest rates. According to their findings, as interest rates increase, no matter domestically or internationally, the Muslim investors tend to purchase more of Shari’ah compliant stocks, thereby enhancing the Islamic stock returns. The authors explained that such a finding is probably contributed by the nature that Muslim investors in Malaysia are basing their investing decisions on level of interest rates. In event of lower interest rates, some Muslim investors may choose not to buy more Shari’ah compliant stocks. As suggested by the authors, such a phenomenon happens because of the misconception that non-excessive interest rate is considered as permissible. Specifically, every amount of interest rates irrespective of its magnitude is not permissible.

Apart from studies on relationships between macroeconomic variables to stock returns, there is also study investigating the relationships between stock returns, returns volatility and trading volume in Indonesia, Malaysia, Philippines, Singapore and Thailand. According to a study conducted by Pisedtasalasai & Gunasekarage (2007), there was strong evidence of asymmetry on the stock returns relationships to trading volume in all of the countries under investigation. In other words, it is found that stock returns were statistically significant in predicting future returns volatility and trading volume. However, returns volatility and trading volume cannot be used to predict stock returns in the future.

As discussed above, it can be observed that there are vast different techniques used to predict stock returns. To use economic variables to predict stock returns are one of the techniques adopted by previous researchers. Secondly, it is found that to have a discussion on all of the techniques used is simply impossible. Thus, in the following section, discussions will zoom down into the context of predicting or investigating stock returns from macroeconomic variables.

2.5 Use of Economic Variables to Predict Stock Returns

The use of economic indicators to predict stock market movement for superior profits is nothing new. Many people are attracted to do so in order to yield above average performance in the financial market. This can be seen even from the title of the investment related books available in the bookstores today. For example, stock market related books on how to utilize macroeconomics information to time or predict the market is widely available. Among those famous books include: The Secret of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities (Baumohl, 2005), The Trader’s Guide to Key Economic Indicators (Yamarone, 2007), and Using Economic Indicators to Improve Investment Analysis (Tainer, 2006). Apart from that, the importance of economic variables towards stock market returns is not only acknowledged by practitioners or investors, but is also widely recognized by scholars. This can be seen as many literatures related to stock market returns are highly associated with macroeconomic variables, as the study of macroeconomics has been perceived as important by academicians.

As mentioned above, many research and literature related to macroeconomics variables and stock market returns or characteristics can be found. A fast review of the literature discovered that the many literatures are arguing the existence of meaningful relationships between economic variables to stock market returns. Depending on the context and objectives of the research conducted by the various scholars, different economic variables are investigated. Among the famously investigated and discussed economic variables include the following: economy growth of a country (as proxy by Gross Domestic Product), industrial production index, unemployment, inflation rate, money supply, variables related to interest rates, exchange rates, and oil prices (Muradoglu et. al., 2000; Patra & Poshakwale, 2006). There are many reasons whereby these economic variables are chosen. Firstly, these variables are selected probably due to their popularity and it is easy to make sense on how these economic variables might affect the stock market. All of these economic variables should sound familiar for any students studying business management, finance or economy, as they are commonly known and understood. Besides, there variables are also often discussed in financial newspaper, and the frequent availability of the discussions on these variables in business management or investment context indicates that they play crucial roles in affecting the business environment or company performances (Siegal, 2002). Secondly, these economic variables are also selected due to the availability of well-recognized and accepted theories in security pricing (Fifield et. al., 2002). For example, it is widely accepted that the Discounted Cash Flow (DCF) model can be used for securities pricing purposes. In the DCF theory, there are two components important to security pricing, firstly the interest rates used as the discount rates; and secondly, the expected cash flow from the securities in the future. Thus, economic variables such as interest rates are used as the input to security pricing purposes, and hence, the change in the level of interest rates will affect the pricing of securities, and hence the stock market returns in the future (Maginn, Tuttle, Pinto & McLeavy, 2007). Then, other economic variables, such as the growth of a country economy, the industrial production index, money supply, inflationary pressure, oil prices as well as exchange rates are likely to affect the cash flow component in the DCF model. For instances, when the economy of a nation grows, it is reasonable to expect that businesses can make more profits and hence, enjoy higher cash flows from business operations. When the future cash flows are expected to be increasing, the valuation of the stock will become more expensive, and thus, better expected stock market returns (Flannery et. al., 2002). As such, it is rational to believe that these economic variables are likely to affect the stock market returns in the future, and should be investigated and considered in the research related to stock market returns. Thirdly, it is also easily observed that these variables are closely related to the business cycles; and business cycle is having huge impacts on stock market returns. For example, in a growing economy environment, the growth of Gross Domestic Products for a country improve, and the industrial production indexes is enhanced, the unemployment rate decrease, inflationary pressures may pick up and central bank may increase the interest rates to counter the inflationary pressures. At such period, stock market returns tend to be positive and encouraging. In contrast, when the economy turn south, recessionary pressures, such as the slow down (or even negative) of economy growth, the rising unemployment rates, the rise of deflationary pressure, and the tendency of central banks to decrease interest rates to revive the economy; are often associated with negative stock market returns. Thus, stock market returns may be predicted if the changes of the business cycle can be discerned effectively through tracking of economic variables related to the economy (Maginn et. al., 2007; Conover et. al., 2005).

Overall, it is shown that it is indeed rational to use economic variables to predict the stock market returns in the future, as there are many economically and common sense linkages between these economic variables to stock market valuation and returns. Anyway, to investigate the relationships of all of the economic variables to stock market returns will be daunting tasks. As such, in the following section, out of the many economic variables available, interest rates will be used in this dissertation, as it is perceived as the most important economic variables expected to affect stock market returns. More explanations and discussion on the importance of interest rates to the financial market will be outlined in the following section.

2.6 Importance of Interest Rates to Stock Market Returns

Many successful practitioners are arguing that tracking the interest rates level of the economy is essential to investment decision making process. For examples, as argued by Zweig (1997), interest rates is very important variables to be tracked because often, when the economy outlook is gloomy and at the moment the central bank is lowering the interest rates to revive the economy, it is often the very best timing to buy into the stock market. The reason is that stocks are generally cheap when the economy outlook is negative (i.e., when most people are pessimistic on the financial market), and when the central bank lower the interest rates significantly, consumptions can often be encouraged and the lowering of interest rates make buying stock attractive. To explain, when the interest rates is low, people are discouraged from saving, and they may be more inclined to invest these money in the stock market for higher returns (Conover et. al., 2005). Besides, perhaps more importantly, when interest rates are low, it makes buying capital intensive things, such as car and house more affordable. Companies will also more willing to invest and spent more to expand their businesses. All of these often put upwards pressure to the economy, and hence, in the long run, is positive to stock market returns. Then, Zweig (1997) also suggested that the excessive increase of interest rates is often indication of restrictionary monetary policies implemented by government. As the government reduce money supply, most of the than not, stock market will suffer. Apart from that, another famous portfolio manager, namely Ken Fisher (2007), has also been using interest rates to make investment decisions. According to him, interest rates are given great importance because it will affect people preferences of types of investment assets. When interest rates are low, people are less likely to invest in bond, and thus, more likely to switch their capital to be invested in assets such as in the stocks or property market. Besides, it is also shown and argued that yield curve can be rationally employed in predicting the changes in the market. Thus, it is indeed reasonable to believe that interest rates are powerful and crucial to more reliable and accurate market timing in the context of stock market returns prediction (Fisher, 2007). In fact, in the recent financial crises, it is observed that stock market returns is very encouraging after the various central banks around the world lower the interest rates in the respective countries to prevent the economy recession from worsening. This should further convince investors on the importance roles of interest rates in affecting or directing stock market returns in the future.

Besides being tracked and studied by the practitioners, interest rates are also found to be highly impactful to stock market returns by academicians or scholars. This is economically reasonable. As commented by Siegal (2002), interest rates can be influential towards stock market because: (a) interest rates is often used as the discount rates to estimates the fair price of securities, (b) long term interest rates will often compete for investors’ dollar (i.e., when interest rates increase, investors pull out from the stock market and then to park their money in the bank), (c) rising interest rates often may hurt the profitability of the various businesses, as the financing costs in running businesses have increase. Not only that, according to Abdullah & Hayworth (1993), rising interest rates will adversely affect the stock market sentiment, as well as limiting the possibilities of merger and acquisition activities (when companies find it more costly to finance these corporate acquisition activities). Indeed, aside from the assertions that interest rates are impactful towards stock market returns, it is also found that that are many empirical evidences proving and suggesting that there are both economically meaningful and statistically significant relationships between interest rates to stock market returns in various countries around the world. All of these empirical evidences and literature in this context will be discussed and in the following sections.

2.7 Relationships between Stock Market Returns to Interest Rates

As mentioned before, there are many literatures available in investigating the relationship between interest rates and stock market returns. For example, it is found that interest rates can be one influential risk factors being priced in explaining stock market returns in the United States (Kim & Wu, 2987). Such a finding is also supported by the finding from Chen (1991). Chen (1991) found out that the 1-month T-bill rate (i.e., short term interest rates) is one of the impactful variables that can be used to predict stock returns in the future. Then, Abdullah et. al. (1993) found and documented that the stock market returns are actually negatively correlated to changes in interest rates (i.e., an inverse relationship – when interest rates increase, stock market returns dropped). Although all of the researches mentioned above are relevant only to findings in the United States, there are also many empirical evidences that stock market returns had strong association to the changes in interest rates. For example, in a cross sectional study by Bodurtha et. al (1989), it is found that the international interest rates is significantly priced in explaining the average stock returns in countries as follow: Canada, United States, United Kingdom, France, Germany, Australia, and Japan. While all of the countries researched by Bodurtha et. al (1989) are developed countries, Fifield et. al. (2002) performs a study on the emerging countries. These authors found that interest rates changes are indeed priced into stock market returns in thirteen emerging countries around the world.

From another perspective, there are also many researches in investigating the causality relationships between stock market returns and interest rates. For example, Kraft and Kraft (2003) found that there is unidirectional causality from stock prices to interest rates, using data from the stock exchange in the United States. Then, Abdullah and Hayworth (1993) also discovered that both the long-term as well as the short-term interest rates indeed Granger cause stock returns. However, the evidences on Granger causality are not uniform. In some studies, Granger causality is not found. For example, a study by Muradoglu et. al. (2000) found that Granger causality between stock market returns and interest rates can only be observed in certain countries, for example, in Brazil, Pakistan, Zimbabwe, Korea, Argentina and Mexico. Under their study, Granger causality between stock market returns and interest rates cannot be observed in another seven countries (i.e., a total of 13 countries are investigated). According to the authors, the inconsistently of empirical evidences on existence of Granger causality between these countries are largely contributed by the fact that certain stock markets are highly affected by country specific events. Specifically, the different degree of financial liberalization and the level of economic integration of the emerging countries to the world market may be affecting the relationships between interest rates and stock returns in those particular countries. However, there are no conclusive reasons suggesting that these possible reasons are valid and justified. Indeed, the authors suggested that further studies are required to investigate the inconsistency of Granger causality between the different countries.

2.8 Concluding Remarks

In short, a review of the existing literature found that there are many strong evidences against Efficient Market Hypotheses around the world. Indeed, most of the literature on the context of market efficiencies in South East Asia indicates that the stock markets in these countries are not efficient, as there are recognizable and popular approaches that can be employed for abnormal returns. Indeed, despite wide publication, it is found seasonality effects still persist in these countries. The literature supporting market efficiencies are only minority. As such, it can be concluded confidently that to study stock returns for abnormal returns is justified. Then, the previous literature concerning stock returns in several countries in South East Asia are also reviewed. There are relatively few studies conducted in this region, when compared to other more popular nations such as in United States, United Kingdom, Japan, India or China. Different techniques used to predict stock returns. To use economic variables to predict stock returns are one of the techniques adopted by previous researchers. It is then argued that to have a complete discussion on all of the techniques used is simply impossible. As such, discussions presented in this study will focus on the topic of investigating stock returns relationships with macroeconomic variables. Among the many different macroeconomic variables, it is found that interest rates are perhaps the most influential and widely monitored indicators. Such an understanding further draw our attention on to use interest rates to predict stock market returns in the context of the various countries in South East Asia.

Then, it seems that there are evidences that changes in interest rates are related to the stock market returns. Practitioners and academicians apparently acknowledge such idea, or at least found that to be economically reasonable. Evidences that there are statistically significant correlations between changes in interest rates to the stock market returns are also available. Apart from that, although not an exact science, there are also evidences of Granger causality between these two variables. Nonetheless, as we had discussed before, there is no research investigating such kind of research in all of the following countries in South East Asia, namely: Malaysia, Singapore, Thailand, Indonesia, Philippines and Brunei Darussalam. Secondly, as far as the author knows, there is no recent research of such kind in these few years (as most financial and economic related journals are concentrating on discussions of subprime or the sovereign crises happen recently) – thus, a research to understand if such a relationships still stay valid in the recent years is necessary.

 

 

 

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