This is a study to investigate if there are any statistically significant correlation relationships between cross sectional stock returns to several firm specific fundamental variables as follow: return on equity (ROE), price to earnings ratios (PE), price to sales ratio (PS), price to net worth ratio (PNet), dividend yield (DY), rate of growth in revenue (RG), payout ratio, rate of growth in earnings per shares (EPSG), and Price Earnings to Growth ratio (PEG), for companies listed under Kuala Lumpur Composite Index (KLCI) in Kuala Lumpur Stock Exchange (KLSE). Some of the fundamental variables, namely, return on equity (ROE), price to earnings ratios (PE), dividend yields (DY), and price to net worth ratios (PNet), exhibits statistically significant correlation coefficients with cross sectional stock returns in certain financial years. It is noted that the sign of those statistically significant correlation coefficients are indeed consistent with the theory or with the assertions from the successful investors. For instance, in financial year 2007, ROE is statistically significant positively correlated to cross sectional stock returns. Then, in financial year 2007, PE is statistically significant negatively correlated to cross sectional stock returns. Next, in financial year 2007, 2009 and 2010, DY is statistically significant positively correlated to cross sectional stock returns. Lastly, in financial year 2006, PNet is statistically significant positively correlated to cross sectional stock returns. Nevertheless, some of the fundamental variables are found to exhibit no statistically significant relationships with cross sectional stock returns in Malaysia. Among these variables include: price to sales ratio (PS), rate of growth in revenue (RG), payout ratio, rate of growth in earnings per shares (EPSG), and Price Earnings to Growth ratio (PEG). As such, from this study, it is suggested that it can be concluded that there are no evidences supporting the notion that these fundamental variables can be used to explain cross sectional stock returns in Malaysia.
Financial markets have always been highly complicated subject for scholars and practitioners. A review of the daily finance news found that there are many different views and propositions on market predictions. One of the highly troubling issues in getting advices from the many different market pundits is that their views are often inconsistent or contradictory with each others. Different pundits seemingly are more comfortable to using different yardsticks or inputs to make predictions and estimations on stock prices or performance of the financial market as a whole in the future. Apparently, different pundits tend to believe in different things. For examples, people who believe in technical analysis tend to believe that charts and past prices of a particular stock or financial market can be used to time the market and make profitable investment decision. In contrast, people who believe in fundamental analysis tend to use fundamental variables, such as price-to-earning ration, dividend yield, book-to-market value, net tangible assets, economic indicators, sentiment indicators, flow of capital, government policy and many other sorts of quantitative and qualitative indicators to guide their investment decision (Reilly & Brown, 2003). Then, they are also people who believe in using a combination of both technical indicators and fundamental indicators to guide their investment decision making process (Malkiel, 2007). Overall, it can be confidently to mention that depending on the motivation, belief, philosophies and the perceptions of the particular investors or scholars, different variables will be used to time the market.
To employ whatever variables to guide a person investing decision making process is nothing wrong. However, there are few problems with choosing the variables to be used in guiding a person investing decision without proper structure or system to ensure that what is being considered or employed as input in estimation or prediction on the stock market is indeed logical or statistically sensible. For example, there are huge chances that the variables adopted by a person may not be the effective or even useful inputs that can truly enhance the investment performances of the individuals. Worst, believing in misconception or betting on the stock market with false concepts or ideas can be detrimental to the health and wealth of the investor (Fisher, Chou & Hoffmans, 2007). In such a case, investors should conduct proper studies to investigate the effectiveness and reliability of any variables to be used in the decision making process before betting real money in the financial market. At the very least, any variables that are to be used must be able to exhibit statistically significant relationships with the future stock prices from the historical perspective. This is important as if any variables are shown to exhibit no statistically significant relationships with future stock prices; there are even little chances that these variables can guide the investors to make profitable trading or investment decisions in the future.
Secondly, it is also problematic for any investors to decide how to choose the few variables to be employed in stock screening or market timing process. There are too many different types of indicators available in the market. Considering using a combination of all of the financial ratios, economic indicators, technical signals and other relevant variable proposed by different apparently brilliant and honest market guru, any investors may end up having to track more than thousands of variables every day. To use all of the signals will be irrational. The only way is to screen out the selected few highly effective variables that have the highest chances of yielding extraordinary investment results. Thus, it is crucial to analyze some of the many possible variables and to analyze the historical relationships of these variables to stock prices.
Then, last but not least, the different market pundit apparently has highly contradictory opinions on what works and not in the stock market. For example, Shiller (2000) performed a study and concluded that Price to Earning (P/E) ratio is useful to predict stock market returns in the future. According to his study, high P/E ratio is often bad for the stock market in the future, while low P/E ratio is often good for the stock market in the future. However, one of the legendary investors, namely, Fisher, Chou and Hoffmans (2007) argued that P/E ratio is not useful to predict stock market returns. One example given by Fisher is that during the Great Depression period in United States, P/E for the entire market had been high, but that is one of the best times in the history to bet on stock. Any investors relying on P/E ratio to guide their investment decision will surely miss out one of the most wonderful opportunities in history. Then, there are also saying that market is efficient, and it is not possible for average investor to beat the market in the long run. The best strategy for investors is to buy and hold index funds, and by doing that, they stand a high chance to beat even the professionals in the stock market (Ellis, 2002). In contrast, Tier (2004) argued that the theory of efficient market is simply wrong, as there are many legendary investors, such as Warren Buffett and George Soros that have been proving capable of making big money by exploiting inefficiencies in the stock market. Then, it is also widely acknowledged that technicians tend to believe that analyzing the financial statement or corporate fundamentals will not able to yield extraordinary investment results as stock prices have been reflecting all of the necessary information before the analysts (Elder, 2002). In contrast, there are a lot of academic attacks on absurdity of technical analysis. Malkiel (2007) asserted that there are many reasons that charting is doomed to fail in the stock market. The primary reason is that chartist or technicians often buy after the price has gone up, or to sell when the price had broken the resistance line. However, sharp reversals on stock market is no something unusual, and the fast reversal will cause the investors to keep buying and selling, based on the different buying and selling signals that can occur in just a single day. Instead of buying high and sell higher, chartists are more likely to keep buying high (when they enter a trade due to price breakout) and selling low (when they exit from a trade due to cut lost signal).
Obviously, any serious investors will want to find out the truth about stock market, to prevent relying on wrong or deadly concepts in investing their hard earn money throughout their investment horizon. For this, there is nothing more appropriate than to perform research to investigate the different factors argued to be affecting stock prices and can be used to make reasonably good prediction or estimation on stock market of the future. As such, in this dissertation, the various factors argued by scholars and practitioners to be capable of predicting stock market will be investigated. Statistical concepts such as Pearson correlation and R-square will be used to judge if any reasonably reliable linkages between the factors or variables argued by the market pundits to be capable of predicting stock returns. Only by having proper research on the different ideas or proposition of the various market pundits, an investor can understand which factors or variables are proven more reliable or predictive than the others. The wrong or false concepts frequently argued by the so-called market pundits can also be demystified (i.e., falsified).
There are many unanswered questions about selecting and employed the reasonably good factors or variables for market prediction or investment decision making purposes. Firstly, it is uncertain are the different variables that can be reasonably selected for research purposes. As there are many different variables available in the literature and books on investment or trading, the different factors postulated to have meaningful or predictive impacts or influences on stock market should be identified. Then, another research questions to be answered in this dissertation is whether the different variables argued by scholars, market pundits, book authors or journalists to be able to predict future stock prices is true. Technically speaking, it is unknown if these factors argued to be able to affect or influence stock prices in the future is true or false. As such, it is crucial for investors to perform studies to investigate if the different factors or variables representing the different factors are indeed having statistically significant relationships (as measured by correlation coefficient) with stock returns in the past. Having these unanswered research questions in mind, research objectives under this dissertation project can be formulated in the following section.
The specific research objectives in this study is stated and articulated in this section. Generally, in the study conducted in this dissertation, the key objective is to investigate if there are any statistically significant relationships between cross sectional stock returns to several firm specific fundamental variables as follow: return on equity (ROE), price to earnings ratios (PE), price to sales ratio (PS), price to net worth ratio (PNet), dividend yield (DY), rate of growth in revenue (RG), payout ratio, rate of growth in earnings per shares (EPSG), and Price Earnings to Growth ratio (PEG). For this purpose, correlation coefficients between the research variables will be computed. The study will be conducted using data from companies listed under Kuala Lumpur Composite Index (KLCI) in Kuala Lumpur Stock Exchange (KLSE), Malaysia. The research period investigated is from year 2006 to year 2010. By identifying if there exist statistically significant relationships between cross sectional stock returns to these firms’ specific variables, investors can then verify the claims of both practitioners as well as scholars if the different fundamental variables are related to cross sectional stock returns. In event there are statistically significant relationships found between stock returns to a particular fundamental variables as outlined above, it is then possible for investors to further understand how the specific variables is related to cross sectional stock returns – and to extend the study to identify possible ways of exploiting the found relationships in yielding above average returns from the stock market. Overall, to summarize, the research objective of this dissertation can be outlined as follow.
Research Objective: to investigate if there are any statistically significant correlation relationships between cross sectional stock returns to several firm specific fundamental variables as follow: return on equity (ROE), price to earnings ratios (PE), price to sales ratio (PS), price to net worth ratio (PNet), dividend yield (DY), rate of growth in revenue (RG), payout ratio, rate of growth in earnings per shares (EPSG), and Price Earnings to Growth ratio (PEG), for companies listed under Kuala Lumpur Composite Index (KLCI) in Kuala Lumpur Stock Exchange (KLSE).
This dissertation is structured as follow. In the next Chapter, literature review on the materials concerning factors used to predict stock returns will be presented. An intensive literature review are required to understand what are the factors/ variables argued by the different scholars or practitioners to be able to predict or forecast stock market prices in the future. Thus, in Chapter 2, the popular factors/ variables that are postulated to have predictive power in affecting or forecasting stock market prices in the future will be identified. Not only the scholars or academicians findings or opinions will be reviewed, but the books written by, or about successful investors will also be studied. Then, the views of economists will also be investigated. After a review of the opinions and views from various parties, the factors perceived to be influential in affecting stock returns will be summarized. Next, In Chapter 3, research methods employed in this dissertation will be outlined. The limitations related to the research design will also be articulated. In Chapter 4 however, data presentation and analysis will be conducted. Pearson correlation coefficients between the factors assumed to be significant in affecting stock returns to historical stock returns will be analyzed, to investigate if there are statistically significant historical relationships (as measured by Pearson correlation coefficient) between the factors/ variables mentioned above to stock returns in the. Regression analysis will be conducted as well, to understand the relationships between these factors to stock returns, in the context of Malaysian stock market. Lastly, in Chapter 5, conclusion related to this dissertation will be articulated. The research objectives and questions will be answered, in light of the findings discovered in this study. Then, recommendations for further research will also be presented, to highlight the areas that deserve further investigation to better understand the real factors driving stock returns in actual financial market. This chapter will also provide economically meaningful and rational explanation on the findings.