From the results findings, it is obvious that not all economic indicators should be studied and followed by investors. As discussed in previous chapter, it is reasonably concluded that short term interest rates are indeed negatively related to stock returns. However, the low R-square of the regression line between short term interest rates and stock returns suggests that short term interest rates variables, however, should not be used solely in making rational investment decision, even in the context of Singapore stock market. Low R-square indicates that changes in short term interest rates explaining only minor percentage of the variation in changes in STI. Then, it is also articulated that there is only weak evidences that the growth of money supply is positively related to stock returns. Two of the definitions of money supply, namely, M1 and M3 do not have any statistically significant relationships with stock returns. Then, although the growth rate of M2 is found to exhibit statistically significant relationships with stock returns, it is largely attributed by a single outlier (from the graphical analysis of the scatter plot). Perhaps more importantly, it is found that, inflation rate (as proxy by CPI) as well as growth of real economy activity (as proxy by growth in IPI) has no relationships with stock returns. Indeed, judging from the R-square of zero, these two variables should not be used to predict stock returns. Investors will likely to do much better if they rely on other macroeconomic indicators such as short term interest rates. In a nutshell, not all macroeconomic variables or indicators should be learned, studied or followed by serious investors. The different explanatory power of the different indicators, strongly suggest that some indicators are simply more effective than other indicators. Indeed, it is likely that, even by employing the other quantitative methods or statistical tools or concepts, it is unlikely to find any reliable relationships between inflation rate, or rate of changes in real economy activity to stock returns. Similarly, in the future study, research should focus more on identifying opportunity of gaining excess returns through following and incorporating interest rates into the investment decision making process.
Stock market is a highly complex invention of human beings. Yet, there are many misconceptions popularized by others. Through statistical methods, some of the advices provided by so-called market wizards or gurus can be ignored. Different weight should be assigned to the different types of advice, depending on the rationale used to predict stock returns in the future.
Purely from the research findings, it is found that the relationships between macroeconomic variables to stock returns, in the context of Singapore are consistent with theories. For example, interest rates are negatively related to stock returns. In theory, this is true, because when interest rates increase, it looks attractive to buy bond or to save more fund to bank accounts. Thus, such a situation is likely to put pressures to the stock markets, whereby more conservative investors may simply sell the stocks they are holding (perhaps partially), to be saved in the bank. As such, stock prices are likely to be depressed. Besides, as argued by Siegal (2002), the rise of interest rates will also likely to affect corporate operations adversely. Rising interest rates indicate rising costs of doing business. For instance, rising interest rates can means rising interest payment and expenses for corporation with debt in the capital structure. Then, rising interest rates will also affect market sentiment (Yamarone, 2007), whereby corporations may delay or give up merger and acquisition activities for growth. In a nutshell, the findings concerning interest rates and stock returns support the notion argued by Kraft & Kraft (1977); Chen, Roll & Ross (1986); Kim & Wu (1987); Chen (1991); Abdullah & Hayworth (1993); McGowan & Dobson (1993); and Resnick & Shoesmith (2002). Then, there is also some weak evidences supporting the notion that growth of money supply is positively related to stock returns. Such weak evidences support the findings from Kraft & Kraft (1977); Pearce & Roley (1985); Abdullah & Hayworth (1993); Zweig (1997); Thorbecke & Coppock (1996); Park & Ratti (2000); and Conover, Jensen, Johnson, & Mercer (2005).
Nevertheless, it is obvious that the research presented in this dissertation is not final. It is preliminary and guiding scholars to areas of better prospects for future research. It points to the direction of relying on short term interest rates as a possible areas or indicators to yield excess returns in the marketplace. Nonetheless, the exact method to employ short term interest rates as powerful trading rules in reaping alpha in the stock markets is not discussed or identified.
However, the study presented in this dissertation can be impactful towards the decision making process for investors. Investors can now understand that any claims or prediction on stock returns on changes in CPI or IPI (particularly those that is already expected) can hardly be accurate. This is because from the historical perspective, there is simply no statistically significant relationship between these variables to stock returns. Indeed, having understand that there is no existence of statistically significant relationship between these variables to stock returns, investors can then understand if these market guru really understand what they are actually talking or predicting about.
In final words, stock market is complex. There is no easy profit available, or else, everybody will already be multi-millionaires, which is something impossible. The findings on this dissertation suggest that investors might better study hard, and never under-estimate the complexities inherent in the stock markets. Theories related to stock market, may be true (i.e., for example, negative relationships between interest rates to stock returns), but solely relying on these theories, may not be able to bring superior profits for investors.
As discussed before, this dissertation only provides preliminary studies on linear relationships between macroeconomic variables to stock returns. In the future, different types of studies can be conducted to better understand how these macroeconomic variables affect stock returns. Several areas are suggested to be possible future research area, to cover the limitations in this dissertation.
As articulated before, in this study, the statistical method of correlation coefficient and linear regression assume existence of linear relationships between stock returns to the macroeconomic variables being investigated. However, the real relationships between macroeconomic variables to stock returns may be more dynamic or complex than people can ever imagine. Indeed, to assume linear relationships may be too simplisistic. Thus, it is possible for investors or scholars to employ other quantitative methods to understand further how the actual dynamic, non-linear or complex relationships between stock returns to indicators such as interest rates and money supply.
Secondly, it is also articulated that it is ignored in this study that the relationships between macroeconomic variables and stock returns may change according to different time period or regime. Structural change and its influences towards the relationships to be observed are simply ignored. Then, it is also outlined lengthily in Chapter 2 that there are evidences supporting the notion that relationships between macroeconomic variables to stock returns may differ in different phase of the business cycle. Apart from that, under different market sentiment, it can be reasonably expected that investors may have different expectation on the release or announcement of macroeconomic indicators. Then, in the future research, investors may separate the research period into different regime, to understand how the relationships between stock returns to economic variables may change accordingly to the changes in stages of business cycles. Perhaps when that is performed, there will be more obvious and statistically significant relationships between stock returns to variables such as IPI or CPI in Singapore.
From another perspective, it is also argued that even through this study, it is unsure if stock returns lead macroeconomic variables or otherwise. The causation between stock returns to macroeconomic variables is not investigated. To do that, other statistical methods, such as Granger causality analysis will be required. Thus, it is possible that scholars may employ statistical methods such as Granger causality to further investigate relationships between stock returns to the macroeconomic variables used in this study.
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