# CHAPTER IV: DATA PRESENTATION AND ANALYSIS

## 4.1 Overview of the Chapter

In this chapter, the data concerning stock return, as well as the specific macroeconomic indicators will be analyzed. Simulated results will be presented. The macroeconomic indicators investigated include the following: Straits Times Index, 1 month interbank rate, 3 month interbank rate, yield of 1 year Treasury bill, yield of 10 years Treasury bonds, Industrial Production Index, Consumer Price Index, M1, M2 and M3. In order to ease the reference and to save space in this chapter, symbols for the respective variables are used. The symbols for all of the variables employed in this study are presented in Table 4.1 below.

## 4.2 Presentation of Simulated Results

In this section, the simulated results concerning relationships between stock returns to the specific macroeconomic variables will be discussed. For each of the macroeconomic variables, the relationships to stock returns will be investigated through three different statistical concepts or tools, namely: correlation coefficient, regression analysis (via ANOVA table), and then scatter plot.

### 4.2.1 Relationships between Change_STI and Change_1m_Interbank

In Table 4.1 below, the correlation coefficient between Change_STI and Change_1m_Interbank is shown. It is found that Change_STI is statistically significant correlated to Change_1m_Interbank, at the 95% confidence interval. The sign of the correlation is negative, indicating that the rate of change in STI is negatively related to chnages in 1 month interbank rate in Singapore. This is consistent with the theory, whereby when interest rates increase, stock returns tend to be lower, as discussed previously, 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. 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 Table 4.2 below, the ANOVA table for regression between Change_STI and Change_1m_Interbank is shown. It is found that the regression between Change_STI and Change_1m_Interbank is statistically significant at 95% confidence interval. In order to gain greater understandings on the regression line graphically, the scatter diagrams, with Change_STI as the dependent variables, and Change_1m_Interbank as the independent variables, will be shown in Figure 4.1 below.

In Figure 4.1 below, the scatter diagram of Change_STI against Change_1m_Interbank is shown. It is found that statistically significant regression line is not something affected by a single outlier. This suggests that there are indeed certain meaningful relationships between rates of change in STI to changes in 1 month interbank rate. The slope of the regression is negative, and this provides some empirical evidences that rates of change in STI are negatively related to changes in 1 month interbank rate.

### 4.2.2 Relationships between Change_STI and Change_3m_Interbank

In Table 4.3 below, the correlation coefficient between Change_STI and Change_3m_Interbank is shown. It is found that Change_STI is statistically significant correlated to Change_3m_Interbank, at the 95% confidence interval. The sign of the correlation is negative, indicating that the rate of change in STI is negatively related to changes in 3 month interbank rate in Singapore. Again, this finding is consistent with the theory, whereby when interest rates increase, stock returns tend to be lower.

In Table 4.4 below, the ANOVA table for regression between Change_STI and Change_3m_Interbank is shown. It is found that the regression between Change_STI and Change_3m_Interbank is statistically significant at 95% confidence interval. In order to gain greater understandings on the regression line graphically, the scatter diagrams, with Change_STI as the dependent variables, and Change_3m_Interbank as the independent variables, will be shown in Figure 4.2 below.

In Figure 4.2 below, the scatter diagram of Change_STI against Change_3m_Interbank is shown. It is found that statistically significant regression line is not something affected by a single outlier. Exclusion of any data point is unlikely to change the regression significantly. This suggests that there are indeed certain meaningful relationships between rates of change in STI to changes in 3 month interbank rate. The slope of the regression is negative, and this provides some empirical evidences that rates of change in STI are negatively related to changes in 3 month interbank rate.

### 4.2.3 Relationships between Change_STI and Change_1y_Treasury

In Table 4.5 below, the correlation coefficient between Change_STI and Change_1y_Treasury is shown. It is found that Change_STI is statistically significant correlated to Change_1y_Treasury, at the 95% confidence interval. The sign of the correlation is negative, indicating that the rate of change in STI is negatively related to changes in the yield of 1 year Treasury bill in Singapore. Again, this finding is consistent with the theory, whereby when interest rates increase, stock returns tend to be lower.

In Table 4.6 below, the ANOVA table for regression between Change_STI and Change_1y_Treasury is shown. It is found that the regression between Change_STI and Change_1y_Treasury is statistically significant at 95% confidence interval. In order to gain greater understandings on the regression line graphically, the scatter diagrams, with Change_STI as the dependent variables, and Change_1y_Treasury as the independent variables, will be shown in Figure 4.3 below.

In Figure 4.3 below, the scatter diagram of Change_STI against Change_1y_Treasury is shown. It is found that statistically significant regression line is not something affected by a single outlier. This suggests that there are indeed certain meaningful relationships between rates of change in STI to changes in the yield of 1 year Treasury bill. The slope of the regression is negative, and this again, provides some empirical evidences that rates of change in STI are negatively related to changes in short term interest rates in Singapore.

### 4.2.4 Relationships between Change_STI and Change_10y_Treasury

In Table 4.7 below, the correlation coefficient between Change_STI and Change_10y_Treasury is shown. It is found that Change_STI is not statistically significant correlated to Change_10y_Treasury, at the 95% confidence interval. The sign of the correlation is positive, indicating that the rate of change in STI is positively related to changes in the yield of 10 year Treasury bill in Singapore. Such a finding is inconsistent with the previous findings, whereby interest rates are statistically significant and negatively related to stock returns. One of the possible reason giving rise to such a findings in Table 4.7 is perhaps investors in Singapore is not sensitive to the long term interest rates. Perhaps investors in Singapore are more short-term oriented, shifting their fund in reaction to changes in short term interest rates.

In Table 4.8 below, the ANOVA table for regression between Change_STI and Change_10y_Treasury is shown. It is found that the regression between Change_STI and Change_10y_Treasury is not statistically significant, even at 90% confidence interval. This is consistent with the previous finding, of non-statistically correlation relationship between the two variables.

In Figure 4.4 below, the scatter diagram of Change_STI against Change_10y_Treasury is shown. Similar to the statistical results presented above, the scatter plot does not suggest that reliable and dependable relationships exists between rates of change in STI to changes in the yield of 10 year Treasury bill. The slope of the regression is positive. However, the slope is negligible.

### 4.2.5 Relationships between Change_STI and Change_IPI

In Table 4.9 below, the correlation coefficient between Change_STI and Change_IPI is shown. It is found that Change_STI is not statistically significant correlated to Change_IPI, even at the 90% confidence interval. The sign of the correlation is negative, indicating that the rate of change in STI is negatively related to rate of change in IPI in Singapore. Nonetheless, due to the non-statistically significant findings, the sign of the correlation is simply not meaningful.

In Table 4.10 below, the ANOVA table for regression between Change_STI and Change_IPI is shown. It is found that the regression between Change_STI and Change_IPI is not statistically significant, even at 90% confidence interval. This is consistent with the previous finding, of non-statistically correlation relationship between the two variables.

In Figure 4.5 below, the scatter diagram of Change_STI against Change_IPI is shown. Similar to the statistical results presented above, the scatter plot does not suggest that reliable and dependable relationships exists between rates of change in STI to rate of changes in IPI The slope of the regression is negative. However, the slope is negligible â€“ and the positive slope coefficient can be confidently ignored.

### 4.2.6 Relationships between Change_STI and Change_CPI

In Table 4.11 below, the correlation coefficient between Change_STI and Change_CPI is shown. It is found that Change_STI is not statistically significant correlated to Change_CPI, even at the 90% confidence interval. The sign of the correlation is negative, indicating that the rate of change in STI is negatively related to rate of change in CPI in Singapore. Nonetheless, due to the non-statistically significant findings, the sign of the correlation is simply not meaningful.

In Table 4.12 below, the ANOVA table for regression between Change_STI and Change_CPI is shown. It is found that the regression between Change_STI and Change_CPI is not statistically significant, even at 90% confidence interval. This is consistent with the previous finding, of non-statistically correlation relationship between the two variables.

In Figure 4.6 below, the scatter diagram of Change_STI against Change_CPI is shown. Similar to the statistical results presented above, the scatter plot does not suggest that reliable and dependable relationships exists between rates of change in STI to rate of changes in CPI The slope of the regression is negative. However, the slope is to shallow, strongly suggesting that both the variables do not have any simple linear relationships in the context of Singapore.

### 4.2.7 Relationships between Change_STI and Change_M1

In Table 4.13 below, the correlation coefficient between Change_STI and Change_M1 is shown. It is found that Change_STI is not statistically significant correlated to Change_M1, even at the 90% confidence interval. The sign of the correlation is positive, indicating that the rate of change in STI is positively related to rate of change of M1 in Singapore. Nonetheless, due to the non-statistically significant findings, the sign of the correlation is simply not meaningful.

In Table 4.14 below, the ANOVA table for regression between Change_STI and Change_M1 is shown. It is found that the regression between Change_STI and Change_M1 is not statistically significant, even at 90% confidence interval. This is consistent with the previous finding, of non-statistically correlation relationship between the two variables.

In Figure 4.7 below, the scatter diagram of Change_STI against Change_M1 is shown. Similar to the statistical results presented above, the scatter plot does not suggest that reliable and dependable relationships exists between rates of change in STI to rate of changes in M1 The slope of the regression is positive. Although the slope is obvious, it is still not step enough to be considered having reliable relationships to stock returns in Singapore.

### 4.2.8 Relationships between Change_STI and Change_M2

In Table 4.15 below, the correlation coefficient between Change_STI and Change_M2 is shown. It is found that Change_STI is statistically significant correlated to Change_M2, even at the 90% confidence interval. The sign of the correlation is positive, indicating that the rate of change in STI is positively related to rate of change of M2 in Singapore. This is surprising, as from the previous analysis, it is found that Change_STI is not statistically significant correlated to Change_M1. In order to form better judgment on such inconsistent finding, scatter plot in the following section will be investigated in greater details.

In Table 4.16 below, the ANOVA table for regression between Change_STI and Change_M2 is shown. It is found that the regression between Change_STI and Change_M2 is statistically significant, at 95% confidence interval. This is consistent with the previous finding, of statistically correlation relationship between the two variables.

In Figure 4.8 below, the scatter diagram of Change_STI against Change_M2 is shown. A glance through the scatter plot indicates existence of reliable and dependable relationships exists between rates of change in STI to rate of changes in M2. However, for discerning researcher, it is easy to discover that such positive relationships are indeed due to a particular outlier. Checking back the data, the particular outlier happened during year 1998, whereby many of the South East ASEAN countries are severely hit by financial crisis. In order to combat the financial crisis, government of Singapore had increased the money supply significantly. At the particular point of time, the crisis is ending (nonetheless, we cannot confidently attribute the end of the crisis to increase of money supply), and the stock returns is positive and encouraging at the particular month. Thus, it is subjective if the outlier should be included in the analysis, or be excluded. If the outlier is excluded, the slope of the regression line will become shallower. Anyway, it is safe to conclude that there exist weak evidences supporting the notion that growth of money supply may affect stock returns positively (considering only M2 have statistically significant relationships with rate of change in STI).

### 4.2.9 Relationships between Change_STI and Change_M3

In Table 4.17 below, the correlation coefficient between Change_STI and Change_M3 is shown. It is found that Change_STI is not statistically significant correlated to Change_M3, even at the 90% confidence interval. The sign of the correlation is positive, indicating that the rate of change in STI is positively related to rate of change of M3 in Singapore. Nonetheless, due to the non-statistically significant findings, the sign of the correlation is simply not meaningful.

In Table 4.18 below, the ANOVA table for regression between Change_STI and Change_M3 is shown. It is found that the regression between Change_STI and Change_M3 is not statistically significant, even at 90% confidence interval. This is consistent with the previous finding, of non-statistically correlation relationship between the two variables.

In Figure 4.9 below, the scatter diagram of Change_STI against Change_M3 is shown. Similar to the statistical results presented above, the scatter plot does not suggest that reliable and dependable relationships exists between rates of change in STI to rate of changes in M3 The slope of the regression is positive.

## 4.3 Summary of Data Findings and Discussions

In this section, the relationships between the stock returns and the various macroeconomic variables will be summarized in Table 4.19 as follow. In the third column, the correlation coefficients between rates of changes in STI to the specific macroeconomic variables are presented. In the bracket, the p-values of the respective correlation coefficients are shown. Then, in the fourth column, the R-square of the regression between the changes in STI to the specific macroeconomic variables are shown. Higher R-square suggests that the specific macroeconomic variables are more powerful in explaining the variation in stock returns in Singapore.

As shown in table 4.19 above, it is found that interest rates is the economic variables that have most statistically significant relationships with stock returns. Out from four interest rates being investigated; only Change_10y_Treasury is not statistically significantly related to stock returns. The other interest rates related variables, such as Change_1m_interbank, Change_3m_interbank and Change_1y_Treasury has negative and statistically significant relationships with stock returns. Overall, this strongly suggests that short term interest rates are indeed negatively related to stock returns. Besides, the R-square of the regression between these short term interest rates and stock returns are also much higher than the other macroeconomic variable. Nonetheless, the low R-square value of 0.028, 0.040 and 0.030 is not truly encouraging. This indicates that short term interest rates variables should not be used solely in making rational investment decision, even in the context of Singapore stock market.

There are however, 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). Fortunately, all of the sign of the money supply is positive. This suggest that perhaps the best conclusion is that there is some weak evidences supporting that growth of money supply is positively related to rate of changes in STI.

Then, 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.

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