4.1 Descriptive Analysis
Table 4A have shown the summary statistics of the index returns of the selected emerging markets. The observed skewness statistics are negative for all markets, indicating their return distribution are negatively skewed. A negative value of skewness signals that the return distribution is not normally distribution as the normal distribution has a long tail on the left (Brooks, 2014). This could be further examined using Jacque-Bera normality test. Based on Table 4A, the null hypothesis of normality is not accepted at one percent significance level. On the other hand, the observed kurtosis statistics are positive for all markets, implying that the distribution is leptokurtic, where the tails are heavier and have sharper peak (DeCarlo 1997). This might be due to the fact that the past extreme events have greatly affected the index return. Since the distributions of stock returns are leptokurtic instead of normal distribution, dummy variable regression analysis might be sensitive to the extreme outliers and thus less robust (Kato and Schallheim 1985).
4.2 Examination of Seasonal Effect
Table 4B presents the result for the test of the existence of seasonal effect based on the estimation of regression models that correspond to regression equation (1) and (2). The estimation method is Cochrane-Orcutt after testing for autocorrelation and heteroskedasticiy. The regression coefficients along with their respective t-statistics are reported on the table. Analytically, seasonal effect exists in all countries except Malaysia, where none of the coefficients are statistically significant. January returns are statistically significant at 5 percent level for Philippines, indicati...
... middle of paper ...
...istical analysis has been carried out to test the difference between the low and high portfolio.
Referring to the table below, since t-ratio (-0.506247) is greater than t Critical (162) at 5% significance level (1.97472), do not reject null hypothesis. The daily mean return of low portfolio in January does not statistically differ from high portfolio. Hence, there is no credible evidence can tell whether the January effect in Philippines stock market is driven by tax-loss selling, but there is no proof to show tax-loss selling cannot explain the January effect since the daily mean return of high portfolio has shown a tendency to rise during the month of December and then continue to rally throughout the month of January. In short, tax-loss selling hypothesis might be the possible reason to explain part of January effect in Philippines stock market, but not fully.
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