In this paper, January anomaly and other monthly return patterns have been examined in the Ghanaian stock market. The sample period under study was divided into two periods. The first period covered the duration when the Ghanaian market was trading only three times within a week and the second sample period also covered the duration when the exchange was trading for five days within a week. The main idea behind this division was to capture and compare the efficiency of the market at these two distinctive periods by examining the pattern of monthly return. The paper employed the logarithmic return of the Ghana BMI indices (dollar) from the stock market and different financial models were applied to examine same.
Also, in an attempt to select the best model to account for return and volatility in the Ghanaian stock market, the symmetric GARCH (1, 1) and two other asymmetric models (EGARCH and GJR) were estimated. The various statistical properties from the estimated models were examined and the standardized residuals were also checked for each model. The result from most of the statistical properties and residual diagnostics checks favored the standard GARCH (1, 1) as the best model to account for return and volatility attributes of the market for the periods under examination. This finding is consistent with the previous conclusion reached in a study conducted on the Ghanaian bourse by Frimpong and Oteng-Abayie (2006) which also concluded that the parsimonious GARCH (1, 1) model is the best specification to account for return and volatility in the market as there is no dissimilar reaction of investors towards good and bad news (no leverage effect) in the market.
Analysis of the dummies which represent the various months from the estimated GARCH (1, 1) model records no January effect or any other type of monthly anomaly for the first period. The Wald test applied to the estimated model also failed to reject the null hypothesis that the return coefficients for the various months are approximately equal. The policy implication of this finding is that investors trading in the market within this period should not consider monthly effects when forming their portfolio since there no information to exploit. However, the absence of any form of monthly anomaly in the market during this period does not necessarily imply that the market is efficient in its weak form. This assertion is informed by the fact that the moving average (MA (1)) parameter included in the mean equation is statistically significant, denoting the presence of serial correlation in the market.
On the contrary, the second period recorded significant anomalous positive returns in the months of January, April, May and June. These months serves as opportune periods for investors to sell stocks to benefit from excess abnormal returns. Moreover, the month of March and July also recorded significant negative returns during the same period. Savvy investors can buy stocks at low prices during those periods with negative returns and sell them for high arbitrage profits in the month of January, April, May and June when returns are very high. Again, investors in the market should exploit this opportunity with caution because of the significance of the moving average parameter. The existences of such market timing opportunities which are readily available to be exploited by prudent investors suggest that the Ghanaian stock market is not informationally efficient in accordance with the efficient market hypothesis.
Moreover, it should be emphasized that per the analysis from the pattern of return in the Ghana bourse, the question of whether the market is efficient is decided by specifying a particular time period. This is inferred from the fact that there are periods without monthly anomalous returns whilst there still exist periods with significant monthly abnormal returns. Variations in time periods as well as differences in the source of stock market data employed in various study on the Ghanaian bourse may be some of the reasons why there are conflicting conclusions on the efficiency or the presence of monthly anomaly on the Ghanaian stock market. Also, because of the mixed nature of the findings from this study, none of the reasons or theories proffered to explain the existence of month anomaly by earlier researchers can wholly be considered appropriate to support the pattern of stock return in the Ghanaian bourse.
Again, there exist an opportunity for improvement on this topic in future research work using the Ghanaian bourse as the case study area. In the first place, the study will be enhanced if the main index which is the Ghana All Share Index is used instead of the proxy indices such as the Ghana BMI and the Databank Indices which are commonly used in current and previous studies on the Ghanaian stock market. Prospective future researchers may have access to high frequency data and with enough time spans to use the Ghana All Share Index contrary to the present constraints imposed on the current and previous researchers on the market.
Also, it is possible for this study to be improved upon in future research work by using daily or weekly data instead of the monthly data used for this study. These high frequency data will be capable of capturing most of the dynamic structures inherent in the series as against the currently available monthly data used for this study.
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APPENDIX 1 WALD TEST FOR FIRST PERIOD MONTHLY