| Pub. Date | : Dec, 2019 |
|---|---|
| Product Name | : The IUP Journal of Financial Risk Management |
| Product Type | : Article |
| Product Code | : IJFRM21912 |
| Author Name | : Nisha Jindal |
| Availability | : YES |
| Subject/Domain | : Finance Management |
| Download Format | : PDF Format |
| No. of Pages | : 07 |
Calendar anomalies is a situation seen a lot in any stock market. As for the Indian stock market, calendar anomalies are a normally seen and used practice there. This study considers a few calendar patterns available in the Indian stock market. The daily closing prices of the index, S&P CNX 500, were taken for the sample period of 11 years from January 2008 to December 2018, collected from the official website of National Stock Exchange. Daily closing prices were converted into returns. The Ordinary Least Square (OLS) and Generalized Autoregressive Conditional Heteroscedastic (GARCH) models were applied for data analysis. The results proved that seasonality was prevalent in Indian stock market. Friday effect was found as the returns of Friday were higher as compared to other days because Friday is considered as the last day of the week, and after that the market remains closed for two days.
It is very difficult to consider any particular reason ultimately responsible for the seasonality in stock market. It is believed that investors prefer to sell more on Monday because they would like to adjust the impact of information received in the previous week, as generally bad news are released on Friday after the closing of the market. So, day-of-the-week effect is a normal practice which is observed in equity market, and there is disparity on the issue whether calendar effects exist or not. Sullivan et al. (2001) argued in their paper that there is no statistically significant evidence for calendar effects in the stock market, and that all such patterns are the result of data dredging. Against this backdrop, the present paper attempts to estimate the seasonality of S&P CNX 500 returns of NSE on the basis of data of 11 years from January 2008 to December 2018.