Seasonality in stock markets is a regular and repetitive phenomenon occurring at some regular intervals of time, which may generate abnormal or excess returns. This paper explores the existence of seasonality in Indian stock market in four forms, namely, day-of-the-week effect, month-of-the-year effect, quarterly effects, and monthly effects. For this purpose, S&P CNX Nifty was taken as the sample. The daily closing, opening, high and low prices were collected from November 3, 1995 to May 31, 2013. ADF test was used for checking stationarity, whereas a dummy variable regression was used for testing seasonality. It was found that all the four effects are present in the Indian stock market. The returns of September, Monday, first quarter and first-half of the month were significantly different. Thus the existence of seasonality in Indian stock markets was proved. All the four effects tested for Nifty indicate that seasonality has changed over the years.

There is a seasonal bias to the stock market, and by paying attention to the seasonal market tendencies you can gain an edge in the stock market over the long haul. Seasonality offers a practical approach to investing and trading. What better way to learn how to employ seasonal systems than learning from Jay Kaeppel, a master in the analysis of seasonal trends? Kaeppel walks you through this phenomenon that continues to work consistently, providing you with his ultimate seasonal index to make the calendar work for you. Stock Market Seasonals provides a never-before-seen definitive guide that illustrates how to utilize a combination of four basic seasonal tendencies in order to maximize returns.

The paper investigates Indian momentum profitability along with its performance stability round the year using the stock price data from National Stock Exchange (NSE). Results show evidence in favor of momentum profitability over the sample period from 1997 to 2013. Moreover, the momentum performance is not specific to any particular month suggesting no influence of calendar effects on momentum anomaly in the Indian stock market. Though, momentum strategies performed differently in different calendar months, with particularly strong negative returns in the month of May. However, no statistically significant difference was observed among the mean monthly momentum returns across calendar months. Contrary to the US market findings, no January or similar April seasonality is observed in the Indian momentum profits suggesting some unique characteristics of Indian momentum profitability. In nutshell, the results from the study suggest support in favor of practical implementation of momentum strategies throughout the year in the Indian stock market.

The presence of the Seasonal or Monthly Effect in stock returns has been reported in several developed and emerging stock markets. This study investigates the existence of seasonality in India's stock market. The Efficient Market Hypothesis suggests that all securities are priced efficiently to fully reflect all the information intrinsic in the asset. The Seasonal Effects create higher or lower returns depending on the Time Series. They are called Anomalies because they cannot be explained by traditional asset pricing models. Examples of such patterns include e.g. the January Effect, the Day-of-the Week Effect and the Week of the Month Effect etc. Studies on the Seasonal Effects in the Indian Stock Market are limited. In an attempt to fill this gap, this study explores the Indian Stock Market's Efficiency in the 'weak form' in the context of Seasonal Effects. The objective of this paper is to explore the Seasonal Effect on the Indian Stock Market. For the purpose this analysis BSE Sensex index was chosen for a period of ten years from 1st April 2000 to 31st March 2010. The study found that the Day of the Week Effect and Monthly Effect Pattern did not appear to exist in the Indian Stock Market during the study period.

This paper investigates the existence of stock returns seasonality on the Nigerian Stock Exchange (NSE). Regression-based approach was used to analyse the monthly stock return data for seasonal pattern from January 1985 to March 2011. The full sample was divided into three sub-sample periods which covers stock market events in Nigeria. Results obtained from the study indicate that the highest return occurs in the month of May. The sub-sample III results support the month of May highest return. Results from sub-sample I and II periods however show that the highest return occur in June. Further analysis reveals that the coefficients of the twelve months of the year are jointly statistically equal to zero, indicating evidence against January effects or any monthly pattern in the NSE returns.

Despite an extensive number of studies documenting evidence of seasonal anomalies in developed markets, only a few studies have comprehensively examined these anomalies within emerging markets. Testing the robustness of seasonal anomalies in emerging markets would first, help to examine the theoretical explanations that have been proposed and second, provide an out-of-sample result for these seasonality anomalies. This study examines the efficiency of advanced emerging markets by testing five seasonal anomalies: the month of the year, other January, day-of-the-week, holiday, and week 44. Evidence is reported that is consistent with all of these seasonal anomalies with the exception of the other January effect; supporting the argument that advanced emerging markets are less than perfectly efficient.