The Efficient Market hypothesis is a cornerstone of modern investment theory that essentially advocates the futility of information in generation of abnormal returns in capital markets over a period of time. However, the existence of anomalies challenge the notion of efficiency in stock markets. Calendar effects, in particular, violate the weak form of efficiency, highlighting the role of past patterns and seasonality in estimating future prices. The present research aims to study the efficiency in Indian stock markets. Using daily and monthly returns of NIFTY 50 data from its inception in January 1995 to December 2015, we employ dummy variable multiple linear regression technique to assess the existence of calendar effects in India stock markets. To correct for volatility clustering and ARCH effect present in the daily returns, the results are modeled using the EGARCH estimation methodology. The study reveals the existence of calendar effects in India in form of a significant Wednesday Effect as well as a significant 'December effect', thereby suggesting that the Indian stock markets do not show informational efficiency even in the weak form, a trait observable in emerging markets.

The Efficient Market Hypothesis believes that it is impossible for an investor to outperform the market because all available information is already built into stock prices. However, some anomalies could persist in stock markets while some other anomalies could appear, disappear and re-appear again without any warning. A Special Issue on "Efficiency and Anomalies in Stock Markets" will be devoted to advancements in the theoretical development of market efficiency and anomaly in the Stock Market, as well as applications in Stock Market efficiency and anomalies.

Any anomaly, including January Anomaly, would enable the investors and speculators to gain abnormal returns. The presence of January Anomaly defeats the basic premises of the efficient market hypothesis. Besides, it has greater implications for the design of investment strategy in the long run. This paper seeks to find out whether the 'January Anomaly', found in many countries, is also found in the fast developing Indian Markets. The study used the logarithmic data for S&P CNX Nifty and S&P CNX 500 sample indices and applied the Dummy Variable Regression Model from 1st April 2002 to 31st March 2011. It is found that the highest mean return was earned in December and the lowest/ negative mean return earned in January Month for S&P CNX Nifty index. The S&P CNX 500 Index recorded the Highest Mean Return in the Month of March and the Highest Negative Mean Returns in the Month of January. It is found that there was significant difference in the mean returns among the different months of the year. The analytical results of seasonality indicate the absence of January Anomaly during the study period.

The efficiency of the capital market raises various issues all over the world. Earlier research studies give evidence that the capital markets are informational efficient and hence, cannot outperform the market consistently on the basis of price change predictions. However, some researchers have also brought into light seasonal effects/calendar anomalies in the developed markets. This paper investigates one such anomaly (Semi-month and Turn of the month effects) in an emerging Indian Capital Market. The S&P CNX Nifty and BSE Sensex Index data have been collected and analyzed for a period of six years from 1st January 2005 to 31st December 2010. The analysis of the study found that the semi-month and turn of the Month Effect not exists in Indian Stock Market during the study period.

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.

In this study we try to briefly revise the day of the week effect (DOW) and to examine why there are conflicting empirical results through the time. Moreover, we try to add a new-alternative view to the specific area of study, adding a further possible explanation in calendar anomalies field of study. Specifically, we try to examine if investors' weekday behavior changes depend on the financial trend. For example, let suppose that there are evidence that Mondays are positive returns days, but there are signs for an upcoming financial crisis. Could this general believed practical rule be strong enough in order to be sustainable even during financial crisis period or does it change? In order to analyze this issue providing empirical support, we examine the US stock market and the S&P index for the time period 2000-13. The results confirm our assumption that the financial trend influences the weekly stock returns' pattern, which may be an alternative explanation for the conflicting empirical findings that have been documented in the literature up today.