This paper examines whether the 'January anomaly' or 'seasonality of monthly returns' found in several advanced markets is also found in the fast developing Indian markets. Any anomaly, which includes January anomaly or effect, would enable the investors and speculators to gain abnormal returns. Although the presence of January anomaly defeats the basic premises of the efficient market hypothesis, it has greater implications to design suitable investment strategies in the long run. We use the logarithmic data of the five most important indices of the National Stock Exchange of India (NSE) for the period from 1999 to 2007 and apply a set of selected statistical parameters to examine the presence of anomaly, if any, in the market. Our analytical results indicate the presence of 'January anomaly' in Samp;P CNX Nifty which is the benchmark index of the NSE. Kruskal-Wallis test shows statistically significant differences in monthly returns in respect of three indices while Wilcoxon-Mann-Whitney test reveals statistically significant differences in the month of April, November and December when compared to January returns. Dummy Variable Regression, yet another test applied to investigate the January anomaly, also reveals statistically significant results in monthly returns. Friedman Anova test suggests that seasonality in stock returns is present in the case of only one index, Samp;P Nifty Jr. Our findings corroborate the results of previous evidences documented in the literature. Our investigation further reveals that March and April turn significant negative returns but prove to be the potential months to buy the scrips (buy low); Contrary to this, November and December show significant positive high returns goading us to conclude that these two months are the best period to sell the securities (sell high). Tax-loss selling hypothesis and Accounting-information hypothesis could be the possible explanations for the anomalous behavior of the scrips in the Indian markets. In a nutshell, our results indicate that the Indian markets show evidences of seasonal anomalies and offer enormous opportunities to gain reasonable returns in the long-run.

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 study of security market imperfections, namely the predictability of equity stock returns, is one of the fundamental research areas in financial modelling. These anomalies, which are not consistent with existing theories, concern the relation between stock returns and variables, such as firm size and earnings-to-price ratios, and seasonal effects, such as January and turn-of-the-month. This book provides the most complete and current account of work in the area. Leading academics and investment researchers have combined to produce a comprehensive coverage of the subject, including both cross-sectional and time series analyses, as well as discussing the measurement of risk and prediction models that have been used by institutional investors. The studies cover many worldwide markets including the US, Japan, Asia, and Europe. The book will be invaluable for courses in financial engineering, investment and portfolio management, and as a reference for investment professionals seeking an up-to-date source on return predictability.

This book makes two key contributions to empirical finance. First it provides a comprehensive analysis of the Thai stock market. Second it presents an excellent exposition ofhow modem econometric techniques can be utilised to understand a market. The increasing globalisation of the world's financial markets has made our un derstanding of the risk-return relationship in a broader range of markets critical. This is particularly so in emerging markets where market depth and liquidity are major issues. One such emerging market is Thailand. The Thai capital market isof particular interest given that it was the market in which the Asian financial crises commenced. As such an understanding ofthe Thai capital market via study of the pre and post-crisis periods enables one to shed light on one of the major financial markets events of recent times. This book provides a quantitative analysis of the Thai capital market using some very useful and recent econometric techniques. The book provides an over view of the Thai stock market in chapter 2. Descriptive statistics and time series models (moving average, exponential smoothing, ARIMA) are presented in chap ter 3 followed by market efficiency tests based on autocorrelations in chapter 4. A richer set of models is then considered in chapters 5 through 8. Chapter 5 finds a cointegrating relationship between macroeconomic factors and stock returns.

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.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.