Academic journal article IUP Journal of Applied Finance

Calendar Anomalies in National Stock Exchange Indices

Academic journal article IUP Journal of Applied Finance

Calendar Anomalies in National Stock Exchange Indices

Article excerpt

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Seasonalities or calendar anomalies are well documented and are perhaps the best-known examples of inefficiencies in the financial markets. It may be in terms of seasonal effects over the day-of-the-week, the months of the year, or over specific years. Evidence of such seasonalities is readily available for the well-established stock markets in the developed economies, as well as in some emerging market countries. While the studies of Keim (1983), Jaffe et al. (1985), and Ariel (1987) revealed the existence of a monthly effect on the US and other developed markets, studies by Rozeff and Kiney (1976), Gulketin and Gulketin (1983), Keim (1983), and Reinganum (1983) revealed the existence of a January effect, where returns in January tend to be larger than returns in other months.

The main argument proposed is the tax-loss selling hypothesis where investors sell in December and buy back in January such that returns are higher at the beginning of the year. Essentially, the tax-loss hypothesis is supported in most countries where the tax year ends in December. For instance, the months of year effect would exist if returns on a particular month are higher than that in other months. This negates the notion of efficiency in markets since traders are able to earn abnormal returns by examining the pattern of monthly returns and framing trading strategies accordingly. Essentially, this entails an inefficient market situation where returns are not proportionate with risk.

The purpose of this study is to investigate the existence of a day-of-the-week effect (weekend effect), financial year effect (April effect) in the NSE indices and frame the trading strategy.

Literature Review

A number of studies have been carried out to examine the stock market seasonality. Seasonalities or calendar anomalies are different share market returns at distinct cusps in time such as on select days of the week, periods of the month or turn-of-the-year, overreaction and underreaction. Wachtel (1942) was the first economist to examine and document seasonality in the Dow Jones Industrial Average for the period 1927-42. He found that there was a January effect in the market as a result of bullish tendency from December to January. These anomalies are well-documented through research for different markets and different periods for three decades. However, the study by Lindley et al. (2004) reveals that many years during the period 1962-2000 did not have a significant January effect, and in fact, some years even had a negative January effect. Bernstein's study (1999) reveals that January effect is moved into November and December; this was due to the reporting of holdings by the mutual funds in October, which induces the individual investor to go for the same kind of portfolio. It has led to the notion that the January effect may have moderated or relocated to the other months of the year in recent periods.

Daily stock returns have been found to be higher in the first-half relative to the last-half of the trading month which is known as Half-Month (turn-of-the-month) effect. Agrawal and Tandon (1994) and Hensel and William (1996) documented that stocks consistently have higher returns on the last day and first four days of the month due to bulk inflow of cash by way of salary, interest payment, etc.

The day-of-the-week effect in Indian market was examined by Chaudhury (1991), Poshakwale (1996), and Goswami and Anshuman (2000). They used serial autocorrelation tests and Ordinary Least Squares (OLS) fitting. Choudhry (2000) examined the seasonality of returns and volatility under a unified framework, but the study has a misspecification issue with regard to conditional mean. Bhattacharya et al. (2003) used the GARCH framework by incorporating the lagged returns (BSE 1001) as explanatory variables in the conditional mean. They used reporting and non-reporting weeks to study the day-of-the-week effect. …

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