Can you capitalize on the turn-of-the-year effect?

Beyer, Scott; García‐Feijóo, Luis; Jensen, Gerald R.

doi:10.1080/09603107.2013.831168

Cited by 10 publications

(14 citation statements)

References 33 publications

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“…A confirmation of window dressing hypothesis would imply that the seasonality is caused primarily by the institutional investors (Henker and Paul, 2012). Ritter and Chopra (1989), Barone (1990), Athanassakos and Schnabel (1994), Porter et al (1996), Ignatius (1998), Parikh (2009), Beyer et al (2013) and Sharma and Narayan (2014) presented this hypothesis as a possible explanation.…”

Section: Calendar Anomalymentioning

confidence: 94%

Calendar anomaly: unique evidence from the Indian stock market

Harshita

Singh

Yadav

2018

JAMR

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Purpose The purpose of this paper is to ascertain the monthly seasonality in the Indian stock market after taking into consideration the market features of leptokurtosis, volatility clustering and the leverage effect. Design/methodology/approach Augmented Dickey-Fuller, Phillips-Perron and Kwaitkowski-Phillips-Schmidt-Shin tests are deployed to check stationarity of the series. Autocorrelation function, partial autocorrelation function and Ljung-Box statistics are employed to check the applicability of volatility models. An exponential generalized auto regressive conditionally heteroskedastic model is deployed to test the seasonality, where the conditional mean equation is a switching model with dummy variables for each month of the year. Findings Though the financial year in India stretches from April to March, the stock market exhibits a November effect (returns in November are the highest). Cultural factors, misattribution bias and liquidity hypothesis seem to explain the phenomenon. Research limitations/implications The paper endeavors to provide a review of possible explanations behind month-of-the-year effect documented in literature in the past four decades. Further, the unique evidence from the Indian stock market supports the argument in the literature that monthly seasonality, by nature, may not be a consistent/robust phenomenon. Therefore, it needs to be examined from time to time. Originality/value As the seasonality in the stock market and resultant anomalies are dynamic phenomena, the paper reports the current seasonality/anomalies prevalent in the Indian market. This would aid investors in designing short-term investment portfolios (based on anomalies present) in order to earn abnormal returns.

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Section: Calendar Anomalymentioning

confidence: 94%

Calendar anomaly: unique evidence from the Indian stock market

Harshita

Singh

Yadav

2018

JAMR

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“…Jacobsen and Zhang (2013) study more than 300 years of UK stock returns and find that the January effect appears around 1830, when Christmas became a public holiday; however, is no longer significant from 1951 to 2009. However, Beyer et al (2013) find no evidence against January effect as they show that a portfolio comprised of small and unfavorable stocks outperformed the market 45 out of 47 Januaries. Moller and Zilca (2008) examine the development of daily pattern of January effect.…”

Section: The January Effectmentioning

confidence: 90%

A Review on the Evolution of Calendar Anomalies

Kumar

2017

Studies in Business and Economics

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“…Then their repurchase in January increases their price -hence, the high January returns. Evidence for such a scenario can be found in Beyer, Garcia-Feijoo, and Jensen (2013), who show that a trading strategy targeting small, out of favor firms achieves superior return performance in January.…”

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confidence: 99%