Can Tax‐Loss Selling Explain the January Effect? A Note

Jones, Charles P.; Pearce, Douglas K.; Wilson, Jack W.

doi:10.1111/j.1540-6261.1987.tb02577.x

Cited by 69 publications

(36 citation statements)

References 25 publications

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“…According to these studies more proof for or against seasonal anomalies can only be verified by investigating markets during different time periods. Pettengill (1986) and Jones et al (1987) using similar pre-WWI period US stock return and linear ordinary least squares (OLS) method find evidence of a Further, as indicated by Connolly (1989) and de Jong et al (1992) most empirical studies dealing with stock market anomalies are usually carried out under the assumption that the error term and hence the returns follow a normal distribution with constant variance. And according to Connolly (1989) the error term from regressions involving stock returns are almost certainly not normally distributed.…”

Section: Introductionmentioning

confidence: 96%

Month of the year effect and January effect in pre‐WWI stock returns: evidence from a non‐linear GARCH model

Choudhry

2001

Int. J Fin Econ

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This paper investigates seasonal anomalies in the mean stock returns of Germany, the UK and the US during pre-World War I (WWI) period. The anomalies studied are month of the year effect and the January effect. The empirical research is conducted using a non-linear GARCH-t model, and monthly returns. Results obtained provide evidence of the January effect and the month of the year effect on the UK and the US returns. The German returns shows the month of the year effect but no January effect. Given the lack of tax treatment of capital gains/loss before 1914 by these countries, the results fail to provide merit to the tax-loss selling hypothesis of the January effect. Since we apply value-weighted returns in all cases, results obtained also fail to provide support for the small firm effect.

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Section: Introductionmentioning

confidence: 96%

Month of the year effect and January effect in pre‐WWI stock returns: evidence from a non‐linear GARCH model

Choudhry

2001

Int. J Fin Econ

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“…1 Evidence which appears to contradict the tax-loss hypothesis, at least as the sole explanation, has been provided in US studies by Pettengill (1986), Jones, Pearce & Wilson (1987) and Brailsford & Easton (1993). In a recent study on a restricted sample of UK companies over the period January 1988 to December 1994, Draper & Paudyal (1996) suggest that the pattern of trading activity supports the hypothesis of tax-driven trading by individual investors around April.…”

Section: Previous Studiesmentioning

confidence: 87%

Firm Size, Monthly Seasonalities and Tax-Loss Selling: Further Evidence From the Uk

Baker

Limmack

1998

The British Accounting Review

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“…This hypothesis was proposed by Ritter and Chopra (1989). However, the portfolio re-balancing hypothesis is inconsistent with the findings of Jones, Pearce and Wilson (1987) who show high January returns existed since 1871, long before institutional investors played a major role in the financial markets in the US. Further, if window dressing is a relevant factor, presumably portfolios should be rebalanced by sacrificing stocks that show losses, not just those experiencing gains.…”

Section: Notesmentioning

confidence: 90%

Is Beta Dead or Alive?

Hsia

Fuller

Chen

2000

Business Fin & Account

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The debate about whether beta is dead or alive has heated up once again. We believe the empirical work supporting either side of the argument is limited because market frictions are not adequately addressed. This study clarifies the controversy about the issue by creating a new moving-average beta and analyzing two market anomalies: the turn-of-the-year and the Monday effects. What is discovered in this research is (1) that a fundamental source of the problem underlying the two types of market anomalies is the persistence of market frictions that retard the arbitrage process; (2) that beta is seriously ill if the effects of market frictions are ignored; and (3) that beta is alive and well if the effects of market frictions are accommodated. Also, we show, by using an optimal lead/lag structure, the moving-average beta provides significantly higher explanatory power for the turn-of the-year and the Monday effects than betas created from ordinary least squares regression and Scholes-Williams and Fama-French methods because the moving-average beta accommodates the effects of market frictions into the body of beta itself. This new type of beta, a moving-average beta, is demonstrated to be robust. Copyright Blackwell Publishers Ltd 2000.

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Can Tax‐Loss Selling Explain the January Effect? A Note

Cited by 69 publications

References 25 publications

Month of the year effect and January effect in pre‐WWI stock returns: evidence from a non‐linear GARCH model

Month of the year effect and January effect in pre‐WWI stock returns: evidence from a non‐linear GARCH model

Firm Size, Monthly Seasonalities and Tax-Loss Selling: Further Evidence From the Uk

Is Beta Dead or Alive?

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