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The Adaptive Dynamics of the Halloween Effect: Evidence from a 120-Year Sample from a Small European Market

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  • Júlio Lobão

    (Portugal School of Economics and Management and CEF.UP, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

  • Ana C. Costa

    (School of Economics and Management, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal)

Abstract

The Halloween effect predicts that stock markets in the winter months (November through April) generate significantly higher returns than in the summer months (May through October). This paper examines the time-varying behavior of the Halloween effect within a new historical dataset that covers about 120 years of Portuguese stock market history. We combine subsample analysis with rolling window analysis to show that the performance of the anomaly has varied in an adaptive fashion over time. The anomaly existed during the first four decades of the 20th century. Afterward, it vanished for 60 years, reappearing only at the beginning of the 21st century. However, in the first two decades of the new century, the effect seems to be a mere reflection of the excess return generated in January. Overall, the time-varying performance of the Halloween effect supports the adaptive market hypothesis for the Portuguese stock market.

Suggested Citation

  • Júlio Lobão & Ana C. Costa, 2023. "The Adaptive Dynamics of the Halloween Effect: Evidence from a 120-Year Sample from a Small European Market," IJFS, MDPI, vol. 11(1), pages 1-11, January.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:1:p:13-:d:1026091
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    References listed on IDEAS

    as
    1. Haggard, K. Stephen & Witte, H. Douglas, 2010. "The Halloween effect: Trick or treat?," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 379-387, December.
    2. Ali F. Darrat & Bin Li & Benjamin Liu & Jen Je Su, 2011. "A Fresh Look at Seasonal Anomalies: An International Perspective," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 10(2), pages 93-116, August.
    3. Edwin D. Maberly & Raylene M. Pierce, 2004. "Stock Market Efficiency Withstands Another Challenge: Solving the "Sell in May/Buy after Halloween" Puzzle," Econ Journal Watch, Econ Journal Watch, vol. 1(1), pages 29-46, April.
    4. Maria Caporale, Guglielmo & Zakirova, Valentina, 2017. "Calendar anomalies in the Russian stock market," Russian Journal of Economics, Elsevier, vol. 3(1), pages 101-108.
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