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Outliers and the Halloween Effect: Comment on Maberly and Pierce

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  • H. Douglas Witte

Abstract

Maberly and Pierce (2004) re-examine the work of Bouman and Jacobsen (2002) that documents significantly lower monthly stock market returns over the period May to October than over the period November to April. The finding has been called the Halloween effect and is present to varying degrees in most equity markets worldwide. Maberly and Pierce focus on the Halloween effect in the United States and contend it is driven by two negative-return outliers. We argue that controlling for two outliers is somewhat arbitrary. We apply robust regression methods—including all the data but limiting the influence of extreme returns—to the estimation of the Halloween effect in the United States. Contrary to the Maberly and Pierce findings, our results indicate statistical significance of a Halloween effect in the U.S. at levels similar to those originally reported in Bouman and Jacobsen.

Suggested Citation

  • H. Douglas Witte, 2010. "Outliers and the Halloween Effect: Comment on Maberly and Pierce," Econ Journal Watch, Econ Journal Watch, vol. 7(1), pages 91-98, January.
  • Handle: RePEc:ejw:journl:v:7:y:2010:i:1:p:91-98
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    References listed on IDEAS

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    1. Unknown, 2010. "Table of Contents," 2010 Conference: Modern management challenges in the agro-food sector, March 18-19, Pivola, Slovenia 183902, Slovenian Association of Agricultural Economists (DAES).
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    4. 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.
    5. Editorial Article, 0. "Contents," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    6. Ejis, 2010. "Table of Contents," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 02, June.
    7. Ejis, 2010. "Table of Contents," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 01, March.
    8. Sven Bouman & Ben Jacobsen, 2002. "The Halloween Indicator, "Sell in May and Go Away": Another Puzzle," American Economic Review, American Economic Association, vol. 92(5), pages 1618-1635, December.
    9. Editorial Article, 0. "Contents," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    10. Lucey, Brian M & Zhao, Shelly, 2008. "Halloween or January? Yet another puzzle," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1055-1069, December.
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    Cited by:

    1. Dichtl, Hubert & Drobetz, Wolfgang, 2015. "Sell in May and Go Away: Still good advice for investors?," International Review of Financial Analysis, Elsevier, vol. 38(C), pages 29-43.
    2. Peter Arendas & Viera Malacka & Maria Schwarzova, 2018. "A Closer Look at the Halloween Effect: The Case of the Dow Jones Industrial Average," IJFS, MDPI, vol. 6(2), pages 1-12, April.
    3. Dragos Stefan Oprea, 2014. "The Halloween Effect Evidence from Romania," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(7), pages 463-471, July.
    4. Carrazedo, Tiago & Curto, José Dias & Oliveira, Luís, 2016. "The Halloween effect in European sectors," Research in International Business and Finance, Elsevier, vol. 37(C), pages 489-500.

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    More about this item

    Keywords

    Halloween effect; outliers; influence vector; robust regression;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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