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A note on the turn of the month and year effects in international stock returns

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  • Mohammed S. Khaled
  • Stephen P. Keef

Abstract

This examination of the turn of the month (TOM) and turn of the year (TOY) effects in 50 international stock indices, for the period 1994--2006, characterises the degree that the effects are influenced by: (i) the gross domestic product of the economy, (ii) the sign of the return on the prior day (called the prior day effect), (iii) a temporal indicator and (iv) the Monday effect. These effects are assessed by the use of an estimated generalised least squares (EGLS) panel regression model incorporating panel-corrected standard errors. Three important results relating to the TOM and TOY effects are observed. When the prior day effect on control days is used as the reference and controls are made for market development and year, we find that: (i) there is a relatively enhanced return on all TOM days, (ii) there is a relatively enhanced return on good TOY days and (iii) returns of bad TOY days are not remarkable.

Suggested Citation

  • Mohammed S. Khaled & Stephen P. Keef, 2012. "A note on the turn of the month and year effects in international stock returns," The European Journal of Finance, Taylor & Francis Journals, vol. 18(6), pages 597-602, July.
    • Handle: RePEc:taf:eurjfi:v:18:y:2012:i:6:p:597-602
    DOI: 10.1080/1351847X.2011.617379
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    Cited by:

    1. Gkillas, Konstantinos & Vortelinos, Dimitrios I. & Babalos, Vassilios & Wohar, Mark E., 2021. "Day-of-the-week effect and spread determinants: Some international evidence from equity markets," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 268-288.
    2. Maher, Daniela & Parikh, Anokhi, 2013. "The turn of the month effect in India: A case of large institutional trading pattern as a source of higher liquidity," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 57-69.
    3. Urquhart, Andrew & McGroarty, Frank, 2014. "Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run U.S. data," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 154-166.
    4. Marcos Albuquerque Junior & José António Filipe & Paulo de Melo Jorge Neto & Cristiano da Costa da Silva, 2021. "Assessing the Time-Frequency Co-Movements among the Five Largest Engineering Consulting Companies: A Wavelet-Base Metrics of Contagion and VaR Ratio," Mathematics, MDPI, vol. 9(5), pages 1-16, March.
    5. KUMAR Satish, 2017. "A Review On The Evolution Of Calendar Anomalies," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 12(1), pages 95-109, April.
    6. Lobão, Júlio, 2019. "Seasonal anomalies in the market for American depository receipts," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 24(48), pages 241-265.
    7. Chhabra, Damini & Gupta, Mohit, 2022. "Calendar anomalies in commodity markets for natural resources: Evidence from India," Resources Policy, Elsevier, vol. 79(C).
    8. Berger, Theo & Gençay, Ramazan, 2018. "Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 30-46.

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