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Stock Market Anomalies: Evidence from Emerging BRIC Markets

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  • Satinder Pal Singh

Abstract

The study investigates and identifies the presence of stock market anomalies for four emerging stock markets of Brazil, Russia, India and China (BRIC) for the period 1 January 2003–15 June 2013. The article uses dummy regression model to examine the existence of ‘Day of the Week’ (DOW) and Month of the Year (MOY) effects of stock market returns of BRIC countries. The study supports the efficient market hypothesis in DOW and MOY form for Brazil, Russia and India. However, it demonstrates statistically significant negative return on Tuesday for Chinese stock market, although no anomaly is found in MOY form. This indicates that an investor may leverage the knowledge of such abnormal returns to enter the stock market a day earlier and make profits. At the same time the findings may also be used by the regulatory agencies of China to investigate why these patterns exist and take necessary actions to allow efficient functioning of the market. The study challenges some of the previous studies of DOW and MOY anomalies for India and Russia.

Suggested Citation

  • Satinder Pal Singh, 2014. "Stock Market Anomalies: Evidence from Emerging BRIC Markets," Vision, , vol. 18(1), pages 23-28, March.
    • Handle: RePEc:sae:vision:v:18:y:2014:i:1:p:23-28
    DOI: 10.1177/0972262913517329
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    References listed on IDEAS

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    Cited by:

    1. Atsin, Jessica A.L. & Ocran, Matthew K., 2015. "Calendar effects and market anomalies on the Johannesburg Stock Exchange," MPRA Paper 87448, University Library of Munich, Germany.
    2. Shuang Feng & Jon T. Stewart, 2015. "A Review of Market Segmentation and Inefficiencies of the Chinese Stock Market," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 4(4), pages 18-28, October.
    3. Alin Marius ANDRIEŞ & Iulian IHNATOV & Nicu SPRINCEAN, 2017. "Do Seasonal Anomalies Still Exist In Central And Eastern European Countries? A Conditional Variance Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 60-83, December.

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