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Herding by Foreign Institutional Investors: An Evidential Exploration for Persistence and Predictability

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  • Manojit Chattopadhyay
  • Ashish Kumar Garg
  • Subrata Kumar Mitra

Abstract

The primary objective of the study is to explore the predictability of herding patterns of foreign institutional investors in the Indian market using high frequency data over a period from January 2003 to June 2014. Herding of an individual stock was measured estimating a simple volume based ratio and persistence of trends was detected using the runs test (Wald and Wolfowitz [1940]) on that ratio. Predictability of herding behavior has been successfully modeled by applying 7 data mining models using various measures of performance. Market regulators may consider our findings to regulate the foreign institutional investors trading to make the financial system more transparent and robust.

Suggested Citation

  • Manojit Chattopadhyay & Ashish Kumar Garg & Subrata Kumar Mitra, 2018. "Herding by Foreign Institutional Investors: An Evidential Exploration for Persistence and Predictability," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(1), pages 73-88, January.
    • Handle: RePEc:taf:hbhfxx:v:19:y:2018:i:1:p:73-88
    DOI: 10.1080/15427560.2017.1373282
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    Cited by:

    1. Puput Tri Komalasari & Marwan Asri & Bernardinus M. Purwanto & Bowo Setiyono, 2022. "Herding behaviour in the capital market: What do we know and what is next?," Management Review Quarterly, Springer, vol. 72(3), pages 745-787, September.
    2. Paramita Mukherjee & Sweta Tiwari, 2022. "Trading Behaviour of Foreign Institutional Investors: Evidence from Indian Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(4), pages 605-629, December.
    3. Yushen Kong & Micheal Owusu-Akomeah & Henry Asante Antwi & Xuhua Hu & Patrick Acheampong, 2019. "Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.

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