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Investor-herding and risk-profiles: A State-Space Model-based Assessment

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  • Harminder B. Nath
  • Robert D. Brooks

Abstract

This paper, using the Australian stock market data, examines the investor-herding and riskprofiles link that has implications for asset pricing, portfolio diversification and foreign investments. As investors may herd towards a specific factor, sector or style to combat market conditions for optimizing investment returns, examining such herding can reveal investors' risk profiles. We employ State-Space models for extracting time series of herd dynamics and the proportion of signal explained by herding (PoSEH). Market volatility has a significant negative effect on PoSEH, with the most/least effect on high/low performance days of stock returns. Using quantile regression, we observe that herding and adverseherding can emerge during the worst and best performance days of stock returns, and that extreme volatility can bring herding to a near halt. The study reveals the presence of a regulated stock market environment and risk-aversion tendencies among investors.

Suggested Citation

  • Harminder B. Nath & Robert D. Brooks, 2020. "Investor-herding and risk-profiles: A State-Space Model-based Assessment," Monash Econometrics and Business Statistics Working Papers 9/20, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2020-9
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp09-2020.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Christian Espinosa-Méndez & José Arias, 2021. "Herding Behaviour in Asutralian stock market: Evidence on COVID-19 effect," Applied Economics Letters, Taylor & Francis Journals, vol. 28(21), pages 1898-1901, December.

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    More about this item

    Keywords

    herd behaviour; risk aversion; state-space models; quantile regression;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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