Investors overconfidence behaviour at Bombay Stock Exchange

Abstract

Purpose The purpose of the paper is to empirically test the overconfidence hypothesis at Bombay Stock Exchange (BSE). Design/methodology/approach The study applies bivariate vector autoregression to perform the impulse-response analysis and EGARCH models to understand whether there is self-attribution bias and overconfidence behavior among the investors. Findings The study shows the empirical evidence in support of overconfidence hypothesis. The results show that the overconfident investors overreact to private information and underreact to the public information. Based on EGARCH specifications, it is observed that self-attribution bias, conditioned by right forecasts, increases investors’ overconfidence and the trading volume. Finally, the analysis of the relation between return volatility and trading volume shows that the excessive trading of overconfident investors makes a contribution to the observed excessive volatility. Research limitations/implications The study focused on self-attribution and overconfidence biases using monthly data. Further studies can be encouraged to test the proposed hypotheses on daily data and also other behavioral biases. Practical implications Insights from the study suggest that the investors should perform a post-analysis of each investment so that they become aware of past behavioral mistakes and stop continuing the same. This might help investors to minimize the negative impact of self-attribution and overconfidence on their expected utility. Originality/value To the best of the authors’ knowledge, this is the first study to examine the investors’ overconfidence behavior at market-level data in BSE, India.

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