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Revisiting overconfidence in investment decision-making: Further evidence from the U.S. market

Author

Listed:
  • Bouteska, Ahmed
  • Harasheh, Murad
  • Abedin, Mohammad Zoynul

Abstract

Investor overconfidence leads to excessive trading due to positive returns, causing inefficiencies in stock markets. Using a novel methodology, we build on the previous literature by investigating the existence of overconfidence by studying the causal relationship between return and trading volume covering the COVID-19 period. We implement a nonlinear approach to Granger causality based on multilayer feedforward neural networks on daily returns and trading volumes from 2016 to 2021, covering 1424 daily observations of the S&P 500 index. The results provide evidence of overconfidence among investors. Such behavior may be linked to the increase in the number of investors. However, there is a decline in the rate of returns during the study period, implying uncertainty caused by the COVID-19 pandemic.

Suggested Citation

  • Bouteska, Ahmed & Harasheh, Murad & Abedin, Mohammad Zoynul, 2023. "Revisiting overconfidence in investment decision-making: Further evidence from the U.S. market," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s027553192300154x
    DOI: 10.1016/j.ribaf.2023.102028
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    Keywords

    Finance; Overconfidence; Granger causality; Artificial neural networks; U.S. stock market;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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