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

Ahmed Bouteska, Murad Harasheh and Mohammad Zoynul Abedin

Research in International Business and Finance, 2023, vol. 66, issue C

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.

Keywords: Finance; Overconfidence; Granger causality; Artificial neural networks; U.S. stock market (search for similar items in EconPapers)
JEL-codes: C45 G11 G41 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:66:y:2023:i:c:s027553192300154x

DOI: 10.1016/j.ribaf.2023.102028

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