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Revisiting Overconfidence In Investment Decision-Making: Further Evidence From The U.S. Market

Ahmed Bouteska, Murad Harasheh, Abedin Abedin

Research in International Business and Finance, Volume: 66, Start page: 102028

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1016/j.ribaf.2023.102028

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 c...

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Published in: Research in International Business and Finance
ISSN: 0275-5319 1878-3384
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63950
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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
College: Faculty of Humanities and Social Sciences
Funders: Swansea University
Start Page: 102028

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