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article · Research in International Business and Finance

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

202325 citationsOpen accessUniversity of Tunis El Manar

In plain language

This research revisits the concept of investor overconfidence, which can lead to excessive trading and market inefficiencies. Using a novel methodology, the study investigated the causal relationship between stock returns and trading volume, specifically covering the COVID-19 pandemic period. A nonlinear Granger causality approach, based on multilayer feedforward neural networks, was applied to daily S&P 500 index data from 2016 to 2021. The findings provide evidence of overconfidence among investors, a behaviour that may be connected to an increase in the number of market participants. However, the study also observed a decline in the rate of returns during this period, suggesting uncertainty caused by the pandemic.

Key takeaways

  • Investor overconfidence can lead to excessive trading and inefficiencies in stock markets.
  • A novel nonlinear Granger causality approach using multilayer feedforward neural networks was employed.
  • The study found evidence of investor overconfidence in the U.S. market, potentially linked to an increase in investor numbers.
  • The research covered the COVID-19 period, noting a decline in returns and increased market uncertainty.

Why it matters

Understanding investor overconfidence is crucial because it can distort market efficiency and lead to suboptimal investment decisions. This research sheds light on how investor behaviour, especially during significant global events like a pandemic, influences market dynamics, offering insights for both individual investors and financial regulators.

Commercialisation angle

This is foundational research into investor behaviour and market dynamics. The abstract does not indicate a direct application pathway for commercialisation, but its findings could inform risk management strategies or educational programmes for investors.

AI-generated from the published abstract. Always read the original work before citing.

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. Data available on request from the authors

Research topics

  • Financial Markets and Investment Strategies
  • Market Dynamics and Volatility
  • Stock Market Forecasting Methods

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1016/j.ribaf.2023.102028

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