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Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach.

Esra Alp Coşkun1,2, Hakan Kahyaoglu3, Chi Keung Marco Lau1,4

  • 1Department of Accountancy, Finance, and Economics, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH UK.

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Investor overconfidence, leading to overtrading, shows asymmetric behavior across market return regimes. This overconfidence is more persistent in low-return environments, potentially causing losses from excessive trading strategies.

Keywords:
Artificial intelligenceFeed-forward neural networksLocal projectionsNonlinear Granger causalityNonlinear impulse-response functionsOverconfidenceReturn regime

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Area of Science:

  • Behavioral Finance
  • Financial Econometrics
  • Artificial Intelligence in Finance

Background:

  • Overconfidence behavior, a type of positive illusion, is historically linked to financial crises.
  • Investor overconfidence, evidenced by overtrading after positive returns, can cause stock market inefficiencies.

Purpose of the Study:

  • To investigate investor overconfidence in an emerging market (Borsa Istanbul) using novel AI and nonlinear methods.
  • To analyze the impact of different return regimes on investor overconfidence attitudes.

Main Methods:

  • Application of a feed-forward neural network and nonlinear Granger causality test.
  • Utilizing nonlinear impulse-response functions based on local projections for time series analysis.
  • Distinguishing between different market regimes to analyze trading volume responses to return shocks.

Main Results:

  • Contradicts existing literature by revealing asymmetric overconfidence behavior across return regimes.
  • Overconfidence is persistent over a 20-day forecasting horizon.
  • Overconfidence is more persistent in low-return regimes than in high-return regimes.

Conclusions:

  • Investor overconfidence exhibits distinct patterns depending on market return regimes and interest rate environments.
  • Investors should exercise caution with aggressive trading strategies, especially in low-return periods, to avoid potential losses.