Can Machine Learning Enhance the Forecasting of Herding Behavior in International Stock Markets?

Panagiotis G. Artikis (), Georgios A. Papanastasopoulos (), Polyxeni G. Tsitsiri () and Antonios M. Vasilatos ()
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Panagiotis G. Artikis: University of Piraeus
Georgios A. Papanastasopoulos: University of Piraeus
Polyxeni G. Tsitsiri: University of Piraeus
Antonios M. Vasilatos: University of Piraeus

Chapter Chapter 16 in Business Analytics and Decision Making in Practice, 2024, pp 187-201 from Springer

Abstract: Abstract Behavioral finance uses psychology to make financial decisions. Investor herd behavior—imitating rather than making independent decisions—drives financial crises. Due of the challenging of herding behavior prediction, our machine learning approach is innovative although while COVID-19 epidemic is well-studied. We employ machine learning algorithms to predict stock investors’ behavior before and after the COVID-19 pandemic to test rational asset pricing in the Americas, Europe, and Asia–Pacific. Empirical research shows the COVID-19 epidemic increased herding in all regions. In Europe, herding-related variables are more important. These findings demonstrate that market-related dynamics, such as liquidity and transaction procedures, greatly influence investor herding. We also noticed good herding behavior prediction findings using machine learning. Our machine-learning model exceeded traditional regression methods in prediction accuracy due to its advanced algorithms and ability to understand complex robust data patterns. Machine learning captures nonlinear relationships, revealing herding behavior causes Our research has two main consequences. It shows that market conditions impact herding. The liquidity and trading methods of investors may affect crowdsourcing. Machine learning predicts herding behavior, thus advanced algorithms and data-driven approaches may help investors make better decisions.

Keywords: Machine learning; Financial forecasting; Herding behavior (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-61589-4_16

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