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Deep Learning Approaches to Market Sentiment and Stock Returns: A Cross-Country Analysis in Africa in the Time of the COVID-19 Pandemic
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Author(s): Imane Boudri (Groupe Institut Supérieur de Commerce et d'Administration des Entreprises, Morocco), Abdelhamid El Bouhadi (Sidi Mohamed Ben Abdellah University, Morocco)and Mimoun Benali (Sidi Mohamed Ben Abdellah University, Morocco)
Copyright: 2025
Pages: 26
Source title:
Behavioral Finance and AI Tools for Sustainability
Source Author(s)/Editor(s): Fouad Daidai (University Chouaib Doukkali, Morocco), Mimoun Benali (Sidi Mohamed Ben Abdellah University, Morocco)and Larbi Tamnine (Sidi Mohamed Ben Abdellah University, Morocco)
DOI: 10.4018/979-8-3693-9684-1.ch002
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Abstract
This chapter explores the intersection of behavioral finance and artificial intelligence through an analysis of market sentiment and stock returns in three African financial markets: the Casablanca Stock Exchange (MASI), the Tunisian Stock Exchange (TUNINDEX), and the Regional African Stock Exchange (BRVM Composite). Using advanced natural language processing techniques, particularly BERT, the study analyzes sentiment from news articles to evaluate its influence on stock performance during the COVID-19 pandemic. The findings reveal diverse dynamics: the BRVM Composite exhibited a significant causal relationship between sentiment and returns, while the MASI and TUNINDEX showed weaker correlations, highlighting the complexity of sentiment-driven behavior. Structural factors, policy interventions, and market-specific dynamics emerged as key influences. This research contributes to behavioral finance by deepening our understanding of sentiment's impact in emerging markets and offering actionable insights for investors and policymakers during crises.
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