Opacité, analystes financiers et évaluation optimisée du risque dans l'industrie bancaire et les FinTech.

Opacity, Financial Analysts, and Optimized Risk Assessment : Evidence from Banking and FinTech Sectors

• Auteur / Autrice : Mehrafarin Shetabi
• Direction : Alain Sauviat, Isabelle Distinguin
• Type : Thèse de doctorat
• Discipline(s) : Sciences économiques
• Date : Soutenance le 08/01/2025
• Etablissement(s) : Limoges
• Ecole(s) doctorale(s) : Gouvernance des Institutions et des Organisations
• Partenaire(s) de recherche : Laboratoire : Laboratoire d'Analyse et de Prospective Economiques
• Jury : Président / Présidente : Laetitia Lepetit
• Jury : Examinateurs / Examinatrices : Christophe Hurlin
• Jury : Rapporteurs / Rapporteuses : Anne-Gaël Vaubourg, Jaideep Oberoi

Mots clés

Mots clés contrôlés

Banques

Mots clés libres

Opacité

Analystes financiers

Stabilité Bancaire

Marché FinTech

Optimisation du Risque

Discipline de Marché

Opacity

Financial Analysts

Bank Stability

Fintech Market

Risk Optimization

Market Discipline

Résumé

Cette thèse examine les interactions entre l'opacité bancaire, l'influence des analystes financiers et l'optimisation du risque, avec un double focus sur le secteur bancaire et celui des FinTech. Composée de trois études, elle explore la stabilité systémique, les comportements des analystes et des outils novateurs de gestion du risque de crédit. Le premier chapitre analyse les banques américaines et européennes, démontrant que l'opacité, mesurée via les erreurs et dispersions des prévisions des analystes, exacerbe les risques, particulièrement dans les petites banques opaques aux États-Unis. La couverture par les analystes amplifie la sensibilité du marché aux signaux négatifs, tandis que les dividendes élevés intensifient les risques liés à l'opacité. Le deuxième chapitre explore les comportements des analystes à l’échelle mondiale, montrant que l’expérience, l’ampleur des portefeuilles et les incitations régionales influencent la précision et l’audace des prévisions. Les analystes américains allient précision et audace, tandis que les analystes européens privilégient l’audace au détriment de la précision. Le troisième chapitre introduit le modèle EFSGA, un modèle d’apprentissage évolutif combinant algorithmes génétiques et apprentissage automatique pour optimiser dynamiquement le risque de crédit dans les FinTech, équilibrant précision et interprétabilité. Cette thèse offre des perspectives essentielles sur l’opacité, le rôle des analystes et des outils de gestion du risque, contribuant à la résilience des systèmes financiers modernes.

This thesis provides a comprehensive investigation into the intricate interactions between bank opacity, financial analyst influence, and risk optimization, focusing on the dual contexts of banking and FinTech. Through three detailed studies, it explores how opacity and analyst behavior shape financial stability across varied regulatory environments and introduces an innovative, adaptive approach to optimizing credit risk assessment in FinTech lending.The first chapter rigorously examines the destabilizing effects of bank opacity, particularly during periods of market overvaluation and economic uncertainty, with a focus on U.S. and European banks. Using analyst forecast errors and dispersion as forward-looking measures of opacity, the study reveals that high opacity significantly heightens risk, especially in smaller, opaque U.S. institutions where analyst coverage paradoxically amplifies market sensitivity to negative earnings signals. By contrast, the effects in European banks are less pronounced, reflecting differences in regulatory frameworks and market structures. Additionally, high dividend payouts are shown to intensify opacity-driven risks, highlighting the intricate relationship between transparency, market discipline, and financial prudence in shaping bank stability.The second chapter investigates the role of financial analyst characteristics and career incentives in forecasting accuracy, boldness, and career trajectories across global banking markets, comparing trends in the U.S., Europe, and Asia. The findings demonstrate that experience, firm affiliation, and portfolio breadth significantly influence forecasting behavior, with distinct regional patterns. In the U.S., experienced analysts at leading firms provide bold and accurate forecasts, while younger analysts tend toward herding. In Europe, larger portfolios reduce forecasting accuracy, and younger analysts employ boldness to stand out, often at the expense of precision. These results highlight how regional labor market dynamics and career incentives shape analysts' forecasting contributions and their impact on market discipline.The third chapter addresses the challenges of credit risk assessment in FinTech lending, introducing EFSGA, an evolutionary-based ensemble learning framework that integrates genetic algorithms with machine learning. EFSGA is designed for dynamic, application-specific credit risk classification, balancing predictive accuracy with interpretability while adapting to evolving market conditions in real time. The model significantly outperforms traditional methods in handling unbalanced datasets and providing timely, actionable insights for credit risk management, establishing itself as a powerful tool for post-loan monitoring and risk mitigation in digital finance.Together, these studies offer a holistic analysis of opacity, financial analyst behavior, and advanced risk assessment techniques within banking and FinTech contexts. By clarifying the role of opacity and analyst pressures in shaping stability across financial systems and presenting cutting-edge tools for managing credit risk in emerging digital markets, this thesis provides crucial insights for financial institutions, regulators, and policymakers striving to foster systemic resilience and transparency in an increasingly interconnected financial landscape.