View source for The Role of Artificial Intelligence in Behavioral Finance

== Details ==
* Author: Emir Esati
* Title: The Role of Artificial Intelligence in Behavioral Finance
* Supervisor: Prof. Dr. Jörg Osterrieder
* Degree: Bachelor of Science
* University: Bern University of Applied Sciences
* Year: 2024
* Status: Submitted Paper

== Summary == 

This thesis explores the role of Artificial Intelligence (AI) in behavioral finance, focusing on how AI can identify and predict behavioral biases in financial markets and examining retail investors' perceptions and attitudes towards AI in financial decision-making. The goal is to bridge the gap between theoretical finance models and practical AI applications to enhance investment outcomes. The relevance of this research lies in AI's ability to address the limitations of traditional financial models, which often fail to predict market anomalies and investor behaviors, by providing more accurate forecasts and strategic decision-making tools. Grounded in behavioral finance, this study integrates psychological insights with economic theories to explain how cognitive biases and emotions influence financial decisions. Key concepts such as Prospect Theory, heuristics, and biases are central to this research. AI technologies like machine learning and natural language processing (NLP) are used to gain deeper insights into market behaviors and investor decision-making processes. The literature review comprehensively examines the integration of AI in behavioral finance, focusing on its applications in portfolio optimization, algorithmic trading, sentiment analysis, risk management, fraud detection, natural language processing, and pattern recognition. Additionally, it discusses the acceptance and trust of retail investors towards AI-based financial services, addressing factors influencing their perceptions, barriers to adoption, and the need for transparency and explainability in AI tools. Methodologically, a survey-based approach is used to gather direct insights from retail investors, capturing their perceptions, acceptance, and trust in AI-based financial services through a mix of Likert scale, multiple-choice, and open-ended questions. Secondary data from academic literature support the primary data. Key findings indicate high theoretical trust in AI's capabilities among retail investors, though practical satisfaction with AI tools varies. Younger investors show higher engagement and trust in AI tools compared to older investors, highlighting a generational divide. The study emphasizes the importance of transparency and explainability in AI tools to enhance user trust and satisfaction. Targeted education and training programs can bridge the gap between different age groups and improve AI tool adoption and effectiveness. In conclusion, integrating AI in behavioral finance offers significant opportunities to enhance financial decision-making by mitigating cognitive biases and improving investment strategies. To fully capitalize on these opportunities, financial institutions must address current shortcomings in AI tool design and deployment. Future research should focus on longitudinal studies to track changes in attitudes and behaviors over time and cross-cultural studies to understand diverse investor perceptions. By addressing these areas, financial institutions can better harness the power of AI, leading to more informed and rational investment decisions for retail investors.

== Important links == 
* [https://drive.google.com/drive/folders/1EkU3KZHli6ASIioqsWL6X_rSJRc01OlS Google Drive Thesis Link]
* [https://bfh.fra1.qualtrics.com/jfe/form/SV_7Pw0fA1zUMrR7rU Qualtrics Survey]


== Contact == 
* [mailto:oste@bfh.ch Prof. Dr. Jörg Osterrieder]
* [mailto:emir.esati@students.bfh.ch Emir Esati]