View source for Artificial Intelligence in Behavioral Finance: Understanding and Predicting Investor Biases

== 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 == 

A detailed study on the role and effectiveness of Artificial Intelligence technologies within the domain of behavioral finance, with a particular focus on identifying and predicting behavioral biases in financial decision-making.

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

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