Artificial Intelligence in Behavioral Finance: Understanding and Predicting Investor Biases
Details
- Author: Emir Esati
- Title: Artificial Intelligence in Behavioral Finance: Understanding and Predicting Investor Biases
- 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 investigates AI's pivotal role in enhancing the understanding of market behaviors that deviate from the rational actor model. It focuses on AI's capability to analyze complex patterns and anomalies exhibited by human investors, aiming to refine and redefine methodologies within behavioral finance. The study specifically examines the effectiveness of Natural Language Processing (NLP), Neural Networks, and Machine Learning techniques in identifying and predicting behavioral biases. The survey conducted reveals a high level of trust in AI's capabilities among retail investors, yet highlights the need for improvements in transparency and user experience of AI tools. The findings suggest that while AI technologies can provide deeper insights and more accurate predictions, there are significant areas for enhancement to increase user satisfaction and adoption.