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Financial Behavior Prediction with Machine Learning
Abstract
The financial markets often experience irrational behaviors, leading to significant overreactions where asset prices deviate from intrinsic values. This research investigates the application of machine learning (ML) techniques in predicting these market anomalies. The study reveals varying levels of awareness among financial professionals regarding market overreaction, with most acknowledging its impact but differing in familiarity with the concept. Confidence in ML's ability to predict market overreaction is mixed, with strong support for its use in high-frequency trading and risk assessment. Key factors for effective ML models include historical price data, trading volume, and news sentiment. Challenges such as data availability and model integration complicate implementation. Respondents foresee ML becoming increasingly integral to financial predictions, emphasizing the need for educational initiatives to enhance understanding of behavioral finance principles. The findings suggest a promising future for ML applications in finance, including trading and risk management.
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