Abstract

This research investigates the synergistic potential of integrating machine learning (ML) techniques with behavioral finance principles to enhance market sentiment prediction and optimize investment strategies. Traditional financial models often fail to account for the irrationalities and cognitive biases that significantly influence market behavior. This study leverages advanced ML algorithms, including recurrent neural networks (RNNs) and sentiment analysis tools, to extract and interpret market sentiment from diverse data sources, such as news articles, social media, and financial reports. By incorporating behavioral biases, such as loss aversion and herding behavior, into the ML models, we aim to develop more accurate and robust predictive models. Furthermore, we propose an algorithmic trading framework that utilizes the predicted market sentiment to dynamically adjust investment portfolios, minimizing risk and maximizing returns. The results demonstrate the effectiveness of the proposed approach in outperforming traditional investment strategies, highlighting the transformative potential of combining ML and behavioral finance in navigating the complexities of modern financial markets.