Abstract

It's well known that financial markets are very unstable and hard to predict, which makes it hard for both traditional analytical models and investors to make decisions. Machine learning methods have recently shown promise in finding complicated patterns in large amounts of financial data. At the same time, behavioral finance insights have shown how investor psychology affects market movements. This study investigates the enhancement of stock market forecasting accuracy through the integration of advanced machine learning (ML) models with behavioral data, including market sentiment and investor biases, to gain deeper insights into investor decision-making. We examine the literature on conventional forecasting techniques in comparison to contemporary machine learning methodologies, and we construct a predictive framework that integrates historical stock data with behavioral indicators such as news and social media sentiment, fear-greed indices, and other relevant metrics. We use a range of models, such as regression-based models, ensemble classifiers, and deep learning (LSTM networks), and we add behavioral features to these models. We anticipate that machine learning models will surpass traditional methods in forecasting stock trends, and that the incorporation of behavioral variables will further improve predictive accuracy. Initial results suggest enhanced predictive accuracy (e.g., diminished error rates and increased directional precision) when sentiment and other investor-related variables are incorporated. This research enhances the fields of finance and AI by presenting a comprehensive forecasting methodology that integrates quantitative data with qualitative behavioral insights, thereby offering potential advantages to traders, investment firms, and policymakers in comprehending and predicting market dynamics.