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Exploring the Efficacy of Artificial Intelligence Techniques in Predicting Stock Market Trends
Abstract
Stock price prediction is a challenging task, traditionally relying on fundamental, technical, and time series analysis. However, AI and ML techniques offer new opportunities for enhancing predictions in equity markets. In recent years, considerable efforts have been made to identify these patterns in stock markets, with the aim of facilitating profitable trading and investment decisions. Consequently, a wealth of studies and research endeavors have emerged in this field. This chapter explores diverse techniques used for stock market prediction, analyzing their effectiveness. The techniques examined in this study are categorized into three groups: traditional ML, deep learning, and sentiment analysis. Results show naive Bayesian and random forests as promising conventional ML models, while LSTM neural network provides accurate predictions among deep learning models. This chapter sheds light on the employed and researched ML models, offering insights into their strengths in forecasting market trends.
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