Stock Market Prediction Using Machine Learning Techniques

Abstract

The prediction of stock market prices holds significant importance within the contemporary economic landscape. Consequently, there has been a notable surge in scholarly interest directed towards exploring novel avenues for enhancing stock market prediction capabilities. Recent research endeavors have illuminated the potential predictive value inherent in various data streams, including historical stock data and user-generated content sourced from platforms such as Twitter and web news. These investigations have revealed a discernible relationship between social mood, as reflected in online discourse, and future stock price movements. However, prior studies have often overlooked the incorporation of such sentiment-derived information, thus presenting an information gap. In the present study, we address this gap by proposing an effective methodology for the integration and analysis of multiple information sources to facilitate more accurate stock price predictions. Leveraging Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models, we conduct a comprehensive analysis of disparate data sources. Furthermore, we employ an ensemble approach, incorporating Weighted Average and Differential Evolution techniques, to refine the predictive accuracy of our models. Our findings demonstrate the efficacy of the proposed methodology in generating highly accurate stock price predictions across varying future time horizons, including one-day, seven-day, 15-day, and 30-day intervals. These predictions offer valuable insights for investors seeking to make informed decisions regarding their investment strategies and enable companies to gauge their anticipated performance within the stock market landscape.