Machine Learning and its Role in Stock Market Prediction

The financial market is notorious for its volatility and unpredictable nature, a fact widely acknowledged by researchers. Over the years, they have devoted considerable effort to studying time series data in order to forecast future stock values. However, the complex interplay of numerous factors makes accurate predictions challenging. While some factors like historical stock data, trade volume, and current pricing can be quantified, other critical elements such as a company's intrinsic value, assets, quarterly performance, investments, and strategic decisions cannot be easily incorporated into mathematical models. Consequently, stock price prediction using machine learning techniques remains difficult and somewhat unreliable. Furthermore, forecasting the impact of major events like pandemics or wars on the stock market in the coming weeks remains a significant challenge. This chapter of the book provides an in‐depth exploration of machine learning methods employed in stock market forecasting, along with a valuable case study, offering valuable insights for researchers working in this field.