Deep Learning Tools for Predicting Stock Market Movements

12Machine Learning and its Role in Stock Market Prediction

Pawan Whig^1*, Pavika Sharma², Ashima Bhatnagar Bhatia¹, Rahul Reddy Nadikattu³ and Bhupesh Bhatia⁴

¹Vivekananda Institute of Professional Studies-TC, New Delhi, India

²Bhagwan Pashuram Institute of Technology, New Delhi, India

³University of Cumbersome, Williamsburg, USA

⁴Delhi Technological University, New Delhi, India

Abstract

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. ...