Deep Learning Tools for Predicting Stock Market Movements

Preface

Predicting the movement of stocks is a classic but difficult topic that has attracted the study of economists and computer scientists alike. Over the last couple of decades, several efforts have been made to investigate the use of linear and machine learning (ML) technologies with the objective of developing an accurate prediction model. New horizons, such as deep learning (DL) models, have just been brought to this field, and the pace of advancement is too quick to keep up with. Moreover, the stock market behavior and pattern have perplexed researchers and mathematicians for decades. Therefore, it is crucial to familiarize oneself with the many investment opportunities, styles, tools, and techniques to study the stock market volatility, and portfolio management solutions that exist in the case of a global financial catastrophe. Therefore, the objective of the current work is to give a thorough view of the evolution and development of DL tools and techniques in the field of stock market prediction in the developed and developing worlds.

Stock market interest has grown in recent years. Investors exchange millions of dollars in assets every day to profit. If an investor can predict market behavior, they may earn higher risk-adjusted returns. DL, ML, soft computing, and computational intelligence research have produced accurate stock market predictions. Financial research is tough but essential for stock market predictions. The efficient market hypothesis (EMH) may not ...