Deep Learning Tools for Predicting Stock Market Movements

4Stock Market Predictions Using Deep Learning: Developments and Future Research Directions

Renuka Sharma^* and Kiran Mehta

Chitkara Business School, Chitkara University, Punjab, India

Abstract

The present study gives a comprehensive overview of the advancements and potential avenues in the realm of utilizing deep learning techniques for forecasting stock market trends. This study surveys the evolving landscape of deep learning methodologies employed in predicting stock price movements and offers insights into their effectiveness across various time frames and market conditions. The research delves into the multifaceted aspects of this field, encompassing architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recent transformer-based models. The analysis underscores the influence of data preprocessing, feature engineering, and model complexity. Additionally, this chapter shows a roadmap for future research directions, emphasizing the need for hybrid models that integrate traditional financial indicators with deep learning approaches, the exploration of alternative data sources like social media sentiment, and the imperative of addressing ethical concerns pertaining to market manipulation. By synthesizing the current landscape and illuminating potential trajectories, this chapter serves as a valuable guide for researchers and practitioners seeking to navigate the evolving landscape of stock market prediction through deep learning. ...