A Comparative Study of Deep Learning Algorithms for Forecasting Indian Stock Market Trends

This research underscores the vital significance of providing investors with timely and dependable information within the dynamic landscape of today’s stock market. It delves into the expanding utilization of data science and machine learning methods for anticipating stock market movements. The study conducts a comprehensive analysis of past research to pinpoint effective predictive models, with a specific focus on widely acknowledged algorithms. By employing an extensive dataset spanning 27 years of NIFTY 50 index data from the National Stock Exchange (NSE), the research facilitates a thorough comparative investigation. The primary goal is to support both investors and researchers in navigating the intricate domain of stock market prediction. Stock price prediction is challenging due to numerous influencing factors, and identifying the optimal deep learning model and parameters is a complex task. This objective is accomplished by harnessing the capabilities of deep learning, thereby contributing to well-informed decision-making and the efficient utilization of predictive tools. The paper scrupulously examines prior contributions from fellow researchers in stock prediction and implements established deep learning algorithms on the NIFTY 50 dataset to assess their predictive accuracy. The study extensively analyzes NIFTY 50 data to anticipate market trends. It employs three distinct deep learning models—RNN, SLSTM, and BiLSTM. The results underscore SLSTM as the most effective model for predicting the NIFTY 50 index, achieving an impressive accuracy of 99.10%. It’s worth noting that the accuracy of BiLSTM falls short when compared to RNN and SLSTM.

@article{Paul2023,
  title     = {A Comparative Study of Deep Learning Algorithms for Forecasting Indian Stock Market Trends},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mrinal Kanti Paul and Purnendu Das},
  doi       = {10.14569/IJACSA.2023.0141098},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141098}
}