Share price prediction of Indian Stock Markets using timeseries data-A Deep Learning Approach

Shravan Raviraj, M. M. Manohara Pai, Krithika M. Pai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stock markets form the largest avenues of investment in India primarily through two stock exchanges: Bombay Stock Exchange(BSE) and National Stock Exchange(NSE). Analysts and investors look into various factors and try to predict the trends in stock prices in these exchanges. Being extremely volatile in nature, share price prediction is a fairly complex task. Despite the abundance of data, technology has not been able to carry out right predictions up to a desired accuracy most of the time. The recent developments in deep learning technology have proven to be a useful resource in improving the accuracy of predictions. The proposed deep learning based prediction algorithms makes use of Recurrent Neural Network, Long Short Term Memory and Gated Recurrent Unit over time series data obtained online. The developed algorithms predicts the trends five days in advance. The results of the prediction on stocks from various industries are explored to derive valuable insights.

Original languageEnglish
Title of host publication2021 IEEE Mysore Sub Section International Conference, MysuruCon 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages744-751
Number of pages8
ISBN (Electronic)9780738146621
DOIs
Publication statusPublished - 2021
Event1st IEEE Mysore Sub Section International Conference, MysuruCon 2021 - Hassan, India
Duration: 24-10-202125-10-2021

Publication series

Name2021 IEEE Mysore Sub Section International Conference, MysuruCon 2021

Conference

Conference1st IEEE Mysore Sub Section International Conference, MysuruCon 2021
Country/TerritoryIndia
CityHassan
Period24-10-2125-10-21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Engineering (miscellaneous)
  • Computational Mechanics
  • Control and Optimization
  • Instrumentation

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