Stock Market Prediction Using Transformers

Authors

  • R. Sampada  B.Tech., CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • J. Shivani  B.Tech., CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • V. Lasya  B.Tech., CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • B. Srujana  B.Tech., CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • Mr. G. Sreenivasulu  Associate Professor, CSE Department, JB Institute of Engineering and Technology, Hyderabad, India

Keywords:

BERT, GPT2, Transformer-based models

Abstract

Predicting stock market trends has been a difficult challenge, but recent research has shown that using machine learning techniques, specifically deep learning, has produced promising results. Transformer-based models, such as BERT and GPT2, have been successful in natural language processing tasks and are now being utilized for stock market prediction. These models can analyse large amounts of data, including financial news and social media data, and historical stock market data to make predictions about future stock prices. One advantage of these models is their ability to process and filter out irrelevant information, but there are still challenges to overcome, such as the high volatility of stock prices and the complexity of the stock market system. Nonetheless, these models can provide valuable insights for traders and investors to make informed decisions.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. Sampada, J. Shivani, V. Lasya, B. Srujana, Mr. G. Sreenivasulu "Stock Market Prediction Using Transformers" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.502-505, March-April-2023.