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Time-Invariant Stock Movement Prediction After Golden Cross Using LSTM

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(8), pp.59-66
  • DOI : 10.9708/jksci.2023.28.08.059
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 24, 2023
  • Accepted : August 14, 2023
  • Published : August 31, 2023

Sumin Nam 1 Jieun Kim 2 Zoon-ky Lee 1

1연세대학교
2연세대학교 정보대학원

Accredited

ABSTRACT

The Golden Cross is commonly seen as a buy signal in financial markets, but its reliability for predicting stock price movements is limited due to market volatility. This paper introduces a time-invariant approach that considers the Golden Cross as a singular event. Utilizing LSTM neural networks, we forecast significant stock price changes following a Golden Cross occurrence. By comparing our approach with traditional time series analysis and using a confusion matrix for classification, we demonstrate its effectiveness in predicting post-event stock price trends. To conclude, this study proposes a model with a precision of 83%. By utilizing the model, investors can alleviate potential losses, rather than making buy decisions under all circumstances following a Golden Cross event.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.