Is it possible to forecast KOSPI direction using deep learning methods?

Songa Choi, Jongwoo Song

Research output: Contribution to journalArticlepeer-review


Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.

Original languageEnglish
Pages (from-to)329-338
Number of pages10
JournalCommunications for Statistical Applications and Methods
Issue number4
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 The Korean Statistical Society, and Korean International Statistical Society. All Rights Reserved.


  • 1D-CNN
  • LSTM
  • deep learning
  • machine learning
  • time-series data


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