KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Deep Learning-Based Stock Fluctuation Prediction According to Overseas Indices and Trading Trend by Investors

해외지수와 투자자별 매매 동향에 따른 딥러닝 기반 주가 등락 예측

  • Received : 2021.02.15
  • Accepted : 2021.05.24
  • Published : 2021.09.30
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Abstract

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

주가 예측은 경제, 통계, 컴퓨터 공학 등 여러 분야에서 연구되는 주제이며, 특히 최근에는 기본적 지표나 기술적 지표 등 다양한 지표로부터 인공지능 모델을 학습하여 주가의 변동을 예측하는 연구들이 활발해 지고 있다. 본 연구에서는 S&P500 등의 해외지수, 과거 KOSPI 지수, 그리고 KOSPI 투자자별 매매 동향으로부터 KOSPI의 등락을 예측하는 딥러닝 모델을 제안한다. 제안 모델은 주가 등락 예측을 위하여 비지도 학습 방법인 적층 오토인코더를 이용하여 잠재변수를 추출하고, 추출된 잠재변수로부터 시계열 데이터 학습에 적합한 LSTM 모델로 학습하여 당일 시가 대비 종가의 등락을 예측하며, 예측된 값을 기반으로 매수 또는 매도를 결정한다. 본 연구에서 제안하는 모델과 비교 모델들의 수익률 및 예측 정확도를 비교한 결과 제안 모델이 비교 모델들 보다 우수한 성능을 보였다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터 지원사업의 연구결과로 수행되었음(IITP-2021-2018-0-01419).

References

  1. G. Hu, et al., "Deep stock representation learning: From candlestick charts to investment decisions," arXiv preprint arXiv:1709.03803, 2018.
  2. J. Patel, S. Shah, P. Thakkar, and K. Kotecha, "Predicting stock market index using fusion of machine learning techniques," Expert Systems with Applications, Vol.42, No.4, pp.2162-2172, 2015. https://doi.org/10.1016/j.eswa.2014.10.031
  3. R. Singh, and S. Srivastava, "Stock prediction using deep learning," Multimedia Tools and Applications, Vol.76, No.18, pp.18569-18584, 2016. https://doi.org/10.1007/s11042-016-4159-7
  4. R. M. I. Kusuma, T. T. Ho, W. C. Kao, Y. Y. Ou, and K. L. Hua, "Using deep learning neural networks and candlestick chart representation to predict stock market," arXiv:1903. 12258v1. 2019.
  5. S. J. Guo, F. C. Hsu, and C. C. Hung, "Deep candlestick predictor: A framework toward forecasting the price movement from candlestick charts," In Proceedings of 9th International Symposium on Parallel Architectures, Algorithms and Programming, pp.219-226, 2018.
  6. T. Kamo, and C. Dagli, "Hybrid approach to the Japanese candlestick method for financial forecasting," Expert Systems with Applications, Vol.36, No.3, pp.5023-5030. 2009. https://doi.org/10.1016/j.eswa.2008.06.050
  7. D. H. Shin,, K. H. Choi, and C. B. Kim, "Deep learning model for prediction rate improvement of stock price using RNN and LSTM," Journal of Korean Institute of Information Technology, Vol.15, No.10, pp.9-16, 2017.
  8. W. S. Lee, "A deep learning analysis of the KOSPI's directions," Journal of the Korean Data & Information Science Society, Vol.28, No.2, pp.287-295, 2017. https://doi.org/10.7465/jkdi.2017.28.2.287
  9. C. Liu, J. Wang, D. Xiao, and Q. Liang, "Forecasting S&P 500 stock index using statistical learning models," Open Journal of Statistics, Vol.6, No.6, pp.1067-1075, 2016. https://doi.org/10.4236/ojs.2016.66086
  10. H. Y. Lee, "A combination model of multiple artificial intelligence techniques based on genetic algorithms for the prediction of Korean Stock Price Index(KOSPI)," Entrue Journal of Information Technology, Vol.7, No.2, pp.33-43, 2008.
  11. W. Bao, J. Yue, and Y. Rao, "A deep learning framework for financial time series using stacked autoencoders and longshort term memory," PloS One, Vol.12, No.7, e0180944, 2017. https://doi.org/10.1371/journal.pone.0180944
  12. Y. H. Lee, "Value investing using deep neural networks," Master's Thesis, Seoul National University, Korea, 2018.
  13. J. Y. Heo, and J. Y. Yang, "SVM based stock price forecasting using financial statements," Journal of KIISE Transactions on Computing Practices, Vol.21, No.3, pp.167-172, 2015. https://doi.org/10.5626/KTCP.2015.21.3.167
  14. B. Liao, C. Ma, L. Xiao, R. Lu, and L. Ding, "An arctan-activated WASD neural network approach to the prediction of dow jones industrial average," In Preceedings of the 14th International Symposium on Neural Networks, pp.120-126, 2017.
  15. S. A. Hamid, and Z. Iqbal, "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Vol.57, No.10, pp.1116-1125, 2004. https://doi.org/10.1016/S0148-2963(03)00043-2
  16. G. J. Kim, "Stock fluctuation prediction based on news sentiment analysis and time series prediction," Master's Thesis, Graduate School of Soongsil University, Korea, 2017.
  17. S. K. Kim, "Stock price prediction neural network system based on financial ratio," In Proceedings of the Korea Intelligent Information System Society, Vol.1, pp.293-307, 1994.
  18. S. Sangsawad, and C. C. Fung, "Extracting significant features based on candlestick patterns using unsupervised approach," In Preceedings of 2th International Conference on Information Technology, pp.1-5, 2017.