DOI QR코드

DOI QR Code

A study on stock price prediction system based on text mining method using LSTM and stock market news

LSTM과 증시 뉴스를 활용한 텍스트 마이닝 기법 기반 주가 예측시스템 연구

  • Received : 2020.06.12
  • Accepted : 2020.07.20
  • Published : 2020.07.28

Abstract

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

주가는 사람들의 심리를 반영하고 있으며, 주식시장 전체에 영향을 미치는 요인으로는 경제성장률, 경제지료, 이자율, 무역수지, 환율, 통화량 등이 있다. 국내 주식시장은 전날 미국 및 주변 국가들의 주가지수에 영향을 많이 받고 있으며 대표적인 주가지수가 다우지수, 나스닥, S&P500이다. 최근 주가뉴스를 이용한 주가분석 연구가 활발히 진행되고 있으며, 인공지능 기반한 분석을 통하여 과거 시계열 데이터를 기반으로 미래를 예측하는 연구가 진행 중에 있다. 하지만, 주식시장은 예측시스템에 의해서 단기간 적중이 되더라도, 시장은 더 이상의 단기 전략대로 움직여지지 않고, 새롭게 변할 수밖에 없다. 따라서, 본 모델을 삼성전자 주식데이터와 뉴스 정보를 텍스트 마이닝으로 모니터링하여 분석한 결과를 나타내어 예측이 가능한 모델을 제시하였으며, 향후 종목별 예측을 통하여 실제 예측이 정확한지 확인하여 발전시켜 나갈 예정임.

Keywords

References

  1. J. P. Tang. (2015r). The Effect on KOSPI 200 Futures after Launching KOSPI 200 Option. In 2015 International Conference on Industrial Technology and Management Science. Atlantis Press. . doi:10.2991/itms-15.2015.347
  2. H. Xue, D. Q. Huynh & M. Reynolds. (2018). SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1186-1194). IEEE. doi:10.1109/wacv.2018.00135
  3. Miner, G., Elder IV, J., Fast, A., Hill, T., Nisbet, R & Delen, D. (2012). Text Mining PubMed. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Academic Press. 703-750. doi: 10.1016/b978-0-12-386979-1.00030-x
  4. S. Stoikov & R. Waeber. (2015). Reducing Transaction Costs with Low-Latency Trading Algorithms. SSRN Electronic Journal. doi:10.2139/ssrn.2661618
  5. R. Sproat & N. Jaitly. (2017). An RNN Model of Text Normalization. Interspeech 2017. doi: 10.21437/interspeech.2017-35
  6. Q. Chen, B. Liang & J. Wang. (2019). A Comparative Study of LSTM and Phased LSTM for Gait Prediction. International Journal of Artificial Intelligence & Applications, 10(4), 57-66. doi: 10.5121/ijaia.2019.10405
  7. R. Aviandy.. (2016). Analysis of Financial performance affecting Stock Price on Pharmaceutical Industry. Business and Entrepreneurial Review, 7(1), 69. doi:10.25105/ber.v7i1.1184
  8. S. H. Koh. (2018). A Comparative Study on the Excess Returns of Growth Stocks and Value Stocks in the Korean Stock Market. Journal of the Korea Convergence Society, 9(7), 213-222. https://doi.org/10.15207/JKCS.2018.9.7.213
  9. S. H. Koh. (2015). Convergent Momentum Strategy in the Korean Stock Market. Journal of the Korea Convergence Society, 6(4), 127-132. https://doi.org/10.15207/JKCS.2015.6.4.127
  10. S. H. Koh. (2016). A Converging Approach on Investment Strategies, Past Financial Information, and Investors' Behavioral Bias in the Korean Stock Market. Journal of the Korea Convergence Society, 7(6), 205-212. https://doi.org/10.15207/JKCS.2016.7.6.205
  11. Supplemental Information 2: GSS Calculations Jupyter Notebook. (n.d.). doi:10.7287/peerj.preprints.26665v2/supp-2
  12. R. Bandi & J. Amudhavel. (2018). Object Recognition Using Keras with Backend Tensor Flow. International Journal of Engineering & Technology, 7(3.6), 229. doi:10.14419/ijet.v7i3.6.14977
  13. M. L. Hetland (2010). Python Algorithms: Mastering Basic Algorithms in the Python Language. doi:10.1007/978-1-4302-3238-4
  14. K. Detlefsen. (2018). Monte Carlo Option Pricing in Tensorflow. SSRN Electronic Journal. doi:10.2139/ssrn.3214058
  15. B. Zhao, X. Li, & X. Lu. (2018). HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2018.00773