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A Research on stock price prediction based on Deep Learning and Economic Indicators

거시지표와 딥러닝 알고리즘을 이용한 자동화된 주식 매매 연구

  • Received : 2020.10.05
  • Accepted : 2020.11.20
  • Published : 2020.11.28

Abstract

Macroeconomics are one of the indicators that are preceded and analyzed when analyzing stocks because it shows the movement of a country's economy as a whole. The overall economic situation at the national level, such as national income, inflation, unemployment, exchange rates, currency, interest rates, and balance of payments, has a great affect on the stock market, and economic indicators are actually correlated with stock prices. It is the main source of data for analysts to watch with interest and to determine buy and sell considering the impact on individual stock prices. Therefore, economic indicators that impact on the stock price are analyzed as leading indicators, and the stock price prediction is predicted through deep learning-based prediction, after that the actual stock price is compared. If you decide to buy or sell stocks by analysis of stock prediction, then stocks can be investments, not gambling. Therefore, this research was conducted to enable automated stock trading by using macro-indicators and deep learning algorithms in artificial intelligence.

거시경제는 한 나라 경제 전체의 움직임을 보여주기 때문에 주식을 분석할 때 선행되어 분석되는 지표 중 하나이다. 실업률, 이자율, 물가, 국민소득, 환율, 통화량, 국제수지 등 국가차원의 경제 상황 전반은 주식시장에 직접적인 영향을 미치고, 경제 지표는 개별 주가와의 상관관계가 있기 때문에 주식을 예측하기 위해 많은 증권사 애널리스트들이 관심 있게 지켜보고, 개별 주가에 영향을 고려하여 매수와 매도를 판단하는 주요한 근거자료가 되고 있다. 주가에 영향을 미치는 경제 지표를 선행지표로 분석하고, 주가예측을 딥러닝 기반의 예측을 통하여 예측 후 실제 주가를 비교하여 차이가 발생하면 거시지표에 대한 가중치를 조절하여 지속적인 반복학습을 통하여 주식의 매수와 매도를 판단한다면, 주식은 더 이상 도박과 같은 투기가 아닌 건전한 투자가 될 수 있다. 따라서 본 연구는 거시지표와 인공지능의 딥러닝 알고리즘방식을 이용하여 자동화된 주식매매가 가능하도록 연구를 수행하였다.

Keywords

References

  1. Tobin, J. (1998). World economy and financial markets. Japan and the World Economy, 10(3), 377-379. doi:10.1016/s0922-1425(98)00038-3
  2. Choi, J., Yoo, S., & Kim, J. (2012). Comparative Analysis of Default Risk of Construction Company during Macroeconomic Fluctuations. Korean Journal of Construction Engineering and Management, 13(4), 60-68. doi:10.6106/kjcem.2012.13.4.060
  3. Choi, J., & Lee, O. (2014). Correlation Analysis Among the Price of Apartments in Seoul, Stock Market and main Economic Indicators. Journal of Digital Convergence, 12(2), 45-59. doi:10.14400/jdc.2014.12.2.45
  4. Tang, J. (2015). The Effect on KOSPI 200 Futures after Launching KOSPI 200 Option. Proceedings of the 2015 International Conference on Industrial Technology and Management Science. doi:10.2991/itms-15.2015.347
  5. Fred Economic Research. (2020). https://fred.stlouisfed.org/graph/?g=w8ko Fear andGreed Index. (2020). https://money.cnn.com/data/fear-and-greed/
  6. Hong, S. (2020). A study on stock price prediction system based on text mining method using LSTM and stock market news. The Society of Digital Policy and Management, 18(7), 223-228. https://doi.org/10.14400/JDC.2020.18.7.223
  7. Joshi, H., Verma, A., & Mishra, A. (2020). Classification of Social Signals Using Deep LSTM-based Recurrent Neural Networks. 2020 International Conference on Signal Processing and Communications (SPCOM). doi:10.1109/spcom50965.2020.9179516
  8. Heesoo Hwang. (2018). Daily Stock Price Forecasting Using Deep Neural Network Model. Journal of the Korea Convergence Society, 9(6), 39-44. https://doi.org/10.15207/JKCS.2018.9.6.039
  9. Yongtaek Lim,Heuiseok Lim. (2020). A Comparative Analysis of the Prediction Models for the Direction of Stock Price Using the Online Company Reviews. Journal of the Korea Convergence Society, 11(8), 165-171. https://doi.org/10.15207/JKCS.2020.11.8.165
  10. Gyeahyung Jeon,Sun-young Kwon. (2018). A Study on the Influence of Economic Factors on Library Use. Journal of the Korea Convergence Society, 9(11), 299-306. https://doi.org/10.15207/JKCS.2018.9.11.299
  11. Yong Jae Shin. (2018). A Study on Effects of 6th Industry types on the Korean Economy. Journal of the Korea Convergence Society, 9(10), 325-338. https://doi.org/10.15207/JKCS.2018.9.10.325
  12. Billah, M., Waheed, S., & Hanifa, A. (2016). Stock market prediction using an improved training algorithm of neural network. 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE). doi:10.1109/icecte.2016.7879611
  13. Jeon, S., Hong, B., Kim, J., & Lee, H. (2016). Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis. Proceedings of the International Conference on Internet of Things and Big Data. doi:10.5220/0005876102230231
  14. Ghosh, A., Bose, S., Maji, G., Debnath, N., & Sen, S. (n.d.). Stock Price Prediction Using LSTM on Indian Share Market. doi:10.29007/qgcz