A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System

  1. Wu, Jimmy Ming-Tai 1
  2. Sun, Lingyun 1
  3. Srivastava, Gautam 3
  4. Diaz, Vicente Garcia 4
  5. Lin, Jerry Chun-Wei 2
  1. 1 Shandong University of Science and Technology, China
  2. 2 Western Norway University of Applied Sciences, Norway
  3. 3 Brandon University, Canada and China Medical University, Taiwan
  4. 4 Universidad de Oviedo, Spain
Show affiliations +
Journal:
International Journal of Data Warehousing and Mining

ISSN: 1548-3924,  1548-3932

Year of publication: 2022

Volume: 18

Issue: 1

Pages: 1-19

Type: Article

DOI: 10.4018/IJDWM.309957 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: International Journal of Data Warehousing and Mining

Abstract

This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

Bibliographic References

  • 10.1016/S0304-405X(98)00052-X
  • 10.1111/j.1540-6261.2006.00885.x
  • 10.1371/journal.pone.0180944
  • 10.1002/for.2585
  • 10.1002/(SICI)1099-131X(199801)17:1<59::AID-FOR676>3.0.CO;2-H
  • C. H.Chen, (2019), Knowledge and Information Systems, pp. 1
  • Chong, T. T., Cao, B., & Wong, W. (2014). A new principal-component approach to measure the investor sentiment. IGEF Working Paper, 24, 1-19.
  • 10.1109/ACCESS.2014.2352261
  • 10.1007/BF00994018
  • 10.2307/1907042
  • 10.1016/j.eswa.2013.06.071
  • L.Di Persio, (2016), Systems and Signal Processing, 10, pp. 403
  • 10.1016/j.knosys.2017.09.023
  • 10.1016/j.dss.2013.02.006
  • 10.1093/rfs/hhm071
  • 10.2139/ssrn.2311310
  • Hiew, J. Z. G., Huang, X., Mou, H., Li, D., Wu, Q., & Xu, Y. (2019). BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability. arXiv preprint arXiv:1906.09024.
  • 10.1145/1569901.1570106
  • 10.1016/j.cor.2004.03.016
  • 10.1016/0304-405X(86)90070-X
  • 10.1371/journal.pone.0212320
  • 10.1109/IJCNN.1990.137535
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint 576 arXiv:1412.6980.
  • 10.1111/j.1540-6261.1991.tb03746.x
  • L.Lin, (2004), Management Information Systems, 33, pp. 273
  • 10.1007/s00521-020-05532-z
  • 10.1016/j.asoc.2015.06.040
  • Samuelson, P. A. (1975). Lifetime portfolio selection by dynamic stochastic programming. Stochastic Optimization Models in Finance, 517-524.
  • 10.1109/IAT.2004.1342986
  • 10.1109/ICACCI.2017.8126078
  • A. F.Sheta, (2015), Soft Computing, 4, pp. 55
  • Siripurapu, A. (2014). Convolutional networks for stock trading. Stanford University Department of Computer Sciences.
  • 10.1016/0261-5606(92)90048-3
  • 10.1007/s00530-021-00758-w
  • 10.1002/spe.2915
  • 10.1109/SMC.2018.00272
  • 10.1080/00036846.2019.1624920