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

Author

Listed:
  • Jimmy Ming-Tai Wu

    (Shandong University of Science and Technology, China)

  • Lingyun Sun

    (Shandong University of Science and Technology, China)

  • Gautam Srivastava

    (Brandon University, Canada and China Medical University, Taiwan)

  • Vicente Garcia Diaz

    (Universidad de Oviedo, Spain)

  • Jerry Chun-Wei Lin

    (Western Norway University of Applied Sciences, Norway)

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.

Suggested Citation

  • Jimmy Ming-Tai Wu & Lingyun Sun & Gautam Srivastava & Vicente Garcia Diaz & Jerry Chun-Wei Lin, 2022. "A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 18(1), pages 1-19, January.
    • Handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-19
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    References listed on IDEAS

    as
    1. Lee, Charles M C & Shleifer, Andrei & Thaler, Richard H, 1991. "Investor Sentiment and the Closed-End Fund Puzzle," Journal of Finance, American Finance Association, vol. 46(1), pages 75-109, March.
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