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
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Revista:
International Journal of Data Warehousing and Mining

ISSN: 1548-3924,  1548-3932

Año de publicación: 2022

Volumen: 18

Número: 1

Páginas: 1-19

Tipo: Artículo

DOI: 10.4018/IJDWM.309957 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International Journal of Data Warehousing and Mining

Resumen

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.

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