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Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models

Author

Listed:
  • Yong Shi

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences
    Southwest Minzu University)

  • Bo Li

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Wen Long

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Wei Dai

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

Technical analysis indicators are widely used in the field of quantitative investment, and they are usually utilized to assist in the search for profitable buy and sell points. In order to make better use of technical indicators, a method of trying to improve the performance of technical indicators by using neural network models is proposed in this work. The method tries to utilize neural network models to learn the possible patterns or features of price and volume before the profitable buy or sell points indicated by technical indicators. In modeling, a certain length of historical market data before the buy and sell points indicated by technical indicators is taken as model inputs, and whether or not can these buy and sell points meet certain profit standard is taken as labels. We validate our method on stock indexes, stocks and futures, and the results show that our method can improve the performance of several simple but common strategies based on technical analysis indicators.

Suggested Citation

  • Yong Shi & Bo Li & Wen Long & Wei Dai, 2022. "Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1027-1068, March.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:3:d:10.1007_s10614-021-10116-7
    DOI: 10.1007/s10614-021-10116-7
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    References listed on IDEAS

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    1. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    2. O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
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    Cited by:

    1. Yanbo Zhang & Mengkun Liang & Haiying Ou, 2024. "Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3243-3278, December.

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