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A second-order fuzzy time series model for stock price analysis

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  • Zhi Liu
  • Tie Zhang

Abstract

It is difficult to model stock market because of its uncertainty. Many methods have been introduced to tackle these difficulties, in which fuzzy time series has shown its advantages in dealing with fuzzy and uncertainty data. In recent years, many researchers have applied the fuzzy time series to analyze and forecast the stock price, and how to improve the accuracy of forecasting has attracted many researchers. In this paper, the data are first preprocessed and a new way to divide the universe of discourse is given, after which the data are fuzzified applying the triangular membership function, then three-layer back propagation (BP) neural network is established. Finally, the generalized inverse fuzzy number formula is applied to defuzzify the relation obtained with the prediction results. The proposed method is applied to predict the stock price of State Bank of India (SBI) and Dow-Jones Industrial Average (DJIA). The experimental results show that the proposed method can greatly improve the accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.

Suggested Citation

  • Zhi Liu & Tie Zhang, 2019. "A second-order fuzzy time series model for stock price analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(14), pages 2514-2526, October.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2514-2526
    DOI: 10.1080/02664763.2019.1601163
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    Cited by:

    1. Eren Bas & Erol Egrioglu & Taner Tunc, 2023. "Multivariate Picture Fuzzy Time Series: New Definitions and a New Forecasting Method Based on Pi-Sigma Artificial Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 139-164, January.
    2. Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
    3. Tai Vo-Van & Ha Che-Ngoc & Nghiep Le-Dai & Thao Nguyen-Trang, 2022. "A New Strategy for Short-Term Stock Investment Using Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 887-911, February.

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