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A Type 2 fuzzy time series model for stock index forecasting

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  • Huarng, Kunhuang
  • Yu, Hui-Kuang

Abstract

Most conventional fuzzy time series models (Type 1 models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize more of that variable's observations in forecasting, this study proposes the use of a Type 2 fuzzy time series model. In such a Type 2 model, extra observations are used to enrich or to refine the fuzzy relationships obtained from Type 1 models and then to improve forecasting performance. The Taiwan stock index, the TAIEX, is used as the forecasting target. The study period extends over the 2000–2003 period. The TAIEX from January to October in each year is used for the estimation, while that covering November and December is used for forecasting. The empirical analyses show that Type 2 model outperforms Type 1 model.

Suggested Citation

  • Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
    • Handle: RePEc:eee:phsmap:v:353:y:2005:i:c:p:445-462
    DOI: 10.1016/j.physa.2004.11.070
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    Cited by:

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    3. Chen, Tai-Liang & Cheng, Ching-Hsue & Jong Teoh, Hia, 2007. "Fuzzy time-series based on Fibonacci sequence for stock price forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 377-390.
    4. Jilani, Tahseen Ahmed & Burney, Syed Muhammad Aqil, 2008. "A refined fuzzy time series model for stock market forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2857-2862.
    5. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    6. Ni, Yensen & Wu, Manhwa & Day, Min-Yuh & Huang, Paoyu, 2020. "Do sharp movements in oil prices matter for stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    7. Chen, Tai-Liang & Cheng, Ching-Hsue & Teoh, Hia-Jong, 2008. "High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 876-888.
    8. Tai-Liang Chen, 2012. "Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-series," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 38-50, February.
    9. Zhou, Qin & Shang, Pengjian, 2020. "Weighted multiscale cumulative residual Rényi permutation entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    10. 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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