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Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-series

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  • Tai-Liang Chen

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    (Wenzao Ursuline College of Languages, Republic of China)

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    Abstract

    Fuzzy time-series models have been utilized in making reasonably accurate predictions in many areas, such as academic enrollments, weather forecasting and stock markets. To refine past fuzzy time-series models, this paper proposes a new model, which employs the concepts of ¡°momentum¡± along with Chebyshev¡¯s theorem in the forecasting process. The proposed model applies a ¡°momentum¡± index to generate forecasting rules (fuzzy logical relationships) to reduce the probability of rules not being found in cases where no rules are available to forecast a testing dataset. Chebyshev¡¯s theorem is adopted to define a ¡°reasonable¡± universe of discourse for the observations in a training dataset. From the refined process, two types of universe, symmetrical and asymmetrical, are given. To verify the proposed model, this paper employs experimental datasets, derived from a seven-year period of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Model comparison results show that the proposed model surpasses in accuracy one traditional fuzzy time-series model and two advanced models, based on neural networks and rough set algorithms.

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    Bibliographic Info

    Article provided by Better Advances Press, Canada in its journal Review of Economics & Finance.

    Volume (Year): 2 (2012)
    Issue (Month): (February)
    Pages: 38-50

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    Handle: RePEc:bap:journl:120103

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    Related research

    Keywords: Fuzzy time-series; Stock price forecasting; Fuzzy linguistic variable;

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    References

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    1. Wang, Hui & Pandey, Ras B, 2004. "A momentum trading approach to technical analysis of Dow Jones industrials," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 331(3), pages 639-650.
    2. 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, Elsevier, vol. 363(2), pages 481-491.
    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, Elsevier, vol. 380(C), pages 377-390.
    4. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 349(3), pages 609-624.
    5. 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, Elsevier, vol. 387(4), pages 876-888.
    6. 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, Elsevier, vol. 353(C), pages 445-462.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
    8. Yu, Hui-Kuang, 2005. "A refined fuzzy time-series model for forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 346(3), pages 657-681.
    9. Tanaka-Yamawaki, Mieko & Tokuoka, Seiji, 2007. "Adaptive use of technical indicators for the prediction of intra-day stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 383(1), pages 125-133.
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