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OWA-based ANFIS model for TAIEX forecasting

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  • Cheng, Ching-Hsue
  • Wei, Liang-Ying
  • Liu, Jing-Wei
  • Chen, Tai-Liang

Abstract

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.

Suggested Citation

  • Cheng, Ching-Hsue & Wei, Liang-Ying & Liu, Jing-Wei & Chen, Tai-Liang, 2013. "OWA-based ANFIS model for TAIEX forecasting," Economic Modelling, Elsevier, vol. 30(C), pages 442-448.
    • Handle: RePEc:eee:ecmode:v:30:y:2013:i:c:p:442-448
    DOI: 10.1016/j.econmod.2012.09.047
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    References listed on IDEAS

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    Cited by:

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    2. Kaur, Gurbinder & Dhar, Joydip & Guha, Rangan Kumar, 2016. "Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 122(C), pages 69-80.
    3. Chen, Shiyu & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A visibility graph averaging aggregation operator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 1-12.
    4. Egrioglu, Erol, 2014. "PSO-based high order time invariant fuzzy time series method: Application to stock exchange data," Economic Modelling, Elsevier, vol. 38(C), pages 633-639.
    5. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    6. Bundzel, Marek & Kasanický, Tomáš & Pinčák, Richard, 2016. "Using string invariants for prediction searching for optimal parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 680-688.
    7. Reza Kiani Mavi & Neda Kiani Mavi & Mark Goh, 2017. "Modeling corporate entrepreneurship success with ANFIS," Operational Research, Springer, vol. 17(1), pages 213-238, April.
    8. Marek Bundzel & Tomas Kasanicky & Richard Pincak, 2016. "Using String Invariants for Prediction Searching for Optimal Parameters," Papers 1606.06003, arXiv.org.
    9. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.

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