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A novel fuzzy-Markov forecasting model for stock fluctuation time series

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Abstract

In order to reveal intrinsic fluctuation rules hidden in a stock market time series dataset with noise, a novel forecasting model combining Markov chain theory with fuzzy set theory is proposed in this study. A fuzzified one-step transition matrix of Markov Chain in the paper represents inherent rules of historical fluctuation. Comparing with existing models, the advantage of the proposed model is that transition matrix can express the relationship between history and current flexibly while the introduction of fuzzy theory can help to alleviate noises. Therefore, the proposed model could handle complex patterns during state transitions and the relatively simple forecasting algorithm could reduce the calculation cost. We apply the proposed method to forecast well-known stock indexes such as (Taiwan Stock Exchange Capitalization Weighted Stock Index) TAIEX, (Shanghai Stock Exchange Composite Index) SHSECI and so on. Experimental results demonstrate that our proposed method outperforms other traditional models.

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Acknowledgements

The authors are indebted to anonymous reviewers for their very insightful comments and constructive suggestions, which help ameliorate the quality of this paper. This work was supported by major projects of the National Social Science Foundation of China under Grants with No. 19VHQ011.

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, 250014, China

    Hongjun Guan, He Jie & Aiwu Zhao

  2. Courant Institute of Mathematical Sciences, New York University, New York, 10012, USA

    Shuang Guan

Authors
  1. Hongjun Guan
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  2. He Jie
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  3. Shuang Guan
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  4. Aiwu Zhao
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Correspondence to Aiwu Zhao.

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Appendix

Appendix

See Tables 6, 7, 8, 9 and 10.

Table 6 Historical training data and fuzzified fluctuation data of TAIEX 1999
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Table 7 The degree memberships for testing data of TAIEX 1999
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Table 8 Forecasting results from 1 November 1999 to 30 December 1999
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Table 9 The probability of state transitions for training data of TAIEX 1999 (five states)
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Table 10 The probability of state transitions for training data of TAIEX 1999 (nine states)
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Guan, H., Jie, H., Guan, S. et al. A novel fuzzy-Markov forecasting model for stock fluctuation time series. Evol. Intel. 13, 133–145 (2020). https://doi.org/10.1007/s12065-019-00328-0

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