Forecasting stock prices based on multivariable fuzzy time series

Zhi Liu; Zhi Liu

doi:10.3934/math.2023643

AIMS Mathematics

2023, Volume 8, Issue 6: 12778-12792. doi: 10.3934/math.2023643

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Research article

Forecasting stock prices based on multivariable fuzzy time series

Zhi Liu ^,

Department of Basic, Shenyang University of Technology, Liaoyang, Liaoning, China

Received: 26 December 2022 Revised: 17 March 2023 Accepted: 20 March 2023 Published: 30 March 2023
MSC : 62P20

With the development of the stock market, the proportion of the stock assets in the asset structure of the residents increases rapidly. Therefore, the research on the prediction of stocks has great theoretical significance and application potential. A key point of researching stock prices is how to pick out the main factors. In this study, principal component analysis (PCA) is applied to find out the main factors which mainly affect the stock price. Then an improved cluster analysis algorithm is proposed to fuzzy the data, and a qualitative analysis method is given to find the most suitable prediction set from the multiple fuzzy sets corresponding to the current fuzzy set. We also extend the inverse fuzzy number formula to a more general form to get the predicted value. Finally, Xishan Coal and Electricity Power (XSCE) and Taiwan Futures Exchange (TAIFEX) time series are predicted, using the proposed multivariate fuzzy time series method. The results show that the prediction error is lower than that of the previous models. The proposed method produces better forecasting performance.
- quantitative analysis,
- qualitative analysis,
- inverse fuzzy number,
- stock prices,
- fuzzy time series
Citation: Zhi Liu. Forecasting stock prices based on multivariable fuzzy time series[J]. AIMS Mathematics, 2023, 8(6): 12778-12792. doi: 10.3934/math.2023643

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Abstract

With the development of the stock market, the proportion of the stock assets in the asset structure of the residents increases rapidly. Therefore, the research on the prediction of stocks has great theoretical significance and application potential. A key point of researching stock prices is how to pick out the main factors. In this study, principal component analysis (PCA) is applied to find out the main factors which mainly affect the stock price. Then an improved cluster analysis algorithm is proposed to fuzzy the data, and a qualitative analysis method is given to find the most suitable prediction set from the multiple fuzzy sets corresponding to the current fuzzy set. We also extend the inverse fuzzy number formula to a more general form to get the predicted value. Finally, Xishan Coal and Electricity Power (XSCE) and Taiwan Futures Exchange (TAIFEX) time series are predicted, using the proposed multivariate fuzzy time series method. The results show that the prediction error is lower than that of the previous models. The proposed method produces better forecasting performance.

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