FINANCIAL TIME SERIES USING NONLINEAR DIFFERENTIAL EQUATION OF GAUSSIAN DISTRIBUTION PROBABILITY DENSITY

Pengbo Wan, Nawaf Alhebaishi () and Qi Liu
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Pengbo Wan: Finance School, Hubei University of Economics, Wuhan, P. R. China
Nawaf Alhebaishi: ��Information System Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Qi Liu: ��School of Employment and Entrepreneurship, Hubei University of Technology, Wuhan, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-12

Abstract: To further explore the research of financial time series prediction and broaden the application scope of Gaussian distribution probability density equation, based on the nonlinear differential equation of Gaussian distribution probability density, the semi-supervised Gaussian process model is taken as the research object to discuss the application of semi-supervised Gaussian process model in the stock market. Shanghai 180 Index, Shanghai (Securities) Composite Index and the yield of three stocks are studied in detail. The specific results are as follows. The prediction accuracy of Shanghai 180 Index is 83%, and the prediction accuracy of Shanghai (Securities) Composite Index is 78%. The stock yield series curves of the three stocks have the characteristics of peak and thick tail, which do not obey the normal distribution. Among the three models of semi-supervised Gaussian process model, initial Gaussian process model and SVM algorithm model, the prediction accuracy of semi-supervised Gaussian process model is the best, and the prediction accuracy of the yield of three stocks is 82.45%, 85.03% and 84.53%, respectively. The research results can fully prove that the semi-supervised Gaussian process model has good application effect in stock time series prediction. The research content can provide scientific and sufficient reference for the follow-up research of financial time series, and also has important significance for the research of Gaussian process model.

Keywords: Gaussian Distribution; Gaussian Process; Financial Time Series Research; Nonlinear (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22400849

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