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On Mixed Model for Improvement in Stock Price Forecasting

Qunhui Zhang1, Mengzhe Lu3,4, Liang Dai2,*

1 Hunan Vocational College of Science and Technology, Changsha, China
2 Hunan University of Finance and Economics, Changsha, China
3 Xiangtan University, Xiangtan, China
4 University Malaysia Sabah, Kota Kinabalu, Malaysia

* Corresponding Author: Liang Dai. Email:

Computer Systems Science and Engineering 2022, 41(2), 795-809.


Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. But the fact is that forecasting stock prices by using various models has been suffering from low accuracy, slow convergence, and complex parameters. This study aims to employ a mixed model to improve the accuracy of stock price prediction. We present how to use a random walk based on jump-diffusion, to obtain stock predictions with a good-fitting degree by adjusting different parameters. Aimed at getting better parameters and then using the time series model to predict the data, we employed the time series model to smooth the sequence utilizing logarithm and difference, which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure. According to the comparative analysis, which focuses on checking the mean absolute error, including root mean square error and R square evaluation index, we have drawn a clear conclusion that our mixed model prediction effect is relatively good. In the context of Chinese stocks, the hybrid random walk model is very suitable for predicting stocks. It can “interpret” the randomness of stocks very well, and it also has an unparalleled prediction effect compared with other models. Based on the time series model’s application in this paper, the above-mentioned series is more suitable for predicting trends.


Cite This Article

Q. Zhang, M. Lu and L. Dai, "On mixed model for improvement in stock price forecasting," Computer Systems Science and Engineering, vol. 41, no.2, pp. 795–809, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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