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Stock Price Prediction Based on Data Mining Combination Model

Stock Price Prediction Based on Data Mining Combination Model

To-Han Chang, Nientsu Wang, Wen-Bin Chuang
Copyright: © 2022 |Volume: 30 |Issue: 7 |Pages: 19
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668435700|DOI: 10.4018/JGIM.296707
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MLA

Chang, To-Han, et al. "Stock Price Prediction Based on Data Mining Combination Model." JGIM vol.30, no.7 2022: pp.1-19. http://doi.org/10.4018/JGIM.296707

APA

Chang, T., Wang, N., & Chuang, W. (2022). Stock Price Prediction Based on Data Mining Combination Model. Journal of Global Information Management (JGIM), 30(7), 1-19. http://doi.org/10.4018/JGIM.296707

Chicago

Chang, To-Han, Nientsu Wang, and Wen-Bin Chuang. "Stock Price Prediction Based on Data Mining Combination Model," Journal of Global Information Management (JGIM) 30, no.7: 1-19. http://doi.org/10.4018/JGIM.296707

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Abstract

Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.