Predicting stock price based on BP neural network model

Predicting stock price based on BP neural network model

Yishuai Tian1*, Botao Liu1, Boying Lv1

1College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China.

Journal of Modern Economy

The increase in the stock will affect the development of the economy, so the description and forecast of the capital are fundamental. Therefore, this paper first analyzes the historical data of Everbright Bank stock (601818) and explains the stock increase, the share affected by the exchange rate, and the index correlation analysis. Then a prediction model based on BP neural network is established. BP is used to train stock descriptors to predict stock prices for the first 30 trading days in early 2019. Combined with ARE and MSE for error analysis, the average relative error is 3.6%, and the mean-variance is 0.041, indicating that the prediction effect of the model is good. To make a specific contribution to the study of stock price prediction.

Keywords: Stock forecast; BP neural network; Stock analysis error check

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How to cite this article:
Yishuai Tian, Botao Liu, Boying Lv. Predicting stock price based on BP neural network model. Journal of Modern Economy, 2020; 3:11. DOI: 10.28933/jme-2020-01-2005


1. Sha Wang. Research on the Application of BP Neural Network in Stock Forecast [D]. Central South University, 2008.
2. Murphy, John J. Intermarket Technical Analysis[J].
3. Deboeck G J. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets[M]. 1994.
4. Massimiliano, Marcellino, And, et al. Macroeconomic forecasting in the Euro area: Country specific versus area-wide information[J].
5. Kimoto T, Asakawa K, Yoda M, et al. Stock market prediction system with modular neural network: Neural Networks, 1990., 1990 IJCNN International Joint Conference on, 1990[C].
6. Liting Gu Zhongxing Ye. Two neural network methods for the classification of stock market change patterns [J]. Journal of Shanghai Jiao-tong University, 1995 (02): 100- 104.
7. Song Li, Lijun Liu, Chen Gu. Comparative study on prediction models of chaotic time series [J]. Computer engineering and application,2009,45 (32):53-56.
8. Kuihe Yang, Baoshu Wang, Lingling Zhao. Selection of input variables in prediction model based on neural network [J]. Computer science, 2003(08):139-140.
9. Dash R, Samal S, Dash R, et al. An integrated TOPSIS crow search based classifier ensemble: In application to stock index price movement prediction[J]. Applied Soft Computing Journal, 2019.
10. Jiang M, Liu J, Zhang L. An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms[J]. Physica A: Statistical Mechanics and its Applications, 2019.
11. Jiayu Q, Bin W, Changjun Z. Forecasting stock prices with long-short term memory neural network based on attention mechanism.[J]. PloS one, 2020,15(1).
12. Liu L, Pan Z. Forecasting stock market volatility: The role of technical variables[J]. Economic Modelling, 2020,84.
13. Hideki T, Reina H, Saori K, et al. [A factor analysis method for contingency table data with unlimited multiple choice questions].[J]. Shinrigaku kenkyu : The Japanese journal of psychology, 2016,86(6).
14. Islam B, Baharudin Z, Nallagownden P. Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid[J]. Neural Computing and Applications, 2017,28(1).
15. Zhiyuan M, Wei Z, Zhongbing L, et al. Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm.[J]. Ul-trasonics, 2020,100.
16. Yingchao Xu, Xiangyou Wang, Xiang Yin, et al. prediction of potato processing quality based on multiple non-linear regression analysis [J]. Journal of Agricultural Machinery, 2018, 49 (04): 366, 373.