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 JFRM  Vol.7 No.4 , December 2018
The Forecasting Model of Stock Price Based on PCA and BP Neural Network
Abstract: Based on Principal Component Analysis (PCA) and Back Propagation neural network, this paper establishes stock forecast model, and takes the Yunnan Baiyao (000538) as example, 29 indicators are selected from stocks technical analysis, and the neural network is input after dimension reduction and further confirms number of hidden layer nodes, learning rate, activation function and training function of the network in accordance with comparison and analysis of Mean Square Error (MSE) and Mean Absolute Error (MAE) in different parameter data experiments. Lastly, the model with steadiness and accuracy is obtained.
Cite this paper: Zhang, H. (2018) The Forecasting Model of Stock Price Based on PCA and BP Neural Network. Journal of Financial Risk Management, 7, 369-385. doi: 10.4236/jfrm.2018.74021.
References

[1]   Bao, Y. L. (2013). Research on the Analysis and Predictive Algorithm of Financial Time Series Based on the Support Vector Machine. Dalian: Dalian Maritime University.

[2]   Bollerslev, T., Cai, J., & Song, F. M. (2000). Intraday Periodicity, Long Memory Volatility, and Macroeconomic Announcement Effects in the US Treasury Bond Market. Journal of Empirical Finance, 7, 37-55.
https://doi.org/10.1016/S0927-5398(00)00002-5

[3]   Chen, J. Y. (2014). The Application of Data Mining in the Stock Analysis. Guangzhou: South China University of Technology.

[4]   Dose, C., & Cincotti, S. (2005). Clustering of Financial Time Series with Application to Index and Enhanced Index Tracking Portfolio. Physical A: Statistical Mechanics and Its Applications, 355, 145-151.
https://doi.org/10.1016/j.physa.2005.02.078

[5]   Du, X. P. (2006). The Securities Situation Assessment System Based on Data Mining. Tianjin: Tianjin University.

[6]   Fiol-Roig, G., Miro-Julia, M., & Isern-Deya, A. P. (2010). Applying Data Mining Techniques to Stock Market Analysis. In Y. Demazeau, et al. (eds.), Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing (Vol. 71, pp. 519-527). Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-12433-4_61

[7]   Golan, R. H., & Ziarko, W. (1995). A Methodology for Stock Market Analysis Utilizing Rough Set Theory. Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr), New York, NY.
https://doi.org/10.1109/CIFER.1995.495230

[8]   Guo, S. H. (2010). Stock Analysis Simulation System Based on Apriori Algorithm. Computer Simulation, 27, 334-337.

[9]   Hao, Z. Y. (2017). A Stock Prediction System Based on Data Mining. Nanjing: Nanjing University of Science and Technology.

[10]   Kumar, L., Pandey, A., Srivastava, S., et al. (2011). A Hybrid Machine Learning System for Stock Market Forecasting. Proceedings of World Academy of Science Engineering & Technology, 315-318.

[11]   Li, Y. Q., & Song, W. (2013). Prediction of Stock Price Trend Based on BP Neural Network. Journal of North China University of Technology, 25, 11-16.

[12]   Liu, N. K., & Lee, K. K. (1997). An Intelligent Business Advisor System for Stock Investment. Expert Systems, 14, 129-139.
https://doi.org/10.1111/1468-0394.00049

[13]   Ma, C. Q., Lan, Q. J., & Chen, W. M. (2007). Financial Data Mining. Beijing: Science Press.

[14]   Panigrahi, S. S., & Mantri, J. K. (2015). Epsilon-SVR and Decision Tree for Stock Market Forecasting. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida.
https://doi.org/10.1109/ICGCIoT.2015.7380565

[15]   Qiao, J. W. (2013). Application of BP Neural Network in Stock Investment Analysis. Chengdu: University of Electronic Science and Technology of China.

[16]   Refenes, A. N., & Latif, E. A. (2015). Assessing the Quality of Service Using Big Data Analytics: With Application to Healthcare. Big Data Research, 4, 13-24.

[17]   Srikant, R., & Agrawal, R. (2010). Mining Quantitative Association Rules in Large Relational Tables. Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, 1-12.

[18]   Sun, L. P. (2013). The Application and Research of Data Mining in Stock Analysis. Chengdu: Southwestern University of Finance and Economics.

[19]   Ticknor, J. L. (2013). A Bayesian Regularized Artificial Neural Network for Stock Market Forecasting. Expert Systems with Applications, 40, 5501-5506.
https://doi.org/10.1016/j.eswa.2013.04.013

[20]   Tsaih, R., Hsu, Y., & Lai, C. C. (1998). Forecasting S & P 500 Stock Futures with a Hybrid AI System. Decision Support System, 23, 161-174.
https://doi.org/10.1016/S0167-9236(98)00028-1

[21]   Wang, B. (2010). Data Mining Concepts and Techniques. Beijing: Mechanical Industry Press.

[22]   Yang, T. (2017). Stock Ranking Based on Machine Learning. Tianjin: Tianjin Polytechnic University.

[23]   Yuan, C. A. (2009). Principles of Data Mining and SPSS Clementine Application Collection. Publishing House of Electronics Industry. 16-23, 176-187, 311-355.

[24]   Zhang, J. J. (2010). Analysis Stocks Based on the Data Mining. Beijing: China University of Petroleum.

[25]   Zhang, J. N. (2009). Data Mining and Application. Beijing: Peking University Press.

[26]   Zhang, W. T., & Dong, W. (2013). SPSS Statistical Analysis Advanced Course. Beijing: Higher Education Press.

 
 
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