Effectiveness of and bp-neural Network for Overnight Stock Price Forecasting
An example of CSI 300
DOI:
https://doi.org/10.54097/fbem.v11i1.11599Keywords:
Neural Network, Prediction Model, Big Data.Abstract
In view of the empirical problem of introducing machine learning into stock price prediction and the unique condition of Chinese stock market represented by the limitation of rise and fall, In our research, we take CSI300 component stock as samples, using BP neural network to predict stock price, introduce the factor representing the Piece Limit System into the analysis, and demonstrate the effectiveness of Chinese A-share market and the feasibility of neural network to forecast stock price. The study find that the BP neural network further improves the accuracy of stock price prediction with the function of logistic regression, and has a better AUC performance in the sample interval. Our research optimizes the machine learning process to provide more efficient empirical evidence of machine learning in Chinese stock market prediction in recent years.
Downloads
References
Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of financial economics, 104(2), 272-287.
Kutsurelis, J. E. (2012). Forecasting financial markets using neural networks: An analysis of methods and accuracy. Monterey, California: Naval Postgraduate School; Springfield, Va.: Available from National Technical Information Service.
Ramezanian, M. R., Shaverdi, M., & Faridi, A. (2011). Combination Neural Network and Financial Indices for stock price prediction. Journal of Applied Sciences, 11(19), 3429-3435.
De Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert systems with applications, 40(18), 7596-7606.
Buscema, M. (1998). Back propagation neural networks. Substance use & misuse, 33(2), 233-270.
Li, J., Cheng, J. H., Shi, J. Y., & Huang, F. (2012). Brief introduction of back propagation (BP) neural network algorithm and its improvement. In Advances in Computer Science and Information Engineering: Volume 2 (pp. 553-558). Springer Berlin Heidelberg.
Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639-1664.
Waqar, M., Dawood, H., Guo, P., Shahnawaz, M. B., & Ghazanfar, M. A. (2017, December). Prediction of stock market by principal component analysis. In 2017 13th International conference on computational intelligence and security (CIS) (pp. 599-602). IEEE.
Wen, Y., Lin, P., & Nie, X. (2020, March). Research of stock price prediction based on PCA-LSTM model. In IOP Conference Series: Materials Science and Engineering (Vol. 790, No. 1, p. 012109). IOP Publishing.
Tanaka, K., Higashide, T., Kinkyo, T., & Hamori, S. (2019). Analyzing industry‐level vulnerability by predicting financial bankruptcy. Economic Inquiry, 57(4), 2017-2034.
Beutel, J., List, S., & von Schweinitz, G. (2019). Does machine learning help us predict banking crises?. Journal of Financial Stability, 45, 100693.
