Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 115, Issue -, Pages 872-879Publisher
ELSEVIER
DOI: 10.1016/j.future.2020.10.009
Keywords
Neural network; BP algorithm; Stock price prediction; Stock price pattern classification
Categories
Funding
- Heilongjiang Province Philosophy and Social Science Fund Project: Research on the Performance and Influencing Factors of Grain Subsidy Policy in the Agricultural Comprehensive Reform Experimental Area of Liangjiang Plain in Heilongjiang Province [17JYC144]
- Youth Innovative Talents Training Program in General Undergraduate Colleges and Universities in Heilongjiang Province: Research on the Development of New Rural Cooperative Financial Organizations in Major Grain Producing Areas Based on Farmers' Perspective [UNPYSCT2017203]
- Harbin University of Commerce School-level Project: Research on the Development of New Rural Cooperative Financial Organizations in Heilongjiang Province Based on Farmers' Perspective [17XN050]
- Heilongjiang Province Philosophy and Social Science Fund Project: Research on the coordination of grain production and farmers' income in Heilongjiang Province Based on the perspective of targeted poverty alleviation [18JYE669]
- Youth Innovative Talents Training Program in General Undergraduate Colleges and Universities in Heilongjiang Province [UNPYSCT2018125]
- Harbin University of Commerce School-level Project [17XN049]
- Special support project for postdoctoral in Heilongjiang Province [LBH-Z19073]
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Under the background of big data and Internet finance, quantitative investment is becoming more critical. The study applied neural network and BP algorithm to classify and predict stock price patterns, with BP algorithm neural network showing better prediction accuracy than deep learning fuzzy algorithm. The best prediction effect was achieved when the prediction range was within 15 days.
Under the background of big data and Internet finance, quantitative investment is becoming more and more critical, and the prediction of the stock price has become the focus of investors' concern and research. The purpose of this work is to apply neural network and BP algorithm onto the classification and prediction of stock price patterns. The method is to use the BP algorithm neural network for the transaction data of 5 consecutive days as input samples, so there are 20 input layer nodes. The final value of the next day is used as the output sample, and the number of nodes in the output layer is 1. The purpose of network training is to find 20 spline functions. After the training of the BP algorithm neural network, the test data (stock price data for 5 consecutive days) independent of the training data is leveraged as the input of the neural network, and the closing price of the next day is used as the target output of the network. Through the error between the actual output and the target output, the stock price prediction performance of the network model is analyzed. The results have shown that the prediction accuracy of the stock price is 62.12% under the prediction of deep learning fuzzy algorithm and 73.29% under the prediction of the BP algorithm neural network. When the prediction range is between 15 days, the error of 30 prediction values relative to the real value is within +/- 10%, accounting for 90% of the total days, and the prediction effect is the best. By analyzing the prediction of the number of hidden layers on the stock price and different ranges, it can be concluded that the prediction of the stock price trend prediction model of BP algorithm neural network is better than that of the deep learning fuzzy algorithm prediction model. This algorithm provides investors with a certain value for stock forecasting, which makes government gain a more active position in macroeconomic regulation and control. (C) 2020 Elsevier B.V. All rights reserved.
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