The application research of neural network and BP algorithm in stock price pattern classification and prediction

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.

Automated summary

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.