Prediction of stock prices based on LM-BP neural network and the estimation of overfitting point by RDCI

The prediction of stock prices has been a major area of interest in recent years, and many methods have been applied in this field. In this paper, to determine the method to predict stock prices, a 25-7-5 three-layer BP neural network based on a time series is constructed considering the daily opening price, highest price, lowest price, closing price and trading volume. A network based on a time series can reflect the trend of stock prices in a period more comprehensively. There are some disadvantages of the traditional BP neural network training algorithm to predict stock prices with large quantities of sample data and large parameters to be estimated in neural networks such as slow training speed and low accuracy. In this paper, the LM-BP algorithm is proposed to overcome these disadvantages. The network structure of stock price prediction based on the LM-BP neural network is given in this paper. Currently, there is no reliable theory to determine the overfitting critical point. In this paper, the repeated division and count in intervals (RDCI) method is proposed for the lack of research in this area. In this paper, the curves of MRE2–MRE1 are drawn, and the fitting accuracy corresponding to the best prediction accuracy of the BP neural network is reasonably estimated based on several independent repeated tests. The experiments indicate that the prediction of stock prices based on the LM-BP neural network and the estimation of the overfitting point by RDCI in this paper achieves better results than existing methods.