Simulation Of Stock Market Forecasting Based On BP Neural Network

As an important part of my country's socialist market economy,the stock market is self-evident.The reason why the stock market can develop healthily and steadily is closely related to the current world economic environment and relevant national policies.Therefore,there are often large fluctuations in the stock market.For investors,such fluctuations translate into the investment risk of every investor.In order to further diversify their investment risks,most investors will choose to invest in multiple stocks to avoid risks.However,if there is a way to provide investors with reasonable advice more accurately,it will bring relatively stable returns for investors.Through analysis,we can see that the stock price trend is similar to the economic cycle and has certain regularity.We can try to describe it through a nonlinear function and further predict the stock price.Therefore,we can introduce neural network technology into stock price prediction.By learning the specified samples,a connection is established between the input layer,the hidden layer,and the output layer to predict the stock price in a certain period in the future..I believe that the analysis of neural network models can bring more scientific and reasonable investment advice to investors in many stocks.First,this article explains the relevant concepts and properties of stocks,and conducts technical analysis in conjunction with the relevant forecasting principles.It introduces three methods of technical analysis in detail,basic analysis,technical analysis,and quantitative analysis;then,introduces this article Using the neural network theory,starting from the basic characteristics of neurons,the concept,basic principles and basic characteristics of neural networks are introduced,and the training process is deduced in detail.Then,the MATLAB that needs to be used in this article is explained.In the process of starting from the concept and basic characteristics,through the MATLAB toolbox-neural network toolbox,the stock price and trend of the stock are simulated;furthermore,the algorithm used is LM.The algorithm applies the LM algorithm to the BP neural network,and then introduces the existing LM-BP neural network model.For the traditional neural network in the stock price prediction,it is easy to fall into the local optimum and the prediction accuracy is low.The advantage of LM-BP neural network model in facing this problem,then,further elaborate on the model's thinking in predicting the stock price of each stock in the stock market.Finally,based on the LM-BP neural network model,MATLAB was used to simulate the above algorithm model.The five stocks randomly selected in the flush software were used to export and organize their historical data as the experimental samples for the study.At the same time,the time series For each stock,the analysis is performed from the perspective of the highest price,the lowest price,the opening price,and the closing price,and the relative error and absolute error charts are generated.By analyzing the relevant experimental results generated,it is proved that the LM-BP neural network prediction model has good accuracy and stability in the short-term prediction of stock prices,and can make reasonable and effective predictions for investors.