Research And Implementation Of Stock Forecasting Method Based On Improved Neural Network

Since the birth of the stock market,many experts and investors at home and abroad have set foot in the research of the stock market.It is a subject to be explored in depth to discuss and predict the change trend of financial industry development from the research on the change law of financial market behavior.It is also a key basis for formulating financial plans and making decisions.As far as the research on financial time series is concerned,stock forecasting has always been a difficult problem and also a hot issue.The traditional stock forecasting technology has become more and more cumbersome,while consuming a lot of time.Today,with the rapid development of artificial intelligence,the application of machine learning to stock research and the application of neural networks to the financial field indicate a big turnaround in stock price forecasting.Effective financial forecasting is of great significance to real life.It plays an important role in the development of the national economy.Therefore,the application of convolutional neural network in stock forecasting is worthy of in-depth discussion by researchers.This paper first summarizes the current domestic and foreign stock price forecasting methods related to machine learning,and explains the application value of convolutional neural network in stock price forecasting.Secondly,the paper focuses on the construction and basic working principle of convolutional neural network,as well as the construction technology of convolutional neural network model.It uses artificial intelligence to predict stocks,and uses machine learning algorithms to study it.In this paper,the prediction model of convolutional neural network is constructed,combined with artificial intelligence to predict stocks,and machine learning method is used to explore it.This paper constructs a prediction model of convolutional neural network.Combining the data of individual stocks in Shanghai and Shenzhen,it uses its strong supervision and learning ability,as well as the unique feature extraction ability of convolutional neural network to study the attributes of financial data,and obtains a model that can predict stock prices.It is applied to predict the future trend of stock prices,and optimizes the model with multiple parameters.Among them,the process of optimization is a crucial step,Finally,satisfactory results were obtained.Finally,combined with the characteristics of stock basic data and financial data,Dropout is introduced to improve the model,and the prediction effect before and after the improvement is compared and analyzed,which shows that the method has good application prospects and improves the prediction effect of the model after adding Dropout.The experimental results show that compared with the improved model,the optimized convolution neural network prediction model has achieved better prediction results.