Research On Stock Prediction Based On LSTM

The stock market is an important part of the national economy.With the improvement of people's living standards in recent years,more and more people are investing in stocks.Stock forecasting is a study that every investor is trying.The ordinary investors confirm the stock selection through technical analysis,and the technical analysts recommend through the combination of fundamentals,technical aspects and news,while the stock data is analyzed by researchers through establishing a mathematical model.With the outbreak of deep learning and the Recurrent Neural Network has achieved good performance in time series.LSTM has to attract broad attention as a classical model in the Recurrent Neural Network and has broad application prospects.Stock data is expressed as a classic financial time series.Predict stock data with using neural network is a research hotspot in recent years.With the rise of ideas such as algorithmic trading and quantitative investment,more and more people are using neural networks to predict stock data.However,there is no good guiding theory for deep learning so far.Many researchers rely on their own groping,or obtain the parameter settings of the model from the experience of their predecessors.Based on the viewpoint of "History repeats itself",this thesis studies the phenomenon of "same rise and fall" between stocks in the same industry,and extracts stock correlation characteristics by combining Pearson correlation coefficient and Dynamic Time Warping.In the linear relationship,Pearson is used to acquire the long and short periods existing in the stock,while in the nonlinear relationship,the dynamic time warping is utilized,and the obtained information is transformed into the correlation feature.On this basis,the LSTM stock prediction method combining correlation characteristics is designed.We used multiple neural network algorithm structures such as Dense,PReLU and Dropout to construce many different models,and discusses the influence of different model structure and parameter settings on stock forecasting.The experimental results show that the classification forecast method proposed in this thesis has more than 3% improvement rate than the traditional SVM and BP models.The regression forecast method is better than the traditional LinearRegressionand BP models in terms of RMSE,R2,error value and self-designed profit value.