Stock linkage prediction based on optimized LSTM model

Stock linkage refers to the correlation or similar performance of two or more stocks in the stock market. The quantification of stock linkage relationship is the trend and difficulty of research in recent years. The study of stock linkage can dig out the potential relationship between stocks at a deeper level. At present, the existing research often only studies the linkage phenomenon from the perspective of the correlation or similarity of stock movement, and there is no unified and standard numerical index to effectively describe the degree of linkage phenomenon, which greatly hinders the progress of research. Aiming at the problem that it is difficult to quantify the phenomenon of stock linkage, we analyze the correlation and morphological similarity of time series, and propose the combination of correlation coefficient and time weighted distance as the numerical expression of stock linkage for the first time, so as to realize the quantification of stock linkage. In addition, the parallel network structure of LSTM model is designed, and the automatic noise reduction encoder and wavelet transform module are added as the noise reduction processing layer, which effectively improves the prediction performance of LSTM model for stock market linkage numerical time series. Three different types of comparative experiments based on 2.309 million stock market sequences show that the proposed optimized LSTM model has more accurate prediction effect, and its RMSE error is 18.68% lower than the compared DB-LSTM model and 46.38% lower than SDAE-LSTM model.