A time-series stock price prediction and asset portfolio optimization model based on long- and short-term memory networks

Aiming at the inadequacy of the existing prediction models for time series data based on variables, this paper proposes an LSTM model based on fused multi-scale convolutional attention (MCA-LSTM). The experimental parameters are designed, the stock price dataset is constructed, and the modeling and prediction of the improved LSTM model is carried out using the closing price of individual stocks as the data base. And the model prediction accuracy was evaluated using RMSE, MAPE, and MAD indicators as evaluation indicators. In order to test the performance of MVA-LSTM portfolio model in arbitrage and generalization, the indicator factors are set to compare and analyze the application performance of MCA-LSTM-BL model. Using the framework of the mean semi-absolute deviation (MSAD) portfolio optimization model, a new portfolio optimization model based on return forecasting is developed. That is, the portfolio optimization model based on improved LSTM network return prediction (MCA-LSTM+MSAD). The asset values of different portfolio models are compared and the return prediction of each portfolio model is analyzed under the consideration of transaction costs. The MCA-LSTM+MSAD portfolio optimization model proposed in this paper is able to achieve an excess return of 56.98%. The MCA-LSTM+MASD model is able to maintain the maximum value consistently during the stock trading days of the study sample. This paper concludes that MCA-LSTM+MASD is a portfolio optimization model that can be further deepened and applied in real investment.