Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

This study utilizes a new approach for short-term stock correlation forecasting using a combination of convolutional neural networks (CNN), bi-directional long and short-term memory (BiLSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to improve the accuracy of short-term equity correlation forecasts, which is essential for the efficient management of financial asset portfolios. The existing time series models usually only take into account the effect of stock historical data characteristics on stock correlation but ignore the fact that stock correlation is affected by multiple factors and undergoes an unknown unstable trend. More efficient methods or algorithms need to be devised to deal with financial data. This paper explores stock metrics and introduces a multi-factor model to quantify the factors affecting stock returns in order to calculate the correlation between stocks. Meanwhile, to cope with the problem of incomplete input data, this paper simultaneously incorporates stock return data that is directly related to the correlation data. In the CNN-BiLSTM-Attention (CLATT) model, the role of the CNN module is to obtain high-dimensional features from the input time series data, and the role of the BiLSTM module is to process the time series information to capture the time-series features in the stock return data and correlation data in the sample. In order to acquire the inherent correlation between the input stock return data and correlation data and to enhance the prediction accuracy of the model, the attention mechanism is finally implemented and used to optimize the weights of the BiLSTM output. In this paper, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) metrics, mean absolute percentage of error (MAPE), and the coefficient of determination
$R^{2}$
, as well as four benchmark models, are used to evaluate the performance of the proposed model. The result shows that the CLATT model has 57.32% and 33% improvement in the correlation prediction accuracy of different stock portfolios compared to the single LSTM model, and the model prediction accuracy has been improved. Compared with the benchmark CNN-BiLSTM, it also has 56.06% and 28.87% improvement.