Deep neural network modeling for financial time series analysis

Modeling stock returns has often relied on multivariate time series analysis, and constructing an accurate model remains a challenging goal for both market investors and academic researchers. Stock return prediction typically involves multiple variables and a combination of long-term and short-term time series patterns. In this paper, we propose a new deep learning network, named DLS-TS-Net, to model stock returns and address this challenge. We apply DLS-TS-Net in multivariate time series forecasting. The network integrates a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) units, and Gated Recurrent Units (GRUs). DLS-TS-Net overcomes LSTM's insensitivity to linear components in stock market forecasting by incorporating a traditional autoregressive model. Experimental results demonstrate that DLS-TS-Net excels at capturing long-term trends in multivariate factors and short-term fluctuations in the stock market, outperforming traditional time series and machine learning models. Additionally, when combined with the investment strategies proposed in this paper, DLS-TS-Net shows superior performance in managing risk during extreme events