Stock return forecasting typically requires a large number of factors and these factors usually exhibit nonlinear relations with each other. Conventional methods of stock return forecasting mainly fall into two categories: Technical Analysis and Fundamental Analysis. Technical Analysis focuses on time-series data, while Fundamental Analysis explores low-frequency fundamental variables. Although there are substantial works on either time-series analysis or fundamental analysis, few studies have enriched the time-series forecasting with fundamental variables, as the features are characterized by different frequencies, scales and types. In this paper, we propose a Long Short-Term Memory and Deep Neural Network (LSTM-DNN) hybrid model to integrate the fundamental information into time-series forecasting tasks. We demonstrate how investors can benefit from the superior performance of LSTM-DNN by constructing a long-short portfolio that takes long positions in stocks with the highest forecasting returns and short positions in stocks that are expected to decline. Extensive experimental results on real data show that our novel framework could improve the profitability of long-short portfolio strategies compared to the state-of-the-art approaches. We also find evidence indicating that the outperformance of LSTM-DNN model comes from its enhanced ability to extract information from the nonlinear relations among various features, rather than bearing more market risks. Besides the novel framework, we propose a cross-section normalization method, which benefits the framework by providing enriched cross-section signals.
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Materials Science
- General Engineering
- Hybrid deep neural networks
- long-short portfolio strategy
- security returns forecasting