Deep Learning for Forecasting Stock Returns in the Cross-Section
Deep Learning for Forecasting Stock Returns in the Cross-Section
This paper investigates the application of deep learning techniques to forecast one-month-ahead stock returns in the cross-section of the Japanese stock market. It addresses the growing interest in using advanced machine learning methods, such as deep neural networks (DNNs), which have shown high performance in fields like image and speech recognition, for financial prediction tasks. The study implements DNNs to model complex patterns in stock data and compares their performance against shallow neural networks and other representative machine learning models, focusing on cross-sectional analysis that considers multiple stocks simultaneously.
The results demonstrate that deep neural networks generally achieve better forecasting accuracy than shallow networks, with the best-performing DNNs also outperforming other machine learning approaches. These findings highlight the potential of deep learning as a skillful method for predicting stock returns in cross-sectional settings, suggesting it can capture intricate market dynamics more effectively. The paper contributes to quantitative finance by providing empirical evidence from the Japanese market, supporting the broader adoption of deep learning techniques in financial forecasting and portfolio management applications.
Highlights
- 1Implementation of deep learning for cross-sectional stock return forecasting in the Japanese market
- 2Comparison of deep neural networks (DNNs) with shallow neural networks and other machine learning models
- 3Demonstration of DNNs' superior performance in predicting one-month-ahead returns
- 4Evidence supporting deep learning as a promising method for stock return prediction
Methods
- MDeep neural networks (DNNs) for modeling complex patterns in stock data
- MCross-sectional analysis focusing on multiple stocks simultaneously
- MPerformance comparison with shallow neural networks and representative machine learning models
- MEvaluation using one-month-ahead forecasting in the Japanese stock market
Results
- RDeep neural networks generally outperform shallow neural networks in return prediction
- RBest-performing DNNs surpass other machine learning models in forecasting accuracy
- RDeep learning shows skillful predictive ability for cross-sectional stock returns
- RResults indicate promise for deep learning applications in financial forecasting
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