Abstract
Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment decisions. A mature investor will thoroughly investigate the state of a stock market at various time scales. To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network. It learns multi-scale patterns from two types of scale-information, wavelet-based and downsampling-based, by eXtreme Gradient Boosting and Recurrent Convolutional Neural Network, respectively. After combining the learned patterns from the two-way, our model achieves state-of-the-art performance on FI-2010 and CSI-2016, where the latter is our published long-range stock dataset to help future studies for STP task. Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information.
Keywords
- Foundation for AI in FinTech: Analyzing big financial data
- Foundation for AI in FinTech: Data mining and knowledge discovery for FinTech
- Foundation for AI in FinTech: Modeling financial market microstructure
- AI for trading: AI for novel financial models
- AI for risk and security: AI for market movement and change analysis
- Other areas: Financial decision-support system
- Foundation for AI in FinTech: General
