Kavli Affiliate: Jia Liu
| First 5 Authors: Chengqi Dong, Zhiyuan Cao, S Kevin Zhou, Jia Liu,
| Summary:
Stock price prediction is of significant importance in quantitative
investment. Existing approaches encounter two primary issues: First, they often
overlook the crucial role of capturing short-term stock fluctuations for
predicting high-volatility returns. Second, mainstream methods, relying on
graphs or attention mechanisms, inadequately explore the temporal relationships
among stocks, often blurring distinctions in their characteristics over time
and the causal relationships before and after. However, the high volatility of
stocks and the intricate market correlations are crucial to accurately
predicting stock prices. To address these challenges, we propose a Dual-branch
Framework of Fluctuation and Trend (DFT), which decomposes stocks into trend
and fluctuation components. By employing a carefully design decomposition
module, DFT effectively extracts short-term fluctuations and trend information
from stocks while explicitly modeling temporal variations and causal
correlations. Our extensive experiments demonstrate that DFT outperforms
existing methods across multiple metrics, including a 300% improvement in
ranking metrics and a 400% improvement in portfolio-based indicators. Through
detailed experiments, we provide valuable insights into different roles of
trends and fluctuations in stock price prediction.
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