用动态知识图谱提升股市预测，融合外部事件影响。
Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Markets
- 将外部事件建模为图上霍克斯过程，捕捉动态关联
- 在多个持有周期下优于基线模型，显著提升选股排名效果
- 适合金融量化、时序建模方向研究者参考
股票价格波动受复杂因素影响，不仅包括历史数据，还涉及跨股票关系、宏观经济、政策变化、战争爆发等外部因素，且这些因素随时间动态演变。本文首次提出在外部影响下进行股市预测的方法，通过学习机制融合历史趋势与来自时序知识图谱的外部知识。由于缺乏现成的数据集和时序知识图谱，我们基于股票市场数据构建了综合性时序知识图谱数据集。所提方法将外部时序知识图谱中的关系建模为图上的霍克斯过程事件。大量实验表明，所学动态表示能有效按回报率对股票排序，在多个持有周期下优于相关基线模型。
原文摘要 · Abstract (English)
Fluctuations in stock prices are influenced by a complex interplay of factors that go beyond mere historical data. These factors, themselves influenced by external forces, encompass inter-stock dynamics, broader economic factors, various government policy decisions, outbreaks of wars, etc. Furthermore, all of these factors are dynamic and exhibit changes over time. In this paper, for the first time, we tackle the forecasting problem under external influence by proposing learning mechanisms that not only learn from historical trends but also incorporate external knowledge from temporal knowledge graphs. Since there are no such datasets or temporal knowledge graphs available, we study this problem with stock market data, and we construct comprehensive temporal knowledge graph datasets. In our proposed approach, we model relations on external temporal knowledge graphs as events of a Hawkes process on graphs. With extensive experiments, we show that learned dynamic representations effectively rank stocks based on returns across multiple holding periods, outperforming related baselines on relevant metrics.
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