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.

翻译：股票价格的波动受到超越历史数据的复杂因素相互作用的影响。这些因素本身受外部力量影响，涵盖股票间动态关系、宏观经济因素、各类政府政策决策、战争爆发等。此外，所有这些因素均具有动态性，并随时间推移呈现变化。本文首次通过提出新型学习机制来处理外部影响下的预测问题，该机制不仅学习历史趋势，同时整合来自时序知识图谱的外部知识。由于当前缺乏此类数据集或时序知识图谱，我们以股票市场数据为研究对象，构建了完整的时序知识图谱数据集。在所提出的方法中，我们将外部时序知识图谱上的关系建模为图结构上霍克斯过程的事件。通过大量实验证明，学习得到的动态表征能基于多个持有期的收益率对股票进行有效排序，在相关指标上显著优于现有基线方法。