Predictive AI with External Knowledge Infusion for Stocks
This addresses stock market prediction for investors by integrating dynamic external factors, but it is incremental as it builds on existing graph and temporal modeling methods.
The paper tackles stock price forecasting by incorporating external knowledge from temporal knowledge graphs, constructing datasets and modeling relations as events of a Hawkes process on graphs, and shows that learned dynamic representations outperform baselines in ranking stocks based on returns across multiple holding periods.
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.