Predictive AI with External Knowledge Infusion for Stocks

ArXiv ID: 2504.20058 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: temporal knowledge graphs, Hawkes process on graphs, external influence forecasting, stock market data, dynamic representations, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including heterogeneous Hawkes processes on temporal knowledge graphs and graph convolution operators, but also demonstrates strong empirical rigor through the construction of two comprehensive datasets (NASDAQ/NSE SmKGs) and extensive experiments outperforming baselines.

  flowchart TD
    A["Research Goal: Forecast Stock Returns Under External Influence"] --> B["Construct Temporal Knowledge Graph TKG Dataset"]
    B --> C["Model External Relations as Events in Graph Hawkes Process"]
    C --> D["Learn Dynamic Stock Representations"]
    D --> E["Experiments: Rank Stocks by Predicted Returns"]
    E --> F["Key Findings: Outperforms Baselines, Effective Dynamic Ranking"]