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Predictive AI with External Knowledge Infusion for Stocks

Author

Listed:
  • Ambedkar Dukkipati
  • Kawin Mayilvaghanan
  • Naveen Kumar Pallekonda
  • Sai Prakash Hadnoor
  • Ranga Shaarad Ayyagari

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.

Suggested Citation

  • Ambedkar Dukkipati & Kawin Mayilvaghanan & Naveen Kumar Pallekonda & Sai Prakash Hadnoor & Ranga Shaarad Ayyagari, 2025. "Predictive AI with External Knowledge Infusion for Stocks," Papers 2504.20058, arXiv.org.
    • Handle: RePEc:arx:papers:2504.20058
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    References listed on IDEAS

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    1. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    2. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
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