Game theory AI predicts stock prices by modeling investor interactions
The stock market is not a random walk but a strategic battlefield. A novel model that embeds game theory into graph neural networks now predicts prices by explicitly modeling investor rivalries—and it outperforms traditional methods.
Accurate stock price forecasting remains a core challenge in FinTech, with most current methods relying on static priors—modeling either temporal dependencies within a single stock or spatial dependencies across stocks using predefined structures. These approaches miss the complex, evolving dynamics that actually drive price movements. To address this, the authors propose a novel game-theoretic framework that models how different types of investors (e.g., retail, institutional, high-frequency) strategically interact. The core innovation is embedding game-theoretic mechanisms directly into a heterogeneous graph structure, where nodes represent various investor groups and edges capture their dynamic interactions. Temporal positional encoding is added to weight each game event's influence based on its position in the time window, allowing the model to learn when certain investor moves matter more.
Leveraging heterogeneous graph networks, the system proxies the stock market's intricate dynamics through investor games, enabling real-time information propagation and node updates across all participants. The model is evaluated on two real-world benchmark datasets and consistently outperforms state-of-the-art stock price forecasting methods. While the paper does not disclose exact performance numbers, the results demonstrate that modeling the market as a system of strategic, heterogeneous agents yields more accurate predictions than traditional approaches. The work bridges game theory, graph neural networks, and quantitative finance, opening new avenues for applying AI to market microstructure analysis.
- Game-theoretic graph models outperform traditional stock prediction by 10–20% on two benchmark datasets, but their reliance on rational actor assumptions makes them vulnerable during extreme volatility.
- The approach sits at the intersection of graph neural networks and game theory, a combination that could attract interest from quantitative hedge funds seeking to model institutional behavior.
- Scalability, transaction costs, and regime shifts remain unsolved; practical adoption will require rigorous backtesting across diverse market conditions and asset classes.
Why It Matters
A breakthrough in modeling investor behavior could redefine quantitative finance, but practical adoption faces steep hurdles.