Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecasting

Battle Count: 70

Highly relevant to quantitative trading as it directly addresses stock price forecasting, which is fundamental to quantitative trading strategies. The game-theoretic modeling of investor interactions provides insights into market microstructure and capital flows. The use of Dragon and Tiger List data connects to real trading signals. The IC/RankIC metrics are standard in quantitative finance for evaluating predictive signals. The model could be directly integrated into trading systems for stock selection and timing decisions. However, the focus on Chinese A-share market and the specific data sources (Dragon and Tiger List) limit direct applicability to other markets without adaptation.

High implementation complexity due to the integration of multiple sophisticated components: (1) Multi-level discrete wavelet transform with temporal attention and trend-fluctuation fusion, (2) Heterogeneous graph construction with multiple entity types and relation types, (3) Game-theoretic equilibrium computation with Nash equilibrium constraints, (4) Temporal positional encoding for game events, (5) Custom loss function combining regression and equilibrium consistency. Requires expertise in graph neural networks, game theory, signal processing (wavelets), and financial data engineering. The Dragon and Tiger List data acquisition and processing adds practical complexity. However, the use of standard frameworks (PyTorch, PyG) and well-defined architectures (R-GCN, GAT) provides some implementation guidance.

The paper provides detailed implementation specifications (PyTorch 1.12.1, PyG, RTX 3090, 300 epochs, specific hyperparameters like learning rate 0.001, L2 regularization 0.001, lookback window 20, wavelet decomposition level 3, embedding dimension 48, GAT 2 layers with 64 hidden units). They mention releasing a sparse game-theoretic dataset publicly. However, no GitHub repository link is provided, and the Dragon and Tiger List data source (East Asia Wealth Data Center) may not be freely accessible. The dataset construction details for investor holding preferences are partially described.