Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Prediction

Battle Count: 87

The paper is primarily a research/proposal paper focused on systemic risk detection rather than direct trading signal generation. It explicitly states it is 'not a trading or investment advisory system.' However, the framework's ability to detect regime transitions and provide early-warning signals is relevant for risk management, portfolio hedging, and macro-level trading decisions. The current results are preliminary and show degenerate GNN behavior (predicting all positive), limiting immediate practical utility. The multi-layer graph approach could inform regime-switching strategies or risk overlay systems if further developed and validated.

The full SRR architecture involves multi-layer graph construction, GNN encoders (GCN/GAT/Transformer variants), temporal sequence modeling (GRU/LSTM/Transformer), and multi-layer fusion. However, the current implementation is simplified: a two-layer GCN with global mean pooling and a single GRU layer (~30K parameters). The graph construction (rolling Spearman correlations, threshold-based edge creation) is straightforward. The main complexity lies in the multi-layer graph management, temporal sequence handling, and the interpretability/attribution components. The paper provides algorithmic pseudocode but no code repository.

The paper describes reproducibility practices including deterministic preprocessing, published hyperparameters, chronological train-test splits, and publicly available data sources. However, no code repository or GitHub link is provided. The implementation details (rolling windows, label construction, architecture configs) are described in text but not in executable form. The paper is explicitly labeled as a 'proposal paper with preliminary empirical evidence,' limiting the completeness of the evaluation.