Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets
Leveraging Graph Neural Networks (GNNs) and complex network theory to detect early warning signals of systemic risk in financial markets.
A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics
Presented at JADT 2026 and awarded the VADISTAT Prize for young researchers, this paper validates 18 analytical approaches on 1M+ texts to identify method-invariant findings.
Network Topology Analysis for Systemic Risk Prediction
Financial machine learning project combining dynamic correlation networks, graph neural networks, and trading backtests to detect systemic risk in U.S. equity markets.
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