3 papers in the archive (2026), average empirical rigor 5/10, average math complexity 2/10. Quadrant mix: Philosophers ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Sumin Kim (3), Alejandro Lopez-Lira (3), Chanyeol Choi (3), Yongjae Lee (3), Yoon Kim (2), Nicole Kagan (2).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this chal
Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilistic forecasts of keyword-mention outcomes. While recent work shows that large language models (LLMs) can generate forecas
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