3 papers in the archive (2026), average empirical rigor 6.8/10, average math complexity 7.7/10, 1 with linked code. Quadrant mix: Holy Grail ×3.
Works mostly in Statistical Arbitrage & Pairs Trading, Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Portfolio Optimization.
Frequent co-authors: Jae-Young Kim (1).
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%).
Statistical arbitrage trades on an estimate of the state of a residual spread, and conventional practice discards the uncertainty of that estimate. We keep the uncertainty and use it in the trading decision. A Neural Filter Estimator (NFE) is trained on simulated paths of a mean-reverting process wh
We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors a
We study whether a risk-sensitive objective from asset-pricing theory – recursive utility – improves reinforcement learning for portfolio allocation. The Bellman equation under recursive utility involves a certainty equivalent (CE) of future value that has no closed form under observed returns; we