3 papers in the archive (2025–2026), average empirical rigor 6/10, average math complexity 8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Portfolio Optimization, Market Simulation & Agent-Based Models.
Frequent co-authors: Jeonggyu Huh (2), Hyun-Gyoon Kim (2), Seungwon Jeong (1), Byung Hwa Lim (1), So-Yoon Cho (1), Jin-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%).
This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sect
Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio cons
We study continuous-time portfolio choice in diffusion markets with parameter $θ\in Θ$ and uncertainty law $q(dθ)$. Nature draws latent $θ\sim q$ at time 0; the investor cannot observe it and must deploy a single $θ$-blind feedback policy maximizing an ex-ante CRRA objective averaged over diffusion