7 papers in the archive (2023–2026), average empirical rigor 7.9/10, average math complexity 5.9/10. Quadrant mix: Holy Grail ×5, Street Traders ×2.
Works mostly in Market Microstructure, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Factor Investing & Asset Pricing.
Frequent co-authors: Jong-Kook 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%).
The application of machine learning to financial prediction has accelerated dramatically, yet the conditions under which complex models outperform simple alternatives remain poorly understood. This paper investigates whether advanced signal processing and deep learning techniques can extract predict
We demonstrate that the choice of normalization for order flow intensity is fundamental to signal extraction in finance, not merely a technical detail. Through theoretical modeling, Monte Carlo simulation, and empirical validation using Korean market data, we prove that market capitalization normali
How information transmits through prices – and why this transmission breaks down – remains poorly understood. We combine regularized deconvolution with Hawkes process analysis to study the impulse response structure of investor flows in the Korean equity market (January 2020 – February 2025). Thr
Whether heterogeneous investor flows transmit private information across stocks or merely reflect coordinated responses to public signals remains an open question in market microstructure. We construct Transfer Entropy (TE) networks from investor-type flows – foreign, institutional, and individual
Most empirical microstructure research assumes that order flow–return parameters are constant, yet these relationships shift substantially across market regimes. Combining adaptive Kalman filtering, Markov-switching regime identification, and asymmetric response estimation, we characterize regime-d
Chae and Kang (2019, \textit{“Pacific-Basin Finance Journal”}) documented a puzzling Low Volume Return Premium (LVRP) in Korea – contradicting global High Volume Return Premium (HVRP) evidence. We resolve this puzzle. Using Korean market data (2020-2024), we demonstrate that HVRP exists in Korea but
Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market leading to the development of techniques to gain above-market returns. Systematic trading has undergone significant advances in recent decades with deep learning schemes emerging as a po