3 papers in the archive (2025–2026), average empirical rigor 7.7/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Crypto Markets & DeFi, Reinforcement Learning for Trading.
Frequent co-authors: Ekaterina Seregina (1), David A. Hsieh (1), Patrick Kuiper (1), Sean Moushegian (1), David Ye (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%).
We propose Mixed-Panels-Transformer Encoder (MPTE), a novel framework for estimating factor models in panel datasets with mixed frequencies and nonlinear signals. Traditional factor models rely on linear signal extraction and require homogeneous sampling frequencies, limiting their applicability to
EIP-4844 gave Ethereum rollups a separate fee dimension for data, governed by a multiplicative update rule that targets three ``blobs’’ per block. Whether this target is reached depends on how many independent, self-interested rollups choose to post data, a question that closed-form models address o
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These mode