5 papers in the archive (2025–2026), average empirical rigor 6.7/10, average math complexity 4.9/10. Quadrant mix: Holy Grail ×3, Philosophers ×1, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, NLP & LLMs in Finance, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Igor Griva (2), Robert Axtell (1), William Kennedy (1), Jason Kinser (1), Prem Sapkota (1), Ujjwal Prajapati (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 study establishes the causal effects of market sentiment on firm profitability, moving beyond traditional correlational analyses. It leverages a causal forest machine learning methodology to control for numerous confounding variables, enabling systematic analysis of heterogeneity and non-linear
This study identifies and quantifies a significant informational friction embedded in the SEC Form 144 disclosure regime, characterized as predictive decoupling. Drawing on a theoretical foundation of welfare economics, the article argues that the current reporting inversion – where trade execution
Traditional models of market efficiency assume that equity prices incorporate information based on content alone, often neglecting the structural influence of reporting timing and cadence. This study introduces the Autonomous Disclosure Regulator, a multi-node AI framework designed to audit the inte
Corporate insiders trade for diverse reasons, often possessing Material Non-Public Information (MNPI). Determining whether specific trades leverage MNPI is a significant challenge due to inherent complexity. This study focuses on two critical objectives: accurately detecting Unlawful Insider Trading
Corporate insiders have control of material non-public preferential information (MNPI). Occasionally, the insiders strategically bypass legal and regulatory safeguards to exploit MNPI in their execution of securities trading. Due to a large volume of transactions a detection of unlawful insider trad