3 papers in the archive (2025–2026), average empirical rigor 7.8/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Microstructure, High-Frequency Trading & Optimal Execution, Crypto Markets & DeFi.
Frequent co-authors: Salam Rabindrajit Luwang (3), Kundan Mukhia (3), Md. Nurujjaman (3), Anish Rai (2), Filippo Petroni (2), Luis E. C. Rocha (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%).
Quantitative understanding of stochastic dynamics in limit order price changes is essential for execution strategy design. We analyze intraday transition dynamics of ask and bid orders across market capitalization tiers using high-frequency NASDAQ100 tick data. Employing a discrete-time Markov chain
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Reque
Financial markets alternate between tranquil periods and episodes of stress, and return dynamics can change substantially across these regimes. We study regime-dependent dynamics in developed and developing equity indices using a data-driven Hilbert–Huang-based regime identification and profiling pi