3 papers in the archive (2026), average empirical rigor 8.2/10, average math complexity 4.3/10, 1 with linked code. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Crypto Markets & DeFi, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Jun Han (1), Shuo Zhang (1), Wei Li (1), Zhi Yang (1), Yifan Dong (1), Tu Hu (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%).
Large language models are moving from producing trading signals to writing the code that executes them. The failure mode of the second role is silent: generated code runs, a backtest plots, yet the risk logic that the trader described is not the logic being executed. Existing code benchmarks test fu
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated exper
Blockchain technology is widely expected to reduce transaction costs by automating contract enforcement and eliminating intermediaries; yet, the execution costs imposed by network congestion have received little attention in the operations management literature. We study on-chain peak shaving, the s