3 papers in the archive (2022–2026), average empirical rigor 6.3/10, average math complexity 4.7/10. Quadrant mix: Holy Grail ×1, Philosophers ×1, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Portfolio Optimization.
Frequent co-authors: Yiyan Qi (2), Saizhuo Wang (2), Lionel M. Ni (2), Hengyi Yang (1), Sida Lin (1), Yankai Chen (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%).
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate int
The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This gap hinders research progress and limits the practical application of academic innovations. We present QuantBench, an in
Quantitative investment (``quant’’) is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathe