4 papers in the archive (2025–2026), average empirical rigor 5.1/10, average math complexity 3.1/10, 1 with linked code. Quadrant mix: Holy Grail ×1, Philosophers ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Portfolio Optimization.
Frequent co-authors: Yongjae Lee (4), Chanyeol Choi (3), Alejandro Lopez-Lira (3), Suhwan Park (2), Wonbin Ahn (2), Junhyuk Seo (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%).
Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynamics. A promising direction is to build semantic representations of investment themes from textual data. However, despite
In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. These conflicts are especially problematic in real-world investment services, where a model’s inherent biases can misalign w
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financi
Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution’s own standard. In FinAutoRubric