3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 4.3/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Portfolio Optimization, Machine Learning for Market Forecasting.
Frequent co-authors: Yongjae Lee (3), Hoyoung Lee (2), Suhwan Park (2), Chanyeol Choi (2), Alejandro Lopez-Lira (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
Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilistic forecasts of keyword-mention outcomes. While recent work shows that large language models (LLMs) can generate forecas
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