4 papers in the archive (2025), average empirical rigor 6.9/10, average math complexity 3.9/10. Quadrant mix: Lab Rats ×1, Street Traders ×3.
Works mostly in NLP & LLMs in Finance, Factor Investing & Asset Pricing, Crypto Markets & DeFi.
Frequent co-authors: Bhaskarjit Sarmah (4), Stefano Pasquali (4), Fabrizio Dimino (2), Alejandro Michel (1), Tejas Prakash Agarwal (1), Reetu Raj Harsh (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%).
Multi-hop reasoning over financial disclosures is often a retrieval problem before it becomes a reasoning or generation problem: relevant facts are dispersed across sections, filings, companies, and years, and LLMs often expend excessive tokens navigating noisy context. Without precise Knowledge Gra
Large language models (LLMs) are increasingly being used to extract structured knowledge from unstructured financial text. Although prior studies have explored various extraction methods, there is no universal benchmark or unified evaluation framework for the construction of financial knowledge grap
Portfolio managers rely on correlation-based analysis and heuristic methods that fail to capture true causal relationships driving performance. We present a hybrid framework that integrates statistical causal discovery algorithms with domain knowledge from two complementary sources: a financial know
The financial domain poses unique challenges for knowledge graph (KG) construction at scale due to the complexity and regulatory nature of financial documents. Despite the critical importance of structured financial knowledge, the field lacks large-scale, open-source datasets capturing rich semantic