4 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 5.2/10, 1 with linked code. Quadrant mix: Holy Grail ×3, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Portfolio Optimization, Risk Management & Tail Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Nariman Khaledian (3), Navid Parvini (3), Alireza Kargarzadeh (2), Sid Ghatak (2), Amir Dehkordi (1), Nazanin Yousefi (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 (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns. We ask how much of that predictability survives a test that rules out look-ahead, memorization and selection. We propose OLIVIA, a protocol for such tests: point-in-time inputs w
Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk – decomposed into aleatoric and epistemic com
Robust financial-risk inference requires more than mathematical correctness. Models must accurately apply conditional rules, maintain evolving financial states, and make decisions that respect operational constraints. This work introduces RiskReason, a benchmark for verifiable financial-risk reasoni
There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in