4 papers in the archive (2025–2026), average empirical rigor 7/10, average math complexity 4.4/10, 2 with linked code. Quadrant mix: Holy Grail ×1, Street Traders ×3.
Works mostly in Machine Learning for Market Forecasting, NLP & LLMs in Finance, Portfolio Optimization, Reinforcement Learning for Trading.
Frequent co-authors: Stefan Zohren (3), Yaxuan Kong (2), Alejandro Lopez-Lira (2), Chanyeol Choi (2), Yongjae Lee (2), Marcus Kaiser (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%).
Robust asset allocation is a key challenge in quantitative finance, where deep-learning forecasters often fail due to objective mismatch and error amplification. We introduce the Signature-Informed Transformer (SIT), a novel framework that learns end-to-end allocation policies by directly optimizing
We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain:
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
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