3 papers in the archive (2025), average empirical rigor 7.5/10, average math complexity 7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Risk Management & Tail Risk, Reinforcement Learning for Trading, Options & Derivatives Pricing.
Frequent co-authors: Paweł Sakowski (2), Zofia Bracha (1), Robert Ślepaczuk (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%).
This paper explores the application of deep Q-learning to hedging at-the-money options on the S&P500 index. We develop an agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, trained to simulate hedging decisions without making explicit model assumptions on price dynam
This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) architectures are used to forecast parameters of three probability distributions: Normal, Student’s t, and skewed Student
The proper design and architecture of testing machine learning models, especially in their application to quantitative finance problems, is crucial. The most important aspect of this process is selecting an adequate loss function for training, validation, estimation purposes, and hyperparameter tuni