4 papers in the archive (2025), average empirical rigor 6.4/10, average math complexity 3.9/10. Quadrant mix: Lab Rats ×1, Street Traders ×3.
Works mostly in Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Probowo Erawan Sastroredjo (3), Marcel Ausloos (3), Fabio Bagarello (1), Francesco Gargano (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%).
Our research investigates the relationship between eco-innovation and earnings management among 567 firms listed on the FTSE All-Share Index from 2014 to 2022. By examining how sustainability-driven innovation influences financial reporting practices, we explore the strategic motivations behind inco
Through its initiative known as the Climate Change Act (2008), the Government of the United Kingdom encourages corporations to enhance their environmental performance with the significant aim of reducing targeted greenhouse gas emissions by the year 2050. Previous research has predominantly assessed
Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income ($PI$) and total assets ($TA$) data from 567 companies listed on
We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of machine learning techniques, including reinforcement