3 papers in the archive (2023–2026), average empirical rigor 8/10, average math complexity 4.8/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Machine Learning for Market Forecasting, Options & Derivatives Pricing, NLP & LLMs in Finance.
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%).
Stock return prediction is fundamental to financial decision-making, yet traditional time series models fail to capture the complex interdependencies between companies in modern markets. We propose the Full-State Graph Convolutional LSTM (FS-GCLSTM), a novel temporal graph neural network that incorp
Institutional allocators often evaluate structured strategies on the basis of marketed backtests – hypothetical track records constructed by applying a strategy’s rules to historical data prior to any live trading, also referred to as pro-forma performance. It is unclear how much of that signal sur
The hedge fund industry presents significant challenges for investors due to its opacity and limited disclosure requirements. This pioneering study introduces two major innovations in financial text analysis. First, we apply topic modeling to hedge fund documents-an unexplored domain for automated t