3 papers in the archive (2025–2026), average empirical rigor 5.8/10, average math complexity 5.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting.
Frequent co-authors: Masahiro Kato (1), Hiromu Ozai (1), Kaito Takano (1), Masanori Hirano (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%).
Modeling time series with long- or short-memory characteristics is a fundamental challenge in many scientific and engineering domains. While fractional Brownian motion has been widely used as a noise source to capture such memory effects, its incompatibility with Itô calculus limits its applicabilit
Accurately forecasting central bank policy decisions, particularly those of the Federal Open Market Committee(FOMC) has become increasingly important amid heightened economic uncertainty. While prior studies have used monetary policy texts to predict rate changes, most rely on static classification
Financial statement auditing is conducted under a risk-based evidence approach to obtain reasonable assurance. In practice, auditors often perform additional sampling or related procedures when an initial sample does not provide a sufficient basis for a conclusion. Across jurisdictions, current stan