3 papers in the archive (2023–2026), average empirical rigor 5.7/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Options & Derivatives Pricing, Machine Learning for Market Forecasting, Portfolio Optimization, Factor Investing & Asset Pricing.
Frequent co-authors: Nozomu Kobayashi (1), Yoshiyuki Suimon (1), Kosuke Mitarai (1), Rihito Sakurai (1), Tsuyoshi Okubo (1), Tassa Thaksakronwong (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%).
Efficient computation of Greeks for multi-asset options remains a key challenge in quantitative finance. While Monte Carlo (MC) simulation is widely used, it suffers from the large sample complexity for high accuracy. We propose a framework to compute Greeks in a single evaluation of a tensor train
In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantu
Quantum computing may speed up numerical problems involving large matrices that are demanding for classical computers, and active research on this possibility is ongoing. In this work, we propose quantum algorithms for the exact simulation of a normalised correlated Gaussian random vector $|x\rangle