NeuroQuant: A Reinforcement Learning Framework with Stochastic Optimal Transport for Adaptive Portfolio Management Under Market Regime Shifts
This paper introduces NeuroQuant, a novel AI-driven framework that combines Reinforcement Learning (RL), Stochastic Optimal Transport (SOT), and unsupervised market regime detection for dynamic and adaptive portfolio optimization. Unlike traditional R
Authors
- A Mohammed Faazil
Institutions
- St. Peter's Institute of Higher Education and Research (IN)
Publication Details
- Published
- 2025-08-18
- DOI
- https://doi.org/10.36227/techrxiv.175554738.80460787/v1
- Primary Topic
- Stochastic processes and financial applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00