# RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets

arXiv: https://arxiv.org/abs/2510.14986 · HTML: https://mlsanity.com/paper/2510.14986/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022)
- [MASTER: Market-Guided Stock Transformer for Stock Price Forecasting](https://mlsanity.com/paper/2312.15235/) (2023)
- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions](https://mlsanity.com/paper/2210.04797/) (2022)
- [Smart HPA: A Resource-Efficient Horizontal Pod Auto-scaler for Microservice Architectures](https://mlsanity.com/paper/2403.07909/) (2024)
- [Towards Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures](https://mlsanity.com/paper/2403.15169/) (2024)
- [A Survey on Immersive Cyber Situational Awareness Systems](https://mlsanity.com/paper/2408.07456/) (2024)
- [Machine Learning Driven Smishing Detection Framework for Mobile Security](https://mlsanity.com/paper/2412.09641/) (2024)
- [The Future of AI: Exploring the Potential of Large Concept Models](https://mlsanity.com/paper/2501.05487/) (2025)
- [Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management](https://mlsanity.com/paper/2506.05693/) (2025)
- [What Skills Do Cyber Security Professionals Need?](https://mlsanity.com/paper/2502.13658/) (2025)

## Built on by (most cited first, 7 of 7)

- [Australian Bushfire Intelligence with AI-Driven Environmental Analytics](https://mlsanity.com/paper/2601.06105/) (2026)
- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [Comparative Analysis of Large Language Models in Healthcare](https://mlsanity.com/paper/2604.10316/) (2026)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning](https://mlsanity.com/paper/2606.04574/) (2026)
- [Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching](https://mlsanity.com/paper/2608.28252/) (2026)

## Abstract (arXiv)

Financial markets are inherently non-stationary, with shifting volatility regimes that alter asset co-movements and return distributions. Standard portfolio optimization methods, typically built on stationarity or regime-agnostic assumptions, struggle to adapt to such changes. To address these challenges, we propose RegimeFolio, a novel regime-aware and sector-specialized framework that, unlike existing regime-agnostic models such as DeepVol and DRL optimizers, integrates explicit volatility regime segmentation with sector-specific ensemble forecasting and adaptive mean-variance allocation. This modular architecture ensures forecasts and portfolio decisions remain aligned with current market conditions, enhancing robustness and interpretability in dynamic markets. RegimeFolio combines three components: (i) an interpretable VIX-based classifier for market regime detection; (ii) regime and sector-specific ensemble learners (Random Forest, Gradient Boosting) to capture conditional return structures; and (iii) a dynamic mean-variance optimizer with shrinkage-regularized covariance estimates for regime-aware allocation. We evaluate RegimeFolio on 34 large cap U.S. equities from 2020 to 2024. The framework achieves a cumulative return of 137 percent, a Sharpe ratio of 1.17, a 12 percent lower maximum drawdown, and a 15 to 20 percent improvement in forecast accuracy compared to conventional and advanced machine learning benchmarks. These results show that explicitly modeling volatility regimes in predictive learning and portfolio allocation enhances robustness and leads to more dependable decision-making in real markets.