---
title: An End-to-End Direct Reinforcement Learning Approach for Multi-Factor Based Portfolio Management
url: https://www.ml-quant.com/papers/ssrn/4729683/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4729683
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4729683
featured: 2024-02-21
citations: 0
topic: Portfolio & Allocation
---


# An End-to-End Direct Reinforcement Learning Approach for Multi-Factor Based Portfolio Management

A new online portfolio decision model combines the multifactor model and mean-variance portfolio optimization in one step, enhancing overall performance.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4729683
- Identifier: SSRN 4729683
- Released: 2024-02-17
- First featured: Quant Letter No. 38 (2024-02-21): https://www.ml-quant.com/issues/2024-02-21/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning](https://www.ml-quant.com/papers/arxiv/2312.15385/): The article discusses a new reinforcement learning-based model for analyzing real-world data, which is more applicable than the continuous-time model.
- [Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation](https://www.ml-quant.com/papers/arxiv/2307.07694/): The study finds that PPO and A2C deep reinforcement learning algorithms are more effective for portfolio optimization due to their noise handling and policy derivation capabilities, despite their high sample complexity.
- [On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management](https://www.ml-quant.com/papers/arxiv/2307.03391/): The paper presents a new adaptive portfolio management framework that merges dynamic Black-Litterman optimization with the general factor model and Elastic Net regression, showing computational benefits and promising trading results.
- [Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2306.07013/): Reinforcement learning and barrier functions used in portfolio management framework
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://www.ml-quant.com/papers/arxiv/2305.17523/): Comparing portfolio optimization approaches on stock data
- [Adaptive and Regime-Aware RL for Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2509.14385/): The study presents a new reinforcement learning framework for portfolio optimization, which performs well under financial stress and supports dynamic asset allocation.