---
title: Enhanced Factor Portfolio Optimization
url: https://www.ml-quant.com/papers/ssrn/5122756/
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 5122756
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5122756
featured: 2025-02-19
citations: unknown
topic: Portfolio & Allocation
---


# Enhanced Factor Portfolio Optimization

The paper explores the use of an Enhanced Portfolio Optimization model for factor investors to optimize asset allocation, considering the recent poor performance of the value factor in the US equity market.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5122756
- Identifier: SSRN 5122756
- Released: 2025-02-04
- First featured: Quant Letter No. 85 (2025-02-19): https://www.ml-quant.com/issues/2025-02-19/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization](https://www.ml-quant.com/papers/arxiv/2502.11701/): A new asset selection method for mean-variance portfolios has been proposed, allowing for quicker optimization and construction of portfolios with fewer assets.
- [Dynamically Optimal Portfolios for Monotone Mean-Variance Preferences](https://www.ml-quant.com/papers/arxiv/2503.08272/): The paper describes the optimal dynamic portfolio choice for the Monotone Mean-Variance utility in asset price models with independent returns, with minimal assumptions.
- [Bayesian Optimization for CVaR-based portfolio optimization](https://www.ml-quant.com/papers/arxiv/2503.17737/): The paper presents new Bayesian Optimization algorithms for portfolio allocation, reducing risk and meeting performance goals, proven through practical examples.
- [Multi-hypothesis prediction for portfolio optimization: A structured ensemble learning approach to risk diversification](https://www.ml-quant.com/papers/arxiv/2501.03919/): The paper introduces a framework for portfolio allocation that uses multiple hypotheses prediction through structured ensemble models, allowing for control of portfolio diversification before decision-making.
- [Causal Portfolio Optimization: Principles and Sensitivity-Based Solutions](https://www.ml-quant.com/papers/arxiv/2504.05743/): The article introduces a new risk management framework that uses Bayesian and neural networks for efficient portfolio optimization, based on Common Causal Manifolds.
- [Breaking the Dimensional Barrier: A Pontryagin-Guided Direct Policy Optimization for Continuous-Time Multi-Asset Portfolio Choice](https://www.ml-quant.com/papers/arxiv/2504.11116/): The article introduces the Pontryagin-Guided Direct Policy Optimization framework for solving large-scale portfolio optimization problems, capable of handling up to 50 assets and 10 state variables.