---
title: Rebalanced Portfolio Optimization
url: https://www.ml-quant.com/papers/ssrn/4526656/
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 4526656
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4526656
featured: 2023-08-02
citations: 1
topic: Portfolio & Allocation
---


# Rebalanced Portfolio Optimization

Regular intraperiod portfolio rebalancing strategies offer a unique solution to portfolio optimization problems without requiring utility or risk tradeoffs.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4526656
- Identifier: SSRN 4526656
- Released: 2023-04-15
- First featured: Quant Letter No. 10 (2023-08-02): https://www.ml-quant.com/issues/2023-08-02/
- Citations (Semantic Scholar): 1
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation](https://www.ml-quant.com/papers/arxiv/2307.12744/): The study highlights the importance of considering the memory effect in market correlations for improving the accuracy of forecasting models and aiding in portfolio selection.
- [Transfer Learning for Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2307.13546/): The study presents the concept of transfer risk in transfer learning techniques for financial portfolio optimization, showing its potential to improve the efficiency of the transfer learning approach.
- [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.
- [Portfolio selection via topological data analysis](https://www.ml-quant.com/papers/arxiv/2308.07944/): A two-stage method using Topological Data Analysis for building a stock investment portfolio consistently outperforms traditional methods across various time periods.
- [Fast Empirical Scenarios](https://www.ml-quant.com/papers/arxiv/2307.03927/): Two new algorithms are introduced to extract key scenarios from large, complex data, showing potential in portfolio optimization.
- [Portfolio Optimization: A Comparative Study](https://www.ml-quant.com/papers/arxiv/2307.05048/): A study comparing three portfolio design methods found that the mean-variance portfolio is best for risk-adjusted returns, while autoencoder portfolios have the highest annual returns.