---
title: Tradeoff Between Tracking Error & Turnover in Portfolio Rebalancing
url: https://www.ml-quant.com/papers/ssrn/4552359/
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 4552359
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4552359
featured: 2023-08-30
citations: unknown
topic: Portfolio & Allocation
---


# Tradeoff Between Tracking Error & Turnover in Portfolio Rebalancing

There's a balance between tracking error and turnover in rebalancing choices, with calendar-based methods being less efficient than tolerance bands, and no proof that rebalancing choices can consistently boost expected returns.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4552359
- Identifier: SSRN 4552359
- Released: 2021-06-03
- First featured: Quant Letter No. 14 (2023-08-30): https://www.ml-quant.com/issues/2023-08-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Formulations to select assets for constructing sparse index tracking portfolios](https://www.ml-quant.com/papers/ssrn/4551632/): The article introduces new methods for asset selection in sparse index tracking portfolios, highlighting its benefits over traditional full replication portfolios.
- [Grover Search for Portfolio Selection](https://www.ml-quant.com/papers/arxiv/2308.13063/): The study presents explicit oracles for Grover's algorithm to align with investor preferences, possibly choosing portfolios with optimal Sharpe ratios, tested using quantum simulators.
- [Analysis of Optimal Portfolio Management Using Hierarchical Clustering](https://www.ml-quant.com/papers/arxiv/2308.11202/): The study improves the Markowitz Model by integrating machine learning through a hierarchical clustering approach, enhancing portfolio performance on a risk-adjusted basis.
- [Choice of a Mixed-Asset Portfolio Based on Third-Degree Stochastic Dominance](https://www.ml-quant.com/papers/ssrn/4545395/): A study finds that adding direct real estate investments and bonds to a mixed-asset portfolio of stocks significantly enhances the portfolio's efficient frontiers.
- [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.
- [Quantifying Outlierness of Funds from their Categories using Supervised Similarity](https://www.ml-quant.com/papers/arxiv/2308.06882/): The study uses machine learning to measure the impact of mutual fund miscategorization, revealing a significant link between outlier measures of funds and their future returns.