---
title: Beating Diversification Strategies
url: https://www.ml-quant.com/papers/repec/cup-jfinqa-v-59-y-2024-i-8-p-3601-3632-3/
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: RePEc:cup:jfinqa:v:59:y:2024:i:8:p:3601-3632_3
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.cambridge.org%2Fcore%2Fproduct%2Fidentifier%2FS0022109023001175%2Ftype%2Fjournal_article%3Bh%3Drepec%3Acup%3Ajfinqa%3Av%3A59%3Ay%3A2024%3Ai%3A8%3Ap%3A3601-3632_3
featured: 2025-01-23
citations: unknown
topic: Portfolio & Allocation
---


# Beating Diversification Strategies

The research indicates that the $1/N$ rule is best in high-dimensionality but can be improved by combining it with other rules or machine learning portfolios.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.cambridge.org%2Fcore%2Fproduct%2Fidentifier%2FS0022109023001175%2Ftype%2Fjournal_article%3Bh%3Drepec%3Acup%3Ajfinqa%3Av%3A59%3Ay%3A2024%3Ai%3A8%3Ap%3A3601-3632_3
- Identifier: RePEc:cup:jfinqa:v:59:y:2024:i:8:p:3601-3632_3
- Released: 2024-08-08
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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