---
title: HighDimensional Portfolio Optimization with Tree-Structured Factor Model
url: https://www.ml-quant.com/papers/repec/eee-pacfin-v-81-y-2023-i-c-s0927538x23001774/
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:eee:pacfin:v:81:y:2023:i:c:s0927538x23001774
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X23001774%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A81%3Ay%3A2023%3Ai%3Ac%3As0927538x23001774
featured: 2023-10-18
citations: unknown
topic: Portfolio & Allocation
---


# HighDimensional Portfolio Optimization with Tree-Structured Factor Model

The paper proposes a new portfolio optimization method that uses multiple characteristic information to predict stock returns and risk exposures, demonstrating its effectiveness in achieving higher Sharpe ratios, smaller standard deviations, and lower turnover.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X23001774%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A81%3Ay%3A2023%3Ai%3Ac%3As0927538x23001774
- Identifier: RePEc:eee:pacfin:v:81:y:2023:i:c:s0927538x23001774
- Released: 2023-10-18
- First featured: Quant Letter No. 22 (2023-10-18): https://www.ml-quant.com/issues/2023-10-18/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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