---
title: Forecasting High-Dimensional Portfolios
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-6-p-1513-d-1102706/
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:gam:jmathe:v:11:y:2023:i:6:p:1513-:d:1102706
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F6%2F1513%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A6%3Ap%3A1513-%3Ad%3A1102706
featured: 2023-05-24
citations: unknown
topic: Portfolio & Allocation
---


# Forecasting High-Dimensional Portfolios

New methodology for forecasting and portfolio formation in large portfolios of assets introduced, resulting in better investment performance.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F6%2F1513%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A6%3Ap%3A1513-%3Ad%3A1102706
- Identifier: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1513-:d:1102706
- Released: 2023-05-24
- First featured: Quant Letter No. 1 (2023-05-24): https://www.ml-quant.com/issues/2023-05-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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