---
title: ML Beats Benchmark Models in Stock Beta Estimation
url: https://www.ml-quant.com/papers/ssrn/4551604/
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 4551604
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4551604
featured: 2023-08-30
citations: unknown
topic: Portfolio & Allocation
---


# ML Beats Benchmark Models in Stock Beta Estimation

Machine learning models, especially random forests, are more effective than traditional models in predicting market trends and reducing errors, improving market-neutral strategies and minimum variance portfolios.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4551604
- Identifier: SSRN 4551604
- Released: 2021-10-01
- 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

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