---
title: Hybrid Machine Learning for Stock Volatility Prediction
url: https://www.ml-quant.com/papers/repec/eee-finana-v-96-y-2024-i-pb-s1057521924006434/
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:finana:v:96:y:2024:i:pb:s1057521924006434
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521924006434%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A96%3Ay%3A2024%3Ai%3Apb%3As1057521924006434
featured: 2024-12-18
citations: unknown
topic: Derivatives & Volatility
---


# Hybrid Machine Learning for Stock Volatility Prediction

A study uses machine learning to analyze stock market volatility, finding the RF-LASSO model to be the most effective predictor.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521924006434%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A96%3Ay%3A2024%3Ai%3Apb%3As1057521924006434
- Identifier: RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924006434
- Released: 2024-12-18
- First featured: Quant Letter No. 79 (2024-12-18): https://www.ml-quant.com/issues/2024-12-18/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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