---
title: Predicting US Stock Market Direction
url: https://www.ml-quant.com/papers/repec/eee-intfor-v-40-y-2024-i-3-p-869-880/
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:intfor:v:40:y:2024:i:3:p:869-880
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169207023000729%3Bh%3Drepec%3Aeee%3Aintfor%3Av%3A40%3Ay%3A2024%3Ai%3A3%3Ap%3A869-880
featured: 2024-06-12
citations: unknown
topic: Derivatives & Volatility
---


# Predicting US Stock Market Direction

Machine learning models, specifically random forests and bagging, are superior in predicting S&P 500 returns using volatility indices.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169207023000729%3Bh%3Drepec%3Aeee%3Aintfor%3Av%3A40%3Ay%3A2024%3Ai%3A3%3Ap%3A869-880
- Identifier: RePEc:eee:intfor:v:40:y:2024:i:3:p:869-880
- Released: 2024-06-12
- First featured: Quant Letter No. 53 (2024-06-12): https://www.ml-quant.com/issues/2024-06-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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