---
title: Online Investor Sentiment and Stock Market Risk
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-12-y-2024-i-20-p-3192-d-1497063/
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:12:y:2024:i:20:p:3192-:d:1497063
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F12%2F20%2F3192%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A12%3Ay%3A2024%3Ai%3A20%3Ap%3A3192-%3Ad%3A1497063
featured: 2024-10-17
citations: unknown
topic: LLMs & Text
---


# Online Investor Sentiment and Stock Market Risk

Machine learning techniques like extreme gradient boosting and random forest are more accurate in predicting the aggregated stock market risk premium based on online investor sentiment than traditional linear models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F12%2F20%2F3192%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A12%3Ay%3A2024%3Ai%3A20%3Ap%3A3192-%3Ad%3A1497063
- Identifier: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3192-:d:1497063
- Released: 2024-10-17
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

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