---
title: Monetary Policy Sentiment and Risk Dynamics
url: https://www.ml-quant.com/papers/ssrn/5233873/
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 5233873
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5233873
featured: 2025-04-30
citations: unknown
topic: LLMs & Text
---


# Monetary Policy Sentiment and Risk Dynamics

A model showing the interaction of monetary policy signals and market sentiment in driving financial tail risk is developed, indicating that aligned signals reduce risk and misaligned ones increase it.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5233873
- Identifier: SSRN 5233873
- Released: 2025-04-28
- First featured: Quant Letter No. 95 (2025-04-30): https://www.ml-quant.com/issues/2025-04-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [ECB Press Conference Sentiment](https://www.ml-quant.com/papers/repec/wly-ijfiec-v-30-y-2025-i-1-p-652-664/): The research uses FinBERT to analyze sentiment in ECB president's introductory statements, finding that the sentiment about monetary policy significantly affects subsequent press conference content.
- [Twitter Sentiment and Financial Trends](https://www.ml-quant.com/papers/ssrn/4467949/): A new financial sentiment index derived from Twitter data shows strong links to market conditions and can forecast stock market returns, particularly in response to changes in U.S. monetary policy.
- [From Tone to Trajectory: Continuous Sentiment and the Shape of Monetary Policy Communication](https://www.ml-quant.com/papers/arxiv/2609.25034/): The study shows that how monetary policy sentiment unfolds across a press conference, not just its average tone, predicts rate changes and shapes forecaster expectations at the ECB and Fed.
- [Value of Textual Data in Forecasting Macroeconomic Tail Risk](https://www.ml-quant.com/papers/ssrn/4509043/): News-based data offers valuable insights not provided by economic indicators, especially for left-tail forecasts, and significantly influences consumer sentiment.
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://www.ml-quant.com/papers/ssrn/4412788/): ChatGPT predicts stock market returns using sentiment analysis, outperforming traditional methods.
- [Sentiment trading with large language models](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2024-105227/): The OPT model, a large language model, has proven superior in predicting stock market returns using sentiment analysis of U.S. financial news, outdoing traditional methods like the Loughran-McDonald dictionary model.
