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Words That Move Markets: Linking News Keywords to Volatility Trends

Swan, Edvard LU and Höyby Fathi Saffar, Jonas LU (2025) STAH11 20242
Department of Statistics
Abstract (Swedish)
This study examines the relationship between global news narratives and market volatility, focusing on the VIX index as a measure of investor sentiment. By analyzing financial news headlines from 2023, the research investigates whether keyword frequencies related to terms like conflict, recession, and bullish can predict changes in VIX. The study employs multiple regression, time series analysis, and cross-correlation techniques to uncover significant patterns. Results indicate that keywords such as conflict and risk act as leading indicators for VIX movements, while bullish and recession demonstrate nuanced interactions with market volatility. The findings support the hypothesis that linguistic cues in financial narratives contribute to... (More)
This study examines the relationship between global news narratives and market volatility, focusing on the VIX index as a measure of investor sentiment. By analyzing financial news headlines from 2023, the research investigates whether keyword frequencies related to terms like conflict, recession, and bullish can predict changes in VIX. The study employs multiple regression, time series analysis, and cross-correlation techniques to uncover significant patterns. Results indicate that keywords such as conflict and risk act as leading indicators for VIX movements, while bullish and recession demonstrate nuanced interactions with market volatility. The findings support the hypothesis that linguistic cues in financial narratives contribute to market sentiment, with implications for forecasting tools and automated investment strategies. This work highlights the predictive potential of narrative-driven analysis while acknowledging limitations, such as data granularity and the exclusion of real-time sentiment metrics. Future research could expand these findings by integrating broader datasets and real-time analytics. (Less)
Please use this url to cite or link to this publication:
author
Swan, Edvard LU and Höyby Fathi Saffar, Jonas LU
supervisor
organization
course
STAH11 20242
year
type
M2 - Bachelor Degree
subject
language
English
id
9184876
date added to LUP
2025-02-14 09:05:49
date last changed
2025-02-14 09:05:59
@misc{9184876,
  abstract     = {{This study examines the relationship between global news narratives and market volatility, focusing on the VIX index as a measure of investor sentiment. By analyzing financial news headlines from 2023, the research investigates whether keyword frequencies related to terms like conflict, recession, and bullish can predict changes in VIX. The study employs multiple regression, time series analysis, and cross-correlation techniques to uncover significant patterns. Results indicate that keywords such as conflict and risk act as leading indicators for VIX movements, while bullish and recession demonstrate nuanced interactions with market volatility. The findings support the hypothesis that linguistic cues in financial narratives contribute to market sentiment, with implications for forecasting tools and automated investment strategies. This work highlights the predictive potential of narrative-driven analysis while acknowledging limitations, such as data granularity and the exclusion of real-time sentiment metrics. Future research could expand these findings by integrating broader datasets and real-time analytics.}},
  author       = {{Swan, Edvard and Höyby Fathi Saffar, Jonas}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Words That Move Markets: Linking News Keywords to Volatility Trends}},
  year         = {{2025}},
}