TY - JOUR
T1 - News intensity and asset returns
T2 - the case of currency volatility
AU - Avioz, Ilanit
AU - Kedar-Levy, Haim
AU - Pungulescu, Crina
N1 - Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - There are both theoretical reasons and empirical evidence for financial markets rewarding investors who put effort into acquiring relevant information. This article shows how a systematic approach of encoding text, ‘semantic fingerprinting’ can be applied to a set of news headlines from The Wall Street Journal to measure the ‘news intensity’ − the volume of relevant news − pertaining to three major currency indices: dollar, pound and euro. In a dataset that spans two decades, we find a persistently positive link between the ‘news intensity’ and the volatility of currency returns, that becomes significantly stronger in times of recession: ‘bad news’ tends to translate into higher volatility.
AB - There are both theoretical reasons and empirical evidence for financial markets rewarding investors who put effort into acquiring relevant information. This article shows how a systematic approach of encoding text, ‘semantic fingerprinting’ can be applied to a set of news headlines from The Wall Street Journal to measure the ‘news intensity’ − the volume of relevant news − pertaining to three major currency indices: dollar, pound and euro. In a dataset that spans two decades, we find a persistently positive link between the ‘news intensity’ and the volatility of currency returns, that becomes significantly stronger in times of recession: ‘bad news’ tends to translate into higher volatility.
KW - News
KW - currency indices
KW - natural language processing
KW - volatility
UR - http://www.scopus.com/inward/record.url?scp=85190429469&partnerID=8YFLogxK
U2 - 10.1080/13504851.2024.2337321
DO - 10.1080/13504851.2024.2337321
M3 - Article
AN - SCOPUS:85190429469
SN - 1350-4851
JO - Applied Economics Letters
JF - Applied Economics Letters
ER -