News intensity and asset returns: the case of currency volatility

被引：1

作者：

Avioz, Ilanit ^{[1
]}

Kedar-Levy, Haim ^{[1
]}

Pungulescu, Crina ^{[2
]}

机构：

[1] Ben Gurion Univ Negev, Sch Management, Beer Sheva, Israel

[2] John Cabot Univ, Finance, Via Lungara 233, I-00165 Rome, Italy

来源：

APPLIED ECONOMICS LETTERS | 2024年

基金：

以色列科学基金会;

关键词：

News; volatility; currency indices; natural language processing;

D O I：

10.1080/13504851.2024.2337321

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

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