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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页数：6
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