Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2613-2618 |
| Number of pages | 6 |
| Journal | Applied Economics Letters |
| Volume | 32 |
| Issue number | 18 |
| DOIs | |
| State | Published - 2025 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- News
- currency indices
- natural language processing
- volatility
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