Economics at your fingertips  

An alternative approach to predicting bank credit risk in Europe with Google data

Marcos González-Fernández and Carmen González-Velasco

Finance Research Letters, 2020, vol. 35, issue C

Abstract: The aim of this paper is to construct an alternative approach based on a sentiment index to measure bank credit risk in European countries using an alternative approach instead of traditional measures. Specifically, we use Google data for a set of keywords related to bank credit risk to capture investor sentiment. The resulting index shows a great similarity to traditional indexes based on bank CDS. The out-of-sample analysis demonstrates that our sentiment index is helpful for predicting bank credit risk during periods of financial distress, since it enhances the accuracy of the estimations.

Keywords: Sentiment index; Google data; Credit risk; Credit default swaps (search for similar items in EconPapers)
JEL-codes: G10 G17 G40 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2024-03-31
Handle: RePEc:eee:finlet:v:35:y:2020:i:c:s1544612319305318