improving the predictability of rating transitions
Ursula Slapnik (Author), Igor Lončarski (Author)

Abstract

In order to identify novel qualitative determinants of transitions in sovereign credit ratings, we construct six different textual sentiment and subjectivity measures using dictionary-based, and machine learning approaches on sovereign credit rating reports issued by Moody's and Fitch in the period from 2002 to 2017. After controlling for macroeconomic and fiscal strength, soft information, as well as known sources of proximity biases, we find that, on average, these novel text-based measures improve the classification accuracy of downgrades and upgrades. The improvement is more notable for sentiment than subjectivity measures, and for downgrades compared to upgrades. Next, we find evidence that credit rating agencies seem to follow the through-the-cycle rating philosophy by taking a longer horizon into account. Finally, to the best of our knowledge, we offer the most comprehensive analysis of textual sentiment measures and their effect on sovereign credit ratings thus far.

Keywords

finančni trg;finančni instrumenti;investicije;politika cen;modeli;financial market;financial instruments;investments;price policy;models;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL EF - Faculty of Economics
UDC: 336
COBISS: 60523267 Link will open in a new window
ISSN: 1042-4431
Views: 357
Downloads: 122
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: finančni trg;finančni instrumenti;investicije;politika cen;modeli;
Type (COBISS): Article
Pages: 19 str.
Volume: ǂVol. ǂ73
Issue: ǂart. ǂ101344
Chronology: Jun. 2021
DOI: 10.1016/j.intfin.2021.101344
ID: 13006551