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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL EF - Ekonomska fakulteta
UDK: 336
COBISS: 60523267 Povezava se bo odprla v novem oknu
ISSN: 1042-4431
Št. ogledov: 357
Št. prenosov: 122
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: finančni trg;finančni instrumenti;investicije;politika cen;modeli;
Vrsta dela (COBISS): Članek v reviji
Strani: 19 str.
Letnik: ǂVol. ǂ73
Zvezek: ǂart. ǂ101344
Čas izdaje: Jun. 2021
DOI: 10.1016/j.intfin.2021.101344
ID: 13006551