Publication:
Performance of default-risk measures: the sample matters

Consultable a partir de

2022-05-01

Date

2020

Director

Publisher

Elsevier
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión aceptada / Onetsi den bertsioa

Project identifier

ES/1PE/ECO2016-77631-R
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104304GB-I00/ES/
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095072-B-I00/ES/

Abstract

This paper examines the predictive power of the main default-risk measures used by both academics and practitioners, including accounting measures, market-price-based measures and the credit rating. Given that some measures are unavailable for some firm types, pair wise comparisons are made between the various measures, using same-size samples in every case. The results show the superiority of market-based measures, although their accuracy depends on the prediction horizon and the type of default events considered. Furthermore, examination shows that the effect of within-sample firm characteristics varies across measures. The overall finding is of poorer goodness of fit for accurate default prediction in samples characterised by high book-to-market ratios and/or high asset intangibility, both of which suggest pricing difficulty. In the case of large-firm samples, goodness of fit is in general negatively related to size, possibly because of the 'too-big-to-fail' effect.

Keywords

Credit-risk measures, Default prediction, Hard to value stocks

Department

Enpresen Kudeaketa / Institute for Advanced Research in Business and Economics - INARBE / Gestión de Empresas

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

This paper has been possible thanks to the SANFI Research Grant for Young Researchers Edition 2015, the financial support from the Spanish Ministry of Economy, Industry and Competitiveness (ECO2016-77631-R (AEI/FEDER, UE)) and the Spanish Ministry of Science and Innovation (PID2019-104304GB-I00/AEI/10.13039/501100011033). Ana González Urteaga particularly acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through grant PGC2018-095072-B-I00.

© 2020 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0

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