1 Performance of default-risk measures: the sample matters Isabel Abinzano 1 , Ana Gonzalez–Urteaga, Luis Muga INARBE and Public University of Navarre Santiago Sanchez Public University of Navarre 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. Key words: credit-risk measures, default prediction, hard to value stocks. JEL Classification: G32, G33. 1. Introduction Credit risk is perceived as the oldest and most important form of financial risk. This is because default is one of the most disruptive events that can befall a company, triggering not only bankruptcy costs in the form of legal and consulting fees, but also causing breaks in 1 Corresponding author. Institute for Advanced Research in Business and Economics (INARBE) and Department of Business Management. Public University of Navarre, Pamplona, Navarre, Spain. E-mail: isabel.abinzano@unavarra.es. We are grateful for valuable comments from participants at the 11 th International Risk Management Conference, the 8 th GIKA Conference, the 26 th Finance Forum, the 2019 INFINITI Conference on International Finance and the 6th International Conference on Credit Analysis and Risk Management. 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 Gonzalez- Urteaga particularly acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through grant PGC2018-095072-B-I00. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/