An analysis of the determinants of S&P ratings assigned to Canadian firms: Application of a multinomial logit
Abstract
This study identifies the factors affecting the ratings assigned to Canadian firms by the rating agency Standard and Poor's, and proposes a multinomial logit model to predict ratings. The authors first propose a two-class model that classifies ratings as either investment or speculative grade. They then extend this model to account for all the rating levels used by the rating agency. The out-of-sample forecasts respectively lead to concordance rates of 86 per cent and 60 per cent in the two-class and extended models. The credit rating models thus capture to some extent the main determinants affecting the ratings changes.
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Author's Biography
Walid Amdouni is a corporate credit analyst at Moneris Solutions Corporation, Toronto. His main interests include credit risk management, rating methodologies and replication, Basel’s credit risk capital requirements measurement using advanced approaches. He holds a MBA in Finance from Université Laval in Canada.
Issouf Soumaré is Associate Professor of Finance and Managing Director of the Laboratory for Financial Engineering at Laval University, Canada. His research and teaching interests include risk management, financial engineering and numerical methods in finance. He co-authored the book ‘Stochastic Simulation and Applications in Finance with Matlab Programs’ (Wiley). His theoretical and applied financial economic works have been published in leading international economics and finance journals. Issouf holds a PhD in business administration in finance from the University of British Columbia (Canada) and is also a certified Professional Risk Manager and Financial Risk Manager.
Citation
Amdouni, Walid and Soumaré, Issouf (2014, September 1). An analysis of the determinants of S&P ratings assigned to Canadian firms: Application of a multinomial logit. In the Journal of Risk Management in Financial Institutions, Volume 7, Issue 4. https://doi.org/10.69554/KVMM8179.
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