Modelling market implied ratings using LASSO variable selection techniques

Sermpinis, G., Tsoukas, S. and Zhang, P. (2018) Modelling market implied ratings using LASSO variable selection techniques. Journal of Empirical Finance, 48, pp. 19-35. (doi:10.1016/j.jempfin.2018.05.001)

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Abstract

Making accurate predictions of corporate credit ratings is a crucial issue to both investors and rating agencies. In this paper, we investigate the determinants of market implied credit ratings in relation to financial factors, market-driven indicators and macroeconomic predictors. Applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), we document substantial predictive ability. In addition, when we compare our LASSO-selected models with the benchmark ordered probit model, we find that the former models have superior predictive power and outperform the latter model in all out-of-sample predictions.

Item Type:Articles
Additional Information:We are grateful for comments and inputs from an anonymous referee, Oldrich Vasicek and from participants at the 2017 European Financial ManagementAssociation Conference, the 2017 European Meetings of the Econometric Society, the 2016 International Conference on Forecasting Financial Markets and the AdamSmith Business School seminars. The third author is grateful to China Scholarship Council for funding (Grant number: 201508060273).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhang, Miss Ping and Tsoukas, Professor Serafeim and Sermpinis, Professor Georgios
Authors: Sermpinis, G., Tsoukas, S., and Zhang, P.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of Empirical Finance
Publisher:Elsevier
ISSN:0927-5398
ISSN (Online):1879-1727
Published Online:29 May 2018

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