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Modelling market implied ratings using LASSO variable selection techniques

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  • Sermpinis, Georgios
  • Tsoukas, Serafeim
  • Zhang, Ping

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.

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  • Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    • Handle: RePEc:eee:empfin:v:48:y:2018:i:c:p:19-35
    DOI: 10.1016/j.jempfin.2018.05.001
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    More about this item

    Keywords

    Market implied ratings; LASSO; Financial ratios; Forecasting;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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