Abstract
The aim of the paper is to investigate different aspects involved in developing prediction models in default risk analysis. In particular, we focused on the comparison of different statistical methods addressing several issues such as the structure of the data-base, the sampling procedure and the selection of financial predictors by means of different variable selection techniques. The analysis is carried out on a data-set of accounting ratios created from a sample of industrial firms annual reports. The reached findings aim to contribute to the elaboration of efficient prevention and recovery strategies.
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Notes
- 1.
The selected predictors and the estimations results are available upon requests from the authors.
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Amendola, A., Restaino, M., Sensini, L. (2012). Variable selection in forecasting models for default risk. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-2342-0_2
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DOI: https://doi.org/10.1007/978-88-470-2342-0_2
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