Browsing by Issue Date, starting with "2023-10-24"
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- Simulation Applications in Company Default PredictionPublication . Castelo, Ricardo Jorge Leandro; Costa, Magali Pedro; Lisboa, Inês Margarida CadimaThis study applies a simulation methodology, Monte Carlo, to the field of corporate default prediction, where its presence is only superficial. It attempts to augment a famous model from a methodology already highly supported in traditional literature – the Z”-score of Altman (1983), created through multiple discriminant analysis – and transform it stochastically without the use of the highly complex intelligent models already available in literature. A sample of 20 000 Portuguese companies from the Agriculture, Forestry, Fishing, Mining and Construction sectors is analyzed, yielding results that support the Monte Carlo method as a strong competitor for simple approaches like the logit transformation. This helps to build the foundation for what may possibly be a path towards models easier to apply in practice for the average Micro, Small and Medium enterprise (MSME) and beyond. The evaluation to the model on this study also takes inspiration off the innovative points of view of Mitton (2021) and Zhang (2022) to scrutinize results through empirical tests of the model under a high number of parameter conditions, instead of relying heavily on statistical significance, which is often overrepresented and overvalued in literature, and easily manipulatable.