Resumen

SABEK, Amine  y  HORAK, Jakub. Gaussian Process Regressions Hyperparameters Optimization to Predict Financial Distress. Retos [online]. 2023, vol.13, n.26, pp.273-289. ISSN 1390-8618.  https://doi.org/10.17163/ret.n26.2023.06.

predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology. The main objective of this paper is to predict financial distress based on the Gaussian Process Regression (GPR) and then compare the results of this model with the results of other deep learning models (SVM, LR, LD, DT, KNN). The analysis is based on a dataset of 352 companies extracted from the Kaggle database. As for predictors, 83 financial ratios were used. The study concluded that the use of GPR achieves very relevant results. Furthermore, it outperformed the rest of the deep learning models and achieved first place equally with the SVM model with a classification accuracy of 81%. The results contribute to the maintenance of the integrated system and the prosperity of the country’s economy, the prediction of the financial distress of companies and thus the potential prevention of disruption of the given system.

Palabras clave : financial distress; Gaussian process regression; deep learning; investment financing; financial risk prediction; Gaussian regression; financial ratios; deep learning models.

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