Abstract
This study explores the use of three algorithms, support vector machines (SVMs), Adaptive Boosting (AdaBoost), and Gradient Boosting models, to distinguish between companies with good and bad credit outlooks. The study uses seven financial variables to classify non-financial companies as good or bad credit outlook. SVMs have several advantages, including their ability to handle non-linearly separable data, their avoidance of overfitting, and their suitability for high-dimensional data. Adaboost and Gradient Boost also have several advantages, including the ability to combine multiple weak classifiers, its computational efficiency, and its ability to handle large datasets. Results suggest that all three models-SVMs, Adaboost, and Gradient Boosting--can effectively distinguish between companies with good and bad credit outlooks.
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Malhotra, R., Malhotra, D.K. (2023). Using Machine Learning Models to Predict Corporate Credit Outlook. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_25
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