Document Type

Article

Publication Date

5-28-2026

Comments

This article is the author’s final published version in Journal of Risk and Financial Management, Volume 19, Issue 6, 2026, Article number 389.

The published version is available at https://doi.org/10.3390/jrfm19060389. Copyright © 2026 by the authors.

 

Abstract

This research explores whether boosting model complexity enhances the forecasting of corporate financial outlook in a multiclass credit outlook setup. Instead of viewing distress as simply a yes-or-no result, companies are divided into negative, neutral, and positive outlook categories to better reflect shifting credit conditions. The study evaluates a parametric baseline against several nonlinear classifiers—including ensemble, kernel-based, and similarity-driven approaches—while applying a consistent validation process and statistical testing. On average, nonlinear models outperform the linear specification in terms of out-of-sample accuracy and provide more homogeneous classification across the three outlook categories. Importantly, they substantially improve the identification of firms with financial vulnerabilities. Among nonlinear models, average performance differences are economically small and statistically insignificant. These findings suggest that there are diminishing returns to additional complexity once nonlinear structure is allowed for in the models. SHAP-based interpretability provides exploratory evidence that model decisions are economically intuitive and broadly consistent with nonlinear, state-dependent credit risk dynamics. Negative financial surprises tend to be penalized more heavily than positive ones are appreciated, demonstrating the convex nature of the underlying risk dynamics.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Language

English

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