SME default prediction: A systematic methodology-focused review

Cheraghali, Hamid; Molnár, Peter

doi:10.1080/00472778.2023.2277426

Cited by 8 publications

(1 citation statement)

References 148 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Financial problems can lead to insolvency and even bankruptcy of the company. Therefore, models that predict these situations are called bankruptcy models, sometimes just prediction models (Cheraghali & Molnár, 2023;Lohmann & Möllenhoff, 2023;Valaskova et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Durica,

Frnda,

Svabova

2023

View full text Add to dashboard Cite

Research background: Financial distress or non-prosperity prediction has been a widely discussed topic for several decades. Early detection of impending financial problems of the company is crucial for effective risk management and important for all entities involved in the company’s business activities. In this way, it is possible to take the actions in the management of the company and eliminate possible undesirable consequences of these problems. Purpose of the article: This article aims to innovate financial distress prediction through the creation of individual models and ensembles, combining machine learning techniques such as decision trees and neural networks. These models are developed using real data. Beyond serving as an autonomous and universal tool especially useful in the Slovak economic conditions, these models can also represent a benchmark for Central European economies confronting similar economic dynamics. Methods: The prediction models are created using a dataset consisting of more than 20 financial ratios of more than 19 thousand real companies. Partial models are created employing machine learning algorithms, namely decision trees and neural networks. Finally, all models are compared based on a wide range of selected performance metrics. During this process, we strictly use a data mining methodology CRISP-DM. Findings & value added: The research contributes to the evolution of financial prediction and reveals the effectiveness of ensemble modelling in predicting financial distress, achieving an overall predictive ability of nearly 90 percent. Beyond its Slovak origins, this study provides a framework for early financial distress prediction. Although the models are created for diverse industries within the Slovak economy, they could also be useful beyond national borders. Moreover, the CRISP-DM methodological framework enables its adaptability for companies in other countries.

show abstract

Section: Introductionmentioning

confidence: 99%