Default prediction modeling (DPM) with machine learning algorithms: case of non-financial listed companies in Pakistan

Abstract

Purpose

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Design/methodology/approach

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

Findings

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Research limitations/implications

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

Originality/value

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

Citation

Alvi, J. and Arif, I. (2024), "Default prediction modeling (DPM) with machine learning algorithms: case of non-financial listed companies in Pakistan", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-09-2023-1888