Predicting financial distress using the worst-practice-frontier data envelopment analysis model and artificial neural network

Abstract

Purpose

The main purpose of this paper is to predicate financial distress using the worst-practice-frontier data envelopment analysis (WPF-DEA) model and artificial neural network.

Design/methodology/approach

In this study, a neural network technique was used to forecast inputs and outputs in the future time-period. Using a WPF-DEA model, financially distressed companies were identified based on the worst performance, and an improvement solution was provided for those decision-making units.

Findings

This study’s findings show that dynamic WPF-DEA has high predictability in corporate financial distress, and it can be used with high confidence. Based on the future time-period results, JOUSH & OXYGEN was predicted to be a financially distressed company in the two future time-periods.

Originality/value

In recent decades, globalization, technological changes and a competitive space have increased uncertainty in the economic environment. In such circumstances, economic growth certainly depends on correct decision-making and optimal allocation of resources. It can be done by introducing appropriate tools and models for assessing corporate financial conditions, including financial distress and bankruptcy.

Citation

Fathi, M.R., Rahimi, H. and Minouei, M. (2023), "Predicting financial distress using the worst-practice-frontier data envelopment analysis model and artificial neural network", Nankai Business Review International, Vol. 14 No. 2, pp. 295-315. https://doi.org/10.1108/NBRI-01-2022-0005