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

Mohammad Reza Fathi, Hamid Rahimi and Mehrzad Minouei

Nankai Business Review International, 2022, vol. 14, issue 2, 295-315

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.

Keywords: Data envelopment analysis; Artificial neural network; Financial distress; Decision-making units; Worst-practice-frontier (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:nbripp:nbri-01-2022-0005

DOI: 10.1108/NBRI-01-2022-0005

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