Efficacy of going concern prediction model for creditor oriented regime via liquidation: A MDA approach

Abstract

Purpose

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

Design/methodology/approach

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

Findings

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

Research limitations/implications

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

Practical implications

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

Originality/value

The proposed accounting-based going concern prediction model is based on liquidated firms.

Citation

Javaid, M.I. and Javid, A.Y. (2018), "Efficacy of going concern prediction model for creditor oriented regime via liquidation: A MDA approach", Journal of Applied Accounting Research, Vol. 19 No. 4, pp. 552-573. https://doi.org/10.1108/JAAR-07-2017-0070