Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Abstract

Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.

Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm

“…According to Ashraf, Félix and Serrasqueiro (2019) [52], in order to test the prediction accuracy of the logistic regression model obtained from the training of sample companies in various regions, data of 300 listed companies nationwide that did not join the regression (150 joined the guarantee network, 150 did not join the guarantee network) and 200 listed companies that did not join the regression in various regions (100 joined the guarantee network and 100 did not join the guarantee network) were obtained as prediction samples. The results of the prediction accuracy test are shown in Table 6.…”

Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis

“…Studying the efficacy of Altman's z-score model in predicting bankruptcy of specialty retail firms doing business in contemporary times, Chaitanya (2005) found that all but two of the bankruptcies (94%) would have been accurately predicted. Ashraf et al (2019) found that both models by Altman (1968) and Zmijewski (1984) are still valuable for predicting the financial distress of emerging markets and can be used by businessmen, financial specialists, administrators, and other concerned parties who are thinking about investing in an organization and/or want to enhance their organization performance. Elviani et al (2020) studied the accuracy of the Altman (1968), Ohlson (1980), Springate (1978) and Zmijewski (1984) models in bankruptcy predicting trade sector companies in Indonesia using binary logistic regression.…”

Financial performance analysis of GIC Re

“…Ohlson's (1980) O-Score The Ohlson's O-Score model has been widely used in the distress prediction literature. This model is based on conditional logit technique (Timmermans, 2014) and uses logistic regression with a set of nine accounting ratios to predict corporate financial distress (Asfraf et al, 2019). The variables and analysis technique is summarized below: O-Score = À 1.32 -0.407X 1 þ 6.03X 2 À 1.43X 3 þ 0.08X 4 -2.37X 5 À 1.83X 6 þ 0.29X 7 À 1.72X 8 À 0.52X9 Where:…”