Bankruptcy Prediction for Non-Financial Firms of Pakistan

Roomi, Muhammad Sohaib; Ahmad, Waqas; Ramzan, Muhammad; Zia‐ur‐Rehman, Muhammad

doi:10.5296/ijafr.v5i2.7782

Cited by 10 publications

(15 citation statements)

References 19 publications

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“…Recently, Tinoco and Wilson (2013) tested the performance of the Altman (1968) Z-score model for UK firms and noticed a significant decrease in the prediction accuracy of the model than the original study. On the other hand, Roomi et al (2015) reported that the Z-score model is a good predictor for predicting financial distress of Pakistani firms. Moreover, Jaffari and Ghafoor (2017) found that the logit model is better than MDA, but the prediction accuracy of the models declines when applied to the Pakistani market.…”

Section: Comparative Positionmentioning

confidence: 99%

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

Ashraf

Fėlix

Serrasqueiro

2019

JRFM

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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.

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Section: Comparative Positionmentioning

confidence: 99%

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

Ashraf

Fėlix

Serrasqueiro

2019

JRFM

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“…The very first step is to find out the defaulted firms in Pakistan for the period of 2014-2015, as this period has been selected as this is the most recent financial data available for all listed companies. The total population of current research involves all the companies listed as non-financial in PSX (Abbas & Ahmad, 2011;Ahmed, 2016;Roomi et al, 2015)and also to identify the firms that has been bankrupt in PSX record. The analysis reports of PSX are obtained to determine the bankrupt companies' sample.…”

Section: Sample Selectionmentioning

confidence: 99%

Predicting Bankruptcy through Neural Network: Case of PSX Listed Companies

et al. 2022

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The paper reconnoiters if logistic regression (LR) and neural network (NN) can estimate bankruptcy for PSX non-financial companies a year ahead of bankruptcy occurrence; particularly it endeavors to explore how exact LR and NN models are? Financial ratios were utilized forecast the bankruptcy in firms. Empirical results demonstrated that both models have capability to predict the event of bankruptcy with NN outperforming LR model. Although both models possess capability to predict bankruptcy, current research demonstrated that use of neural networks (NN) enhances the precision of prediction by being a superior approach over logistic regression method (this is based on accuracy level achieved earlier by NN over LR). These results will cover the literature gap existent in bankruptcy research in Pakistan especially about NN estimation model, proposing an advanced forecasting with precision as proven through figure 4.1.

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“…Therefore, we used Z-score (Altman 1968) and ZMI-score (Zmijewski 1984), two widely used measures of insolvency risk (Laeven and Levine 2009;Kanagaretnam et al 2011) to proxy a firm's bankruptcy risk. Researchers have found Altman's Z-score an effective and accurate tool to assess the financial distress of nonfinancial firms in Pakistan (Awais et al 2015;Hussain et al 2014;Roomi et al 2015). In the Altman (1968) model, low (high) values of the Z-score indicate that firm is at a higher (lower) level of risk.…”

Section: Dependent Variablementioning

confidence: 99%

Sentiments–Risk Relationship across the Corporate Life Cycle: Evidence from an Emerging Market

Akbar

Qureshi³

et al. 2021

Economies

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The influence of market sentiments on the bankruptcy risk propensity of firms has been extensively explored in the literature. However, less attention has been paid to whether the corporate life cycle plays any role in this nexus. The purpose of this research is to unveil how the corporate bankruptcy risk propensity responds to market sentiments, and whether this sentiments–risk relationship varies over different stages of the corporate life cycle. Using a sample of 301 Pakistani non-financial listed firms for 2005–2014, we employ two-step generalized method of moments (GMM) regression estimation to address the issue of endogeneity. Empirical evidence reveals that managers tend to escalate a firm’s bankruptcy risk during high market sentiments. Further analysis indicates that during the period of positive market sentiments, introduction stage firms prefer to assume the highest bankruptcy risk followed by decline and growth firms, while mature firms continue to be risk-averse. This research contributes to the corporate finance literature by suggesting that managerial risk-taking is influenced by market sentiments and corporate managers show a different attitude towards risk at different stages of the corporate life cycle. Therefore, to ensure enterprise sustainability, capital market regulators should have a robust risk management framework in place to discipline the excessive risk-taking by firm managers over different stages of the corporate life cycle. Moreover, investors and creditors shall take into consideration the respective life cycle stage of the firm to minimize the risk exposure of their investment portfolios. Our results are robust to alternate econometric specifications and alternate variable specifications.

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Bankruptcy Prediction for Non-Financial Firms of Pakistan

Cited by 10 publications

References 19 publications

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

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

Predicting Bankruptcy through Neural Network: Case of PSX Listed Companies

Sentiments–Risk Relationship across the Corporate Life Cycle: Evidence from an Emerging Market

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