Predicting financial distress: Applicability of O-score model for Pakistani firms

Waqas, Hamid; Md-Rus, Rohani

doi:10.15208/beh.2018.28

Cited by 22 publications

(16 citation statements)

References 34 publications

(57 reference statements)

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“…A review of 162 scientific papers that analyze research of bankruptcy likelihood revealed that traditional statistical methods are being applied in the majority cases of bankruptcy prediction, being followed by artificial intelligence models as a second choice. Waqas and Md-Rus (2018) state that model groups differ in components used for calculations and calculation techniques applied. Namely this shows which different aspects of financial and economic stability are the best ones, and what conditions cause the bankruptcies of companies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is not only because it was one of the first models, but also because it rather precisely forecasts the bankruptcies of companies that are different in nature. As Waqas and Md-Rus (2018) research shows, almost all bankruptcy research is based on financial indicators, as are the majority of traditional models. Meanwhile, non-financial variables are rarely used and constitute a minority of all research on bankruptcy prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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“…Menurut Shilpa & Amulya (2017) financial distress didefinisikan sebagai ketidakmampuan perusahaan untuk memenuhi kewajiban keuangannya saat ini. berbagai cara penelitian untuk mengetahui suatu perusahan mengalami kondisi financial distress antara lain menggunakan Interest Coverage Ratios (Asquith et al, 1994), dan (Makeeva & Khugaeva, 2018), ekuitas negatif (Waqas & Md-Rus, 2018), debt service coverage ratio (Indriyanti, 2019), earning per share negatif (Mittal & Lavina, 2018) dan (Witiastuti & Suryandari, 2016). Perusahaan yang mengalami financial distress dapat dilihat dari beberapa kondisi yaitu EBITDA lebih kecil dari biaya keuangan dalam dua tahun berturut-turut, penurunan market value (nilai pasar) dalam dua tahun berturut-turut, dan Operating Cash Flow kurang dari biaya keuangan dalam dua tahun berturut-turut (Gupta et al, 2019), (Manzaneque et al, 2016), (Pindado et al, 2008), dan (Rezende et al, 2017).…”

Section: Pendahuluanunclassified

Pengaruh Rasio Keuangan pada Financial Distress

Mahaningrum

Merkusiwati

2020

E-Jurnal Akuntansi

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The purpose of this study is to obtain empirical evidence about the effect of financial ratios on financial distress. This research was conducted on companies in the basic and chemical industry sectors listed on the Indonesia Stock Exchange in the 2016-2018 period. The sample was determined using the nonprobability sampling method with a purposive sampling technique. The number of samples used was 39 companies and the overall observation data for the 2016-2018 period was 117 observation data. The analysis technique used is logistic regression analysis. Based on the results of the study, it was stated that liquidity ratios had no effect on financial distress, leverage ratios had a positive effect on financial distress, profitability ratios had a negative effect on financial distress, activity ratios had no effect on financial distress and growth ratios had no effect on financial distress. Keywords: Financial Distress; Financial Ratios; Logistic Regression.

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“…Ohlson created a model by using the sample of 105 failed and 2 058 healthy firms. Ohlson concludes that the Logit-based model is more useful as MDA-based models (Waqas and Md-Rus, 2018;Bandyopadhyay, 2006;Shumway, 2001;Jones et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Logit business failure prediction in V4 countries

Ďurica

Valášková

Janošková

2019

Engineering Management in Production and Services

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The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.

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Predicting financial distress: Applicability of O-score model for Pakistani firms

Cited by 22 publications

References 34 publications

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Pengaruh Rasio Keuangan pada Financial Distress

Logit business failure prediction in V4 countries

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