Forecasting Bankruptcy for organizational sustainability in Pakistan

Inam, Fraz; Inam, Aneeq; Mian, Muhammad Abbas; Sheikh, Adnan Ahmed; Awan, Hayat Muhammad

doi:10.1108/jeas-05-2018-0063

Cited by 17 publications

(9 citation statements)

References 45 publications

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“…The important role of financial ratios in predicting financial distress is explored in the study of Waqas and Md-Rus (2018), whose inclusion of profitability, liquidity, leverage, and cash flow ratios improved the accuracy rate of financial distress modeling using a logit model. Inam et al (2019) confirm that profitability and leverage indicators are the best financial discriminants to predict the financial distress which was proved analyzing the Pakistani firms of the non-financial sector. Another important aspect is presented in the study of Korol (2019) who investigated the effectiveness of bankruptcy prediction models in short-and long-term horizons.…”

Section: Literature Reviewsupporting

confidence: 61%

Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

Valášková

Ďurana

Adamko

et al. 2020

JRFM

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The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

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Section: Literature Reviewsupporting

confidence: 61%

Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

Valášková

Ďurana

Adamko

et al. 2020

JRFM

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“…Menurut Khani, rasio likuiditas merupakan indikator terbaik untuk melihat perusahaan akan bangkrut atau tidak (Inam et al, 2019) serta adanya hubungan yang signifikan antara rasio likuiditas dan kebangkrutan perusahaan (Defitria et al, 2019). Oleh karena itu, hipotesis yang dikembangkan adalah H3 : Ada perbedaan kinerja signifikan rasio likuiditas sebelum dan pada masa pandemi covid-19.…”

Section: Kinerja Rasio Likuiditas (Cr) Sebelum Dan Pada Masa Pandemi Covid-19unclassified

Analisis Perbedaan Kinerja Perusahaan Sebelum Dan Saat Pandemi Covid-19

Hilman

Laturette

2021

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This study aims to determine differences in the performance of the profitability ratio (ROA), sales growth ratio (SG), liquidity ratio (CR) and leverage ratio (DER) to bankruptcy prediction (Altman Z-score) before and during the Covid 19 pandemic. Descriptive statistical method with secondary data collection, namely quarterly financial reports from 2019-2020. Samples were taken by purposive sampling method and obtained 62 samples of the construction industry and consumer goods in 2019-2020. Normality testing using the Kolmogorov-Smirnov test results in data not normally distributed with a significance value below 0.05. Different tests using the non-parametric Wilcoxon sign test resulted in differences in ROA (0.000 <0.05), SG (0.004 <0.05) and CR (0.005 <0.05) on the decreased bankruptcy predictions during the pandemic. Meanwhile, DER (0.803 <0.05) did not differ from the prediction of bankruptcy before and during the COVID-19 pandemic

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“…Its main strength is a relatively easy interpretation of between-group differences and reduction of error rates, while it suffers from several assumptions -the analysis is extremely sensitive to outliers, it needs an adequate sample size, multivariate normality, homoscedasticity, independence of observations, and low (non) multicollinearity (Svabova et al, 2022). This method, despite the number of assumptions considered, is frequently used to create bankruptcy prediction models in different conditions (see Wieprow & Gawlik, 2021;Bărbuță-Mișu & Madaleno, 2020;Inam et al, 2019;Kliestik et al, 2018b;etc.). In the current research of Visegrad group environment, the assumptions were complied as indicated in the methodological steps of the research.…”

Section: Methodsmentioning

confidence: 99%

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Valášková

Gajdosikova

Belás

2023

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Research background: Effective monitoring of financial health is essential in the financial management of enterprises. Early studies to predict corporate bankruptcy were published at the beginning of the last century. The prediction models were developed with a significant delay even among the Visegrad group countries. Purpose of the article: The primary aim of this study is to create a model for predicting bankruptcy based on the financial information of 20,693 enterprises of all sectors that operated in the Visegrad group countries during the post-pandemic period (2020?2021) and identify significant predictors of bankruptcy. To reduce potential losses to shareholders, investors, and business partners brought on by the financial distress of enterprises, it is possible to use multiple discriminant analysis to build individual prediction models for each Visegrad group country and a complex model for the entire Visegrad group. Methods: A bankruptcy prediction model is developed using multiple discriminant analysis. Based on this model, prosperity is assessed using selected corporate financial indicators, which are assigned weights such that the difference between the average value calculated in the group of prosperous and non-prosperous enterprises is as large as possible. Findings & value added: The created models based on 6?14 financial indicators were developed using different predictor combinations and coefficients. For all Visegrad group countries, the best variable with the best discriminating power was the total indebtedness ratio, which was included in each developed model. These findings can be used also in other Central European countries where the economic development is similar to the analyzed countries. However, sufficient discriminant ability is required for the model to be used in practice, especially in the post-pandemic period, when the financial health and stability of enterprises is threatened by macroeconomic development and the performance and prediction ability of current bankruptcy prediction models may have decreased. Based on the results, the developed models have an overall discriminant ability greater than 88%, which may be relevant for academicians to conduct further empirical studies in this field.

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Forecasting Bankruptcy for organizational sustainability in Pakistan

Cited by 17 publications

References 45 publications

Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

Analisis Perbedaan Kinerja Perusahaan Sebelum Dan Saat Pandemi Covid-19

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

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