Efficacy of industry factors for corporate default prediction

Agrawal, Khushbu; Maheshwari, Yogesh

doi:10.1016/j.iimb.2018.08.007

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…The sensitivity variable for industry factors, industry beta, is found to be statistically significant in predicting defaults. Higher sensitivity to industry factors leads to an increased probability of default (Agrawal and Maheshwari 2019).…”

Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy.

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Section: Earnings Management and Bankruptcy Predictionmentioning

confidence: 99%

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Ogachi

Ndege

Gaturu

et al. 2020

JRFM

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“…However, the financial literature proposes a wide range of possibilities, with different terms appearing in an attempt to portray the formal process that affects the firm and to categorize the economic problems involved. Altman and Hotchkiss (2006) highlight four terminologies commonly used in the financial literature to refer to firms in financial difficulties: failure, insolvency, default, and bankruptcy (Agrawal & Maheshwari, 2019).…”

Section: Definition Of Bankruptcymentioning

confidence: 99%

Probability of Corporate Bankruptcy: Application to Portuguese Manufacturing Industry SMEs

Pacheco

Madaleno

Correia

et al. 2022

IJBS

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This paper aims to develop a model for predicting corporate bankruptcy for SMEs in the Portuguese manufacturing industry where this question remains rather unaddressed. Using profitability, activity, liquidity, leverage, and solvency ratios, it was added the size and age variables, for a group of 208 firms, including 49 bankrupt firms and 159 active firms, during the years 2011 to 2015. The logit model allowed us to estimate a model with 82.3% of predictive capacity. The most important variables identified were profitability, solvency, and size. Estimations only with the data closest to the bankruptcy date improved predictive capacity. It is evidenced that financial and non-financial variables can predict bankruptcy probability. A possible future approach would be to analyze a larger sample. Also, a larger period could be considered, allowing to test either the effects of the 2007/8 crisis or the effects of the recent economic turmoil related to Covid-19. Important for both corporate managers and investors. Conclusions may be disclosed regarding the influence that economic turmoil certainly has on corporate defaults and bankruptcies allowing its extension to other countries. The contribution of this paper is to find the best specification for a bankruptcy prediction model applied to the Portuguese manufacturing industry SMEs. This paper also contributes to the existing literature by using non-financial variables and analyzing a sector still unexplored in Portugal, albeit its conclusions can be extended to other countries.

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“…In India, some of the prominent credit default prediction models were formulated by Bandyopadhyay (2006), Datta (2013), Desai and Joshi (2015), Singh and Mishra (2016) and Agrawal and Maheshwari (2019). Bandyopadhyay (2006) used the MDA technique to develop a default prediction model and employed logistic regression model to directly estimate the probability of default in the Indian manufacturing industry.…”

Section: Predictors Of Credit Default: a Review Of Literaturementioning

confidence: 99%

“…Desai and Joshi (2015) formulated the MDA model for the non-financial sector in India. Datta (2013) and Agrawal and Maheshwari (2019) compared the predictive ability of MDA and logit. Datta (2013) developed MDA and logit model for the Indian industrial sector and concluded that logit model exhibits a high level of classification accuracy.…”

Section: Predictors Of Credit Default: a Review Of Literaturementioning

confidence: 99%

Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

Shetty

Vincent

2021

Vision: The Journal of Business Perspective

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The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

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Efficacy of industry factors for corporate default prediction

Cited by 14 publications

References 31 publications

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya

Probability of Corporate Bankruptcy: Application to Portuguese Manufacturing Industry SMEs

Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

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