Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?

Gupta, Jairaj; Gregoriou, Andros; Healy, Jerome

doi:10.1007/s11156-014-0458-0

Cited by 68 publications

(86 citation statements)

References 50 publications

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“…The results described in this section are not entirely consistent with the relevant studies, such as Gupta et al (2015). Both CTA and RETA are not statistically significant for estimation in the multivariate model without the cash flow variables.…”

Section: Micro Firmscontrasting

confidence: 77%

“…This result seems to be inconsistent with the results of Gupta et al (2015). An examination of the correlation coefficients for this sample (a) admitted earnings (EBITDATA), liabilities divided by assets (TLTA) and the (log of) current assets divided by current liabilities (LCR) to the model; and (b) excluded the trade creditors over total assets (TCTA).…”

Section: Medium Firmsmentioning

confidence: 57%

“…Investigating capital structure choices, Mateev, Poutziouris, and Ivanov (2013) find that medium-sized firms are mainly dependant on long-term bank loans as their preferred method of external financing, whereas short-term loans and trade credits are the main sources of external finance for micro and small businesses. After a certain threshold, the insolvency risk ultimately declines with company size, as shown in the study by Gupta et al (2015), in which the authors demonstrate that risk and default characteristics of "micro" firms differ from those of larger SMEs size classifications. The empirical literature also argues that the stability of cash flow and diversity increases with firm size (Gill, Biger, Pai, & Bhutani, 2009), leading to a negative relationship between firm size and default likelihood (Pettit & Singer, 1985).…”

Section: Smes and Size Factormentioning

confidence: 87%

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Does size matter in predicting SMEs failure?

2018

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This study acknowledges the diversity between micro, small, and medium‐sized firms while predicting bankruptcy and financial distress of the United States small and medium‐sized enterprises. Empirical findings suggest that survival (failure) probability increases (decreases) with increasing firm size and firms in different size categories have varying determinants of bankruptcy, whereas factors affecting their financial distress are mostly invariant. Magnitude of significant covariates changes across the size categories of both bankrupt and financially distressed firms. Further, operating cash flow information does not add any marginal increment in prediction performance of multivariate hazard models above baseline models developed using information from income statements and balance sheets. This result holds for failure likelihood of small and medium‐sized enterprises and their respective size categories.

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Section: Micro Firmscontrasting

confidence: 77%

Section: Medium Firmsmentioning

confidence: 57%

Section: Smes and Size Factormentioning

confidence: 87%

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Does size matter in predicting SMEs failure?

2018

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“…The development of a robust variety of evaluation systems has resulted in significant improvements in the assessment of credit applications in recent years (for a categorized review, see Gupta et al 2014a). These evaluation tools, commonly known as credit scoring systems, allow credit providers to make more informed and assertive decisions.…”

Section: Related Workmentioning

confidence: 99%

“…Lopez and Saidenberg (2000: 152) note that "these limitations create a serious difficulty for users' own validation of credit risk models and for validation by third parties, such as external auditors or bank regulators". Therefore, the discussion has not been put to rest and there is a need for the development and/or adoption of new models, techniques or approaches, which might help overcome the limitations presented by the most commonly used valuation models (Gupta et al 2014a). Accordingly, by making complementary use of cognitive mapping techniques with TODIM, the main objective of the current study is the development of a multicriteria system, which can allow the process of credit risk assessment for SMEs to become more informed, transparent and comprehensive.…”

Section: Related Workmentioning

confidence: 99%

An Idiosyncratic Decision Support System for Credit Risk Analysis of Small and Medium-Sized Enterprises

Gonçalves¹,

Ferreira

Jalali

et al. 2015

Technological and Economic Development of Economy

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Small and medium-sized enterprises (SMEs) are currently considered an important driving force of economic growth. Several studies have been developed to analyse this issue and, in particular, to assess the credit risk of SMEs. Most of these applications, however, share the same methodological limitations, such as the manner by which criteria are selected, or the methods used for calculating the weights between them. Based on the integrated use of cognitive mapping techniques and the Interactive Multiple Criteria Decision Making (TODIM) approach, this study aims to create an idiosyncratic decision support system for the identification of multiple criteria and the calculation of their respective weights (i.e. the trade-offs) in the evaluation of SME credit risk. The results show that the model created in this study allows for simple and straightforward credit concession decisions, facilitating the evaluation of SME credit applications through more informed and transparent risk assessments. Practical implications, strengths and weaknesses of the proposed framework are analysed and discussed.Keywords: credit risk analysis, multiple criteria decision analysis (MCDA), small and mediumsized enterprises (SMEs), TODIM (Portuguese acronym for interactive multiple criteria decision making).JEL Classification: C44, D70, M21.

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Corporate failure prediction using threshold‐based models

Veganzones

2022

Journal of Forecasting

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Corporate failure prediction literature indicates that models' performance depends on more than the complexity of the prediction method. Recent advances cite the relevance of sampling approaches to model performance.Therefore, this study proposes a novel approach that implements threshold models for corporate failure prediction efforts. Threshold models estimate asymptotically conservative confidence regions, in which the samples are split by size. Then, single-based classifiers and a combination of multiple classifiers are employed in each region to estimate the prediction accuracy. This article offers a comparison of the proposed threshold-based corporate failure model with two benchmark models, previously used in studies in the same context.The empirical results show that the proposed threshold-based model clearly outperforms conventional models, in particular with the combination of multiple classifiers. The superiority of the threshold-based model stems from its ability to discern failed firms, which represent the most important class in financial terms. This study thus provides initial evidence of the utility of threshold models in corporate failure prediction efforts.

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Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?

Cited by 68 publications

References 50 publications

Does size matter in predicting SMEs failure?

Does size matter in predicting SMEs failure?

An Idiosyncratic Decision Support System for Credit Risk Analysis of Small and Medium-Sized Enterprises

Corporate failure prediction using threshold‐based models

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