Variable selection in default risk models

Amendola, Alessandra; Restaino, Marialuisa; Sensini, Luca

doi:10.21314/jrmv.2011.066

Cited by 16 publications

(10 citation statements)

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“…Agarwal & Taffler, 2008;Altman, 2000;Altman & Branch, 2015;Amendola, Restaino, & Sensini, 2011;Dimitras, Zanakis, & Zopoudinis, 1996;Jackson & Wood, 2013;Kim et al, 2016;Sensini, 2015Sensini, , 2016Wang et al, 2014). While most studies did not use a robust variable selection technique to identify the most relevant corporate bankruptcy predictors (Du Jardin, 2009), the backward stepwise procedure used in this study helps overcome this critical issue (Kim & Gu, 2006).…”

Section: Independent Variablesmentioning

confidence: 94%

“…Backward stepwise selection consists in starting with all potential predictors, testing the deletion of each potential predictor using a chosen model fit criterion, deleting the potential predictor (if any) whose loss gives the most statistically insignificant alteration of the model fit, and repeating this process until no further potential predictors can be deleted without a statistically significant loss of fit. Referring to prior works in the literature, this study includes 30 financial predictors that are commonly used and selected in predicting corporate bankruptcy (Agarwal & Taffler, 2008;Altman, 1968;Altman & Branch, 2015;Amendola, Restaino, & Sensini, 2011;Balcaen & Ooghe, 2006;Beaver, 1966;Dimitras, Zanakis, &Zopoudinis, 1996;Jackson & Wood, 2013;Kim et al, 2016;Ohlson, 1980;Wang et al, 2014). These indicators are reported in Table 1.…”

Section: Independent Variablesmentioning

confidence: 99%

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Exploring Corporate Bankruptcy in Belgian Private Firms

Cultrera

Bauweraerts

2017

IJEF

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The main purpose of this paper is to identify the financial antecedents of corporate bankruptcy by employing a robust variable selection procedure. Based on a sample of 1,338 Belgian private firms, a backward stepwise logistic regression technique is employed. The findings indicate that the likelihood of corporate bankruptcy is higher at lower levels of solvency, liquidity and profitability. Furthermore, our results reveal that corporate bankruptcy is more likely in older and larger firms.

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Section: Independent Variablesmentioning

confidence: 94%

Section: Independent Variablesmentioning

confidence: 99%

Exploring Corporate Bankruptcy in Belgian Private Firms

Cultrera

Bauweraerts

2017

IJEF

View full text Add to dashboard Cite

show abstract

“…Amendola et al (2011) véleménye szerint e témakör ugyanolyan releváns és kutatott terület ma, mint az 1930-as években. Ennek legfőbb oka, hogy a hiteladósok nemfizetése jelentős veszteséget okozhat az üzleti élet szereplőinek, valamint a nemzetgazdaság egé-szének is (Horta -Camanho, 2013).…”

Section: Cikkek Tanulmányokunclassified

Stock és flow típusú számviteli adatok alkalmazása a csődelőrejelző modellekben

Nyitrai¹

2017

Veztud

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“…In Section 3.1, we briefly introduce Lasso and Adaptive Lasso, two variable selection methods that we compare our proposed procedure with. We excluded the stepwise procedure (widely used in the business failure literature) from further investigation because Lasso was found to outperform it [36]. It is important to underline that the variables are selected within the linear regression framework, because in Section 2, we proved that there is a relationship between the probability of failure and profitability ratios and therefore, the business failure can be evaluated in terms of financial stability based on the ROA and/or ROE.…”

Section: Step 1: Variable Selection Criteria In Business Failure Modementioning

confidence: 99%

Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction

Amendola

Giordano

Parrella

et al. 2017

Appl Stoch Models Bus & Ind

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Business failure prediction models are important in providing warning for preventing financial distress and giving stakeholders time to react in a timely manner to a crisis. The empirical approach to corporate distress analysis and forecasting has recently attracted new attention from financial institutions, academics, and practitioners. In fact, this field is as interesting today as it was in the 1930s, and over the last 80 years, a remarkable body of both theoretical and empirical studies on this topic has been published. Nevertheless, some issues are still under investigation, such as the selection of financial ratios to define business failure and the identification of an optimal subset of predictors. For this purpose, there exist a large number of methods that can be used, although their drawbacks are usually neglected in this context. Moreover, most variable selection procedures are based on some very strict assumptions (linearity and additivity) that make their application difficult in business failure prediction. This paper proposes to overcome these limits by selecting relevant variables using a nonparametric method named Rodeo that is consistent even when the aforementioned assumptions are not satisfied. We also compare Rodeo with two other variable selection methods (Lasso and Adaptive Lasso), and the empirical results demonstrate that our proposed procedure outperforms the others in terms of positive/negative predictive value and is able to capture the nonlinear effects of the selected variables. Copyright © 2017 John Wiley & Sons, Ltd.

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Variable selection in default risk models

Cited by 16 publications

References 0 publications

Exploring Corporate Bankruptcy in Belgian Private Firms

Exploring Corporate Bankruptcy in Belgian Private Firms

Stock és flow típusú számviteli adatok alkalmazása a csődelőrejelző modellekben

Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction

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