Tests of the generalizability of Altman's bankruptcy prediction model

By J. Grice, Robert W. Ingram. 2001

389 citations recorded by the source corpus.

Subfields: Accounting, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/4f4f18b2476776cc07585efda32723f8d678c1e0

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Citations in the corpus

Corporate governance effect on financial distress likelihood: Evidence from Spain (2016, background): “Financial distress is defined as the lack of company’s capacity to satisfy its financial obligations (Grice & Dugan, 2001; Grice & Ingram, 2001; Pindado et al., 2008).”
Corporate Financial Distress: An Empirical Analysis of Distress Risk (2007, background): “Similarly to Griffin and Lemmon (2002), the authors observe that the underperformance of distressed stocks is stronger when arbitrage is expensive.”
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy (2007, background): “Grice and Ingram (2001) reported that Altman’s Z-score model declined when applied to various industries.”
Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress? (2019, methodology): “Later, MDA was used by many researchers, including Deakin (1972), Grice and Ingram (2001), and Agarwal and Taffler (2007).”
Does brand management of corporate reputation translate into higher market value? (2010, neutral): “This ratio is commonly used to predict bankruptcy and cost of debt issuances (Grice & Ingram, 2001 ).”
Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms (2015, background): “…on economic– financial ratios elaborated on the basis of information gathered from balance sheets (among others, Altman, 1968; Beaver, 1967; Blum, 1974; Grice & Ingram, 2001; Ohlson, 1980; Pindado, Rodrigues, & De la Torre, 2008), or from initial public offerings prospects (e.g., Platt, 19951).”
Estimating financial distress likelihood (2008, background): “More recent studies focus on re-estimating the models above to determine whether they remain useful for predicting bankruptcy in more recent and longer periods and, more importantly, for predicting other financial distress conditions besides bankruptcy (Begley et al., 1996; Grice and Dugan, 2001; Gr”
Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model (2014, neutral): “(2012) US non-financial companies issuing debt securities from Lehman Brothers Bond Database, data on unionization described in Hirsch and Macpherson (2003) supported by financial and demographic data.”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, background): “Grice and Ingram [42] studied on the sensitivity of Altman's model, and concluded that the model trained with only manufacturing samples had significantly higher overall accuracy than the model trained with both manufacturing and non-manufacturing samples.”