Corporate bankruptcy prediction: a high dimensional analysis
Stewart Jones ()
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Stewart Jones: The University of Sydney Business School, The University of Sydney
Review of Accounting Studies, 2017, vol. 22, issue 3, No 11, 1366-1422
Abstract Much bankruptcy research has relied on parametric models, such as multiple discriminant analysis and logit, which can only handle a finite number of predictors (Altman in The Journal of Finance 23 (4), 589–609, 1968; Ohlson in Journal of Accounting Research 18 (1), 109–131, 1980). The gradient boosting model is a statistical learning method that overcomes this limitation. The model accommodates very large numbers of predictors which can be rank ordered, from best to worst, based on their overall predictive power (Friedman in The Annals of Statistics 29 (5), 1189–1232, 2001; Hastie et al. 2009). Using a sample of 1115 US bankruptcy filings and 91 predictor variables, the study finds that non-traditional variables, such as ownership structure/concentration and CEO compensation are among the strongest predictors overall. The next best predictors are unscaled market and accounting variables that proxy for size effects. This is followed by market-price measures and financial ratios. The weakest predictors overall included macro-economic variables, analyst recommendations/forecasts and industry variables.
Keywords: Corporate bankruptcy modelling; Gradient boosting; Logit; Market prices; Financial ratios (search for similar items in EconPapers)
JEL-codes: C1 M4 (search for similar items in EconPapers)
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