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- W1891609966 abstract "This study pursues two issues in the context of reduced-type bankruptcy prediction. First, we investigate the marginal usefulness of some non-traditional ratios and indicators. We find that the performance of traditional ratios can be considerably improved by their squared terms and technical ratios built from itemized accounts. Also, missing values for many items are found informative.Second, the non-traditional ratios dramatically increase the number of candidate variables, which makes the variable selection particularly important. We show that the innovative lasso regularization performs for these purposes significantly better than the traditional stepwise method.All in all, the non-traditional information increases the cross-validated accuracy ratio by 1.9%. When applying the lasso method instead of stepwise, this advantage increases to 2.3%, along with a considerable reduction in computer processing time but, admittedly, at the cost of a significantly higher number of variables chosen. Lasso also has some methodological advantages, being e.g. able to efficiently select non-traditional ratios which complement (rather than replace) traditional ones." @default.
- W1891609966 created "2016-06-24" @default.
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- W1891609966 date "2009-01-01" @default.
- W1891609966 modified "2023-09-24" @default.
- W1891609966 title "Bankruptcy Prediction Revisited: Non-Traditional Ratios and Lasso Selection" @default.
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- W1891609966 doi "https://doi.org/10.2139/ssrn.1518084" @default.
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