Variable selection and corporate bankruptcy forecasts |
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| Institution: | 1. Lucas College and Graduate School of Business, San Jose State University, San Jose, CA 95192, USA;2. Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221, USA;3. Economics and Management School, Wuhan University, Wuhan 430072, China;1. School of Business, Macau University of Science and Technology, Taipa, Macau;2. School of Business, SiChuan Normal University, SiChuan Province, PR China;3. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan;1. Department of Information Management, National Central University, Jhongli, Taiwan, ROC;2. Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan, ROC;3. School of Economics and Business, 226 Netzer Administration Building, SUNY College at Oneonta, Oneonta, NY 13820, United States |
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| Abstract: | We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980–2009 period, LASSO admits the majority of Campbell et al. (2008) predictive variables into the bankruptcy forecast model. Interestingly, by contrast with recent studies, some financial ratios constructed from only accounting data also contain significant incremental information about future default risk, and their importance relative to that of market-based variables in bankruptcy forecasts increases with prediction horizons. Moreover, LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell et al. (2008) and (2) the distance to default from Merton’s (1974) structural model. |
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| Keywords: | Discrete hazard model Financial ratios LASSO Market information |
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