[{"jel":["C63","C52","C53","G33","M41"],"year":"2017","citation":{"mla":"Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","apa":"Klobucnik, J., Miersch, D., &#38; Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>.","bibtex":"@article{Klobucnik_Miersch_Sievers_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, journal={SSRN Electronic Journal}, author={Klobucnik, Jan and Miersch, David and Sievers, Sönke}, year={2017} }","short":"J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).","chicago":"Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","ieee":"J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” <i>SSRN Electronic Journal</i>, 2017.","ama":"Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>. 2017."},"department":[{"_id":"275"}],"date_created":"2018-10-31T12:19:42Z","keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"status":"public","_id":"5199","author":[{"full_name":"Klobucnik, Jan","first_name":"Jan","last_name":"Klobucnik"},{"first_name":"David","last_name":"Miersch","full_name":"Miersch, David"},{"last_name":"Sievers","first_name":"Sönke","full_name":"Sievers, Sönke"}],"user_id":"64756","publication_status":"published","language":[{"iso":"eng"}],"title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","publication":"SSRN Electronic Journal","type":"journal_article","abstract":[{"text":"This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners. ","lang":"eng"}],"date_updated":"2019-01-03T13:16:51Z"}]