[{"page":"84","year":"2017","type":"working_paper","citation":{"mla":"Sievers, Sönke, et al. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. 2017, doi:10.2139/ssrn.2237757.","bibtex":"@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, DOI={10.2139/ssrn.2237757}, author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }","chicago":"Sievers, Sönke, Jan Klobucnik, and David Miersch. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017. https://doi.org/10.2139/ssrn.2237757.","apa":"Sievers, S., Klobucnik, J., & Miersch, D. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. https://doi.org/10.2139/ssrn.2237757","ama":"Sievers S, Klobucnik J, Miersch D. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.; 2017. doi:10.2139/ssrn.2237757","ieee":"S. Sievers, J. Klobucnik, and D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. 2017.","short":"S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017."},"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757"}],"doi":"10.2139/ssrn.2237757","_id":"20868","date_updated":"2022-01-06T06:54:41Z","publication_status":"published","jel":["C63","C52","C53","G33","M41"],"date_created":"2021-01-05T11:44:45Z","status":"public","department":[{"_id":"275"}],"keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"author":[{"last_name":"Sievers","id":"46447","first_name":"Sönke","full_name":"Sievers, Sönke"},{"last_name":"Klobucnik","full_name":"Klobucnik, Jan","first_name":"Jan"},{"full_name":"Miersch, David","first_name":"David","last_name":"Miersch"}],"title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","user_id":"46447","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"}]},{"title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","user_id":"64756","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"}],"jel":["C63","C52","C53","G33","M41"],"publication_status":"published","status":"public","date_created":"2018-10-31T12:19:42Z","author":[{"last_name":"Klobucnik","first_name":"Jan","full_name":"Klobucnik, Jan"},{"full_name":"Miersch, David","first_name":"David","last_name":"Miersch"},{"last_name":"Sievers","first_name":"Sönke","full_name":"Sievers, Sönke"}],"publication":"SSRN Electronic Journal","keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"department":[{"_id":"275"}],"_id":"5199","date_updated":"2022-01-06T07:01:43Z","type":"journal_article","citation":{"ieee":"J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” SSRN Electronic Journal, 2017.","short":"J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).","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} }","mla":"Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic Journal, 2017.","apa":"Klobucnik, J., Miersch, D., & Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic Journal.","ama":"Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic Journal. 2017.","chicago":"Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic Journal, 2017."},"year":"2017","language":[{"iso":"eng"}]}]