[{"keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"language":[{"iso":"eng"}],"_id":"20868","department":[{"_id":"275"}],"user_id":"46447","abstract":[{"lang":"eng","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."}],"status":"public","type":"working_paper","title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","doi":"10.2139/ssrn.2237757","main_file_link":[{"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757"}],"date_updated":"2022-01-06T06:54:41Z","date_created":"2021-01-05T11:44:45Z","author":[{"first_name":"Sönke","full_name":"Sievers, Sönke","id":"46447","last_name":"Sievers"},{"last_name":"Klobucnik","full_name":"Klobucnik, Jan","first_name":"Jan"},{"first_name":"David","last_name":"Miersch","full_name":"Miersch, David"}],"year":"2017","page":"84","citation":{"ieee":"S. Sievers, J. Klobucnik, and D. Miersch, <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017.","chicago":"Sievers, Sönke, Jan Klobucnik, and David Miersch. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>, 2017. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>.","ama":"Sievers S, Klobucnik J, Miersch D. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>.; 2017. doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>","apa":"Sievers, S., Klobucnik, J., &#38; Miersch, D. (2017). <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>","bibtex":"@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, DOI={<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>}, author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }","short":"S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017.","mla":"Sievers, Sönke, et al. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017, doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>."},"jel":["C63","C52","C53","G33","M41"],"publication_status":"published"},{"title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","date_updated":"2022-01-06T07:01:43Z","date_created":"2018-10-31T12:19:42Z","author":[{"last_name":"Klobucnik","full_name":"Klobucnik, Jan","first_name":"Jan"},{"first_name":"David","last_name":"Miersch","full_name":"Miersch, David"},{"full_name":"Sievers, Sönke","last_name":"Sievers","first_name":"Sönke"}],"year":"2017","jel":["C63","C52","C53","G33","M41"],"citation":{"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).","mla":"Klobucnik, Jan, et al. “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.","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."},"publication_status":"published","keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"language":[{"iso":"eng"}],"_id":"5199","department":[{"_id":"275"}],"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"}],"status":"public","publication":"SSRN Electronic Journal","type":"journal_article"}]