Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence
J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).
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Journal Article
| Published
| English
Author
Klobucnik, Jan;
Miersch, David;
Sievers, Sönke
Abstract
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.
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SSRN Electronic Journal
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Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic Journal. 2017.
Klobucnik, J., Miersch, D., & Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic Journal.
@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} }
Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic Journal, 2017.
J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” SSRN Electronic Journal, 2017.
Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic Journal, 2017.