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