@techreport{20868,
  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.}},
  author       = {{Sievers, Sönke and Klobucnik, Jan and Miersch, David}},
  keywords     = {{Financial distress prediction, probability of default, accounting information, stochastic processes, simulation}},
  pages        = {{84}},
  title        = {{{Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}}},
  doi          = {{10.2139/ssrn.2237757}},
  year         = {{2017}},
}

@article{5199,
  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. }},
  author       = {{Klobucnik, Jan and Miersch, David and Sievers, Sönke}},
  journal      = {{SSRN Electronic Journal}},
  keywords     = {{Financial distress prediction, probability of default, accounting information, stochastic processes, simulation}},
  title        = {{{Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}}},
  year         = {{2017}},
}