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Kombinierte Liquiditäts- und Solvenzkennzahlen und ein darauf basierendes Insolvenzprognosemodell für deutsche GmbHs

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  • Volodymyr Perederiy

Abstract

Eine große Herausforderung der multivariablen Analyse mit bilanziellen Kennzahlen besteht in der Identifikation derjenigen Kennzahlen, die zur besten Modellperformance führen und dabei möglichst leicht interpretierbar und intuitiv bleiben. Die Menge der in Frage kommenden Kennzahlen ist in der Regel groß; viele Kennzahlen weisen Abhängigkeiten und Korrelationen auf, was im multivariaten Kontext zu Multikollinearitätsproblemen führt. Diese Aussagen betreffen insbesondere auch die Insolvenzprognosemodellierung auf Grundlage von bilanziellen Informationen. In der vorliegenden Studie wird mittels einer einfachen Gewichtung und Division von geeigneten Bilanz- und GuV-Posten eine kombinierte Kennzahl gebildet, welche die Informationen aus den meisten traditionellen Liquiditäts- und Solvenzkennzahlen komprimiert und somit eine diesbezügliche Kennzahlenauswahl überflüssig macht. Die kombinierte Kennzahl wird anschließend – neben einigen Kennzahlen aus anderen Kennzahlenkategorien (Rentabilität, Effizienz, Unternehmensgröße) – zur Insolvenzprognose für deutsche GmbHs verwendet. Es wird demonstriert, dass die kombinierte Kennzahl die Insolvenzprognose verbessert und dabei leicht interpretierbar bleibt.

Suggested Citation

  • Volodymyr Perederiy, 2007. "Kombinierte Liquiditäts- und Solvenzkennzahlen und ein darauf basierendes Insolvenzprognosemodell für deutsche GmbHs," SFB 649 Discussion Papers SFB649DP2007-060, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2007-060
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    References listed on IDEAS

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    Keywords

    Logistische Regression; Variablenauswahl; Insolvenzprognose; Bilanzanalyse; bilanzielle Kennzahlen; Liquidität; Solvenz;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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