破产数据的质量及其对预测模型评价的影响:创建和测试一个德国数据库

Martin Huettemann, Tobias Lorsbach
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摘要

本研究评估破产数据质量对破产预测模型估计与评价的影响。为了实现这一目标,我们开发了一种系统的方法,从公司新闻稿和公共资源中获取破产信息。然后,将此方法应用于德国市场,我们创建了一个破产数据库,其中包括比Bureau van Dijk和Compustat Global常用数据库中更多数量和更准确的破产事件以及更准确的破产日期。我们利用我们的破产数据进行了两个实证分析。首先,使用我们更准确的数据库,我们比较了德国背景下几种破产预测模型的表现。其次,我们将我们的数据库与Bureau van Dijk的数据进行比较,发现破产数据的质量显著影响破产预测模型的参数估计和样本外评价。因此,我们建议修改使用不准确破产信息的破产研究提供的证据。
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The Quality of Bankruptcy Data and its Impact on the Evaluation of Prediction Models: Creating and Testing a German Database
This study assesses the impact of the quality of bankruptcy data on the estimation and evaluation of bankruptcy prediction models. To meet this objective, we develop a systematic methodology to obtain bankruptcy information from corporate news releases and public sources. Then, applying this methodology to the German market, we create a bankruptcy database that includes a higher number of and more accurate bankruptcy events as well as more accurate bankruptcy dates than those in the frequently used databases of Bureau van Dijk and Compustat Global. We use our bankruptcy data to conduct two empirical analyses. First, using our more accurate database, we compare the performance of several bankruptcy prediction models in the context of Germany. Second, we compare our database with Bureau van Dijk data and find that the quality of bankruptcy data significantly affects the parameter estimates and the out-of-sample evaluation of bankruptcy prediction models. Therefore, we suggest revising evidence presented by bankruptcy studies that use inaccurate bankruptcy information.
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