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    <title>EconStor Community: Lehrstuhl für Rechnungswesen und Prüfungswesen, Universität Erlangen-Nürnberg</title>
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      <url>http://www.econstor.eu/retrieve/99826</url>
      <link>http://hdl.handle.net/10419/23952</link>
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      <title>Content analysis of XBRL filings as an efficient supplement of bankruptcy prediction? Empirical evidence based on US GAAP annual reports</title>
      <link>http://hdl.handle.net/10419/58246</link>
      <description>Title: Content analysis of XBRL filings as an efficient supplement of bankruptcy prediction? Empirical evidence based on US GAAP annual reports
&lt;br/&gt;
&lt;br/&gt;Authors: Henselmann, Klaus; Scherr, Elisabeth
&lt;br/&gt;
&lt;br/&gt;Abstract: Most of the bankruptcy prediction models developed so far have in common that they are based on quantitative data or more precisely financial ratios. However, useful information can be lost when disregarding soft information. In this work, we develop an automated content analysis technique to assess the bankruptcy risk of companies using XBRL tags. We develop a list of potential red flags based on the U.S. GAAP taxonomy and assign the elements to 2 categories and 7 subcategories. Then we test our red flag item list based on U.S. GAAP annual reports of 26 companies with Chapter 11 bankruptcy filings and a control group. The empirical results show that in total, the red flag item list has predictive power of bankruptcy risk. Logistic regression results also show that the predictive power increases the nearer the bankruptcy filing date approaches. We furthermore observe that the category 2 red flags (bankruptcy characteristics and influencing factors) have higher discriminatory power than category 1 red flags (earnings management indicators) for one year before the bankruptcy filing date. This difference narrows for two years before the bankruptcy filing date and may turn in favor of category 1 red flags for three years before the bankruptcy filing date.</description>
      <pubDate>Sat, 29 Oct 2011 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Content analysis of XBRL filings as an efficient supplement of bankruptcy prediction? Empirical evidence based on US GAAP annual reports</title>
      <link>http://hdl.handle.net/10419/58246</link>
      <description>Title: Content analysis of XBRL filings as an efficient supplement of bankruptcy prediction? Empirical evidence based on US GAAP annual reports
&lt;br/&gt;
&lt;br/&gt;Authors: Henselmann, Klaus; Scherr, Elisabeth
&lt;br/&gt;
&lt;br/&gt;Abstract: Most of the bankruptcy prediction models developed so far have in common that they are based on quantitative data or more precisely financial ratios. However, useful information can be lost when disregarding soft information. In this work, we develop an automated content analysis technique to assess the bankruptcy risk of companies using XBRL tags. We develop a list of potential red flags based on the U.S. GAAP taxonomy and assign the elements to 2 categories and 7 subcategories. Then we test our red flag item list based on U.S. GAAP annual reports of 26 companies with Chapter 11 bankruptcy filings and a control group. The empirical results show that in total, the red flag item list has predictive power of bankruptcy risk. Logistic regression results also show that the predictive power increases the nearer the bankruptcy filing date approaches. We furthermore observe that the category 2 red flags (bankruptcy characteristics and influencing factors) have higher discriminatory power than category 1 red flags (earnings management indicators) for one year before the bankruptcy filing date. This difference narrows for two years before the bankruptcy filing date and may turn in favor of category 1 red flags for three years before the bankruptcy filing date.</description>
      <pubDate>Sat, 29 Oct 2011 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Applying Benford's Law to individual financial reports: An empirical investigation on the basis of SEC XBRL filings</title>
      <link>http://hdl.handle.net/10419/55146</link>
      <description>Title: Applying Benford's Law to individual financial reports: An empirical investigation on the basis of SEC XBRL filings
&lt;br/&gt;
&lt;br/&gt;Authors: Henselmann, Klaus; Scherr, Elisabeth; Ditter, Dominik
&lt;br/&gt;
&lt;br/&gt;Abstract: This study examines whether investors could use Benford's Law as an aid in determining high-risk areas for investing within their process of decision-making. The business reporting standard XBRL offers the opportunity to easily extract and analyze a sufficient number of monetary items out of single annual reports for statistical analysis purposes. Using SEC XBRL filings of S&amp;P 500 companies (Fiscal Year 2010), we derive first digit distributions for single companies and measure the deviation from the Benford distribution. On average, we find that for all monetary numbers that are contained in the examined XBRL reports, the first digit distribution follows Benford's Law. A firm and industry-specific analysis reveals the industry Financials as being most conspicuous. Taken together, the empirical results suggest that the application of Benford's Law to annual reports might be a useful analytical tool for investors.</description>
      <pubDate>Sat, 29 Oct 2011 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Applying Benford's Law to individual financial reports: An empirical investigation on the basis of SEC XBRL filings</title>
      <link>http://hdl.handle.net/10419/55146</link>
      <description>Title: Applying Benford's Law to individual financial reports: An empirical investigation on the basis of SEC XBRL filings
&lt;br/&gt;
&lt;br/&gt;Authors: Henselmann, Klaus; Scherr, Elisabeth; Ditter, Dominik
&lt;br/&gt;
&lt;br/&gt;Abstract: This study examines whether investors could use Benford's Law as an aid in determining high-risk areas for investing within their process of decision-making. The business reporting standard XBRL offers the opportunity to easily extract and analyze a sufficient number of monetary items out of single annual reports for statistical analysis purposes. Using SEC XBRL filings of S&amp;P 500 companies (Fiscal Year 2010), we derive first digit distributions for single companies and measure the deviation from the Benford distribution. On average, we find that for all monetary numbers that are contained in the examined XBRL reports, the first digit distribution follows Benford's Law. A firm and industry-specific analysis reveals the industry Financials as being most conspicuous. Taken together, the empirical results suggest that the application of Benford's Law to annual reports might be a useful analytical tool for investors.</description>
      <pubDate>Sat, 29 Oct 2011 22:58:59 GMT</pubDate>
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