A Graph Mining Approach to Identify Financial Reporting Patterns: An Empirical Examination of Industry Classifications
ABSTRACT This study proposes a quantitative method using the eXtensible Business Reporting Language financial accounting taxonomies to identify firms' common business characteristics and demonstrates that this graph mining approach can effectively identify industry boundaries. The premise of th...
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Published in | Decision sciences Vol. 50; no. 4; pp. 847 - 876 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Atlanta
American Institute for Decision Sciences
01.08.2019
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Subjects | |
Online Access | Get full text |
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Summary: | ABSTRACT
This study proposes a quantitative method using the eXtensible Business Reporting Language financial accounting taxonomies to identify firms' common business characteristics and demonstrates that this graph mining approach can effectively identify industry boundaries. The premise of this method is based on the previous findings that financial accounts and the structural semantic information represented in financial statements reveal firms' general business operations and common characteristics if they have similar business models. Specifically, we introduce a graph similarity metric combined with spectral clustering algorithm to quantify the similarity of financial disclosures. Through industry classification comparison with the traditional classification schemes, the Standard Industrial Classification and the North American Industry Classification System, we show that the proposed method consistently clusters firms into their respective industries based on financial disclosures with significantly lower variance in a time‐varying fashion. This novel graph mining method provides an automated way for decision makers to identify common business operations as well as detecting potential financial fraud and uncovering accounting information misrepresentation. |
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Bibliography: | Corrections added on November 13, 2018 after first publication on October 30, 2018: The second author Fang‐Chun Liu's email address has been corrected. The authors would like to thank the ICIS 2016 conference attendees for their valuable comments, and also thank Anzhela Knyazeva, Marco Enriquez, Roman Ivanchenko, Walter Hamscher, Mike Willias, Seung Won, and other 2016 internal seminar participants at the Division of Economic and Risk Analysis of the U.S. Securities and Exchange Commission for their valuable suggestions. |
ISSN: | 0011-7315 1540-5915 |
DOI: | 10.1111/deci.12345 |