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 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.

An Earth Mover's Distance Based Graph Distance Metric For Financial Statements

“…Furthermore, several studies [2], [9] have suggested that organizations with similar business activities should have similar financial statements. This idea is confirmed by Yang et al [6], where they provide evidence that a company distance metric can effectively identify industry boundaries.…”

“…One of the drawbacks is that classification systems do not evolve at the same rate as the market conditions, making it difficult to classify new industries [11]. Another drawback is the lack of uniform classification standards, which results in a different company classification depending on the industry classification standard being used [6]. This implies that we cannot merely distinguish organizations based on their size and industry classification.…”

“…In the past, several attempts have been made to define company similarity. This appears to be beneficial for company classification and fraud detection purposes [2], [4]- [6]. However, the previously proposed methodologies only analyze a For reproducibility, the source code and synthetic data are publicly available at https://github.com/snoels/earth-movers-graph-distance-metric.…”

The investigation on infoecology in the field of smart manufacturing

“…Different studies have used the graph mining technique for data analysis. Yang et al (2009), using eXtensible Business Reporting Language, identified common business characteristics of companies. They showed that graph mining could effectively identify industry boundaries.…”

An artificial intelligence-enabled industry classification and its interpretation

“…As advances in computing power enhance the ability of scholars to deal with complex calculations, researchers in information systems have adopted a variety of approaches to classify industries for grouping homogeneous firms (Fang et al, 2013;Chowdhuri et al, 2014;Yang et al, 2019;Xu et al, 2020). Among many academic works related to alternative industry classifications, Hoberg and Phillips (2016) develop the text-based network industry classification (TNIC) derived from the firms' business descriptions of 10-K reports.…”