An Exploratory Study of Financial Reporting Structures: A Graph Similarity Approach Using XBRL

Abstract

Financial reporting information is crucial to both investors and regulators for their decision-making purposes as it reveals corporate financial position and performance over a period of time. This study proposes a quantitative method to identify companies who opt for different financial statement reporting structures from their industry peers. We first introduce a graph similarity measure to quantify structural differences of financial disclosures chosen by firms, and then we conduct two experiments focusing on reported balance sheets as examples to validate effectiveness of our proposed method in identifying discrepancies between reporting structures. The first experiment shows the proposed method is sensitive to changes made on balance sheets and helps capture major structural differences. The second experiment shows this method is able to cluster more than 92% of firms under study from 2011 to 2014 correctly into their respective industries based on their common reporting structures. We also show that the “misaligned” firms are true outliers who either opted for different reporting structures than their industrial peers or simply made technical errors. The novel approach presented in this paper provides an automated means to identify unique financial disclosure structural patterns and therefore offers a potential method to help information users to identify distinct business strategies or financial fraud.

Keywords: Financial reporting structural patterns, Corporate disclosure, Graph similarity metric, String edit distance, Balance sheet, XBRL