Financial statement similarity

Abstract

We propose financial statement similarity as a measure of financial reporting comparability. The firm‐pair version of our measure reflects the degree to which two firms report similar relations within their financial statement items; this version can help managers and market participants identify peer firms. The firm‐year version of our measure reflects the degree to which a firm reports financial statement relations that are similar to other members of its industry; this version can help market participants, regulators, and auditors screen firms for further attention. Our measure uses the presence and amounts of almost all financial items reported by a firm. We validate our measure in four sets of analyses to establish concurrent validity and in three sets of analyses to establish predictive validity. In all these tests, we contrast our measure with the comparability measure in De Franco et al. (2011) and a multivariate measure that considers the presence, but not amounts, of financial statement items. Our measure outperforms the alternatives and can be a useful tool for users.This article is protected by copyright. All rights reserved.

An efficient graph‐based peer selection method for financial statements

“…The pairwise distance is obtained by computing the Jaccard similarity between two sets of ledger accounts present within each company's financial statement (Hoitash et al, 2018). Cosine similarity score : Brown et al (2021) represent a company as a vector where each element represents a ledger account value. The pairwise distance is computed by computing the cosine similarity between two company vectors.…”

“…The pairwise distance is computed by computing the cosine similarity between two company vectors. This means that only the value information is considered. Mahalanobis similarity score : This method, introduced by Brown et al (2021), starts from the company ledger account value vectors and computes the pairwise company distances by computing the Mahalanobis distance between the company vectors while ignoring the internal structure of a financial statement. Random method : This method randomly selects a set of companies and ranks them in a random way. This ranking introduces a degree of similarity based on the ranking of the companies. EMD‐GraD : The earth mover's distance‐based graph distance metric we propose in this paper (see Section 3.3).…”

“…In previous research, various attempts have been made to define company similarity based on financial statements for purposes such as company classification, fraud detection, and strategic peer selection (Brown et al, 2021;Hoitash et al, 2018;Jan, 2018;Kanapickienė & Grundienė, 2015;Yang & Cogill, 2013;Yang et al, 2019). However, these previously proposed methodologies only analyze a portion of the information present in a financial statement: either the values on the ledger accounts or the structural relationship between the ledger accounts present within a financial statement.…”

“…Another line of research tries to address this issue by looking at the financial statements as a whole. Brown et al (2021) represent a company as a vector where each element represents a ledger account value. They define the similarity between two companies as the cosine or Mahalanobis distance between these vectors.…”