  Quantifying the similarity between a group of companies has proven to be
useful for several purposes, including company benchmarking, fraud detection,
and searching for investment opportunities. This exercise can be done using a
variety of data sources, such as company activity data and financial data.
However, ledger account data is widely available and is standardized to a large
extent. Such ledger accounts within a financial statement can be represented by
means of a tree, i.e. a special type of graph, representing both the values of
the ledger accounts and the relationships between them. Given their broad
availability and rich information content, financial statements form a prime
data source based on which company similarities or distances could be computed.
  In this paper, we present a graph distance metric that enables one to compute
the similarity between the financial statements of two companies. We conduct a
comprehensive experimental study using real-world financial data to demonstrate
the usefulness of our proposed distance metric. The experimental results show
promising results on a number of use cases. This method may be useful for
investors looking for investment opportunities, government officials attempting
to identify fraudulent companies, and accountants looking to benchmark a group
of companies based on their financial statements.

Computational Engineering, Finance, and Science