TL;DR
This paper introduces a novel graph distance metric based on Earth Mover's Distance for comparing financial statements represented as trees, aiding in company benchmarking, fraud detection, and investment analysis.
Contribution
The paper proposes a new Earth Mover's Distance-based graph metric specifically designed for financial statement trees, with comprehensive real-world experimental validation.
Findings
Effective in measuring similarity between financial statements
Promising results in fraud detection and benchmarking
Useful for investment opportunity identification
Abstract
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…
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