  The role of Network Theory in the study of the financial crisis has been
widely spotted in the latest years. It has been shown how the network topology
and the dynamics running on top of it can trigger the outbreak of large
systemic crisis. Following this methodological perspective we introduce here
the Accounting Network, i.e. the network we can extract through vector
similarities techniques from companies' financial statements. We build the
Accounting Network on a large database of worldwide banks in the period
2001-2013, covering the onset of the global financial crisis of mid-2007. After
a careful data cleaning, we apply a quality check in the construction of the
network, introducing a parameter (the Quality Ratio) capable of trading off the
size of the sample (coverage) and the representativeness of the financial
statements (accuracy). We compute several basic network statistics and check,
with the Louvain community detection algorithm, for emerging communities of
banks. Remarkably enough sensible regional aggregations show up with the
Japanese and the US clusters dominating the community structure, although the
presence of a geographically mixed community points to a gradual convergence of
banks into similar supranational practices. Finally, a Principal Component
Analysis procedure reveals the main economic components that influence
communities' heterogeneity. Even using the most basic vector similarity
hypotheses on the composition of the financial statements, the signature of the
financial crisis clearly arises across the years around 2008. We finally
discuss how the Accounting Networks can be improved to reflect the best
practices in the financial statement analysis.
