Neural Networks in Accounting: Clustering Firm Performance Using Financial Reporting Data

This paper considers the use of neural networks—namely self-organizing maps (SOMs)—to analyze and cluster firms' financial performance. Applying SOMs to financial statement data is a consolidated practice; however, in this paper SOMs are used to overcome several limitations encountered in previ...

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Bibliographic Details
Published in:The Journal of information systems 2020-06, Vol.34 (2), p.149-166
Main Authors: Dameri, Renata Paola, Garelli, Roberto, Resta, Marina
Format: Article
Language:English
Subjects:
Accounting
Financial analysis
Financial performance
Financial reporting
Financial statements
Neural networks
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Summary:This paper considers the use of neural networks—namely self-organizing maps (SOMs)—to analyze and cluster firms' financial performance. Applying SOMs to financial statement data is a consolidated practice; however, in this paper SOMs are used to overcome several limitations encountered in previous works on financial reporting indicators such as the small number of companies in the sample, the limited number of ratios, the homogeneity of the economic sector, and the lack of explanation and further analysis of the SOM outputs. This study uses a large financial dataset related to more than 3,000 companies belonging to every economic sector; it demonstrates that SOMs can effectively process a large dataset of heterogeneous data. Moreover, the SOM results are supported by detailed explanations of the research methodology applied, and further traditional financial analysis addresses the black box nature of the SOMs and can help professionals in the understanding and use of SOMs.
ISSN:0888-7985
1558-7959
DOI:10.2308/isys-18-002