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.

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