In this work, we explore the application of machine learning models (MLM) to the analysis of firms’ performance. To such aim, we consider a bunch of financial indicators on firms operating in the Information and Communication Technology (ICT) sector, with attention to enterprises providing ICT related-services. The rationale is to highlight the potential of MLM to exploit the complexity of financial data, and to offer a handy way to visualize the related information. In fact, instead of performing classical analysis, we discuss how to apply to those indicators Self-Organizing Maps-SOMs—that are well suited to manage high dimensional and complex datasets to extract their relevant features. It emerges that SOMs are useful in clustering companies depending on multi-dimensional criteria and in analysing hidden relations in companies’ performances.

Smart Mobility portfolio analysis: the case of Barcelona

Dameri, R. P.;Garelli, R.;Resta, Marina
2018

Abstract

In this work, we explore the application of machine learning models (MLM) to the analysis of firms’ performance. To such aim, we consider a bunch of financial indicators on firms operating in the Information and Communication Technology (ICT) sector, with attention to enterprises providing ICT related-services. The rationale is to highlight the potential of MLM to exploit the complexity of financial data, and to offer a handy way to visualize the related information. In fact, instead of performing classical analysis, we discuss how to apply to those indicators Self-Organizing Maps-SOMs—that are well suited to manage high dimensional and complex datasets to extract their relevant features. It emerges that SOMs are useful in clustering companies depending on multi-dimensional criteria and in analysing hidden relations in companies’ performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/882920
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