This research applies neural networks – namely: Self-Organising Maps (SOMs) - to analyse a bunch of financial indicators drawn from the balance sheet of academic spin-offs. The goal of the work is twofold: first, it aims at processing financial data to extract knowledge about the still uncertain role and strategic profile of academic spin-offs; and second, it aims at understating whether SOMs are able or not to support investigations on firms’ performance, and to decide strategic orientation thanks to the processing of financial indicators. After a deep literature review about both the application of SOMs to financial reporting data and the business profile of academic spin-offs, the paper carries on an empirical investigation on 810 Italian academic spin-offs, using their financial reporting data. The results show that SOMs are able to extract the main features of different academic spin-off archetypes that can be then explained via traditional financial analysis instruments.
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|Titolo:||Data analytics e intelligenza artificiale per l’analisi di bilancio. Performance e profili di business degli spin-off accademici|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.01 - Articolo su rivista|