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This paper addresses the importance of industry-specific models for SMEs bankruptcy prediction, building on earlier research finding larger predictive accuracy and enhanced temporal stability. Using Italian data, we propose separate bankruptcy prediction models for a few industries based on balance sheet data and explore the predictive power of SMEs' website html code structure. Our findings suggest that website data can serve as a valid complementary source for bankruptcy prediction, with different performances across sectors. We observe a certain degree of sectoral heterogeneity in the importance of financial ratios, firm-specific characteristics, and website structure, calling for an industry-tailored approach in bankruptcy prediction models.

Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies.

Research in innovation usually builds on conventional data such as balance sheets, surveys, patents, or product catalogs. This paper intends to explore unconventional data, specifically web-scraped data, as an information source for innovation studies, proposing a careful procedure to establish the veracity of the linkage between web-based data and firm-level information retrieved from conventional sources. The study regards a sample of Italian manufacturing small and medium enterprises active in 2016, comprehending both innovative and non-innovative firms. It is based on HTML tags, whilst most of the previous literature worked on the web-pages text and related semantics. Our paper provides evidence that the way HTML language is applied to build a corporate website unveils the capabilities of the owner firm, helping to distinguish innovative from non-innovative SMEs.

The Small Business Act of the European Commission in 2008 acknowledges the key role of Small and Medium Enterprises (SMEs) in the EU economy. This is particularly relevant for Italy, which has the largest share of SMEs in Europe, as well as for other countries such as Portugal, Spain and Greece. On the other hand, SMEs experience more difficulties in their early stages mainly due to high market competition and credit constraints, as highlighted by Fritsch and Weyh (2006). For these reasons, the study of SMEs default risk is always relevant. There are several papers studying firm default factors in a single country (see Ciampi, 2015, Fantazzini and Figini, 2009, Flix and dos Santos, 2018). The literature concentrates mainly on financial indicators built on businesses’ balance sheets, which are available about two years late with respect to their reference period. This diminishes the significance of the results, both for credit risk and policy aims, and particularly in a forecasting perspective. The purpose of this paper is to provide a preliminary study on a sample of spanish firms selected from the SABI, Sistema de Análisis de Balances Ibéricos, which is listed among Bureau van Dijk databases. The analysis will be carried out according to both parametric and non-parametric discrimination techniques, with the standard construction of a training set on which to build a model and a validation set to test the validity and robustness of the results, and, in the end, the reliability of the model in predicting default. Finally we present a new proposal: a scheme to understand to what extent firms’ default can be predicted by substituting the traditional data sources (offline information) with data collected from their corporate websites (online information) in order to exploit more up-to-date information.

The aim of this paper is to propose a method to explore and synthesize social media data in order to aid businesses to make their communication decisions. The research was conducted at the end of 2014 on 5607 Italian Facebook subjects interested in drugs and health. In this study, we refer to the pharmaceutical market that is characterized by strict legal constraints, which prevent any promotional activities (such as advertising) of companies on prescription drugs. Thus, pharmaceutical businesses tend to promote their corporate brand instead of a single product brand. In such context, social media offer the opportunity to gather customers’ information about their attitudes and preferences, helpful to address marketing activities. Through a multivariate statistical approach on Facebook data, we have highlighted the associations existing between TV channels and users’ profiles. Therefore, depending on the value proposition to promote, every business could choose, first, the target group to reach and, then, the nearest suitable channel where to develop the corporate brand communication.