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Research in innovation studies usually relies on financial statements, surveys, or patents as primary data sources, although these sources of information show some limitations when applied to Small and Medium Enterprises (SMEs). Our paper explores whether the HTML code of a company's website is a further source to better inform innovation policies, under the assumption that how HTML is employed in crafting a corporate website provides insights into the company's innovation capabilities. In particular, we leverage HTML tags and their associations to empirically show that the websites of innovative SMEs are different from non-innovative ones both in terms of their size and coding practices. Our findings, based on a sample of Italian companies, indicate that the features of the HTML code of corporate websites reflect unobservable characteristics related to the skills and creativity present in businesses.

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.

Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black-box type of models. In order to overcome this drawback and maintain the high performances of black-boxes, this paper has chosen a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Networks) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm still maintaining a rich interpretation framework to support decisions.

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.

Small and medium enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. On the same time, SMEs are more fragile and likely to default as compared to larger firms so to induce public policies targeted on them. Specific default predic- tion models, accounting for SMEs idyiosincratic traits, are based on several types of data, mainly accounting indicators. We explore the possibility of complementing accounting information with data scraped from the firms’ websites.

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.

L’obiettivo di un modello di rischio di credito e` quello di sviluppare una regola accurata in grado di distinguere tra osservazioni buone e cattive. In questo lavoro le aziende sono le nostre osservazioni, in particolare ci concentriamo sulle Piccole e Medie Imprese (PMI), come la maggior parte dei lavori nella letteratura finanziaria, dato il loro contributo all’economia dell’Unione Europea sia per il val- ore aggiunto sia per la creazione di posti di lavoro. Lo scopo dell’articolo e` duplice: esplorare l’uso degli indicatori ottenuti dal web per migliorare le previsioni di in- solvenza delle imprese e confrontare le prestazioni dei modelli alimentati con tali indicatori rispetto a quelli costruiti con le informazioni finanziarie standard. I risul- tati, ottenuti su un campione di aziende spagnole, includeranno anche un focus sulla capacita` degli indicatori web di riconoscere le aziende insolventi non comuni., The objective of a Credit Risk model is to develop an accurate rule that can distinguish between good and bad instances. In this work instances are firms, specif- ically we focus on Small and Medium Enterprises (SMEs), as most of the papers in the financial literature, thanks to their contribution to the European Union economy for both value added and the creation of jobs. The aim of the paper is twofold: to explore the usage of the web-scraped indicators to improve firms’ default predic- tions and to compare the models’ performances fed with the web-based indicators with the ones built with the standard financial information. The results, obtained on a sample of Spanish companies, will also include a focus about the ability of the web features in recognizing uncommon defaulted firms.

Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises default. The usage of accounting indicators does not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing to credit. This complicates matters both for private lenders and for public institutions supporting policies. In this paper we propose corporate websites as an additional source of information, ready to be exploited in real-time. We also explore the joint use of online and offline data for enhancing correct prediction of default through a Kernel Discriminant Analysis, keeping the Logistic Regression and the Random Forests as benchmark. The obtained results shed light on the potentiality of these new data when accounting indicators lead to a wrong prediction.

The paper explores the possibility to employ the source code of corporate websites as an information source for research in innovation studies. Research in this area is generally based on studies that collect data on patents or official data sources. Our paper links the standard economic information of the firm with web-based data and joins the ongoing debate with a threefold contribution. First, whereas the majority of the literature focused on the linguistic content of web-pages, we mostly use HTML tags. Second, we propose a method to assess the quality of the linkage of Web data to firm-level information. Third, we show that the data retrieved from corporate websites can aid to identify 'innovative SMEs'.

Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises (SMEs) default. Generally, papers in the field use accounting indicators as regressors of bankruptcy prediction models, although it has been demonstrated they do not allow to completely overcome the information opacity that is one of the main barriers preventing SMEs from accessing to credit. Plus, companies balance sheet data are published at least one year late with respect to the reference period, so that they prevent a real time prediction as requested by financial operators. In this paper we propose websites as an additional source of information for forecasting SMEs default. We borrowed this idea from contributions the have used corporate websites to retrieve online proxies of firms’ economic characteristics, such as corporate culture or firm performance. Our work explores the joint use of online and offline data for enhancing correct prediction of default through kernel discriminant analysis. We also study in detail the firms where the combination is successful in order to highlight possible patterns for future applications.

As recently stated by EUROSTAT, Small and Medium Enterprises (SMEs) play a crucial role in the economy of European Union (EU). This is particularly true for Italy that has one of the largest share of SMEs in the Euro zone. In such a context, the assessment of the Italian firms creditworthiness is a priority, especially in the early stage of a SMEs life. The aim of this work is to explore the effectiveness of the Feedforward Neural Network for Italian firms defaults prediction in comparison with standard linear models (Logistic and Probit). Our analysis considers the Italian manufacturing SMEs in a short time span (2016-2017). Results, obtained via two different sampling strategies, are not clear-cut.

We propose the use of online indicators, scraped from the firms’ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model.

The growth of cultural firms is important in developing local economies, enhancing employment, and improving urban sustainability, but it is difficult to achieve in fragmented industries that are populated by the smallest firms and offer a particularly unfavorable context for growth. The study takes a contingency perspective and contributes to both the literature on business strategy in fragmented industries and that on the growth of small and medium-sized enterprises (SMEs) by identifying the strategies implemented in a fragmented cultural industry, determining which of them are associated with a firm’s expected growth, and finding the few firm-specific factors that are associated with growth. It also complements the extant literature on art galleries by looking at them from the understudied strategic perspective. Suggestions for practitioners and policy makers are included.

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.

Usury regulations focus both on explicit recommendations, such as disclosure statements in lending acts or interest rate caps, and on incentives for the formal banking sector to reach the poor. Considerable attention is also devoted to adequate sanctions for the practice of usury. We propose a theoretical model to investigate where to direct reforms to curb usury. Our primary policy implication is the convenience of polarizing the allocation of public resources in either legal safeguards for formal lenders or sanction enforcement against usurers. Furthermore, we demonstrate that in developing countries such policy interventions may backfire when borrowers' higher wealth implies increasing inequality in its distribution. Therefore, countries undertaking reforms against predatory lending should be aware of their growth path during transition

In this paper we develop a weighted forward search (WFS) approach to the correction of outliers in GARCH(1,1) models relying on the foward search (FS) method introduced by Atkinson and Riani (Robust diagnostic regression analysis. Springer, New York, 2000). The WFS is based on a weighting system of each unit and is an extension of the FS from independent to dependent observations. We propose a WFS test for the detection of outliers in GARCH(1,1) models and a WFS estimator of GARCH(1,1) coefficients which automatically corrects outliers. Extensive Monte Carlo simulations show the good performance of the WFS test with respect to other methods of outlier detection for the same models. The marked similarity between the distribution of MLE before strong contamination of the time series and after decontamination through the WFS proves the reliability of the WFS estimator. Finally, the application of the WFS procedure to several financial time series of the NYSE reveals the effectiveness of the method when extreme returns are observed.

In this paper the relationship between the Industrial Production Index (IPI), the confidence index for the manufacturing sector and Google searches for several words linked to the economic situation is explored. In particular, time series referred to the period January 2004 - September 2016 on Italian data. An analysis of significant correlations between the selected indicators is achieved to explore the probable comovements of same. Adding one observation at a time since the first forewarning signs of the 2008 crisis, we find that a few Google searches and the IPI cointegrate, particularly during the strong downward trend leading to January 2009, while there is no cointegration between confidence indicators and the IPI. These results suggest that searches in google and the IPI or the confidence indexes are influenced by common circumstances. Finally forecasts of the IPI obtained through VECM models suggest that the evolution of the IPI can be well represented using the real time Gtrends selected variables.

Worldwide, monthly CPIs are mostly calculated as weighted averages of price relatives with fixed base weights. The main source of estimation of CPI weights are National Accounts, whose complexity in terms of data collection, estimation of aggregates and validation procedures leads to several months of delay in the release of the figures. This ends up in a non completely consistent Laspeyres formula since the weights do not refer to the same period as the base prices do, being older by one year and then corrected by the elapsed inflation. In this paper we propose to forecast CPI weights via a compositional VAR model, to obtain more updated weights and, consequently, a more updated measure of inflation through CPIs.

In this paper we explore the relationship between the Industrial Production Index (IPI), the confidence index for the manufacturing sector and its sub-indexes and Google searches for several words linked to the economic situation, for the period January 2004 - September 2016 on Italian data. Significant correlations between the selected indicators point to probable comovements of same. Adding one observation at a time since the first forewarning signs of the 2008 crisis, we find that a few Google searches and the IPI cointegrate, particularly during the strong downward trend leading to January 2009, while no confidence indicators cointegrate with the IPI. These findings suggest that concern about economic conditions expressed through searches in google and the IPI or the confidence indexes are influenced by common circumstances. Recursive forecasts of the IPI through VECM models suggest that the evolution of the IPI can be well mimicked using the real time Gtrends selected variables.

L’analisi dell’evoluzione della soddisfazione nel contesto di business, è di solito basato su studi pseudo-panel, perchè sono meno costosi e facili da costruire con i dati disponibili. Come in casi cross-section, informazioni dettagliate sui clienti vengono raccolte in ogni istante temporale, ma il confronto dinamico avviene generalmente considerando soli pochi ritardi temporali (a causa del breve ciclo di vita di prodotti e servizi). Di conseguenza, nel nostro contributo proponiamo l’utilizzo dell’analisi fattoriale multipla duale. Tale tecnica permette la sintesi delle tabelle multivariate e la visualizzazione delle stesse su uno spazio comune che fa luce su traiettorie di soddisfazione dei clienti. I vantaggi della tecnica vengono illustrati in un caso di studio relativo ad una banca italiana, The analysis of the evolution of satisfaction in business context is usually based on pseudo panels studies, because they are less costly and easy to build with the available data. As in the cross-section case, detailed information about customers are collected at each time point, but the dynamic comparison generally involves few temporal lags (due to short life time of products and services). Accordingly, in our paper we apply the Dual Multiple Factor Analysis. Such a technique allows the synthesis of the multivariate tables and their visualization on a common space that sheds light on customers’ trajectories of satisfaction. A real case study of an Italian bank is illustrated

In this paper we analyze the relationship between the distribution of firm size and stochastic processes of growth. Three main models have been suggested by Gibrat (1931), Kalecki (1945) and Champernowne (1973). The first two lead to lognormal distribution and the last to Pareto distribution. We fitted lognormal and Pareto distribution to two Italian sectors: ICT and mechanical. For ICT we found that lognormal distribution must be rejected and Pareto fits reasonably well to the last 30% of largest companies. For mechanical sector we can not reject lognormal distribution. Furthermore, we perform some experiments to corroborate the theoretical models. By means of transition matrices we found that ICT shows features very close to Gibrat’s and Champernowne’s models, while Kalecki’s model strongly fits to mechanical

Building on the recognition of the variety of aspects associated with international expansion, we present a framework for identifying different archetypes of firm internationalization. Our model is based on six indicators: internationalization from the demand side, resources located abroad, geographical scope, international orientation, internationalization of the business network, and financial internationalization. Drawing from data on 63 Italian SMEs, four archetypes of internationalization strategy are identified through a cluster analysis: 'marketer', 'investor', 'networker', and 'weak internationalizer'. This study offers a methodological contribution to the analysis of firm-level internationalization, grounded in configurational theory, which defines strategies as multidimensional archetypes. We also discuss potential directions for future research.

Inequality measurement based on grouped data is usually performed by non-linear squares. We propose here an alternative approach building on the compositional nature of the shares of income derived from quantiles. Three parametric Lorenz curves are estimated after trasforming the income shares with the isometric logratio transform. The performance of our simplicial approach is then compared by means of a Monte Carlo Experiment with the methodology proposed by Chotikapanich and Griffiths (2002) based on the Dirichlet distribution and with the non-linear squares. To this purpose, the resulting bias in the Gini coefficient associated with the estimated curves is evaluated across increasing quantiles and different income distributions.

The combined use of administrative data and sample surveys has become increasingly important in statistical applications. In this paper, we focus on the combination of administrative tax records and sample survey data collecting information on household expenditures. Our purpose is to create a new dataset containing information from both data sources following an almost-exact matching approach.

A common practice to determine the extension and heaviness of heavy tails of income, return and size distributions is the sequential estimation and fitting of one or several models, starting from a group of the largest observations and adding one observation at a time [14]. In the early stages this kind of procedure shows high sensitivity of the shape parameter estimates to single observations, the end of the search being fixed when the shape parameter value estimates reach a plateau. In this paper we propose a stepwise fitting of a heavy-tailed model, the Pareto II distribution [1], previously applied to the size distribution of business firms. The procedure, based on the forward search technique [2], is data-driven since observations to be added at each iteration are determined according to the results of the estimation carried out at the preceding step and not, as in sequential fitting, according to their rank.

A livello regionale, una pubblicazione Eurostat (2008) attesta che nel quinquennio 2000-2004 si è avuta una diminuzione del reddito primario delle famiglie per la regione Lombardia pari al 9,2% rispetto alla media dei paesi EU E Milano? Purtroppo non esistono statistiche ufficiali a livello così dettagliato. Per questo motivo l’anagrafe tributaria rappresenta un patrimonio informativo che non può giacere inutilizzato: si tratta di 850.228 contribuenti nel 2000 e più di 856.000 contribuenti nel 2004. A questi si aggiungono circa 280.000 non dichiaranti, in calo nel 2004 di circa 12.000 unità (-4,3%). La copertura totale dell’archivio costituisce una grande ricchezza e assicura la robustezza statistica dei risultati evidenziati, che, pur risentendo dei fenomeni di elusione ed evasione che caratterizzano i dati fiscali, rispecchiano la realtà “dichiarata” dai milanesi. Va ricordato, in ogni caso, che anche le indagini sul reddito possono risentire dell’effetto di underreporting: difficilmente l’evasore dichiarerà i suoi reali guadagni all’intervistatore. La capillarità della banca dati e l’ampio ricorso allo studio complessivo della distribuzione del reddito ci hanno permesso di disaggregare i dati, incrociando età, genere, categoria di reddito, per considerare tutte le diverse sfaccettature del fenomeno. Cosa è rimasto nelle tasche dei milanesi? Come si evoluta la situazione delle donne, dei giovani e degli anziani? In questo lavoro si è cercato, tra l’altro, di dare una risposta a queste domande. Il principale obiettivo dello studio, evidenziare le potenzialità di questo archivio amministrativo, trova sostegno nei risultati, che incoraggiano il proseguimento di questo tipo di analisi poiché coerenti, in termini tendenziali, con quelli citati a livello nazionale.

We propose here a new implementation of the forward search, which is a powerful general method usually suitable for detecting extreme observations and for determining their effect on fitted models (Atkinson and Riani, 2000). Through the forward search we iteratively fit the Pareto II distribution to firm size data. In particular, a threshold is fixed to the fit of the Pareto IIistribution through a progressive adaptation technique, performing at each iteration the chi(2) test to check for the acceptance of the null hypothesis. Yearly Zipf-plots of the truncated empirical distribution with superimposed theoretical Pareto II distribution highlight the adherence of the estimates to data for different size ranges. Possible economic interpretations of the results are then provided, referring in particular to the role of the stock market in shaping firm size distribution and to the firm size effect (Banz, 1981; Reingaum, 1981). More in general, we discuss possible implications of introducing our methodology in macroeconomic models

The paper analyzes the change in the size distribution of Italian banking groups over the period 1999 to 2007 following a wave of M&As among large banks. Had this process increased the degree of concentration we would have expected greater credit rationing for small firms, given the central role of Italian banks in financing small firms. We measure this change through widely used measures of concentration on branches. First, we observe a steady increase in concentration that can be captured only by looking at the overall size distribution. Other measures do not perceive this change until the year 2007, when the very large banks merged. Second, by focusing on the banking groups that have been active players in M&As we do see a decline in concentration, since smaller players have caught up with the larger ones in terms of rate of size increase. This contrasts with the role of the new entries and the disappearance of banks following mergers, that has increased the dispersion of market shares. The implications are that: i) there is a credit termination risk due to the rise in active players’ size, but ii) credit rationing may not occur due to a substitution effect in credit supply from new entries.

A common practice to determine the extension and heaviness of heavy tails of income, return and size distributions is the sequential estimation and fitting of one or several models, starting from a bunch of the largest observations and adding one observation at a time. This kind of procedure shows high sensitivity of the shape parameter estimates to single observations at early stages, the end of the search being fixed when the shape parameter value estimates reach a plateau. In this paper we propose a stepwise fitting of a heavy-tailed model, the Pareto II distribution, applied to the size distribution of business firms. The procedure, based on the forward search technique, is data-driven since observations to be added at each iteration are determined according to the results of the estimation carried on at the preceding step and not, as in sequential fitting, according to their rank.

Through the Forward Search we fit iteratively the Pareto II distribution to firm size data. In particular, a threshold is fixed to the fit of the Pareto II distribution through a progressive adaptation technique, performing at each iteration the chi-square test to check for the acceptance of the null. Yearly Zipf-Plots of the truncated empirical distribution with superimposed theoretical Pareto II distribution highlight the adherence of the estimates to data for different size ranges. Possible economic interpretations of the results are then provided, referring in particular to the role of the stock market in shaping firm size distribution and to the firm size effect [Banz, 1981], [Reingaum, 1981]. More in general, we discuss possible implications of introducing our methodology in macroeconomic models