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This paper investigates the capability of forecasting models for bankruptcy prediction referring to annual balance sheet information of Italian firms in the limited liability sector. The performance of default risk models in terms of forecast accuracy is mainly related to the selection of the set of best predictors. Therefore our main research question refer to the identifications of the determinants of corporate financial distress, comparing the performance of innovative selection techniques. Furthermore, several issues involved in default risk analysis are considered, such as the structure of the data-base and the sampling procedure The predictive performance of the proposed default risk model has been assessed by means of different accuracy measures. The results of the analysis, carried out on a data-set of financial ratios expressly created from a sample of industrial firms annual reports, give evidence in favour of the proposed model over traditional ones. Keywords: Default Risk, Bankruptcy, Variable Selection, Lasso.

Credit cards, both as mean of payment and borrowing, rise many economic issues. The credit card services can be viewed as a two-sided network platform affected by indirect network externalities. The distribution of prices faced by the two sides influences market participation and the overall volume of demand. Consumers may hold or use credit cards from multiple networks leads to a ‘multi-homing’ effect that is of great importance in determining the outcome of the industry. Moreover, some studies show that the multiple credit cards can be seen as a device to access to more financing, making family bankruptcy more likely. In this paper we model the number of credit cards held by a panel of Italian household over the period 1991-2010 using demographic, socio-economic and geographical variables as potential predictors and panel data techniques for count data. The reached results can be of interest for implementing market strategies in credit card industry and, in particular, to investigate peculiar effect such as “multihoming” and “co-holding”. Keywords: Credit cards, Panel data, Count-data models

Credit cards, both as mean of payment and borrowing, rise many economic issues. The credit card services can be viewed as a two-sided network platform affected by indirect network externalities. The distribution of prices faced by the two sides influences market participation and the overall volume of demand. Consumers may hold or use credit cards from multiple networks leads to a ‘multi-homing’ effect that is of great importance in determining the outcome of the industry. Moreover, some studies show that the multiple credit cards can be seen as a device to access to more financing, making family bankruptcy more likely. In this paper we model the number of credit cards held by a panel of Italian household over the period 1991-2010 using demographic, socio-economic and geographical variables as potential predictors and panel data techniques for count data. The reached results can be of interest for implementing market strategies in credit card industry and, in particular, to investigate peculiar effect such as “multihoming” and “co-holding”. Keywords: Credit cards, Panel data, Count-data models

This paper investigates the capability of forecasting models for bankruptcy prediction referring to annual balance sheet information of Italian firms in the limited liability sector. The performance of default risk models in terms of forecast accuracy is mainly related to the selection of the set of best predictors. Therefore our main research question refer to the identifications of the determinants of corporate financial distress, comparing the performance of innovative selection techniques. Furthermore, several issues involved in default risk analysis are considered, such as the structure of the data-base and the sampling procedure The predictive performance of the proposed default risk model has been assessed by means of different accuracy measures. The results of the analysis, carried out on a data-set of financial ratios expressly created from a sample of industrial firms annual reports, give evidence in favour of the proposed model over traditional ones. Keywords: Default Risk, Bankruptcy, Variable Selection, Lasso.