Int. J. Business Performance Management, Vol. X, No. Y, XXXX
Risk insolvency predictive model maximum expected utility Daria Marassi* Eu-Ra Europe Rating S.p.A., L.go Don Francesco Bonifacio 1, Trieste 34125, Italy E-mail: daria.marassi@eu-ra.com *Corresponding author
Valetino Pediroda Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, via Valerio 10, Trieste 34127, Italy E-mail: mciprian@units.it E-mail: pediroda@units.it Abstract: This paper presents a new approach to develop the probability of default for private firms. This work provides a global perspective regarding the credit risk prediction, starting from the work of the Basel Committee on Banking Supervision, with a deep study of the more predictive variables for default prediction and, finally, building a new mathematical model based on machine learning. The used method is called Maximum Expected Utility (MEU) and represents the most promising methodology for the default prediction. The main idea is to use the interaction between variables to improve the final model efficiency. The development model has been tested on complex analytical function (in which the classical models fault) and finally has been developed to assess the distress of industrial companies according to Basel II guidelines. The evidence was related to Italian industrial enterprises and took into consideration, the situation of the Italian economy both from a micro and macro perspective. Keywords: rating; probability of default; credit risk prediction; Basel II; methodology for the default prediction; risk insolvency model; default model; predictive model; interaction between variables; predicted default variables; self organizing maps; statistical analysis; credit rating; risk model development; cumulative accuracy profile. Reference to this paper should be made as follows: Marassi, D. and Pediroda, V. (XXXX) ‘Risk insolvency predictive model maximum expected utility’, Int. J. Business Performance Management, Vol. X, No. Y, pp.XXX–XXX. Biographical notes: Daria Marassi is a Senior Database Analyst and Statistical Analyst at Eu-Ra Europe Rating Spa, an Italian credit rating agency that makes use of the most modern analysis and techniques in the field of Company Finance and Financial Engineering. She is engaged, in particular, into research of innovative methodologies for credit rating analysis and for risk management. Copyright © XXXX Inderscience Enterprises Ltd.
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