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CPS2129 Matilde Bini et al.
                  is consistent with the general economic cycle. The level of profitability (ROE)
                  is well predictable by the same quadratic function. The industries in which the
                  Italian manufacturing is stronger (i.e. mechanical and chemical productions)
                  are the ones for which the recovery in terms of riskiness indicators after the
                  crisis is faster and stronger. From an economic point of view: it is relevant in
                  the period the impact of double crisis (subprime and sovereign debt). This
                  implies a W-shaped trend for quite all economic indicators. This is particularly
                  true for firms’ riskiness and the indicators representing it. The estimated curve
                  parameters imply a first period of increasing riskiness and a second period
                  characterized by a more and more decreasing riskiness. The second period can
                  be partitioned, in turn, into two sub periods in which two different factors
                  impact  on  riskiness.  Right  after  the  sovereign  debt  crisis  (2011-2014)  the
                  growth of riskiness was stopped by the effects of ACE (Aiuto alla Crescita)
                  provision, that strongly boosted the deleveraging process in the larger firms
                  (Zeli, 2018). The coming of the global economic recovery in 2014-2015 further
                  improved the riskiness indicators.

                  References
                  1.  Altman, E., Marco, G., & Varetto, G. (1994). Corporate distress diagnosis:
                      comparison using linear discriminant analysis and neural networks (the
                      Italian experience). Journal of Banking & Finance., 18, 505–529.
                  2.  Arbuckle, J. L. (1996). Full information estimation in the presence of
                      incomplete data. In G.A. Marcoulides & R.E. Schumacker [Eds.] Advanced
                      structural equation modeling. Mahwah, New Jersey: Lawrence Erlbaum
                      Associates.
                  3.  Bagozzi, R.P., Yi, and Y. (1988). On the evaluation of structural equation
                      models. Academy of Marketing Science, 16(1): 76–94.
                  4.  Bellovary, J.L., Giacomino, D.E. & Akers, M.D. (2007). A review of
                      Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial
                      Education, 33, 1–42.
                  5.  Bollen, K. A. (2002). Latent variables in psychology and the social
                      sciences. Annual Review of Psychology, 53, 605–634.
                  6.  Bollen, K. A., and Curran, P. J. (2006). Latent Curve Models. New York:
                      Wiley.
                  7.  Bottazzi, G., Grazzi, M., Secchi, A., & Tamagni, F. (2011). Financial and
                      economic determinants of firm default. Journal of Evolutionary
                      Economics., 21, 373–406.
                  8.  Di Clemente, A. (2008). Rischio d’insolvenza e ciclo economico: un’analisi
                      di macro stress-testing per le imprese non finanziarie italiane In:
                                            th
                      Proceedings of the 49  Annual scientific conference of Società Italiana
                                                               th
                      degli Economisti – Perugia University - 24  October.


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