Peres M., C. J., Instituto Superior de Contabilidade e Administração de Lisboa, Instituto Politécnico de Lisboa, Avenida Miguel Bombarda, No. 20, 1069-035 Lisboa, Portugal.

Antão, M. A., Faculdade de Ciências da Economia e da Empresa, Universidade Lusíada de Lisboa, Rua da Junqueira, No. 188-198, 1349-001 Lisboa, Portugal.

Abstract
Over the years, economy’s cyclicality and the Disaster Myopia problem, which, according to Vasconcelos (2017), Cornand and Gimet (2012, p. 301) consists of an excessive optimism about market conditions whereby economic agents tend to underestimate risk, have repeatedly brought to the forefront the harmful effects of “bankruptcy” in varied economy sectors. With this, came a recurrent demand for better ways of anticipating “bankruptcy” or, at least, to look for contingency plans that could allow it not to spread out.In the same way that no two persons are alike, bankruptcies also differ significantly from one another, by either causes or consequences, tending to create difficulties to prediction. This article reviews the main models of multivariate discriminant analysis when applied in the multi-sectorial scope for the prediction of corporate “bankruptcy”. The focus is on the different components of bankruptcy.We gathered 78 multi-sectoral discriminatory functions, developed or reviewed by researchers between 1968 and 2016, for various time-horizons and countries. We intend to identify, in addition to the common procedures and characteristics of these analyzes and their base samples (Peres, 2014; Peres and Antão, 2017), also their components and their stability, when applied to different sectors.

Keywords
Multivariate Discriminate Analysis, Corporate Bankruptcy, Prediction models, Forecast, Multi-sector.

Cite this paper
Peres M., C. J., Antão, M. A., The Use of Multivariate Discriminant Analysis – The multi-sectorial approach applied to the Portuguese economy , SCIREA Journal of Economics. Volume 4, Issue 1, February 2019 | PP. 1-23.

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