Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/98587
Title: Insolvency risk: characterisation and prediction
Author: Xaus Pariente, Adrià
Director: Fortiana Gregori, Josep
Marti Pidelaserra, Jordi
Keywords: Avaluació del risc
Tesis
Fallida
Anàlisi multivariable
Anàlisi de regressió
Xarxes neuronals (Informàtica)
Risk assessment
Theses
Bankruptcy
Multivariate analysis
Regression analysis
Neural networks (Computer science)
Issue Date: Jan-2016
Abstract: The present document sets out to analyse the concept of insolvency risk in a firm and how it can be objectively measured. Our main objective is to predict whether a firm will face an insolvency situation, based on its most recent historical data stored in its accounts. In order to achieve it, the prediction of insolvency risk is studied reviewing some of the most relevant literature and explaining the accounting and financial implications which lie behind it, understanding the concept of insolvency from this perspective. In mathematical terms, this is an example of the so-called Problem of Classification (or Discriminant Analysis), which is usually approached using Statistics. More specifically, the chosen way to mathematically measure insolvency risk is through some of the most popular statistical prediction methods which deal with this problem. Some of these methods consist of the classical Altman’s Z Score, essentially equivalent to the Linear Discriminant, or more contemporary methods like Classification and Regression Trees or Neural Networks. These methods are applied on two samples. The first one is a sample of 40 Spanish firms selected under some certain criteria, gathering its data from SABI database (Sistema de Análisis de Balances Ibéricos). The second one is the sample that Professor E. I. Altman used in his famous 1968 article, where he introduced its aforementioned Z Score. A balanced approach between financial theory and statistical theory is used in order to effectively convey the message that we cannot totally rely on the statistical methods without taking into account the non-mathematical implications, for this is a complex issue involving many other areas such as finance, accounting or economics.
Note: Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Josep Fortiana Gregori i Jordi Martí Pidelaserra
URI: http://hdl.handle.net/2445/98587
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

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