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Altman Z-Score Indicators

Filippo Mattio

Altman Z-Score Indicators.

Rel. Federico Caviggioli, Matteo Tubiana. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2024

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This thesis provides a comprehensive examination of the Altman Z Score model, a seminal tool developed by Edward I. Altman in 1968 for evaluating the financial health and insolvency risk of companies. Renowned for its simplicity and practicality, the Altman Z Score model has become a staple in financial analysis, requiring minimal input data and resources for effective implementation. Altman's dedication to usability has spurred the development of various iterations of the model, tailored to diverse industries and economic environments while preserving its fundamental principles. The research, after an introduction about the concept of crisis and the main historical crisis that affected the world, delves into the origins of the model, tracing its conceptual foundations and evolution through subsequent versions. Emphasis is placed on understanding the methodology behind the model, including its reliance on multivariate discriminant analysis and the interpretation of its results. The thesis critically evaluates the model's efficacy in predicting insolvency and assessing financial stability, drawing insights from empirical studies and practical applications. Furthermore, the thesis explores the adaptation of the Altman Z Score model to different business contexts and industries, highlighting its versatility and applicability across various sectors. It examines how practitioners have customized the model to suit specific organizational needs and regulatory requirements, shedding light on best practices and potential pitfalls in its implementation. By offering a comprehensive analysis of the Altman Z Score model and its adaptations, this thesis contributes to a deeper understanding of financial risk assessment and management. It provides valuable insights for practitioners, researchers, and policymakers seeking to leverage the model's predictive capabilities in navigating complex financial landscapes and safeguarding corporate solvency.

Relators: Federico Caviggioli, Matteo Tubiana
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 92
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Ente in cotutela: Columbia University (STATI UNITI D'AMERICA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/31425
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