Financial distress prediction in colombian infrastructure firms using logistic regression and support vector machines

Bankruptcy describes the condition in which a business cannot repay their outstanding debts, which forces them to follow legal and financial liquidation processes where many of the companyþs assets are used to repay a portion of their liabilities. Bankruptcies incur severe consequences to shareholders, creditors, and employees. Advanced statistics and machine learning techniques have been used in the past years to predict many business failure cases. Such models have been of great use for investors, creditors, auditors, banks and government policymakers. In this study, logistic regression and support vector machine models have been carried out with the aim of predicting the financial distress risk of firms belonging to the construction industry in Colombia, one-year prior of its occurrence.

• Autores: Arango Giraldo, Jacobo
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2019
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/45270
• Acceso en línea: http://hdl.handle.net/1992/45270
• Palabra clave: Quiebra; Análisis de regresión; Aprendizaje automático (Inteligencia artificial); Ingeniería
• Rights: openAccess
• License: https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf