Bankruptcy prediction using machine learning models - Empirical results in the Colombian manufacturing industry (2018-2022)

Aug 5, 2026

Upload by Quinn

The purpose of this study is to examine financial indicators that signal corporate failure in manufacturing firms in Colombia. The analysis builds on prior research using predictive approaches such as multiple discriminant analysis, logistic regression, and machine learning models. The work applies logistic regression and random forests to financial ratios computed from company data reported between 2018 and 2022 in the SIREM database from the Superintendence of Companies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages50

Reading Time2 h 6 min

AccessFree to download

Introduction

Research background and motivation

Objective and scope (2018-2022)

Structure of the work

A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Finance from the Nova School of Business and Economics. Bankruptcy prediction using machine learning models: empirical results in the Colombian manufacturing industry (2018-2022) Miguel Angel Parra Castro Work project carried out under the supervision of: Yeny Rodríguez Virginia Gianinazzi 19/12/2023 Abstract The purpose of this study is to examine financial indicators that reveal the situation of corporate failure in manufacturing companies in Colombia. The use of these indicators is based on previous studies that have used predictive models of corporate fragility: multiple discriminant analysis, logistic regression, and machine learning. This work uses logistic regression and random forests models. This work is based on financial indicators made of the data reported between 2018 and 2022 in the database Sistema de Información y Riesgos Empresariales (SIREM) of the Superintendence of Companies. Keywords: Corporate finance, business failure, bankruptcy prediction, insolvency, financial analysis, financial ratios, random forest. Introduction A company is an economic entity that engages in commercial, industrial or service-providing activities, with the objective of generating economic profit (Sepúlveda, 2021) . Gelashvili & Segovia-Vargas (2020) state that the function of a company is to generate value for its shareholders, maintain a profitable and sustainable operation, effectively manage its financial resources, and meet its financial and commercial obligations. The survival of firms contributes to economic growth, employment promotion, market competition and financial stability (Alarcón & Mejía, 2017; Umaña, 2012) . To ensure the survival of firms, financial analysis serves as a primary tool. Financial analysis of a firm comprises a thorough review to financial statements, such as balance sheet, profit and loss statement, cash flow statement, to monitor financial health, performance, and financial position in the industry (Faxas & Fuentes, 2011; Zeng, 2013) . Thus, financial analysis includes the use of various financial ratios in order to measure aspects of liquidity, solvency, profitability and efficiency (Lakshmi et al., 2021; Rahim, 2021) . In addition, financial management decisions such as capital structure, investment strategies and risk management are an essential factor to ensure the success and financial viability of a company in the long term (Faxas & Fuentes, 2011) . Finding the factors that give rise to corporate failure is one of the objectives of governments, guilds, financial agents, and society (Parra & Peluffo, 2022) . Corporate failure generates a notable concern to all stakeholders of the company, competitors, investors, debtors, creditors, customers, employees, suppliers, and partners (Galán-Barrera & Torres-García, 2017) . For that reason, this problematic has been the subject of various research and models that serve as predictors of situations that can be rectified. In the field of finance, predicting bankruptcy has been considered as a relevant topic to investigate (Zięba et al., 2016) . Research on financial stress based on financial indicators and statistical methods began in 1932 with Fitzpatrick who established the descriptive stage and then in 1968 Altman gave rise to the predictive stage with the formulation of multivariate models (Altman, 1968) . Discriminant analysis and financial ratio analysis have been the main techniques used in several studies that have been carried out to measure their effectiveness in predicting corporate bankruptcy (Casanova, 2011; Correa & Mejia, 2019) . Thus, Altman (1968) proposed the scope of financial ratio analysis as a statistical tool to anticipate a business crisis. In this way, Ohlson (1980) revealed empirical results in the prediction of business failure using the maximum likelihood estimation methodology of the conditional logit model.

FAQs

What is the main objective of the study?

To estimate the probability of bankruptcy for Colombian manufacturing companies during 2018–2022 using financial ratios and machine learning classification methods.

Which machine learning models are used for bankruptcy prediction?

Logistic regression and random forests are used to model the insolvency risk based on the selected financial indicators.

What data source and period does the analysis rely on?

The models are based on financial data reported between 2018 and 2022 in the SIREM database of the Superintendence of Companies.

Aug 5, 2026

Upload by Quinn