Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation
This research aims to determine the existence of inflection points when companies’ credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations w...
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doaj-23e4f8de23644af297723fbd553209b92021-04-16T23:01:32ZengMDPI AGMathematics2227-73902021-04-01987987910.3390/math9080879Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability EstimationNora Gavira-Durón0Octavio Gutierrez-Vargas1Salvador Cruz-Aké2Department of Banking and Investments, School of Business and Economics, Universidad de las Americas Puebla, Puebla 72810, MexicoHigher School of Economics, Instituto Politécnico Nacional, Santo Tomas Campus, Ciudad de México 11350, MexicoHigher School of Economics, Instituto Politécnico Nacional, Santo Tomas Campus, Ciudad de México 11350, MexicoThis research aims to determine the existence of inflection points when companies’ credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations with homogeneous Markov chains and the k-means clustering method to determine thresholds and migration among clusters. To prove this, we used quarterly financial data from a sample of 35 public enterprises over the period between 1 July 2006 and 28 March 2020 (companies listed on the USA, Mexico, Brazil, and Chile stock markets). For simplicity, we make the assumption of no revolving credits for the companies and that they face their next payment only with their operating cash flow. We found that Ponzi companies (1) have a 0.79 probability average of default, while speculative ones had (0) 0.28, and hedge companies (−1) 0.009, which are the inflections point we were looking for. Our work’s main limitation lies in not considering the entities’ behavior when granting credits in altered states (credit relaxation due to credit supply excess).https://www.mdpi.com/2227-7390/9/8/879homogeneous markov chainsk-meanscredit riskdefault clusters |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nora Gavira-Durón Octavio Gutierrez-Vargas Salvador Cruz-Aké |
spellingShingle |
Nora Gavira-Durón Octavio Gutierrez-Vargas Salvador Cruz-Aké Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation Mathematics homogeneous markov chains k-means credit risk default clusters |
author_facet |
Nora Gavira-Durón Octavio Gutierrez-Vargas Salvador Cruz-Aké |
author_sort |
Nora Gavira-Durón |
title |
Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation |
title_short |
Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation |
title_full |
Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation |
title_fullStr |
Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation |
title_full_unstemmed |
Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation |
title_sort |
markov chain k-means cluster models and their use for companies’ credit quality and default probability estimation |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2021-04-01 |
description |
This research aims to determine the existence of inflection points when companies’ credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations with homogeneous Markov chains and the k-means clustering method to determine thresholds and migration among clusters. To prove this, we used quarterly financial data from a sample of 35 public enterprises over the period between 1 July 2006 and 28 March 2020 (companies listed on the USA, Mexico, Brazil, and Chile stock markets). For simplicity, we make the assumption of no revolving credits for the companies and that they face their next payment only with their operating cash flow. We found that Ponzi companies (1) have a 0.79 probability average of default, while speculative ones had (0) 0.28, and hedge companies (−1) 0.009, which are the inflections point we were looking for. Our work’s main limitation lies in not considering the entities’ behavior when granting credits in altered states (credit relaxation due to credit supply excess). |
topic |
homogeneous markov chains k-means credit risk default clusters |
url |
https://www.mdpi.com/2227-7390/9/8/879 |
work_keys_str_mv |
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