Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data
This paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical models that can identify consumers in a difficult financi...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/10/2/24 |
| _version_ | 1797476769494728704 |
|---|---|
| author | Magdalena Brygała |
| author_facet | Magdalena Brygała |
| author_sort | Magdalena Brygała |
| collection | DOAJ |
| description | This paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical models that can identify consumers in a difficult financial situation that may lead to consumer bankruptcy. In the face of the current global pandemic crisis, the future of household finances is uncertain. The change of the macroeconomic and microeconomic situation of households requires searching for better and more precise methods. The research relies on four samples of households: two learning samples (imbalanced and balanced) and two testing samples (imbalanced and balanced) from the Survey of Consumer Finances (SCF) which was conducted in the United States. The results show that the predictive performance of the logit model based on a balanced sample is more effective compared to the one based on an imbalanced sample. Furthermore, mortgage debt to assets ratio, age, being married, having credit constraints, payday loans or payments more than 60 days past due in the last year appear to be predictors of consumer bankruptcy which increase the risk of becoming bankrupt. Moreover, both the ratio of credit card debt to overall debt and owning a house decrease the risk of going bankrupt. |
| first_indexed | 2024-03-09T21:08:24Z |
| format | Article |
| id | doaj.art-a5ff63d9689c46df836441a7c9c76f1b |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-09T21:08:24Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-a5ff63d9689c46df836441a7c9c76f1b2023-11-23T21:56:32ZengMDPI AGRisks2227-90912022-01-011022410.3390/risks10020024Consumer Bankruptcy Prediction Using Balanced and Imbalanced DataMagdalena Brygała0Faculty of Management and Economics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, PolandThis paper examines the usefulness of logit regression in forecasting the consumer bankruptcy of households using an imbalanced dataset. The research on consumer bankruptcy prediction is of paramount importance as it aims to build statistical models that can identify consumers in a difficult financial situation that may lead to consumer bankruptcy. In the face of the current global pandemic crisis, the future of household finances is uncertain. The change of the macroeconomic and microeconomic situation of households requires searching for better and more precise methods. The research relies on four samples of households: two learning samples (imbalanced and balanced) and two testing samples (imbalanced and balanced) from the Survey of Consumer Finances (SCF) which was conducted in the United States. The results show that the predictive performance of the logit model based on a balanced sample is more effective compared to the one based on an imbalanced sample. Furthermore, mortgage debt to assets ratio, age, being married, having credit constraints, payday loans or payments more than 60 days past due in the last year appear to be predictors of consumer bankruptcy which increase the risk of becoming bankrupt. Moreover, both the ratio of credit card debt to overall debt and owning a house decrease the risk of going bankrupt.https://www.mdpi.com/2227-9091/10/2/24bankruptcy of householdspredictionlogitUShousehold financechoice-based sample |
| spellingShingle | Magdalena Brygała Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data Risks bankruptcy of households prediction logit US household finance choice-based sample |
| title | Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data |
| title_full | Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data |
| title_fullStr | Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data |
| title_full_unstemmed | Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data |
| title_short | Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data |
| title_sort | consumer bankruptcy prediction using balanced and imbalanced data |
| topic | bankruptcy of households prediction logit US household finance choice-based sample |
| url | https://www.mdpi.com/2227-9091/10/2/24 |
| work_keys_str_mv | AT magdalenabrygała consumerbankruptcypredictionusingbalancedandimbalanceddata |