Predicting Nursing Home Financial Distress Using the Altman Z-Score
This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-07-01
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| Series: | Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| Online Access: | https://doi.org/10.1177/0046958020934946 |
| _version_ | 1818849354821140480 |
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| author | Justin Lord PhD Amy Landry PhD Grant T. Savage PhD Robert Weech-Maldonado PhD |
| author_facet | Justin Lord PhD Amy Landry PhD Grant T. Savage PhD Robert Weech-Maldonado PhD |
| author_sort | Justin Lord PhD |
| collection | DOAJ |
| description | This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool to identify nursing homes that are at risk of financial distress. |
| first_indexed | 2024-12-19T06:31:55Z |
| format | Article |
| id | doaj.art-5af5c580b7e34dcdb3baee72325d4dc8 |
| institution | Directory Open Access Journal |
| issn | 0046-9580 1945-7243 |
| language | English |
| last_indexed | 2024-12-19T06:31:55Z |
| publishDate | 2020-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| spelling | doaj.art-5af5c580b7e34dcdb3baee72325d4dc82022-12-21T20:32:21ZengSAGE PublishingInquiry: The Journal of Health Care Organization, Provision, and Financing0046-95801945-72432020-07-015710.1177/0046958020934946Predicting Nursing Home Financial Distress Using the Altman Z-ScoreJustin Lord PhD0Amy Landry PhD1Grant T. Savage PhD2Robert Weech-Maldonado PhD3Louisiana State University Shreveport, USAThe University of Alabama at Birmingham, USAThe University of Alabama at Birmingham, USAThe University of Alabama at Birmingham, USAThis article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool to identify nursing homes that are at risk of financial distress.https://doi.org/10.1177/0046958020934946 |
| spellingShingle | Justin Lord PhD Amy Landry PhD Grant T. Savage PhD Robert Weech-Maldonado PhD Predicting Nursing Home Financial Distress Using the Altman Z-Score Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| title | Predicting Nursing Home Financial Distress Using the Altman Z-Score |
| title_full | Predicting Nursing Home Financial Distress Using the Altman Z-Score |
| title_fullStr | Predicting Nursing Home Financial Distress Using the Altman Z-Score |
| title_full_unstemmed | Predicting Nursing Home Financial Distress Using the Altman Z-Score |
| title_short | Predicting Nursing Home Financial Distress Using the Altman Z-Score |
| title_sort | predicting nursing home financial distress using the altman z score |
| url | https://doi.org/10.1177/0046958020934946 |
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