The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and perform...

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Bibliographic Details
Published in:Information (Basel) 2024-06, Vol.15 (6), p.355
Main Authors: Horváthová, Jarmila, Mokrišová, Martina, Schneider, Alexander
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Bankruptcy
Classification
Construction companies
Construction industry
decision tree
Decision trees
Deep learning
Discriminant analysis
ensemble methods
Forecasts and trends
Insolvency
Machine learning
Neural networks
Neurophysiology
Nonparametric statistics
Regression analysis
Support vector machines
Transition economies
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Summary:Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability.
ISSN:2078-2489
2078-2489
DOI:10.3390/info15060355