Articlegold2024

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

Jarmila Horváthová · Martina Mokrišová · Alexander T. Schneider

15 citations0/0 files storedInformation

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Financial Analysis and Corporate Governance Stock Market Forecasting Methods

Abstract

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 15 citations exist upstream.

References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress 1999Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis 2007Using neural network ensembles for bankruptcy prediction and credit scoring 1999Business failure prediction using rough sets 2018Deep learning models for bankruptcy prediction using textual disclosures 1993Forecasting with neural networks 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.