Abstract
This study explored the role of artificial intelligence (AI) in predicting companies’ financial distress. We used Artificial Neural Networks (ANN) to develop and test financial distress prediction models for the financial services and manufacturing companies listed on the Johannesburg Stock Exchange (JSE) for the period 2000–2019. Our constructed ANN Models achieved classification accuracy rates of 81.03 and 96.6 percent for the financial services and manufacturing industries, respectively. Both models could also predict financial distress up to five years prior to the firm being classified as distressed. This study provided key theoretical and practical contributions to the current literature by highlighting the potential role of AI models in solving financial problems. Creditors can use the models built in this study as a default prediction tool, investors as an investment decision-making tool, and for business managers a performance guidance tool to ensure long term financial sustainability.
| Original language | English |
|---|---|
| Pages (from-to) | 723-743 |
| Number of pages | 21 |
| Journal | Journal of Sustainable Finance and Investment |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2023 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Artificial neural networks
- bankruptcy prediction
- financial distress
- financial ratios
- macroeconomic factors
ASJC Scopus subject areas
- Business and International Management
- Finance
- Economics, Econometrics and Finance (miscellaneous)
Fingerprint
Dive into the research topics of 'Application of artificial neural networks in predicting financial distress in the JSE financial services and manufacturing companies'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver