Application of artificial neural networks in predicting financial distress in the JSE financial services and manufacturing companies

Fikile Dube · Ntokozo Patrick Nzimande · Paul‐Francois Muzindutsi

38 citations0/0 files storedJournal of Sustainable Finance & Investment

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 Corporate Insolvency and Governance Stock Market Forecasting Methods

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.

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 — 38 citations exist upstream.

References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)2017Machine learning models and bankruptcy prediction 2004Bankruptcy Prediction with Industry Effects 2008Using neural networks and data mining techniques for the financial distress prediction model 2007DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

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.