Using fundamental, market and macroeconomic variables to predict financial distress: A study of companies listed on the Johannesburg Stock Exchange

Sibusiso W Sabela,Leon M Brummer,Hendrik P Wolmarans,John H Hall

doi:10.4102/jef.v11i1.168

Sibusiso W Sabela, Leon M Brummer + Show 2 more

Open Access

https://doi.org/10.4102/jef.v11i1.168

Copy DOI

Abstract

This study presents a three-stage approach in determining financial distress of companies listed on the Johannesburg Stock Exchange. A novel feature of the present study is that it deviates from a binary classification of corporate distress prediction to present a multinomial outcome where the model predicts distressed, depressed and healthy companies. The research results show an improvement in the prediction accuracy rate when fundamental data is combined with market-based data. However, the further addition of macroeconomic indicators does not enhance the prediction accuracy.

Highlights

The need for reliable and rigorous corporate financial distress prediction models is of paramount importance, especially in today’s world of financial uncertainty
As the existing literature is ambivalent regarding the value of fundamental data when predicting corporate financial distress, the aim of the present study is to add to the body of knowledge by adopting a three-stage approach in predicting corporate financial distress
The level of accuracy and performance of this technique was evaluated against the -popular statistical technique and the results indicated that neural network methods provide superior results to those obtained from the logistic analysis method (Yim & Mitchell 2005)

Summary

Introduction

The need for reliable and rigorous corporate financial distress prediction models is of paramount importance, especially in today’s world of financial uncertainty. Since the inception of statistical financial distress prediction models in the 1960s, there has been a constant improvement in methodologies and statistical techniques to accurately forecast corporate financial distress. The purpose of these models is to enhance the efficiency and stability of both the credit markets and the broader financial system, and to warn company managers and shareholders of the possible impending danger of financial distress of the corporations in which they are stakeholders. The early detection of corporate financial distress is essential for the protection of various financial and social investments For this reason, the prediction and classification of companies to determine whether they are potential candidates for financial distress has become a key topic of debate and research

Objectives

Methods

Findings

Conclusion