Predicting Companies in Financial Distress Explored in Research
A Master’s degree in Finance is what Ms Fikile Dube achieved cum laude for her thesis titled: Industry-Specific Financial Distress Prediction for Companies in the Financial Services and Manufacturing Sectors of the Johannesburg Stock Exchange.
Dube set out to develop two models capable of predicting the probability of companies in financial distress, using artificial neural networks (ANNs) which make up an artificial intelligence (AI) model based on machine learning.
‘The findings of this study showed that the ANNs were able to predict financial distress - with an accuracy of over 80% for both sectors - using industry-specific performance indicators,’ said Dube. ‘These results speak to the applicability of AI models in the South African context and have inspired me to continue to further explore the application of machine learning models in finance for my PhD with the aim of discovering new and innovative ways to solve financial problems.’
Dube says when she enrolled at UKZN in 2015, obtaining a master’s degree had not been a priority in her future plans. Her goal was to get a B Com, possibly pursue honours, and then leave! ‘However, the incredibly inspiring and passionate staff in the School of Accounting, Economics and Finance convinced me to stay on. One is constantly motivated by them to achieve more,’ she said.
There were challenges during her master’s journey, but with assistance from her supervisors and her determination to achieve, she overcame all obstacles.
‘Coming from a finance background I knew this was not going to be easy, however, after a few python coding and machine learning courses, countless failed attempts and immense motivation and assistance from my supervisors Professor Paul Francois Muzindutsi and Dr Ntokozo Nzimande, I developed two fully functional ANNs for predicting financial distress for the financial services and manufacturing industries,’ she said.
Describing his interaction with Dube, Muzindutsi said: ‘I first met Fikile as a third-year Finance student who intended to seek work after completing her honours degree. During her honours year, she tutored in undergraduate Finance modules, developing an interest for academia and decided to enrol for a master’s degree. She is an intelligent, hardworking student who challenges herself and has the potential to become a good researcher. Congratulations to her for delivering a high-quality master’s thesis. She is now working on a research proposal for her PhD and I will be happy to supervise and mentor her all the way on her new journey.’
Dube thanked her family for their support and is excited her achievement has inspired her father to also pursue a master’s degree.
Currently working on getting her master’s published, Dube is reading for a PhD and hopes to continue in academia.
Words: Lungile Ngubelanga