Professor Stewart Jones says the new technologies are set to benefit banks, companies and investors, as their outputs can assist with making the right choices when it comes to lending money, managing business operations and making investments.
This new research will help a variety of stakeholders better understand how and why companies fail. “We can learn from those failings and actually make business stronger in the future,” said Stewart.
I think the US market is at some points going to falter. It’s hard to judge how serious the correction will be. Hopefully it won’t be a GFC 2, but many of the issues that created the first GFC are re-emerging.
As examples, Professor Jones points to the under regulated over the counter derivatives market which has again risen in value to over 500 trillion US dollars globally and the rapidly expanding trillion dollar student loan market in the US.
He says that the hedge funds and investment banks are now “cutting and dicing up these loans into collateralised loan obligations. They’re being rated by the credit rating agencies and it seems to be a replay of what actually happened during the GFC”.
“With default rates on student loans at around 11% and with a much higher number in a deferral or repayment program it’s looking like an overvalued market,” Professor Jones said.
Financial modelling in the field of credit risk and bankruptcy forecasting, developed by Professor Jones, is also pointing toward a downturn.
“Since the GFC, there’s been a lot more concern about the measurement of risk, the pricing of risk using more sophisticated models for prediction,” said Professor Jones. “I’m working in more advanced technologies, such as machine learning technologies, which are known to be a lot more accurate in the prediction of risk events.”
Jones S, Johnstone D and Wilson R 2017 'Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Models', Journal of Business Finance and Accounting, vol.44:1-2, pp. 3-34
Jones S 2017 'Corporate Bankruptcy Prediction: A High Dimensional Analysis', Review of Accounting Studies, vol.22:3, pp. 1366-422