Most of us have now been working from home for the last 3 to 4 weeks. Some of us may have already lost our jobs or been told that they are at risk. During that time many businesses have been shut - a week of quarantine may equate to 2% of their annual sales.

Similarly, as a rule of thumb, a 2% fall per week can be applied to the GDP of any nation currently under quarantine. If a quarantine lasts for 8 weeks, it is likely that GDP will fall by around 15%.

These are incredibly significant numbers. During my time in finance, I have never seen anyone assume a decline of more than 10%, as a worst case scenario in their models. We are predisposed by our own experiences to compare the current situation to situations from the recent past. Currently, the economic impact of COVID-19 is being to only be compared to the 2008 Financial Crash. Automatically assuming that this is likely to be the worst case scenario put us at a significant risk of underestimating the gravity of the situation. In the last few days, IMF predicted "the worst economic crisis since 1930s depression". This might just about exemplify the situation.

Historic Data

Looking at the UK's Quarterly GDP Growth data for the past 65 years, the values range between -2.7% fall and 5% increase. Therefore, the falls in GDP to be announced over the coming months will without doubt be the largest on historical record.

Modelling this data using a t-distribution (it assumes outlier events are more likely to happen than in a Normal Distribution), we can calculate the frequency of any GDP growth rate over a period of time. Below is a list of different GDP rate thresholds and the expected frequency of occurrence over 1,000 years time period. For example, a decline of -15% or more in GDP shouldn't happen more than 1 quarter in 1,000 years.

GDP Rate: -40% Occurrence in 1,000 years: 0.0 years or 0.1 quarters

GDP Rate: -35% Occurrence in 1,000 years: 0.0 years or 0.1 quarters

GDP Rate: -30% Occurrence in 1,000 years: 0.1 years or 0.2 quarters

GDP Rate: -25% Occurrence in 1,000 years: 0.1 years or 0.3 quarters

GDP Rate: -20% Occurrence in 1,000 years: 0.1 years or 0.5 quarters

GDP Rate: -15% Occurrence in 1,000 years: 0.2 years or 1.0 quarters

GDP Rate: -10% Occurrence in 1,000 years: 0.6 years or 2.4 quarters

GDP Rate: -5% Occurrence in 1,000 years: 2.7 years or 10.7 quarters

GDP Rate: 0% Occurrence in 1,000 years: 193.7 years or 774.9 quarters

GDP Rate: 5% Occurrence in 1,000 years: 995.5 years or 3981.9 quarters

Most economic and financial models are built on assumptions with similar distributions. This means they significantly underestimate the occurrence of sudden economic shocks, like we are currently experiencing. Because events like this are so extraordinary and their impact extremely large, the majority of people are naturally underestimating them and their subsequent economic consequences.

Why does this matter?

Our mutual underestimation of the economic significance of these events also creates an unintentional risk. As businesses experience tremors in their daily activity, they may suddenly change their financial "budgets", which will amplify the economic shock.

In order to mitigate this risk, it is important for businesses to prudently manage their cash payments from client companies. Expectation of a future payment from someone, naturally creates a potential credit risk that needs to be carefully monitored during the present uncertain times.

To help companies with monitoring their credit risk exposure, I have built RiskTails. Using data from Companies House and other publicly available data sources, I apply machine learning in order to predict the probability of any company in the UK going bankrupt in the next 12 months.

Adding a list of companies allows users to get a holistic view of their exposure and which companies pose the greatest risk to them. As new data becomes available, all the numbers are constantly updated to give users the latest snapshot of the credit risk exposure.

To learn more about the tool and and how it can help you better manage your credit risk exposure during the Coronavirus crisis, please get in touch with me directly on LinkedIn or via email: dmitry [at] rastorguev.co.uk

Ultimately, extra attention and effort is required from companies to better manage their cash flow and credit management during the impending turbulent times. Fortunately, there are plenty of established financial services companies and FinTechs offering a wide range of services that can help businesses with their challenges.

Risk awareness and forward planning is a must.