Brace! Brace! Tackling COVID-19 Induced Startup Financial Distress
Updated: Jun 22, 2020
How can Startups assess the impact of the COVID-19 outbreak on their immediate future? Common sense and some Excel magic.
As the pandemic outbreak plays out, it imposes a heavy toll on the economy, leaving no sector unscathed. Entrepreneurs and managers must reassess their work plans, goals, and devise corrective measures to avoid financial distress.
Changing fortunes are not a new phenomenon, and adverse effects can run business down. The lending community, as well as regulators, has developed analyses to assess the chance for default for borrowing companies.
A modern tool has been developed by Edward I Altman in the 60’s - A multi variate analysis resulting in what he called Z-Score. The model took in accounting data, assigning coefficient derived from a wide selection of public companies in the US. Since then, several modifications have been suggested to the coefficients, allowing for different cohorts from other countries, other industries, and variations of this tool are used to this day by lenders, investors, and regulators.
Useful as it may be, Altman’s Z-Score does not come without drawbacks - specifically pertinent to the Startup condition:
Feeds on audited data, rarely available for startups
Assumes linear liquidity, as opposed to chronic loss backed by future (contingent) cash injections
Does not account for explosive growth and resulting need for working capital
Suffers from high rates of false-positives.
Cash is King
The single most important factor for startup financials is cashflow. Cash on hand provides the assurances of meeting the company’s obligation for the immediate future.
Sensible planning takes into account a financial cushion, allowing for discrepancies in revenues, R&D delays, cost runaways.
In tight, aggressive, startup state-of-mind, such cushion tends to be minimal - cash reserve providing for orderly liquidation.
Declining cash balances approaching reserve level indicate distress for the company that will need to be rectified aggressively to avoid default on the company’s obligation, a blow startups rarely recover from, given the intangible nature of most of their assets, and the resulting liquidation value thereof.
Assessing Your Resilience
By nature, startups entrepreneurs are obsessed about the future. In the context of this discussion, it is a GOOD thing.
In a previous post (Hebrew), I offered a cashflow driven approach to evaluate how much cash do you need for your startup.
In a similar take, I propose to analyze your future facing resilience now, facing the COVID-19 storm, or towards any future crisis, for that matter. While not replacing professional, rigorous consulting, it offers a framework to assess your situation, allowing you to take the measures you must to assure the survival of your venture.
A Simple Cashflow Forecasting Tool:
Request Access to view a google sheet version. Use at your own risk!
Using a spreadsheet, lay out the past six months of your financial execution:
It is a pretty standard approach, listing in the top line your revenue stream(s), summing up in line 6.
Note: In this example I have accounted for 35% variable cost (rounded, shows in line 7)
It is important, though, to discern between different types of revenue, in this discussion relating to different sources of revenue. These should be sorted by customer types, product types, payments schedule, and other parameters that may be risk-adjusted.
Note that I have left out the end of month balance, since “the past is past”. I did, though, include the last known bank balance - for it will be the basis for our projections.
Forecasting your revenue - A risk adjusted approach
Now comes the tricky part, in which you have to estimate the probability of each revenue stream materializing as a function of the last known result. This probability may shifts from month to month as the crisis first worsens, stabilizes, and finally alleviates. Use your judgement and market intimacy to predict which customers may rebound, which might not, what products will die.
True, these are calculated guesses, but you should be able to defend those predictions, either by questioning customers, or deducing from publicly available market predictions.
Now comes the mechanical part, in which you plug in the revenue streams (lines 3,4,5 in the example) the product of the last known revenue (column H in the example) and the respective probability. This goes for all future periods: