I was discussing the SVB blowup situation yesterday with one of my friends who manages the public markets portfolio for a large family office. He and I both have deep financial services backgrounds, having worked across diverse services & asset classes (VC, PE, public markets, Investment Banking, debt etc.). Both of us came to the same conclusion regarding what has unfolded:

Given the complexity of financial markets, with many direct & indirect stakeholders, influencers, interconnections, interdependencies, manual & robo decision engines at play, it’s almost impossible for even the smartest operating teams & regulators to stay on top of systemic risks building up across thousands of organizations in our financial system.

Of course, this risk management challenge gets further exacerbated in pure capitalist markets such as the US, that consciously allow free market cycles, driven by excessive greed followed by excessive fear, to play out without much intervention.

Going beyond the macro discourse around SVB, of which there is enough now in the media & on Twitter, I want to highlight one learning that all of us need to pay attention to from this episode – the real risk in most things in life is in the “unknowable”, not the “unknown”.

What does this mean? In most planning exercises we do around risk management both professionally (eg. what’s the sensitivity around my company’s 2023 revenue?) & personally (if I plan to do a startup, how much personal runway do I need to be able to operate without a salary?), we focus mainly on outlining the “unknowns” – variations in outcomes of visible & obvious elements. Things like revenue from existing customers, attrition of top performers, house rent, holiday budgets etc.

Planning for unknowns is largely driven by first-order thinking. This includes the classic sensitivity analysis playbook of (1) listing out all obvious elements of the game, (2) thinking of a range of values for them (best case/ likely case/ worse case) & (3) using these values as inputs to model out various output scenarios that consequently drive the overall decision-making process.

But if most organizations & individuals follow this kind of solid decision-making framework, why is the real-world full of surprising blow-ups – bank runs, hedge fund unravels, fast-growing companies unexpectedly going bankrupt etc.?

It’s because the real world is a complex adaptive system with emotion-driven humans as actors. Michael Mauboussin, legendary analyst, academic & public markets investor, beautifully outlined the qualities of this type of system in his recent conversation with Tim Ferris:

So, “complex” means lots of agents. Those could be neurons in your brain, ants in an ant colony, people in a city, whatever it is. “Adaptive” means that those agents operate with decision rules. They think about how the world works, and so they go out in there and try to do their thing. And as the environment changes, they change their decision rules. So that’s the adaptive part, their decision rules that are attempting to be appropriate for the environment. And then, “system” is the whole is greater than the sum of the parts. It’s very difficult to understand how a system works, an emergent system works, by looking at the underlying components.

Michael Mauboussin

In such a system, while some risks fall under “unknowns”, a majority of them are “unknowable” given the system is self-evolving & therefore, impossible to predict at a granular level. Many words are used to describe these unknowables – edge cases, tail events, black swans etc.

Even if we do get some additional visibility into a few of these probabilistic unknowables & can foresee their 1st-order impact to an extent, their 2nd & 3rd order effects are really hard to model out.

Given this context, classic risk management approaches work well most of the time, until they don’t. And when they don’t, participants are caught unaware, unprepared, & often facing the Risk of Ruin.

So, how can organizations & individuals prepare better to deal with the unknowables? The following steps can help:

1. Start by recognizing the presence of “unknowables” – a major first step is to acknowledge one’s ignorance, & consciously keep overconfidence bias at bay by reminding oneself that even after all this data & analysis, there is a lot that is just not possible to predict. Approaching risk management with humility & in defense mode creates a conducive mindset for this.

2. Add a significant “Margin of Safety” on top of your analysis – while a rigorous Sensitivity Analysis will cover the unknowns well, adding a Margin of Safety goes a long way in providing a buffer for the unknowables. How much of it you want to add depends on context but given we live in a highly risky world, it should be significant enough. As an example, legendary value investors like Buffet & Munger insist on a 50% Margin of Safety while buying public securities (buying at half of the intrinsic value of a company).

Btw, this isn’t anything new. Engineers who design everything from trains & storage tanks to nuclear reactors & space shuttles, recognize error rates in their assumptions & therefore, always include an “allowance” in their computations. Millions of lives depend on this method!

3. Routinely stress-test & update your assumptions – with software continuing to eat the world at an exponential pace, cycles are becoming shorter & feedback loops quicker. The Fed raised rates from under 0.5% in Mar’22 to ~5% in less than a year! With information transmitted in real-time, especially via networks like Twitter, & decisions manifested at the push of a button, we saw how SVB unraveled in literally a day. Given this speed of change, it’s important to frequently stress-test your state-of-state, accounting for changes in external & internal environments & updating your assumptions (esp. Margin of Safety) accordingly.

While the Treasury, the Fed & FDIC have joined forces to save everyone impacted by this specific SVB case, most of us can’t count on such White Knights bailing out our families or our startups each time. A pragmatic & defensive risk management approach that accounts for unknowables, incorporates a healthy Margin of Safety, & includes periodic stress testing, can help us cope with outlier events & keep us in the game.