Bob Rosenberg
Educator (Associate Professor) / Entrepreneur / Leader of angel communities /
Entrepreneur in residence at PorterShed and BioExcel
A View From the Fridge:
Reassessing who we are and what we do
A recent article at Seeking Alpha describes how the pandemic has transformed how investors look at stocks (https://seekingalpha.com/article/4358515-stocks-turned-upside-down-covidminus-19-beta-effect). I couldn’t get this article out of my mind and, as a result, I’ve been down a rabbit hole reviewing the roots of the capital asset pricing model (CAPM).
Simply stated, the model is an equation.
Plug in data and the equation spits out a number that represents the anticipated return on an investment; in other words, the equation uses historical and current market data to determine whether an asset is a bargain or if it’s overpriced. The data it uses includes the value of ‘riskless’ assets (safe harbors like US Treasury bills), the volatility of the overall market, the volatility of the individual asset, and the length of time you plan to keep the asset.
It’s a great little tool, a major contribution to finance and investment, a spreadsheet must-have that has found its way into gambling, sports, and even the climate change debate.
So why am I going on about it here?
Because like so much else, Covid-19 has laid bare the weakness of historical models as predictors. In my mind I still hear the economic modellers who said, with confidence, that the 2007 crash couldn’t happen because we had created a worldwide system for mortgages, diversifying the risk. Even if one country defaulted on mortgages, it never happens that the mortgage markets worldwide go south.
Until 2007, that is.
You’ll notice that the article focuses on beta, one aspect of the CAPM model. Beta is a measure of volatility – how much an asset moves in relation to the market. If the asset gyrates more than the market as a whole, it’s a volatile asset. Moves less? it’s a relatively safe albeit sleepy asset.
A beta of 1 means that the asset tracks the underlying market, and an investor should expect market-level returns.
A beta above 1 means that the asset magnifies market volatility. One assumes more risk with high-beta assets, but with the risk comes the opportunity for outsized rewards. A beta of 2 means that expected return would be twice the expected market return, and perhaps twice the loss if things go south.
Finally, a beta below one is usually a stodgy, slow moving asset that changes little over time. Mature companies in segments (often regulated) like groceries, insurance, mining, utilities and the like.
So how did high-flyers – high-beta assets – suddenly become stodgy? And vice versa?
Did the Covid-driven need for Zoom calls and online retail suddenly upset sentiment? Or the volatility of the assets? Or the volatility of the underlying market? Or the current price of the assets? Or is something else going on?
Hmmmmm.
You probably don’t know Harry Markowitz. Or Louis Bachelier. They were mathematicians who used stock price data to prove that the best predictor of a stock price tomorrow is its price today. That stocks move in unpredictable ways, what is known as a random walk (often pictured as a drunk moving fitfully around a light pole).
You can have all the backward-looking data your models can accommodate, but if today is volatile and unpredictable, then the odds of being right about tomorrow fall away pretty quickly. If the light pole is moving, what hopes for the drunk staggering around it? Models are pretty in the short term, but as insight into the future???? Good luck.
Which, if you’ve survived the digressions, brings me to startups today. VCs have schemes for measuring value and risk – variations on CAPM, Monte Carlo simulations, bespoke VaR (value at risk) models – but admit that investing in startups is, at best, a ‘visceral’ business. They are looking for 10-20X returns because the volatility is through the roof. Too many variables, too much uncertainty. No historical data, even if the business model is a carbon copy of a successful business.
Especially today.
Which means that we all need to reassess our businesses, in particular our go-to-market strategies, to reflect today’s new realities, while acknowledging that things are changing much too quickly for our own good. The investors are going to increase the discount factors to reflect the increased uncertainty, the inability to apply models. In a crisis, the price of an asset today may not be the best predictor of its price tomorrow. Risk is much more than simple volatility.
Especially when the market risk is at unacceptably high levels.
In the short term, raising money is going to get more difficult unless you’re in the charmed circle of companies that address very near-term realities. Covid-related healthcare products, or tools to facilitate on-line transactions and communications, for example. A new restaurant concept or entertainment venue might not survive a business review today, or you might need to put the concept on ice for the time being. And you might want to reconsider the risk of being a startup when economic conditions are precarious. It’s a good time to have some conversations with mentors, investors and potential investors, as well as others in the industry. Including competitors.
Models and historical analyses are like umbrellas. They can provide comfort in a shower, not so useful in a hurricane. Old hands have lots of experience, but I doubt that even those who survived the Spanish Flu (or the Great Depression or the Rust Belt years) have much insight given current conditions. I’m talking with as many people as I can; I’ve never been so busy with companies at all stages of development. And I’ve never questioned my own assumptions more.
As always, if you’ve got comments or questions – or just want to talk – let me know.
Be safe out there.
