(Bloomberg Opinion) -- World Cup predictions.
The World Cup kicks off today, minutes after this newsletter publishes, which means that there will be no finance news for the next month. That will make this newsletter a bit of a slog, sorry.
So I guess let’s talk about the World Cup? There is a long tradition of investment banks publishing research notes in which they predict the winner of the World Cup using some sort of half-baked statistical method. These predictions are always pretty dumb, which makes sense, because the analysts are in the business of predicting investment returns—which is hard enough!—and not in the business of predicting soccer results.
But of course the analysts are not really in the business of predicting investment returns. They are in the business of producing content that will improve their banks’ relationship with their customers, giving the customers a reason to trade with the bank, giving the banks’s salespeople a good excuse to call the customers. The World Cup reports fit perfectly with that business. If you go write a report about a clever interest-rate options strategy, and send it out today, and your salespeople follow up by calling clients tomorrow afternoon, no one will answer the phone because, come on, Spain will be playing Portugal. But if you go write a report about who will win the World Cup, your salespeople can call up customers and be like “hey bro, Brazil, good stuff, good stuff,” and the clients will be like “oh yeah man, true, true.” Everyone is going to be wasting a month on the World Cup anyway; you might as well waste some of it building client goodwill.
Of course this only works (I mean, “works,” whatever) in a world in which banks give away investment research for free as a relationship-building tool, and monetize it in the form of commissions on trades. The Mifid II regime is shifting European research away from that model and toward one where customers have to pay for research. Customers are not going to pay for World Cup research. If European regulators kill sell-side World Cup research … honestly it will be fine, no one will miss it all that much, but it’ll be a little bit sad. If you make research more transactional and professional and value-oriented, then it will just be so transactional and professional and value-oriented. The old murkier business models preserve some inefficiencies, but here and there they also preserve some charming quirks.
[ING] used one of the more unusual techniques described here, opting to calculate the chances of success using a measure based on market value of the nation’s team and its previous performance.
Being analysts, [Nomura] have to apply some rigor to our World Cup predictions, so we’ve decided to apply portfolio theory and the efficient markets hypothesis to the World Cup. We look at the value of players in each team, the momentum of team performance and historical performance to arrive at three portfolios of teams to watch.
But then there is this from Goldman Sachs:
We feed data on team characteristics, individual players and recent team performance into four different types of machine learning models to analyze the number of goals scored in each match. The models then learn the relationship between these characteristics and goals scored, using the scores of competitive World Cup and European Cup matches since 2005. By cycling through alternative combinations of variables, we get a sense of which characteristics matter for success and which stay on the bench. We then use the model to predict the number of goals scored in each possible encounter of the tournament and use the unrounded score to determine the winner.
This is a little different: It’s not just using the analysts’ financial expertise to predict soccer results (why?); it’s using their machine-learning expertise to predict soccer results. The thing about machine learning in finance is that domain expertise always seems so optional. I am constantly reading stories about biology researchers or hobbyist coders, who barely know what a stock is, building machine-learning models that outperform the experts. (“It's a little embarrassing, no,” I once wrote: “That investing is best understood by people who don't understand investing? That it's a trivial application of broader data-science principles, best addressed by people who were trained on harder and more interesting applications?”) This is the same line of thinking but in reverse: Goldman’s analysts know enough about soccer to throw in an awkward “stay on the bench” metaphor, but it is not exactly the subject of their Ph.D. research. (I hope.) But if you can build a machine-learning model, you can build a machine-learning model; what it is learning doesn’t much matter.
What if Goldman’s model works? I mean, I doubt it; I suspect there are not nearly enough data points for a machine-learning model to get good at predicting the World Cup. (Also: “Goldman has predicted a Brazilian victory for the last three World Cups, and has been wrong every time.”) But if it does? On the one hand, the magic of machine learning would have allowed some random finance types to outperform the experts at predicting the World Cup. On the other hand, that same magic should allow random … biology, or physics, or internet-advertising, or sports-analytics … types to outperform the experts at predicting stock returns. If the World Cup is as easy as that, maybe finance is too.
“Hedge Funds' Best Ideas? Those Are Just Stocks They're Dumping,” reads the headline here, but the story—by Bloomberg’s Sarah Ponczek, based on a paper called “Talking Your Book: Evidence from Stock Pitches at Investment Conferences” by Patrick Luo of Harvard—is actually rather heartening. What you learn about hedge funds’ “best ideas” that they pitch at investment conferences is:
- They are good ideas: “Pitched stocks earn a cumulative abnormal return of 20% over 18 months before the pitch and continue to outperform the benchmark by 7% over 9 months afterwards.”
- They are the funds’ best ideas: “Pitched stocks also tend beat other stocks that hedge funds hold but that don’t get name-checked in an on-stage presentation.”
- You can benefit from listening to them: “Returns of pitched stocks diverged from market immediately after the pitches—long pitches spike up and short pitches spike down.”
That’s all good! Famous hedge-fund managers have some skill at picking stocks. They have some metacognitive skills that allow them to recognize which of their ideas are particularly strong. And when they tell you to buy a stock, you can believe them. It’s all sort of sweet and charming. On the other hand they’re not doing it out of pure altruism:
“Hedge funds take advantage of the publicity of these conferences and strategically release their book information to drive market demand,” Luo wrote in a new study. “Specifically, hedge funds sell pitched stocks after the conferences to take profit and create room for better investment opportunities.”
I mean, they’re former best ideas, not future best ideas, but you can’t have everything.
Op-risk cat bonds.
Once upon a time, the big global investment banks were wild places, where creative bankers competed to find novel and aggressive ways to slice up risks and sell them to buyers who were hungry for attractively packaged risks. Then the global financial crisis happened, and the banks lost a lot of money, and the attractively packaged risks stopped looking so attractive, and the banks started paying multibillion-dollar fines every 15 minutes, and the regulators stepped in to rein in the worst excesses of the risk-repackaging business, and the whole thing got a lot less wild. And if, like me, you get a lot of aesthetic enjoyment out of crazy financial products, then you will find less aesthetic enjoyment in banking these days. It’s all, like, ooh, zero-commission mobile trading, whatever.
But Credit Suissse AG’s operational risk catastrophe bonds are among my very favorite post-crisis financial products, not only because they are pretty wild, but also because they are wild in such a specifically post-crisis way. After the crisis, regulators started imposing big fines and monitoring banks more closely for misbehavior and fraud. And Credit Suisse looked around and said: Yes, sure, we can sell that.
And so they bought operational-risk insurance from Zurich Insurance Co. Ltd., which would pay out if Credit Suisse lost money due to rogue traders or fraud by its employees or computer problems or whatever. And Zurich sold catastrophe bonds to reinsure that risk, so that the capital markets would ultimately own (some of) the risk that Credit Suisse would commit fraud. “What if Credit Suisse is committing fraud when it sells the bonds?,” I asked. “Can the bondholders sue Credit Suisse for fraud? If they win, do they have to pay Credit Suisse back?” (I also suggested that the obvious buyer for these bonds would be Credit Suisse’s bonus pool: Why not make the employees own the sliced-up risk that they will misbehave?)
Anyway they are at it again, or at least, Zurich is selling new catastrophe bonds that Artemis reports will cover Credit Suisse:
As with the first operational risk cat bond, we assume this new transaction will provide cover for a range of risks, including certain cyber risk exposures, such as IT system failure that causes business interruption; fraudulent behaviour both of external parties and employees of the investment bank; fiduciary issues; losses due to improper business practices or unauthorised activity; accounting errors; documentation errors; regulatory compliance issues; HR issues; discrimination in the workplace; or even personal injury.
In the decade since the financial crisis, the focus at investment banks has shifted from the creative slicing of risk to more mundane stuff like regulatory compliance and cybersecurity. And Credit Suisse is creatively slicing that mundane stuff and selling it. It is a real triumph of the human spirit in the face of adversity, or of boredom anyway.
People are worried about unicorns.
It feels like it’s been a while since I have read any dire warnings about overstretched valuations of private technology companies and the imminent crash of the unicorn bubble. I guess everything is fine? “E-scooter company Bird is seeking to raise around $200 million in new funding at a $2 billion valuation,” reported Dan Primack on Tuesday, “just weeks after it raised $150 million at a $1 billion valuation, and only three months after raising at a $300 million valuation.” Pretty normal! It’s a bird unicorn? A painted bunting? A scooticorn? I kind of admire the approach. Sure you must be exhausted from raising a big funding round weeks ago, but if you wait even another few weeks, there's a risk that your buzz will wear off and people will have a new random obsession. “When the ducks are quacking, feed them,” capital markets bankers will tell you, and Bird took that lesson to its avian heart.
SoftBank Group Corp. is in discussions to invest another giant slug of capital in WeWork Cos., with a deal that would value the shared-office company at $35 billion to $40 billion, according to people familiar with the matter.
Such an investment would roughly double WeWork’s $20 billion valuation, set last August when SoftBank invested $4.4 billion in the company.
I have to say, if SoftBank is going to become the entire market for hot private technology startups, then every valuation is going to be marked-to-SoftBank, and the numbers will start to lose their meaning.
SoftBank: Would you like some money at a $10 billion valuation?
SoftBank: Here you go. Would you like some more at a $20 billion valuation?
SoftBank: Here you go. How about a $40 billion valuation?
Startup: This is dumb but it’s not like we’re going to say no.
SoftBank: Here you go.
Startup: Thanks brb buying a yacht.
SoftBank: Our mark-to-market investment returns are tremendous, we must be good at this.
“WeWork doubled its revenue to $886 million last year, though its net loss also doubled to $933 million,” so I guess the valuation doubling is justified on a multiple of revenues or earnings.
Oh and here is venture capitalist Fred Wilson with, I won’t call it a dire warning about overstretched unicorn valuations exactly, but complaining about the obsession with valuation numbers anyway:
CEOs and their talent organizations frequently tell me that it is easier to recruit people to companies that have raised at eye popping values. This is particularly perverse because the higher the valuation, the less money the employee will make on their equity. But, it seems, the talent market is looking to the investment community to signal to them what companies are worth working for.
Is that wrong? I’m kind of an efficient-markets guy; I am open to the notion that capital markets convey information, and that one sort of information they convey is that big valuable companies are doing something big and valuable while small broke companies are doing something small and broken. In general, I assume, if the capital markets tell you that Alphabet Inc. is doing something big and important, but the charming recruiter at the magic-bean startup tells you that its business is vastly more revolutionary than anything Google has ever done, and you believe him, then the probability is that the markets are right and you are wrong.
But this is just a matter of probability—of course the capital markets are sometimes wrong—and, crucially, if you’re thinking of going to work at a startup, you shouldn’t believe in market efficiency. You should want to buck conventional wisdom, to pursue the dream of taking something small and implausible and making it huge and inevitable. You should want to change the world, not just index it.
But that is perhaps the old way of thinking about “startups,” as small scrappy companies pursuing improbable dreams. The new way of thinking about “startups” is that they are, you know, giant multinational real-estate companies with thousands of employees and $40 billion valuations. If you’re going to be the four-thousandth employee somewhere, sure, why not, look to the capital markets for a signal about whether it’s worth doing.
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