Fed vs ECB Rate Hike; Big Data; The Talented Mr. Ripley by Ben Hunt, Epsilon Theory – Salient Partners
The more I practice, the luckier I get. – Gary Player (b. 1935)
Luck is the residue of design. – Branch Rickey (1881 – 1965)
I’ve found that you don’t need to wear a necktie if you can hit. – Ted Williams (1918 – 2002)
They say that nobody is perfect. Then they say that practice makes perfect. I wish they’d make up their minds. – Wilt Chamberlain (1936 – 1999)
It took me 17 years to get 3,000 hits in baseball. I did it in one afternoon on the golf course. – Hank Aaron (b. 1934)
Talent is cheaper than table salt. What separates the talented individual from the successful one is a lot of hard work. – Stephen King (b. 1947)
At one time I thought the most important thing was talent. I think now that – the young man or the young woman must possess or teach himself, train himself, in infinite patience, which is to try and to try and to try until it comes right. He must train himself in ruthless intolerance. That is, to throw away anything that is false no matter how much he might love that page or that paragraph. The most important thing is insight, that is … curiosity to wonder, to mull, and to muse why it is that man does what he does. And if you have that, then I don’t think the talent makes much difference, whether you’ve got that or not. – William Faulkner (1897 – 1962)
Talent is its own expectation, Jim: you either live up to it or it waves a hankie, receding forever. – David Foster Wallace, “Infinite Jest” (1996)
What is most vile and despicable about money is that it even confers talent. And it will do so until the end of the world. – Fyodor Dostoyevsky (1821 – 1881)
Talent is a long patience, and originality an effort of will and intense observation. – Gustave Flaubert (1821 – 1880)
There is nothing more deceptive than an obvious fact. – Arthur Conan Doyle, “The Boscombe Valley Mystery” (1891)
|Sheriff Metzger:||Mrs. Fletcher! Can I see you for a minute? [pause] Do me a favor, please, and tell me what goes on in this town!|
|Jessica Fletcher:||I’m sorry, but …|
|Sheriff Metzger:||I’ve been here one year, and this is my fifth murder. What is this, the death capital of Maine? On a per capita basis this place makes the South Bronx look like Sunny Brook farms!|
|Jessica Fletcher:||But I assure you, Sheriff …|
|Sheriff Metzger:||I mean, is that why Tupper quit? He couldn’t take it anymore? Somebody really should’ve warned me, Mrs. Fletcher. Now, perfect strangers coming to Cabot Cove to die? I mean look at this guy! You don’t know him, I don’t know him. He has no ID, we don’t know the first thing about this guy.|
– “Murder, She Wrote: Mirror, Mirror, on the Wall: Part 1” (1989)
Dr. Yen Lo: His brain has not only been washed, as they say … It has been dry cleaned. – “The Manchurian Candidate” (1962)
|Dickie Greenleaf:||Everyone should have one talent. What’s yours?|
|Tom Ripley:||Forging signatures, telling lies … impersonating practically anybody.|
|Dickie Greenleaf:||That’s three. Nobody should have more than one talent.|
– “The Talented Mr. Ripley” (1999)
My singular talent is seeing patterns that others don’t. That’s not a boast, but a fact, and frankly it’s been as much a source of alienation in my life as a source of success. As my father was fond of saying, “You know, Ben, if you’re two steps ahead it’s like you’re one step behind.” I can’t explain how I see the patterns – they just emerge from the fog if I stare long enough. It’s always been that way for me, for as far back as I have memories, and whether I’m 5 years old or 50 years old I’m always left with the same realization: I only see the pattern when I start asking the right question, when I allow myself to be, as Faulkner said, “ruthlessly intolerant” of anything that proves false under patient and curious observation.
For example, I think the wrong question for anyone watching “Murder, She Wrote” is: whodunit? The right question is: how does Jessica Fletcher get away with murder this time? Once you recognize that it’s a Bayesian certainty that the woman is a serial killer, that she controls the narrative of Cabot Cove (both figuratively as a crime novelist and literally as a crime investigator) and thus the behavior of everyone around her, you will discover a new appreciation for both the subliminal drivers of the show’s popularity as well as the acting genius of Angela Lansbury. Seriously, go back and watch the original “Manchurian Candidate” and focus on Lansbury. She’s a revelation.
Or take the Masters tournament earlier this month. I was lucky enough to attend Wednesday’s practice round, and I was sitting in a shady spot on the 10th green watching the players come by and try their luck at 15 foot putts. At first, like the other spectators, my question was: how are they such good putters? This was “the obvious fact,” to quote Sherlock Holmes, and I watched for any clues that I could adopt for my laughable game – a forward tilt of the wrist, a stance adjustment … anything, really. We all watched carefully and we all dutifully oohed and aahed when the ball occasionally dropped in the cup. But suddenly, a new pattern emerged from the fog, and I realized that we were all asking the wrong question. Instead, I started to ask myself, why are they such poor putters?
Now I realize that I just alienated at least half of the reading audience, but bear with me. I’m not saying that professional golfers are poor putters compared to you or me. Of course not. They are miracle workers compared to you or me. But it’s a stationary ball with a green topography that never changes. The speed of the greens is measured multiple times a day to the nth degree. These players have practiced putting for thousands of hours. They have superior eyesight, amazing muscular self-awareness, and precision equipment. And yet … after charting about 50 putts in the 12 – 15 foot range, the pattern of failure was unmistakable. These professional golfers were aiming at a Point A, but they would have sunk exactly as many putts if the cup had actually been located 6 inches to the right. Or 6 inches to the left. Or 12 inches back. Or 12 inches forward. The fact that a putt actually went in the hole from a distance of 12 – 15 feet was essentially a random event within a 15 x 30 inch oval, with distressingly fat probabilistic tails outside that oval. This from the finest golf players in the world. I saw Ben Crenshaw, a historically great putter who was playing in something like his 44th Masters and probably knows the 10th green better than any other living person, miss a long putt by 6 feet.
But here’s the thing. When a player took a second putt from the same location, or even close to the same location, his accuracy increased by well more than an order of magnitude. Suddenly the ball had eyes. So I went to the practice green, where I saw Jordan Spieth putt ball after ball from exactly the same location about 10 feet from the hole. He made 50 in a row before I got tired of watching. Now granted, Spieth is a wizard with the putter, a lot like Tiger was at the same age. See it; make it. But then I watched one of the no-name amateurs for a while, a guy who had no chance of making the cut, and it was exactly the same thing – putt after putt after putt rolled in from the same spot at a considerable distance.
The best golfers in the world are surprisingly poor aimers. Surprising to me, anyway. They are pretty miserable predictors of where a de novo putt is going to end up, even though we all believe that they are wonderful at this activity. But they are phenomenally successful and adaptive learners, even though we rarely focus on this activity.
I think the same pattern exists in other areas of the sports world. Take basketball free throws. I’d be willing to make a substantial bet that whatever a professional’s overall free throw shooting percentage might be – whether it’s DeAndre Jordan at 50% or Steph Curry at 90% – their shooting percentage on the second of two free throws is better at a statistically significant level than their shooting percentage on the first of two free throws. I have no idea where to access this data, but with the ubiquitous measurement of every sports function and sub-function I’m certain it must exist. Someone give Nate Silver or Zach Lowe a call!
I think the same pattern exists in the investing world, too. We are remarkably poor aimers and predictors of market outcomes, even though we collectively spend astronomical sums of money and time engaged in this activity, and even though we collectively ooh and aah over the professional who occasionally sinks one of these long putts. True story … in 2008 the long/short equity hedge fund that I co-managed was up nicely, and we were deluged by investors and allocators asking the wrong question: how did you have such a great year? At no point did anyone ask the right question: given your fundamental views and avowed process, why weren’t you up twice as much? Most investors, just like the spectators at Augusta, are asking the wrong questions … questions that conflate performance with talent, and questions that underestimate the role of process and learning in translating talent into performance.
I’m not saying that idiosyncratic talent doesn’t exist or that it isn’t connected to performance or that it can’t be identified. What I’m saying is that it’s as rare as Jordan Spieth. What I’m saying is that the talents that are most actionable in the investment world are not found in the predictions and the aiming of a single person. They are found within the learned and practiced behaviors that exist across a broad group of investment professionals. Jordan Spieth is a very talented putter and he works very hard at his craft. But there is no individual golf pro, not even Jordan Spieth, who I would trust with my life’s savings to make a single 15 foot putt. On the other hand, I would absolutely put my life’s savings on the line if I could invest in the process by which all golf pros practice their putting. I am far more interested in identifying the learned behaviors of a mass of investment professionals than I am in identifying a specific investment professional who might or might not be able to sink his next long putt.
What’s the biggest learned behavior of professionals in the investing world right now? Simple: QE works. Not for the real economy– I don’t know any professional investor who believes that the trillions of dollars in Fed balance sheet expansion has done very much at all for the real economy – but for the inflation of financial asset prices. This is what I’ve called the Narrative of Central Bank Omnipotence, the overwhelmingly powerful common knowledge that central bank policy determines market outcomes. The primary manifestation of this learned behavior today is to go long Europe financial assets … stocks, bonds, whatever. QE worked for US markets – that’s the lesson – and everyone who learned that lesson is applying it now in Europe. China, too. Here’s a great summary of this common knowledge position from a market Missionary, Deutsche Bank’s Chief International Economist Torsten Slok:
In my view, every asset allocation team in the world should have this chart hanging on their wall. Based on forward OIS curves the market expects the Fed to hike in March 2016 and the ECB to hike in December 2019. A year ago, the expectation was that the Fed and the ECB would both hike in November 2016. This discrepancy has significant relative value implications for FX, equities and rates. EURUSD should continue to go down and European equities will look attractive for many more years. Another consequence of this chart is that with ECB rates at zero for another five years, many European housing markets should continue to do well. The investment implication is clear: Expect that the benefits we have seen of QE in the US over the past 3 to 5 years will be playing out in Europe over the coming 3 to 5 years. – Torsten Slok, Deutsche Bank Chief International Economist, April 9, 2015
Just as a recap on how to play the Common Knowledge Game effectively, the goal here is to read Torsten’s note for its description and creation of common knowledge (information that everyone thinks that everyone has heard), not to evaluate it for Truth with a capital T. That’s the mistake many investors make when they read something like this … they start thinking about whether or not they personally agree with the Fed hike expectations embodied in forward OIS curves, or whether or not they personally agree with Torsten’s macroeconomic predictions on things like the European housing market, or whether or not they personally agree with the social value of the Fed or ECB policies that are impacting markets. In the Common Knowledge Game, fundamentals – whether they are of the stock-picking sort or the macroeconomic sort – don’t matter a whit, and your personal view of those fundamentals matters even less. The only thing that matters is whether or not the QE-works lesson has been absorbed by the learning process of investment professionals, and that’s driven by the lesson’s transformation into common knowledge by Missionaries like Torsten. From that perspective I don’t think there’s any doubt that what Torsten is saying is true, not with a capital T but with a little t, and that the long-Europe-because-of-ECB-QE trade has got a lot of behavioral life left to it.
One last point … I know that I’m a broken record in the fervency and persistence of my belief that Big Data is going to rock the foundations of the investment world, but this topic of talent, learning, and asking the right question is just too on-point for me to let it slide. I started this note with the alienating observation that I don’t believe that professional golfers are particularly good putters, certainly not in their ability to size up and sink a de novo putt from 15 feet or more. On the other hand, I am pretty certain that with a few months and a few million dollars, it’s possible to build a mobile robotic system with the appropriate sensors and mechanical tolerances that would sink pretty much every de novo putt it took from a distance of 15 feet. Or a robotic system that would hit 99% of its free throws. Machines are far more accurate aimers and more precise estimators of the environment than humans, and that’s a useful observation whether we’re talking about sports or investing.
But that’s not my point about Big Data. My point about Big Data is that such systems are ALSO better than humans at learning. They are ALSO better than humans at pattern recognition. I can remember when this wasn’t the case. As recently as 20 years ago you could read artificial intelligence textbooks that praised the computer’s ability to process information quickly with various backhanded compliments … yes, isn’t it amazing how wonderfully a computer can sort through a list, but of course only a human brain can perform tasks like facial recognition … yes, isn’t it amazing how many facts a computer can store in its memory chips, but of course only a human brain can truly learn those facts by placing them within the proper context. We have entire social systems – like sports and markets – that are designed to reward humans who are superior learners and pattern recognizers. Why in the world would we believe that clever and observant humans will continue to maintain their primacy in these fields when challenged by non-human intelligences that are, quite literally, god-like in their analytical talents and ruthless intolerance of what is false? At least in sports it’s illegal to have non-human participants … honestly, I can see a day where investing is reduced to sport, where we maintain human-only markets as part of a competitive entertainment system rather than as a fundamental economic endeavor. In some respects I think we’re already there.
I’ll close with a teaser. There’s still a path for humans to maintain an important role, even if it’s not a uniformly dominant role, within markets that we share with non-human intelligences. Humans are more likely than non-human intelligences to ask the right question within social systems, like markets, that are dominated by strategic interactions (i.e., games). That’s not because non-human intelligences are somehow thinking in an inferior fashion or aren’t asking questions at all. No, it’s because Big Data systems are giant Induction Machines, designed to ask ALL of the questions. The distinction between asking the right question and asking all of the questions is always interesting and occasionally vital, depending on the circumstances. More on this to come in future notes, and hopefully in a future investment strategy …