This is the entirety of a three part series that was originally published byIntegrity Research and titled The Great Quant Makeover – Part 1: How Discretionary Managers Can Cope with the New Systematic Realities, Part 2:The Rise of the Quants and How Some Successful Discretionary Managers are Responding, Part 3: Revenge of the Humans or How Discretionary Managers Can Crush Systematics
Six months ago I found myself in our Estimize office sitting across the table from a hedge fund portfolio manager who said something I honestly couldn’t believe. According to this PM who runs a $500M long/short book at a large multi-manager fund, he was taking a data science course at night, after work. He told me, “if I don’t learn how to do quantitative analysis I’m not going to have a job in two years.”
A second said the same thing to me a week later.
Two weeks after that I received an email from the “school” providing that very course, inquiring if I could teach a data science class, specifically for finance, to 25 members of a hedge fund who had contracted them.
These are just a few anecdotes among many in the absolutely massive transformation taking place right now within the discretionary institutional management industry. Discretionary managers have woken up, and are now scrambling to understand what’s taking place and how they must change in relation to it. Many will not survive the shift. Others will take advantage and be better off for it.
This piece takes a deep dive into the following themes and how institutional managers can begin to effectively redirect themselves:
- Investors have woken up to the asymmetric risk they were taking on with active discretionary mutual funds, hedge funds and RIAs who were basically playing with beta instead of generating alpha. Now they are pulling their money.
- Asset flows are moving into “passive” ETF strategies and will continue to move further into smart beta ETF strategies, long only active management is headed to the grave.
- Hedge fund assets are flowing out of discretionary and into quantitative systematic strategies which have produced far more consistent alpha. They also blow-up less often.
- Most classic systematic alpha strategies are based on price; volume and fundamentals have been arbitraged out and are now betas. This has precipitated a race to build new alphas with new data sets.
- Discretionary managers are scurrying to incorporate new data sets, but lack the understanding of how to analyze their efficacy and more importantly, how to incorporate them into their discretionary trading processes.
- If discretionary managers remain disciplined and execute their rubric faithfully, they can crush systematic quants, but they must solve the religion vs. science question first.
- The organizational structure of discretionary management teams along with the type of people they hire is broken and outdated for today’s challenges. Changes are starting to take place, but all too slowly for many players to survive.
- Building the right infrastructure will remain pertinent to surviving this shift. Both quant and discretionary firms must hire teams that include engineers, product managers and quants to suss out new data sets.
On June 20th, Estimize will be hosting the L2Q (Learn to Quant) Conference, a one day seminar designed for discretionary institutional PMs, analysts, and traders who know they need to move quickly and efficiently towards building quantitative processes. Segments will be taught by preeminent buy side, sell side, and unique data experts with vast quantitative investment experience at Two Sigma, PDT Partners, WorldQuant, Wolfe Research, Deutsche Bank, and others.
But before that, let’s take a deeper dive into the topics above, and why we felt a whole conference was necessary to explore them.
1. Getting Paid For Playing With Beta Is Over
Looking back, it’s hard to understand why anyone was willing to give most discretionary fund managers money in the first place. The truth is, most PMs were simply playing with beta, whether it be momentum, mean reversion, value, growth, sector or market cap. Managers were leveraging these far more often than they were actually generating alpha. Now we can all argue over whether correctly timing the use of betas is in itself alpha, but that argument is made moot by the fact that the vast majority of PMs were unsuccessful at this in the long run and eventually blew up.
The greatest trick the industry ever pulled was making LPs believe that they could consistently leverage beta and not get caught with their hand in the cookie jar, giving up years of returns in a matter of months. Over and over, fund managers took their “two and twenty” to the bank in the years they happened to be on the right side of that equation. Then they blew up. Instead of fighting back to their hurdle, they just closed shop and opened up a new one, somehow convincing investors to play the same asymmetric game of risk once again. Heads I win, tails I take a vacation for a year and someone gives me another coin to flip later.
Don’t get me wrong, there are managers who have proven track records of not blowing up while playing with beta, and some even generate true alpha, but they are few and far between. Good luck picking the correct fund manager.
Why did it take the market so long to wake up? We can start with the great answers you’ll hear from friends of mine like wealth manager, Josh Brown. He fully understands the social and egotistical aspect of being invested in these funds, not because it’s the rational thing to do, but because of the accompanying prestige. The same can be said for managing your own personal portfolio; it’s something to talk about at a cocktail party. And while it seems our current political climate echoes the movie Idiocracy, financial market education and investor behavior have actually taken a huge leap forward since the ‘08 crash. I find it interesting that retail investors actually got smart before pension funds, pulling money from active managers, closing their brokerage accounts, and investing in passive low cost ETF strategies.
As for the tens of thousands of small RIAs, why would I give them my money either if I can buy a smart beta ETF for 20bps that does basically the same thing they were for 100bps? You’re gonna tell me that all those mom and pop RIAs managing $40M are executing those smart beta strategies as efficiently and accurately as iShares? Please. It’s only a matter of time before Betterment or some other robo-advisor allows its clients to algorithmically allocate a portion of their portfolio to these strategies. Heck, I wouldn’t be surprised if one of them also provided the ability to use simple, proven, market timing overlays in order to rotate in and out or long and short certain smart beta strategies.
Hedge fund PMs have to realize that even though they are in last car on this disruption train, the conductor is coming to clip their ticket as well. They will either evolve or die, like any other industry disrupted by better efficiency. I think it’s obvious that there will be far fewer of them as most will not successfully shift to generating alpha.
2. All Investing Is Active, Even The Passive Kind
Let’s clear something up, there’s no such thing as “passive investing”. The words we use matter because they form the basis for how we think about things and the actions we take. The developed western world is ripping itself apart over an inability to win a “war on terrorism” because, for propaganda purposes, we decided to say we were fighting a war on a military tactic (you didn’t have to study war theory in school like me to know you can’t win a war against a tactic).
All investing is active, even the decision of how to weight an index, what goes into that index, and how to allocate your capital amongst different asset classes. Just because the computer keeps your allocation levels static does not mean you’ve abdicated responsibility for investment decisions. This is why I’m such a big fan of smart beta, because it does away with the ignorant notion that you can avoid making a decision on beta to begin with. We all have to, so we might as well make that decision in an informed and active way.
In any event, we’re going to continue to see massive flows of capital out of “active” long only mutual fund and long/short hedge fund strategies and into these. The question on everyone’s mind is, how will this affect the market? My best guess is that we’re not going to see the downside of massive systemic risks some are warning about when everyone is indexing. The latter part of 2016 and beginning of 2017 prove that even with all the indexed money, correlations can still drop quickly when macro factors evolve. After the 2016 election, cross-asset correlations that have existed for the past decade began to break down as pictured in the charts below.
Exhibit 1: Cross-asset correlations have fallen sharply
3. Assets Are Flowing From Discretionary to Systematic
You don’t have to look too deeply to see this massive trend in strategy allocations playing out. At Millennium, we’ve seen WorldQuant blow the doors off the barn with returns and inflows of capital. At Point72 (SAC) we’ve seen Cubist outpace the discretionary side of the firm by a wide margin with now over 40 systematic PMs. Balyasny has quickly shifted focus and is building a stable of systematic managers to effectively do something with their huge AUM growth. Other multi-manager platforms like Schonfeld, Paloma, AHL, Engineer’s Gate and GSA have added significant assets. Paul Tudor Jones is attempting to remake his firm by hiring a bunch of systematic managers, and others are following suit. And let’s not even get started with the continued dominance of firms like Renaissance, AQR and Two Sigma, where you probably can’t even give them your money if you tried.
I would say that the nerds are the new kings of Wall Street (Midtown), but frankly they (myself included) would cringe at that statement given their propensity to run in very different circles than the rest of the money manager crowd. This group is mostly made up of unassuming nerdy PhD types that you would probably take for accountants on the subway. They have serious mathematical and scientific training and have usually honed their craft on other data sets before coming to the financial world.
The fact of the matter is that there’s simply more efficacy to what these managers are doing than the vast majority of the discretionary trading world, and they’ve (mostly) put up the numbers to prove it. And I’m not just talking about returns, these groups are producing real alpha. Their strategies are meticulously backtested in and out of sample before going live, and are scaled up over time. Many discretionary managers launch a book with $500M in play from day one, I can count on one hand the number of systematic funds that have done that in the past 5 years.
And while some systematic funds don’t perform well, you’ll be hard pressed to find any massive blow ups akin to what’s regularly seen on the discretionary side. Pension funds can certainly deal with paying 2 and 20 if they have more confidence that their returns from year 1 through 3 aren’t going to all disappear in year
The flow of capital from discretionary to systematic strategies is going to continue, as it should. That will have its own repercussions, which we’re already starting to see.
4. Quants Dig For New Alpha
A 2012 tell-all book from a former Goldman Sachs trader revealed how the Great Vampire Squid often endearingly referred to their unsophisticated clients at “muppets.” While they rightfully got skewered for that comparison, they were certainly onto something when their trading desks would remark internally that they were basically taking candy from babies.
However, many of the muppets are gone now and that’s left far less alpha in the market to capture. Relative value and statistical arbitrage strategies are about capturing asset mispricings associated with the irrational behavioral aspects of fear and greed. This isn’t going to change any time soon, the muppets aren’t coming back, they’ve wised up. Less alpha overall will lead to a drop in the number of hedge funds and the amount of hedge fund assets that can generate enough alpha to command high fees.
It truly is amazing to watch a data set go from being an alpha to a beta over time. I’ve seen the sell side analyst estimates data set owned by Thomson Reuters IBES travel this path over the past 15 years. Yes, there will always be alpha available to be arbitraged which is associated with the irrational behavior of humans in markets, but most alpha generated by systematic traders is associated with an informational advantage.
About five years ago many of the classic stat-arb strategies stopped working due to an influx of competitors. There simply wasn’t enough alpha to go around. This precipitated the smartest firms to search for new data sets with predictive power, or reflexivity. Fast-forward a few years and an all out arms race is now under way.
I love to use the example of the company that is selling data captured from new car insurance registrations. They get this data daily, and it’s incredibly accurate at calling new car sales. So instead of waiting until the end of the quarter to find out how many vehicles GM sold, you can basically get a running count of growth on a daily basis. Obviously that’s going to give you an advantage in trading those auto names, that is until everyone else is using that data. At that point, the data set goes from providing alpha you can capture, to a data set that you must be looking at in order to avoid an informational disadvantage. In a sense, it becomes beta.
So the arms race is in full swing, and there is now a serious lack of qualified talent to analyze all of these different data sets and incorporate them into the existing multi-factor models. While the quantitative research process into the efficacy of a data set hasn’t changed much, firms are struggling to build a process around the testing pipeline. The most efficient firms like WorldQuant have been able to take advantage of that competency to move quickly and decisively to incorporate new alphas.
This brings me to my last point about the systematic testing process. In the next section of this article, I’m going to heavily malign the discretionary buy side for being fairly clueless about how to undertake this entire process. The truth is, even most (but not all) systematic quants suffer from a severe lack of creativity and original thought when it comes to generating hypotheses around how to take advantage of a given data set. From our experience working with discretionary firms at Estimize, they are two steps even further behind the quants as it relates to incorporating new data sets.
Let’s just go back to the car sales example for a second. Would you know exactly how to take advantage of that data to run an event study and generate alpha? Probably not. You’d likely want to talk with someone who’s been trading autos for 10+ years to get their take on what they think moves auto stocks and how having a good projection of sales would impact those names. A good quantitative research process requires an ex-ante hypothesis for some level of causation and not just correlation. We need to know roughly why something works, not just that it works, or else we won’t know why it stops working, and as history has proven, everything stops working at some point.
Being able to hand over an easily testable clean data set, and a bunch of original thoughts about how to generate alpha is imperative for data firms to succeed at this process.
5. Quantamental, Systamental, Factor Aware…Call It What You Want
The rise of the systematic quants and their use of these new data sets also had an impact on the poor returns of the discretionary world over recent years. First, the HFT guys killed the day traders making it impossible to pick up pennies. Next, the stat-arb guys crushed the swing traders playing in the couple of hours to one week timeframe. Were they the primary factor of poor discretionary returns? Probably not, but significant none of the less.
A few years ago the first big discretionary firms started making attempts to hire data scientists and acquire new data sources. They’ve mostly failed to integrate any of this into an actual investment process. Then about 6-9 months ago another chunk of the more forward thinking discretionary firms gave in to the realization that they needed to make big changes. It’s not as if discretionary PMs weren’t using data driven statistical approaches to gain an edge, or that none of them had quants on the desk to help, they were just very few and far between.
You may have seen Paul Tudor Jones almost publicly berating his organization in a strange showing of frustration from such a legendary investor. Steve Cohen has been very public about his attempt to shift Point72 in the data driven direction, even commenting that it’s incredibly hard to find good talent these days (we’ll get to this in a minute). The guys who have been successful in this game historically see the writing on the wall. Hell, even the first episode of season two for the show Billions features main character Bobby “Axe” Axelrod giving his team the condensed 3 minute version of this piece, albeit in a much louder tone. So whomever the producers of that show are talking to, this whole thing has seeped into the mainstream buy-side consciousness now.
The shift that needs to happen is similar to the way players were drafted in Michael Lewis’ book, “Moneyball”. Consider how hard the scouts fought against being replaced by algorithms that were far more accurate than they were, and even in the face of all this evidence, refusing to change. Then consider how much money was on the line in baseball, and the astronomically larger amount on the line in our world. You would think that would precipitate a much quicker shift, but in fact, it will only mean a slower one due to the fear of change when dealing with so much money.
As quants, we are taught how to go through the research process to validate the efficacy of a data set or tool. Everything is derived from this process, and there isn’t too much leeway, it is designed as good science. Yes, as mentioned above, you still need a level of creativity in order to do good research. However, discretionary managers don’t even have the framework for understanding how to do that research, or incorporate new things into their decision making process. This is the largest hurdle to making the shift, and I believe less than 20% of managers will clear it.
This shift isn’t just about using new data sets, like Estimize, or the car sales example, it’s about fundamentally buying into the notion that PMs need to be making investment decisions based on putting the odds in their favor by looking at statistics, and not just being gunslingers or bottoms up value guys. That’s an affront to their entire way of doing things, just as it was for the baseball scouts.
6. Algorithms + Human Experience = Optimal Trading
A passage from Michael Lewis’ latest book, “The Undoing Project,” speaks so directly to the issue discretionary firms face today. Lewis writes about a specific behavioral experiment performed on a set of first year residents and accomplished oncologists. In the experiment, the scientists asked the accomplished doctors to tell them how they make a decision regarding whether a patient has cancer from looking at an x-ray. The doctors all tended to give the scientists a 10 point checklist with a 1-10 rating for each of the 10 points, add up the points and you can accurately determine whether it’s cancer or benign. The scientists proceed to give a set of x-rays (the outcomes of which are known only to them) to the doctors and the residents, asking them to determine whether each is cancer or not. They also give the doctor’s checklist to the residents to use.
I think you can guess what happens next. The oncologists who supplied the rubric in the first place show almost zero ability above random to accurately determine whether the x-ray was cancer or not. They didn’t follow their own rubric, suffered from an astounding amount of representative heuristic, and failed to do their job well. Meanwhile, the first year residents were able to score far higher accuracy rates on average and therefore would have been able to help their patients. They were simply acting as the human measurement component of an algorithm.
Similarly, most discretionary PMs would likely supply a rubric for how they make decisions, but when it comes down to it, they don’t actually adhere to it. No set of new data or analytical tools thrown into the “mosaic of information” that the PM is supposed to be paying attention to will matter unless they are disciplined enough to remove their ego from the equation and reduce themselves to being a human algorithm.
There’s an inevitable question that arises from the above, what’s the point of the human PM if we’re going to ask humans to basically be algorithms? Why not just run a fully systematic strategy and remove the human all together after the quantitative research process is complete? Could a first year analyst and some good portfolio construction software more faithfully execute the signals than a PM with 20 years of experience? Science would seem to say yes. That said, there’s obviously a more optimal scenario where that 20 years of experience alongside the discipline to execute the rubric faithfully results in better outcomes due to the ability to see regime changes in the market, something quantitative strategies built on linear analysis have a hard time doing.
It’s my belief that good quantamental / systamental / factor-aware PMs can crush the systematic quants if they are disciplined. Systematic strategies are designed to make small bets across a lot of names using half a dozen or more different signals that each have a weighting in the stock selection and exposure model. A lot of them hit for singles, consistently. But that also means that when a really fat pitch comes down the plate based on all the data, they can’t swing for the fences. This is the advantage of discretionary managers. With the right discipline, they can take a big cut with a 7% position in their book when all the data lines up, and reap the rewards of the hard work.
While it’s been a tough run of it recently, there are reasons to believe this is a great time to enter the market with a solid quantitative approach to discretionary trading. The chart below shows that while there may be many secular headwinds for the discretionary investing world, the cyclical nature of this industry is extremely strong, and we’re certainly at the deepest part of the trough regarding performance, with only one direction to go.
7. There’s Plenty of Talent, You’re Just Hiring the Wrong People
The last part of this puzzle is obviously the people. And here’s the sad truth: the way that discretionary hedge funds have staffed themselves historically is almost criminal (there were actually some real criminals in there too!).
Picture the normal funnel to becoming a PM running a $500M long/short equity book. You grew up in a wealthy family in a wealthy town, usually in the New York metropolitan area, parts of Silicon Valley, Chicago or Michigan. You went to Harvard, Yale or Princeton. You took an IB analyst position at Goldman or another bulge bracket. You spent a few years there learning how to build a financial model before a hedge fund picked you up for an analyst spot. You made friends with your PM, who if you were lucky did well, and 5 years later when the firm had more capital than it knew what to do with, your PM told the firm to give you $200M to play with.
At no point in this process did you ever have to exhibit a lick of skill for the job that you’ve just been given. Yes, you are probably a very smart individual, and you worked hard, but we all know that smart does not equal good in the investment world. Every step along the way you were selected not for the trait which would make you the best qualified to do that job, you were selected because you jumped through the hoops which lead to the correct selection bias. The sad truth is that hedge funds are run by white dudes who grew up in Greenwich, and they like (and trust) working with white dudes who grew up in Greenwich and look like them.
And look, this isn’t some idealistic push for equality bullshit comment, it’s about results. If you are hiring these people exclusively, you are not selecting for skill and you will not be able to make the shift to a more data driven quantitative approach, I guarantee it. If I were starting a fund from scratch, I’d rather have a more racially, socioeconomically diverse group of kids from schools other than the Ivy’s than those from Yale who studied political science.
And don’t get me started on the lack of women running money. Every single study ever done says that they are more successful than men due to a range of behavioral and psychological factors. Yet firms tend to overlook women for PM positions due to their inability to play the game that gets them the capital allocation. And of course, we come back to the fact that the entire industry is designed to hire for people that look like the people who are currently in charge.
Firms need to start incorporating measurement of variables pre hiring that actually correlate to success as a PM. They need to start selecting for skill, not just smarts. Our Forcerank platform is beginning to be used for this purpose, and I expect others will pop up over time. I also expect some kind of psychometric testing firm to be created soon which has done the research to identify certain skills and traits that correspond to success in different strategies. You don’t want the same kind of people running momentum models as the ones running deep value.
There isn’t a lack of talent, you just need to look in the right places and be willing to elevate people who might not look, talk, or act like you.
8. Building the Right Team
The other major personnel issue we’re seeing firms grapple with is the question of how to structure their teams to incorporate the quantitative research and data science capability. Some approaches have been successful, and others have failed.
Each firm, whether quant or discretionary, is going to need a centralized infrastructure that is capable of imbibing a new data set and making it available across the firm. Many systematic multi-manager funds, and large centralized managers are already setting up data teams to search for, ingest, clean, and quickly analyze new data sets to test for alpha in their multi-factor models. The heads of these teams are getting paid big dollars, upwards of $2M a year to run this process that feeds the heart of the machine – and there aren’t many good ones out there. The imbalance of supply and demand for this position is causing some funds to make poor hiring decisions in order to simply get someone in the door. The role itself is incredibly multidisciplinary in nature and requires a strong understanding of the quantitative research process, a decent technical background, the ability to travel across the globe to conferences meeting with hundreds of potential vendors, sniffing out what’s real from what’s bullshit, determining what startups will be around tomorrow and which won’t, and then haggling over price. Please tell me which previous role prepares you for all of that?
The firms that don’t hire well here are going to fall behind and see their returns suffer as data sets more quickly than ever move from being alphas to betas as they get arbed. This doesn’t happen overnight, it takes years for alpha to get arbitraged from a data set, but many won’t have as much capacity as those previously, along with a larger stable of systematic managers, things will speed up.
The centralized infrastructure and data acquisition team is going to also house engineers, a product manager, and optimally a quant who can do basic descriptive work on a data set to determine whether it’s clean and reliable enough to have PMs use.
And that’s where the centralized team should end.
Each PM or “pod” should then have a quant, an engineer or two, and a data analyst placed on their desk directly. Here’s why. Each PM is going to be trading different names, and have a need to access different sets of information. Fighting over centralized quantitative research capacity with other pods is a disaster. And then receiving some kind of report that doesn’t fit into your actual process is useless. Each PM is going to have a different checklist or rubric with different signals. And the key is the data analyst, they need to have a deep understanding of the industries the PM is trading so that they can work in coordination with the PM and the quant to build a process that can be effectively utilized. I’ve seen people in this role who also have some coding experience so that they can rapidly prototype stuff for the quant before the centralized team goes out and does the job in a production-ready way. The quant, of course, will be testing different data sets for efficacy, and handing them over to the engineers to build factor models.
A quantitative approach and a commitment to data science by firms is not a thing you do in some other room. The only way this is going to work is if you build cross functional teams on the PM’s desk and support them with a data and infrastructure team at the top.
How Far Down the Rabbit Hole?
So if you’re a PM, do you need to take that data science class at night? Yes, but not for the reason you think. PMs aren’t going to be writing python code and working in R to do quantitative research, that’s not their job. But in order to effectively communicate and run their teams they are going to have to understand all the pieces to the process. And most of all, if they aren’t educated as to how all of this works, how are they ever going to trust the data and signals coming out of the process when the time comes to make buy and sell decisions?
On June 20th the L2Q conference hosted by Estimize is going to give discretionary PMs, analysts, and traders a one day overview of the different pieces they need to get up to speed on in order to effectively build and run their teams. The goal of the conference is not to have everyone walking away knowing everything, it’s meant as a jumping off point, to give a sense of perspective for where managers need to go next, and we’ll have the vendors there that can help them take the next steps to getting educated. We’ll also have a number of heavily vetted data vendors which can fit into this process and add alpha generating signals, including our own Estimize and Forcerank data sets.
Hope to see you there!
And if you are interested in discovering more alpha using the Estimize data set, please contact us today!
Article by Estimize