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Assembling the "Investment Avengers" Using Skill-Set Analytics [3 of 3]

How do you push performance higher for an already successful team of investors?

Andrea Gentilini
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  • Skill-set analytics are – similarly to Environmental, Social and Governance analytics – an opportunity to use digitization – big data and analytics – to derive value.
  • Financial returns, the ultimate measure of success, is the wrong thing to look at when trying to improve them. Performance anxiety, anyone?
  • The recipe for improving returns by fine-tuning the way you make investment decisions is quite simple: Measure what you’re good at and what you’re not so good at; do more of the former, less of the latter (or work on it), and use the right data and analytics.
  • Working on perfecting investment skill has added a median 200 bps of incremental alpha over a rolling three-year period among Novus clients.

View Part 1 of this series, 3 Reasons Why Your Firm Might Be Making Bad IT Decisions

View Part 2 of this series, Saving the World (While Generating Returns) with ESG Data

I wrote recently about how some hedge fund managers are naturally gifted, like Superman, while others are, like Iron Man, heroes who built their powers via technology. But nobody can save the universe alone. It takes a super-team. Where would Superman be without the Justice League, or Iron Man without the Avengers?

A critical portion of that team are those who monitor superheroes and, more importantly, their tools. What would Iron-Man be without the attentive eye of Pepper Potts, providing actionable feedback on how to do better? And for the feedback to be effective, yes, you need big data and relevant analytics. Iron Man’s armor is ripped with sensors, providing feedback in real-time.  

Don’t Look at Returns in Isolation

If returns are your output, and if returns are what you’re looking to improve, you might conclude: "Well, I should analyze returns all the time, right?” Wrong.

I don’t know if you have ever needed physical therapy in your life but, as I approach my late 40s, I tend to appreciate this allied health profession more and more. Crippled by some muscular tension, I decided to give physical therapy a chance. I entered the office and, animated by my usual self-confidence, I told the therapist before he even asks me a question, that I have pain in my lateral quadriceps and that I have been “working hard at stretching precisely where it aches”. To which he cooly replied, “Well, stretching the area where it hurts is the mother of bad ideas.” That was a stark (no pun intended, Mr. Stark, a.k.a. Iron Man) reminder that advice given is advice best taken.  

Returns in asset management are the equivalent of aching muscles; improving is the ultimate objective, but not the focus of your attention.  

The reason is that, in asset management, luck plays a big role in the outcome, which creates noise in the data. And to extract signals from noisy data, you need another ton of data. Monthly returns are useless because, to infer anything meaningful about skillset, you’d need 150 years of monthly returns, a time horizon that is about 6x the length of the average investment career out there.  

(If you’re eager to possess what’s needed to call yourself a legitimate quant, we recommend you follow Attilio Meucci. His books, impressive courses and teaching materials are an inspiration of rigor for those of you out there who like math and, more importantly, solid explanations that goes along with it.)

Then What?

So now we know what the problem is. What’s the solution?

Let’s borrow from the science of performance improvement. Once the realm of psychologists (or psycho-something-else) with more conjectures than data (Freud, anyone?), the realm of performance studies graduated to a true scientific discipline, with rigorous, well-designed double-blind randomized study and the inevitable success stories hitting the press.

Obligatory nod to Billy Beane and Moneyball here – available on Netflix at the time of this writing.

Few authors have extracted the general principles to ascend to stardom in a given discipline. Matthew Syed’s Bounce comes to mind, if you want to read about it, a book that flows very nicely while maintaining scientific rigor.

Here the Reader’s Digest condensed version: Energy, intelligence and dedication only get you halfway in the world’s ranking. That said, they’re of course needed to get up there. The bad news is that they don’t get you further; it’s sort of a necessary condition to ascend to the top. The worse news is that if you insist on them as your only levers (e.g., you practice harder and harder), you risk deteriorating your ranking by ‘cementifying’ bad habits through bad practice and working yourself to the bone.  

The good news is that, if you know what you’re good at and what you’re not so good at (among all the degrees of freedom you use in your craft), you can do more of the former and less of the latter, which may get you to the top. Yes, “may” and not “will” because we need to leave room for a bit of luck after all.  

So now the question is: “How can I find out what I am good at and not so good at?” That’s where Novus comes into play. Through analytics on a sizeable amount of data, Novus quantifies how good you are at the degrees of freedom which managers use to generate returns. These are

  1. Exposure management
  1. Risk allocation
  1. Idea selection
  1. Trading acumen
  1. Position sizing

The degrees of freedom mentioned above can be seen as intrinsic sources of alpha, i.e., levers you can pull by simply looking inwards at your investment process and data. Evidence shows that intrinsic sources of alpha offer the largest alpha improvement for a given investment process, versus an incremental investment into the traditional, extrinsic source of alpha, as depicted below.

A diagram of sources of alpha
Figure 1—Intrinsic vs. extrinsic sources of alpha

In light of the evidence, why is it that 92% of all investment team resources (time of employees and hard dollars spent) are devoted to push extrinsic source of alpha further? Could that be an opportunity?

If you’re interested in knowing what each one of them signifies, please take a dive into the Novus Framework. Now, armed with the insights, you can decide what to work on. Suppose the Novus framework highlighted a weakness (oops, I meant improvement opportunity) in position sizing. After a few analytical deep dives – also available on Novus – you can choose whether you’re too diversified, too aggressive, too slow or a variety of other behavioral causes.

Da Bottom Line?

During our close-to-a-decade-and-a-half history, we’ve dissected thousands of portfolios and worked with hundreds of investment professionals on extracting insights and devising programs to act on them.  

The most common improvement areas?

  • Wasting time modulating gross and net exposures,
  • “Diworsifying” portfolios by adding an excessive number of positions,
  • Deploying more exposure in areas with weaker skillset
  • …and 12 others.

(I can’t trace the source of the term “diworsifying” in the literature, but I am personally indebted to Stan Altshuller, one of the Novus founders, for introducing me to the concept.)

Simply not making mistakes gets you 80% of the way. And yes, clearly, everyone is unique, so not all the above applies to everyone the same way. Most of it depends on your investment philosophy, investment personality and asset class.  

That said, taking a completely inappropriate average across all realized alpha improvements across all managers who did something about their skillset, we found that acting on insights leads to 200 bps of incremental performance.  

Performance was measured as excess returns over the reference benchmark for the rolling three-year period following the end of the performance improvement program compared to the period prior, normalized for the level of average exposures.

The figures above match the impact of similar initiatives taken at multi-manager platforms which, by their very same construct, may possess reasonably large data set. Spoiler alert: In the upcoming episode of the Novus Successful Investor Podcast with Joe Peta, the author of Trading Bases, you’ll hear similar considerations on the dataset and analytics used to arrive at similar conclusions.  

Of course, skill-set analytics is – similarly to Environmental, Social and Governance analytics – just one opportunity to use digitization to derive value. There are others; these are just the low-hanging fruit. Novus can help you gain transparency, identify where your IT spend might be out-of-balance, manage the growth of your portfolio data and otherwise embrace digitization.

Maybe you haven’t gone full-throttle on machine learning and artificial intelligence adoption. You probably have by now, though, but in either case you should consider how solid your digital foundation is.

All superheroes have their weaknesses so, if you don’t know if technology might be yours, maybe that’s another article.

View Part 4 of this series, Are We Leaving $800 Billion on the Table?

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