Speaker 1: Welcome to Inside Active, a podcast about active managers that
Speaker 1: goes beyond sound bites and headlines and looks deeper into
Speaker 1: their processes, challenges, and philosophies and security selection. I'm David
Speaker 1: cone I, lead mutual fund and active Research at Bloomberg Intelligence.
Speaker 1: Selecting active managers has been one of the biggest challenges
Speaker 1: in investing. While performance, risk, attribution, and other portfolio metrics
Speaker 1: can tell us how fun performed, they don't necessarily tell
Speaker 1: us whether that performance was the result of repeatable skill
Speaker 1: or simply favorable market conditions. For institutional investors, consultants, and
Speaker 1: financial advisors, separating genuine investment skill from luck remains one
Speaker 1: of the industry's most difficult and most important questions. Recent
Speaker 1: advances in portfolio analytics are challenging some long held assumption
Speaker 1: about how active managers should be evaluated. Rather than focusing
Speaker 1: primarily on investment outcomes, a growing body of research suggests
Speaker 1: investors may gain deeper insights by examining the decision makers,
Speaker 1: what they buy, what they sell, how they seize positions,
Speaker 1: and how consistently those decisions add value over time. So
Speaker 1: today I wanted to explore how investors should think about
Speaker 1: measuring manager's skill, why traditional performance metrics may not tell
Speaker 1: the full story, and whether a more decision based approach
Speaker 1: could improve active manager selection. So join me to discuss
Speaker 1: that is Michael Ervolini, author of Skill Versus Luck taking
Speaker 1: the guests out of Equity fund selection. Mike, thanks for
Speaker 1: joining me today.
Speaker 2: It's a pleasure, David, and I appreciate the opportunity to
Speaker 2: speak with you and your audience great.
Speaker 1: So what problem were you trying to solve when you
Speaker 1: wrote this book Skill Versus Luck?
Speaker 2: Fundamentally, it was helping the industry overcome conventional thinking. It's
Speaker 2: a challenge in every industry trying to move forward rather
Speaker 2: than holding fast to the current activities and beliefs we have.
Speaker 2: And as you mentioned in your intro, there's quite a
Speaker 2: few activities going on right now where people are using
Speaker 2: decision based analytics to give better insights into manager skill.
Speaker 2: That area hasn't received as much traction as it needs,
Speaker 2: and so in writing the book, I was hoping to
Speaker 2: stimulate an industry wide conversation about why we need to
Speaker 2: go beyond outcome based analytics and look at decision based
Speaker 2: analytics in combination so that people can get a deeper
Speaker 2: and richer understanding of what skills are driving the fund results,
Speaker 2: what skills are persistent, and all of that can allow
Speaker 2: investors to be more confident in selecting managers that have
Speaker 2: a greater likelihood of outperforming in the future. And really
Speaker 2: that's what cation decisions are all about, is who you
Speaker 2: want to give your money to in order to get
Speaker 2: better returns going forward.
Speaker 1: So you're the title of the book kind of suggests
Speaker 1: we consistently are constantly confused luck with skill. Why do
Speaker 1: you think intelligent people make that mistake so often?
Speaker 2: Well, two points on that one is I really believe
Speaker 2: that skill and luck are just opposite ends of a continuum,
Speaker 2: and so when a fund manager has more skill, they're
Speaker 2: less dependent upon luck for their results. So that's part
Speaker 2: one is skill and luck are involved in every decision.
Speaker 2: You just want managers that are more skilled. But a
Speaker 2: more specific answer to your question is, when you're making
Speaker 2: decisions using conventional analytics, they bring you to the threshold
Speaker 2: of understanding skill, but then you need to make a jump.
Speaker 2: You need to overinterpret the results or see in them
Speaker 2: something that you want to see. In other words, you
Speaker 2: have to guess a little bit. And I think because
Speaker 2: of that inadequacy of conventional analytics at addressing skill it
Speaker 2: makes it hard to figure out which managers are skilled
Speaker 2: and which aren't, and that has been plaguing the industry
Speaker 2: for a while. It continues to do so. But I
Speaker 2: have worked with institutional investors in particular who've been using
Speaker 2: decision based analytics, and their ability to sort out superior
Speaker 2: managers has improved as a result of this. So we
Speaker 2: can get past this. But most of the confusion has
Speaker 2: to do with working with inferior analytics as opposed to
Speaker 2: people not trying their best.
Speaker 1: So do you think we're kind of wired to judge
Speaker 1: outcomes rather than decisions.
Speaker 2: Yeah. One of the confusions that we have is how
Speaker 2: we make decisions. And what is not commonly understood is
Speaker 2: that more than ninety five percent the decisions we make
Speaker 2: every day are made completely automatically by our unconscious and
Speaker 2: so a lot of what's happening, whether it's a buying
Speaker 2: a stock, picking a manager, or ordering something off the menu,
Speaker 2: these automatic decisions generally work. But they only work really
Speaker 2: well where there's been great feedback about how good the
Speaker 2: decision is and how good the outcome has been. And
Speaker 2: so in that automatic decision environment. When you're using outcome
Speaker 2: based analytics, you see in them what you want, and
Speaker 2: so I think when asset owners or allocators are shown
Speaker 2: decision based results against outcome based results, they can take
Speaker 2: the best of both. But lacking any kind of decision
Speaker 2: based analytics and using only outcome based analytics, our natural
Speaker 2: instinct is to see in those results something that seems
Speaker 2: reason and often what we see is completely wrong.
Speaker 1: Okay, and you know you redefine skill as the combined
Speaker 1: effect of expert judgment and investment processes. Why did you
Speaker 1: feel the industry needed a new definition?
Speaker 2: Well, I think partly is because we had been looking
Speaker 2: at skill the wrong way. We've been looking at it
Speaker 2: as outcomes, and when you look at that, it's hard
Speaker 2: to figure out what's in there. So if we're going
Speaker 2: to look at decisions, then we want to focus on
Speaker 2: what is it the manager brings to the fund and
Speaker 2: the manager brings to qualities, their expert judgment and their
Speaker 2: investment process, and that's what they should be judged on.
Speaker 2: And fortunately, decision based analytics can allow you to rigorously
Speaker 2: evaluate and quantify both of those elements judgment and process,
Speaker 2: and when you understand them well you can then get
Speaker 2: a sense of how consistently good the men is making buying, selling,
Speaker 2: sizing decisions, and using that, I think what we've seen
Speaker 2: in the industry is that people then have more confidence
Speaker 2: that the manager they're working with, well not perfect, has
Speaker 2: a better than average chance about performing going forward.
Speaker 1: So then how do you actually separate you know, a
Speaker 1: good decision from a lucky outcome, Like, how do you
Speaker 1: go in and do that?
Speaker 2: Well, there's always going to be some luck. Okay, that's
Speaker 2: just we'll call that the market.
Speaker 1: You know, if you were.
Speaker 2: Lucky enough to buy the Magnificent seven some years ago
Speaker 2: and overweight them extensively, part of that was a good decision,
Speaker 2: and part of that is they've just done remarkably well.
Speaker 2: So there's a little bit of luck, or if your
Speaker 2: style is in favor and so forth. But given that
Speaker 2: we can look at individual decisions, let's say a buying decision.
Speaker 2: What we can do is over a period of time,
Speaker 2: we use ten years just for easy number. We look
Speaker 2: at every position that was purchased over that ten year period,
Speaker 2: and then we evaluate, independently from how the manager sized
Speaker 2: a position or when they sold it, on average, how
Speaker 2: did those positions do in aggregate that they generate excess
Speaker 2: return or not on their own. And so we can
Speaker 2: do that for sizing, we can do that for adding
Speaker 2: on the way up, adding on the way down. We
Speaker 2: can look at decisions broadly or very very narrowly, and
Speaker 2: the idea is to isolate individual types of decisions and
Speaker 2: evaluate if they, on average are helping to generate more
Speaker 2: excess return or in fact are taking away excess return,
Speaker 2: which is obviously a negative skill.
Speaker 1: Okay, and you know, one of the most interesting concepts
Speaker 1: I think in the book is the counterfactual portfolio. Can
Speaker 1: you kind of explain that idea and plan English.
Speaker 2: Sure, a counterfactual portfolio is simply a copy of the
Speaker 2: actual portfolio, but then one decision type is adjusted to
Speaker 2: understand its impact on the portfolio. So a very straightforward
Speaker 2: example is this, there's a fund where the manager periodically
Speaker 2: purchases stocks that are outside the benchmark of the fund,
Speaker 2: and presumably these out of benchmark purchases are intended to
Speaker 2: generate excess return or alpha. Well, one way to evaluate
Speaker 2: that is to create a copy of the fund and
Speaker 2: then go through it every day, and whenever the manager
Speaker 2: buys a position that's outside the benchmark. Just simply eliminate
Speaker 2: that from the copy or the counterfactual. And so when
Speaker 2: you're done doing that exercise, the counterfactual portfolio on every
Speaker 2: single day owns exactly what's in the actual portfolio, except
Speaker 2: it doesn't own any out of benchmark positions. And now
Speaker 2: with that counterfactual portfolio, you generate its returns and its
Speaker 2: excess return. You can do information ratios, whatever metrics you want,
Speaker 2: and compare those to the actual. Now, if we're looking
Speaker 2: at relative return, if the actual portfolio has a higher
Speaker 2: relative return than the counterfactual, that tells us owning those
Speaker 2: out of benchmark positions really helped it generated more excess return. Conversely,
Speaker 2: if the actual portfolio underperforms the counterfactual, it tells us
Speaker 2: that owning those out of benchmark positions actually hurt the results.
Speaker 2: So that's the idea of a counterfactual. You take one
Speaker 2: decision type at a time, you either eliminate it or
Speaker 2: alter it, and then compare it back to the actual
Speaker 2: and that way you're able to see whether specific decisions
Speaker 2: are additive or detracting from overall success in the fund.
Speaker 1: Now it makes sense, it makes a lot of sense.
Speaker 1: It's really interesting. You know. I also couldn't help but
Speaker 1: think that, you know, what you're looking at applies even
Speaker 1: beyond investing. Do you think your framework could be used
Speaker 1: to evaluate, you know, even CEOs or even sports coaches.
Speaker 2: I'm not sure I've thought about that. It's possible. It
Speaker 2: definitely was meant to be used across any publicly treated
Speaker 2: asset type, that's for sure, so option swaps, fixed income.
Speaker 2: But beyond that, we've never tackled anything like that, So
Speaker 2: I'm not sure it's possible, But I'm not sure.
Speaker 1: It's definitely interesting concept. And you know, obviously the biggest,
Speaker 1: you know, one of the biggest criticisms you've had is
Speaker 1: allocators spend way too much time evaluating performance, not enough
Speaker 1: time evaluating decisions. If you were sitting on an investment
Speaker 1: community today, what would you stop measuring and what would
Speaker 1: you start measuring?
Speaker 2: Well, I'm not sure I would stop measuring anything, but
Speaker 2: I would add a whole lot of decision based analytics.
Speaker 2: I would want to know how effective the manager is
Speaker 2: at buying, selling, and sizing, and then I would want
Speaker 2: to look at them from multiple perspectives. I would want
Speaker 2: to look, let's say, ten years again, how good was
Speaker 2: buying over ten years, how good was it year by
Speaker 2: year to see how consistent it was, How good is
Speaker 2: the buying across sectors, to make sure that we're not
Speaker 2: just leaning on a few sectors, but the manager's good
Speaker 2: across the board. And I might want to look at
Speaker 2: it by factor. If there's a certain element in the
Speaker 2: strategy that the managers expressing in their coversations or their
Speaker 2: marketing material about we look for stocks with these characteristics,
Speaker 2: I would want to use those factors to evaluate the
Speaker 2: buying as well. As an example, if the manager says
Speaker 2: they'd like to buy high quality companies, well, we could
Speaker 2: use any number of factors to evaluate the quality of
Speaker 2: a company balance sheet, debt, or whatever we want to
Speaker 2: do and see if in fact, higher quality balance sheet
Speaker 2: companies show up as being the stronger buys in the portfolio.
Speaker 2: So I'd want to do a lot of work like that,
Speaker 2: And when you get to that place, you know that
Speaker 2: the manager is doing what they said they're going to do,
Speaker 2: They're doing it very well every year and over time,
Speaker 2: it's really adding value to the fund. That, in combination
Speaker 2: with conventional analytics, allows you to know that over the
Speaker 2: past few years the results have been great too. The
Speaker 2: amount of risk taking was not disproportional to the return,
Speaker 2: and they're well within their peer group, and all that
Speaker 2: kind of stuff. All these conventional analytics are important, they
Speaker 2: simply don't provide enough information about skill.
Speaker 1: Okay, you know you obviously spent decades looking at how
Speaker 1: professional investors make decisions. What do you think the biggest
Speaker 1: misconception people have about how decisions are actually made?
Speaker 2: Well, I mentioned one, which is this unconscious bit thinking.
Speaker 2: Recognizing that most of your decisions are unconscious really convinces
Speaker 2: me and a lot of other people that it's imperative
Speaker 2: that you have a strong process because that can arrest
Speaker 2: some of the automatic decision making and make sure that
Speaker 2: you stay in the area where you make your best decisions.
Speaker 2: Another misconception is emotions. We hear frequently, at least I
Speaker 2: have in my career an investor or money manager or
Speaker 2: other profession I will say, well, we take all the
Speaker 2: emotions out of our decision making. We do everything here
Speaker 2: by the numbers. Well, that sounds great, but it's actually impossible.
Speaker 2: What neuroscience tells us is it's literally impossible to make
Speaker 2: a rational choice without emotions. You can add up a
Speaker 2: column of numbers without emotions because there's no judgment involved.
Speaker 2: You're just adding numbers. But when you're trying to make
Speaker 2: a choice, what discount rates to use? What fund am
Speaker 2: I ultimately going to pick for my allocation? What research
Speaker 2: am I going to use to help me get to
Speaker 2: that place? Those choices, you get to difficult spots where
Speaker 2: it's not easy to differentiate one option from another, and
Speaker 2: a little emotional spark is what helps in the tie breakers.
Speaker 2: And so understanding that your emotions play a role in
Speaker 2: your decision making is another reason you want to have
Speaker 2: a good process because it can slow you down and
Speaker 2: allow you to arrest. Automatic decision making allow you to
Speaker 2: harness emotions in a positive way. And when you embrace
Speaker 2: those two concepts and use them to help build your process,
Speaker 2: that result is you get much better decision making.
Speaker 1: Do you think there's you know, a danger we've become
Speaker 1: obsessed with measuring everything, you know, in other words, can
Speaker 1: skill itself become over engineered.
Speaker 2: I think we can overmeasure things. Yeah, we can lose
Speaker 2: sight of what we're really after. And if that happens,
Speaker 2: it's because we didn't have a good plan going in.
Speaker 2: You know, we have to understand why we're measuring skill
Speaker 2: and what we want to come out of it. But
Speaker 2: I will caution this frequently, the fear of overmeasurement can
Speaker 2: be used as an argument to hang onto convention, to
Speaker 2: not take a step forward to state where we are
Speaker 2: more comfortable in presenting these analytics. To some fund managers,
Speaker 2: sometimes the pushback is, well, you know, I don't want
Speaker 2: you coming in and messing up what I'm doing now. Well,
Speaker 2: so I don't want to see this stuff now. I
Speaker 2: think there's a little fear in there about learning what
Speaker 2: you do well and what you don't. So there's a
Speaker 2: balance point. Yes, we can't go overboard and measure too
Speaker 2: much or follow analytics too far, because no analytic is perfect.
Speaker 2: But reasonable people can take on new analytics and learn
Speaker 2: to make better decisions. And that's what it's all about.
Speaker 2: Being proportional, being reasonable and understanding that the purpose here
Speaker 2: is to make better decisions, not just to measure things
Speaker 2: or to follow analytics one hundred percent.
Speaker 1: You know your approach depends on analyzing thousands of decisions.
Speaker 1: Could someone argue that investing is still too uncertain to
Speaker 1: isolate skill.
Speaker 2: I don't think so now, because I myself have been
Speaker 2: involved in analyzing four or five six hundred funds, and
Speaker 2: there's no question that skill can be measured. And there
Speaker 2: are other companies out there that have collectively looked at
Speaker 2: perhaps eight or nine thousand funds, and so skill can
Speaker 2: be measured, can be isolated, can be quantified. It's done
Speaker 2: differently by different analytics shops, but there's no question that
Speaker 2: can be isolated. It simply can't be done using conventional analytics.
Speaker 1: Okay, you know Warren Buffett has said that you know,
Speaker 1: temperament matters more than IQ. Does your framework capture qualities
Speaker 1: like patients, discipline, conviction or is it only you know,
Speaker 1: kind of measurable decisions.
Speaker 2: No, In fact, it can help you determine some of
Speaker 2: those things. One example, let's say patients. Okay, we could
Speaker 2: look at when a manager is buys stocks and they
Speaker 2: take off relatively early after purchase. Does the manager hang
Speaker 2: on to them or does the manager sell them far
Speaker 2: too quickly and give up a lot of alpha. Now
Speaker 2: there's a patience issue. So by looking at new winners
Speaker 2: and evaluating whether they're being sold too quickly or not,
Speaker 2: that's a way of getting into patients conviction. We can
Speaker 2: look at when positions are purchased and maybe in the
Speaker 2: first few months they take a dip in price. Sometimes
Speaker 2: an early dip in price is an indication that the
Speaker 2: manager bought a bad stock. Other times it's just that
Speaker 2: the market has had a bad reaction to that company,
Speaker 2: but the fundamentals really haven't changed, and more often than not,
Speaker 2: those stocks are going to bounce back. And by evaluating
Speaker 2: that behavior, we can see whether the manager has sufficient
Speaker 2: conviction in stocks that wiggle a little bit right after
Speaker 2: they're purchased, but ultimately do really, really well. So we
Speaker 2: can look for behavioral kinds of aspects in the manager
Speaker 2: looking at the decisions they make.
Speaker 1: Okay, you know another thing that you know kind of
Speaker 1: really jumped out was that you know you've concluded that
Speaker 1: buying is usually the biggest drive of alpha. Why do
Speaker 1: you think buying is more important than selling. Well, if
Speaker 1: you're not.
Speaker 2: A good buyer, you're basically starting out in a hole,
Speaker 2: and it's hard to sell your way out of a
Speaker 2: hole and generate alpha. It can be done, but it's
Speaker 2: really hard. But if you start out buying good stocks,
Speaker 2: you can have a few mistakes on sizing and selling
Speaker 2: and still do pretty well, so it's just a question
Speaker 2: of starting off on the strongest platform possible. And that's
Speaker 2: with good buying. In general speaking, most successful managers are
Speaker 2: really really good buyers. There's a few that are not,
Speaker 2: but and I can explain why if that's of interest,
Speaker 2: But generally speaking, they're good buyers.
Speaker 1: So that kind of brings me to the next part
Speaker 1: is you know the you argue that selling is probably
Speaker 1: that the industry's weakest skill. You know, and when I
Speaker 1: speak to managers, I always I don't want to know
Speaker 1: just what their buying philosophy is, but you know what
Speaker 1: makes them sell? And so what do you think a
Speaker 1: lot of professionals struggle so much? Was selling?
Speaker 2: It's the least understood. If you were to do a
Speaker 2: quick search on equity investing strategy, everything you.
Speaker 1: Found would be about buying.
Speaker 2: When it so that's the whole industry in academy itself,
Speaker 2: when they think about the strategic decisions inside of equity
Speaker 2: investing or any other asset class, it's generally they're looking
Speaker 2: at buying. Selling is often a discipline. Well, if buying
Speaker 2: is strategic and selling is a discipline, it sort of
Speaker 2: tells you that selling is sort of the orphan of
Speaker 2: thinking in asset management, and so it's not well understood.
Speaker 2: The second thing is what is a good sell. Often
Speaker 2: people believe that a good sell is after I sell
Speaker 2: the stock the price goes down. Well, that's not necessarily
Speaker 2: a good description. A better one is A good sell
Speaker 2: is when you sell a stock and after you sell it,
Speaker 2: its return is less than the fund's average. So, in
Speaker 2: other words, you want to hang onto stocks that can
Speaker 2: lift up the fund average return, and you want to
Speaker 2: sell stocks that will take down the fund's average return,
Speaker 2: meaning that if the market is going down, holding onto
Speaker 2: a stock that only goes down five percent when a
Speaker 2: lot of other stocks are going down ten percent, well
Speaker 2: you shouldn't sell that stock even though it's priced going down.
Speaker 2: So we get confused a little bit about what a
Speaker 2: good sell is. And again it's selling a stock before
Speaker 2: it drags down the average. That's the fundamental test. And
Speaker 2: then the fact that academia and other analytics really don't
Speaker 2: look at selling particularly well. Conventional analytics are really nonexistent
Speaker 2: in their ability to help you understand selling. The decision
Speaker 2: based analytics do a real nice job at looking at
Speaker 2: selling selling when stocks are up selling when stocks are down.
Speaker 2: You can look at selling all different manners and really
Speaker 2: understand it. But most managers do not have good analytics
Speaker 2: to help them understand their selling, and that's why they
Speaker 2: try their best, but they really can't improve it because
Speaker 2: they don't have the right feedback.
Speaker 1: Okay, and you know, if you had to pick one
Speaker 1: investment habit, you know you think every active manager should
Speaker 1: stop doing tomorrow. What do you think that would be?
Speaker 2: Reaching for conviction too quickly? Conviction is an important element.
Speaker 2: When you talk to managers, they talk about it. When
Speaker 2: consultants took the managers, they ask them about their conviction.
Speaker 2: Conviction is a well bandeed word in the industry and
Speaker 2: it's important.
Speaker 1: But what the.
Speaker 2: Research shows is coming to it slowly and allowing it
Speaker 2: to develop, particularly using your analysts, using the investment committee,
Speaker 2: using your research team. Allowing conviction to develop slowly allows
Speaker 2: the manager to be more confident that they're really dealing
Speaker 2: with well airned conviction and not just bluster. And telling
Speaker 2: the difference when you're excited is really really hard. So
Speaker 2: that again brings me back to why process is so important.
Speaker 2: Getting to a discipline process where you can let your
Speaker 2: unconscious do its best job. Let your emotions help you
Speaker 2: as much as they can, but let others in your
Speaker 2: organization offer questions, criticism, or support. All of that allows
Speaker 2: the manager to develop real fiction and make their best decisions.
Speaker 1: Okay, you know we're obviously entering an error where AI
Speaker 1: can analyze enormous amounts of information. Do you think AI
Speaker 1: makes skill more valuable or less valuable?
Speaker 2: Right now? There's no question it makes skill more valuable.
Speaker 2: There's a recent research report from Lauren Cohen out of
Speaker 2: Harvard just came out in the past seven or ten
Speaker 2: days where they use some large language models to mimic
Speaker 2: a number of mutual funds over an extended period of time,
Speaker 2: and what they found out is that these largers language
Speaker 2: models were really really good at anticipating something between seventy
Speaker 2: five and eighty percent of the stocks that the fund
Speaker 2: actually purchased over time. What they also found is that
Speaker 2: the stock the large language models didn't buy were the
Speaker 2: ones that contained most of the alpha. So there's something
Speaker 2: about human judgment at the edge of the marketplace that
Speaker 2: is pretty impressive today. It allows people to really do
Speaker 2: some spectacular stuff. Will that always be the case, I
Speaker 2: don't know, but my sense is markets move so fast,
Speaker 2: humans are always going to have a role to play.
Speaker 2: One other supporting piece of evidence I can offer is
Speaker 2: back in the Great Crash and eight nine, I remember
Speaker 2: being at a conference of chief investment officers and some
Speaker 2: of the risk managers, global risk managers, and what they
Speaker 2: were saying is, at that moment they abandoned their models
Speaker 2: because they weren't trained on such an extreme situation. So
Speaker 2: to the extent things move rapidly and we see extreme
Speaker 2: activities going on, I think humans are going to be
Speaker 2: a little bit smarter because they can see and pull
Speaker 2: things together a little faster today. Again, who knows what's
Speaker 2: going to happen years to come, But right now there's
Speaker 2: some evidence that AI can make humans do even better.
Speaker 2: It's not clear that they can supplant them completely.
Speaker 1: Okay, And kind of on that note or a little bit,
Speaker 1: you know, kind of going away from AI. But you know,
Speaker 1: if we talk about ten years from now, if you
Speaker 1: rewrote this book in ten years, what do you think
Speaker 1: would change.
Speaker 2: Well, my challenge, as I said earlier, is to overcome convention.
Speaker 2: So what would be great to be able to write
Speaker 2: is that overcoming convention was not as hard and didn't
Speaker 2: take as long as I originally thought that would be
Speaker 2: a great thing to be able to write ten years. No,
Speaker 2: that's my hope. I also think we'll see, without question,
Speaker 2: a more greater adoption of decision based analytics, and I
Speaker 2: think we will see a lot more AI being used
Speaker 2: in the industry, and hopefully we'll see the opportunity for
Speaker 2: active management to become stronger, and we may find a
Speaker 2: different balance point between active and passive investing. Right now,
Speaker 2: I think there's such a challenge in identifying a skilled
Speaker 2: manager that many investors are choosing to allocate more capital
Speaker 2: passively simply because they're afraid of picking the wrong manager.
Speaker 2: But if we can make skill as transparent as it
Speaker 2: needs to be and as it can be, then I
Speaker 2: think that offers the opportunity for a different balance point
Speaker 2: between active and passive, and that's another benefit we get
Speaker 2: by going down this road of improving our skilled metrics.
Speaker 1: Okay, I've got just one more question before I let
Speaker 1: you go. Every investor listening right now, remember just one
Speaker 1: idea from skill versus luck. What would you hope it
Speaker 1: would be that.
Speaker 2: There are skilled managers out there. There are plenty of them,
Speaker 2: the difficulty is finding them. So whether you're an institutional investor,
Speaker 2: a manager search consultant, or an individual, what it's incumbent
Speaker 2: upon you to do is to begin to demand better
Speaker 2: skill metrics from whomever you work with, whether it's an
Speaker 2: internal team, if you're a pension fund, or whether it's
Speaker 2: a manager search firm, if you're a large investor, or
Speaker 2: if you're an individual, whatever platform you're using, start demanding
Speaker 2: better skill metrics. They exist, they're relatively inexpensive, and they
Speaker 2: add tremendously to your ability to make better allocation decisions.
Speaker 2: And what we're all trying to do is meet some
Speaker 2: financial need in the future, whether it's retirement or an endowment,
Speaker 2: or making investments in the community or in healthcare or
Speaker 2: whatever it is. The better we do with our investments,
Speaker 2: the better off we all are. And asking and demanding
Speaker 2: for better skill analytics can allow you to make better
Speaker 2: investment decisions. And that's certainly what I would be pushing
Speaker 2: for right now, is we need to change the industry.
Speaker 2: And it starts by saying, hey, why don't we have
Speaker 2: some different skill metrics on this platform.
Speaker 1: That we're using. Well, this is great. I really learned
Speaker 1: a lot and enjoyed speaking with you. And so, Mike,
Speaker 1: thank you again for joining me today. Well, thank you
Speaker 1: very much.
Speaker 2: It's my pleasure.
Speaker 1: I also want to thank our listeners. If you like
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