Speaker 1: Welcome to trillions. I'm Joel Webber and I'm Eric bel Tunis. Eric.
Speaker 1: We've been doing this for a little bit now, but
Speaker 1: we haven't spent that much time talking about what's basically
Speaker 1: the hottest trend in ETFs, which is smart beta. Yeah,
Speaker 1: it's one of them. I think people forget that smart
Speaker 1: beta is of e t F assets, so it's a
Speaker 1: big chunk um. The other virtually is all what we
Speaker 1: call cheap beta, which is you know, the smp F
Speaker 1: t F and we called the plane vanilla side. But
Speaker 1: this is up from nothing ten years ago, so it
Speaker 1: is a it's growing faster on a percentage basis than
Speaker 1: e t F as a whole, and it's a really
Speaker 1: interesting dynamic area. This is where a lot of the
Speaker 1: innovation and new products are being launched. So how are
Speaker 1: we supposed to think about this thing called smart beta. Well,
Speaker 1: it's very it can be confusing a lot of times.
Speaker 1: People think it sounds like something you get at the
Speaker 1: g n C store. Yeah, I mean right, I know,
Speaker 1: you're like a way container little double chocolate smart beta.
Speaker 1: It's definitely jargon e um and it's got a buzzword
Speaker 1: type feel to it. But um, you know. Essentially, a
Speaker 1: lot of people will give you different definitions of it.
Speaker 1: To me, you know, the metaphor I use is the
Speaker 1: way people describe different religions, right they there's this thing
Speaker 1: that they say, it's like a bunch of people touching
Speaker 1: an elephant in a dark room, in the way different
Speaker 1: religions describing God. It's the same thing, but it's just
Speaker 1: being Uh, they're feeling a different part of it, but
Speaker 1: ultimately it's the same thing to me. Smarting Right now,
Speaker 1: what is that I'm in a dark room? You don't
Speaker 1: want to know. No, let's say you're touching the trunk.
Speaker 1: Somebody else is touching the side. My point is that
Speaker 1: smart beata kind of can mean different things to different people. Uh,
Speaker 1: it's controversial, and I think that a lot of people
Speaker 1: instead of thinking you need to like capture it and
Speaker 1: be done with understanding it in two seconds, I think
Speaker 1: you need to just accept that it's a little bit
Speaker 1: of a mystery to some people. There's a complexity to
Speaker 1: it and there's differing opinions. As long as you embrace
Speaker 1: that and sort of go into this conversation we're about
Speaker 1: to have knowing that you're gonna understand a little more.
Speaker 1: I think that's the way to go. You gotta lower
Speaker 1: the bar on this because you can't really just get
Speaker 1: it done in like two or three sentences. If I
Speaker 1: had to do it in one sentence, what would you
Speaker 1: how would you describe it? I would say smart Beta
Speaker 1: is trying to take Peter Lynch's brain and put it
Speaker 1: into our two D two's think. In other words, you're
Speaker 1: trying to take the best of active management, but putting
Speaker 1: it into a rules based index that an e t
Speaker 1: F track. So it's basically a conversion of active and
Speaker 1: trying to make it better and more efficient and cheaper
Speaker 1: and deliver it in a better structure. This week, Gun Trillion,
Speaker 1: the Mystery of smart Beta. Okay, Eric, we've got some guests.
Speaker 1: This is really important because I think every one of
Speaker 1: these guests, including you and me, we'll have a different
Speaker 1: perspective on smart Beta. So we're all sort of touching
Speaker 1: the elephant in your dark room, your creepy dark room
Speaker 1: filled with an elephant and us. Yeah, it's almost akin
Speaker 1: to when you go to a conference. So an ETF conferences,
Speaker 1: there's a panel, you tend to have people from different lenses.
Speaker 1: So we're trying to sort of capture that so that
Speaker 1: you can get all of the viewpoints and then you
Speaker 1: can sort of come up with your own sort of
Speaker 1: view on it. One of them is remote, so he's
Speaker 1: in his own dark room touching a different elephant. Near
Speaker 1: case are a columnst with Bloomberg gad fly high near Hello,
Speaker 1: he's in d C. Joining us in the booth is
Speaker 1: Tom Seraphagus, who is a colleague of yours at Bloomberg Intelligence,
Speaker 1: where he's an et F analyst. Also came from Oppenheimer
Speaker 1: before he was at Bloomberg uh and has some experience
Speaker 1: with smart beata et F. Sah, how are you. I'm
Speaker 1: doing great, Thanks for joining. So Tom, can you help
Speaker 1: start this discussion around what is smart beata. Let's definance
Speaker 1: so that you know, before people get in the deep
Speaker 1: to the pool with us, we have an understanding of
Speaker 1: what we're talking about. Yeah. Sure. And so before I
Speaker 1: joined here, I was at on the other side of
Speaker 1: the table, so on the issue side. And you have
Speaker 1: to remember that a lot of these active shops have
Speaker 1: a tremendous amount of resources, right They've got portfolio managers
Speaker 1: and analysts. So I think in a very brief way,
Speaker 1: what they were trying to do is take the mind
Speaker 1: of the portfolio manager Peter Rearle and sure someone else saying, Hey,
Speaker 1: when you're picking stocks, what are things you're looking at?
Speaker 1: How are you running your screens? What are you looking at?
Speaker 1: Let's take your approach, put it, write it down, put
Speaker 1: it in an index, and launch an e t F
Speaker 1: over it. So, um, you know, the the definition of
Speaker 1: smart data. It's not so clear, right, it's a very
Speaker 1: gray area. But it's really trying to take a lot
Speaker 1: of these approaches, democratize it, put in e TF and
Speaker 1: make it available to the masses as a product, as
Speaker 1: a problem, buy off the shelf, right, how do you
Speaker 1: think about it? I think those are both good. Both
Speaker 1: Eric and Tom's definitions I think are both good. I'll
Speaker 1: just try to simplify it even further because I think
Speaker 1: it's really important to convey to to investors that this
Speaker 1: is actually very simple in concept. All this is is
Speaker 1: traditional active management. All that all the strategies that they've
Speaker 1: always loved and have been used for decades by every
Speaker 1: active manager ever. Value you know, buying cheaper companies, buying
Speaker 1: you know, high growth companies, buying smaller companies, whatever the
Speaker 1: strategy that you like is effectively giving it to a
Speaker 1: computer and letting a computer run it. That's really all
Speaker 1: it is. And the reason that's important to convey is
Speaker 1: the the the act of taking it away from the
Speaker 1: from the human and giving it to the computer gives
Speaker 1: the investor several advantages that are really important. One, it
Speaker 1: makes it a lot cheaper because you can you know,
Speaker 1: the computer doesn't cost as much as the human. That's
Speaker 1: the first thing that's you know, that's a good thing.
Speaker 1: The second thing is they're more transparent because you've turned
Speaker 1: it over to a computer. You've had to write rules
Speaker 1: about how this thing will look. And investors can read
Speaker 1: these rules and they can see exactly what they're buying,
Speaker 1: So that's useful to And the other thing is that
Speaker 1: it allows them. It allows us to to um to
Speaker 1: fine tune how we think about these various styles. So
Speaker 1: for example, me and you, we might both like value, right,
Speaker 1: but you might like it more than me, So I
Speaker 1: can write a computer program for you that gives you
Speaker 1: more value and less value for me, and so on.
Speaker 1: And these are things that are more difficult to do
Speaker 1: with human beings. So that's all it is. That sounds simple.
Speaker 1: Let me add in here to dovetail to my original
Speaker 1: definition and just go and how the term came about,
Speaker 1: because this probably will help. A consulting firm named Towers
Speaker 1: Watson is who coined the phrase smart beta, and this
Speaker 1: is exactly how they defined it in their literature in
Speaker 1: your two thousand. Smart beta is simply about trying to
Speaker 1: identify good investment ideas that can be structured better. So
Speaker 1: think about it. What do people not like about an
Speaker 1: active mutual fund? Their high cost uh they have the
Speaker 1: manager could be emotional and make decisions based on the
Speaker 1: current trends, and they have distribute capital gains regardless of
Speaker 1: whether you if even if you stayed invested, you've got
Speaker 1: tax bill. Now, if you take that active mutual fund
Speaker 1: strategy and you put it into a rules based index,
Speaker 1: you essentially eliminate the emotion, right because it's gonna rebalance
Speaker 1: at least us said it's a robot. You're gonna lower
Speaker 1: the feet because there's less overhead. And the ETF structure
Speaker 1: so usually like a third as cheap as or a
Speaker 1: third as expensive as an active mutual fund, and they're
Speaker 1: et f s are very tax efficients. You can have
Speaker 1: some decent turnover inside the fund, and yet no capital
Speaker 1: gains get distributed, so you eliminate three of the baggage
Speaker 1: of the active mutual fund when you cross it over. So,
Speaker 1: in a way, Towers Watson definition is true. It's just
Speaker 1: been mutated over the years and piss people off because
Speaker 1: they think that it means smart means better, and it's
Speaker 1: not necessarily better. It's better, a better way to deliver something,
Speaker 1: not you're gonna definitely outperform unless we're clear. There's also
Speaker 1: different names for smart beta. It's not like smart beta
Speaker 1: is the only one, right Tom, Like, what other what
Speaker 1: other terms can mean smart beta? Yeah, I mean there's
Speaker 1: a lot of terminology that's been thrown out. People call
Speaker 1: it strategic beta, tarnative beta, active beta, um, you know.
Speaker 1: And a lot of people will get caught on, get
Speaker 1: caught up in the semantics because they think that, oh,
Speaker 1: if it's not smart beta, it's dumb beta. Right. It's not.
Speaker 1: It's not so binary that it's just one or the other.
Speaker 1: So a lot of people send they get caught up
Speaker 1: in the semantics and kind of forget, like, what is
Speaker 1: the bigger theme here? What are these products giving you?
Speaker 1: What are they offering you? Can you help me embrace
Speaker 1: the complicated a little bit here, because one of the
Speaker 1: main differences with smart beta is with a with a
Speaker 1: normal ETF the most popular ones to play vanilla ones,
Speaker 1: those are all market cap weighted, right, So help me
Speaker 1: better understand what sometimes the differences with smart beta. Right
Speaker 1: when the Towers Watson definition, they would call all that
Speaker 1: bulk beta. So there's just two bulk beta, which is
Speaker 1: market cap weighted. This I think a lot of people
Speaker 1: just called beta. So just tell me what market cap
Speaker 1: is right like that? Right, So the SMP five hundred,
Speaker 1: the bigger the stocks market cap, the bigger the waiting, right,
Speaker 1: so as stocks get bigger, that sort the top waitings
Speaker 1: in the Sapier, Apple, Microsoft, Amazon, Right, you got it. Now,
Speaker 1: that is another way to view smart beta and how
Speaker 1: some people do it. If you're in the index fund
Speaker 1: were world or the vanguard world and you're used to
Speaker 1: market cap weighted products which are very popular, smart beta
Speaker 1: to you seems like a mutation or a twist on
Speaker 1: the market cap waiting structure. So you could actually view
Speaker 1: smart beta from that lens and either look at it
Speaker 1: as you know, a way to try to do better
Speaker 1: than the market cap waited next, because you're gonna change
Speaker 1: the way the index works. You're gonna maybe wait by
Speaker 1: dividends instead of market cap. You're gonna wait by size.
Speaker 1: You could wait by a collection of fundamentals. That is
Speaker 1: another way to view it. So you can see you
Speaker 1: can see smart beta from the active side or from
Speaker 1: the passive side and describe it different ways. But it
Speaker 1: sounds kind of controversial, right, Well, it is. The word
Speaker 1: smart is the controversial part. But I equated to Obamacare,
Speaker 1: right remember that what was the real name of the bill?
Speaker 1: I don't even remember the patient and affordable care branding
Speaker 1: right there, right, so even Obama started calling it Obamacare.
Speaker 1: Smart beta is just a buzzword that you cannot beat down.
Speaker 1: People have come along, like Tom said morning Star Targic
Speaker 1: called a strategic beta, scientific beta, alternative beta, quantiment indexes.
Speaker 1: How about that one anti benchmark is the France calls
Speaker 1: it that active betas You just keep coming back to
Speaker 1: smart beta. Smart beta sticks. It's sticks. But the words
Speaker 1: smart is what has made it controversial because at the
Speaker 1: end of the day, you may or may not outperform.
Speaker 1: You're just going to have a different return stream than
Speaker 1: a market cap weighted index. Okay, Eric, can we actually
Speaker 1: break this thing down and talk about smart beta in
Speaker 1: real life. Let's call it a case study. Do you
Speaker 1: have an example of a smart beta e t F
Speaker 1: and can you walk me through how how it did
Speaker 1: what it did? Right? So, here's a classic one came
Speaker 1: out in two thousand five. So this is like early
Speaker 1: settler smart beta product, right, This was the Power Shares FO.
Speaker 1: I can picture it out in the homestead it Well,
Speaker 1: back then it was a lot of open frontier people
Speaker 1: put flags everywhere. Now that's why the t flants. He
Speaker 1: is very crowded, hard to come up with new ideas.
Speaker 1: There's not much white space left, so to speak. But
Speaker 1: this really was one of the first ones. It's the
Speaker 1: Power Shares Foutsie Raffie US one thousand portfolio tik R
Speaker 1: PRF can see that again. No, Um, here's what it does.
Speaker 1: It's pretty simple. It tracks stocks based on four fundamental
Speaker 1: measures right book value, cash flow, sales, and dividends. So
Speaker 1: it just screens stocks a thousand stocks by those metrics,
Speaker 1: comes up with a score, sorts them by that, and
Speaker 1: then every quarter it looks for which ones go to
Speaker 1: the top, and then it waits them by the score,
Speaker 1: and that's the that's how the index works, and that's
Speaker 1: what the e t F tracks. It just that's all
Speaker 1: it does. And it it goes on like a little robot.
Speaker 1: It is. It's a total how often does it rebalance quarterly?
Speaker 1: So it is it's almost like a robotic version of
Speaker 1: a stock analyst, right, because it's doing things that a
Speaker 1: researcher would look for. Those are definitely popular fundamental metrics.
Speaker 1: Here's where I think this is a great example of
Speaker 1: how it works, especially on the lack of emotion side.
Speaker 1: In two thousand and eight, right bank stocks were hated.
Speaker 1: Nobody wanted to buy City Group at any of these stocks. Well,
Speaker 1: as you know, they got cheaper on a value ation
Speaker 1: basis because everybody sold them. Well, this thing is a robot.
Speaker 1: It doesn't it doesn't read the news, it doesn't know
Speaker 1: what's going on. It isn't not getting yelled at by clients.
Speaker 1: So it rebalanced into financials in a big way. It
Speaker 1: had fifty financials in the beginning of two thousand nine.
Speaker 1: Doubled down on it, doubled down it went. It went
Speaker 1: into the burning building, as Warren Buffett would say, big
Speaker 1: time with with a suit of gasoline. And on top
Speaker 1: of that, I mean, like, I assume maybe the guy
Speaker 1: in charge of this who is whom so the person
Speaker 1: who made the indexes Rob or not, and I'm guessing
Speaker 1: Rob maybe got some heat for that he did. So
Speaker 1: I interviewed him from my book and he said that
Speaker 1: institutional funds that tracked that index on a separate account
Speaker 1: basis called up and said, do not do the rebalance.
Speaker 1: We do not want to do this exactly, and in
Speaker 1: separate account you can do that. The e t F
Speaker 1: cannot well guess who won after five years. Basically, that
Speaker 1: rebalance accounted for almost all of the firm's out performance
Speaker 1: over the next five years and came from the financials
Speaker 1: read that one rebalance. Now Ben Johnson the Morning Star
Speaker 1: calls it the immaculate rebalance. He thinks that I'm bringing
Speaker 1: up a extreme situation. He also brings up that a
Speaker 1: lot of investors in the e t F left too
Speaker 1: because they couldn't handle it, and so part of smart beta,
Speaker 1: this brings up the behavior if you really want to
Speaker 1: benefit from it, and the lack of emotion, you have
Speaker 1: to hang in there. So the e t F did
Speaker 1: its job going the burning building, but a lot of
Speaker 1: investors couldn't handle the heat either. So that's why all
Speaker 1: of that is important to point out there. But that
Speaker 1: is an example of how the smart beta can work
Speaker 1: for you and arguably be better than a human being manager.
Speaker 1: Near do you have a case study that rivals erics? Well,
Speaker 1: and I don't know if it rivals erics, but it's
Speaker 1: a little bit different. Okay, why not? Okay, yeah, let's
Speaker 1: let's let's let's get it on. Um. There are two
Speaker 1: et f s that I wanted to bring up that
Speaker 1: illustrate the point that I made earlier about giving you
Speaker 1: different types different depths of exposure. Um. So, for example,
Speaker 1: I looked at value at the at value as as
Speaker 1: an active strategy, and one value e t F, which
Speaker 1: is basically a factor, right, like to simplify that, like
Speaker 1: value and there are many different types of factors. Value
Speaker 1: is basically probably the most famous one, right, Yeah, it's
Speaker 1: most famous, and for a good reason, by the way,
Speaker 1: because in the it has it works very often. I mean,
Speaker 1: in other words, if if you looked, if you back
Speaker 1: tested all these various types of factors value and quality
Speaker 1: and momentum and so on, the batting the success rate
Speaker 1: for value over rolling periods is exceedingly high, so it's
Speaker 1: very popular over time, that's right. And it's also intuitively appealing,
Speaker 1: right because it makes sense that if you buy a
Speaker 1: company that's beaten down, you should get paid more than
Speaker 1: a company that's not. Right. It just that has intuitive
Speaker 1: appeal and the and the you know, and the numbers
Speaker 1: check out. So that's why it's so popular. And there
Speaker 1: are there are two different ETFs that you can buy
Speaker 1: that give you different types of exposure to it. One
Speaker 1: is VTV, which is the Vanguard Value e t F,
Speaker 1: and that E t F sorts stocks based on a
Speaker 1: value uh based on a value sort, and then awaits
Speaker 1: them by market cap. So that's one way that you
Speaker 1: can get value. But by contrast, you can also buy
Speaker 1: a different value t F that's v l U E,
Speaker 1: which is the I Shares Edge m S C I
Speaker 1: U S a value factor E t F. Don't ask
Speaker 1: me why these names have to be this long. And
Speaker 1: the difference here is that this two sorts by value,
Speaker 1: but then it also waits by value, and so the
Speaker 1: difference is you get more value exposure in the Eye
Speaker 1: Shares Fund than you do in the Vanguard fund. That
Speaker 1: that doesn't make the Eye Shares fund better, but it
Speaker 1: allows investors to express a preference as to how much
Speaker 1: value they want in their portfolio. If they want more,
Speaker 1: they go for the eye shares. If they want less,
Speaker 1: they go for the vangord Um. I would like to
Speaker 1: use a metaphor we Tom and I we use that.
Speaker 1: We have this thing called the smart data spectrum and
Speaker 1: what Near just described. If you could picture a spectrum.
Speaker 1: On one side, you have v t V. It's got
Speaker 1: just a little bit of value. It's going to do
Speaker 1: better if the regular market to doing well because it's
Speaker 1: mostly bulk beta. V l u E is sort of
Speaker 1: in the middle um and the value factor in the
Speaker 1: name is important because when you see that, that means
Speaker 1: it's waiting by the factor that's going to give you
Speaker 1: more juice. It's almost like v t V is o
Speaker 1: duels and vlu E barely any alcohol, and we have
Speaker 1: to think like like two cases to get you know,
Speaker 1: buzz the middle. There is vlu E that's going to
Speaker 1: be more like just a regular beer like a Budweiser.
Speaker 1: But then there's even ones that go further that are
Speaker 1: more hardcore value. Because v l u E and Near
Speaker 1: probably knows this has a volatility screen, So it's got
Speaker 1: this like screen to kick out really hardcore value stocks
Speaker 1: because it's trying to sell it to advisors who don't
Speaker 1: want that crazy of a ride. But there are more
Speaker 1: pure value like west Gray's q val the quant shares
Speaker 1: that are on the far end, which would be like
Speaker 1: that German beer that has exactly So I think this
Speaker 1: is a fascinating development in the E t F world.
Speaker 1: In this could make smart beta confusing to people, but
Speaker 1: really what you got to figure out is how much
Speaker 1: juice or factor content, how much aggressiveness do you want
Speaker 1: in this smart beta e t F because they have
Speaker 1: all flavors, Tom, can you give me a case study
Speaker 1: the tops erics. Yeah, so mine's a little bit different
Speaker 1: in that, you know what the example that Eric gave
Speaker 1: is more takes the mind of the act of manager
Speaker 1: and it kicks out the emotion. Right. This the product
Speaker 1: I want to talk about is a little bit more
Speaker 1: about targeting a specific outcome or helping somebody targeted outcomes.
Speaker 1: So let's just say you want to own the market,
Speaker 1: but you want to own it in the least vollowed
Speaker 1: a way. You don't want to own the riskiest stocks.
Speaker 1: So I want to bring up SPLV, which is the
Speaker 1: SMP five Low Volatility et F by Power Shares. The
Speaker 1: name long enough. I was gonna say, Eric, we should
Speaker 1: do a whole episode about the longest names in the
Speaker 1: e t F in the et F world. A new
Speaker 1: one was just filed that broke the record. Really Yeah,
Speaker 1: the goobelly pet parents, Pet companions and their parents and
Speaker 1: pet Ecosystem TMF. What I know, there's a multiple reasons
Speaker 1: why that's insanely amazing. We don't know yet, but it's
Speaker 1: gotta have five. I think it's gonna be kitty or doggie,
Speaker 1: but um, that's a whole different story. A couple of
Speaker 1: east also have thirteen fourteen words in the name, but um,
Speaker 1: that one's long. Okay, back to SPLV, uh sure, And
Speaker 1: why I wanted to highlight this one is because it's
Speaker 1: in a smart bit of category that's gotten pretty popular.
Speaker 1: It's just it's low volatility, right, so it's owning the
Speaker 1: market in the least volatial way. So this launched back
Speaker 1: in two thousand and eleven. And if you remember what
Speaker 1: happened in the summer of two thousand eleven was the
Speaker 1: U S got downgraded by the SMP. There's a lot
Speaker 1: of market volatility, and this thing kind of got put
Speaker 1: on the map like, hey, it's down less than the market.
Speaker 1: So it's very simple messogy it is and it's very simple,
Speaker 1: and I think this is why it's had some success
Speaker 1: is it takes the SMP five hundred and it says
Speaker 1: we are only going to take the one hundred least
Speaker 1: vottle stocks. It's very simple, right, so you're gonna own
Speaker 1: on hundred least bottle stocks in the SMP. So when
Speaker 1: you look at you know, some big draw sounds in
Speaker 1: the SMP, it's down less, holds up a little bit better.
Speaker 1: So it's kind of maybe like a little bit of
Speaker 1: a foam O E t F that you still want
Speaker 1: to own the market, but you want to do it
Speaker 1: in at least volowed away um. And it's twenty five
Speaker 1: basis points. It's been out for a lot. It's gotten
Speaker 1: pretty big, about six six or seven billion. Also like
Speaker 1: that you didn't uh, you didn't pampa product that you
Speaker 1: used to work on. That's that's classic of you. And
Speaker 1: let me talk. I want to add on to this
Speaker 1: because it's interesting. When SPLV came out, it tracks just
Speaker 1: those one stocks, so it has no sector. It doesn't
Speaker 1: care where it just goes where it ends up, having
Speaker 1: typically overweight to utilities and staples, which are always usually
Speaker 1: the less volatile stocks. So some investors think, oh, well
Speaker 1: those are more susceptible to rising rates, or they don't
Speaker 1: want to be overweight these sectors. So our Shares came
Speaker 1: out with a version that has sector bands, and then
Speaker 1: there's multiple products that do a little of both and
Speaker 1: so but SPLV was like sort of the speaking of
Speaker 1: the frontier. That was like the first one that nailed
Speaker 1: down low vall. Now there's probably forty five produc that
Speaker 1: do some twist on that to sort of again it's
Speaker 1: like soda flavors or whatever, and they serve it up
Speaker 1: in different ways. But SPLV was the first. How often
Speaker 1: does it change those one stocks that it holds. It
Speaker 1: does it every quarter. Every every quarter, it's going to
Speaker 1: rank the stocks and it's going to take the one hundred,
Speaker 1: which can be tough because let's say utilities got creamed, right,
Speaker 1: You're gonna have to take some down performance until let
Speaker 1: thing rebalances into wherever the next low ball is. That's
Speaker 1: why these things take a stomach Okay, I love these
Speaker 1: case studies that really helped me figure out sort of
Speaker 1: how these products can perform nearer. I'm gonna come back
Speaker 1: to you because in addition to writing for Bloomberg, Godfly,
Speaker 1: you also touch other people's money right right Well, in
Speaker 1: addition to writing for Bloomberg, I also run an asset
Speaker 1: management firm. Um, so you know, I manage money for
Speaker 1: other people, and um, I am. I've always been a
Speaker 1: big believer in active management. I think that the evidence
Speaker 1: for are certain types. There's a lot of quackery out there,
Speaker 1: without question, but I think, um, the evidence for the
Speaker 1: types of factors that we're talking about today, Um, you know,
Speaker 1: value and some of the other's quality, momentum and so on,
Speaker 1: I think is is well documented in the academic literature,
Speaker 1: and I think not controversial. Everyone agrees about it and
Speaker 1: so on, um and so um. And so I've I've
Speaker 1: been sort of an early adopter of these smart beta
Speaker 1: funds just because their active management more cheaply. So, if
Speaker 1: you believe in active management, and you should pay as
Speaker 1: little as possible for it, and so smart data is
Speaker 1: perfect for that. The question is, Okay, now you've got
Speaker 1: all these options, what do you do with them, And
Speaker 1: this is where it's early days, and there's a lot
Speaker 1: of confusion about what to do. But what I would
Speaker 1: propose and what I actually do is I use them
Speaker 1: for simple diversification. You know, it stands to reason that
Speaker 1: if you don't know which strategy is going to do
Speaker 1: well over the next period, then you just want to
Speaker 1: diversify across these strategies. In my opinion, it just makes
Speaker 1: sense to instead of putting all your eggs in the
Speaker 1: market cap index or value index or whatever, have exposure
Speaker 1: to various types of things. And these smart data products
Speaker 1: allow you to do that relatively cheaply, and I think
Speaker 1: that's the first step on the bus. If investors take
Speaker 1: away anything from this podcast, in my opinion, what they
Speaker 1: ought to take is look at your portfolio, see what
Speaker 1: kind of exposures you have. If you have too much
Speaker 1: in one particular type or style of investing or factor,
Speaker 1: however you want to call it, then look at look
Speaker 1: at smart data funds and diversify across them so that
Speaker 1: your eggs are in different baskets. Which puts me back
Speaker 1: in a dark room touching an elephant um tom when
Speaker 1: you are on the other side of the table. How
Speaker 1: are you guys seeing investors use this both from like
Speaker 1: a retail perspective but also like the institutional Yeah, so
Speaker 1: I think it really comes down to, now the ets
Speaker 1: that are coming out, they're trying to solve a problem, right,
Speaker 1: So it really comes down to what are you trying
Speaker 1: to solve? So are you using an active manager that
Speaker 1: maybe you're not happy with, like you have a value manager,
Speaker 1: Maybe you might want to look at a value smart
Speaker 1: beta et F Do you want to be in the market,
Speaker 1: but you want to be in the lowest vault as possible.
Speaker 1: So it's about talking to your clients, are talking to
Speaker 1: investors and finding a problem. It's not just all the
Speaker 1: tools are now out there, right, the toolbox has gotten
Speaker 1: really big. It's just about figuring out what are you
Speaker 1: trying to accomplish. And that's the conversation we constantly have
Speaker 1: with clients that this is the problem that I have.
Speaker 1: I want income, but I also want low vall income.
Speaker 1: So let's build a product that will give to you,
Speaker 1: let's say, dividend paying stocks, but also with like a
Speaker 1: low vall component to it. Solutions, Yeah, it has them exactly.
Speaker 1: Heyntroll here now that I'm the editor of Bloomberg business Week.
Speaker 1: I pulled a few strings for our loyal podcast listeners.
Speaker 1: Go to business Week mag dot com slash trillions right
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Speaker 1: dot com slash trillions for your free thirty day trial. Thanks. Okay,
Speaker 1: last topic, what kind of due diligence should investors be
Speaker 1: doing before they pick up these tools? Um, okay, I'll
Speaker 1: take a crack at this. So when I look at
Speaker 1: smart beta again, we have this spectrum and the way
Speaker 1: we determine where on the spectrum it goes. Again, the
Speaker 1: more quote beta that is in it, the more it
Speaker 1: could just be a replacement. I think. The more volatile
Speaker 1: it is, the more you could add it on as
Speaker 1: like a little bit of hot sauce. But anyway to
Speaker 1: determine what you're looking at, I would suggest a few things.
Speaker 1: One is the index waiting methodology. Does it wait by
Speaker 1: market cap? If so, it's probably gonna be not that volatile,
Speaker 1: not that much exposure to any factor or anything. But
Speaker 1: you get a little is it weighted towards the factor itself?
Speaker 1: That's important. That's number one. Number two is the standard deviation,
Speaker 1: which it's a simple term. If you fell asleep and
Speaker 1: stats class like I did, I had to relearn it,
Speaker 1: but so useful. It's just basically looking at past volatility
Speaker 1: of this fund or stock or whatever it could be,
Speaker 1: use for anything, and just telling you how bumpy the
Speaker 1: ride will be. It's like almost like a ski slopes line,
Speaker 1: like is this a black diamond or a blue square?
Speaker 1: So standard deviation is basically how bumpy the ride will be.
Speaker 1: But you need it relative to something. So I said,
Speaker 1: just taking the standard deviation of s P Y or
Speaker 1: the SMP fire, which I believe right now might be
Speaker 1: around nine. That means there's a two thirds chance that
Speaker 1: the annual return of the SMP over the next year
Speaker 1: will be one way or the other, up or down.
Speaker 1: So where is it versus nine percent? If it's you
Speaker 1: are going to have a real volatile smart beta product.
Speaker 1: If it's nine, it's about the same. And there's some
Speaker 1: that are lower, like the low volley t F might
Speaker 1: be more like eight percent, So figure that out. That
Speaker 1: is huge because you don't want to buy something that's
Speaker 1: too spicy for you or too lame or you know,
Speaker 1: doesn't move around enough. So I think those are two
Speaker 1: important stats. And then of course the fee I think
Speaker 1: ast matter here and now you can get a smart
Speaker 1: beta e t F for under twenty basis points. Uh,
Speaker 1: so you should shop around a little bit. Although when
Speaker 1: you're swinging for the fences, the fee isn't quite as important.
Speaker 1: But I think expense ratio is interesting because the range
Speaker 1: now is from a D to nine. There's a big range,
Speaker 1: and there's a lot of cost pressure going on in
Speaker 1: this space. So near when when our two D two
Speaker 1: with the head of Peter Lynch shows up at your door,
Speaker 1: what what do you do? Well? I think the I
Speaker 1: think as an investor, you want to look at UM
Speaker 1: if you look at nothing else, you want to look
Speaker 1: at what's what's the factor that I'm buying and how
Speaker 1: much am I paying for it? I think those are
Speaker 1: one and two. But if I if I can UM,
Speaker 1: let me, let me just use the time I have
Speaker 1: for just just a little bit of advocacy for this industry.
Speaker 1: I think I think there's something very important that we
Speaker 1: need to solve for UM and I think we need
Speaker 1: to solve for it very quickly if if smart beta
Speaker 1: is going to take off, and that is, we need
Speaker 1: to have some sort of labeling system to continue the
Speaker 1: analogy from earlier UM that tells you how much alcohol
Speaker 1: content is in the e t F is in this factor. So,
Speaker 1: in other words, you need you if you're looking at
Speaker 1: five value value factory t s. Let's just say you
Speaker 1: have no idea how much value is in one versus
Speaker 1: the other, and so as a consumer you can't make
Speaker 1: it a smart decision about which one to buy. And
Speaker 1: so we need to come up with a standardized system
Speaker 1: to be able to say, right there almost like an
Speaker 1: expense ratio, like you know what it costs, right It's
Speaker 1: almost like an expense ratio that tells you this has
Speaker 1: this percentage of value in it. And we can do
Speaker 1: in any We can do it on a ten point scale,
Speaker 1: we can do it on a D scale, we can
Speaker 1: do it in any way you want. But we have
Speaker 1: to do it, and there's a way to measure it.
Speaker 1: It would be very easy to do. The industry is
Speaker 1: going to push back against it because I don't think
Speaker 1: ultimately I think they want this to be UM somewhat
Speaker 1: confusing because it's very difficult to differentiate um if you
Speaker 1: make a lot of information available to investors. But I
Speaker 1: think we have to insist on it, and we have
Speaker 1: to give investors that tool so they can they can
Speaker 1: discriminate among the products. Spoken like a true Bloomberg Gadfly
Speaker 1: columnist Tom, we set out to explore the mystery of
Speaker 1: smart beta. Have we explored it? I think it's going
Speaker 1: to be an ongoing debate, not just you know, beyond
Speaker 1: this room, but it's going to be something that's going
Speaker 1: to keep evolving in the ETF industry because the lines
Speaker 1: are getting very, very blurry. Even the new players atting
Speaker 1: in into the market, their traditional active shops that are
Speaker 1: now launching ETFs. Now there's actively managed ETFs. The lines
Speaker 1: getting very blurry. Well, you just heard a lot of
Speaker 1: different takes on it, and it really depends on where
Speaker 1: you're coming from. And you may find smart beta as
Speaker 1: a solution to a boring core passive portfolio. You want
Speaker 1: a little spice in your life, or you may have
Speaker 1: too much spice and paying too much for the active managers.
Speaker 1: So you think underperforming the mutual fund structure, and you
Speaker 1: want to basically lower your fees and make it a
Speaker 1: more clean, um cheaper version of what you were getting before.
Speaker 1: So there's a lot of angles to this. Yeah, I
Speaker 1: like the hot sauce. Hot sauce. Hot sauce is better
Speaker 1: than the elephant. Alright near case are Tom Saraphagus. Thank
Speaker 1: you so much for us for Trillians, Thanks, thanks for
Speaker 1: having me, Thanks for listening to Trillions until next time.
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