Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: This week on the podcast, I have an extra special guest.
Speaker 1: His name is Rob are Not and he is the
Speaker 1: creator and chairman of Research Affiliates, a fundamental indexing firm
Speaker 1: that has strategies that manage over two hundred billion dollars
Speaker 1: in assets. Rob is perhaps best known for popularizing the
Speaker 1: concept of fundamental indexing UH, often called smart data, although
Speaker 1: he has uh takes issue with with that, um moniker.
Speaker 1: He thinks it's just become a marketing term. Uh. This
Speaker 1: is really a fascinating conversation. Every time I speak with Rob,
Speaker 1: I learned something new. Uh. This time I discovered that
Speaker 1: he has co authored a number of papers with the
Speaker 1: great Peter Bernstein, which was something I I didn't previously know.
Speaker 1: If you were at all interested in things like fundamental indexing,
Speaker 1: what's wrong with market cap waiting? How large that sector
Speaker 1: can grow? The advantages of factor based investing. And I
Speaker 1: also learned that there are now over five hundreds such factors.
Speaker 1: I had no idea. I knew it was hundreds, I
Speaker 1: had no idea was that many. Uh. This is really
Speaker 1: a masterclass in this space. So with no further ado.
Speaker 1: Here is my conversation with Rob or Not. My extra
Speaker 1: special guest this week is Rob are Not. He is
Speaker 1: the founder and chairman of Research Affiliates, perhaps best known
Speaker 1: for popularizing the concept of smart beta, better known or
Speaker 1: better described as fundamental indexing uh rafie does not consider
Speaker 1: market cap, focusing instead on four primary factors sales, profit, book, value,
Speaker 1: and dividend are not formed the firm in O two
Speaker 1: in Newport Beach, California. Investment strategies developed by Research Affiliates,
Speaker 1: or Raphy as it's better known, manages over two hundred
Speaker 1: billion dollars in assets. Rob are Not, Welcome back to Bloomberg.
Speaker 1: Thank you. So I mentioned you started Research Affiliates and
Speaker 1: I'm used to just calling it raffi is the nickname
Speaker 1: everybody uses. Uh back in two thousand two. Could you
Speaker 1: have imagined then that it's barely fifteen years later and
Speaker 1: your strategies are now managing over two hundred billion dollars?
Speaker 1: Did you have any expectation of how wildly successful smart
Speaker 1: data would become? Those are two different questions. Did I
Speaker 1: expect to be this successful? Yes, nobody starts a business
Speaker 1: without a healthy dose of optimism. Okay, perhaps sometimes undeserved,
Speaker 1: but but even still, that's a big chunk of change.
Speaker 1: Did you expect to hit those sort of numbers this quickly? Um, Well,
Speaker 1: I don't know how quick that is. Sixteen years is
Speaker 1: a long time. But as for RAFI the fundamental index,
Speaker 1: UH and it's growth. I couldn't have anticipated that because
Speaker 1: we hadn't come up with the idea yet. When when
Speaker 1: did that first roll out? We first thought about the
Speaker 1: idea of indexing in non capitalization ways in two thousand three.
Speaker 1: We formalized our research and UH completed development of the
Speaker 1: idea by mid O four. We were live with assets
Speaker 1: by the end of O four. First fund was launched
Speaker 1: by Pimco and OH five, first published index was by
Speaker 1: Footsie UM in November of oh five. So barely a
Speaker 1: dozen years. That's even faster. Scale up to not a
Speaker 1: lot of fun scale up to two and a dozen years. No,
Speaker 1: that's true, that's true, but keep in mind it's not
Speaker 1: a hundred billions of assets under management. We licensed the
Speaker 1: idea three distribution partners, so we have the help of Pimco, Schwab, Investco, Nomura,
Speaker 1: foot See and Russell UH and couldn't get any names
Speaker 1: that anybody recognized. We keep looking, We keep hoping somebody
Speaker 1: known actually signs onto the idea. So that let's let's
Speaker 1: digress and talk about that a moment. That's a fascinating
Speaker 1: model where now I know you guys also manage money directly,
Speaker 1: not anymore, not anymore. Oh so that's that's a change
Speaker 1: since the last time you were here. When did you
Speaker 1: stop directly managing money? We decided in two thousand fourteen
Speaker 1: that UM five of our assets under quote management close quote,
Speaker 1: where money was money that we actually managed. Fift pcent
Speaker 1: of our revenues came on that, and the tail was
Speaker 1: wagging the dog. We were winding up with distribution partners,
Speaker 1: fearful that we were going to compete directly with them.
Speaker 1: That makes sense, and we decided we don't need these complications.
Speaker 1: Let's stick to what we're best at. And so we
Speaker 1: approached Pimco, our largest affiliate, and in effect said would
Speaker 1: you like this eight billion dollar book of business UM
Speaker 1: And they said, they said, at a price tag of zero,
Speaker 1: that's not a bad deal. I think we'll go for you.
Speaker 1: You know, if you would have come to me, I
Speaker 1: would have I would have given you the same deal
Speaker 1: they gave you. Oh my goodness, that didn't cross my mind.
Speaker 1: I wish, I wish I had thought of that. So
Speaker 1: so this is kind of fascinating. So what I find
Speaker 1: so intriguing about your model in addition to the intellectual property,
Speaker 1: but essentially, you're in the business of licensing these indices
Speaker 1: to other people to do you know, the roll up
Speaker 1: your sleeves and do the trading, do the clients everything
Speaker 1: do which is complex and filled with risk and expensive.
Speaker 1: So you really have the sweetest part of of the pie.
Speaker 1: For us, it's the sweetest part because, uh, it's what
Speaker 1: we love to do, and it's what you you, I
Speaker 1: ostensibly would imagine you do best. Let the other people
Speaker 1: who are experts in trading and what have you. Um
Speaker 1: I think not being involved in trading back office call
Speaker 1: desks is a wonderful thing. Um I think there are
Speaker 1: people who are really good at that, and I want
Speaker 1: them to do what they're really good at. In effect,
Speaker 1: being in the business of product innovation and licensing, we
Speaker 1: face an unusually high hurdle because our clients are distribution partners.
Speaker 1: They don't need us, They have their own R and
Speaker 1: D teams and so for our business to succeed, UM,
Speaker 1: they have to see us as an extension of their
Speaker 1: own R and D team, complimentary to what they do,
Speaker 1: and UM, not all organizations can think that way. You
Speaker 1: have to be willing to go against the not invented
Speaker 1: here syndrome. So that sort of puts a little more
Speaker 1: heft on the name of the firm research affiliates, because
Speaker 1: essentially you provide intellectual um property and research strength to
Speaker 1: the affiliates you work with. I never really thought of
Speaker 1: it in quite those terms, but really that's what Raffie does.
Speaker 1: That's exactly right. Was that intentional or did we you
Speaker 1: just happen to stumble on that? Um, a little bit
Speaker 1: of both. I was running first Quadrant at the time
Speaker 1: and had decided if I'm ever going to start my
Speaker 1: own business, I had to do it sooner rather than later,
Speaker 1: and so I was running both in parallel for two years.
Speaker 1: Running both in parallel for two years meant I had
Speaker 1: to be studiously avoid conflicts of interest. So initially we
Speaker 1: started out saying, let's subadvise, let's license our ideas, and
Speaker 1: we found that that business model works, so we stuck
Speaker 1: with it. Quite fascinating. Let's talk a little bit about
Speaker 1: factors investing um Fama French famously identified first the three
Speaker 1: factor model, then the five factor model, then the seven
Speaker 1: factor model. Are there still factors out there that have
Speaker 1: yet to be discovered? Two answers to that question. Firstly, yes,
Speaker 1: there will be. They've already been five factors published five
Speaker 1: and um there will be hundreds more. Now the more
Speaker 1: pertinent question is how much of this is data mining
Speaker 1: finding relationships that prevailed in the past that have no
Speaker 1: reason to prevail in the future, And how much of
Speaker 1: it is um truly factors that drive price that can
Speaker 1: be truly a reliable source of access return, Meaning is
Speaker 1: there enough outperformance here that can be captured by a portfolio? Exactly? So?
Speaker 1: Of those five So if these were really good factors,
Speaker 1: why wouldn't we create a if not a five hundred
Speaker 1: factor portfolio. Hey, let's put these in order of the
Speaker 1: strongest out performance generators, and here are the fifty or
Speaker 1: five best factors. Why can't we do that? Well? I
Speaker 1: wrote a paper a couple of years ago called how
Speaker 1: can smart Bay to Go Horribly Wrong? And I remember
Speaker 1: that it was massively controversial. I thought the controversy was
Speaker 1: amusing because if I'd written a paper entitled how can
Speaker 1: Stockpicking go Horribly Wrong? And I offered the the sage
Speaker 1: inside that if a stock soars and its fundamentals haven't,
Speaker 1: so it's valuation multiples have sword, it's past returns will
Speaker 1: look artificially wonderful, and if there's any mean or version
Speaker 1: on valuations, it's future performance will, to use a technical term,
Speaker 1: suck so um. People would have read that paper and said,
Speaker 1: what's this nitwit talking about? Everybody knows that by saying
Speaker 1: exactly the same thing about factors and smart beta strategies,
Speaker 1: I was pilloried for suggesting that the same thing could
Speaker 1: apply for strategies. Now, if you go back historically, you
Speaker 1: find that the alpha of many of the smart beta
Speaker 1: factors have has not been tested in terms of how
Speaker 1: much of that access return came from rising valuation multiples.
Speaker 1: We might as well have a an Apple factor that
Speaker 1: simply says Apple has outperformed magnificently. It is a powerful factor.
Speaker 1: You have a long Apple, short everything else factor, and
Speaker 1: because of the past performance, we know it's going to
Speaker 1: continue to work. Let's digress a little bit and and
Speaker 1: define factors for people who may not be familiar with
Speaker 1: Gene Fama, who won the Noble Prize a few years
Speaker 1: ago for his for a lot of his work. UM
Speaker 1: the original and its Gene Farm in Ken French at
Speaker 1: Dartmouth Farm is a Chicago. The original Fama French factor
Speaker 1: paper was small capitalization value, and I'm trying to remember
Speaker 1: was that momentum, quality, market market beta market. So that's
Speaker 1: the three, okay, and then when we moved to five,
Speaker 1: we added um uh, quality and momentum. That's the next lot,
Speaker 1: and then seven. I don't even know what the next
Speaker 1: two are. I'm not certain, but I think it's illiquidity
Speaker 1: and investment. Right. So, so all of this comes back
Speaker 1: to let's let's keep it simple. Low cost stocks over
Speaker 1: long periods of time, low uh better value stocks, not
Speaker 1: expensive stocks. I don't mean low price tend to outperform
Speaker 1: expensive stocks over the long haul, exactly right now. Advocates
Speaker 1: of efficient markets will say it's got to be because
Speaker 1: of some kind of hidden risk, because you can't get
Speaker 1: something for nothing. Um. I would push back against that
Speaker 1: and say it's not risk with value. Might be risked
Speaker 1: with small cap because there and then there's a liquidity issues,
Speaker 1: but with value, it's the psychology it's the behavior of
Speaker 1: who wants to buy this. Dominoes is one of my
Speaker 1: favorite examples. Domino's had a big, a whole run of
Speaker 1: issues late nineties early two thousands. The stock did poorly.
Speaker 1: People were thinking, all right, well, that chain is pretty
Speaker 1: much done. And Dominoes over the past I think it's
Speaker 1: either fifteen or twenty years, has handily outperformed Apple. May
Speaker 1: be getting the timeframe wrong. So and that's the psychology
Speaker 1: of who wants to touch that? And if you look
Speaker 1: from the trough in two thousand nine, Uh City ever
Speaker 1: so briefly dipped below a buck, be of a dipped
Speaker 1: to roughly two bucks, and since then those have handily
Speaker 1: outperformed Apple. Um so, but they've outperformed it from a
Speaker 1: starting point of being thought on death's door. So value,
Speaker 1: it seems to me, does have a behavioral basis. Basically,
Speaker 1: when you have a value orientation, you're buying what's unloved,
Speaker 1: what people want to shun. That should be rewarded. Now
Speaker 1: is it a hidden risk factor only psychologically? Well, let
Speaker 1: me let me push back a little bit over there.
Speaker 1: Two companies in financial crisis look terrible. One of them's
Speaker 1: a I G. The other's Lehman brothers. You can buy
Speaker 1: a I g rescued and and since pretty decent, not great,
Speaker 1: but pretty decent returns off of the bottom and Lehman,
Speaker 1: I guess we could call that a value trap. Although
Speaker 1: there were elements of fraud and RepA one oh five,
Speaker 1: there was a whole different set of problems with Lehman.
Speaker 1: But the risk with value is am I buying something
Speaker 1: that's going to be a zero? That's known as a
Speaker 1: value trap. It's a stock that looks cheap on its
Speaker 1: way to zero. Now it's hard to have a whole
Speaker 1: sector that looks cheap on its way to zero. I
Speaker 1: coined the term anti bubble in two thousand nine to
Speaker 1: describe what I perceived as the inverse of a bubble,
Speaker 1: where an entire sector of the economy is priced as
Speaker 1: if they're all headed for oblivion, when in fact, every
Speaker 1: failure clears the runway for the survivors to have higher profits,
Speaker 1: higher margins, and greater success us ahead. So, unless you
Speaker 1: wanted to um except the notion of armageddon the end
Speaker 1: of the economy, uh you, it made sense to think
Speaker 1: that collectively this sector was being dismissed when it shouldn't be.
Speaker 1: There's a whole group of people that are the armageddon traders.
Speaker 1: I think the Ft called them the plastic bears. They'll
Speaker 1: scream arm again, but then they'll he here's what we
Speaker 1: can sell you while we're waiting for armagedon um. The
Speaker 1: other thing about anti bubble is so fascinating. You could
Speaker 1: say there's an anti bubble in what was it? Home
Speaker 1: builders and oh five and mortgage brokers and bankers and
Speaker 1: oh six and pretty much anything else in finance and
Speaker 1: O seven and each almost every there's almost always some
Speaker 1: sort of bubble in the market somewhere and some sort
Speaker 1: of anti bubble in the market somewhere. You go back
Speaker 1: to early two thousand sixteen emerging mark, It's deep value
Speaker 1: was priced at two to three times cash flow, and
Speaker 1: it's not as if the emerging economies of the world
Speaker 1: were collectively all going to go bust, so it represented
Speaker 1: an extraordinary opportunity. Rafi, the fundamental index in emerging markets
Speaker 1: was briefly trading for a Schiller Pe ratio below six. Wow,
Speaker 1: that's amazing. Let's talk about a study you did way
Speaker 1: back when looking at the SNP five index, which theoretically
Speaker 1: is a passive, and we'll put a footnote on exactly
Speaker 1: what cassive means. In this you found from to the
Speaker 1: last year there was a full year of data available,
Speaker 1: stocks added to the index underperformed those that were kicked
Speaker 1: out by an average of twenty three percentage points over
Speaker 1: the ensuing twelve months. That's not a bad gap in return.
Speaker 1: Oh my goodness, that's amazing. So so what are the
Speaker 1: occations of this and what does this mean about the
Speaker 1: stock picking acumen of the editors on the committee of
Speaker 1: the SMP Index who select the stocks that go into it.
Speaker 1: I don't fault the index committee for the way they
Speaker 1: make choices. They they are under. Their clients are the
Speaker 1: people who license the index, and the people who license
Speaker 1: the index want the index to include all the names
Speaker 1: that are hot, beloved, and are embarrassing not to have
Speaker 1: in the index. Just added was Twitter as of this
Speaker 1: tape in okay Um. And if there's a stock that
Speaker 1: has been brutalized, unloved, dirt cheap and nobody wants it,
Speaker 1: it's embarrassing to have it in the index. So of
Speaker 1: course they're going to take those out. Now, what happens
Speaker 1: is two things. Firstly, they announce a change, and they
Speaker 1: announced the date the change will take effect. That gives
Speaker 1: the index funds a grace period in which to trade
Speaker 1: where they're going to move those stock prices, and those
Speaker 1: stocks will still be in the index, so it won't
Speaker 1: create a performance drag. There. Everybody, please front run our trades. Well,
Speaker 1: there's a hedge fund community that does exactly that. So
Speaker 1: the trades are made, the positions built, and then given
Speaker 1: over to the index funds on the effective date, preferably
Speaker 1: at or near the close. The stocks added when you
Speaker 1: compare them with the discretionary deletions deletions that aren't related
Speaker 1: to corporate actions, um outperformed by nearly nine percent during
Speaker 1: that grace period. So when index funds say we don't
Speaker 1: move stock prices with our trading, that's that's rubbish. Um.
Speaker 1: They let other people move it on their behalf, and
Speaker 1: they could say, oh, look we haven't moved this. Well
Speaker 1: there's that, and then there's also the simple fact that
Speaker 1: the ads beat the discretionary deletions by nine during a
Speaker 1: period of days. Well, that's a big move. So you
Speaker 1: also have part of that return difference in the subsequent
Speaker 1: year is simply mean reversion taking the price impact of
Speaker 1: the index funds back out, and part of it is
Speaker 1: quite simply. The stocks added are extravagantly expensive on average,
Speaker 1: and the stocks dropped are usually a deep discounts um
Speaker 1: over nine of the ads are companies trading at premium multiples.
Speaker 1: Over the discretionary deletes are trading at a discount. And
Speaker 1: the average gap between valuation multiples of the ads and
Speaker 1: of the deletes is more than three to one. That's amazing.
Speaker 1: So let me make the case for quote unquote passive indexing.
Speaker 1: I think smart data. Let me caveat this. Smart data.
Speaker 1: Is that the wrong phrase. It should be fundamental indexing.
Speaker 1: Passive investing is a wrong phrase. It should be low
Speaker 1: cost indexing. But here's the pro indexing argument, and I
Speaker 1: want you to take this argument apart. This is the
Speaker 1: most cost effective, least expensive way to get exposure to equities.
Speaker 1: It has the lowest amount of turnover, the least amount
Speaker 1: of tax consequences, and everybody seems to have the greatest
Speaker 1: difficulty outperforming the SNP five hundred. So we're gonna keep
Speaker 1: using that as a benchmark going forward. Discuss the Bill
Speaker 1: Sharp wrote a piece in the nine nineties entitled the
Speaker 1: arithmetic of active investing, and it's a very simple thesis.
Speaker 1: The market is capitalization weighted. The index fund span almost
Speaker 1: all of the market and is capitalization weighted. You take
Speaker 1: that portfolio, all of the indexers out, and what's left
Speaker 1: is what active managers collectively own. Well, it's the same portfolio,
Speaker 1: giver take some wiggle room. So if it's the same portfolio,
Speaker 1: active managers should have the same per formances index funds
Speaker 1: minus costs, and the costs are higher for active managers.
Speaker 1: That arithmetic is not just true, it's a truism that
Speaker 1: means that if you're choosing active managers with absolutely no skill,
Speaker 1: you should expect to earn index returns minus um when
Speaker 1: you choose an active manager. This doesn't mean in choosing
Speaker 1: active managers as a waste of time. What it does
Speaker 1: mean is you'd better have a good answer to the
Speaker 1: question if this active manager is a winner, so there's
Speaker 1: a loser on the other side of their trades, who's
Speaker 1: the loser and why are they are willing loser for
Speaker 1: fundamental index The answer to that is really simple. This
Speaker 1: is a strategy that contratrades against the market's biggest and
Speaker 1: most extravagant bets, and so the loser on the other
Speaker 1: side of the trade is the performance chasing lemmings who
Speaker 1: are legion. Here's the Bill Miller pushed back. Most active
Speaker 1: managers are as you've described, with a very low active
Speaker 1: share and essentially their closet index ers. So why on
Speaker 1: earth should anybody pay a high fee when you could
Speaker 1: pay a low fee and get the same portfolio. Fair
Speaker 1: fair criticism, It's a fair criticism. There's a lot of
Speaker 1: active managers hiding the bushes near the benchmark. Most active
Speaker 1: managers are constantly looking over their shoulder at the benchmark
Speaker 1: and worrying about beating it. The beauty of fundamental index
Speaker 1: and of smart beta as it was originally defined. Smart
Speaker 1: beta originally meant strategies that break the link with price,
Speaker 1: that don't pay any attention to market capitalization or a
Speaker 1: price in setting the weight of a stock. This term
Speaker 1: has been stretched to the point of meaninglessness. But um,
Speaker 1: under that definition, you do have the advantage that you're
Speaker 1: going to be contratrating against the market's biggest bats um,
Speaker 1: whether you're equal weighting or fundamental index or minimum variants,
Speaker 1: you're going to be having an anchor a target weight
Speaker 1: that isn't related to price, so as the price sores
Speaker 1: and tumbles, you're gonna be selling and buying. It's built
Speaker 1: in structural cell high Bilow discipline. Fascinating. We were talking
Speaker 1: earlier about traditional passive investing or or low cost indexing.
Speaker 1: Let's let's do a little bit of a compare and
Speaker 1: contrast with UM fundamental indexing. And we touched on this.
Speaker 1: I want to give you a quote from Burton Malchiol.
Speaker 1: Smart beta strategies are riskier than index funds and not
Speaker 1: right for individual investors. What is professor Malkiel getting wrong there?
Speaker 1: Malkiel comes from the efficient markets community. He believes he
Speaker 1: wrote random Walk down Wall Street back in I think
Speaker 1: the sixties. UM. It's interesting to note that if you
Speaker 1: believe that UM the the share prices equal a fair
Speaker 1: value that we cannot see plus or minus and error
Speaker 1: adding up to the price, And if you believe the
Speaker 1: market is constantly hunting for those errors and trying to
Speaker 1: fix them so that the error is mean reverting, then
Speaker 1: contratrating against big price moves has a structural alpha that
Speaker 1: is in fact the key driver of the value effect.
Speaker 1: If you look at value strategies, the alpha comes from
Speaker 1: the rebalancing, not from the cheapness of the stocks. Well,
Speaker 1: isn't that the same thing. When we say a stock
Speaker 1: is cheap, we're essentially saying it's trading at a discount
Speaker 1: to its fair value, and once that rebalance o cards
Speaker 1: it should snap back to that and there are gains. Well,
Speaker 1: just as an example, fundamental index overweight stocks that are
Speaker 1: trading at discounts proportional to the magnitude of the discount,
Speaker 1: underweight stocks trading at disc premiums proportional to the magnitude
Speaker 1: of the premium. So your systematically downweighting the growth stocks
Speaker 1: and upweighting the value stocks. You're saying, thanks for the
Speaker 1: gains on the growth stocks, let me trim it back
Speaker 1: to its economic footprint, thank you for the discounts on
Speaker 1: the value stocks, reweighting it back up. So there's a
Speaker 1: structural value tilt. Well, why then, does this strategy relentlessly
Speaker 1: beat cap weighted value indexes Russell value EFA, Value IFA
Speaker 1: Emerging Markets value UM winning against the value index is
Speaker 1: seventy or eighty percent of the time, year by year. Uh.
Speaker 1: It does so for the simple reason that value indexes
Speaker 1: are themselves cap weighted so they're going to overweight the
Speaker 1: overvalue and underweight the undervalued value stocks, even within the
Speaker 1: universe of value correct um. Think of it this way.
Speaker 1: If a stock outperforms in the future, then it must
Speaker 1: have been under under price today. If it underperforms in
Speaker 1: the future, it must have been overpriced today. And what
Speaker 1: stocks get the most weight in a cap weighted portfolio
Speaker 1: those that are overpriced. So let's let's address that. Casette
Speaker 1: raises a really interesting question. When we look at cap
Speaker 1: weighted indexes, we end up owning more of the names
Speaker 1: that are outperforming have been out, I've been out before,
Speaker 1: and since we know momentum as a factor, the assumption
Speaker 1: is that those outperformers will, at least for some finite
Speaker 1: period in the future, continue to outperform. So we end
Speaker 1: up owning more of the winners that are keeping winning
Speaker 1: and less of the losers that are keeping losing. Or
Speaker 1: so goes the cap weighted indet argument. What's wrong with
Speaker 1: that claim? Don't we want more exposure to stocks that
Speaker 1: are winning. Here's what's fascinating. Yeah, momentum is a powerful
Speaker 1: factor across multiple asset classes spanning decades, but within stocks,
Speaker 1: momentum was first published by professors jagged Yaian Titman back
Speaker 1: in nineteen two or three, and since nineteen momentum is
Speaker 1: a strategy in the stock market hasn't worked well, hasn't worked,
Speaker 1: it's underperformed some other factors. No high performing stocks have
Speaker 1: underperformed low performing stocks on average since ninety nine. So
Speaker 1: is that due to the dot com crash in the
Speaker 1: Great Financial Crisis? How is it operated? And I know
Speaker 1: you can't say I only want to invest when we're
Speaker 1: not in a bubble or in a crisis, but how
Speaker 1: significant were those events to the underperformance of outperformed tremendously significant.
Speaker 1: And you put your finger on it that when you
Speaker 1: have a strategy that outperforms for a while, then crashes,
Speaker 1: outperforms for a while, then crashes net net, it can
Speaker 1: look terrible if you can use it only when it's
Speaker 1: gonna work. Great an index that does that exactly well.
Speaker 1: In point of fact, when um, when momentum is telling
Speaker 1: you the extravagantly priced growth stocks are the ones with momentum,
Speaker 1: That's when it's more likely to have a crash than
Speaker 1: at other times. When momentum is saying the value stocks
Speaker 1: have turned and are showing positive momentum. That's when momentum
Speaker 1: historically works best. So the combination of the two value
Speaker 1: and momentum tends to be a really nice combination. But
Speaker 1: be very careful about using momentum when it's telling you
Speaker 1: to chase bubble stocks. If you can switch it off,
Speaker 1: do so. Let's talk about the combination of value and momentum. Uh.
Speaker 1: West Gray of Alpha architect wrote a piece Who's Who's um?
Speaker 1: Title I love about combining value and momentum, which essentially
Speaker 1: says even God would get fired as an active manager.
Speaker 1: And he said he suggests that while we know the
Speaker 1: combination of value and momentum outperforms just about any other
Speaker 1: combination can come up with, there are these occasional draw
Speaker 1: downs that can be brutal. Absolutely so, So how do
Speaker 1: we explain why value and momentum outperforms just about any
Speaker 1: other combination of factors if we still get these really
Speaker 1: horrific periods of time where gee, this doesn't seem to
Speaker 1: be working. Well, simple fact is, no strategy is going
Speaker 1: to work all the time. UH. Contraining investing is arguably
Speaker 1: the most powerful and reliable UH long term form of investing. Basically,
Speaker 1: whatever is newly cheap is going to have performed terribly.
Speaker 1: It will have inflicted pain and losses. People don't want
Speaker 1: more of that. Imagine an investor saying, oh, I just
Speaker 1: lost a ton of money on this, give me more
Speaker 1: of that. Uh, that's what a contraining investor does. Um
Speaker 1: a strategy that's provided great joy and profit, a stock
Speaker 1: that's provided great joy and profit. How many people look
Speaker 1: at that and say, oh, get me out of here.
Speaker 1: That's what a contraining investor does. And so with contraining investing,
Speaker 1: you're going against the crowd that's profoundly uncomfortable, and whenever
Speaker 1: it fails, whenever it doesn't add value because the expensive
Speaker 1: popular stocks are getting more expensive and the cheap garbage
Speaker 1: stocks are getting more cheap. Anytime it does fail, people
Speaker 1: start to think you're an absolute idiot. So let's talk
Speaker 1: about that for a second. Because my office has a
Speaker 1: value tilt, as does everything that the research affiliate or many.
Speaker 1: I shouldn't say everything, but many, everything just just about.
Speaker 1: And yet we're in a period where we have the
Speaker 1: fang stocks, we have these tech companies doing great, and
Speaker 1: value is going through one of its periodic stretches of
Speaker 1: under performance where people stop and say, I understand the
Speaker 1: concept behind value, but look at Netflix, why why do
Speaker 1: we want to own these cheap stocks? The expensive stocks
Speaker 1: are doing great. So two part question, A, why do
Speaker 1: we go through these periodic spasms of of significant underperformance?
Speaker 1: And B is that what enables value to over the
Speaker 1: long haul outperform growth. Let me answer be first, Yes,
Speaker 1: that's exactly why it outperforms in the long run, because
Speaker 1: it's uncomfortable, but the periods, and because when it goes
Speaker 1: through a period of disappointment, is very easy for people
Speaker 1: to abandon a strategy that's on comfortable the making the
Speaker 1: cheap stocks that have become cheaper even cheaper still correct.
Speaker 1: So it is really important. When we wrote the paper
Speaker 1: how can smart be to go horribly wrong? We showed
Speaker 1: that when a strategy becomes more expensive, it creates a
Speaker 1: a surge in relative performance, making the strategy look unusually powerful,
Speaker 1: while so sewing seeds for future disappointment because the strategy
Speaker 1: is newly expensive. The same thing applies to value. Value
Speaker 1: in two thousand was profoundly cheap. The gap between growth
Speaker 1: stocks and value stocks was eight to one in valuation
Speaker 1: terms um By two thousand and seven it was two
Speaker 1: and a half to one too, and after one sounds
Speaker 1: like a big gap, it's not. It's considerably narrower than
Speaker 1: you usual. And so in two thousand seven, objectively value
Speaker 1: was priced high relative to its own history, and that's
Speaker 1: what set the stage for the quant crash in August
Speaker 1: of two thousand seven. That was a very crowded trade
Speaker 1: a lot of people had. But that you know, you,
Speaker 1: you you reference quants. That makes me think of a
Speaker 1: quote of yours which I don't believe is still true.
Speaker 1: Or maybe you could disabuse me of that notion. You
Speaker 1: once wrote, our industry hates arithmetic. Now that was certainly
Speaker 1: true decades ago, given the amount of things, amount of
Speaker 1: assets managed by quantitative strategies, and just the flood of
Speaker 1: smart quant analysts coming into the industry, does finance still
Speaker 1: hate arithmetic? Oh my goodness, yes, it doesn't hate mathematics.
Speaker 1: The notion of quantitative methods probably almost more popular than
Speaker 1: ever before. Only two thousand seven might even come close.
Speaker 1: But the notion of simple arithmetic, the arithmetic of returns
Speaker 1: is you earn a yield, you have a growth in income,
Speaker 1: and you have changes in valuation multiples. If you know
Speaker 1: those three numbers, you know your rate of return. Disaggregating
Speaker 1: historical returns into those three components gives you a very
Speaker 1: clear picture of where returns came from, and looking ahead,
Speaker 1: you know what the yield is. You know that historical
Speaker 1: growth is probably not a bad predictor for future growth,
Speaker 1: which leaves you valuation change. If valuations are high, you're
Speaker 1: more likely to have mean reversion down. If valuations are low,
Speaker 1: you're more likely to have mean reversion up. So when
Speaker 1: markets are expensive, you have a lousy yield. Growth is
Speaker 1: what it is, and valuations are more likely to come
Speaker 1: down than rise, and when markets are cheap, the opposite
Speaker 1: holds true. So that's the arithmetic people hate. When we're
Speaker 1: in the tenth year of a bull market, tenth year
Speaker 1: of an economic expansion, when times are good and people
Speaker 1: have had wonderful success, their expectations for future returns go up,
Speaker 1: not down. Now, that's interesting because when markets are rising
Speaker 1: and yields are falling, your forward looking return is eroding.
Speaker 1: You expect the return should be lower when things are
Speaker 1: pricing and higher when they're cheating, right, and people think
Speaker 1: the opposite way. Um, it's interesting. At the market lows
Speaker 1: in eight seven in two thousand two, uh, and again
Speaker 1: in two thousand nine. Each of those three episodes, I
Speaker 1: found myself on a plane next to somebody who was saying,
Speaker 1: I'm never gonna be investing in the stock market again.
Speaker 1: Are all three time? So that's fascinating. Can you stick around?
Speaker 1: I have a ton more questions for you. We have
Speaker 1: been speaking with Rob are Not. He is the founder
Speaker 1: and chairman of Research Affiliates. If you enjoy this conversation,
Speaker 1: be sure and check out our podcast extras when we
Speaker 1: keep the tape rolling and continue discussing all things smart Beta.
Speaker 1: You can find that wherever final podcasts are sold. We
Speaker 1: love your comments, feedback and suggestions right to us at
Speaker 1: m IB podcast at Bloomberg dot net. Check out my
Speaker 1: daily column on Bloomberg dot com. Follow me on Twitter
Speaker 1: at Ridholts. I'm Barry Ridhults. You're listening to Masters in
Speaker 1: Business on Bloomberg Radio. Welcome to the podcast, Rob, Thank
Speaker 1: you so much for doing this. You we were discussing earlier.
Speaker 1: You were one of the original old people. We kinda
Speaker 1: first tested the water with the podcast with series back
Speaker 1: in I want to say, right, that's that's when we
Speaker 1: launched it, and you're you're one of the people where
Speaker 1: a couple of years later, I kind of said I
Speaker 1: wanted to ask him about this. I didn't even bring
Speaker 1: up that I forgot this. I forgot that. I gotta
Speaker 1: bring him back and go over some of these things.
Speaker 1: So thank you so much for for returning. I'm fond
Speaker 1: of saying this is the most fun I have all week.
Speaker 1: So uh, this is uh yeah, right, I have to
Speaker 1: sit down with people like you and Danny Kanaman and
Speaker 1: Ray Dally and Howard Marks. That is my cross to
Speaker 1: bit and hopefully yes, so it's an ugly dirty job,
Speaker 1: but somebody's got to do it. Let's um, there's a
Speaker 1: ton of stuff I didn't get to And since we
Speaker 1: were talking about expensive stocks and tech stocks, let's talk
Speaker 1: a little bit about fang Uh we see the fang
Speaker 1: stocks that have you know, just taken off over the
Speaker 1: past five or so years. But there's also let's do
Speaker 1: a compare and contrast with the es. These are real
Speaker 1: businesses with actual revenues some of them and profits well, Apple, Amazon, uh, Netflix, Google,
Speaker 1: go down the top ten tech stocks. It's not like
Speaker 1: the ephemeral or is it. Let me ask you the
Speaker 1: question as opposed to answering what are the parallels between
Speaker 1: today and the sort of mania we saw in the nines?
Speaker 1: Are there parallels? There? Sure are? UM. I wrote a
Speaker 1: paper recently entitled Yes, It's a bubble? So what? Okay,
Speaker 1: that's one of my questions. Let's let's go over that
Speaker 1: because I love the title. Yeah, the point of the
Speaker 1: paper is twofold. Um. One point that we make is
Speaker 1: that the term is bandied about without a definition, a bubble, bubble,
Speaker 1: and so we make an effort to give a um
Speaker 1: an objective definition that people can actually use as a
Speaker 1: test for saying, is this a bubble? And that objective
Speaker 1: definition is one where simple measures of fundamental business success
Speaker 1: you have to make um exceptionally aggressive assumptions to justify
Speaker 1: the price of a stock, or a sector or a country.
Speaker 1: So you don't like the Potter Stewart definition, I know
Speaker 1: it when I see it. It's a little vague, a
Speaker 1: little vague. And the second part of the definition is
Speaker 1: that the marginal buyer is somebody who is not buying
Speaker 1: because of a careful analysis of fair value, but is
Speaker 1: buying because they expect to be able to resell to
Speaker 1: somebody else at a higher price. Greater fool theory correct.
Speaker 1: So you look at the fangs today, the marginal buyer
Speaker 1: in most cases is not somebody who's done careful analysis
Speaker 1: of forward looking expected rates of return. The other parallel
Speaker 1: with the tech bubble is that at the peak of
Speaker 1: the tech bubble, five of the eight largest market cap
Speaker 1: companies on the planet. We're tech companies today seven of
Speaker 1: the eight largest our tech companies. The valuations aren't as
Speaker 1: extravagant as they were in two thousand, but the concentration
Speaker 1: at the top of the list in global market cap
Speaker 1: is even more. So let me push back on that
Speaker 1: because we've we've been debating this um with some folks
Speaker 1: in the office. So technology today represents the elimination of
Speaker 1: human ingenuity and effort. And why wouldn't technology companies that
Speaker 1: don't require a giant infrastructure. Think back to the railroads.
Speaker 1: It costs inflation adjusted billions to lay thousands of miles
Speaker 1: of tracks and all the manpower you had to hire
Speaker 1: in locomotives and rail cars and all that stuff. Right,
Speaker 1: Completely tapping intensive, completely labor intensive. Now a startup is
Speaker 1: a couple of guys on a laptop and an internet connection,
Speaker 1: so you don't have the same capital requirements you don't
Speaker 1: have the same labor intensity and the ability to scale
Speaker 1: is immense. So shouldn't the A shouldn't these companies be
Speaker 1: trading at a premium? And B shouldn't they be dominating
Speaker 1: the market? Courts? Hey, this is the future and technology
Speaker 1: is driving us all with robots and driverless cars and
Speaker 1: automation and your name it. But you tell me which
Speaker 1: companies are going to be the center of innovation ten
Speaker 1: years from now, and I will happily acknowledge that those
Speaker 1: companies deserve massive valuations today, huge multiples. The problem with
Speaker 1: that thesis is really simple, and that's casting a BroadNet.
Speaker 1: You're going to have a lot of companies with a
Speaker 1: fantastic story. Back in two thousands, this story was these
Speaker 1: companies are changing. It's a new paradigm. These companies are
Speaker 1: changing the way we do business, the way we communicate.
Speaker 1: They're radically reshaping the macro economy, and this industry is
Speaker 1: going to be changing our world. So of course they
Speaker 1: deserve massive premium multiples. What was overlooked was that these
Speaker 1: companies were disruptors, and the disruptors themselves get disrupted, often
Speaker 1: very fast. So how many search engines preceded Google how
Speaker 1: many how many handheld devices preceded the iPhone, and we're
Speaker 1: seen as utterly dominant. BlackBerry own that market for the palm.
Speaker 1: Palm briefly was trading for market cap greater than General
Speaker 1: Motors and and both of them eventually went to zero.
Speaker 1: So you have an industry with massive change. Here's a
Speaker 1: fascinating thing. The ten largest market cap companies in technology
Speaker 1: in the US in the year two thousand, um how
Speaker 1: many of those outperformed in the eighteen years before a
Speaker 1: start of two thousand and the start of this year.
Speaker 1: Zero a single one, not one beat the market. The
Speaker 1: fascinating part about tell me the companies ten years hence,
Speaker 1: look back ten years ago. There was no go down
Speaker 1: the list. Facebook was in public, Twitter was in public.
Speaker 1: Netflix wasn't public. Actually Netflix was public, but they were
Speaker 1: sending DVDs through the MAIP. Go down the whole list
Speaker 1: and go to ten years previous to that. Google wasn't public.
Speaker 1: You could, you could. Apple was thought to be going bankrupt.
Speaker 1: Amazon was a glimmer and Jeff Bezos is I So
Speaker 1: it's ten years doesn't seem like a long time, but
Speaker 1: making a forecast ten years, hence, it's all but impossible.
Speaker 1: You could get lucky. But here's another fascinating thing about
Speaker 1: um bubbles and about markets in general. If you take
Speaker 1: the ten largest market cap companies on the planet, on average,
Speaker 1: only two of them are still on that list ten
Speaker 1: years later. That's amazing. It's eight or under performers. Now,
Speaker 1: the eight that fall off the list are obviously and
Speaker 1: assuredly performing worse than the eight that replaced them, and
Speaker 1: the eight that are on definition by definition, and the
Speaker 1: eight that are on the list today have a bigger
Speaker 1: weight than the ones that replace them. So you have
Speaker 1: this drag associated with capitalization waiting. So even the top
Speaker 1: ten reinforces the notion of what's wrong with indexing. That
Speaker 1: doesn't make it easier for active managers to add value
Speaker 1: if they're looking over the shoulder and wondering, how am
Speaker 1: I hurting myself by not owning Netflix? Or shouldn't I
Speaker 1: really take advantage of the dip and buy more Tesla? Now,
Speaker 1: I just we're recording this on a day when the
Speaker 1: Wall Street Journal had a column out, I'm sorry it
Speaker 1: was Bloomberg had a column out that said Apple a
Speaker 1: stone's throw away from a trillion dollars and the biggest
Speaker 1: market cap company is actually trading in a much lower
Speaker 1: multiple than the previous times, when, at least on an
Speaker 1: inflation adjusted basis, we had companies approaching the same level.
Speaker 1: Apple at eighteen times is much cheaper than go down
Speaker 1: the list when it was Qualcom, when it was Microsoft,
Speaker 1: when it was whoever. Uh, so are the faning the problem?
Speaker 1: I guess we run into Apple. A new favorite of
Speaker 1: Warren Buffett's is cheap Amazon, now that they're starting to
Speaker 1: show a profit by any rational measure other than pure
Speaker 1: growth and market share, looks very expensive, so as does
Speaker 1: we Work, and a bunch of others. Well, the private
Speaker 1: company's uber we Works. Yeah, tharaos that one didn't work
Speaker 1: out too well. But go down the list of the
Speaker 1: so called unicorns, what does that tell us about how
Speaker 1: much capital is chasing? Exactly right? And when you're talking
Speaker 1: about the unicorns, the very simple fact is some of
Speaker 1: them are worth every penny of what their valuation is today.
Speaker 1: I just don't know which one. And uh, if you
Speaker 1: can pick those um Google since it's i p OH
Speaker 1: has been a persistent favorite for value managers to underweight
Speaker 1: it and for value manager for deep value managers to
Speaker 1: even short it. Okay, well that's not working out so well.
Speaker 1: But for every Google, there's a palm Um, there's Uh,
Speaker 1: there's an array of companies where you look back and say,
Speaker 1: as you did with Saraos that didn't work out so well.
Speaker 1: Thin Nos was hot, it was going to change the world, right,
Speaker 1: But that was an outright fraud. That's very different. And
Speaker 1: and Elizabeth Holmes, the founder and CEO, just settled fraud
Speaker 1: accusations from the s with the sec The company is
Speaker 1: laid off almost all of its staff. Heartily recommend Carrie
Speaker 1: Row's book Bad Blood. It's a fascinating read. Uh. And
Speaker 1: we'll get to books in a minute. But where there
Speaker 1: aren't cases of outright where it isn't a scam, where
Speaker 1: it's which a legitimate company who, for whatever reason, its
Speaker 1: moment may have passed. How can you determine with any
Speaker 1: degree of confidence this is a company that's going to
Speaker 1: be around for ten or twenty years. You can't. You
Speaker 1: can't um x andity. You can't say which of the popular,
Speaker 1: beloved star companies are going to be around in ten years,
Speaker 1: let alone which ones are going to outperform. If history
Speaker 1: is an example, Uh, there were tech companies that were
Speaker 1: uh uh described as being sensibly priced in two thousand
Speaker 1: because they were only twenty times the two year forward earnings. Well,
Speaker 1: look at Microsoft, which has since under the new CEO,
Speaker 1: Saudia Nadela, has since returned to very quietly, has become
Speaker 1: the third largest or third most profitable company in the world,
Speaker 1: third largest market cap. I don't know if that's in
Speaker 1: tech stocks or stocks in general, and that's something that
Speaker 1: kind of was left for dead after the dot coms,
Speaker 1: so no one would have very few people were predicting
Speaker 1: Microsoft was going to make a comeback. I'm surprised Buffett,
Speaker 1: with his friendship with Gates, didn't recognize the value and
Speaker 1: Microsoft UM. But the question that all this comes to
Speaker 1: is if history tells us that these top ten stocks,
Speaker 1: most of them are going to drop out of the
Speaker 1: top ten, is its simple valuation mean reversion or is
Speaker 1: something else at work here? Part of it is valuation
Speaker 1: mean reversion. Part of it. We wrote a piece UM
Speaker 1: called too Big to Succeed, kind of a play on
Speaker 1: the notion of too big to fail, and the point
Speaker 1: of that paper was the largest market cap companies get
Speaker 1: there because they're big, successful businesses and trading at high multiples.
Speaker 1: You don't get to the top of a sector or
Speaker 1: top of the market without both conditions generally being true.
Speaker 1: For that reason, UH, these companies are likely to disappoint
Speaker 1: just on evaluation basis. Now, add on top of that
Speaker 1: the fact that now these companies are so visible that
Speaker 1: everyone's taking shots at them. Their competitors are after them,
Speaker 1: regulators want to put a new notch on their gun barrel. Uh.
Speaker 1: The list goes on and on, and so Apple goes
Speaker 1: from being a beloved trendy darling to being under attack
Speaker 1: from regulators all over the world, UH, questioned and challenged
Speaker 1: because of the buggy nous of some of their um
Speaker 1: uh software these days. Look at General Electric is a
Speaker 1: perfect example. They're a greater love darling than g in
Speaker 1: the in the nineties, not at all, And I, best
Speaker 1: of my recollection, it has not outperformed since then by
Speaker 1: much it's gotten. So I have to ask you. You
Speaker 1: keep referencing various white papers, um how smart batic could
Speaker 1: go disastfully wrong, too big to succeed. There was one
Speaker 1: other you mentioned When you and the research staff at
Speaker 1: Research Affiliates are thinking about writing a white paper putting
Speaker 1: it together, who is your target audience for that? Are
Speaker 1: you writing that? You know? Daniel Borstein very famously said,
Speaker 1: I write to figure out what I think. I'm going
Speaker 1: to suggest that's not the motivation with your white papers.
Speaker 1: You guys already know what you think. Who is the
Speaker 1: target audience for those papers? You know it's a really
Speaker 1: good question, because there's not a really good answer. Yeah,
Speaker 1: we'd love for our papers to be accessible to the
Speaker 1: ordinary Joe on the street, mom and pop. Investors aren't
Speaker 1: really reading white papers. That they aren't reading them, and
Speaker 1: the papers are in many cases too subtle, to mathematical,
Speaker 1: too complicated. When I get to page seven and it's
Speaker 1: all formulas, that's where it's happened. Well, we don't do that.
Speaker 1: We don't do formulas the way I finance general would,
Speaker 1: but we do. I think our general target would be
Speaker 1: somebody with the sophistication of the average financial advisor. If
Speaker 1: we're writing over the heads of the average financial advisor,
Speaker 1: we're not doing anybody any good. If we're writing below
Speaker 1: that level, we run a risk of oversimplifying. And uh,
Speaker 1: we do love challenging conventional wisdom. I've made a career
Speaker 1: out of looking at things that are generally perceived to
Speaker 1: be true and testing them, and sometimes they are true
Speaker 1: and sometimes they're not. And when they're not, you get
Speaker 1: a paper out of it that rattle some cages and
Speaker 1: that maybe even can change the business in a modest way.
Speaker 1: So um, one of the things that I've found is
Speaker 1: that when you write a paper that challenges conventional wisdom,
Speaker 1: there's somebody out there. In fact, usually there's lots of
Speaker 1: people out there who have made a career on that
Speaker 1: conventional wisdom. And boy are they angry to say to
Speaker 1: say the least. So one of the questions that keeps
Speaker 1: coming up on the just low cost indexing side is,
Speaker 1: given the growth of Vanguard and black Rock four trillion
Speaker 1: and five trillion respectively, I think they're gonna be five
Speaker 1: and six in a short period of time. The question
Speaker 1: that always comes up with low cost passive indexes is hey,
Speaker 1: how big can these get before it no longer works?
Speaker 1: I look at smart Beta, from it tripled to over
Speaker 1: six billion, it's a stone throw Uh now from a trillion,
Speaker 1: it's almost as big as Apple, almost as biggest Apple,
Speaker 1: almost as big as one stock. At what point? So
Speaker 1: there's well, the biggest stock, But at what point does
Speaker 1: this top out When does it become so large that
Speaker 1: everybody's doing it and therefore there's no outperformance in it. Firstly,
Speaker 1: smart beta has been to become a catch all phrase
Speaker 1: that spans a lot of strategies, many of which aren't
Speaker 1: smart at all. So the notion that it's just a label, right,
Speaker 1: So let's stick forget smart beta. I don't like passive indexing.
Speaker 1: I don't like smart beta. Let's stick with fundamental indexing,
Speaker 1: and we'll stick with price to sales, price to earnings,
Speaker 1: price to book value, price to dividend, which I think
Speaker 1: is a fundamentals where where research affiliates began. And that's
Speaker 1: did we say that's or more of the indices you create? Um,
Speaker 1: it's it's about UM eight of our A U M.
Speaker 1: The rest is global asset allocation strategies. Now, in terms
Speaker 1: of capacity, different so called smart beta strategies have different capacity.
Speaker 1: Some of them are very high turnover UM, heavily involved
Speaker 1: in illiquid companies, for instance, um UH. Roger Robbertson's favorite
Speaker 1: strategy is an aliquidity strategy. You're gonna focus on companies
Speaker 1: with low liquidity. If you're trying to run a hundred
Speaker 1: billion using that forget not even a chance that, but
Speaker 1: that works for a small niche funds, and that's absolutely
Speaker 1: now fundamental index still to this day pretty much unique
Speaker 1: among the so called smart beta strategies, does have vast capacity.
Speaker 1: Why you're spanning the broad macro economy, you're owning pretty
Speaker 1: much everything you're trading consists of contratrading against the market's
Speaker 1: most extreme bets. So when a price is soaring, you're trimming, well,
Speaker 1: it's easy to find a counterparty to those trades. If
Speaker 1: a stock is tumbling, you're buying. It's easy to find
Speaker 1: a counter party of those trades. You're you're trading is
Speaker 1: spread across a thousand and companies instead of concentrated as
Speaker 1: with SMPI indexation in whatever stocks are being added or
Speaker 1: deleted that particular week. So let me channel Jack Bogel
Speaker 1: and say, all that makes perfect sense, But all that
Speaker 1: trading is expensive, and the gains are offset by your
Speaker 1: turnover and your trading costs. We're selling what people want
Speaker 1: to buy, We're buying what people want to sell. So
Speaker 1: are trading costs at this stage have been immeasurably small.
Speaker 1: At some stage, at some size, we start to move
Speaker 1: prices we're not there yet. How did eighty billion and
Speaker 1: we're not there yet? You know, there's a ways to
Speaker 1: go before you have to really be concerned about that.
Speaker 1: The trading costs for classic capuated indexing are much more vivid,
Speaker 1: much more substantial, measurably so because during that grace period
Speaker 1: you get a nine percent spread between additions and discretionary deleting.
Speaker 1: But that's not the trading costs. That's the actual na
Speaker 1: V gain or loss from the trade. They what do
Speaker 1: they do? They lose half a dozen stocks, they had
Speaker 1: half a dozen stocks. They do that once a year.
Speaker 1: Their trading costs accord. He does it multiple times a year,
Speaker 1: one or two or three stocks, when there's mergers, when
Speaker 1: there's takeouts, when there's things like that. But when I
Speaker 1: look at something like a price to to earnings ratio,
Speaker 1: when you're buying things fundamentally ranked by earnings that changes quarterly,
Speaker 1: how often do you have to rebalance that that index?
Speaker 1: There's two flavors of fundamental index. The original Footsie RAFFI
Speaker 1: rebalances once a year and has ten to annual turnover.
Speaker 1: That's all. And that ten to fifteen percent is spread
Speaker 1: across a thousand companies. It's not concentrated in a dozen
Speaker 1: additions or deletions. The um. Other flavors of Fundamental Index,
Speaker 1: our own Raffi series and the Russell Fundamental Index are
Speaker 1: use what we call quarterly staggered rebalancing, which means every
Speaker 1: quarter you move one fourth of the way to the target.
Speaker 1: Makes sense, you're letting it out. You let momentum run
Speaker 1: on three fourths of the portfolio, and one four of
Speaker 1: the portfolio. You say, okay, enough momentum, we're going to rebalance.
Speaker 1: And by doing it that way, you have the same
Speaker 1: turnover as once a year rebalancing. Still ten to fifteen
Speaker 1: percent annual turnover. It's very low. It's very easy to trade.
Speaker 1: So I don't I don't fret about implementation costs, trading costs,
Speaker 1: moving market prices until we're in the trillion dollar range.
Speaker 1: So we've talked about Van Goard as a competitor in
Speaker 1: black Rock. Let's let's let me bring up something just
Speaker 1: because I'm looking black Rocks. So one of our licensees
Speaker 1: they run over ten, so they're an affiliate, not really
Speaker 1: a true competitor. Yeah, they have competing product and they
Speaker 1: have uh licensed product they're running over ten billion globally
Speaker 1: and RAFFI strategies, so you're you're happy with them, Um,
Speaker 1: I'm looking at price to sales, price to book. Let's
Speaker 1: talk about Dimensional Funds, which is about six billion, and
Speaker 1: their core fundamental index is similar to yours, and that
Speaker 1: they look at price to book and other FAMA based
Speaker 1: FAMA French based factors. But really that's the core of
Speaker 1: of what they do. How do you look at them
Speaker 1: relative to to research affiliates and what what you guys
Speaker 1: do well. RAFFIE in US international and emerging markets has
Speaker 1: has um pretty reliably trounced the d f A value
Speaker 1: products and for an extremely simple reason. D f A
Speaker 1: hues to the religion of efficient markets and says we're
Speaker 1: gonna we're gonna anchor on cap waiting for I mean,
Speaker 1: within their price to book, it'll be get they're gonna
Speaker 1: cap you know, I'm gonna get pushed back and they're
Speaker 1: gonna say here's wrong. I'm gonna forward you that email
Speaker 1: and you can look forward to the email. But in
Speaker 1: any event, they anchor on cap waiting, which is which
Speaker 1: is a mistake, and they do wonderful work. I don't
Speaker 1: want to be seen as suggesting that that they're doing
Speaker 1: something bad. They're doing something better than conventional cap weighted indexing,
Speaker 1: but they anchor on cap waiting. That's that's their starting point,
Speaker 1: so and that's their mistake. I'm gonna have to follow
Speaker 1: up with that. So I only have you for another
Speaker 1: ten or fifteen minutes. I have dozens more questions. We'll
Speaker 1: have to have you back for another time. But let
Speaker 1: me jump into some of my favorite questions that we
Speaker 1: ask everybody, and I'm looking forward to hearing your answers.
Speaker 1: Tell us the most important thing that people don't know
Speaker 1: about you. A lot of people see me as a
Speaker 1: very serious UM research guy. Uh. In point of fact,
Speaker 1: life is short. Having fun is absolutely crucial, and I
Speaker 1: know that about you from camp Ko talk. And so
Speaker 1: I don't do anything. I don't do any research. I
Speaker 1: really don't do anything that isn't fun. I like that answer.
Speaker 1: I'm going to skip ahead to my question number eight.
Speaker 1: What do you do for fun outside of work? Well,
Speaker 1: I collect vintage motorcycles, fastest of their era. I like
Speaker 1: good wine, and I'm a movie junkie. I I have
Speaker 1: watched UM. I write down the movies that I watch
Speaker 1: and I rate them on a five star scale. And
Speaker 1: I've watched over movies in the last six years. Wow,
Speaker 1: that's a lot of movies. Do you modern stuff, classic movies?
Speaker 1: What what's your favorite job? I don't have a favorite.
Speaker 1: I don't watch a lot of the really old movies,
Speaker 1: old as in twenties and thirties and forties, fifties. I
Speaker 1: just saw Roman Holiday the other day. Oh, that's a
Speaker 1: wonderful film. She's just delightful. Yeah, she was taken from
Speaker 1: us much too young. But in any event, the the uh,
Speaker 1: I like variety in films. Uh, that's grace who was
Speaker 1: taken too young. I think Audrey Hepburn lived a fairly
Speaker 1: long she was doing Katherine Hepburn lived a long time.
Speaker 1: Audrey Hepburn I think died in her late sixties. You
Speaker 1: could be right. I am just let's google that. Actually
Speaker 1: this comes up as bing, which I don't know why. Uh,
Speaker 1: sixty three, you are correct, very good memory. Yeah, yeah,
Speaker 1: she had a cerebral hemorrhage while walking down the street
Speaker 1: in Manhattan, just suddenly dropped. Really sad. But in any event,
Speaker 1: very eclectic tastes in films, if it's mainstream Hollywood actable.
Speaker 1: I'm bored. If it is weird and strange, I love it,
Speaker 1: have you? Uh? I was a giant fan of the
Speaker 1: original Blade Runner. Did you see the sequel? So the sequel,
Speaker 1: and then I went back to watch the original for
Speaker 1: a second time after the original is still an astounding
Speaker 1: piece of it is I I saw the new one
Speaker 1: in the theater and I walked out a tad disappointed.
Speaker 1: But that was almost inevitable, and I'm waiting a full
Speaker 1: year to see it again with a little more open minded.
Speaker 1: Sequels are always done on the basis of an original
Speaker 1: film that was brilliant, that was extraordinary, and so it
Speaker 1: suffers by comparison. Patty Shack too, I did not see that.
Speaker 1: Neither did I. But the assumption is um. So let's
Speaker 1: talk about mentors. Who do you look at, who mentored
Speaker 1: you earlier in your career, and who influenced your approach
Speaker 1: to investment. Um. Early mentors were mostly people I worked
Speaker 1: for who were brilliant. Dick Crowle at the Boston Company, UM,
Speaker 1: Bob Lovell at Crumb and Forster at First Quadrant. He
Speaker 1: founded First Quadrant and brought me in his uh president
Speaker 1: and then as CEO later and um um those would
Speaker 1: be two of my most influential mentors. People I didn't
Speaker 1: work for who were mentors. Peter Bernstein was massively influential
Speaker 1: god and the way I think, oh he was he
Speaker 1: was such a mench He was against the against the gods. Well,
Speaker 1: here here's the here's the pitch story. I want you
Speaker 1: to publish my book. It's going to be a book
Speaker 1: on the history of probability theory in finance, and it's
Speaker 1: going to be a best seller. What And it was
Speaker 1: the best seller. It's sold to famillion copies. Such an
Speaker 1: amazing book. I'm slowly working my way through the rest
Speaker 1: of his uh, the rest of his work. And he
Speaker 1: influenced you from afar. Did you have talk with him
Speaker 1: or meet? Oh? We became very good friends, very good friends.
Speaker 1: We worked on two journal articles together, one for Harvard
Speaker 1: Business Review and one for Financial Analyst Journal, which were
Speaker 1: two of my favorite papers that I've been involved in,
Speaker 1: partly because he's such an effortless writer. The paper for
Speaker 1: the Harvard Business Review, he said, why don't you do
Speaker 1: the first draft? I did, and he called me after
Speaker 1: he received it and he said, Rob, there's some gems
Speaker 1: in here, but this is a turgid mess and right
Speaker 1: blunt a little a little blunt, but in a nice way,
Speaker 1: in a in a hey, let me polish this up
Speaker 1: a little bit. Yeah, let me do a complete, massive
Speaker 1: rewrite and turn it into something that the average businessman
Speaker 1: without any investment savvy to understand. And how did the
Speaker 1: paper come out? Came out in the Harvard Business Review?
Speaker 1: I mean, I mean what was what was your final
Speaker 1: read of it? Did you like what he did? And
Speaker 1: how could you not? How could I not? Um? The
Speaker 1: paper was titled by Harvard Business Review. We didn't come
Speaker 1: up with the title The Right Way to Manage your Pension. Uh.
Speaker 1: That's a little arrogant. But it's attention grabbing. It's attention grabbing. Uh.
Speaker 1: And it was. It was an influential paper. So since
Speaker 1: we're talking about Peter Burnestein, let's talk about your favorite books.
Speaker 1: What are some of the favorite things that you have read?
Speaker 1: And I'm just gonna assume iron rand and move beyond that.
Speaker 1: What I really like you did touch on politics, which
Speaker 1: is another of my passions. The uh, for those um
Speaker 1: who don't know me, I'm a libertarian. I believe in
Speaker 1: limited government, which too few people do. The well there
Speaker 1: are people who claim to be libertarians, but really they're
Speaker 1: libertarians for a specific issue, and then they want the
Speaker 1: government intervening elsewhere wherever it's convenient for them. That's human nature.
Speaker 1: The the UH in investments against the gods would be
Speaker 1: tough to beat. It's fantastic. Anything by Ben Graham is
Speaker 1: a must read intelligent investor. Yeah, um, security and security analysis.
Speaker 1: You want to dive in deep um. These are some
Speaker 1: of the giants of our industry. Um. Outside of investing,
Speaker 1: and actually most of my reading when I'm not working
Speaker 1: is outside of investing. A couple of really fun books.
Speaker 1: Four Really, there was a year after Columbus sailed for
Speaker 1: the New There was a book called fourteen nine one
Speaker 1: that examined the America's before Aumbus landed, and the population
Speaker 1: of the America's back then was probably in the in
Speaker 1: the couple of hundred million range. Really, I would not
Speaker 1: have guessed that massively wiped out by measles and other
Speaker 1: diseases where they had no no built up, no genetic
Speaker 1: ability to fight those diseases, so wiped out a hundred
Speaker 1: million plus people. Native Americans were here before Columbus landed.
Speaker 1: I would earlier arrivers. Earlier arrivers said that the shores
Speaker 1: were teeming with people. Fascinating. Uh was the sequel to
Speaker 1: that book, and I think it was even better. It
Speaker 1: shows how global commerce has reshaped the world and how
Speaker 1: it creates seeds of tremendous risk. Another book that I've
Speaker 1: been enjoying immensely, partly because it's so you can pick up,
Speaker 1: pick it up, read a page or two, and then
Speaker 1: put it down and come back to it a month
Speaker 1: later and you haven't missed a beat. It's Letters of Note,
Speaker 1: which is filled with letters written from person AID to
Speaker 1: person B going back three thousand years to modern times.
Speaker 1: Letters that are just fascinating. A letter from a woman
Speaker 1: in China to her husband headed off to war UM
Speaker 1: written four b C and the passion in her worrying
Speaker 1: about him, wanting him to be spectacular in battle, but
Speaker 1: please come home, and uh, you know, it just reminds
Speaker 1: you that all of these people were human beings. Even
Speaker 1: a letter from um Jack the ripper to the police
Speaker 1: taunting them, and you feel the evil humanity in the letter.
Speaker 1: It's just fascinating. I'm gonna definitely check that out. That
Speaker 1: that is quite impressive. Tell us about a time you
Speaker 1: failed and what you learned from the experience. Well, I
Speaker 1: was invited to be a global equity strategist at Solomon
Speaker 1: Brothers in nineteen eighty seven. UM. I lasted there for
Speaker 1: fourteen months. UM it was a wonderful learning experience. UM
Speaker 1: I naively, I was only thirty two at the time,
Speaker 1: and I naively thought I was coming into be global
Speaker 1: equity strategist, But in fact I was coming into UM
Speaker 1: reinforced sales. Of course, of course that's the job. And
Speaker 1: I remember I was brought into a T T S
Speaker 1: pension fund um UH the week after the market crash.
Speaker 1: They were using a mean variance optimizer mark What's optimizer
Speaker 1: to look at their asset allocation, and they were puzzled.
Speaker 1: They said, we did an optimization. We pushed up return
Speaker 1: expectations for stocks, pushed down return expectations for bonds because
Speaker 1: the yields tumbled, pushed up the risk assumption for everything.
Speaker 1: By it seemed like a very reasonable, even conservative approach.
Speaker 1: And our optimizer is telling us to get out of
Speaker 1: stocks and put it all in bonds, and we're it's
Speaker 1: setting wrong with that, optimizes. So I came in and
Speaker 1: I explained the mathematics of why the optimizer would do
Speaker 1: that and ackwards looking as it is. No, it was
Speaker 1: forward looking. It was forward looking. They were assuming higher
Speaker 1: the inputs coming from which is now happens on misremembering this,
Speaker 1: Their inputs were forward looking. So they pushed up the
Speaker 1: return for stocks because they'd crashed, down the return for
Speaker 1: bonds because the yields had crashed, and it still wanted them,
Speaker 1: and it still wanted to put more into bonds because
Speaker 1: they pushed the risk up for everything. Now I went
Speaker 1: through with them the mathematics of why it was saying that,
Speaker 1: and then I concluded by saying, throw out the optimizer
Speaker 1: by stocks. Well, oh my goodness. The reaction at Solomon
Speaker 1: in response to that was fascinating because they were salivating
Speaker 1: over the possibility of a ten billion dollar bond portfolio
Speaker 1: construction exercise that would make them uh thirty forty basis
Speaker 1: points on on ten billion an instant fifty million dollars.
Speaker 1: And here comes this strategist telling the client to do
Speaker 1: the opposite. Um. Uh, they weren't. They were livid. And
Speaker 1: the lesson. The lesson learned was you've got to know
Speaker 1: who your client is. You've got to know what they want,
Speaker 1: and you've got to know whether what they want is
Speaker 1: what you're comfortable delivering. And and our final question, tell
Speaker 1: us something you know about investing today that you wish
Speaker 1: you knew thirty plus years ago, oh even fifteen years ago.
Speaker 1: When we launched fundamental Index. We published the paper and
Speaker 1: immediately set about saying, if you can build an enhanced
Speaker 1: index relative to cap weight, you can build an enhanced
Speaker 1: index relative to fundamental index. Who better to do that
Speaker 1: than us? Well, why don't we figure out which of
Speaker 1: the fundamental measures works best and have the mix of
Speaker 1: fundamental metrics be dynamic. And what I didn't realize at
Speaker 1: the time, we failed miserably. Everything we tried didn't work.
Speaker 1: Um what I didn't realize at the time was when
Speaker 1: a strategy becomes massively more expensive, it'll have wonderful past
Speaker 1: returns and terrible future returns. So we were zeroing in
Speaker 1: on the fundamental metrics that the market was loving more
Speaker 1: and was poised to disappoint. So the enhancements failed. Today,
Speaker 1: I have a better understanding that valuation matters for factors
Speaker 1: and strategies just as much as it does for stocks.
Speaker 1: Quite fascinating, we have been speaking with Rob are not
Speaker 1: of research affiliates. If you enjoy this conversation, be showing
Speaker 1: look up an inch or down an inch on Apple iTunes,
Speaker 1: Bloomberg dot com, Stitcher, overcast, wherever finer podcasts are sold,
Speaker 1: and you can see any of the other two hundred
Speaker 1: plus such conversations we've had. We love your comments, feedback
Speaker 1: and suggestions right to us at m IB podcast at
Speaker 1: Bloomberg dot net. I would be remiss if I did
Speaker 1: not thank the crack staff that helps me put together
Speaker 1: this conversation each week. Medina Parwanner is our producer, Slash
Speaker 1: audio engineer, Taylor Riggs is our booker. Michael bat Nick
Speaker 1: is our head of research. I'm Barry Hults. You've been
Speaker 1: listening to Masters in Business on Bloomberg Radio.