Speaker 1: Welcome to Inside Active, a podcast about active managers that
Speaker 1: goes beyond sound bites and headlines and looks deeper into
Speaker 1: their processes, challenges, and philosophies and security selection. I'm David Cohne,
Speaker 1: I lead mutual fund and active research at Bloomberg Intelligence.
Speaker 2: Today.
Speaker 1: My cost is Christopher Kine, us quantitative strategist at Bloomberg Intelligence. Chris,
Speaker 1: thanks for joining me today.
Speaker 3: Thank you so much for having me.
Speaker 2: David, So, I wanted.
Speaker 1: To ask you about the small cap BMVP portfolio, as
Speaker 1: I really think it's partin into our discussion today. How
Speaker 1: has the focus on certain factors helped that portfolio up
Speaker 1: perform an index like the Russell two thousand.
Speaker 3: Sure, so, our small cap BMVP multi factor portfolio is
Speaker 3: very similar to our large cap version. So it's a
Speaker 3: only multi factor portfolio using four main factors, which is value, momentum,
Speaker 3: low volatility, and profitability. So our small cap version beat
Speaker 3: the Russell two thousand equweight index by about ten percentage
Speaker 3: points this year or last year, I should say, up
Speaker 3: about twenty percent versus about ten to eleven percent for
Speaker 3: the index. You know, it's not really a mystery why.
Speaker 3: You know, most of the factors did work in small
Speaker 3: caps last year similar to large caps. Would I would
Speaker 3: flag that value seem to work a bit better, you know,
Speaker 3: in small caps, but the devils and the details kind
Speaker 3: of about how you define value. But yeah, it's been
Speaker 3: a strong factor year for both market capitalizations and hopefully
Speaker 3: continues great well.
Speaker 1: I think our guests can add some color to our
Speaker 1: discussion on factors. I'd like to welcome Joel Schneider, Joel's
Speaker 1: deputy head of portfolio management at Dimensional Fund Advisors. Joel,
Speaker 1: thank you for joining us today.
Speaker 2: Hey David, Hey Chris. Happy to be here.
Speaker 1: So before we die into factors and discussion in general,
Speaker 1: we'd love to hear how you got your start in
Speaker 1: the investment business.
Speaker 2: Yeah. Sure. I'll start with the short answer first, which
Speaker 2: is serendipity, meaning I didn't really set out to work
Speaker 2: in investment management. The longer answer is, you know, I
Speaker 2: was one of those inquisitive kids that used to take
Speaker 2: things apart to understand how they worked, and it's probably
Speaker 2: no surprise I wound up in engineering, but before I
Speaker 2: got there. After school, growing up, I used to go
Speaker 2: over to my grandparents' house and my parents both worked,
Speaker 2: so my grandparents watched US for a couple hours, and
Speaker 2: my grandpa would always watch CNBC, and so it was
Speaker 2: riveting for me, as a young person that like numbers,
Speaker 2: to see all these prices flying by, to see all
Speaker 2: these company fundamentals being talked about. But there was sort
Speaker 2: of a randomness to the stock market that never really
Speaker 2: made a lot of sense to me, so I sort
Speaker 2: of put that interest aside. I ended up going to
Speaker 2: college and studying computer engineering, and then I worked at
Speaker 2: Lockey Martin and I designed communication systems for the Navy
Speaker 2: and the Air Force. And as I was there, I
Speaker 2: began to realize that if I was going to progress
Speaker 2: in that role, I needed to learn corporate finance. So
Speaker 2: to be the head of a large program, or to
Speaker 2: be the head of a division, you had to have
Speaker 2: your own P and L. So I needed to understand
Speaker 2: corporate finance better than I did. So I went back
Speaker 2: to business school at University of Chicago to get an MBA,
Speaker 2: and while I was there, I learned that there were
Speaker 2: a lot of theories and frameworks and evidence that actually
Speaker 2: explained how financial markets worked, So to me, this was
Speaker 2: really cool. There were valuation frameworks like discounter cash flow
Speaker 2: models that help you value companies. There were portfolio theories
Speaker 2: that talked about the benefits of diversification based on the
Speaker 2: covariance of different assets. There was arbitrage pricing theories that
Speaker 2: helped explain how the prices of different financial instruments were
Speaker 2: all kept inlign with each other. And then importantly for
Speaker 2: today's discussion, there were factor pricing models that helped explain
Speaker 2: what drove returns in equities and bonds. And so after
Speaker 2: seeing all this, and actually really importantly to me was
Speaker 2: these weren't just theories. People were actually rigorously testing them
Speaker 2: using the scientific method which I'd come to learn in engineering.
Speaker 2: So all of this allowed me to see through the randomness.
Speaker 2: And when I graduated from University of Chicago, there's really
Speaker 2: no place other that I wanted to work than dimensional
Speaker 2: because we'd been associated with a lot of the academics
Speaker 2: that founded a lot of those frameworks and did a
Speaker 2: lot of that research.
Speaker 1: That's great, and so you know, actually, let's talk about
Speaker 1: dimensional or you know DFA as I call it. And
Speaker 1: I'm sure a lot of others, and I know many
Speaker 1: of our listeners know of DVA, but we'd love to
Speaker 1: hear you know, from your I guess experience working there,
Speaker 1: you know the basic tenants behind the DFA investment philosophy.
Speaker 2: Yeah, sure, if you don't mind to understand our philosophy,
Speaker 2: and might be helpful if I spend a couple minutes
Speaker 2: describing our background in our history. So, yeah, we've been
Speaker 2: around for about four decades and we've become one of
Speaker 2: the largest investment managers and more recently the largest active
Speaker 2: ETF manager in the world, and we manage about eight
Speaker 2: hundred billion dollars of publicly traded securities, so across equities,
Speaker 2: fixed income, real estate, and commodities. And we have this
Speaker 2: really deep academic heritage and so a lot of our investments,
Speaker 2: and specifically our investment philosophy that you asked about, is
Speaker 2: rooted in research that's been done by multiple Nobel Prize
Speaker 2: winners as well as many other leading academics. And what
Speaker 2: we see as our job at Dimensional is to implement
Speaker 2: their research in real world portfolios. So what that ends
Speaker 2: up looking like is we run very low cost, broadly
Speaker 2: diversified portfolios, but they are not exactly indexed. So we
Speaker 2: have active strategies that actually outperform our benchmark index is
Speaker 2: at a greater rate and over longer periods of time
Speaker 2: than most other managers. In fact, I'm not aware of
Speaker 2: anyone that has sort of the types of numbers we have,
Speaker 2: So just to give you a sense of what those are,
Speaker 2: if you were to go back over the last decade
Speaker 2: and say what percentage of managers beat their benchmark index
Speaker 2: in the industry, it's a pretty low number. It's only
Speaker 2: twenty three percent of managers. But at dimensional seventy eight
Speaker 2: percent of our funds have beating their benchmark index over
Speaker 2: the last ten years. And if you extend that over
Speaker 2: twenty years, it gets even worse. For the industry, only
Speaker 2: eighteen percent of funds have beaten their benchmarks, but ninety
Speaker 2: two percent of our funds have. And so I want
Speaker 2: to get into the investment philosophy now to sort of
Speaker 2: explain how we do that. But a key insight, remember
Speaker 2: when I said our job is to implement the best
Speaker 2: ideas in finance. Well, our co founders actually founded some
Speaker 2: of the first index funds in the early nineteen seventies,
Speaker 2: and in doing that they came to realize that trading
Speaker 2: in a really rigid way where you have to buy
Speaker 2: and sell the stocks that the index tells you to
Speaker 2: is a recipe for high trade and cost, and so
Speaker 2: they realize back then that there's a difference between passive
Speaker 2: and indexed. So I think this may help us in
Speaker 2: our conversation today. But I just wanted to find passive
Speaker 2: and active, or sorry, passive and index For passive, I
Speaker 2: think that means just treating market prices is fair and
Speaker 2: generally holding securities at their marketcap weight. For indexed, that
Speaker 2: is an implementation approach. It's basically where you are forced
Speaker 2: to buy and sell securities when some third party index
Speaker 2: provider tells you to. And so a lot of the
Speaker 2: key to doing better than indexes is avoiding that type
Speaker 2: of implementation. All right, So that was a really long
Speaker 2: way to get to your question. But in terms of
Speaker 2: our investment philosophy, I think it boils down to in
Speaker 2: competitive liquid markets, prices are generally fair, they're forward looking,
Speaker 2: and they already represent a consensus prediction about the future.
Speaker 2: And so the challenge that a lot of traditional active
Speaker 2: managers have had is that they're trying to outguess those
Speaker 2: market prices. And as we all saw yesterday in the news,
Speaker 2: all of a sudden there was news that the Chinese
Speaker 2: company behind deep Seek had this great new AI model
Speaker 2: that challenged the business model of some of the US
Speaker 2: based companies and the suppliers of chips, and so really
Speaker 2: quickly prices adjusted, and I didn't really see a lot
Speaker 2: of people out there calling that ahead of time. So
Speaker 2: markets are moving in real time. Prices are adjusting, and
Speaker 2: so our philosophy is embrace that, just make use of it.
Speaker 2: And so it starts with just saying the prices are
Speaker 2: what they are, they're a reasonable prediction. How do we
Speaker 2: bring other pieces of information to combine with the price
Speaker 2: to understand which stocks have higher or lower future expector
Speaker 2: returns given that price today? And so that to many
Speaker 2: people they call that factor investing. But you're combining different
Speaker 2: variables or financial metrics from companies' income statements or balance
Speaker 2: sheets with the price to make some inferences about which
Speaker 2: stocks are likely to do better in the future.
Speaker 1: No, that makes sense. I do want to touch upon
Speaker 1: what you said about indexing and passive and so, you know,
Speaker 1: this being a podcast focused on active management, if you
Speaker 1: could further elaborate on what you consider the inefficiencies of indexing.
Speaker 1: You know, you mentioned you know the news yesterday and
Speaker 1: you know obviously, you know, holding an index, you know
Speaker 1: that that becomes an issue, and so i'd just love
Speaker 1: to hear more about that.
Speaker 2: Yeah, for sure. So again, indexing to us is an
Speaker 2: implementation decision, and it's basically outsourcing your trading decisions to
Speaker 2: some third party index provider. And I think there's multiple
Speaker 2: issues when you outsource to them. Now, if we step back,
Speaker 2: a lot of people think indexing is really sort of
Speaker 2: low cost, and I would actually take issue with that.
Speaker 2: I think it's low fee, but it's not necessarily low cost.
Speaker 2: And what I mean is that there's a lot of
Speaker 2: hidden costs involved with indexing. A big one of those
Speaker 2: is that the indexers are all forced to trade the
Speaker 2: same names on the same day, the same time as
Speaker 2: all the other indexers. And so a really fun analogy
Speaker 2: is it's sort of like buying roses on Valentine's Day?
Speaker 2: Do you think you're going to get a good price? No, Right,
Speaker 2: if you bought roses a week or two before or after,
Speaker 2: you're going to get a much better price than if
Speaker 2: you're buying them on that day. So that's called the
Speaker 2: index reconstitution effect. And you guys may know these numbers
Speaker 2: better than me, but if you look at the growth
Speaker 2: of indexing, the total dollars chasing after the same names,
Speaker 2: I saw numbers las year at the end of last
Speaker 2: year around like twelve trillion dollars in index products. So
Speaker 2: that means that when there's these rebalance events, you've got
Speaker 2: billions of dollars, tens of billions of dollars that are
Speaker 2: all chasing the same stocks. And what that does is
Speaker 2: it tends to on average, push up prices of names
Speaker 2: that are being added to the index and pushed down
Speaker 2: prices of names that are being dropped. And our research
Speaker 2: we actually just did an updated set of research papers
Speaker 2: on this. There's a lot of academic work about ten
Speaker 2: years ago, so we decided to do an update for
Speaker 2: the next decade. We looked at both US and non
Speaker 2: US equity indices, and we found that prices get pushed
Speaker 2: by about four percent on average. Four percent. I don't
Speaker 2: know if that feels like a big or small number
Speaker 2: to the people listening, but let me just put that
Speaker 2: in context a little. Let's say an index has five
Speaker 2: percent turnover a year. It's pretty low turnover. Eight Well,
Speaker 2: if five percent of your portfolio is getting four percent
Speaker 2: worse prices that's a potential drag of twenty basis points
Speaker 2: a year. Now, when people think of the low expense
Speaker 2: ratios or management fees of indexing, that twenty basis points
Speaker 2: of hidden performance drag is in many cases much bigger
Speaker 2: than the fee they're paying, So really their total cost
Speaker 2: of ownership is a lot higher than they think it is.
Speaker 3: Now.
Speaker 2: The challenge with seeing that is both the index and
Speaker 2: the index fund suffer from that because they're both adding
Speaker 2: the stocks at the close on the rebalanced day. So
Speaker 2: your index fund will have maybe no tracking here with
Speaker 2: your index, but both of them have that performance drag
Speaker 2: baked in. So I think that's one of the biggest
Speaker 2: inefficiencies of indexing. I think there's other ones, though we
Speaker 2: can get into this a little bit later, especially when
Speaker 2: you're trying to capture factor. Premium indexes have a lot
Speaker 2: of style drift, and that becomes a big issue. I
Speaker 2: heard Chris at the beginning talking about capturing some of
Speaker 2: the premiums within small cap like profitability and value. Well,
Speaker 2: if the Russell two thousand is only rebalancing once a
Speaker 2: year in June, then that means they've got eleven months
Speaker 2: where the stocks they hold have drifted. Many have become
Speaker 2: midcaps or large caps, and so the index style drift
Speaker 2: is a major issue. And then you know, I talk
Speaker 2: with a lot of institutional clients and one of the
Speaker 2: things that they've come to really realize is that a
Speaker 2: lot of their index managers may be charging them a
Speaker 2: very low management fee, but then they're keeping a pretty
Speaker 2: large percentage of the securities lending profits from those stocks,
Speaker 2: and in some ways that serves as sort of a
Speaker 2: shadow management fee, right it looks like you're only paying
Speaker 2: a couple BIPs and expensory sue or in management fee,
Speaker 2: but then they're keeping a slug of the sect lending
Speaker 2: revenues for themselves. So those are just some of the issues.
Speaker 2: I'm sure we'll get into more of them later. But
Speaker 2: there's there's definitely issues with index implementation, and you can
Speaker 2: do better than indexing by not being so rigid.
Speaker 1: No makes sense, and so you know, switching from passive
Speaker 1: over to active, and you know, I know Chris has
Speaker 1: a bunch of questions for you in terms of you know,
Speaker 1: factors as you touched upon a little bit, but I
Speaker 1: would just love to hear you know, what factors in
Speaker 1: the research at Dimensional have you found that historically driven performance.
Speaker 2: Yeah, I think the answer to that depends on what
Speaker 2: time frame you're measuring. So we like to think about
Speaker 2: three different timeframes. So in the long term, let's say
Speaker 2: we're measuring over a year or more. In terms of
Speaker 2: those long term drivers, it's company's valuation, their profitability, and
Speaker 2: their size. Then you start to get into some of
Speaker 2: the more intermediate or shorter term drivers. Let's say we're
Speaker 2: measuring those over months or weeks. They're things like momentum
Speaker 2: or asset growth, or interestingly, stocks that are expensive to borrow.
Speaker 2: In the securities lending market, that's actually, to me a
Speaker 2: really cool factor. We often say that we extract information
Speaker 2: from market prices. So earlier you asked me about our
Speaker 2: investment philosophy, and I said, we take the prices for
Speaker 2: what they are and we see what information we can
Speaker 2: extract from that. Well, here's an example where the securities
Speaker 2: lending market that is another market just like the stock
Speaker 2: markets a market, the stock lending markets market, and it's
Speaker 2: got prices, and if people are willing to pay very
Speaker 2: high fees to borrow stocks, often to shorten them, that's
Speaker 2: actually a really negative sign that explains under performance. Of
Speaker 2: stocks over the next few weeks after they become expensive
Speaker 2: to borrow. And then the last very sort of short
Speaker 2: term factors are things related to liquidity. So one of
Speaker 2: them that's in the academic research is price reversals, and
Speaker 2: then the other are things related to implicit trade and costs,
Speaker 2: so spreads, price impact. So I think, really zooming back out,
Speaker 2: you want to think about which factors are reliable, but
Speaker 2: then which ones apply over different timeframes, and how you
Speaker 2: implement will depend on the timeframe that they apply over.
Speaker 2: And so I think maybe the last thing to add
Speaker 2: before I'm sure you have questions on that is some
Speaker 2: listeners may be wondering why I didn't mention some of
Speaker 2: the commonly cided factors that they've heard of. And unfortunately,
Speaker 2: you guys have probably heard the term the factor zoo.
Speaker 2: So there's so many people publishing papers about different factors.
Speaker 2: You know, some people say three hundred or four hundred
Speaker 2: factors have been identified. Well, those are mainly filled with
Speaker 2: either deplicative or unreliable factors. And so when I say deplicative,
Speaker 2: I just mean that once you control for the factors
Speaker 2: I already mentioned that the other ones don't add any
Speaker 2: new explanatory power. It's not to say there's anything wrong
Speaker 2: with those, it's just it could be you know that
Speaker 2: you're going to approach that factor using some other definition.
Speaker 2: That's fine as long as you know and you're not
Speaker 2: being redundant and applying basically sort of the same factor twice.
Speaker 2: And then the other reason why I didn't mention some
Speaker 2: is some of them just don't pass the high standards
Speaker 2: of the scientific method which I talked about earlier, and
Speaker 2: that is you need to have a strong economic theory
Speaker 2: for why a factor should exist. You need to be
Speaker 2: able to reproduce those results, and they need to be
Speaker 2: able to hold up an out of sample testing. Otherwise
Speaker 2: it's hard to be confident that those will occur in
Speaker 2: the future.
Speaker 3: So interesting, I you know, it's a jewel. I reserve
Speaker 3: the right to steal your buying roses on Valentine's Day analogy.
Speaker 3: I love that. So my questions around combining multiple factors,
Speaker 3: I mean, you know, thank you for walking through that.
Speaker 3: That was really interesting. With the different timeframes, you know,
Speaker 3: I don't see many people frame it that way, So
Speaker 3: you know, can you talk us through how those different
Speaker 3: timeframes you know, apply to a multi factor process. How
Speaker 3: do you combine factors, especially if they have different time frames?
Speaker 3: It is any element, and I guess, I guess I
Speaker 3: can ask do you believe in factor timing, Like, do
Speaker 3: you think there's a way to time factors or do
Speaker 3: you think it's a better approach to just have a
Speaker 3: relatively constant exposure to factors that you believe, you know
Speaker 3: are are advantages for the long term.
Speaker 2: Sure, well, it's a two part question, So let me
Speaker 2: take the second part of your question first, which is
Speaker 2: we have looked for every way that we could possibly
Speaker 2: think of time factors, and we really wish that you
Speaker 2: could just Unfortunately there's no evidence that you can, and
Speaker 2: that oftentimes the cost of getting it wrong is really significant.
Speaker 3: Right.
Speaker 2: One of the things I learned in engineering is you
Speaker 2: always have to think about if a certain part or
Speaker 2: system fails, how bad is the outcome when it fails?
Speaker 2: And getting factor timing wrong can be a really expensive
Speaker 2: and so generally it's better to take multiple factors that
Speaker 2: are reliable but that oftentimes are not highly correlated with
Speaker 2: each other and include them into a multi factor portfolio.
Speaker 2: This is going to give you a little bit more
Speaker 2: risk control and the ability to sort of ride out
Speaker 2: or survive different periods in the market when certain factors
Speaker 2: are in or out of favor. So I think, Chris,
Speaker 2: that gets to your second question, which is, then how
Speaker 2: do you start to combine multiple factors into a portfolio?
Speaker 2: And I think there's a few lessons to keep in mind.
Speaker 2: The first one that I said earlier is, you know,
Speaker 2: more factors are not necessarily better. There's a quote that's
Speaker 2: often attributed to Einstein that I really like that you know,
Speaker 2: supposedly pretty much every quote is either attributed nowadays to
Speaker 2: either Einstein or Mark Twain, so you never really know
Speaker 2: if they're said, but anyway, it's a good quote which
Speaker 2: says everything should be as simple as it can be,
Speaker 2: but not simpler, right, So there is room for things
Speaker 2: that are complicated in this world. But just throwing additional
Speaker 2: factors into something, even though it may seem sophisticated, sometimes
Speaker 2: actually is detrimental. And so I would say you want
Speaker 2: to start with factors that are rigorously tested or not
Speaker 2: duplicative with one another, and then, like you said, understand
Speaker 2: the timeframes. And so the way that we approach it
Speaker 2: is those long term factors that help explain returns over
Speaker 2: years those are good things to actually build a strategy's
Speaker 2: construction around, right, because you can do that in a
Speaker 2: pretty stable way without a lot of turnover, and so
Speaker 2: things like you mentioned earlier you were talking about that
Speaker 2: small cap strategy, So things like valuation, profitability, size, those
Speaker 2: are great to include in a long term strategy. And
Speaker 2: the way that we do it is we will start
Speaker 2: with market cap weights of securities, and then to the
Speaker 2: extent that securities look good across multiple factors are bad,
Speaker 2: we'll overweight and underweight relative to market cap weights. Now,
Speaker 2: then this brings in some of those shorter time period factors,
Speaker 2: and so with those, I think it's important to not
Speaker 2: apply them in the construction because if a factor is
Speaker 2: changing its signal or it's information is providing you every
Speaker 2: couple of weeks, then it's going to cause a lot
Speaker 2: of turnover in the portfolio. That could cause very high
Speaker 2: trading cost or if you have taxbile investors, that could
Speaker 2: cause them a huge capital gains tax bill. So there,
Speaker 2: I think the best thing to do is to use
Speaker 2: them as delays. So let's say that you would have
Speaker 2: purchased or sold some security based on the long term factors,
Speaker 2: but then you screen them for the short term factors,
Speaker 2: and then you may decide to either delay that buy
Speaker 2: or sell and then substitute in another name that isn't
Speaker 2: having that maybe negative short run expected return. And so
Speaker 2: I think that tends to be the way that we
Speaker 2: think about it. And then the last thing that I
Speaker 2: would say is you really want to understand how those
Speaker 2: factors interrelate with one another. And so for example, value
Speaker 2: and profitability, they tend to be complements. So more often
Speaker 2: than not, when the value premium is negative, the profitability
Speaker 2: premium is positive, or vice versa. So therefore those are
Speaker 2: great to combine in a portfolio, whereas profitability and let's
Speaker 2: say growth, those tend to be positively correlated. So if
Speaker 2: you aren't careful, you could just be doubling down and
Speaker 2: increasing your risk without really increasing your expected return. So
Speaker 2: I think that's the main way that we think about
Speaker 2: including multiple factors.
Speaker 3: That's so interesting. Thank you. I mean, I think it's
Speaker 3: relative unique that you guys do like the you know,
Speaker 3: the long term and in the short term, and I
Speaker 3: thank you for explaining that. I mean, that's really a
Speaker 3: really cool perspective. Who knows if Leonardo da Vinci actually
Speaker 3: said this, But people say, Leonordo da Vinci said, simplicity
Speaker 3: is the ultimate sophistication. That's what that's I like that.
Speaker 2: Yeah, that's great. So you can borrow my Roses's Day
Speaker 2: and I'll borrow your supposedly Anaro DaVinci.
Speaker 3: And who knows if you said it or not. But
Speaker 3: you know what, when you when you name drop Leonardo
Speaker 3: da Vinci, you sounds smart. So there you go.
Speaker 2: Maybe it was Mark Twain, Yeah.
Speaker 3: Exactly, who knows it was Yogi Berra? No? Yeah, you
Speaker 3: kind of let me do another question I had so,
Speaker 3: you know, like I write a lot about factor investing
Speaker 3: and and sometimes people that maybe you know, certainly aren't
Speaker 3: as sophisticated as you and might not know much about
Speaker 3: this stuff, they'll come back to me and say, why
Speaker 3: don't you have growth as a factor? Is in growth
Speaker 3: a factor? You know? What's the difference between something like
Speaker 3: profitability or maybe a more broad definition you would say
Speaker 3: quality and value? I'm sorry, and growth? Are they the
Speaker 3: same thing? Is one better than the other? Why do
Speaker 3: you always talk about quality slash profitability and our growth?
Speaker 3: What would you say to a question like that?
Speaker 2: Yeah, I'm glad you bring it up because I do
Speaker 2: think it's confusing for a lot of people, and they
Speaker 2: often conflate these different factors, and so I see the
Speaker 2: same thing when I talk with people, and I think
Speaker 2: some of it just comes down to not being clear
Speaker 2: about how these things are defined. And so for us,
Speaker 2: we define value as companies that have low valuations. So
Speaker 2: you can use various metrics. The good news is that
Speaker 2: they all actually contain some information. But some companies have
Speaker 2: low valuations, some have high. To us, the low valuation
Speaker 2: or low relative price is value and the high relative
Speaker 2: price is growth. Where when you get into quality and look,
Speaker 2: I know other people have different definitions of growth, including
Speaker 2: companies that are growing their earnings, which is also kind
Speaker 2: of related to profitability and momentum. There's some interesting work
Speaker 2: Robert Novi Marx who's a professor at the University of Rochester.
Speaker 2: He's looked at momentum and profitability growth, and so there's
Speaker 2: sort of a version of earnings momentum that's really interesting research.
Speaker 2: But at least those you can tie them to specific
Speaker 2: line items on a company's income statement or balance sheet.
Speaker 2: I think with quality, unfortunately, I have to say it's
Speaker 2: a bit more of a marketing term. Than a financial term.
Speaker 2: And what I mean is I think it's designed to
Speaker 2: appeal to people's sort of intuitive sense of, oh, well,
Speaker 2: this company has quality earnings or quality balance sheet, but
Speaker 2: there's no standard definition of that, and so I think
Speaker 2: this causes a lot of the confusion. And when we
Speaker 2: look at the research on quality, there's a number of
Speaker 2: variables that managers tend to use, so return on equity, leverage, earnings, variability, others.
Speaker 2: And unfortunately, when you add those factors in to a
Speaker 2: model that already contains profitability, they don't add any additional
Speaker 2: explanatory power. So I think for listeners, if you have
Speaker 2: a portfolio that is already focused on valuations, profitability, and
Speaker 2: then momentum considers momentum as well, you're pretty much picking
Speaker 2: up all the effect that you're going to get from
Speaker 2: both quality and growth.
Speaker 3: I couldn't agree with that more. I mean, even in
Speaker 3: my own work, you know, I've kind of moved away
Speaker 3: from saying quality for those exact reasons you said. People
Speaker 3: have different ideas, and you know it does play on
Speaker 3: people's like, of course you want high quality on low quality, right,
Speaker 3: who wouldn't. Yeah, But I mean in my research and
Speaker 3: I'm sure you'd agree with this, Like, profitability is by
Speaker 3: far the biggest driver of quality, and the other things
Speaker 3: really add negligible value, and so why don't we just
Speaker 3: use profitability. It's much more easy to understand, and I
Speaker 3: think that the you know, the research there is kind
Speaker 3: of more clear. So I totally agree with you.
Speaker 2: Yeah, I agree with you, and I think Leonardo da
Speaker 2: Vinci would agree with you as well.
Speaker 1: Talking about factors, you know, you mentioned long term factors
Speaker 1: kind of you know, it's the basis for the portfolio management.
Speaker 1: So certainly if you can kind of go into the
Speaker 1: investment process a little bit of you know, from a
Speaker 1: manager's standpoint of, you know, what are they? What is
Speaker 1: the process of, you know, taking this research and implement
Speaker 1: that into an actual portfolio or you know, our funds.
Speaker 2: Yeah, sure, I think I would describe our investment process
Speaker 2: using three words. The first is stematic, the second is daily,
Speaker 2: and the third is flexible. So let me say what
Speaker 2: I mean by that. Every single day we take current
Speaker 2: market prices and the most recent company fundamentals and we
Speaker 2: use that to assign companies to in terms of different factors,
Speaker 2: so value or profitability. Also, we then calculate sort of
Speaker 2: theoretical weights for every security in every portfolio. And I'm
Speaker 2: really gonna highlight the word theoretical there because this is
Speaker 2: only using the long term factors, so we haven't enriched
Speaker 2: that with additional information about the short term ones yet.
Speaker 2: So it's sort of a rough work and process, if
Speaker 2: you will. And so every day, though, we have a
Speaker 2: description of those securities and where they sit across the
Speaker 2: different factors, and that's very different from an indexed based approach.
Speaker 2: So let me just contrast it real quick. If you're
Speaker 2: invested in a value index, for example, or a profitability
Speaker 2: or quality or whatever index, they're only doing those updated
Speaker 2: calculations and bucketing of securities maybe at most every quarter. Oftentimes,
Speaker 2: even when they say they have quarterly rebalances, they're only
Speaker 2: really redefining the breakpoints between those factors on a semi
Speaker 2: annual basis, So they're working with stale information. And so
Speaker 2: we have a daily process to combine securities and financial
Speaker 2: metrics categorize stocks. At that point, our portfolio managers will
Speaker 2: review that updated information and where those stocks are sitting,
Speaker 2: and we'll compare it to our current holdings, and so
Speaker 2: then that may suggest that we may want to do
Speaker 2: some rebalancing. So you're familiar that in X is rebalanced
Speaker 2: maybe a couple times a year. Well, we rebalance a
Speaker 2: little bit every single day, so it's like having two
Speaker 2: hundred and fifty or so rebalance events throughout the year.
Speaker 2: And so every day we're looking at the cash flows
Speaker 2: coming in and out of the portfolio and thinking, how
Speaker 2: do I use those as efficiently as possible. If there's
Speaker 2: some security that became a lower valuation or more profitable,
Speaker 2: and we want to increase our weight in that, how
Speaker 2: do we use the cash flows coming in from either
Speaker 2: clients or maybe from corporate actions like dividends, how do
Speaker 2: we redeploy that cash just to the stocks that we
Speaker 2: want to increase our weight in. And then that's when
Speaker 2: we start to apply those shorter term criteria, those shorter
Speaker 2: term factors, And what that will do is it will
Speaker 2: cause us to delay from trading some of the names.
Speaker 2: So we will say, all right, well, there's maybe ten
Speaker 2: securities in the US arch cap space that we want
Speaker 2: to buy more of, but three of them maybe have
Speaker 2: these short term negative factors, We'll go by the other
Speaker 2: seven instead. And from there what we do, and this
Speaker 2: is this part becomes very unique now compared with anyone
Speaker 2: else to know in the industry, is we will send
Speaker 2: those over to our traders, and let's just say hypothetically
Speaker 2: that we want to spend fifty million dollars, we will
Speaker 2: give our traders, let's say three times that amount of
Speaker 2: order candidates, So we'll give them one hundred and fifty
Speaker 2: million in order candidates, and we will give them the
Speaker 2: exact share counts and price limits and everything, so they
Speaker 2: don't have discretion on which securities to eventually buy. But
Speaker 2: what we do is we give them flexibility over the
Speaker 2: timing and the quantity. So we say, buy anything off
Speaker 2: of this list today, We'll come back and do it
Speaker 2: tomorrow and the next day and the next day. And
Speaker 2: so what ends up happening is our portfolio managers get
Speaker 2: all the positions they want, but our traders also get
Speaker 2: the flexibility to not have to cross spreads or push prices.
Speaker 2: And so this helps avoid an issue that both index
Speaker 2: funds and active managers have, which is, in some ways
Speaker 2: most of them are demanding liquidity from the market. They're
Speaker 2: going and saying I need to trade a specific stock
Speaker 2: in a specific quantity at a specific time and when
Speaker 2: you do that, you just don't get great prices. Whereas
Speaker 2: if our traders can sit over on the favorable side
Speaker 2: of the spread and let other people cross and we
Speaker 2: can get some price improvement, that actually is a value
Speaker 2: add in our process. So that's how the process works.
Speaker 2: When we say it's systematic, we've built systems to do
Speaker 2: this daily rebalancing in the lowest cost way that we can.
Speaker 3: Really interesting, it's like buying one ros a day going
Speaker 3: up to Valentine's Day.
Speaker 2: Yeah, Chris, I've tried to extend this analogy where sometimes
Speaker 2: I say, we give our traders a shopping list and
Speaker 2: tell them to go to the grocery store, but then
Speaker 2: we only give them a budget to buy like a
Speaker 2: third of the shopping list, and so every day they
Speaker 2: have to buy what's on sale. It's just at some
Speaker 2: point you stretch the analogy so far that people are like,
Speaker 2: who would go to the grocery store that often? But
Speaker 2: with electronic trading, actually you can go to the grocery
Speaker 2: store all the time. It's fine.
Speaker 3: Sure, Yeah, And I bet that working those orders, I
Speaker 3: bet that means you know, a big difference over time.
Speaker 3: I could totally see that, and like you said, I mean,
Speaker 3: when you have an index, everyone knows when you're rebalancing,
Speaker 3: and it's not you know, you can front run that stuff.
Speaker 3: All right, let me ask you a question. This is
Speaker 3: like as controversial as it gets, right when it comes
Speaker 3: to factors small size. So when I first learned factor investing,
Speaker 3: it was like small size as a factor. And by
Speaker 3: a factor, I don't mean like a risk factor, I
Speaker 3: mean like an alpha factor, like it's going to give
Speaker 3: you higher risk adjusted returns. And then I feel like
Speaker 3: over the last decade or two, the evidence of small
Speaker 3: size being an actual premium has really been hit, and
Speaker 3: some argue that it was never a premium at all.
Speaker 3: It was just you're taking tail risk because you're buying
Speaker 3: small companies and they could go bankrupt and there's more
Speaker 3: volatility and more left tail risk there, so you should
Speaker 3: be compensated for higher returns. It's not actually a premium.
Speaker 3: As you know, small sizes in many you know academic
Speaker 3: factor models. So where do you come down on this debate?
Speaker 3: I mean, do you think small size is still a
Speaker 3: factor or have you kind of reassess that over the
Speaker 3: last couple of years.
Speaker 2: Yeah, So I think two things. The first is it's
Speaker 2: always important to continuously reassess the evidence. No one should
Speaker 2: ever just stick with what was done at some point
Speaker 2: in the past. Being a statistics nerd myself, for those
Speaker 2: other listeners out there that are this is called taking
Speaker 2: a basin approach. It's you have your priors, you get
Speaker 2: new data, you update you know, you add it to
Speaker 2: the information, you update your priors. And so that's a
Speaker 2: big part of what our research team does here is
Speaker 2: continuously test if these things are still reliable. And then
Speaker 2: I think the other thing to point out is in
Speaker 2: the US we have seen a couple decades where small
Speaker 2: caps have done worse than large caps, which I think
Speaker 2: is interesting for two reasons. One is that hasn't been
Speaker 2: the case outside the US, so across all the other
Speaker 2: non US countries in aggregate, we've actually seen positive size premiums.
Speaker 2: And then in the US, I think there's two things
Speaker 2: going on. One is that people tend to use the
Speaker 2: Russell two thousand as their proxy for how small caps
Speaker 2: have done. But we talked earlier about some of the
Speaker 2: performance drag associated with index reconstitution, and so we said
Speaker 2: that's coming out of the index. So really that small
Speaker 2: cap index has particularly low returns compared with some other
Speaker 2: small cap indices. And then the other thing else is
Speaker 2: you asked me earlier about multiple factors. You always have
Speaker 2: to control for all the other So size is just
Speaker 2: a one dimensional concept. Actually, isn't that helpful? Right? It's
Speaker 2: kind of like if you were a medical researcher and
Speaker 2: you said, like, did someone eat a healthy diet. Well,
Speaker 2: I'm a cyclist, and so I actually eat a lot
Speaker 2: of sugar, but I need that for exercise, and the
Speaker 2: net effect is it's good for me in some quantity
Speaker 2: at some time. So you always have to think about
Speaker 2: multiple explanatory variables. And with small the issue in the
Speaker 2: US actually has not been most small cap stocks. It's
Speaker 2: been a very small subset of small cap stocks that
Speaker 2: have very high valuations and very low or often negative profitability.
Speaker 2: And so some people like to sort of casually say,
Speaker 2: you know, there's a lot of junk and small caps,
Speaker 2: and so I think in small cap investing you have
Speaker 2: to be very careful that you are controlling for those
Speaker 2: other factors as well. Once you do that, yeah, there
Speaker 2: is a premium for small cap stocks, and we tend
Speaker 2: to see that some of the other premiums are actually
Speaker 2: a little bit stronger in small cap than in large.
Speaker 3: Yeah, I found that too, of the other factors, and
Speaker 3: you know, just simply doing a profitability screen on the
Speaker 3: Russell two thousand goes a long way, you know, like.
Speaker 2: Absolutely, yeah, absolutely, I mean it's sort of we like
Speaker 2: to geek out about factors and be quantitative, but just
Speaker 2: step back common sense. Wise investing is always about what
Speaker 2: am I paying for something versus what am I expecting
Speaker 2: to get right, And if you pay a high valuation
Speaker 2: for something that has little to no profits, that's not
Speaker 2: generally regarded as a high expected return investment. And so
Speaker 2: I think it's always important in any area of the
Speaker 2: market to use that framework, but especially in small caps
Speaker 2: because there are just a lot of those unprofitable small
Speaker 2: cap companies in the US.
Speaker 3: All right, let me ask you about kind of the
Speaker 3: topic of the day, which is AI machine learning. You know,
Speaker 3: you you mentioned deep seek. That was the you know,
Speaker 3: big topic of the markets yesterday. Have you found you know,
Speaker 3: applications of it could be just mL models or even LM,
Speaker 3: you know, AI models in factor investing or if you know,
Speaker 3: if not, like, do you think that's a thing we're
Speaker 3: going to you know, really focus on in the future
Speaker 3: trying to have machine learning and such help us with
Speaker 3: the factors. What's your thoughts there.
Speaker 2: Yeah, it's something we've looked into a lot. And the
Speaker 2: interesting thing you mentioned deep Seek again is they actually
Speaker 2: started out as a quantitative hedge fund, and that hedge
Speaker 2: fund ran into some troubles and so they've sort of
Speaker 2: pivoted over to a generative AI model. And I think
Speaker 2: it just highlights that it's been very difficult to apply
Speaker 2: AI to try to whether it's time the market or
Speaker 2: find new factors. In some ways, you start to worry
Speaker 2: about what researchers would call data mining, which is you know,
Speaker 2: we talked about the four hundred factors and the factor zoo. Well,
Speaker 2: you could probably enumerate hundreds of thousands or millions of
Speaker 2: combinations of different variables if you were to combine things
Speaker 2: off of the income statement and the balance sheet, so
Speaker 2: you would make all sorts of esoteric variables and then
Speaker 2: you would run them all through a factor model and
Speaker 2: by chance some would work right. So this is called
Speaker 2: pa hacking in the statistics community. It's one of those
Speaker 2: things where you know, even if there's only a five
Speaker 2: percent chance that something happens by random. If you run
Speaker 2: one hundred tests, well, then five of them are going
Speaker 2: to turn up positive, even though they're just by chance.
Speaker 2: So i'd mentioned Robert nobe Marx. He actually wrote a
Speaker 2: paper on this topic recently. He not only only created
Speaker 2: a bunch of AI models to find new factors, he
Speaker 2: also then employed AI models to write the academic papers
Speaker 2: for him. And it was done as sort of a
Speaker 2: way to demonstrate to the industry what can go wrong
Speaker 2: if you use this approach. So it is something we've
Speaker 2: looked at a lot so far. The ways that we
Speaker 2: have experimented with using it is more to gather some
Speaker 2: of that unstructured data that may be like in company
Speaker 2: filings so or oftentimes when companies have corporate actions, they're
Speaker 2: putting out a bunch of text based information. And if
Speaker 2: you're using different factors, so for example, if you're using
Speaker 2: a profitability factor a value factor, it's relying on the
Speaker 2: numbers in the income statement to be interpreted properly. And
Speaker 2: so let's say that you know the auditor qualifies their
Speaker 2: opinion about the income statements, Well, do you want to
Speaker 2: trust the metrics from there? Probably not. So if you
Speaker 2: had some sort of a model that could scan through
Speaker 2: and highlight to you areas where there may be concerns
Speaker 2: or maybe changes. So let's say a company spins off
Speaker 2: a division. Well, now anything on the balance sheet about
Speaker 2: you know, assets or book value of equity has changed
Speaker 2: because they've spun some of that off. So finding ways
Speaker 2: to flag some of those changes in the data so
Speaker 2: you can go update your metrics so you're not using
Speaker 2: stale metrics has been an area where we've experimented with
Speaker 2: using it.
Speaker 1: Oh, this is great. We just have one more question
Speaker 1: before we let you go. You know, like to ask
Speaker 1: this a lot to a lot of our guests. But whatever,
Speaker 1: you're some of your favorite financial or investing books.
Speaker 2: Yeah, so I read financial literature all day long at
Speaker 2: my job, and so when I get a chance to
Speaker 2: read books, I tend to like books that come from
Speaker 2: other fields. And I really enjoy history. I think we
Speaker 2: have a lot to learn from history, and specifically I
Speaker 2: like biographies, and so one of my favorite authors is
Speaker 2: Walter Isaacson, and he's written biographies on people like Steve Jobs,
Speaker 2: Leonardo da Vinci, which you were talking about earlier, Elon Musk,
Speaker 2: Benjamin Franklin, and others. And one of the things I
Speaker 2: really like reading about in these biographies is each of
Speaker 2: these individuals accomplished a lot. They were all great problem solvers,
Speaker 2: and each of them went through a lot of challenges.
Speaker 2: They went through big ups and downs along the way.
Speaker 2: And I think intellectually we all know that success, whether
Speaker 2: that's in life or in investing, it's not a line
Speaker 2: that goes straight up into the right right. But there's
Speaker 2: a difference between knowing that and then actually being able
Speaker 2: to have the discipline to sort of live through the
Speaker 2: downs and capture the ups. And so for me, reading
Speaker 2: biographies really brings to life the lessons from history, so
Speaker 2: I can learn from them and not have to repeat
Speaker 2: them myself. It's great, Joel.
Speaker 1: I enjoyed this. Thanks again for joining us today.
Speaker 2: Absolutely, Thank you for having me and Chris.
Speaker 1: Thank you for being my co host again today.
Speaker 3: Thank you, and thank you Joel.
Speaker 1: Until our next episode, this is David Cohne with the
Speaker 1: Inside Out.