DFA’s Schneider on Systematic Flexible Investing

You know, most of the factors did work in small caps last year similar to large caps. Would I would flag that value seem to work a bit better, you know, in small caps, but the devils and the details kind of about how you define value. But yeah, it's been a strong factor year for both market capitalizations and hopefully continues great well.

got there. After school, growing up, I used to go over to my grandparents' house and my parents both worked, so my grandparents watched US for a couple hours, and my grandpa would always watch CNBC, and so it was riveting for me, as a young person that like numbers, to see all these prices flying by, to see all these company fundamentals being talked about. But there was sort of a randomness to the stock market that never really made a lot of sense to me, so I sort

of put that interest aside. I ended up going to college and studying computer engineering, and then I worked at Lockey Martin and I designed communication systems for the Navy and the Air Force. And as I was there, I began to realize that if I was going to progress in that role, I needed to learn corporate finance. So to be the head of a large program, or to be the head of a division, you had to have your own P and L. So I needed to understand

corporate finance better than I did. So I went back to business school at University of Chicago to get an MBA, and while I was there, I learned that there were a lot of theories and frameworks and evidence that actually explained how financial markets worked, So to me, this was really cool. There were valuation frameworks like discounter cash flow models that help you value companies. There were portfolio theories that talked about the benefits of diversification based on the

covariance of different assets. There was arbitrage pricing theories that helped explain how the prices of different financial instruments were all kept inlign with each other. And then importantly for today's discussion, there were factor pricing models that helped explain what drove returns in equities and bonds. And so after seeing all this, and actually really importantly to me was

these weren't just theories. People were actually rigorously testing them using the scientific method which I'd come to learn in engineering. So all of this allowed me to see through the randomness. And when I graduated from University of Chicago, there's really no place other that I wanted to work than dimensional because we'd been associated with a lot of the academics that founded a lot of those frameworks and did a lot of that research.

fixed income, real estate, and commodities. And we have this really deep academic heritage and so a lot of our investments, and specifically our investment philosophy that you asked about, is rooted in research that's been done by multiple Nobel Prize winners as well as many other leading academics. And what we see as our job at Dimensional is to implement their research in real world portfolios. So what that ends up looking like is we run very low cost, broadly

diversified portfolios, but they are not exactly indexed. So we have active strategies that actually outperform our benchmark index is at a greater rate and over longer periods of time than most other managers. In fact, I'm not aware of anyone that has sort of the types of numbers we have, So just to give you a sense of what those are, if you were to go back over the last decade and say what percentage of managers beat their benchmark index in the industry, it's a pretty low number. It's only

twenty three percent of managers. But at dimensional seventy eight percent of our funds have beating their benchmark index over the last ten years. And if you extend that over twenty years, it gets even worse. For the industry, only eighteen percent of funds have beaten their benchmarks, but ninety two percent of our funds have. And so I want to get into the investment philosophy now to sort of explain how we do that. But a key insight, remember when I said our job is to implement the best

ideas in finance. Well, our co founders actually founded some of the first index funds in the early nineteen seventies, and in doing that they came to realize that trading in a really rigid way where you have to buy and sell the stocks that the index tells you to is a recipe for high trade and cost, and so they realize back then that there's a difference between passive and indexed. So I think this may help us in

our conversation today. But I just wanted to find passive and active, or sorry, passive and index For passive, I think that means just treating market prices is fair and generally holding securities at their marketcap weight. For indexed, that is an implementation approach. It's basically where you are forced to buy and sell securities when some third party index provider tells you to. And so a lot of the key to doing better than indexes is avoiding that type

of implementation. All right, So that was a really long way to get to your question. But in terms of our investment philosophy, I think it boils down to in competitive liquid markets, prices are generally fair, they're forward looking, and they already represent a consensus prediction about the future. And so the challenge that a lot of traditional active managers have had is that they're trying to outguess those

market prices. And as we all saw yesterday in the news, all of a sudden there was news that the Chinese company behind deep Seek had this great new AI model that challenged the business model of some of the US based companies and the suppliers of chips, and so really quickly prices adjusted, and I didn't really see a lot of people out there calling that ahead of time. So markets are moving in real time. Prices are adjusting, and so our philosophy is embrace that, just make use of it.

And so it starts with just saying the prices are what they are, they're a reasonable prediction. How do we bring other pieces of information to combine with the price to understand which stocks have higher or lower future expector returns given that price today? And so that to many

people they call that factor investing. But you're combining different variables or financial metrics from companies' income statements or balance sheets with the price to make some inferences about which stocks are likely to do better in the future.

hidden costs involved with indexing. A big one of those is that the indexers are all forced to trade the same names on the same day, the same time as all the other indexers. And so a really fun analogy is it's sort of like buying roses on Valentine's Day? Do you think you're going to get a good price? No, Right, if you bought roses a week or two before or after, you're going to get a much better price than if you're buying them on that day. So that's called the

index reconstitution effect. And you guys may know these numbers better than me, but if you look at the growth of indexing, the total dollars chasing after the same names, I saw numbers las year at the end of last year around like twelve trillion dollars in index products. So that means that when there's these rebalance events, you've got billions of dollars, tens of billions of dollars that are

all chasing the same stocks. And what that does is it tends to on average, push up prices of names that are being added to the index and pushed down prices of names that are being dropped. And our research we actually just did an updated set of research papers on this. There's a lot of academic work about ten years ago, so we decided to do an update for the next decade. We looked at both US and non US equity indices, and we found that prices get pushed

by about four percent on average. Four percent. I don't know if that feels like a big or small number to the people listening, but let me just put that in context a little. Let's say an index has five percent turnover a year. It's pretty low turnover. Eight Well, if five percent of your portfolio is getting four percent worse prices that's a potential drag of twenty basis points

a year. Now, when people think of the low expense ratios or management fees of indexing, that twenty basis points of hidden performance drag is in many cases much bigger than the fee they're paying, So really their total cost of ownership is a lot higher than they think it is.

of style drift, and that becomes a big issue. I heard Chris at the beginning talking about capturing some of the premiums within small cap like profitability and value. Well, if the Russell two thousand is only rebalancing once a year in June, then that means they've got eleven months where the stocks they hold have drifted. Many have become midcaps or large caps, and so the index style drift

is a major issue. And then you know, I talk with a lot of institutional clients and one of the things that they've come to really realize is that a lot of their index managers may be charging them a very low management fee, but then they're keeping a pretty large percentage of the securities lending profits from those stocks, and in some ways that serves as sort of a shadow management fee, right it looks like you're only paying a couple BIPs and expensory sue or in management fee,

but then they're keeping a slug of the sect lending revenues for themselves. So those are just some of the issues. I'm sure we'll get into more of them later. But there's there's definitely issues with index implementation, and you can do better than indexing by not being so rigid.

measuring those over months or weeks. They're things like momentum or asset growth, or interestingly, stocks that are expensive to borrow. In the securities lending market, that's actually, to me a really cool factor. We often say that we extract information from market prices. So earlier you asked me about our investment philosophy, and I said, we take the prices for what they are and we see what information we can

extract from that. Well, here's an example where the securities lending market that is another market just like the stock markets a market, the stock lending markets market, and it's got prices, and if people are willing to pay very high fees to borrow stocks, often to shorten them, that's actually a really negative sign that explains under performance. Of stocks over the next few weeks after they become expensive to borrow. And then the last very sort of short

term factors are things related to liquidity. So one of them that's in the academic research is price reversals, and then the other are things related to implicit trade and costs, so spreads, price impact. So I think, really zooming back out, you want to think about which factors are reliable, but then which ones apply over different timeframes, and how you implement will depend on the timeframe that they apply over.

And so I think maybe the last thing to add before I'm sure you have questions on that is some listeners may be wondering why I didn't mention some of the commonly cided factors that they've heard of. And unfortunately, you guys have probably heard the term the factor zoo. So there's so many people publishing papers about different factors. You know, some people say three hundred or four hundred factors have been identified. Well, those are mainly filled with

either deplicative or unreliable factors. And so when I say deplicative, I just mean that once you control for the factors I already mentioned that the other ones don't add any new explanatory power. It's not to say there's anything wrong with those, it's just it could be you know that you're going to approach that factor using some other definition. That's fine as long as you know and you're not being redundant and applying basically sort of the same factor twice.

And then the other reason why I didn't mention some is some of them just don't pass the high standards of the scientific method which I talked about earlier, and that is you need to have a strong economic theory for why a factor should exist. You need to be able to reproduce those results, and they need to be able to hold up an out of sample testing. Otherwise it's hard to be confident that those will occur in the future.

timeframes you know, apply to a multi factor process. How do you combine factors, especially if they have different time frames? It is any element, and I guess, I guess I can ask do you believe in factor timing, Like, do you think there's a way to time factors or do you think it's a better approach to just have a relatively constant exposure to factors that you believe, you know are are advantages for the long term.

This is going to give you a little bit more risk control and the ability to sort of ride out or survive different periods in the market when certain factors are in or out of favor. So I think, Chris, that gets to your second question, which is, then how do you start to combine multiple factors into a portfolio? And I think there's a few lessons to keep in mind. The first one that I said earlier is, you know,

more factors are not necessarily better. There's a quote that's often attributed to Einstein that I really like that you know, supposedly pretty much every quote is either attributed nowadays to either Einstein or Mark Twain, so you never really know if they're said, but anyway, it's a good quote which says everything should be as simple as it can be, but not simpler, right, So there is room for things

that are complicated in this world. But just throwing additional factors into something, even though it may seem sophisticated, sometimes actually is detrimental. And so I would say you want to start with factors that are rigorously tested or not duplicative with one another, and then, like you said, understand

the timeframes. And so the way that we approach it is those long term factors that help explain returns over years those are good things to actually build a strategy's construction around, right, because you can do that in a pretty stable way without a lot of turnover, and so things like you mentioned earlier you were talking about that small cap strategy, So things like valuation, profitability, size, those

are great to include in a long term strategy. And the way that we do it is we will start with market cap weights of securities, and then to the extent that securities look good across multiple factors are bad,

we'll overweight and underweight relative to market cap weights. Now, then this brings in some of those shorter time period factors, and so with those, I think it's important to not apply them in the construction because if a factor is changing its signal or it's information is providing you every couple of weeks, then it's going to cause a lot of turnover in the portfolio. That could cause very high trading cost or if you have taxbile investors, that could

cause them a huge capital gains tax bill. So there, I think the best thing to do is to use them as delays. So let's say that you would have purchased or sold some security based on the long term factors, but then you screen them for the short term factors, and then you may decide to either delay that buy or sell and then substitute in another name that isn't having that maybe negative short run expected return. And so I think that tends to be the way that we

think about it. And then the last thing that I would say is you really want to understand how those factors interrelate with one another. And so for example, value and profitability, they tend to be complements. So more often than not, when the value premium is negative, the profitability premium is positive, or vice versa. So therefore those are great to combine in a portfolio, whereas profitability and let's

say growth, those tend to be positively correlated. So if you aren't careful, you could just be doubling down and increasing your risk without really increasing your expected return. So I think that's the main way that we think about including multiple factors.

quality and value? I'm sorry, and growth? Are they the same thing? Is one better than the other? Why do you always talk about quality slash profitability and our growth? What would you say to a question like that?

low valuations, some have high. To us, the low valuation or low relative price is value and the high relative price is growth. Where when you get into quality and look, I know other people have different definitions of growth, including companies that are growing their earnings, which is also kind of related to profitability and momentum. There's some interesting work Robert Novi Marx who's a professor at the University of Rochester.

He's looked at momentum and profitability growth, and so there's sort of a version of earnings momentum that's really interesting research. But at least those you can tie them to specific line items on a company's income statement or balance sheet. I think with quality, unfortunately, I have to say it's a bit more of a marketing term. Than a financial term.

And what I mean is I think it's designed to appeal to people's sort of intuitive sense of, oh, well, this company has quality earnings or quality balance sheet, but there's no standard definition of that, and so I think this causes a lot of the confusion. And when we look at the research on quality, there's a number of variables that managers tend to use, so return on equity, leverage, earnings, variability, others.

And unfortunately, when you add those factors in to a model that already contains profitability, they don't add any additional explanatory power. So I think for listeners, if you have a portfolio that is already focused on valuations, profitability, and then momentum considers momentum as well, you're pretty much picking up all the effect that you're going to get from both quality and growth.

use profitability. It's much more easy to understand, and I think that the you know, the research there is kind of more clear. So I totally agree with you.

theoretical weights for every security in every portfolio. And I'm really gonna highlight the word theoretical there because this is only using the long term factors, so we haven't enriched that with additional information about the short term ones yet. So it's sort of a rough work and process, if you will. And so every day, though, we have a description of those securities and where they sit across the different factors, and that's very different from an indexed based approach.

So let me just contrast it real quick. If you're invested in a value index, for example, or a profitability or quality or whatever index, they're only doing those updated calculations and bucketing of securities maybe at most every quarter. Oftentimes, even when they say they have quarterly rebalances, they're only really redefining the breakpoints between those factors on a semi

annual basis, So they're working with stale information. And so we have a daily process to combine securities and financial metrics categorize stocks. At that point, our portfolio managers will review that updated information and where those stocks are sitting, and we'll compare it to our current holdings, and so then that may suggest that we may want to do some rebalancing. So you're familiar that in X is rebalanced

maybe a couple times a year. Well, we rebalance a little bit every single day, so it's like having two hundred and fifty or so rebalance events throughout the year. And so every day we're looking at the cash flows coming in and out of the portfolio and thinking, how

do I use those as efficiently as possible. If there's some security that became a lower valuation or more profitable, and we want to increase our weight in that, how do we use the cash flows coming in from either clients or maybe from corporate actions like dividends, how do we redeploy that cash just to the stocks that we

want to increase our weight in. And then that's when we start to apply those shorter term criteria, those shorter term factors, And what that will do is it will cause us to delay from trading some of the names. So we will say, all right, well, there's maybe ten securities in the US arch cap space that we want to buy more of, but three of them maybe have these short term negative factors, We'll go by the other

seven instead. And from there what we do, and this is this part becomes very unique now compared with anyone else to know in the industry, is we will send those over to our traders, and let's just say hypothetically that we want to spend fifty million dollars, we will give our traders, let's say three times that amount of order candidates, So we'll give them one hundred and fifty million in order candidates, and we will give them the exact share counts and price limits and everything, so they

don't have discretion on which securities to eventually buy. But what we do is we give them flexibility over the timing and the quantity. So we say, buy anything off of this list today, We'll come back and do it tomorrow and the next day and the next day. And so what ends up happening is our portfolio managers get all the positions they want, but our traders also get the flexibility to not have to cross spreads or push prices.

And so this helps avoid an issue that both index funds and active managers have, which is, in some ways most of them are demanding liquidity from the market. They're going and saying I need to trade a specific stock in a specific quantity at a specific time and when you do that, you just don't get great prices. Whereas if our traders can sit over on the favorable side of the spread and let other people cross and we can get some price improvement, that actually is a value

add in our process. So that's how the process works. When we say it's systematic, we've built systems to do this daily rebalancing in the lowest cost way that we can.

it was like small size as a factor. And by a factor, I don't mean like a risk factor, I mean like an alpha factor, like it's going to give you higher risk adjusted returns. And then I feel like over the last decade or two, the evidence of small size being an actual premium has really been hit, and some argue that it was never a premium at all.

It was just you're taking tail risk because you're buying small companies and they could go bankrupt and there's more volatility and more left tail risk there, so you should be compensated for higher returns. It's not actually a premium. As you know, small sizes in many you know academic factor models. So where do you come down on this debate? I mean, do you think small size is still a factor or have you kind of reassess that over the last couple of years.

continuously test if these things are still reliable. And then I think the other thing to point out is in the US we have seen a couple decades where small caps have done worse than large caps, which I think is interesting for two reasons. One is that hasn't been the case outside the US, so across all the other non US countries in aggregate, we've actually seen positive size premiums. And then in the US, I think there's two things

going on. One is that people tend to use the Russell two thousand as their proxy for how small caps have done. But we talked earlier about some of the performance drag associated with index reconstitution, and so we said that's coming out of the index. So really that small cap index has particularly low returns compared with some other small cap indices. And then the other thing else is you asked me earlier about multiple factors. You always have to control for all the other So size is just

a one dimensional concept. Actually, isn't that helpful? Right? It's kind of like if you were a medical researcher and you said, like, did someone eat a healthy diet. Well, I'm a cyclist, and so I actually eat a lot of sugar, but I need that for exercise, and the net effect is it's good for me in some quantity at some time. So you always have to think about multiple explanatory variables. And with small the issue in the

US actually has not been most small cap stocks. It's been a very small subset of small cap stocks that have very high valuations and very low or often negative profitability. And so some people like to sort of casually say, you know, there's a lot of junk and small caps, and so I think in small cap investing you have to be very careful that you are controlling for those

other factors as well. Once you do that, yeah, there is a premium for small cap stocks, and we tend to see that some of the other premiums are actually a little bit stronger in small cap than in large.

generally regarded as a high expected return investment. And so I think it's always important in any area of the market to use that framework, but especially in small caps because there are just a lot of those unprofitable small cap companies in the US.

the factors. What's your thoughts there.

we talked about the four hundred factors and the factor zoo. Well, you could probably enumerate hundreds of thousands or millions of combinations of different variables if you were to combine things off of the income statement and the balance sheet, so you would make all sorts of esoteric variables and then you would run them all through a factor model and by chance some would work right. So this is called

pa hacking in the statistics community. It's one of those things where you know, even if there's only a five percent chance that something happens by random. If you run one hundred tests, well, then five of them are going to turn up positive, even though they're just by chance. So i'd mentioned Robert nobe Marx. He actually wrote a paper on this topic recently. He not only only created a bunch of AI models to find new factors, he also then employed AI models to write the academic papers

for him. And it was done as sort of a way to demonstrate to the industry what can go wrong if you use this approach. So it is something we've looked at a lot so far. The ways that we have experimented with using it is more to gather some of that unstructured data that may be like in company filings so or oftentimes when companies have corporate actions, they're

putting out a bunch of text based information. And if you're using different factors, so for example, if you're using a profitability factor a value factor, it's relying on the numbers in the income statement to be interpreted properly. And so let's say that you know the auditor qualifies their opinion about the income statements, Well, do you want to

trust the metrics from there? Probably not. So if you had some sort of a model that could scan through and highlight to you areas where there may be concerns or maybe changes. So let's say a company spins off a division. Well, now anything on the balance sheet about you know, assets or book value of equity has changed

because they've spun some of that off. So finding ways to flag some of those changes in the data so you can go update your metrics so you're not using stale metrics has been an area where we've experimented with using it.

Benjamin Franklin, and others. And one of the things I really like reading about in these biographies is each of these individuals accomplished a lot. They were all great problem solvers, and each of them went through a lot of challenges. They went through big ups and downs along the way. And I think intellectually we all know that success, whether that's in life or in investing, it's not a line

that goes straight up into the right right. But there's a difference between knowing that and then actually being able to have the discipline to sort of live through the downs and capture the ups. And so for me, reading biographies really brings to life the lessons from history, so I can learn from them and not have to repeat them myself. It's great, Joel.