We Asked a $4.5B Quant Manager Why the S&P 500 Is Just 46 Stocks — and Why Small Caps Aren't Dead

[00:00:00] We're now at 46 as of kind of the end of the you know last year when they did the study. So it's effectively less than 50 companies in S&P 500 are driving the returns. So what does it mean for investors? It means that you think you're getting diversification, you think you're investing in a diversified market index with 500 names, but that's not the case. They question how we think about small

[00:00:26] companies. And what they're suggesting and what they've done in the paper is that not only they want the stocks to be small now when we're ranking them, but they want the stocks to have been small a year ago. For the companies that have higher level of intangibles, high intensity companies, high intangible intensity companies, we still use multiple measures. We still look at value,

[00:00:52] quality and sentiment characteristics, but we de-emphasize value. We put lower weight on value, and we put higher weight on sentiment. Hi Elena, welcome to Excess Returns. It's great to have you here with us. Thank you, Jack and Justin. It's my pleasure. We always enjoy having the folks from Bridgeway come on and join us. You and your team do some very thoughtful work around evidence-based

[00:01:19] investing and factor research. And Bridgeway has long been known in the industry as being very rigorous around the quantitative aspect of building portfolios, but also asking the why, when and before building those strategies. And that's something we're going to sort of talk about and work through with you today. But some of the other topics, which is factor investing in general, things like

[00:01:45] the value and the size premium and sort of how you're thinking about that and how investors should be thinking about that now. And over the long-term things like intangibles and how those can kind of be thought about and worked into traditional metrics. And then also, you know, get some thoughts on market concentration and maybe how passive flows are or are not from your opinion affecting sort of the markets and stocks. So a lot of ground to cover, but certainly looking forward to this

[00:02:13] conversation. It kind of goes back to sort of where this podcast really started, which was talking about factor-based investing between Jack and I, but also with guests and some of the Bridgeway folks in the early days. So really looking forward to getting into some of this with you today. Where we thought we would kind of start is what I hit on before. And that's, you know, there's obviously a very strong core quantitative aspect to how you go about constructing investment strategies and building portfolios.

[00:02:43] But talk about this idea that it can be dangerous if you're building a quantitative strategy, if you're not asking sort of the why before acting. So can you explain how you kind of think about those two things? Of course. I want to mention that how you described Bridgeway, it's one of the biggest compliments to the company and the process and just how you described our reputation just made

[00:03:09] me very happy and very proud. So yes, you know, I'm glad that we're known for the discipline process. So I would say that it's both have validity, the humans and the computers and the process. And I would say it's the intersection between the two that creates very strong results. Discipline process is crucial. It keeps you out of behavioral biases. It allows you to remove recency bias. It allows you

[00:03:38] to not be too overconfident or on the other hand, to be scared of the recent news and, you know, pull out everything at the very long, a very wrong time. And while that process takes the emotions out, just running the numbers and just using the machines, in our view is not enough. Same way as AI cannot

[00:04:02] exist without the human oversight, I think that the investments and, you know, creating patollars cannot exist just by following the process. You always have to have the, you know, the human ability of of questioning things. We are able to analyze, question, think big picture, wonder why this works. It's not supposed to work. You know, how come in this particular period, this particular model is

[00:04:29] working? What is going on? So being very skeptical about the results. Our head of research says that, you know, when the computer model produces the results, the work is just starting. That's when you're actually starting to analyze and think about it. So I'll give you an example. Let's say we find something promising. We read literature, we attend conferences, and we think that this particular paper is, has a potential. You know, we'll try to replicate that. We'll have

[00:04:57] the analysis, we'll have the numbers, and then we'll do the screen. So screening for X, Y, or Z, removing something. And let's say it's in a value area, the factor in the value area. And so we have our results, and they look promising. And kind of the danger and the mistake in just using the statistical process is, okay, let's just code that in, put it in, and we're done. This is our model to go in. That's not Bridgeways approach. That's just the beginning.

[00:05:27] And so that's where you would say, okay, let's look at holdings. Let's look at this period. This was a, you know, low volatility period, why we're having the results where the value model is not supposed to act this way. And then there's a lot of statistical techniques that we're using to make sure that we are not data mining, that we're not naive, that we're not expecting something that's not even

[00:05:52] supposed to happen since the beginning, but we missed it. And the last thing I'll add, it's also where the teamwork comes in, and the power of the team, the power of diversity. I would say humility on the investment management team is incredibly important, because by bringing the results of your research to the table, and opening that up to criticism of others, it's actually the best thing you can do,

[00:06:21] because you're going to catch everything. And the people that are giving you your feedback, ask the other why questions, they're going to make better results. And at the end, it will not be a, you know, a, oops, that was not supposed to work since the beginning, because we ask those questions, and it's not just one person, it's we. So I would say that that's why it's crucial. Yeah, a lot of great, valuable stuff in there. I particularly think, you know, thinking about

[00:06:47] the market context or regime, you know, if you're looking at a, let's just say a 20 to 30 or 40 year study or test, and then thinking about what types of stocks or markets were doing well or doing poorly, you know, without kind of having that context, you can kind of get lost or lose, I think,

[00:07:12] important details that, you know, you would want to understand when looking at an investment strategy over long term. So I think that's a very important point that gets oftentimes lost when you're looking at these backtests. Yeah, definitely. The more Markle cycles we can have an hour backtest, the better. And of course, history is not going to repeat. It's not, it's different now than it was 10 and 20 years ago. But there are patterns. And there's also risk, there's always volatility, there's always

[00:07:41] a lot of things that are going to be a lot of things that are going to be a lot of things. But frequently, the market reaction is similar. And so studying those in different cycles and different environments is important. One of the things that I think gets often confused with investors, because there's so many different ETFs out there, and many of them are factor based. But I think this idea between the difference between a smart data strategy and in terms of how you build

[00:08:11] investment models. Can you just make that distinction for our audience so they understand? Absolutely. So smart data strategies is a great invention, I would just say. I'm not going to say it's a bad product. They're typically one factor to portfolios. There could be value, growth, high dividend ETF. They typically use a single measure, single matrix. And then they will

[00:08:38] have a certain revalid schedule. They follow these rules. But it's typically one factor portfolios. It's somewhere in between active and passive, kind of in some way. And I think they're a good tool for a sophisticated investor who can build portfolio allocation, who can have their own core

[00:09:03] investment. And then they would add on factors, smart data portfolios. And then they would add on value. They would add on high dividend. And then they would look at it periodically, rebalance, and make smart investment decisions about those portfolios. There is the basis for that. And I'm sure there are people that are doing it. The issue is that not every investor has the time or expertise to

[00:09:30] be doing this. And I strongly believe that using just one smart beta portfolio is not going to lead to success because factors go in and out of favor. And also, I think that that's where humility comes in. I am sure it's not possible to time the market. Maybe you'll do it once. Maybe you'll do it twice. But if you invest in a one smart beta portfolio and you think that you can time the factors by getting

[00:09:59] in and out exactly at the right time and make the money this way, I would like to meet that person. It's going to be great to just have that expertise and knowledge. Timing the market, timing the factors is very difficult. So the difference between this type of investing for sophisticated allocators that do have this exposure to various factors. And what we offer is we kind of

[00:10:24] do that work. We have multi-factor portfolios that are built to deliver more consistent results. You know, we can have a small cap value portfolio, but it's not going to be only about small cap and value. It could be about quality and sentiment in that particular portfolio. It could be a value portfolio, but the way we're going to define value is not going to be a one measure. It's going to be a multi-metric approach to value. And that also adds diversification and it adds to consistency.

[00:10:53] And then over time, we will monitor the portfolios. We will see the exposures that we're delivering. And we do the job of rebalancing, changing, positioning to make sure that whatever that portfolio is supposed to deliver, that's what is delivered. Whether it's a multi-factor exposure with this kind of allocations to value or quality, this is exposure, this is what it's going to do.

[00:11:16] So we offer these combinations and not only the combination and rebalancing in a disciplined fashion, but also multi-variable, multi-metric approach, which is very crucial, I think, in consistency of the results. And so does that mean that for the multi-factor, multi-variable approach, are you building sleeves of stocks and then combining those sleeves to get that different factor exposure?

[00:11:46] Or are you looking for stocks that pass or have value quality, I don't know, defensive type character? So I'm just trying to flush out when you say multi-factor, how are you getting that multi-factor exposure in most of your portfolio? What is the process? Yes, to both. So I think that's one of the, you know, what I'm kind of excited about Bridgeway and Bridgeway's potential, it's our ability to develop various techniques to offer different

[00:12:15] solutions. So we are not set on, you know, one particular one. We test, we develop, and there's different places where different techniques and different approaches can be used. For example, your question about the combining the various factors into one super factor, there is a potential to be using it and reusing it in some strategies. And it has its benefit. It's better for lower turnover

[00:12:42] strategies. It's looking for names with good quality value sentiment characteristics. And it allows us to remove, for example, what's called fallen knives. Like how do you then address the names that are continuing to go down in price and becoming cheaper and cheaper companies if you don't screen for them on quality or potentially on sentiment? So there is a validity in this approach. It allows you to have

[00:13:09] consistency of exposures and have good stocks in the portfolio, typically with some lower turnover. On the other hand, there is also an approach and validity in what we've researched as well on this individual sleeves or individual factors that are independent in the portfolio. And that brings purity of the factor. So now we're looking for the names that are only scoring high on

[00:13:38] sentiment that tend, and it's very difficult sometimes, depends on the correlation of the factors in the market, of course, in this particular time. But it's not frequent that you'll have high sentiment scores that are also high value scores. So the independent approach allows you to bring the deep exposure to each individual factor in the portfolio. That's where risk management is crucial.

[00:14:02] That's where research of how much to have in each of these factors is crucial. So it's not an easy, let's just do 30-30-30. It's what are the risk parameters? What are the boundaries? How do we monitor the overall positioning of the portfolio? But both have their kind of pros and cons? So are you a believer that in all factor portfolios, even if let's say you have a value portfolio,

[00:14:29] they all are multi-factors to some extent. If you're running a value portfolio, you're always coupling it with some of the other factors to try to get the benefit of them. Is that a fair way to look at it? Yeah. Again, yes, we have portfolios like that. And I look at that portfolio almost every day and I love the combination of the factors because it's more consistent over time, because factors come in and out of favor. And I call it diversified value portfolio. It's really well positioned for the

[00:14:57] investor that wants consistent results that's not going to be dependent only on value exposure. And like, I would like to be value, but I also would like quality in my portfolio. I also would like to have a sliver of sentiment in my portfolio. So from that perspective, that combination is very important. You know, the information ratio of that portfolio is going to be very nice and consistency of returns is going to be very nice. On the other hand, we have clients that are looking for value exposure.

[00:15:25] And so for that, for those particular clients, they were part of the allocation in the bigger, in the bigger scheme, and they are looking for deep value exposure. So it is about value. There are some parameters about us, you know, not going into negative momentum and we have some quality characteristics because we're using multiple measures of value. But the primary factor there is

[00:15:48] going to be value with their, you know, positive profitability and neutral momentum. But it's not intentionally that we're raising momentum, for example, in those particular strategies. So it depends on the investor. And I'm just happy that we're able to offer those solutions depending on the kind of investment that we have. I want to ask you about size because you guys did a really good paper earlier this year about this. And this is probably one of the most debated things in my career, I think, in the factor community.

[00:16:15] It's like, early in my career, there was a size premium and then there wasn't a size premium. And then there is, but it only works best if you combine it with other factors. It's like, you can come up with a million different ways. What did you guys find your most recent research about this? This is what I would love to, I don't know how we're going to do it in an hour, but I'll try it. So this, you're right. This paper was published very recently and it's by our head of research, Andy Berkman and Christine Way. And it's interesting. To me, it's a great read,

[00:16:45] because it's simple and it's intuitive and the results are really kind of like speak for themselves. That is very interesting. So let's think about how people, how academics define size. You're right. Small size is, it's a well-documented phenomena, but there is a lot of criticism because recently it's been out of favor. And so is this no longer a alpha factor? Is it now only a risk factor? And you know, how do you think about size? So academics typically, you know, when you look

[00:17:14] at the Fama-Franche studies and you know, all the other studies, they define size as, for example, SMB, small minus big, the most famous, one of the most famous Fama-Franche factors. It's basically, you take the smaller half of the stocks minus the larger half of the stocks by their size, or some of the Fama-Franche factors using the top 30 minus bottom 30, some use quintile deciles.

[00:17:41] And it's pretty standard. What this paper did, they looked further in, Andy and Christine looked further into definition of size. And they questioned how we think about small companies. And what they're suggesting and what they've done in the paper is that not only they want the stocks to be small now when we're ranking them, but they want the stocks to have been small a year ago. So the name of the

[00:18:10] paper is, I know what you did last summer. So I thought it was very creative name as well, but it basically says, I want to look at the names that are small now, and I want to invest in the names that were small a year ago. And in addition to requiring the names to be small for this, you know, two periods of time, they also removed the names that were not available. There were NAs a year ago. That could

[00:18:35] be the names that used to be out. The universe was a lot of exchanges, except for OTCs and pink sheets. So that could be a stock on pink sheets or OTC over the counter. There could have been IPOs, which by the way, their papers proving that IP was done too well in the beginning. So they only formed portfolios as small, you know, small quintiles, small desks, the small portfolios as the names that have

[00:19:00] been small, that did not become small this year. And the names that existed last year and were small. And that adjustment showed some really improved small size premium. And there is some intuitive ideas behind that. Like it basically relates to negative momentum. The names that are going down tend to continue to go down. So there could have been negative momentum in those names. There could

[00:19:26] have been weak profitability. Why did those names become small a year ago? Interestingly, they repeated that with shorter periods of time and a longer look back. And they also did it not only annually, they did it kind of shift in every quarter. And the results were pretty robust, kind of indicating a very interesting idea for investors, you know, invest in names that are not only small now,

[00:19:53] but were small before. So that's the nature of that study. And it's an interesting paper. Yeah, it's really interesting because of both groups you're kind of removing here, like the full and large caps, I can make a strong case you want to remove them. Obviously, there's a reason they went from bigger caps to smaller caps. And also the data on IPO performance is not very great. Exactly. So like getting the IPOs out of there too is good. So it's just interesting. It's interesting how you, no matter how you play with this data with small caps, you can come to different conclusions. And you guys did one of the most interesting things I think I've seen in terms of how to think about

[00:20:23] this and how to analyze it, you know, with removing the groups that maybe you don't want in there. Thank you. And then Christine, kudos to you. So as a team. What do you think about the small cap universe, like in general? You know, one of the things you hear all the time is small cap, the small cap universe is a lot worse than it used to be, you know, because companies are staying private longer. If you look at the percentage of the rest of 2000s losing money, it's much higher than it's been in the past. I mean, do you think that's a generally worse universe than it's been historically? Okay. I'm going to remove the

[00:20:52] qualitative definition of worse and better, but I'll say it's different. So I agree it's different. And not only, so the big impact that is companies don't want to go public. So that's kind of the underlying reason. And they don't want to go public because of one, they don't need to. There is such an influx and such desire, you know, from the private capital investment, you know, like the growth of private debt and private equity right now. Like I've just talked to recently ourselves,

[00:21:22] gentlemen, there's all these searches in private equity. There's an appetite there. So there is, they're being invested in, they get the money from the private capital, and it's so expensive to go public with all the legislation and regulation and Serbian Oxley. So they just don't want to incur the expense of being in public. So they remain private. And there are studies out there that those companies tend to be higher quality, and therefore the quality characteristics of the small cap indices are lower.

[00:21:52] And I wouldn't argue with that. So there is a structural shift. And yes, the lower, different measures we're studying, but yes, there is lower quality of the smaller cap index than it used to be. Now, the question is if it's good or bad. On one hand, yes, it's, you know, it is lower quality. On the other hand, it may create more opportunities, especially if you look for quality in different ways, especially if we run, you know, we run our quality measures, quality value, quality factor models,

[00:22:21] and there are multiple measures in there. So you can still pick quality companies among the universe. And, you know, they may be, they are better than the benchmark. There is different ways of identifying those companies and multiple measures of quality basically address that. I would add a little bit more. The other set that's changed over time are the very small companies. The number of names in

[00:22:45] the smallest decile of the universe is, you know, shrunk significantly. That's where the entries and the IPOs are more frequent. So that actually reduced the opportunity set. It doesn't mean that you cannot invest in those names, but then yes, the number of companies is shrinking as well there. Yeah. I think you made an important point because that doesn't necessarily mean that the opportunity set for a manager like you is worse. I mean, you, you've probably always screened out a lot of these companies that are unprofitable and all these other things. So it doesn't necessarily mean just because

[00:23:14] there's a lot more of them that there's not great companies within the index. I mean, it's, it's, if you look at like the S&P small cap 600, which just screens on profitability, I mean, that's been a far, far better index than the Russell 2000, just for that simple thing of just screening on profitability. So it doesn't necessarily mean the universe is worse. So yes, either whether for our own new strategies, we have our, you know, bankruptcy, poor quality companies screens for our select strategies. We are looking for higher quality companies. There

[00:23:41] are still opportunities there. The overall kind of bar has shifted. You're correct at that. I want to shift and talk about value. And that's something, another thing people have been talking about has been changing because we've had many of these, the Facebooks and the companies of the world that are these high intangible companies are becoming a bigger and bigger part. Of the universe. Like we had Michael Bobison on recently and just intangible assets across the economy. If you look at the line of intangible relative to tangible, it's going up and up and

[00:24:08] up over time. And it raises the question for a value manager. Like, how do you think about that? Do the traditional value metrics we've been using all these years, do they still work or do I need to enhance those metrics? So how do you guys think about that? We definitely do. We definitely published papers and now I'd say the expertise of, uh, of the team is, uh, is very high in this. And, um, there's multiple papers with, you know, Andy and

[00:24:32] Jacob Paggiani and, uh, one of our other, uh, uh, partners published. And so we've done a lot of research in that area, but just to kind of set to, to start a little bit back, what are the high intangibles? Like, I, I don't know if you want me to, it's, you mentioned the Facebook. Yes. So it's all the know-hows, the brand names, uh, the brand, um, recognition of value. The next step, what to do in R and D and you know, all this knowledge that the companies are developing to create the next drug,

[00:25:02] to go to the next trial. All of that, um, is, has value. And for companies in certain industries that has more value, there's more of this intangible assets. But our current accounting principles are not requiring to put them on the, this assets on the balance sheet. So they're missing on balance sheet and that basically understates the book value of the companies. And then on the other hand, um, companies are required to expanse this. So they are expensing their, you know,

[00:25:34] reducing, effectively reducing their earnings in the short term. So that creates distortions. And so we recognize that we've done studies on that. Um, we see the importance of making this, what we call contextual factor application adjustments in how we value companies. And, um, it's, it's actually pretty simple for the companies that have higher level of, uh, intangibles, high intensity, uh, company, how intangible intensity companies, we, um, we still use

[00:26:04] multiple measures. We still look at value quality and sentiment characteristics, but we de-emphasize value. We put lower weight on value and we put higher weight on sentiment. So sentiment measures matter more for companies in high intangible space, value, traditional value matter less. For companies that are in low intangible intensity industries, um, we continue to use regular

[00:26:30] valuation measures. The traditional valuation is looking at their cash flows, at their earnings, at their sales, uh, patterns, you know, we look at quality, but we don't put as much weight on the sentiment in there. So they continue, it's the value, the, the impact in this overall scoring of like, you know, how you think about the companies we've talked about the kind of multiple, um, uh, factor, uh, metric. And so over, over-emphasizing sentiment for high intangible intensity compared to low

[00:26:57] intangible intensity is how we can approach that on the practical matter. And the way we classify companies, it's kind of based on a sector in industry classification that we developed internally. So the idea is you have different models you're running or your model gets tweaked somewhat, depending on the industry you're dealing with. You, you have a model that's a best fit for that specific industry. It's, it's, it's applying depending on this ICI classification. So the models,

[00:27:23] the underlying models could be the same, but it's the weighting of the value versus the sentiment factor in the overall evaluation of the company. That's what's different for those companies. Do you also adjust the value metrics? Like, do you capitalize intangibles to try to adjust them? Or do you think about more just applying different factors? Oh, it's interesting. We've done one of that adjustment in quality model, interestingly. So it was actually the intangibles matter, not only for value factors, but also for the evaluation of

[00:27:51] the qualities. And as we've done an adjustment for one of our quality metals, um, um, models, uh, looking in some R and D expenditures in there. So there is an adjustment, uh, related to the intangibles. How do you think about value in general? It's something we've talked a lot about on the podcast. Now you obviously had a long period, it's been better recently, but value had a long period where it didn't work. It led to a lot of people questioning, you know, does value work anymore? How do you think about value in general? I mean, you could argue spreads are

[00:28:19] still pretty wide. So there's probably still for long-term investors who are not thinking about this next year. There's probably still an opportunity at value, but how are you thinking about that? Values still one of those, uh, factors that Bridgeway believes in strongly. Um, value issue matters. Um, value is, uh, one of the most researched and, you know, um, long-term outperforming factor. Um, there are various reasons, you know, including the intangibles, including the

[00:28:46] growth of the, you know, ETFs that, you know, factors is not working as consistently as before. Um, but I also know that catching that it, the reversal to the mean matter, like that does happen. It's just to catch that the change can be so quick. So I would say that it's the combination of small and value is what I wear would look for. And even right now, like, look what happened in the

[00:29:14] year to date period, you know, the, the large cap grows a flat, the, the Russell 1000, you know, the small cap values up 14, 15%. And it's not consistent on a daily basis, you know, catching those four or 5% in there's periods of time when the market moves four or 5% a day. But if you have a location to that small cap value, it is actually a great diversifier to either your core allocation

[00:29:37] or a growth allocation. So I depending on the investor, uh, you know, interest in of their risk tolerance. I am not saying that small cap value is a place to be as your core allocation. I am saying that small cap value, especially with current valuations is a place to consider as part of your allocation in order to diversify and know that those continue small cap and small cap value stocks still

[00:30:03] continue to provide diversification to the large cap as well as the international markets. Do you have any opinion? I'm just curious, you know, thinking back to where we started the conversation around understanding the type of market environment or the context of it, like a lot of systematic value investors, you know, when you go back to the late nineties, early two thousands, you know, value had a fantastic run for five to six years and, you know, blew away the NASDAQ and

[00:30:32] the market, but it had trailed for a long number of years kind of coming into that. But then there's other, so you had that period, but then, you know, I've seen some evidence that when you look back at like values at outperformance historically, it tends to be, um, you know, coming out of those bear market environments where value does, cause all these stocks are priced by the business. So value like rallies. And so it's very episodic. You don't get this like long duration of

[00:31:01] value outperformance. So how do you, how do you, I guess, view those two? There's kind of a question in there and hopefully you kind of understand it. So what you are describing, um, in some ways, what could be called a junk rally or a risk type environment, a risk on, and all of a sudden we're coming back from, you know, either a COVID errors or, you know, the tariffs or something that we're seeing where there's such a risk on that companies with

[00:31:28] poor quality, um, you know, no earnings, um, no, even, you know, no sales are actually doing really well. To me, it's not really a value environment. Like it's interesting to look back at 2025 where, you know, it was called like, well, value models should have worked, um, price to book work, you know, like it's, there's different measures that would work in different environments. And,

[00:31:53] um, the junk rally does not necessarily mean a value rally. So, I mean, that's to me is a kind of a distinguishment there. And, um, some of our strategies, you know, we've had periods of time with huge junk rallies where we lack because of our quality overlay or because of the kind of this cutting out the bad players from there. Um, so the answer to me probably be diversification

[00:32:18] for your value measures. Do not rely only on one measure. Um, I've seen plenty of times when I'm studying the, just the universe by, that's out by price to book, price to sales, price to cash, for price to earnings, simple measures, and they're performing differently. You know, there's plenty of time when price to book is rallying and everything else is lagging and you're like, well, did the value work or not? You know, did former French value work and what about the other value measures? So,

[00:32:43] um, diversifying, um, from and, and how the value is measured, not only using only one measure to me is part of that answer. And, and you're right, the junk rallies happen, um, and it's, they're short-lived. Um, but then if you're an investor that is long in, you know, in there for long term, then you're looking for something that's more consistent and, um, that does not necessarily

[00:33:09] be value as defined by price to book. And also to just this point, and you guys do a great job on this on the education side. Like it's so important for people to understand the factor they're invested in and sort of how it behaves and value has had this tendency to maybe have a little bit longer periods where it struggles, but then also have big bursts of returns. That's right. Um, and so if you know that, like, and you're invested in the factor, it allows you to stay the course better. I think when you see the periods of struggle, knowing that that's sort of is the history. I don't know if you've heard that or not.

[00:33:36] I do agree. And I think that they, they switch the inflection point, you know, like I used to think when I was studying that momentum is your fastest moving signal, you know, like you're like, yeah, it's, it's the flexion point of momentum. And all of a sudden, you will, it's, you know, the force is out of the barn and you know, it's, it's immediate, but I've seen much faster movements on, you know, small and value factors, especially with the combination of the two, you know, let's say we're, you know,

[00:34:01] value in ultra small space, you know, those are going to be just bouncing. Uh, and it's very hard to catch for investors because investors typically will, you know, oh, it's already too late. And it's, it's, it is a hesitation of, do I catch the right or am I too late? And so when you already have that allocation, you're not subject to your emotions of like, well, do I just get on right now? Am I too late? Then, you know, what do I do now when there is a rally of 14% of, you know,

[00:34:28] small value? If you want to be invested in small key value, you should be invested. You know, so you don't chase the returns, be they negative or positives. You just have to be consistent in your allocation. Yeah. It's interesting. Cause it's kind of like market bottoms in that way. Like I remember in 2009, like we all thought the world was going to end. Like by the time you realize things were okay, the market had rallied so much. It's kind of the same thing with value. Like just timing those turns is so, so difficult. Yeah. That's right. And I, and looking back at, you know, if you ask me what are my biggest learning,

[00:34:57] uh, you know, lessons in life and like in, you know, in the, in my investment career, I was like, I would say living through 2008, 2009 had the biggest impact on me. So as, as just kind of learning, being disciplined, how things can happen and, you know, any other market turmoil after that was easier to process and you kind of learn to use your expertise and discipline again to handle those. Another topic we've been talking about a lot on the podcast is a market concentration.

[00:35:25] And you guys had a paper, I think it was like the beginning of last year. So it's a little bit while a while back, but what I really liked about it is everybody says the market's really concentrated and we've been saying the market's really concentrated for a very, very long time, but there's ways to some degree quantify how concentrated the market is. Um, in that paper, you guys use that, let me, I'll probably blow off the pronunciation of this, but the Herfindahl-Hirschman index, I believe is the way it's pronounced. Um, and you looked at the idea of like how concentrated the market is relative to like how it behaves relative to like a certain size

[00:35:54] stock portfolio. So can you talk about that? I can, but I am going to call the index HHI. So that's my way to, I should have, I should have done that the first place. I could use it and I'll read it correctly. But then if you ask me like an hour later, I would, I would say, oh, this is HHI. So, um, it is a very simple and creative way to, uh, to measure the concentration. And I'll give you an example. So the paper was done by Indian, um,

[00:36:19] Kai, uh, you know, about a year ago and we called it how many stocks are effectively in S&P 500. So we all know S&P 500 tends to have about 500 stocks, maybe 502, 503, currently 504. But, um, does it really matter that there is 500 stocks in there? What drives the returns of the index? And so they've done this study going back to, I want to say 92 or 94. So, you know, taking,

[00:36:47] you know, several decades of data, um, and using this index, um, and the way this index works is, let's say you have a portfolio of, um, and this is an example from the paper that I remember is that you have 10 stocks in a portfolio and each one of them represents the, uh, you know, the 10%, they equally, uh, weighted. So the index will be 10, you know, by the virtue of the calculation and the burden it will be 10. If you have a portfolio with, let's say about 90% in one stock, and then

[00:37:16] nine other stocks have about 9% or 91%, other ones have 1%, then the index will be close to one. It's effectively the impact of one stock in the portfolio that effectively is driving the returns of the, of this portfolio. So they calculated the index using the history of, you know, going back to early nineties. Um, and what it showed is that the priest, um, it's kind of being, it's probably around

[00:37:43] 160 or so. And that was kind of in, um, uh, in, oh, I'm taking it back. No, I think the study was to 1970. Um, so you long, even longer periods of time. And then the highest concentration was in 94. The, the highest, um, number of, the highest effective number of stocks was in 1994. Now we're talking about less than 50. So we're now at 46 as of kind of the end of the, you know, last year when they did the study.

[00:38:12] So it's effectively less than 50 companies in S&P 500 are driving the returns. So what does it mean for investor? It means that you think you're getting diversification. You think you're investing in a diversified market index with 500 names, but that's not the case. It's only those few names, the mega caps, and then, you know, next 30 that are driving the returns. And so the risk, um, exposure

[00:38:39] of your portfolio of your investment is actually higher than you might perceive because you invested in the market diversified index. So that's the implication. Um, and the possible solutions are, you could do equal weighted S&P, you know, then you're getting the small cap bias. So that may not be what you're, what you want for your corporate portfolio. But some of the suggestions that, uh,

[00:39:04] you know, we had in that paper was diversify, look for other allocations, add allocation to small caps because they're diversifier of this big companies that tend to be growthy. And by the way, the, you know, the growth characteristics of the S&P 500 right now is just incredible. So we're like a 27, I think right now, which is higher than the, you know, history. Uh, and

[00:39:27] another alternative is to invest in international markets. Um, so look for, um, alternative investments, and I'm not speaking specifically about alternatives, but other opportunities that are not as correlated, uh, have low correlation with the, uh, the large core holdings in your portfolio. You know, what's so great about this is we all talk to clients about the concentrated market and it's kind of this ambiguous concept. They don't really understand like, oh, it's concentrated.

[00:39:57] What does that actually mean? When you can say like you effectively own 46 stocks instead of 500, it's a great way to explain to someone like how concentrated it is. And also like, that's the lowest number it's ever been in history. It's another great way to explain to people the concept where they actually understand it. I think, yeah. And seeing the graph, you know, I am the number person, but I love the graph. So if, if, you know, somebody wants to pick up the paper, it's just, it's just kind of like the increase in this and then gradual decline right

[00:40:22] here that, you know, where now the concentration keeps, um, getting worse and worse in the way. Yeah. And I kind of think, and you can correct me if you think I'm wrong about this. I sort of look at factor strategies as a diversifier in an environment like that, because we, we could be, you know, that could continue. The concentration could continue to go up. The largest companies could continue to drive. So I always say to clients, like it's, it's great if you have exposure to the S&P 500, but if you have this other exposure, that's more fundamentally driven, it sort of ends up being a diversifier in your portfolio because we don't know which one of

[00:40:51] those worlds is going to happen here. We don't know the world where the value stocks are going to come back. That might happen or the world where the biggest companies lead that might happen. Like, do you think it's a fair way to look at it? Absolutely. It's, you know, like you've asked me about blending the signals or having the, you know, independent buckets. So in this case, think of it as an independent bucket. Yes, you have your core allocation and it could be your S&P 100, but what can be added to that allocation that's not diversified, that's

[00:41:16] going to bring a pure signal based on something else, whether it's a value factor, whether it's a quality factor. And it could be that, you know, you look for exposures in the emerging markets. That's very, you know, there's even lower correlation with this. So building that portfolio layer in increasing the impact from this diversification, it's even more crucial when the core position is so concentrated. Do you have any thoughts on passive investing?

[00:41:44] Um, it's something we've talked about a lot in the podcast, this idea that as more and more people are investing passively, you know, they may be driving up the biggest companies relative to the index more. And, you know, that might be having an impact on the opportunity set for managers. So it may be, you know, like the stocks that are in the small cap index that are completely ignored, they may become cheaper, they may become a better opportunity. Like, do you think about anything like that? Do you think anything about like that, the way that's behaving might

[00:42:09] impact your opportunity set as a manager? Um, so my, my take on index investing, and I will say something that probably contradictory, but I want to qualify that. I actually like the growth of the index investing because to me, more and more people invest. Like I actually like seeing people invest in stock markets or just, you know, I believe in, uh, um, in, in having your money investment. And so to me, that's just kind of indicates that, you know, the growth in index

[00:42:39] investing maybe creates more opportunity generally for a people of public to be invested. So, but the reality is that yes, the growth of the index investing creates this distortion between, uh, in the equities and volumes traded in algorithms that are working, especially towards the end of the day for all this index funds. And so how do you, how do you work around that? Um, and I would say that for a small manager, it actually creates opportunities. So we are a boutique,

[00:43:07] we are a boutique manager, we are four and a half billion, we have multiple strategies, but because we're small, we are able to expand our universe. Like our, our investable universe, where we are looking, uh, for the opportunities is actually larger than, you know, other big managers. And, um, we have, uh, expertise in trading small caps. We have expertise in our, you know, trader, uh, uh, trading team. They have years and years, decades of experience of, of trading small

[00:43:36] cap and ultra small. So we can work around that. So we found the techniques and abilities to, um, you know, source liquidity, to create liquidity and that to us now, this opportunity. So yes, the index investing does create this. And I would say it's more of chasing the benchmark. It's what the benchmark returns are now that sometimes you are wondering, Mark, it's really rational that everybody's, you know, the, the index volumes are driving the performance of the names,

[00:44:05] not as much as the fundamentals. So yes, there is this kind of somewhat of a frustration or, you know, wonder what's going on. But at the same time, if you're disciplined and looking for opportunities in the broader universe and able to deal with liquidity and spreads and, uh, uh, lower volumes, there are opportunities there, especially for smaller managers. Markets are always volatile. There's always risks out there that investors,

[00:44:32] some of the, sometimes we know them, sometimes we don't, but, um, you know, I think particularly when you look at like what's happened in the past 12 to 18 months with, you know, first starting with the tariffs and then the trade wars and this trend of deep globalization, that's sort of taking place. And now with this war in Iran and the price of oil and how the market's trying to digest this,

[00:44:56] do you have any thoughts about other ways that investors might be able to still stay invested, but, you know, manage some of that risk systematically? Like I know you have some, I think, um, you know, absolute return type of strategies that you guys run. So how do you guys kind of view those types of strategies for the investor and kind of where they fit in the portfolio?

[00:45:20] Um, absolutely. So I would say it all depends on investors, as you said, risk tolerance and their ability to handle this, um, the volatility specs. The answer for some could be embrace it. You know, volatility is just those ups and downs, those swings, those spreads, those changes, the drawdowns, they create the equity, uh, risk premium. This is the, the, the features of the equity market. And,

[00:45:49] you know, that just means that if you're investing for long-term, this will eventually create the long-term results for you. So if, if you can embrace the volatility, buy and hold investors, that could be for somebody who is just reading the news, but not acting on those. On the other hand, for investors that are like, want to stay away from the volatility, they still want to, as you said, participate in, in, uh, in returns and does not want to take the inflation risk from fixed income. I mean, fixed income

[00:46:19] is always out there, but there is inflation risk there. There is interest rate is risk. So if somebody does not want to handle this, um, risks, then the opportunity would be in the market neutral strategies. Um, and that's something that we've, um, kind of developed, uh, and, uh, offering, uh, currently, but I would say the benefits there is that where you are creating a balance of long

[00:46:45] and short positions and strive to have either zero or even slightly negative correlation to the markets. And so what, what ends up happening is that the strategies generate returns, regardless of market direction, whether the markets go up and down because of the combinations of the long and short positions and, and being market intentional and market neutral, we deliver returns, regardless of

[00:47:11] the environment that we're in. And so that's something that, you know, these investors could consider. And I think it's a very profile location. So are you guys actually in some of those portfolios, are you actually going to short individual stocks or is it asset class based? Oh, they are individual stocks and they are done in swap formats, but they are in, you know, the individual stock, uh, uh, swap positions. We offer them for, you know, the global opportunity

[00:47:38] is something that actually expands this opportunity set to not only be in the United States, but capture the, uh, developed markets and develop independent, um, emerging markets. And so that creates even more opportunities for investors that wants to diversify from the, um, high octane US markets, um, bring the diversification of the global markets of the emerging markets, and at the same time, not be dependent on where the direction of the markets are.

[00:48:07] With those particular strategies, what we've developed is, I would say, very unique approach, goodest to the team is that where we, um, take exposures, take desired exposures to value, quality sentiment. But at the same time, we minimize exposures, what we find undesirable. We don't take country risk. We don't take sector risk. So minimize that. Where you end up having a portfolio that's not

[00:48:31] biased towards a particular area of the, uh, globe or a particular sector. Not only they're market neutral, they strive to be near neutral on the country and sector exposures. So it's all about the stock selections, the signals, um, the ideas of, you know, your loan portfolios delivering the loan alpha and in the short portfolios, um, you are able to find locates and, and do your, uh, the, uh, short portfolios in such a way that you also drive alpha from there.

[00:49:01] David Pérez And by the way, just kind of tying back to that H HHI index, you know, I bet even you had 46 stocks that drive, you know, the vast majority of the returns, but, you know, I bet if you looked on a sector basis, you know, it's like 90% tech or something in there. Like I, I didn't read the paper, but you know, that's also, you know, over concentration in one particular sector, just given how much technology represents. HHI index Well, currently I think it's the, if you just take the allocation S and P is about 35% in IT, but you know,

[00:49:30] Russell 1000 growth at some point was close to 50. So yes, it's that, you know, and at time people would say communications are related. So it's even more so effectively. David Pérez Right. Yeah. How do you think about as a systematic evidence-based manager, like how does the team go about assessing, you have a strategy in place, it's made up of certain factors, um, those factors,

[00:49:54] are based on long-term evidence, but how do new factors come in? How do old factors possibly get retired or tweaked? I mean, I think that's a challenge for, at least that I, but what I've seen in systematic strategies is, you know, how long do you give it to run before you say, you know, well, wait a minute, like maybe we should be looking at something else here? Like, how do you guys approach that as a firm? LESLIE KENDRICK Your questions are getting harder.

[00:50:21] This is not an easy one. You know, the question, I can answer the question kind of at the broad level, but to make that call, to make that decision, especially to discontinue the model or take something out. It, it takes conviction and conviction is based on your research and kind of, again, the, um, team expertise in making that decision. So I would say the core principles, the core ideas, they, they, they do matter what you do with portfolio matters. You want to have

[00:50:51] exposures, you know, multi-factor exposures in a portfolio. So, um, I look back at our portfolios. They, from that perspective, they remain unchanged. We deliver to investors what we said we're going to deliver. Um, at the same time, continuing improvement, looking at what's out there, what's working, what's not working, what got eaten away because it's so commoditized that it's no longer working in this particular, you know, in the recent environment or what is out there that we can

[00:51:20] add that's not already present in the portfolio. Like changing for the sake of change is not what we do, but there are opportunities to make enhancements to strategies. Um, and I would say, uh, it's, um, studying it, um, doing, uh, various market cycles of back testing, uh, making sure that you have the additional value at what is it that it's adding to the portfolio and also having some

[00:51:44] conceptual reasons of why would that factor perform, uh, you know, in this particular environment. And you could say that adding some factors, low volatility is an example. This low volatility, when it was, um, really doing well after, you know, at the big market turmoil, is this something to add to your portfolio or could you achieve the results and, and, and a better consistency, but putting some risk constraints. So it's a question of how you want to, um, bring what you

[00:52:14] want in a portfolio. We, I remember taking one model out of, with a portfolio, um, actually the reasons, uh, that was on the easier side because the correlations between the two models became much higher over history. The data is more widespread. There is more access to the data. So we had two quality models, um, in, you know, quality kind of category that, you know, were much more diverse, created more diversification historically. And over time became high and higher correlated.

[00:52:43] So I would rather use the allocation and kind of the risk balance to do something, bring something new in the portfolio, then have more of that kind of same, um, going. So high correlations may be, uh, one of those to improve the overall, uh, consistency of results. It could be, again, the commoditized signal. Um, we haven't done major, major changes where it would remove value

[00:53:08] quality or sentiment from the portfolios, but we have put a different risk constraints in place, uh, for some of the strategies when we were seeking more consistency of performance. Some of it had to do with, you know, what are the active weights, exposures, things like that. But it's, it's the combination of the, uh, process that has a very high bar. So it's not done easily. It's, it's a discussion.

[00:53:35] You want to have somebody who disagrees with you. Um, that's an important, actually, the kind of the outlier opinion is important because you want to question all the different, um, uh, ways of why you're doing it in order to have that high conviction. You know, I, I, have you ever read Jim O'Shaughnessy's What Works on Wall Street? Are you familiar with that book? Oh yeah. Okay. So I, and I believe in the audience or maybe even Jack, you'll correct me. I believe like he started with

[00:54:01] the price to book as the, like the value factor. Then he went to the price to sales and then he went to like a value composite. So, you know, he was able to test, I think, and he tested over longer term periods as he came out with new editions of the book. So I could see how you can see how like you start with one variable and then like ultimately in the sixth edition, it was like the best way to get the, the, the value multiple was through a value composite of five different things. I think it was price to book, price to sales,

[00:54:30] price to cashflow, price to dividend, and then enterprise value to EBITDA is what he, he, and then he combined all those into one composite score. Exactly. Very similar to how we worked in our, you know, uh, quality, one of our quality models, instead of the last variables. And then over time we added something because we saw the value add. And there was also an economic reason to do it. Like it's not just because they, you know, the data and where data might, you know, we're trying to see what works better in the last 20 years.

[00:54:57] It's also expectations. Why would it continue to work in the next 20 years? So, you know, this wouldn't be a podcast in 2026 if I didn't ask you about AI and how you're thinking about AI and your investment process. So just kind of tell us, give us a little bit of inside baseball. Like, how are you guys using AI? How is it coming into your investment process? I guess what are, you know, you're, I'm guessing you're not saying, you know, to chat GBT, build me a strategy, you know, because that doesn't get the why, which is very important to you guys. But how are you

[00:55:27] thinking about using AI currently? Well, we have developed the AI policy. That was an important compliance step that, uh, that, you know, I'm, I'm happy that we have. Um, so, um, kudos to the team. Um, I would say, of course, it's on everybody's mind. Of course it's in the markets. Of course, it's do we have enough resources to support AI from, you know, the warehouses and, you know, data centers.

[00:55:53] And at the same time is like how you can use AI and make proper decisions. Um, so there are some, I'll, I'll give you, I'll give you kind of the, my kind of high level thoughts and maybe just an example of how we're using it. I would say, first of all, it helps with data. Um, and I've started at Bridgeway 25 plus years ago. And the data collection process was, uh, me, um, sometimes printing the

[00:56:22] earnings report. Um, and, um, sometimes hopefully if I had two screens, having one on one screen and then entering data, um, in Excel from the, uh, financial statements. So, uh, and then trying to figure out what's the, if it's a proper gap number, what, what's the company done previously. So they, I learned the process from very first from manual steps. It's like, so I've seen it being very manual. Um, and

[00:56:51] over time, you know, we have our systems that grab the data and they put it in a databases and we match different, um, uh, two different vendors to make sure we have, you know, high quality data. We study it, we massage it. And then I would just do three clicks on Bloomberg and I have all of that data in front of me, including historical ones. So, you know, the data availability and how quickly it can be gathered, systematized, uh, you know, clicks through, um, this is not a commercial to Bloomberg,

[00:57:20] by the way, but I would give them kudos about getting to the underlying sources from their, uh, uh, AI tools up to financials. It's good to see what's, what's those summaries are based upon. So I think that's a great use of it. Uh, so quick access to data and systematized data. Um, then also, um, something, of course, you know, you've heard this before is something that was not able to be put in numbers previously. And then all of a sudden it can be turned into the numbers. It

[00:57:50] can be consumed by the models and that's textual data. It's something that, uh, you know, how, um, and again, going back to me, five years ago, I would read the commentaries from CEO, a CFO, and I would put my assessment of it cautiously optimistic four out of five, you know, uh, strong on X, Y, and Z three out of five. Like we actually were sort of creating our sentiment signals 25 years

[00:58:17] ago based on our assessment of it. And so imagine thousands of those kind of, you know, trained, brain trained models making the assessment of, you know, what's the, uh, tone of the earnings analysis. What is the language? Is it really that this forecast is as optimistic as the numbers indicate, or are there any concerns and they're kind of like in the back of the sentence because the tone

[00:58:43] kind of deteriorated towards the end. That to me is incredible. And so having, um, uh, AI process that and turn the text using LLMs, turn text into numbers is great. Um, the cost of those data is, is high right now. So yes, it's, it's expensive. Um, but nonetheless, that's the application. And, uh, a lot of companies are striving to use that and to develop models like that. So, um, the last one,

[00:59:11] I would say it's, uh, probably on the trading side that I see the application is, you know, ability of traders to process the patterns of, um, uh, how, uh, trades are done, what can be done, um, to improve the execution. And so having those tools, uh, available to them, I think it's, uh, also a potential on, on doing that. So three types of application and just anecdotal examples of,

[00:59:37] yes, we are using it to the degree that we're using it for the, uh, gather data. Uh, we have done some, uh, uh, risk models on the risk management side as well using the, uh, you know, uh, AI, uh, applications, but we have not done and have not used it for the stock selection or to generate the stock signals. That's further out there. It's an interesting area, but we're not there. Well, this has been a great conversation. Thank you so much for having it with us in our audience.

[01:00:06] We like to ask all of our guests one standard closing question, and that is based on your experience in the market. You just said the questions were getting harder. So this is, this should be the hardest question. We'll see. Um, based on your experience in the markets, if you could teach one lesson to your average investor, what would that be? Please invest. That's my first plea. Like, please invest. Don't stay away from stock market. Um, start with educating yourself. Um, there's so

[01:00:34] much information out there, but it's easy to access. There's basics of investing. There is podcast like this. There is a random walk on wall street. There is intelligent investor. There is all the books that are classics. And at the same time, there is information that, um, that you could use to educate yourself. What does it mean? Like, do not not invest because you're scared and it's unknown.

[01:00:58] It's not difficult to educate yourself to have enough of the information to make a decision. So I would encourage that. My recent, um, not read, but I listened to this book was, uh, uh, how not to invest by Barry Ritzholt. So, uh, maybe you can have a podcast with him if you haven't yet. Yeah. I did look up, but it's an incredible book and it talks about the bitfalls of investing. Um, then I would say,

[01:01:23] start early. Uh, I'm encouraging my children to do that. You know, I would just say the power of compounding will do the magic. Just start investing early. It's done. Don't delay. Um, diversify so that mistakes going to be there. You make mistakes, but if you diversify, then each of those mistakes will be a learning opportunity. It's not going to be then the end of the world. And I will not touch investment ever again. Like diversify. Don't put all the X in one basket.

[01:01:52] And, um, lastly, that same thing about the AI, just be cautious about the AI as investment advice. Um, use it as data gathering. Um, but don't follow exactly what the AI tells you to do. So, um, make your own decisions. Um, use the Y. So that would be my advice. Great. Thank you very much. This has been a great conversation. Appreciate it. Yeah, me too. Thank you for tuning into this episode.

[01:02:20] If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Returns Network at Excess Returns Pod.com. If you have any feedback or questions, you can contact us at Excess Returns Pod at gmail.com. No information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.