Talk Your Book: Large Cap Alpha

0:00Today's Animal Spirits Talk Your Book is brought to you by FM Investments. Go to fm-invest.com to learn more about their quantitative strategy for large cap U.S. stocks. That's fm-invest.com. Welcome to Animal Spirits, a show about markets, life, and investing. Join Michael Batnick and Ben Carlson as they talk about what they're reading, writing, and watching. All opinions expressed by Michael and Ben are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. This podcast is for informational purposes only and should not be relied upon for any investment decisions.

0:35Clients of Ritholtz Wealth Management may maintain positions in the securities discussed in this podcast.

0:43Welcome to Animal Spirits with Michael and Ben. Ben, when you think concentrated manager, stock selector, you probably think of a value investor, no? Buffett style, Munger, that value and yes, value investing, not necessarily growth and not necessarily quant either. Yeah, those are two things that you don't see that often. Number one is quant growth. And number two is concentrated growth. I wonder why that is. Is it because it's been so difficult to beat the Nasdaq? That can't be it. Or maybe I'm just making that up. Maybe there are and I just don't know about it. It may be. Maybe the range of outcomes is wider in growth stocks between the really big winners and the big losers?

1:24That could be it. Yeah, that's a good question. It's also, I don't want to step in too much from this episode, but I think in my experience, large cap stocks, beating like the S &P 500, for instance, has to be one of the hardest benchmarks in the world to beat. That's my personal opinion on this thing. And we talked to a portfolio manager today whose fund has managed to beat the S &P and he. Well, Russell 1000 growth. Oh, Russell, sorry. Close enough. Oh, yeah, but it has been the S &P. And it's interesting to hear his thoughts about this. So we talked to Francisco Badeau today from FM Investments.

1:58They use a quant process, but also have a qualitative screen in it to make it more concentrated, which makes sense. It's almost like the old Joel Greenbat. Remember he had his - The magic formula? Yeah, the magic formula. And then you could pick 30 stocks from it or something. It kind of reminded me of that a little bit, where you have an overlay that's the quant screen, but then you also, based on the environment, pick stocks based on your thoughts on how things are going. Here's our conversation with Francisco Bido. We are joined today by Francisco Bido. Francisco is the Senior Portfolio Manager at FM Investments.

2:33Francisco, welcome to the show. Thanks for having me. We're talking today about the FM Investment Large Cap Focused Fund. And we recently had another focused mutual fund manager on the show. And it's very nice speaking to somebody that has conviction because one of the trends in your industry, in the mutual fund space, has been do no harm or don't stray too far from the benchmark. And then, of course, you get something that ends up looking very much like the benchmark. Talk to us a little bit about the genesis of your fund. Why the concentration? How long have you been in business and that sort of stuff?

3:07Well, I've been managing the same strategy in large-carb form and in all-cap Ed Poorman also has a long shirt. Our mutual fund is the flagship. And that one is, that's where the ticker is, IAFLX comes around. And it's been around for a good 12 years. And Performa has been pretty sterlet throughout. If you look at the SMA account for the strategy itself, it's a five-star morning star. And the mutual fund is four stars. And, you know, it's been a great year so far. And the reason for that is that we have a very strong process, which you follow very, very strictly that we call quant active. And you've got to wonder, quant active is really spelling out what the process is about.

3:51It's part quant. That's phase one. Phase two is active. And a typical question that I get is, isn't everybody quant active nowadays? And my answer to that is, we're different. You know, it's quant active, but we have a framework to address risk management, to address stock picking, to address all the pieces we're going to put with other management in a very rigorous way, almost that it could be done by a computer, except that we don't trust the computer 100%. For obvious reasons, computers don't know anything about COVID, about the pandemic when it happened, it didn't know anything about Brexit, and so on.

4:23So you still need a human in the loop there, but we have a lot of the things we said in a very set framework, which you call quanta active. How much narrowing down does your, if it's a screen or whatever it is, how much narrowing down do you do? And is your universe, I don't know, the Russell 1000 or the S &P 500? And then how much does your process of the quant side of things narrow things down? And then how much is the qualitative side of things from there? Well, the quant side, we start out with about 3 ,000 names, all cap names. And then we classify them by market capitalization and see where they land and so on.

4:57But basically, after we apply our screens, we end up typically with about 80 names. And from those names, then you've got to parse it and pick up which are your large caps and your small caps and so on. So it's not a fixed cutoff, which we want to have that way. You know, a lot of these quant screens just pick the top 100 names, for example, right? We don't do it that way. And we don't normalize our scores, meaning that when the scores are bad, we want it to tell us the truth, the things are bad. So if we only have 12 candidates and it has happened, we saw that over COVID and so on, only 12 names we can pick from.

5:33Then we want to see it that way because it's telling us something about the market environment. But overall, if I have to pick an average number for you, it'd be 80, about 80 names. So if you get into a situation like that where you only have a handful of names, are you overweighting those names or are you raising cash in that instance? No, no. What we do is we look at what we own in contrast to what is being suggested to bring in into the portfolio. So we look at two things. We look at the risk. You know, if we decide to bring a certain company in, how does it impact the risk of the portfolio?

6:06Does my tracking error, projected tracking error, does it go up? Does it make it higher? Does it diversify the risk a little more? If I have a lot of IT and I bring in a bank, you know, that's going to take the risk down a little bit. So we look what is proposed by the Kwan system, and then Alex and myself use a common sense, basically, right? And our years of experience to really nail it. And it's consistent. We've done the same thing since the beginning of time. Francisco, this is a growth-oriented strategy. And so the things that you're screening for, I'm guessing, you can correct me if I'm wrong, probably look different than what a traditional value investor might screen for.

6:46So I'm sure you're looking at maybe typical things that everyone would look for, like things like margin. But what else is in the process that might be distinct from how you think about it versus what somebody else might look at? That is really a good point. And I would like to emphasize that we're not strictly a growth portfolio. We look at earnings and revenues acceleration. So we're looking at companies that, you know, at a certain point in time, they're acting like really strong growth companies. It could be Campbell's Soup, it can be Clorox, it can be American Express, it could be McDonald's or Starbucks, and so on, as well as it can be Netflix or Meta or Apple.

7:28So it does tend to overweight a lot of the growth companies, but it doesn't mean that we can't pick a value company because we're looking at the earnings and revenues momentum. We believe that over time, this leads to price momentum. And it's important to know that we're not chasing any trends. We're looking at fundamentals first. So we're looking at earnings and revenues acceleration. Why is it that it's doing what they're doing? Why is it accelerating? And you can go back and reverse engineer that, and you can see that these companies, for example, Campbell Soup, to give you an example, a few years back, they came up with a new line of organic products.

8:03And all of a sudden, they became one of the companies in our screen. Is it typically a growth company? Absolutely not. But at that point in time, they were acting like a growth company. And that's when we want to hold it. Is there any valuation component where you might say, hey, this company is doing great, their earnings and their revenue are accelerating. However, the market seems to have a consensus this view because it's trading at 30 times sales. Is there any sort of risk management on how much you're willing to pay for this earnings acceleration? Well, it depends. And it all depends, you know, what the name is and what are, how strong our scores are, which it comes from the quant system, obviously.

8:40But then Alex and myself sit down and really have a, you know, a heart to heart about the company and about the fundamental numbers and everything else that is not captured by the quant screen, which includes a macro environment and so on. So we do look at valuation, but it's not in the quant screen. It's a discretionary component that we look afterwards. For example, if we're looking at Apple, you know, if Apple came in today, it's been in the portfolio for a long time, but Apple is 30 times. You just remind me of that. So it's 30 times. It's a time to buy Apple. And, you know, depending on what the portfolio looks like at the time, in terms of outcome capture in terms of what sector is exposed and so on, then it might not be the best choice.

9:19It's a very eclectic view of what happens in the active layer afterwards. And the important thing to realize is that I created the active layer, I mean the quant layer. So I know his strengths and his weaknesses. So when Alex and I talk, I know it's talking not because we are shooting the breeze or anything like that. We know where the strategy is probably going to benefit from any holes that the quant layer might have or any blind sites, right? Because it's looking back at data. And here's a market with a rising interest rate environment, for example. Well, where is that? There have been interest rate increases over time, but which ones are the same as this one?

9:57You can go back and not find one single example. So for that reason is that we have the two layers. All right. So this is a bit of a two-parter. So my first question is, and then I'll ask the follow-up. My first question is, what's the turnover on this sort of strategy? So you're active? How active? It's very active. I think in terms of turnover, it's high. It's over 100%. We only have 27 names. And a lot of that, you know, and it depends on your computer turnover. Those 27 names, 27 do not lead to the component by year's end. It's not that. It's that outflows and outflows make the turnover look the way it does.

10:35So if you look at the names that we had at the beginning of the year and the names that we have now, we're probably different about four names, but there've been a lot of movement in the flows that creates a turnover. So Apple, for example, you said Apple's been in the portfolio for a long time. One of the best performing companies of all time, certainly one of the best companies of all time.

10:56However, Apple is not necessarily growing the way that they once were, at least in the top line. Now there's all sorts of levers that they can pull. Who knows what the next$10 billion category that they seem to create every few years. They really are in a class of their own. But I was looking at revenue growth year over year. It's been negative year over year for the last three quarters. Is Apple entering a different phase of its life cycle, for lack of a better word? Right. I don't believe so. I think it's easy for us to get spoiled and think that they can retain that for years to come. But it's okay for an incredible company like that to take a pause, to actually go down to earth for a year or a year or so.

11:45And that's where I see Apple. If you look, for example, at the last earnings report, then you see that the services sector actually did quite well, and it's growing. What took ahead was basically hardware, iPhones, Macs, and so on. Anything that's hardware took a hit, you know, decreased, but the services went up. That's the Apple TV and so on and the music and you name it, right? So these things tend to be cyclical. And every once in a while, both cycles for the hardware and the services align and you get a bigger wave and you get a bigger bump, right? We love those when they come over, but it's not always the case.

12:22I think the important thing is just to survive when the hardware is not doing great and just keep pushing on the ones that are doing great. And by the way, just to add more color on Apple, it would not be, this is not the first time that it's done this. It had negative revenue growth for a couple of quarters in 2016. It did it again in 2019. So this is not without precedent. Right, exactly. Yeah, it tends to go in cycles. But if you look at the Intel report, the latest Intel report, I think there is a quote there about the PC cycle bottoming out. So their numbers are a little bit more encouraging.

12:54So if you map that to Apple and you map that to the fact that the consumer in the US is spending, you know, look at the MasterCard report. They're spending on cruises, they're spending on a lot of things that appear with discretionary. So despite the high interest rates, you know, things are turned around. So these things are cyclical. We expect the hardware to come back up. I'm doing this podcast on a Mac. I have AirPods, an Apple Watch, and an iPhone, so I'm not going to question Apple anytime soon. Personally, from my experience dealing with active managers, I think the large cap US space is probably one of the hardest places to outperform.

13:28I don't know if it's the research or the fact that the US makes up so much of the global market cap, and it's gotten so much more concentrated in recent decades, but it seems like that space is much harder to outperform. What has your experience been like trying to do this in a more concentrated manner? And how do you feel about investing in large cap or all cap stocks? Because, I mean, I guess you could say all cap is also$3 ,000, but it's still dominated by the biggest stock. So what is your feeling about how difficult it is to help perform in this space? Right. Yeah, it's not easy, but it's easier than small cap.

14:02Why do you say that? Because a lot of the small cap companies have the same amount of analyst coverage and analysis that the large caps have. So the markets tend to be, if you want to use a more academic term, a little bit more efficient as you go up in market size. So there's a lot more research on it, a lot of more good data available for the big companies, right? And for the small companies, you go and maybe one analyst or no analyst follows them. So what do you rely on in the last earnings reports and a lot of hope? So there are pros and cons to everything. Yes, it is a hard area to outperform in, but I think we found our niche there.

14:36And our niche is to be concentrated. Today, we hold 27 names in the mutual fund. And we are benchmark aware. We know where the Russell 1000 growth is at any point in time. We know that. But we're not focused on tracking it. We just pick the things that we like, and we manage our risk. So we're not picking something because it's on the benchmark. If you look at our active share, it should be north of 60 % for the mutual fund. That's interesting. I've heard what you just said about small versus large. but it's usually the opposite. That's what I was thinking. People usually say, because there is so much analyst coverage, that's what makes the market much more efficient and therefore difficult to beat.

15:19I've never heard anybody say that and then conclude that it's actually easier. So I'd be curious to hear a little bit more on that. Sure. I mean, and it depends on how you treat the benchmark, right? If you treat the benchmark, it's something that you've got to match. I work with funds that hold 120, 150 names. if you have all that many names you really haven't much of a choice but to be the bench you know and then you find one or two names and try to beat it there maybe get a 50 beeps here and there and that's about it the way that we do it is completely different we don't do that at all we have 27 names and there was a point I kid you not in the past I don't remember what year it was where I have 90 % active share so everything I have was not on the bench so if you pick the right names then you really have a chance So you just can't be the benchmark.

16:05If you're more concentrated, have more conviction in what you're doing and your process, which we do. Alex and I really believe in what we're doing. And, you know, our process has not changed as an exception. It's the same scores. It's the same exact ideas. And everything is exactly the same. The discretion component does change because market change and evolve, right? So we can't treat the market now as we charted it five years before the interest rates were not going up, right? So it's a different situation, but there's a way to factor that in. I'm curious about your process. You talked about how you built this model and you created it, and you know and understand the model, but occasionally you'll get more active with it.

16:41So does that mean that you disagree with your model at times when it'll say, this stock looks great, it should go in the portfolio, on the quant side of things, and you say, I don't think so, I know, because of the macro environment or whatever else is going on. How often are you disagreeing with the model that you created? That's a really good question. And you've got to be surprised by my answer. The answer is never, and I can tell you why. And I can tell you why in a second because I designed the process in such a way that it gives you choices. So if I need three names to come out of the portfolio and it's giving me 10, you know, the quality is going to give me 10.

17:14I'm making this up, right? So I don't have to argue with it too much. I just got to find the one that we all agree. The model agrees because they're giving me 10 and I'm going to pick from those 10. I'm not going to stray from that. So we call that what the screen delivers the approved list and we never, absolutely never stray from the approved list. So we're never overriding the model. The model is always giving me, here are your 10 choices across different sectors. Pick three. And then we sit down and do our analysis and say, oh, these are the best for the corporate. So we're never disagreeing with it.

17:42It's done on purpose. This is a good question. Because if you look at a lot of quant products, they override their processes every so often. We don't. We design the processes in such a way that it gives us options. So we don't have to do those overwrite. Francisco, you mentioned earnings acceleration a bunch of times. One of the craziest earnings acceleration in terms of guidance that I've seen, especially from a large cap company recently is NVIDIA. I think they guided from like maybe seven to 11 billion or it was really outrageous. I would guess that guidance does not factor in because that's not necessarily quantitative.

18:15It's just where management sets their goalposts. How do you think about what companies have to say about their future earnings potential? Is that completely noise or is there a quantitative aspect of that that you take seriously? Yeah, there's both a quantitative aspect and an active aspect to that. So in the quantitative score, we have a score we call the F score, F as in fundamental score. And that's really the main driver of the portfolio. We use the F score really to handicap the stocks that we like. And after that, we have another score independent of that, which you call the M score, which is a momentum score.

18:52but I must clarify that the fundamental score is really the important one. The other score is there to ensure that we're actually being paid for the risk that we take. So the fundamental score, the F score, has about 70 time series to go into it. There's a lot of information. And some of that information is forward-looking information. It's FY1, FY2, forward-looking consensus views for any stock. So some of that does factor into the score itself, into the quant layer. However, we do pay attention to what the company is saying, what, you know, verbally was not going into the score and if there are any concerns.

19:27So that's the active side. So it factors in both ways, both on the quant side and on the active side. One of the companies that is conspicuously absent from this portfolio, and listen, you're a large growth manager. It's hard to avoid what's being called the magnificent seven. Two of them that are not in the portfolio, correct me if I'm wrong, are Tesla and Meta. Can you talk to those? Right. Actually, Meta is in right now. So it's only one. Oh, OK. Yeah. The only one that's missing there is Tesla. And the reason that Tesla is missing is because it's just not meeting our screen. You know, as I mentioned earlier, we do follow a very rigorous process.

20:07And Tesla has been in the portfolio in the past. At some point, it fell out of favor and it never has come back through the screen. It's not that we don't like the company is that we don't think it's a great company, but we would be very foolish to actually overwrite the screen and we know we can pick well from there. So Tesla's not on that screen. So it's not meeting the quant layer. So that's why it's not there. When it does, again, you know, when a company starts to be giving the way that we like it, that's our strength that we're going to go and, you know, enter with an entry-level position, which is right in the middle of the pack of the portfolio, not, you know, around two and a half percent.

20:41So that position sizing was going to be the next question, actually, because a lot of quant strategies, if they're more diversified, will just equal weight a portfolio or maybe market cap weight it. How do you handle the weighting of it? Because you have a more concentrated portfolio, but you do have higher weightings in your big names. Do they grow to that size or do you double down on positions at times? So 90 % of the names in the portfolio, we try to keep below 6%, that they never get below 6%, 90 % of the name cap. And the reason that we want to do that is because in my experience, or many years of experience working for big mutual funds and so on, that you don't want your portfolio to win because of one name.

21:23I'd rather that it wins because the portfolio is built well. So with that, it pretty much ensures that, you know, my experience and the way that our quant system works, they'll let any position go over 6%. If they go over 6%, say that as AMD, and that's happened a few times. It goes, oh, here it is, 6.5%, almost 7%, trim it down back to court. Allow it a little bit time to grow and catch a little bit off, you know, take a little bit off the top there. But in general, any name in the portfolio enters at, here's a darky name, at the centroid. You look at the standard, you know, at the bell curve of all the weights in the portfolio.

22:00And some are bigger than two and a half, some are less than two and a half. It's said that two and a half is right at the center of the bell curve. So that's how we enter. We do not incrementally enter a position. We just slap right into it. And when we actually do the same thing, we're gone. Either we like it or not. Here's another, forgive me for another two-part question, but how often are you getting inputs? I imagine it's quarterly as companies release earnings. Is that accurate or is there something else that I'm missing? The F-score, which you're referring to, will behave like a step function.

22:30It will go flat for a while, for about a quarter, right? And then jump when the new numbers come in. But we actually compute our scores on a daily basis. And that's because we also track the consensus views, as I mentioned earlier. And those tend to change on a daily basis, right? So we track those very carefully. So yeah, so we have this really, you know, ripply line here, and then it jumps over earnings reports or jumps down or up. That's how it works. So the second part of that question would be, how do you avoid the buy the rumor, sell the news effect? Because I was always taught that price leads fundamentals, right?

23:09that people anticipate or the market anticipates earnings acceleration. So let's use Uber as an example. And now maybe this is short-term. You would say this is noise and it's not significant for how you're managing money. But Uber has been knocking it out of the park, had an incredible run into an incredible earnings report, and the stock is down 10 % since it reported earnings. Which, is that just noise to you? Do you not care about those short-term pullbacks? How do you think about that? Right. Uber is not one of our holdings. I haven't seen that name in the list for a long time, maybe in a year or two.