Animal Spirits Podcast - Talk Your Book: Breaking the Market Cap

Episode Date: December 24, 2018

On this week's Talk Your Book we discuss why market cap weighting is so hard to beat when constructing a fund, why cash outperforms the majority of stocks, the thinking behind reverse cap weighting the S&P 500, how to invest in companies with high customer satisfaction, some surprisingly strong consumer brands, qualitative investment factors and much more.   Find complete shownotes on our blogs... Ben Carlson’s A Wealth of Common Sense Michael Batnick’s The Irrelevant Investor Like us on Facebook And feel free to shoot us an email at [e-mail] with any feedback, questions, recommendations, or ideas for future topics of conversation. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's Out of the Spirits talk your book is presented by Exponential ETFs. Exponential ETFs manages ETFs that help investors build better portfolios. Exponential manages the reverse cap weight ETF, the American customer satisfaction ETF, and help select asset managers launch and manage ETFs through its operational and sub-advisory ETF partnership platform. Exponential also produces the ETF Experience podcast, which I was just on with Nick Mejuli last week.

Welcome to Animal Spirits, the podcast that takes a completely different look at markets and investing. Hosted by Michael Batnik and Ben Carlson, two guys who study the markets as a passion and invest for all the right reasons. Michael Battenick and Ben Carlson worked for Ritt Holt's wealth management. All opinions expressed by Michael and Ben or any podcast guests are solely their own opinions and do not reflect the opinion of Rithold's wealth management. This podcast is for informational purposes only and should not be relied upon for investment decisions. Clients of Rithold's wealth management may maintain positions in the securities discussed in this podcast.

On today's Animal Spirits, Talk Your Book, we will be talking with Phil Bach, CEO and founder of exponential ETFs. So one of the questions that we've asked two of our prior guests on Talk Your Book is if market cap weighting is so suboptimal, why is it so hard to beat? And so you and I pulled some data on this to show why. It's just kind of crazy. So every quant that there is will tell you market cap waiting is like the worst way. you can set up a fund structure.

Yeah, you're better off waiting by letter of the alphabet. But the S&P 500 has beaten something like 95% of all actively managed mutual funds over the past 15 years, call it. Caviot, what percentage of funds outperform the S&P gross of fees? Because obviously that's a much lower number. Yeah, Morning started a review on this. And they actually showed, I'll see if I can dig the stud up. I think it was our friend Jeff Battack.

They showed that fund managers do have some skills. in picking stocks, it's just that it's all in no way by fees. And so the majority of the time it is just the fees that does it. So doesn't I go to the point that a lot of these ETFs with lower fee structures and that typically actively managed mutual funds have a better chance about performing potentially. And here's the other side of the thing, the equation. A lot of these mutual funds, if you look at the stats, they all go out of business. So the fact that these indexes really don't go out of business, that's part of their longevity. That's part of the reasons so hard to be is because they don't have fickle investors that pile in and pile out and they

just stick around and all these, a lot of these actively managed mutual funds will throw stuff against the wall and hope it sticks. So why is market cap so hard to beat? So we looked at, there's some crazy stats on this and there's been some really good research in the last few years. So there was a guy Hendrick Bessonbinder who did a paper a few years ago and he looked at returns from 1926 to 19 or 2015. And this is the one that, I remember when this one came out and it was flying around Twitter and the blogosphere, less than half of all monthly stock returns in the U.S. are larger than one month treasury bills, meaning the majority of stocks don't outperform holding cash. And just 42% of stocks have a lifetime return greater than T bills, while half

deliver negative lifetime returns. And over half the wealth created in the stock market since 1926 came from 86 of the top performing stocks, which constitutes around 33 basis points of the total number of stocks. So holding a market cap weighted ETF or index guarantees you exposure to the biggest winners, proportional exposure to the biggest winners, allowing you to take advantage of the facts that you just mentioned. And some people will say, well, that means you also hold the biggest losers. But in a lot of ways, it's almost like they sell the losers because it's these companies that go out of business or get taken over or whatever. But the numbers are pretty crazy. So Longboard funds also did a wonderful study that I think both of us have probably

were written about. And they looked at the stock market from 1980 to 2015. They looked at almost 15,000 stocks in that time and looked at the best performers on an annualized and total basis. And they found that 7.7% of the stocks outperformed to the S&P 500 by at least 500% in their lifetime, which is roughly 1,100 of those stocks. And actually, almost 1,000 stocks lagged by at least 500, and the remaining 12,000 basically was a little above, a little below or about the same. So you have these huge tails where there's a ton of stocks that do really well, a ton of stocks that do really poorly, and a bunch of stocks in the middle. But the big winners more than offset the loser. So there's actually a cool new website called coiffin, k-o-f-in.com, and it allows

you to see performance attribution inside of different indexes. So I'm looking at the SB 500 right now, for instance. And year to date, the biggest contributor to the winner is Microsoft of 59 basis points or adding 59 basis points to performance to the SP 500. The biggest detractor is Facebook down 53 basis points or taking 53 basis points of performance away from the SP 500. But here's the point. A lot of these stocks that go out of business that are in the SB 500 that go away, when they go from, say, let's say they go from $20 billion down to zero, which is an extreme example. But even that is taking very little off of the overall pie, whereas Facebook, for example, comes into the index at $400 billion and adds another $400 billion. It's just, it's not,

it's not symmetrical. And you see these stories all the time. I wrote about this last year. They show how three stocks in the index, this is as of July of this summer. So it seems like a long time ago now. showed Amazon, Netflix, and Microsoft made up for 70% of the gains in the S&P 500 and NASDAQ 100. This is as of this summer because they had such outsized returns. And the reason they had such a big bearing on these returns is because they're so such a big part of the index, as you said. But this happens all the time. So there was, there was some other stats I've run before. So from 1994 to 2014, the S&P 500 returned roughly 9.3% a year. The top 10 stocks accounted for 4.1% of that annual return, so almost 45% of the gains. And so this is kind of just

how it works. The biggest stocks, it's kind of like letting your winners run. And honestly, I think that's another reason it can be so hard to beat and pick stocks is because these stocks just keep going. Think about how many times you've tried to short Amazon over the years. What do you mean, tried? How many times do you think you did short Amazon over the years? More than I care to admit. Okay. So this is like, Is it, Amazon, I literally was early. I mean, I think it's down like 30%. Five years and 3,000% early, but...

I was just early. So the S&P 500 is basically the 8020 rule on steroids. And I've written about this before, and I don't think you and I've ever talked about this because we've written dueling posts on this, but I wrote is the S&P 500 the world's largest momentum strategy. And obviously, if you take the quant definition of momentum,

where they try to look at it at a six or nine or 12-month look-back period, It's not. But with the idea of letting your winners ride, the S&P really does that in a lot of ways. So I think in some minor sense, it is more of a growthy momentum strategy. It's a let your winners ride strategy. I think that's appropriate. Yeah, I just said that. No, I'm agreeing with you. Yes. Agree to agree. Okay.

Well, this also ties back into the pie chart that I did earlier in the year, which showed that the top five stocks in market cap are equivalent to the bottom 282 stocks. So a company like Foot Locker or Hershey or whatever could disappear and it wouldn't affect the returns of the index. Exactly. Because of the market waiting, that's just the way it works. Those big stocks are going to trump anything those littler stocks do. But how many hundreds of billions of dollars in market cap have Amazon, Microsoft, Berkshire, whatever, added along the years? Exactly. It's hard to, yeah, it's hard for those smaller companies to do it. And we're going to, after we go to interview with Phil. We're going to talk about the other side of that coin where that doesn't mean

you don't invest in small caps or anything. So why don't we go to our interview with Phil and we will come back for some more talking points later. Here's Phil. We're sitting here with Phil Bach founder and CEO of exponential ETFs. Phil, thank you very much for coming on the show today. Hey, guys. So we're going to start off talking about the reverse cap weighted US large cap ETF, which has a really nice ticker, RVRS. So the first question that we have is tell us about the economic rationale for this. Why do you expect that this is going to perform or outperform? Why would you want Apple to be the smallest holding and FlowSurf Corp, for example, to be the largest holding? There's really three ideas behind the fund. One is it's a size tilt.

So it's a simple size tilt. People think of playing the size factor by taking money out of large cap and moving it into small cap, but what we found is that by tilting within large cap to capture that tilt, you have a quality filter. So a lot of the research around the size premium talks about the liquidity and the poor valuations and some of the smaller companies. So it calls the size factor into question. But what we found is by doing it within large cap, you kind of solve a lot of those issues. You solve the quality issue. You solve the liquidity issue. And you can capture that premium in a in a rules-based systematic way. So how does this solve the quality issue? what exactly do you mean? So you're limited in the universe to the S&P 500 holdings, which means that

you're going to have analyst coverage. So the valuations are, you know, real market valuation is not just something that's moving around on light volume or on a couple people's opinion. You're going to have a lot of liquidity because this is, you know, trillions of dollars benchmark to the S&P 500. So as it turns out, now there's a value bias to it, an anti-tech bias in the index. Tech is about 11% compared to 22 for the S&P 500. But that's just the way it falls. If you look at the way reverse cap falls prior to the global financial crisis, it looks like an anti-financials index. And, you know, it's really, it's just always going to be responding to the market.

The second rationale, the second reason why we launch a fund is, if you think about how indexes rebalance, so what they do is typically on a quarterly basis, they'll either reconstitute or rebalance, they'll take money out of the winners. Well, they'll take money out of the losers in a market cap-weighted fund, and they'll put it back into the winners, and it's kind of, you know, you get a little bit of a momentum effect. what we're doing is by a rules-based systematic process, we're selling the winners and putting the money back into the smallest stocks with the most room to run at every quarterly rebound. So it's a profit-taking mechanism.

It builds in a mean reversion factor, and that has contributed as much alpha, in fact, a tiny bit more alpha historically than the size premium has. So is there typically a lot of turnover in this type of strategy then if you're doing these rebalances where the stocks are shifting within the S&P 500? Yeah, there is. slightly more than you'll see in a cap-weighted fund because stocks that come out of the S&P 500, they were higher weights and now they go down to zero. But it's still relative to the overall universe of large-cap mutual funds and ETFs. It's still an above-average turnover fund,

meaning below-average total turnover. So looking at this back test, which starts on December 31, 1996 and runs to October 23rd, 2017. This returned 971% versus the S&P 500 total return of 491, an excess return of 3% a year, which is obviously amazing. And I'm sure you're familiar with, you know, you see these back tests, and then once they go live, they fall flat on their face. So in something like this, execution is everything. So talk to us about, you said that you do a quarterly rebalance. Talk to us about some of the execution issues and, and ways that you can combat that? The execution is really simple. So we rebalance every time the S&P rebalances. And if you think about the way the S&P 500, the market cap weighted version, if you look at

the S&P 500 equal weight and then the reverse cap weight, they're kind of three ends of the same tilt or the same spectrum. We rebalance and reconstitute the indexes administered by S&P, and we follow the exact same process on the exact same days. No different. And you're right, the back test was quite good, but not every year in the back test. And certainly, and nobody should expect that every year the fund will outperform now that the fund will is live. In fact, the fund is out just over one year and it underperformed for the first year, not by much. That's certainly within the range of possibilities and within one standard deviation of possible outcomes. Over the long term, our expectation is that it will more often than not outperform, but it won't always.

So thinking about this in terms of portfolio management and where a fund like this could fit within someone's diversified portfolio, obviously you're still fishing in the large cap pond because you're in the S&P 500, but because the way that this fund is constituted, does this act more like a mid-cap fund? Is that, or is this more like a, you want to use this fund around a core holding, like a core large cap? How would you think people are using this to position their portfolios? There are two ways to use it. We see it as a factor fund within large-cap. So the weighted average market cap in reverse is right now, it's $18 billion. We see that as a large-cap fund. And the constituents are exactly the same as the S&P 500. So you have no mapping issues.

We see it as a large-cap fund. Now, 18 billion weighted average market cap. If you look at the S&P 400, the mid-cap, it's $6 billion. So it's three times this. large. Now, 18 billion is still a lot lower than the market cap weighted version. Equal weight is currently $52 billion, and the S&P is over $200 billion. It's really, we see the S&P is this outrageously mega-cap biased portfolio. But we see this as a factor fund within large cap. The other way that it can be used is alongside a market cap-weighted index or index fund. And this really gets back to Michael's blog post about the top five companies in the S&P 500 and their relative percentage. If you want to flatten out your distribution,

of your exposure to stocks in the S&P 500, maybe you want to take a little money off the table in the fang trader. Maybe you're a little concerned about antitrust risks and that type of thing. Well, if you combine reverse cap with market cap weighted in different degrees, you can customize that distribution

where instead of having 20% of your portfolio locked up in just five stocks, you can bring that down drastically. We measure it by HHHHHFindell-Herschman index. You can cut by going 50-50 reverse and cap-weighted. You can cut that HHHSI down from 90, to 33. So it's a really drastic improvement in diversification. So you talked about these stocks having more room to run, the smaller ones. But if you're rebalancing quarterly, doesn't that sort of

stunt their growth potential if you're bringing them back? Is it over rebalancing? Yeah. I suppose that, you know, there is a potential you might want to let things run more. But then you're back to the momentum side of the equation. By profit taking at every quarter along the way, you're really taking advantage of the mean reversion aspect of the fund. So moving along to your other ETF, the American customer satisfaction investable index, the ACSI. Oh, uh-oh. I know this is an anti-survey podcast. Yes. We are an anti-survey podcast, so this is an interesting one to us. But it is interesting from the fact of, do you consider this almost like a qualitative factor as opposed to the more usable quantitative factors? Yeah, it is. I mean, it's not a, if you want a pure quality factor

fund, then buy a quality factor fund. It's not exactly a quality factor, but it is very close to it. And of all the factor loads, that is the most similar for American customer. satisfaction. I mean, look, at the end of the day, it's, you know, it's, it's not, there's a quantitative process in gathering the data, and we use a quantitative process in applying the data to the portfolio level and the way we put together the index. Sorry to cut you up, but to Ben's point, so it's, it's quantitative, but what you're measuring is qualitative because you measure how people feel, which is sort of a new, I don't know if it's a factor or what it is, but it's an interesting way to do this. Well, what it is is an economic principle. So it tells you, customer satisfaction

tells you what kind of pricing power the company has over the customer. It tells you what you recurring revenue expectations should be if you're going to have repeat buyers a word of mouth and you know it tells you a number of things if you do it on the level of the goods and services not the stock we're not trying to capture sentiment on stocks we feel like the market does that we're trying to understand the buyers and will they be back will they be bringing their friends will they be continuing to engage in the goods and services that they're that they're paying for can you explain how is this survey data then translated into a portfolio so how are these companies picked for the portfolio and how does that how does that translate

So the ACSI is the gold standard globally in quantifying customer satisfaction, but what they measure are goods and services, not companies. So we have data on private companies. We have data on different revenue lines and different businesses. So let's take Young Brands, for example, we might get divergent opinions on some of their restaurants. What we do is we roll it together. We have a proprietary model that we used to estimate relative percentage weights of the different brands under a stock, and then we aggregate it together. But we also put in sector constraints around the different sectors. So we don't want to be providing alpha or not or negative alpha. We don't want to be providing that because we've made sector bets. We want to provide

apples to apples alpha within each sector. So we have a tolerance at every rebalance of plus or minus 10 percent from where the benchmark is in any given sector. And then within every single sector, we wait the top half of the companies that we have a statistically significant sample on. We wait them by their customer satisfaction score. So the three largest holdings in the portfolios of the last reporting, or as far as I have data is Apple, Amazon, and Alphabet. Now, do we think that it's sort of obvious that the companies that people like are going to be decent stocks?

So it's kind of an unusual thing. There are two functions. One is what's the relative customer satisfaction of a company within each sector? So in that case, the tech sector. The other is how many companies do we have a statistically significant sample that we can include? So how much of that sector allocation is getting divided up over how many companies? So some companies that have inferior customer satisfaction could be overinflated because

there's just a bigger allocation to go around to that sector. Each sector has to be looked at individually because each sector has a very different elasticity. So for example, if you have a bad experience, if you get sick eating at Taco Bell, you're not going to Taco Bell anytime soon. But if you lease a car for three years and you're like, ah, you know, it's not that great. Yeah, in three years, you might switch from your Ford to a Toyota, let's say, or whatever it is. That's a longer cycle. When we get into the banking industry, it's an even longer cycle. So we have to look at each sector individually. So because of that, is it a a relatively low turnover strategy because these things take time unless there's a huge event,

like, say, like the United Airlines or something where people get really mad at that brand. Yeah, like how often are these surveys held? Is it annual? It's, we get monthly data. We publish publicly on theacSI.org annual data. So you can see the annual scores and we put out reports all the time. But there is fairly low turnover in the fund. It's a very long process. You know, we've seen our research shows that depending on the elasticity, depending on the sector, anywhere from three to 11 months from the time that we see a significant change in customer satisfaction until it hits

the stock. Typically, the way it hits the stocks, it'll be a big earning surprise. So a big hit or miss, and everyone's scratching their head saying, oh, how did that happen? Well, we saw it into data. I mean, Facebook's a good example of that. The customer satisfaction cratered earlier this year. Did that affect the waiting in the portfolio? We took it

out of the portfolio. So, you know, you mentioned how we have Apple, Amazon, and Google, that's right. Google's got declining customer satisfaction scores in most of their facets of their business, but not search. And search is so strong that it keeps them into portfolio. But Facebook and all the social sites had cratering customer satisfaction. People are not happy with the experience. They're leaving. That's going to affect the advertisers. And as such, it was taken out of our portfolio back in, I believe, June. So beyond the big names that everyone kind of knows, the apples and

Amazon's and Googles, what are some surprising brands that are pretty relatively consistent in this survey that people would be kind of shocked by? Yeah. But before you answer, I just want to piggyback off what Ben said, because it's a very fair point that I said that Apple, Amazon, an alphabet of the top three, but four and five are center point energy and UPS. So those are companies that you probably don't think of right away when you think of like the giant sort of market dominated companies. Yeah, that's exactly right. Like some interesting things are Costco and dollar general and dollar store also very good, but especially Costco, very loyal, very good customer satisfaction scores. A lot of the discount stores, you think, well, it's a poor customer experience. That may be so,

certain consumers that for them, you know, if I can go shopping and save 30 bucks, it's worth it for me. I'm really happy, even if I had, you know, not quite as good experience. I mean, for that reason, Duncan Donuts is a higher score than Starbucks, which surprises a lot of people. I'm a Starbucks guy, which goes to the show the power of the survey. But let me ask you, so where would something like this fit in a portfolio? Is this a core holding? Is this a, is this large cap? Like, where does somebody put this? We believe it's a core holding. Again, it's a highly diversified basket were allocated across all sectors. Most people that are using it are using it as a satellite position to try to get a little alpha in their U.S. large cap equity, which is fair. It's a

fairly new fund. It's been out a little over two years. It's done what it's, you know, what it's intended to do, and we're happy with it. And hopefully we'll start seeing some allocations as a core holding as well. Phil Bach is also the host of ETF experience where you could find available wherever you listen to podcasts. Phil, thank you so much for coming in. Awesome. Thanks, guys. So earlier in the show, we spoke about why market cap is so difficult to beat. But, Ben, let's talk about now about why market cap might not actually be so difficult to beat. So if you look at any of the historical look back periods, most of your historical data will show that something like small cap stocks or small cap value stocks have outperformed large cap stocks like the SP 500. You could also look at any other factors, high quality, low ball. So there's a lot of other factors that show. breaking that market cap actually does improve your results historically.

So I would say that it does maybe at the individual stock level. So Ned Davis has a chart that we can't share, but I'll just tell you what it shows. It shows only the highest cap S&P 500 stock going back to, I think, 1970 versus the S&P 500. And the S&P 500 over that time has gained almost 10,000 percent. The highest cap S&P 500 stock over the same time is gained just 1,000 percent. And that's companies like AT&T, Alphabet, Altria, Apple, Cisco, Exxon, GE, IBM, Microsoft, and Walmart. So I think that owning the single biggest stock might not be a great strategy, but I don't know if this works like X to the 10 biggest stocks.

And I would love if some quant nerd could check me on this. What would the SEP 500 X the 10 biggest names look like over the years? Oh, that's a good one. Right? Because we know that just holding the biggest stock is not a good strategy, but I doubt that holds true when you exclude the top 10 biggest stocks. So I think one of the reasons that a lot of the smart. beta factor investing stuff really came to fruition was because of the tech bubble. And you've heard

people like Jeremy Siegel talk about why this is why he wanted to start a place like Wisdom Tree so they could break market cap. And because it really was, I mean, the last few years I think have been kind of normal that we've had a few stocks. So I actually did this for a post last year. And I looked at it from the 1995 to 1999. And usually if you compare the market to the market breadth. So actually, why don't we do what Michael explains technical analysis to Ben to explain what the advanced decline line shows because I feel like you could explain it better than me. So this is just showing all the stocks that are going up added to the previous days tally of stocks that were going up, minus stocks that were going down. So it's just how many stocks are participating in the rally. And in 1999, there was a crazy stat that the market was up 25%, but like more than half of all stocks were down on the year. So I totally agree with you. in 1999, it's a culprit for everything. It's a culprit for smart beta. It's a culprit for

value dominating for hedge funds in the early 2000s. Like, if you mitigated your giant tech exposure in any way in the aftermath of the bubble, you were a genius. Right. So you want to see typically in a quote unquote healthy market, again, I'm using technical analysis terms here, you want to see the advanced decline line rising with the actual market because that would mean more stocks are rising than are falling. But in 1990, it started in 1998, the advanced decline line just crashed and the SP 500 continue to go up. And so that was like a huge screaming signal of, okay, there really are just a few stocks propping up the market, which people have been saying for years lately, but that wasn't been the case lately. So again, I think that is one of the

things that really, like you said, value came out of that. Hedge funds did amazingly well. So if you went short, expensive stocks and long cheap stocks, you did phenomenal from 2000 to 2002. And I think a lot of hedge funds are probably still living off that track record and which is one of the my one of Ben's institutional rules of thumb is if you have a hedge fund that's track record goes back to the 90s just like cut it off like after the tech bubble because anything before then it I don't know if you can really even count it so I am really looking forward to seeing how RVRS does because Phil made a good point about wanting to get exposure to the smallest of the biggest companies My thought to him was like, well, if you want small exposure, why don't you just own midcaps or small cap stocks?

But I do believe that RVRS is going to act a little bit differently than mid and small. I don't know that it's going to outperform large. I mean, obviously nobody does, but I would be curious to see how this one ends up doing. And to be fair, it's also been very hard to beat market cap for the last eight or nine years because the S&P has kind of been the only thing that's been doing really well. Small caps have had, you know, a couple of bare markets, which they're in the midst of one right now. The S&P has had much shallower drawdowns than elsewhere. Tech stocks have been killing it, so value has been doing poorly.

So it seems like it's been a long time coming, but a lot of these factor strategies, if we believe in mean reversion, should do better? And the way that I think about it is a lot of people ask us the question of, you know, do I really need to diversify into these factor strategies if I'm okay with just a total market index? And what we've told people most of the time, no, if you're really, feel okay with that and you don't want to figure out that's fine. But let's assume that the markets are kind of sort of efficient. And obviously, they're not totally efficient, but they're kind of

efficient, I think, over the long term. And if you take these sort of evidence-based factors, and let's say that they all have a similar risk-adjusted return profile, which is kind of another way of thinking about investing internationally as well, if over the very, very long-term these strategies and these companies are going to give similar returns, let's say they don't even give you the same premiums they did in the past. Wouldn't you want to diversify into them because you really don't know which one's going to do the best over the next even 10 to 20 years, let alone the next 90 or 100, which someone will do a back test on then and then prove it. So I think that's one of the reasons that you invest in these things. It's not so much for the risk premium. It's for the risk management

that you get from diversifying by not knowing what's going to do the best. Well said, I have no conviction that large will outperform this or this will outperform large. So good point. Yeah. Okay. Well, thanks again to Phil Bach for joining us. We'll put a bunch of these. We got a bunch of really good charts and stuff. We'll put in the show notes. Thanks for listening.