In their latest episode of the VALUE: After Hours Podcast, hosts Tobias Carlisle and Jake Taylor are joined by Marcel Schwartz and Matthias Hanauer to discuss:
- Analysis of The Four Formulas: Piotroski, Magic, Acquirer’s, Conservative
- Why Formulas May Be Losing Power
- The Value vs Growth Debate Revisited
- Jake’s Veggies: Cantillon Effects and Eutrophication
- Rethinking Quality and Cycles in Factors
- How Each Formula Was Adapted for the Study
- Key Findings and Performance Breakdown
- Airlines – Market Cap for Ants!
- Unexpected Outcomes from the Research
- Factors, Flows, and the Future
Paper discussed in this episode: Formula Investing (Marcel Schwartz, Matthias X. Hanauer)
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Transcript
Matthias: Just that it’s not an investment advice. [chuckles]
Tobias: And we’re live, gents. This is Value: After Hours. I’m Tobias Carlisle. Joined as always by my cohost, Jake Taylor. Our special guests today are Marcel Schwartz and Matthias Hanauer. It’s not investment advice, just so you know. We’re just talking a papers of the day. Formula investing. How are you, gents? Welcome.
Matthias: Great to be here.
Marcel: Thank you for having us.
Tobias: I came across you, guys. I’ve known Matthias for a while, virtually. You guys have just completed a paper called Formula Investing. Why don’t you tell us a little bit about who you are, how you came to work together on this paper and what the paper’s about?
Marcel: Maybe I will start on that one. So, Matthias and me, we met via my university during the studies. So, we were both at the technical university in Munich. During my postgrad studies, I took up the concept of formula investing. And there, I came in contact with Matthias, and we teamed up basically to write a paper about it.
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Analysis of The Four Formulas: Piotroski, Magic, Acquirer’s, Conservative
What we mean with formula investing is basically simple stock screeners that are aiming on outperformance, even for retail investors. What we did was we tested four very popular investing formulas, the F-score by Piotroski, The Magic Formula, which is probably known to most of the audience from the little book that beats the market and consequently also your formula, The Acquirer’s Multiple, Tobias.
Tobias: Good call, fellas.
Jake: Yeah. [laughs]
Marcel: Definitely. And also, The Conservative Formula from Blitz and Van Vliet. We try to bring all those formulas together on a unified framework really where we tested them on an extensive period of time. So, we used US data from 1963 until end of 2022, and thereby, we were able to compare them also against each other but also test them for time. So, we really– [crosstalk]
Tobias: Let’s talk a little bit about–
Jake: How would zig do during the civil war? Because everyone wants to know.
Tobias: I can’t say that. Can’t say the tickers. Otherwise, I put-
Jake: The Acquirer’s Multiple.
Tobias: -the whole thing through compliance. Let’s talk a little bit about this. So, the F-score is the Piotroski F-score. Just remind us what is in the F-score or roughly how the F-score works.
Matthias The F-score is like an indicator of financial fundamental strength. It’s like the sum of nine binary indicators. It’s about the profitability of a company, the probability growth, the liquidity. Like, if it can surf its debt or also if it’s issue on new shares or not. I think leverage is also included. I think it was, I think, defined in 2000 the paper came out and it was seen as the other side of value. But value measures like book to price. You buy deep value and do not look that much about the fundamental strength. So, it was complementary to the valuation part having also something that is looking at the fundamental strength for a firm.
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How Each Formula Was Adapted for the Study
Tobias: Did you have to adapt that for the data set that you looked at?
Matthias: No. I think we followed closely what is done in the original paper. Maybe some of the details we had to adjust, but we tried to stick as close as possible to the original formulas. But I think we had some adjustments regarding the universe that we had tested the formulas. Of course, we updated the sample length, so we end in 2022. But also, we want to really have comparability across formulas, but maybe masala can add a bit on that.
Marcel: Yeah. So, basically, what we try to achieve is a comparable stock universe for all formulas. I think Piotroski was subsampling first for high book to market ratio stocks so far value stocks and use that subsample, where he applied his nine binary signals on. And to not use subsamples, but the same stock universe for really old formulas. We tried to make a ranking for the formulas course and for the F-score. We basically 50/50 weighted the nine signals and the value factor, so just to make them a little bit more comparable.
Tobias: What’s The Conservative Formula? That’s not one that I’m so familiar with.
Matthias: It’s inspired by a paper of my colleague Pim van Vliet. So, I work for Rubicon. It’s a global asset management firm headquartered in the Netherlands and also founded in the Netherlands. But next to my position, there is a quant researcher. I’m also affiliated researcher at Technical University, as Marcel said.
The Conservative Formula combines low risk measured by low volatility with momentum and what we call net-payout yield. This is a measure that also looks at dividends, but also at share buybacks. So similar to Marc Faber shareholder yield measure.
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Key Findings and Performance Breakdown
Tobias: And The Magic Formula, everybody will know it’s return on invested capital as one factor, and then EBIT earnings yield on enterprise value and then Acquirer’s Multiple as mine, EBIT on EV or operating income on EV. So, what were the findings, gents, just in broad terms? Marcel or Matthias?
Matthias: Yeah. Maybe Marcel can go.
Marcel: I haven’t gotten the question. Sorry.
Tobias: Oh, sorry. What were the findings?
Jake: Which one was the best?
Marcel: Okay. [chuckles]
Tobias: Without putting too fine a point.
Marcel: Yeah. No, our key findings were basically in line with the books and the original findings also for longer time period. So, we were witnessing monotonically increasing returns when we were sorting the stocks into decile portfolios. So, the top decile portfolio was always able to outperform the product market, and also the bottom portfolio and also statistically significant and had high return spreads which were not explained by the CAPM alpha. That was a main finding. We also found that it was not due to some magic, but more due to an efficient exposure to well established factors. So, momentum, value, size. So, most of the returns were explained by well-established asset pricing factors.
On the second part of the research, we were taking a bit more of a do-it-yourself perspective, how we called it. So, really the retail investor perspective where we built long only portfolios of 40 stocks and tested whether they were still able to outperform the market also in the post-2000 period. What we found there was that all formulas were able to outperform the market in raw and risk adjusted terms, but we also noticed some performance decay, especially during the most recent years from the sample.
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Why Formulas May Be Losing Power
Jake: So, all for the last 10 years didn’t look as much like the 50 before them?
Matthias: Yeah, you’re absolutely right. So, this is also something that you see for many factors in literature. So, you see typically good returns before 2000. And then, you see sometimes around 2004 that there’s some decay of all factors. Probably has to do a bit that many of these factors were published just before or that liquidity improved the markets after 2004. I think before the decentralization of prices were introduced around that period that led to a higher liquidity. Maybe also more hedge fund quant firms trading on these signals.
So, I think one of these lessons from our paper, is that also these simple formulas probably need some continuous innovation. It’s not that you once come up with this one magic formula and you don’t have to revise it, because it’s just so magic.
Tobias: Or, you don’t tell anybody about it when you publish it. Or, you don’t tell anybody about it, you don’t publish it.
Matthias: Maybe. Yeah.
Tobias: Because it’s a phenomenon that it’s not just these formulas, I think most fundamental approaches to investing have struggled more over the last 10 years or so or even 15 years. It’s beyond publishing and being disseminated. What do you think is causing that?
Matthias: I think there are sometimes cycles also across factors. I think you always have to be careful when looking at back tested results, because these back tests, also papers, they are back tests are sometimes optimized. Researchers might look at 10 different ways how to measure one economic idea and then report just the best one. And therefore, if they all maybe have the same expected return but one was just lucky over the sample period, then you expect some decay for the best performing one.
But also, sometimes people say “Okay, this factor or this style is now dead. Typically, once this is set, the style reverses.” There we also have to be a bit looking at, “Okay, what is like the performance of a long-short signal versus of a long only strategy that’s versus the market?” because for instance in these long only strategies.
I remember for instance 2023 was a good or the average value stock was often outperforming the average growth stocks if you look at different universes. But most value strategies were underperforming the value weighted market, because you did not have included the magnificent seven stocks. So, there’s a bit the differentiation that you have to do. But for instance, some signals like accruals was told to be that and afterwards, this was written, we saw some comeback in this signals.
Tobias: Accruals went away for a little while after the paper was published, but it seems to be it started working again as the signal over the last few years.
Matthias: Exactly.
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Rethinking Quality and Cycles in Factors
Tobias: What has worked over the last decade in your experience, Matthias?
Matthias: I think momentum has been over the last year something a good factor. Also, quality. Yeah. Although there’s the question how to measure it was strong in recent years. What has not worked was value, except maybe for 2021, 2022, parts of 2023. So, momentum and quality being the other side of value, this worked. But it’s really, usually I would prefer blending good signals together, just because one single style will always fail. By combining different proofing factors, you enjoy diversification benefits across the factors. But it’s also important to continuously innovate.
For instance, you have now so much alternative data you have to be careful. Sometimes it’s just an old measure in disguise. But you can also come up with alternative data new techniques like machine learning, NLP, try to discover something new, shift more to short term factors that you can blend in with your like slower moving factors. These are things that I think could be ways to enhance this investing formulas.
Tobias: How do you think about quality? What’s your definition of quality?
Matthias: Well, I don’t share what proprietary definitions we use at Rubicon, but we have one paper I think it was published in 2000 and it has– Yeah, basic thing, everybody wants to have quality. Nobody says, “We have a chunk portfolio and we just hope or we are sure that next month, we can sell it as even more expensive.” So, everybody will claim that they have a quality portfolio, and therefore you would rather expect that quality is overpriced and the style would have a negative return.
But what we found in our research was that there’s a big variety in quality definitions and those quality metrics that can predict also future earnings growth. These are also the ones that have carrier premium. This is sometimes not the definitions that you find at the top line of the income statement or at the bottom line, like earnings and ROA or ROE. But there’s something more between and you have to fine tune your probable definition. Maybe this is missed by the market.
Tobias: It seems that quality’s done very well for a very long period of time. It doesn’t seem to me that it’s any more simple or complex than value necessarily, but why do you think it has persisted for so long?
Matthias: Well, I think it’s just maybe cycles, and we are in a quality cycle now. It might be also that, yeah, this markets, well, you need platforms and firms that are more profitable and can invest further in their technology that they then grow and establish a position and have then again a good profits that might be something this platform economy. But I don’t have a definite explanation for that.
Tobias: Let me give a quick shoutout to everybody, and let’s talk about some of the things that you found in the paper that were a little bit unexpected. Petah Tikva, Israel. What’s up? Tampa, Florida. Tomball, Texas. Lausanne, Switzerland. Gothenburg, Sweden. Toronto, Canada. Savonlinna, Finland. Are there two Savonlinna, Finlands? No.
Valparaiso. What’s up, Mac? Bremerton. Breckenridge. Toronto. Cleveland. Bellevue. Ballynamullan, Ireland. Camas. Boise. Dead Cat Gully, New South Wales. I hope that’s a real place. Birmingham. North Miami. Stirling, Scotland. Jupiter, Florida. Adelaide. Good early start for you. Jämtland, Sweden. Mendocino. Tallinn, Estonia. And Dubai, UAE. What’s up, folks? Thanks for joining us.
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Unexpected Outcomes from the Research
As you were going through the paper, Marcel, what sort of things did you find that were unusual? Anything stand out?
Marcel: So, basically, I found most of the results unusual, because when I first read the books and read the results, I thought, okay, they might be a bit too good to be true. So, that was also part of the reason why I wanted to conduct the research, not only to prove the results but more to find what’s the catch. I thought, okay, maybe the time period was chosen wisely. Maybe it’s due to the mythology.
Really what I found most astonishing in the beginning that it really worked for the extensive period and also without micro-caps and really for investable stock universe. So, what I found most interesting was that I couldn’t find anything, except for the performance decay during the last years that contradicted the original claims.
Tobias: What’s your investable stock universe? How do you define investable?
Marcel: Investable for the paper was all stocks that were listed one of the three major US stock exchanges. And then, we excluded all stocks which were below the 20th percentile of [00:17:22] stocks.
Tobias: In size? in terms of size?
Marcel: In terms of market cap. Yes, exactly. So, I think it was roughly 60% of stocks that we took out quite from the beginning, and then we also excluded all stocks where data was missing in order to calculate all the metrics for all four formulas and also financials, because most of the ratios were not– [crosstalk]
Matthias: On average we had 900 firms. So, it’s close to the Russell 1000. I think in the beginning only 200 and then the peak was 1,100. So, probably you would say this is on US investor investable universe. Bit broader than S&P 500, but it’s still good.
Tobias: Yeah. So, comparable to the Russell 1000. Did you look outside the US or is it just US data only?
Marcel: It was only US data for the scope of the research.
Matthias: Maybe this is a follow-up paper testing international investing formulas.
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The Value vs Growth Debate Revisited
Tobias: Let’s talk more broadly. What do you think investors who are approaching this problem now, what’s a sensible approach for them into the future? Does the deterioration in value mean that– is that cyclical because it looks a little bit secular in that data or do you feel like it’s cyclical? God, I hope it’s cyclical.
Jake: Disgusting. [laughs] No dog in this race, but yeah.
[laughter]Matthias: I didn’t look at it in this paper, but I have other publication and inside articles where I look at value investing, in general. I think value had a strong period in the early 2000 after the dot-com bubble. During the dot-com bubble or the buildup of dot-com bubble, it also had problems. When we zoom into this period, 2018 to 2020, then the similarities to 1999 were quite similar. So, value mainly underperformed, because already expensive stocks became even more expensive, and already cheap stocks became even more cheap. If you’re investing in these cheap stocks and they’re getting cheaper, then it’s bad for your realized returns.
But the more wide this valuation spread, how we call it– I think you also shared some updates on that. Actually, it should be more promising to invest in these stocks. We saw that this partly paid off in 2021 and 2022, and the valuation spread came down over this period. It’s not as low as it used to be in 2015. So, I think we have still some lag to go from the valuation perspective.
But it’s also important to note that value, maybe even it is like smaller returns in the past or even have its neutral returns, it can be still a great diversifier to quality or momentum, because it tends to be negatively correlated. Even if the premium is zero, it could for instance lower the volatility of a strategy. But I would expect like a positive return, maybe not as high as in the 1980s or whatever, but still I would expect a positive return on value.
But you should blend it with also other factors and to lower the tracking error, because what we also saw in the paper, in the long run, these investing formulas give you a premium. But there also are periods where you really have to pay. We measure this via drawdowns. You saw the drawdowns around the global financial crisis, but also in 2018, 2020.
We not only look at relative drawdowns, but we also look at absolute drawdowns. We also saw that The Magic Formula, Acquirer’s Multiple F-score, they behave more similar because they are essentially like value quality mixes. Maybe the conservative formula is the most distinct one, because it has this low volatility element which leads that you maybe lack in really strong bull market, but it gives you the best downside protection. By further including momentum and net payout yield, you also higher the long-term premium.
Tobias: Do you find it a little bit unusual that the low volatility generates lots of performance when it flies in the face of the entire basis for the efficient markets theory that high volatility that it’s working 180 degrees against what the orthodoxy is.
Matthias: Well, we should have invited Pim van Vilet for that. [chuckles] So, he came up with this conservative formula, and he also provided great feedback when we saw the draft and gave a lot of input. So, especially, we want to thank him here. I think this is really the core of all these factor models that typically people extended the CAPM with size value, later profitability investments. But they always kept the relationship that higher market beta should lead to higher returns.
But actually, it’s the opposite that the relationship is not linear. There’s a little difference between beta low-risk stocks and medium-risk stocks. But there’s really a cliff where really the stocks with the highest beta with the highest volatility, they really underperform the market, and these are the stocks that you should maybe avoid.
Tobias: I asked this question on Twitter a few years ago now, so I can’t really remember what the answer was. But someone had done some research showing that the higher the volatility, the worse the returns. It wasn’t Pim. It was a little bit before then. I wondered when they were getting their Nobel Prize for Economics, because I think they’re probably due for one. I don’t think that there’s been any movement in that direction given that it breaks down the entire structure that’s taught in just about every university in the world and has been for 50 years.
Matthias: With the five-factor model, they somehow can explain the high minus low volume or high minus low beta returns in the time series regression. But I haven’t seen any evidence, for instance, that in a cross-sectional Fama-MacBeth regression that beta became positively priced. So, I think they’re still not convincing evidence that maybe including other factors than this relationship really holds.
Tobias: What do you guys think about the markets generally? What are you watching? What are you keeping an eye on? What’s interesting?
Matthias: Maybe what Trump says about tariffs?
Tobias: Is that the only thing that drives it?
Marcel: [laughs] At the moment.
Tobias: It does seem to be that way a little bit in the short-term, but it’s recovered a little bit. Do you think it’s recovered, because he’s reduced the tariffs a little bit? I don’t even know what the– I have no idea where we are in it. I don’t even watch it. I don’t even pay attention.
Jake: It is interesting to think– I think, what it was, China was 100 and something percent and now we’re back down to 70% or something. You would have thought if we just took out that first 130 comment or whatever it was—
Tobias: Yeah.
Jake: If he just said like it’s going to be 70 out of the gates, you would think the market would crash. But instead, it crashed on 130 and then rips when it goes to 70. So, [chuckles] it’s all relative.
Tobias: Haven’t they settled around three or four? I don’t know, maybe I’m imagining that.
Jake: For China? I thought it was still pretty high. Oh, I don’t know.
Tobias: It’s too confusing. It’s too complicated. I have no idea. What do you guys think? What about you, Matthias? You don’t give away any state secrets, but what’s interesting?
Matthias: Just yesterday, I looked with a colleague into earnings calls and how companies mention tariffs. Actually, the mentioning is record high. Maybe you have seen some graphs from Bloomberg or Financial Times that were mentioning that. But it’s also interesting when looking at the sentiment, it’s fairly neutral.
A lot of companies are neutral on tariffs, say, they are managing, or just waiting. Some are even positive. Maybe they also see opportunities. No foreign competition maybe. But also the ratio is increasing that they become a bit more worried. We saw similar patterns, for instance, in 2018 in this first trade controversy with China. It will be interesting to follow, but hard to predict.
Tobias: I’ve seen one of the retailers that I own, which I won’t mention, so I don’t have to go through compliance. But they sell what looks to me completely discretionary stuff. They’ve said some positive things about tariffs and their last earnings results were pretty good. But I guess what I think is completely discretionary. My wife might disagree about what is completely discretionary.
Jake: [laughs] There’s no discretion in the wife’s budget, is there?
Tobias: There are a few baskets that turned up yesterday that I was surprised that we were buying those. But I digress.
Matthias: How often though do you rebalance your portfolio?
Tobias: Quarterly. Yeah, two portfolios rebalance quarterly back to equal weight. I’ve done some research on rebalancing. I think that Corey Hoffstein had the best stuff where he said there’s obviously a lot of timing luck in rebalancing. If you missed March 2009 and you rebalanced instead in September of 2009, your returns are about half what they would otherwise be, so getting that rebalancing right is important. But then you also have these other considerations where if you rebalance too often, then–
I think there’s maybe a little bit of momentum creeps into the portfolio. The longer you hold, the more momentum you get in your position. So, monthly rebalancing is much more fiddly and there’s a lot more going on. But I don’t know that necessarily improves returns. But it may take away some of that timing error. Have you looked at any of that? What do you think?
[crosstalk]Marcel: Please go ahead.
Matthias: My colleagues had done a similar analysis as Corey, and I think looking at fundamental indexation and the dependency on when you rebalance. In general, this is true. I think if you would switch to a monthly rebalance schedule, then you also need to include some buffer rules, because if you have concentrated portfolio of 40 stocks, it’s just 0.4% of the 1,000 stocks or 4% of the biggest 1,000 stocks. So, they will just change from one month to the other, especially if it includes something like momentum. So, it would definitely make sense to include some buffer rules or really compare the expected alpha gain with the expected trading costs.
But then, it becomes less simple investing formulas and it becomes really an optimization problem. But I think initially we looked at the results if we would only do annual rebalancing and I think they were fairly similar if I remember correctly, isn’t it?
Marcel: Yeah. I think they were a bit lower, especially for the conservative formula where momentum is a key factor which is more of a short-term signal. So, overall, I think for all formulas the results improved when we changed to quarterly rebalancing. Tobias, you had yearly rebalancing in your book, right?
Tobias: We had yearly and we lagged the data by six months. I think we traded mid-year as well. We traded in end of June based on year-end data just to avoid a few biases in the data. I had Mikhail Samonov on the program a few years ago, and he has this paper 200 years of value. And he’s also done a paper called 200 Years of Momentum. He was looking at there was quite a big drawdown in value at that time.
This continues on to this day. But he was saying that momentum had a giant crash in 2009 with the GFC, global financial crisis. There was a quake in 2007 and through that whole period had a giant drawdown. It seems that out of that drawdown, momentum’s done quite well. Do you think that values had enough of a drawdown to have a good cycle at the other side?
Matthias: Yeah, I definitely hope so. I also think there is reasons why you can be more positive on value. I think it’s true. Momentum had this big momentum crash, particularly, because it was after the global financial crisis more long in low beta stocks and short in high beta stocks. And then, in March, April 2009 that the market recovered, then you were short these high beta stocks and that people were carmakers, banks, more cyclical stocks, and they really were winning most and that led to this momentum crash.
But we also have done some research, we published on that, my paper is called papers residual momentum or idiosyncratic momentum. And there you see, you can take out these time varying beta bets. Not only market beta, but can also take out other style betas. Over the long run, there’s fairly similar return but have much lower drawdowns and volatility. These are examples of these kind of enhancements that we do and try to improve the process, taking out unrewarded risk.
Similar with value, for instance, these formulas are all comparing valuation across different sectors. Maybe it’s better to do more something like sector relative or valuations, taking maybe you lose a bit of the premium but also you also reduce tracking error a lot.
Tobias: Jake, it’s coming up to the top of the hour. Do you want to do your vegetables?
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Jake’s Veggies: Cantillon Effects and Eutrophication
Jake: Yes, sir, Happy to do it.
Tobias: Market fellas, it’s 32 minutes past the hour. [chuckles] No, it’s two minutes past the hour. What am I talking about?
Jake: 32 minutes in.
Tobias: 32 minutes in.
Jake: Imagine a river so polluted that it bursts into flames. This isn’t a metaphor. This actually happened. The Cuyahoga River, in the summer of 1969. Kids were eating popsicles on Lake Erie’s edge when just upstream, the water literally caught fire. So, here’s the mystery. How does a body of water like Lake Erie go from a regional jewel, the pride of the Great Lakes, to dead in a matter of years? As Munger always said, “You can learn a lot from studying failure.” Resolving this mystery for us could help us understand other ways that we’re likely to die and maybe avoid them.
So, for a little bit more background, Lake Erie was shallow and warm and nutrient rich and really perfect for supporting biodiversity and midwestern economies. And then came the post war fertilizer boom. Synthetic nitrogen and phosphorus flooded in, but not uniformly. The Maumee River drained this massive chunk of Ohio farmland right into the Erie. Algae then didn’t just grow, it exploded. The blue green blooms, they turned deadly, killing fish, suffocating aquatic life. This phenomena is called eutrophication.
By the early 1970s, Lake Erie was officially declared dead. How did all this abundance create collapse? Was it just pollution, urban sewage, overfishing? The environmentalists pointed to the runoff. Policymakers blamed industry and greedy farmers. The public just watched on in horror. But something deeper and invisible was at play. And oddly, it might be a useful metaphor for understanding a key force in economics.
So, here’s a clue. Richard Cantillon, C-A-N-T-I-L-L-O-N, was an 18th century economist that you probably skipped over in Econ 101. But his big idea was that new money doesn’t enter an economy evenly. It’s not as if it’s dropped from the sky by millions of helicopters with bags of fresh cash that Ben Bernanke is just throwing out the window.
It flows through existing pipes into preferred pockets, those closest to the spigot, banks, financial institutions, government contractors, defense firms, large asset owners who benefit from inflation, venture capital and private equity, who benefit from cheap money. Really, the moneyed class are the preferred pockets in here. They all get to shop with the fresh money at yesterday’s prices. Those furthest from the spigot, hourly wage earners, retirees on fixed incomes, anyone living paycheck to paycheck, they all feel the price increases, but just have to hope that some of it trickles down to them. They’re too far downstream, and usually, they just get the higher prices and the hangover.
So, let’s take this back to Lake Erie. Think of that upstream farm using supercharged fertilizers. Those nutrients don’t spread evenly either. They gush in through the Maumee River. And the algae closest to the inlet get these first dibs on it. They bloom, they party, they dominate, [crosstalk] then they die sucking oxygen from the room. The fish suffocate, the whole ecosystem collapses. And eutrophication is this dark outcome of Mother Nature’s Cantillon effects.
This system, the fragility, the systemic fragility, can be hiding in plain sight and even look like prosperity when the algae is in full bloom. So, whether it’s in money or nature, it’s not just what gets added. It’s also important to examine when and where. So, unequal timing of inputs can create these feedback loops that look like growth right up until they implode.
So, policies, whether they’re ecological or monetary, need to account for the where the enrichment happens, not just how much. And so, like inflation, this eutrophication is a story of timing and power and unintended consequences. And really, nature is not exempt from Cantillon’s ghost, and neither are we when we’re running these trillion-dollar deficits. So, there’s your veggies for the day.
Tobias: Good one, JT. Have you guys looked at central bank money printing, and what factors benefit or get hurt by inflation and money printing?
Jake: By money printer go brrr.
Matthias: I looked at this claim that value just depends on interest rates. I think it was very popular three years ago, five years ago or something like that, around 2000, when value was really suffering and a lot of people said, “Yeah, with higher interest rates, the future earnings of growth firms are discounted more. Therefore, the valuation is lower when interest rises.”
Jake: I think it was a very academic term that was used at that time, which was rates, bro.
Matthias: [chuckles] And for some time, it was really when 10-year was going up, value was going up, and when the yield was going down, value was going down. But when you look at longer example periods that we found really zero relationship between value and rates. It was very particular between this period, I don’t remember exactly, 2018, 2021 and also afterwards that it really broke down. So, this was something we looked at.
I think we extended also it’s to the other factors. But of course, there are sometimes dependencies or correlations let’s say like that. But if you really have a few on interest rates, then I wouldn’t invest it through factors, but then I would buy long-term bonds or short-term– Yeah.
Tobias: I think Cliff Asness looked at it around that time.
Matthias: Yes, yes.
Tobias: His colleagues, I think Cliff did it personally as well. He looked at lots of different ideas whether it was the shape of the curve for the rates or whether it was the absolute level of rates or the change in time. I don’t think he could find anything that was robust that explained the movements.
What about rates and equities generally rather than any kind of factor in equities? Because it makes sense to me that as rates go up, bonds come down, but so should equity prices because they’re similar, if you’ve got somewhere else that you can invest and get a reasonable return. If you’ve got a 6% yield on call, it doesn’t make a lot of sense to be going out and buying stocks at 25 times earnings. What do you think?
Matthias: I think this is more like the demand perspective. Sometimes higher rates also indicate future economic growth and then it’s also good for equities, of course, if earnings grow in the future. My colleague, Laurence Winkles, has a paper on it on the time varying correlation of bonds and equities. But I have to be honest, I don’t remember the exact results, but I think it’s time varying the correlation. I think he explains some of the drivers that explain the correlation.
Tobias: I think that there’s this enduring idea that the equity risk premium. I think that including the Federal Reserve in the US tracks this equity risk premium and says that “There are times when the equity zone over whatever the definition of is the 10-year treasury bond” or something like that. When you get that premium, that means that equities are reasonably safe. I think that John Hussman looked at it and he said that he couldn’t find any relationship does. A higher equity risk premium didn’t mean greater returns to equities.
Matthias: I think Cliff has a paper on it or Norton is fighting the Fed model. Isn’t it that one?
Tobias: It could be. Hussman has one as well, but I don’t know if Cliff has done– Yeah, fighting– Is that what it’s called, Fighting the Fed model?
Matthias: I think the main critique, is that 10-year bond yields are nominal returns, but earnings and future earnings are real. So, if you have high inflation periods, then you also expect maybe that earnings of the market will grow at inflation rate and therefore, it’s not really like a fair comparison. I think this was the main critique, but maybe I’m also wrong.
Jake: It doesn’t make sense to me though, because some businesses can grow their top line faster than their input costs, but plenty of them can’t. That’s just as likely to hurt profit margins as it is to help. I don’t know, that seems a little– I would imagine that, in general, most businesses are hurt by inflation more than helped.
Tobias: Yeah. I guess maybe it depends a little bit on your ability to control your– if you’ve got margins and you can control what price you sell with no diminution in demand for your product.
Jake: That’s a tiny percentage of companies that-
Tobias: Yeah, it’s not for many.
Jake: -actually have pricing power. True pricing power.
Tobias: Yeah. No, I guess we’re agreeing. But I’m saying those companies probably like inflation or they don’t care about inflation, but everybody else suffers. That’s one of the things that Buffett has written about that there’s this myth that you want an asset intensive business, because the assets in the business go up, but it’s the replacement of those assets that creates the problem. You can have 10 years of earnings based on depreciated assets. And then, at the end of the 10 years, you have to rebuy that asset and now you have to buy it at a much higher price and all of those 10 years of profits that are completely an illusion because now you’ve got to spend that money again on much more expensive equipment.
I think that’s the argument for why airlines have been such a bad investment category, because the planes get so much more expensive over time as they get safer, I guess.
Jake: Yeah. And you also have the barriers to entry are very low. It’s also a sexy thing to say you’re starting an airline, and there’s psychological benefits that happen for somebody that’s over and above the potential economics.
Tobias: And the assets move around pretty easily too. You can get them from one location to another pretty easily.
Jake: Right.
Tobias: What do you think about value being the beneficiary of that– I mean, value was more traditionally heavier industry, mining and heavy machinery and things like that. And that early 2000s value boom, it did come off the back of a period where value hadn’t done very well, so the prices were all cheap. But it was also the beneficiary of China doing its giant infrastructure build and so spending enormous amounts of money probably where it hadn’t been expected, so pushing up all the basic materials and– [crosstalk]
Jake: Commodity boom.
Tobias: Yeah, the commodity boom. Do you think that–
Jake: Can I redirect the question a little bit?
Tobias: Yeah.
Jake: Is there anything systematic in book to market that would make you select more of those types of industries, and then therefore, perhaps, end up with a bias that creeps in to the portfolio that would be built based on a price to book?
Matthias: I would like to answer it a bit differently.
[laughter]Matthias: I think it’s also more like what Tobias said at the beginning. Like, I think value after a period of bad performance, a lot of people have come up with explanation afterwards why it felt. There was this critique that value misses intangibles, and therefore, we have moved to a more intangible economy, less machine, asset heavy. You have all these software companies, and the value of the software, and the patents are not on the balance sheet, and therefore, value investing fails.
Most of these papers, they always use just book to price. I think book to price is a great value variable. I think really undervalued. But a lot of people that just look at the standalone performance, because it’s a great diversifier with quality, with momentum. But I think if you think about the enterprise multiple, for instance, it uses EBITDA or EBIT. If all these entanglements produce profits– So, if they have value, these entanglements, then they should also produce profits. I think then this critique is less valid.
I think this is always like these papers that mention these entangles. They only look at book to price. But I think this is not an explanation why, for instance, cash flow to price or EBIT to EV was doing not that good over this period or only partly.
Jake: That was a better answer than the two questions.
Matthias: I think there are also ways to, yeah, integrate entanglements into book to price, enhancing it trying to consider human capital, brand capital, knowledge capital, these kind of things. But of course, then it’s more complicated. You have to enhance the common academic definition, but I think there are ways to do that.
Tobias: I know why book value hasn’t really worked that well for that reason. It doesn’t capture the intangibles, but it is more stable as a metric than– One of the things that you find if you’re looking at one of the risks that you use if you use a flow metric, is that one of the positions that you’re looking at has had an unusual flow. They’ve sold an asset or something like that, and so they look undervalued but really on a more normalized basis they’re not. So, book value would get around that problem, wouldn’t it? It’s more stable from quarter to quarter or from period to period. But no, it still doesn’t work.
Jake: I think the other issue too is the LBO that happened in the last 10 years of companies borrowing and then buying back their own shares is also naturally lowers book value. Some companies even up with negative book value eventually. So, that’s, of course, seems like it might distort the signal of what’s a fundamental intrinsic value. And if you’re using book value as a proxy for that, it can get distorted by this LBO effect.
Matthias: Definitely. You have maybe a bit of a US centric view. When you look emerging markets, I think book to price did very well over the last years. So, it also depends and also maybe shows that you can do international diversification when investing actively, that maybe one factor does good for one region but not for the other, and the other way around too.
Tobias: Cliff Asness, when he looked at the performance of book value and over the last few years, it’s been one of the better performed strategies in the States as well.
Jake: Because it gives least value– [crosstalk]
Tobias: Because it’s the least value.
[laughter]Tobias: Yeah, because it’s the one that gives you the least value. But how do we explain why the flow models like EV, but PE, EV free cash flow or PE cash flow, however you want to construct them? Why have they struggled as well over this last period when surely they should be picking up– they’re picking up good companies that are generating flows and trading at a reasonable price relative to those flows? Why do you think they’ve struggled so much?
Matthias: I think one of the explanations or the best explanation that I could find was really that the valuation changes were really explaining the negative performance that expensive stocks already became more expensive. These firms maybe had high prices, they had good growth in the past and maybe people just expected that this growth will continue for the next 5 years to 10 years.
But we often see that like although they are also growing more in the future, not as much maybe as expected by market participants. So, value firms that have been maybe not growing that much in the past, they surprise positively. The firms that have been growing quite a lot and being really priced to perfection, they disappointed with the market.
Tobias: O’Shaughnessy Asset Management had a paper where they break down the performance of value and growth by exactly those metrics, looking at the performance of the earnings afterwards. They found that the growth firms did deliver higher growth, as you would expect, but they delivered less growth than the market expected. As a result, they underperformed, and the value firms delivered worse growth than the growth firms, but better growth than the market expected, and so they tended to outperform. But why do you think that changed over the last 10 years or 15 years with a growth firm still having better growth than they had than the market expected even?
Matthias: I think we looked at it in one small insight and also in unreported results. I looked into three subsets, like if the behavior has changed, and I found little differences actually. But of course, these are still 10 years, 15 year sample periods even in the three sub periods and it might take some time until the market price of it fully.
I think sometimes we saw some, like let’s say corrections, rotations from growth into value in 2021, 2022. I think it’s also different, as I said before, the average value stock was sometimes doing good or great, even better than the average growth stocks. But just by the dominance by this magnificent seven stocks, this was a bit [unintelligible 00:50:10] because value strategies were missing out on these seven stocks.
[crosstalk]Jake: Maybe we talked about it a little bit, Toby, on like– I did a bit of a mea culpa on like, how did I miss Google? We’re talking about base rates. The ability of large companies to grow and continue to grow at rates that just historically had never really been done before on a consistent basis. That turns out that that wasn’t the actual base rate for those companies. The real base rate was quite a bit higher than one would have predicted.
Tobias: Yeah, that’s a Mauboussin or Credit Suisse, wherever it came out of-
Jake: Yeah.
Tobias: -where he looked at the base rates for those biggest companies and they were defying the odds at scale, because there were different types of businesses. I guess previously, it had been an oil company which is limited to the amount of oil it can pull out of the ground, whereas the–
Jake: The amount of assets that you needed to create that revenue just grew at a relatively– You need a pump, and you need a train to move it and you need a refinery to turn it into something.
Tobias: Yeah. You actually have to do something with it.
Jake: Yeah, these guys were able to– The returns on scale were just increasing perhaps because of network effects and just incredible businesses.
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Factors, Flows, and the Future
Tobias: There’s another thing that we haven’t discussed, and that was Michael Green’s passive flows, which is– What do you think about that, Matthias? Is that something that you’ve looked at, the influence of passive flows on the biggest companies in the market?
Jake: The indexation.
Matthias: So, this inelastic price hypothesis.
Tobias: Yeah.
Matthias: I have read it only. I think from the flows perspective, you think like big stocks with more market liquidity, better market liquidity also then gets a higher share, that should be fair. But he finds empirically, I think some results that don’t fully support that. Yeah, but it can be temporary price pressure and then this comes down. I don’t know that really exactly.
One explanation that I heard that could also support it, that’s the most expensive stocks profit from indexing, is that all dividends distributed from the stocks are reinvested not in the stock itself that are issued as dividends, but in the index. And therefore, you see some distribution from the smaller stocks to the bigger stocks on average, because maybe the smaller stocks have higher dividend yields. This is one explanation. But at the end, it pushes up the valuations and at some point, you would expect this gets repriced.
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Airlines – Market Cap for Ants!
Tobias: I think to be fair to those businesses though, a lot of them have– They might be a little bit stretched, but they’re not massively overvalued. The businesses are reasonable. They are that much bigger than the other stock. There was one thing that I hadn’t realized until the pandemic when the airline stocks got beaten up, how small the airline stocks were relative to the tech stocks. This might be the bottom of the–
Jake: Market cap for ants?
Tobias: Well, the airline stocks had like $30 billion, $50 billion market caps, and Google was making– That’s a quarter of cash flow for Google. Google could buy an airline stock every quarter if they wanted to. It’s probably more than that now. They are the right size for their market cap. They are that much bigger. And the flows for those size stocks are probably–
They’re not overblown. They’re not just receiving giant flows, because the market cap is bigger, like the businesses are that much bigger too. So, I don’t really see why that should then create– I understand why it creates a lower valuation for the companies that don’t receive the flows, but then I don’t see why you can’t take advantage of that as a value investor of those why those smaller companies that don’t then generate outsized returns as a result. Because on a fundamental basis, they should be growing at the same rate. The same effect should be happening. It’s just now it’s happening at a lower valuation, so they should be generating better returns.
That’s the thing that I found most confusing about the whole of the last 10 years or 15 years, that if quality is a good metric, then it should get bid up and it shouldn’t continue to generate these excess returns. If value’s a bad metric, then it should be bid down and it should start to generate better returns. That’s the nature of this business, that if something gets missed or left behind, then all of a sudden, it starts working again because it’s so undervalued. But that hasn’t been the case.
Jake: Well, you just have to survive long enough for that to–
Tobias: Well, you have to survive that long.
Matthias: Well, there’s also a reason maybe why you want to include other factors next to value and quality like momentum, where you can profit from these trends and maybe that are not fully fundamentally explained.
Tobias: The two factors that you want are US listing and a big market cap that seems to have been the thing that drove returns over the last 10 years or 15 years.
Matthias: Yeah, I think size has been, yeah, not usually small cap, so equally weighted indexes were outperforming market cap index. But over the last 10 years, it was the other way around. Also, people are predicting now that might reverse and making the case for small-caps.
I think as an investor you should have allocation to small-caps, just because they are in the market portfolio and maybe due to the– Also, the performance of small-caps versus large-caps, for instance, can be a lot explained by changing in valuations and not just by operating performance. And of course, within small-caps, there might be also opportunities to generate alpha, because you have just thousands of stocks that you can invest in and find.
Tobias: William Bock is a friend of mine had this index or he had an ETF called the reverse ETF, where he too– If you take an equal weight that outperforms the market cap weight just because it’s quasi value, I guess, because it’s sizing up the smaller firms a little bit more. And so, he took that one step further and he reversed it. He said, “To the extent that the market capitalization exceeds equal weight will then apply that as a discount to it, so they’re going to be the smallest in the index. So, it’ll be the exact inverse of a market capitalization weighted index.”
Unfortunately, he launched it just in this period of time where it’s been completely dominated by size, and it didn’t work very well and didn’t attract any assets. But really, it’s quasi value index in the S&P 500 that should have– It’s like a Dogs of the Dow or something like that. You’re sizing up the things that are the smallest that didn’t work. It seems like it’s an unusual period of time in the markets. So, the only question I have, is when does it reverse? Tell us, Matthias. Tell us, Marcel. When does it stop? What’s it going to take? Is it a big crash?
Matthias: I think there’s a 60% chance that it will reverse.
[laughter]Tobias: 40%, 40%. All right. Thanks, fellas. I think we’re coming up on time. If folks want to follow along with what you’re doing or get in touch with you, what’s the best way of doing that? Marcel?
Marcel: Email, [unintelligible [00:57:45] I think that would be the best way. Or, via LinkedIn. I think that’s probably the best ways to reach me.
Tobias: And Matthias?
Matthias: I’m both on LinkedIn and on X, both with @matthiashanauer. And you can also follow my research on SSIN, where I have a profile.
Tobias: I will post the paper under this podcast, and Matthias is linked up in the tweet announcing this one. JT, any final words?
Jake: No. [chuckles] Nothing to add.
Tobias: Marcel and Matthias, thanks so much for joining us today. Once again, folks, thanks so much. We’ll be back next week, same time, same bat channel. See you everybody then.
Jake: Thanks, guys.
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