Deep Value Investing | Tobias Carlisle | Talks at Google

MALE SPEAKER: Welcome everyone. We have a very special guest here with us today, Toby Carlisle. Welcome to our Talk at Google, at Author series. He's going to talk about his new book, "Deep Value: Why Activist Investors and Other Contrarians Fight for Control of Losing Corporations." Toby has a unique perspective on

value

investing

, much like Google. His approach to

value

investing

is data-driven, just like Google's engineering approach. In his book he uses statistical analysis and in-depth research to provide evidence for a simple but counterintuitive idea. Losing stocks with failing businesses and uncertain futures can sometimes offer unusually attractive investment potential.

His work in this area is very similar to Ben Graham's classic work in his 1934 book "Security Analysis." The funny thing about the classics is that everyone has heard of them, very few people have read them. Toby is one of those few individuals (investors, I would say) who have been living and breathing Ben Graham's profound value investing philosophy. So please help me welcome Toby Carlisle. TOBIAS CARLISLE: Thank you. Hello friends, thank you very much for inviting me here today. I really want to talk to Google. Just a little about me, my name is Tobias Carlisle, I am the CEO of Eyquem Investment Management and I manage the Fund and some separately managed accounts.

My most recent book is “Deep Value,” and before that I published “Quantitative Value,” co-authored with a gentleman who did qualitative research on it through Booth. I blog on Greenbackd and am just launching a new site called Acquirer's Multiple that hasn't gone live yet. Today's talk takes place in four parts. The first part we are going to discuss is the philosophy of

deep

value. What it is, its genesis. We will then look at some of the behavioral reasons why stocks are undervalued in Contrarians at the Gate. Third, let's examine some ways we can avoid making those behavioral mistakes that create undervalued stocks.

And finally, let's examine the simple metric to generate outstanding returns in the market. In 1927, 33-year-old Benjamin Graham began teaching an evening class at Columbia that he called Security Analysis. He offered practical advice to students taking the class, but the essence of what he taught was that a security had intrinsic value and was distinct from its market price, which could be observed in the stock market on a given day. . And he argued that when there was enough error in the price, or the price was at a discount to intrinsic value, or, in some cases, when the price was at a premium to intrinsic value, you could trade long or short and trust in the fact that there would be some mean reversion to return the price to intrinsic value.

In 1929, the stock market suffered its worst crash and remains the worst to date. From its peak to its lowest point it fell almost 90%. In 1932, three years after that crisis hit bottom, the stock market still had not recovered. Graham authored a series of articles for Forbes magazine where he pointed out some research he had done that said that of the 600 issues on the stock market at the time, 200 of them were trading for less than their liquidation value. So they negotiated for less than the shareholders of those companies could get from them if they wanted to liquidate them.

And then a considerable portion of them were trading at a discount to their net cash backing, meaning after paying off all of the company's liabilities, there would still be increased cash in a bank account and the entire company could be bought for less than that amount. And he said... he compared it to corporate gold dollars that trade for $0.50 or less with conditions. That kind of enduring iconic image is still the way we think about value investing, which is trying to buy $0.50. A few years later, John Burr Williams added an additional color to the way we value a company, and that is to look at all the cash inflows and outflows, discount them back to today, and that gives us an intrinsic insight. worth.

So, as you search the market for these particular stocks, you may find that those with the largest discount to intrinsic value will tend to underperform and those with the largest premium to intrinsic value will tend to... sorry, will tend to have lower performance. This chart shows that if we rank all the stocks in the universe (and this is a global universe, i.e. 22,000 positions), if we give them all a rank, price to cash flow, price to book, price to profits, we take the average of them and then classify them into five groups. The glamor quintile, which is the most expensive, tends to underperform on average, and the value quintile, which is the cheapest, tends to outperform.

So when we look to invest in these companies, one thing we can observe is that the price tends to fluctuate more than the intrinsic value, which just moves much more slowly. So when you find those occasional times where there is a big discount or a big premium, you can invest to capture that discount. The question I am often asked is what causes the discounted price to return to its intrinsic value? And it's a question that Benjamin Graham got when he appeared before a Senate committee in 1955, and the president said: How do you close that gap? Is it for advertising or what's going on?

And Graham gave one of the great answers of his where he said it's a mystery, and it's as much a mystery to him as it is to everyone else. But we understand that it means reversion to the mean, so what this talk is about is what the actual mechanisms of mean reversion are. What are the things that cause reversion to bad to occur? Actually, this is Graham's list. In one, he

talks

about a general improvement in the industry. And it's very common for the industry to be in a slump, for the business to be in a slump, to have a period of bad luck, maybe some bad management, and for current managers to change the company or outside managers to be hired.

Less frequently, a sale, merger or some type of liquidation. Some time later, behavioral researchers DeBondt and Thaler examined values ​​based on a variety of different metrics, but they look, particularly in this case, for undervaluations and overvaluations as measured by price relative to book value. So they found that by selecting the decile, which is 1/10 of the most undervalued stocks, and the decile of the most overvalued stocks, and then examining the earnings per share trend, you find this unusual phenomenon where earnings For the most undervalued stocks, they have been falling over the years prior to forming the portfolio, and for the most overvalued stocks, they have been rising.

And that is the reason why one is overrated and the other undervalued. But then something very unusual happens once the portfolios are formed. Overvalued stocks continue to grow, but they slow down a lot, and the earnings per share of undervalued stocks start to improve at this stage. And within four years, on average, the undervalued stocks that have been selected simply based on their undervaluation have earned more and are earning more than the overvalued stocks that had the highest growth rate. It's a phenomenon we see again and again in research. So this is another interesting study following the Debondt and Thaler research done by Lakonishok, Shleifer and Vishny.

They said, well, if in fact there is a major reversal in the fundamentals of the business, if there is a mean reversion in earnings, if the undervalued stocks start to create earnings growth and the earnings of the overvalued stocks slow down, then, What would happen if we selected portfolios specifically? We could find an undervalued portfolio with very slow growth and an overvalued portfolio with very high growth, and an undervalued portfolio with very high growth, which would be the best portfolio? So they classified the universe into three portfolios based on their valuation, from the most overvalued to the average to the most undervalued.

And then they divided each of those portfolios into the highest growth rate, the average growth rate, and the slowest growth rate. Nine portfolios were then formed. I have selected three of them here to show you the phenomenon. So the glamor portfolio has the highest growth rate and you can see the earnings, cash flow, book value and operating profits, they are all growing more than the other two value portfolios. But you can also see that you have to pay a higher multiple to acquire this stock, so at 19.6x 10.8x cash flow, it's an expensive stock. At the other end of the scale are two of the three value portfolios.

The high-growth value portfolio is still growing at a fairly high growth rate, but is available for a much cheaper multiple, and the opposite value portfolio, which is the very low-growth portfolio, is growing at a low rate and It is available at a low price. multiple. As expected, value portfolios outperformed glamor portfolios. But this is what's really interesting. The undervalued low-growth portfolio outperforms the undervalued high-growth portfolio, and this is something that's really unusual, and I think it's something that's a little bit counterintuitive because we like to think that undervalued high-growth stocks are bargains, gems. in the rough.

And it turns out that mean reversion also acts on those portfolios to slow high growth and improve low growth. This is just the previous slide where the glamor portfolio was removed, so we can see specifically. What I want to bring to your attention here is that the contrarian value portfolio, in many of these categories, is actually a little bit more expensive than the high-growth portfolio. The reason is possibly that earnings are a little more anemic and cash flow is a little more anemic. As you can see, in terms of book value, the high growth value portfolio is slightly more expensive.

But it's definitely growing much faster than the opposite value portfolio. So the real driver of returns here was undervaluation and not growth rate. And then there was also some mean reversion that reduced the high growth rate and made the high growth stock less attractive over a period of time compared to the opposite stock. This was a previous exam done by Michelle Clayman. There was a book written by Tom Peters that came out in the early '80s called "In Search of Excellence," and Peter said that he had identified the characteristics that created excellent companies. He looked at a variety of these quantitative financial elements.

The book has been described as the book of the century, possibly by Tom Peters himself. I've never read it, but I have read the research, and the research is compelling. These are his criteria for an excellent company, so they have very high rates of asset growth, very high rates of capital growth, they are expensive and they have excellent returns on equity, assets and equity. So Clayman said, well, let's create the opposite portfolio. Call them non-great companies, and we will find those with the worst asset growth, capital growth, low valuation, anemic returns on equity, equity and assets, and then track their performance over a period of time.

What I will draw your attention to is just the assessment. You can see here, this is research that appears in the book. It was conducted by Barry Bannister at Stifel Financial. Non-excellent portfolios have far outperformed excellent portfolios. For an initial period, and it's hard to see on this chart, the great portfolios actually outperformed the market. But not because you get a better return investing in the S&P 500 than investing in these very high-quality, high-growth companies, these excellent companies. And you will get a much higher return by investing in the ugliest of the ugly. The question people naturally ask when they see that the data is good is: is there an advantage to investing in great portfolios because they offer some kind of risk protection?

Is it a downside protection phenomenon? Is it that when the world goes into recession, you want to be in those things that generate higher growth rates? And the answer is no. Bannister analyzed periods in which global growth was below and above average. The shaded area is where global economic growth is below average, and the lighter areas are where global economic growth exceeds the average. And you can see quite clearly that there is no real method to determine when a non-excellent performance or an excellent performance. You occasionally outperform, but you don't necessarily need a bad state of the world to do so.

I always like to use this motif to describe the nature of what happens in these undervalued companies. Most of theCompanies are going to go through this cycle. There are a very small number of companies that can avoid this cycle, but a very small number of them. This is from a wood engraving by Albrecht Durer in 1494 from a book called "The Ship of Fools", which is about some fools on a ship to a fool's paradise. And they happen, they have a series of crazy things. One of them is arrogance about where they are and a disregard for the role of luck in their own lives, good and bad.

So it's the Ferris wheel, that's Fortuna's hand driving the wheel in the top left corner. The donkey at the top is regno, "I reign." And you can see that it's at the pinnacle, and it's about to descend to the other side, but instead it reaches for the sun. And this is what, in the academic financial literature, they describe as naive extrapolation. You imagine your current state will continue when, in reality, reversion to the mean is a much more likely outcome. Then the next, regnavi, "I reigned." He is slowly becoming a man as he comes down the other side.

And this is, perhaps, where

deep

value investors want to assert their rights. Sum sine regno, "I am without a kingdom," is not even visible in the woodcut. And then back the other way, regnabo, "I will reign", maybe that's another good time to invest too. If what we're looking for is fundamental weakness, declining earnings, and undervaluation, what makes it difficult for other investors to buy makes it equally difficult for us to buy. So how can we avoid making the behavioral mistakes that other investors make? How do we not be the naive extrapolation investor and become a contrarian value investor capturing that positive return from buying companies with ugly fundamentals?

Much research has been done in this area of ​​the role of experts and statistical prediction models. For this reason, it is a counterintuitive area of ​​study. So a professor went and studied 1,000 hospital admissions for depression or psychosis and apparently, on initial presentation, they may look very similar. Then there is a subsequent diagnosis, several weeks or months later, in which they can adequately determine whether the patient who presented was truly psychotic or depressed. So, by examining admission records and subsequent diagnoses, he created a simple questionnaire that psychologists could administer when these people showed up at the hospital and could make a determination.

Without the benefit of the model, the worst psychologists were right about 55% of the time, on average about 62%, and the best psychologists were right 67% of the time. The simple model in the previous test was correct 70% of the time. The professor distributed the questionnaire to several different psychologists at the hospital. There were two basically inexperienced students, and then there were the clinical psychologists who were actually treating patients, and they found that without the benefit of the model, the inexperienced students were right about 59% of the time, the experienced clinical psychologists were right. gets it right about 64% of the time.

The inexperienced psychologist, with the benefit of the model but with the ability to override the model's result, was correct about 2/3 of the time, and the experienced psychologists, with the benefit of the model, were correct 75% of the time. But this is what's really incredible. The simple model alone trumped them all. The reason is that we make mistakes when we try to apply a model and do not follow the output of the model, and that leads us to underperform what the simple model alone does. The reason why people... when people look at this research, they often say, if you have a case that is so different from the basis, right, of the statistical research that you've done, doesn't it make sense then to be able to override the model?

And the example they always give, you have some kind of algorithm that predicts whether John is going to the theater on Friday, and now you know that he has a broken leg, shouldn't you be able to factor that into your model to determine? if he's really going to the theater this Friday. And the answer is no. And the reason is that we usually find more broken legs than there really are. And particularly in this type of deep value investing, all stocks have what appear to be broken legs. You may be familiar with “The Little Book That Beats the Market,” it is a book by Joel Greenblatt.

He took Warren Buffett's exhortation to buy wonderful companies at fair prices and translated it into a quantitative model that we'll examine in detail in a moment. But for now, he just needs to know that it means high quality, however that is defined, and good value. He did an experiment at his own company in which he handed out lists of stocks selected by the formula and allowed people, in their own separately managed accounts, to pick the stocks they wanted or for him to apply the formula. automatically. He found that, in fact, the automatically applied formula outperformed the market for two years and by a fairly substantial margin.

He also found that when people were allowed to manage the portfolio themselves, they tended to underperform, and the reason is that they picked the best stocks, and they were the ugliest. You could say well, they were not experts, they were people who trusted their experience. But then Greenblatt says we tried doing the same thing and he found out that he got the same result. He underperformed his own model of him. This brings us to the golden rule of statistical prediction rules, and that is that simple models outperform experts. That simple models continue to outperform experts even when they have the benefit of the model.

When we think about designing a quantitative system, there are several things we must take into account. One of them is that the rules must be simple and concrete. They should be simple so that they can be followed and concrete so that they can be understood. This is a picture of a pipe; actually, this is not a pipe. This is an image of a pipe. And when we value companies, in many ways, this is what we are doing. We are using some proxy. And there are a variety of different models or proxies that we can select from.

We can choose the liquidation value, we can look at the franchise value, the growth value, the earnings power value, the multiple of the acquirers. We can use any number of these simple multiples, but we don't really understand the truth of the business. That is why it is important that we recognize, first, that the model has limitations. By recognizing that the model is somewhat imperfect, we can then recognize that we do have some tool to make a decision. Therefore, the 80/20 rule also applies to investment information. You get 80% of the way there with a simple model, and the urgency of most investors is to continue finding that latest information that eliminates the uncertainty they have.

But often, when this happens, the uncertainty has disappeared for everyone else too, so those low prices that attracted you disappear. I think the simplest rule, and one of the most effective, is the current net asset value rule. Graham wrote about this in 1934. He described it as an approximate measure of liquidation value. As you can see, they just look at the balance sheet, ignore the earnings, the cash flow statement... it didn't exist at the time Graham put this together, but you can ignore it for the purposes of this analysis. - and then you treat only current assets as having value.

So, cash is worth cash, accounts receivable, some discount is applied to them, inventories would vary depending on whether it is food that can spoil, couture that will be less valuable in 12 months or something that will remain valuable well into year. the future, and then you can look at fixed assets and determine if they have value. Research shows quite comprehensively that the net value of current assets greatly exceeds. And it is something extraordinary that it is such a simple analysis. So in these cases, the market is always small cap, microcap equivalent to what we're seeing for net current asset value stocks.

You can see that in the United States, in a study from 1970 to 1983, the net value of current assets of the portfolio obtained 29% compared to 11.5% for the market. This is a study I conducted in the US, between 1983 and 2010, and continued with the second study. So that was a second 27 years and a similar kind of top performance, maybe more. Which is surprising, because you would think that with all the access to information, many of these positions would have been arbitraged out, but they haven't. They still exist in the UK and also exist globally. But there are some unusual things that are found when doing this type of research.

The first is that individual net current asset values ​​are more likely to go to 0 than the rest of the market, and the rate is around 6% for a net current asset value stock versus 2.5%. for the average market shares. . But as a portfolio, there are fewer down years than the market. And this is perhaps the most controversial finding, the most counterintuitive, and that is that loss-making net-nets actually outperform profitable net-nets. Within profitable net networks, those that do not pay dividends outperform those that do. So your instinct might be to find a net-net that pays a dividend with positive earnings, and that would lead you to underperform what you can do with net-net alone.

Many of you will know that Warren Buffett started out as a student of Benjamin Graham, then worked with him at the Graham-Newman Corporation, and then left him and created his own company. In the early days of his partnership, he was very much a Graham-type investor, looking at liquidation value. One of the first positions he put into the fund was the Sanborn Map. He had a stock portfolio and was trading at $0.65 per dollar of that stock portfolio, giving no value to the business, which was making about $100,000 a year. He joined the board, took control, liquidated the securities portfolio, paid it off in a tax-effective manner and thus earned a 50% return on the portfolio alone, and then the business remained.

And the business continues to exist to this day as a geographic information systems business in the US. He had this evolution after meeting Charlie Munger, and in 1963, he found American Express mired in scandal. American Express is a financial company and had provided some warehouse receipts to Anthony "Tino" De Angeles, who was a commodities broker and trader. He had discovered that he could take soybean oil to the port, show inspectors that his tanks contained soybean oil and then, through pipes and valves, could fill what had been soybean oil with seawater. They would then check a new tank and it would hold the old soybean oil.

At one point, he controlled nearly 10 times more soybean oil than existed. And that was the secret of their very low prices. He finally went bankrupt. American Express had said the warehouses actually contained salad oil. His brokerage went bankrupt when the people who had lent against those warehouse receipts came looking for deep pockets and found American Express. And American Express owed something on the order of $175 million, which was 10 times its average annual earnings in recent years. Therefore, there was a real risk that American Express would go bankrupt at that time. Buffet put 40% of his portfolio in stocks and he recovered.

He bought $13 million worth of those shares. That current position, if he had maintained it (which he did not maintain until the end because he changed investment entities), that position today is worth something on the order of 14 billion dollars, which is an enormous return. He learned from that that it was not necessary for these things to have liquidation value. In fact, he could buy them through a franchise. He made sure people continued using American Express cards, which taught him there was a different way to invest. In 1972, he founded See's Candies. He was earning something like $2 million on $8 million of invested capital.

They paid 27 million dollars for it. He estimated the value to be on the order of $45 million, so he got a pretty substantial discount. And he said the moment someone asked him about it, are you still a Grahamite type investor? He said I'm 85% Graham, 15% Phil Fisher, who recommended the rumored investment method, which is to find as much information as possible about the quality of the business and its ability to grow. So See's Candies, between 1972 and 2011, returned $1.35 billion to Berkshire Hathaway, which they have continued to invest. And it only requires reinvesting about $70 million in the business to generate those profits.

That is what is known as a franchise. The lesson he learned from investing in See's Candies was that you are much better off with these businesses that can grow over a long period of time. He said that even if there is only one puff left on the cigarette butt, and that puff is pure profit, after smoking that puff, there is nothing left. So he stopped being an investor in cigarette butts and became an investor in companies.wonderful at fair prices. He discusses Buffett's investment methodology and says it has been very lucrative over a very long period of time.

Assuming we don't have Buffet's great mind, will we be able to create a quantitative version of the methodology that Buffet describes in his letters and wrote a book about that process? So he decided that good quality, as defined by Buffett, means a high return on invested capital. So invested capital is the money that needs to be invested in the business to run it, the assets of the business that are actually used to produce income. The higher the return on invested capital, the better the business and the faster it can grow. And for valuation, it uses a trailing yield, earnings before interest and taxes, because it is independent of the capital structure where interest payments on debt affect the taxes paid.

If you take out the interest and taxes, you get this idea of ​​the operating profits coming into the business. We tested this qualitative value and found that the magic formula does, in fact, beat the market, a comparable stock market. And quite broadly speaking, it has beaten it by 3.5% every year from 1974 to 2011. What's really shocking is that the earnings yield alone, what I describe as the acquirer multiple, beat the magic formula itself, and the quality measure actually underperformed the market. . The quality measure actually led to the magic formula of underperforming earnings performance alone. And it's not just a performance, it's not just a raw comeback story.

In fact, it is also a story of risk-adjusted returns. Earnings performance alone leads to a better Sharpe ratio and a better Sortino ratio, which is basically the amount of growth relative to the amount of variability in returns. So you get better returns and better risk-adjusted returns just by using the earnings yield. And the reason is that there is a mean reversion in the return on invested capital. Michael Mauboussin has proven it. He examined several of the top thousand US listed stocks, ranked them in order of return on invested capital, and then divided them into five groups.

He then the highest, and then he classified them into the highest and the lowest. And then he looked at those same companies 10 years from now. And what he finds is that the higher average return on invested capital reverts back to the average return, and the lower return on invested capital also improves slightly. So I think the highest return on invested capital would be regno, at the top of the wheel, and sum sine regno, at the bottom of the wheel. What is the acquirer multiple? Well, it's the multiple company. The reason it is called the acquirer multiple is that it is the metric used by leveraged buyout firms, private equity firms and activists, to analyze the hidden value of the business and, in terms of its mechanics, its market capitalization plus debt .

Because the company has to finance the debt, you can use cash to pay it off. It is also responsible for preferred stock, it is also responsible for minority interests and for underfunded pensions, off-balance sheet liabilities. So it's the real cost of buying the business. And then you get access to EBITDA or EBIT, which is the cash flow coming in. It turns out that it doesn't really matter much which one you choose. But when we tested them using data from 1964 to 2011, we tested a variety of different possible metrics. Earnings Yield, which is the inverse of the price earnings metric, just to highlight the two.

The acquirer multiple using EBIT, the acquirer multiple using EBITDA. Free cash flow over enterprise value, gross profit yield, so it's just the revenue minus the cost of goods sold to give you the third line on the income statement and the book to market, which is the inverse of the book price so that they are ordered. in the same way. We found that the acquirer multiple performed better. And again, not just in terms of raw performance. It also outperforms comprehensively and in terms of risk-adjusted performance. The four things I want to take away from today are that the most undervalued stocks outperform the highest quality, highest growth stocks, even in the undervalued portfolio.

Therefore, undervalued portfolios divided into high growth and low growth, low growth undervalued portfolios will perform better. Therefore, it is better to assume that there will be some mean reversion, positive and negative, rather than naively extrapolating earnings growth. Simple models, the application of these ideas will always exceed the discretion of experts. So at the beginning of your process, you decide what is important in assessing value and then apply it rigorously without fear of failure. And the acquirer multiple is the best multiple. If you are looking for a very simple application, a very simple rule, this is very good.

This is a companion to the book, so I go much deeper into these studies. Each of them... there are several different versions of this study, so you can see it as we go through the entire book. The Acquirer's Multiple site has a place to capture your email and details if you want more information when it's up and running. Basically, it will provide a free screen of the acquirer's multiple companies, and some commentary and research as you go. So if you have any questions, I'd be happy to hear them. AUDIENCE: This question is about mean reversion. Within the universe of companies, do you think there are some particular companies or industries that are more flexible towards mean reversion and others that are not, or do you think it's the entire universe that this mean reversion is reverting to there?

TOBIAS CARLISLE: There are certainly some companies that demonstrate perseverance in their ability to maintain a high return on invested capital. And the question is: if we look at a large enough universe of stocks, would we expect there to be no persistence at all? Would we find some simply by chance? Therefore, it is not entirely clear whether there is a reason for its persistence or whether it is just the luck of observing a 10-year period. has looked at this specifically and found that pharmaceuticals and biotech and another group demonstrated some persistence, thus maintaining high returns on invested capital.

But he couldn't determine the factors that... prospectively, if you look at a set of data without knowing the outcome, you can determine which ones are going to persist and which ones are going to reverse, and he hasn't. I haven't been able to do that yet. AUDIENCE: You mentioned that EBIT divided by EV is actually better than the magic formula. Is Joel Greenblatt aware of this? I can't imagine he doesn't know, right? It doesn't make much sense to me. TOBIAS CARLISLE: He's almost certainly aware of that. AUDIENCE: But you think he knows, but he still put it in the book.

TOBIAS CARLISLE: Well, the magic formula outperforms. The magic formula beats the market. AUDIENCE: Yes, but I'm sure he's also done the study on separate metrics, right? Do you think he hasn't done the... TOBIAS CARLISLE: Well, I'm sure he has. AUDIENCE: But, okay, okay. I guess... TOBIAS CARLISLE: So why do it that way? Well, you know there's... AUDIENCE: You understand my question, yes. TOBIAS CARLISLE: Yes. There are elements to the business. It is both a marketing business and a return business. AUDIENCE: But I get the impression that Joel Greenblatt doesn't really... I mean, he has enough money and he doesn't really care about making more money.

I mean, he could be wrong. TOBIAS CARLISLE: In different states around the world, the magic formula will outperform earnings. So when the markets are going up, in a bull market, the playing field is pretty much even. But there are some periods (the late 1990s, for example, which was an unusual period in the markets) when the magic formula outperformed pure earnings. But earnings performance has outpaced the magic formula over the past 15 years. So it may be an idea of ​​the kind of job security that if you apply the magic formula instead of earnings performance, you will have fewer years of underperformance.

When I try that, that's not what I find. Basically, earnings performance beats the magic formula pretty consistently, and pretty consistently over consecutive five- and 10-year periods. I think it's the best metric for the reason I've outlined here: that return on invested capital, mean reversion, is a very real phenomenon. And it is an easily explainable phenomenon. When you find companies that are very profitable, you invite competitors to enter those industries. In industries with low profitability, people leave the industry. That happens all the time. It's happening right now in the oil and gas industry with low rates of return, because the price of oil is so low that they will stop drilling holes.

So that has a flow-on effect. AUDIENCE: Joel Greenblatt

talks

about the fact that a lot of these economic companies are too cheap and don't have enough liquidity for the fund managers to own, and also that a lot of the fund managers just want salable products. Those are the popular names, etc. So could you comment on the issue of liquidity and how to get in and out of some of these smaller issues? TOBIAS CARLISLE: The book, "The Little Book That Beats the Market," shows a return on the magic formula that is much higher than what can be achieved by adjusting for liquidity and size, but you can still get a higher return by applying it in a very large cap universe way.

And market cap weighted, meaning you've sized the holdings in the portfolio relative to the market cap of the company you're buying rather than weighting them equally, meaning you're putting more money into larger companies. . That is proving precisely that problem. So the results I showed here were actually market cap weighted. So, again, you get slightly better results if you equalize the weight, for the simple reason that you are investing more money in smaller positions. So the market cap weighting adjusts to that. The problem with something like current net asset value is that it's not really an investment strategy for anyone other than an individual.

They simply do not exist frequently enough and are not large or liquid enough to invest in. If you have a million dollars of investable capital, it's probably too small for you. But the magic formula and multiple acquirers scale beautifully. You can take the acquirer multiple in an S&P 500 universe, pick 5% or 10%, so the 25 or 50 stocks in that universe, apply it once a year, and you'll find pretty consistently that you get quite a bit. superior performance by doing that. As a broad issue, liquidity and size are issues that reduce returns, but these metrics still work in large universes.

You get incredible outperformance if you are able to invest in small things, something that small managers and individuals can do. Does that answer the question? AUDIENCE: Yes, that is a very good answer. AUDIENCE: I think your latest statements have answered many of the questions I was going to ask. But for the magic formula backtest, I would like to know more details. One is that you said rebalancing is once a year. TOBIAS CARLISLE: Yes. AUDIENCE: Okay. So how does the backtest treat survival buyers? TOBIAS CARLISLE: That's a good question. So the database that we use, Compustat, keeps the companies that have failed in the data.

So if it failed in 1975, the backtester would have bought it and could have bought it in 1974, because the data remains in the database. So we do a series of things. In quantitative value, we describe in some detail the process we went through to perform backtesting. We basically do a series of things. We are behind on data, so one of the problems we have is that we have this lookahead bias, which is the possibility of trading on information that we don't have yet. And on top of that, you have this January effect, which is quite pronounced. So there are some tax losses from selling at the end of the year, and then if you invest assuming you can buy your entire position on the first day of January, you get a big increase in return.

So what we did was we rebalanced the portfolios on June 30 and used data from the previous December. So we used K's data and rebalanced in June. So we avoid the January effect and we avoid the lookahead bias and then we use a very good database. AUDIENCE: So the basket contains approximately 30 stocks? TOBIAS CARLISLE: It varies depending on the size of youruniverse. The universe, and depending on the size of how much you're investing and how much time you want to spend doing it, the smallest number of stocks you could do it with might be 20. And the largest number of stocks, you don't really make much profit beyond 30.

So between 20 and 30 is the appropriate size for the wallet, with the same weight. That also means you can buy them quarterly. So you could buy: If you buy 20, you buy five quarterly and then you rebalance those five 12 months and a day to capture the tax effect. Makes sense? AUDIENCE: Okay. Yes, thanks. AUDIENCE: So if more people used the same strategy, wouldn't your strategy be equal to the market? TOBIAS CARLISLE: Possibly. But Joel Greenblatt wrote that book in 2006. The magic formula doubled the stock market's return last year. So the magic formula has continued to work. And the reason it's still working is because there aren't many value investors out there.

It is a new strategy, and within that new strategy, deep value investing is a new strategy. Most value investors would try to emulate Buffett; They are franchise type investors. There are very few guys who have really deep value. And there is also this problem that you will encounter. If you look at the data, I'm giving these positions away for free at theacquirersmultiple.com, and you can go ahead there and you can see that they are pretty scary positions. They will be buying very cheap iron ore mining companies, they will be buying very cheap oil and gas companies.

A few years ago, you would have been buying education for profit. It's scary to buy them. And that's what really drives returns. That's why I reviewed that part of the presentation that says you have to follow the model. That's the most important thing, without fear of failure, you can't choose. Because what you do when you choose is do what everyone else does and avoid the things that generate really big outperformance. AUDIENCE: Okay. I have a follow-up question. Have you tried the same strategy in different markets? TOBIAS CARLISLE: Yes. Yes, then the magic formula works. And I talk about this "deep value", but the magic formula works in Japan, it works in the UK and it works in Europe except the UK.

In each of those cases, the acquirer's multiple alone exceeds. The only place where that hasn't happened during the data period that we looked at, which was about 14 years, because there just isn't that much international data. But in Japan, the magic formula appears to exceed the acquirer multiple alone in that one region during that 14-year period we examined. AUDIENCE: Hello. So I was wondering, what does your company do then? Do you follow the model too? TOBIAS CARLISLE: Basically, we have some additional things that we do. The quantitative value goes, quite exhaustively, through a very large model that could be used.

And in a way we argued. You can do things like avoid companies with high risk of financial distress, avoid fraud, avoid earnings manipulators, look for financial strength, look for earnings quality. That means making sure cash flow in the business matches accounting profits. All of those things add a little extra performance around the edges. But the big muscle movement, the big driver of performance, is the acquirer multiple. So yeah, that's what I do. That's what I apply. AUDIENCE: A question. Why doesn't Buffett use the liquidator to operate? Is it because he had too much money? And another question related to Joel Greenblatt's first book.

It refers to the division and also to the special certification. I think his point is that buying them would be nice, but if you can choose your place, yes, it would be better. I think his performance on the test was like 50%, it's the highest I've ever known. So their way is that this is the area that you want to choose from, and then you want to choose the best one. That's not much different from what you're saying here. You're saying things like they don't involve any human beings. Just buy everything, right? Yeah. I just want to know your...

TOBIAS CARLISLE: The first book of his, "You Can Be a Stock Market Genius," which is a terrible name for a really good book, the process he describes in there is an investment strategy. reasonably complex. It's a strategy that only a human being could really implement right now, because it requires reading unusual documents and finding spin-offs or companies about to pay a big dividend, investing in special situations. I don't think I could have invested as much money in that strategy as the magic formula, and I think it's a harder strategy to implement. You can certainly gain experience in one area and understand it better than another.

Derived special situations are actually a very broad set of potential things that you can do, and you can become good at a particular industry or something, and that could lead to better performance. In some ways I think the broader your strategies and the broader your potential universe of stocks, the better performance you will get. I think to keep that rate of return that high, I was paying out a lot of the capital I had, so I would make a profit and pay it off, and then do it again the next year. While the magic formula is a compound strategy.

You reinvest everything you get and reinvest that compounded amount, and it's a strategy that really shows how good it is over a longer period of time, because you get to that point where you're investing larger sums of money at a higher rate. than a special situation, which requires you to continue paying and you are a little limited as to where you can apply. AUDIENCE: ? TOBIAS CARLISLE: Why did it change? AUDIENCE: Yes, yes, yes. Because change? TOBIAS CARLISLE: Possibly the challenge. Maybe he had too much money to invest. AUDIENCE: So one of the biggest criticisms of any model built based on backward data is that you're fitting data, right?

So the strategy described seems a little like, well, let's ignore quality and go with liquidation value, enterprise value, and purchase price of the company. And that might be kind of a valid criticism. What would be your response to that? TOBIAS CARLISLE: Well, we've also looked at it in different markets. So I certainly didn't go into that: It's not a great marketing strategy to tell someone that what we're going to do is buy the lowest quality stocks we can find, or that we're not really going to care about quality. It's a much better marketing strategy to say: What we do is look for very high-quality stocks that are undervalued, and that's what we buy.

Because it seems like a really safe strategy to me. If you said I'm going to ignore quality, people will think you're crazy. So I wrote a whole book explaining why I ignore quality. We didn't set out to find that. It was just something... it's something inevitable in literature. You find this every time you read. I give a sample of the studies in the book, there are many more studies in the book. There is another thing that looks at admiration. So what are your positive or negative feelings about a company? They rank all companies based on what they like or hate them and then examine their performance the following year.

The most hated companies outnumber the most admired. Morningstar gives ratings to companies: this is an A company, A plus, this is a B, this is a D, this is an E. The Es far outperform the A's. Every time you look at the research, it's so contrary to everything you think. You see in other places, where everyone says that you find very high quality ones. So I didn't go looking for him. But when we analyze this type of strategy outside of this market, we find almost the same thing. The only exception to this is Japan. For some reason, the magic formula over the data period we examined outperformed earnings performance alone.

Japan is an outlier in several different strategies. Momentum hasn't worked in Japan, while it has worked in many other markets. Although Japan has been falling gently (this is just an interesting side note), although it has been falling gently since 1990, value investing strategies in Japan have worked very well, simply buying the cheapest companies based on price for the Earnings, price to cash flow, and price to book have increased about 20% annually. So the magic formula might have captured a little more of that performance. But yes, there is always the risk of data mining in this type of thing.

I think the acquirer multiple is how value investors think about investing. So if you think about the way you're instructed in "Security Analysis" or "The Intelligent Investor" or any of Buffett's letters, he says think about buying the company, and think about buying the entire business. You're not buying a part, you're buying the whole thing. And that's exactly what the acquirer multiple does. He says this is what you pay for market capitalization, but don't forget that there are preferred shares, debt, minority interests, off-balance sheet liabilities, underfunded pensions and other things that need to be funded. But you get the benefit of the cash that's in the bank, all the net cash, and then you have the discretion to spend the operating profits as they come in on capital expenditures or paying down debt, or various other things.

So it's the same thing that big acquirers see and I often find that I'm in positions that someone else has been buying right behind me. It's a Carl Icahn or another activist, or a private equity firm. Something happens in them. Because when they get very cheap, there is a bit of instability. It is not a situation that should persist like this. There is a sort of invitation to external managers to come and try to rectify the situation. Data mining is always a problem, but I think there are a variety of reasons why this strategy should work quite well, and it does work quite well over different time periods and in different markets.

AUDIENCE: Buffett talks about holding stocks for the long term, and one of the reasons he likes it is because of the tax consequences. Can you talk a little bit about the difference between trying to find companies that you can hold for a long period of time versus rebalancing and using the deep value strategy? TOBIAS CARLISLE: This is one of the arguments for... this is what franchise investors are trying to do. They want to buy that company that can sustain those very high returns on invested capital and, ideally, grow and capitalize, wasting cash while doing so so that in 10 or 20 years you will be out of there. in dividends what you invested in it, and it is a much more valuable company.

So if you think about the process of going out and finding one of those companies, knowing that there is a mean reversion in the return on invested capital, what would you rather buy? Do you want to buy one that has a very high return on invested capital that could mean a reversal, or is it better to find one that is very cheap, that could be at a low point, that you can hold for seven, eight, nine, ten? years that is reversed. I think you have many possibilities to select. The challenge is not to buy something that now seems high quality, the challenge is to buy something and keep something that is high quality.

That's why we all want to buy the same stocks. Maybe I haven't made it clear enough. We all want, I want to buy, everyone wants to buy very high quality stocks, everyone wants to own very high quality stocks, the compounding machine that has very high returns on invested capital. But can you use historical data to identify them? It's not clear. But what is clear is that buying those that have higher returns on invested capital does not necessarily become those that subsequently have high returns on invested capital. AUDIENCE: One thing you talk about in the book is that one of the reasons this strategy works is because activist investors are arbitraging in an alternative market where companies are traded, not because they want the stocks to trade high, but because They want to take control of the company.

Therefore, activism is a fairly important type of catalyst for this strategy. What do you think about this strategy working for companies where the current management is an owner operator and they have motivations that are completely independent of the motivations of the minority shareholders and do not allow activists in? So I'm thinking like countries. in Asia, where many of the companies are proprietary operating companies. They really have very different motivations, and while it might be cheap on the acquirer's multiple, what are your perspectives on why it would work in those markets when the founders have no reason to let anyone in?

TOBIAS CARLISLE: You may need to look at what they're doing with the cash flows when they get them. Are they paying them, are they using them to expand the size of their kingdom, are they investing them in less profitable businesses? I think it definitely works in the US, because there's that tension when someone walks in.But I think if you look in the US, and this is something I looked at, the number of those companies that are actually taking over or holding some kind of activist event is still quite small. It's only about 1/20. And so, for the other 19 it is simply that mean reversion in the business that generates the returns.

So when you look at Asia, if you look at Japan, for example, where it's quite an insular society, but there is some kind of progress by American activists there. Even though there wasn't much activist activity in the time period, there were still pretty good returns for those undervalued companies, even across all holdings. And now I recognize that they have been very undervalued, and perhaps less so in recent weeks. But he still has a job there. I don't think it's necessary for it to work, but there are always metaphysical reasons outside the scope of backtesting that I can't answer, and in a way you're relying a little.

Maybe if those markets open up too. They continue to open up and adopt more external management, because it is a globalizing world. They have to sell outside their borders, so they need different types of management. AUDIENCE: Thank you very much for your talk. It was great. TOBIAS CARLISLE: Thank you.