Tag Archives: Warren Buffett

This Just Blew My Mind: The Moneyball Secret & Warren Buffett (#valueinvesting, #baseball, #success)

I read Michael Lewis’s Moneyball a few months ago after having seen the film. I would’ve preferred to do it in the other order (if I had ended up seeing the film at all) but I hadn’t gotten to the book yet on my reading list and an opportunity to see the movie presented itself that I decided not to turn down.

As I understood the story, the basic premise was the principles of Grahamite value investing in baseball– buy cheap things rather quality things and wait for reversion to the mean to kick in. These cheap things may not be worth much, but you can buy them at such a discount it doesn’t matter as they’d have to be truly worthless for you to have made a mistake in the aggregate.

Specifically, Billy Bean, the GM of the Oakland Athletics at the time, was recruiting players with no star power and no salary-negotiation power that could fill his roster with an above-average on base percentage. In contrast, all the big teams with the big budgets were buying the massive stars who were known for their RBIs and home run percentages. Billy Bean’s motto was “don’t make mistakes”, like a value investor who looks for a margin of safety. The other big teams with their massive budgets were operating with the motto “Aim for the stands, hit it out of the park”, like the huge mutual funds with their marketing machines and their reliance on investor expectations to add super fuel to the market.

That’s the story I thought I read, anyway, and it made a lot of sense. Inspiring stuff for a little value investor guy like me.

Today, I sat in a marketing presentation from a vendor who used Moneyball as a metaphor and he threw this image up on the projector during his slide show:

oakland_asThis is a picture of the Oakland A’s stadium. It is a shared stadium meaning it is not dedicated to the A’s but also serves as the Oakland Raiders football team home field. As a result, the baseball diamond has a lot of extra foul zone on the first and home base lines, which you might be able to see if you get real close to your monitor and squint.

I had never seen the A’s stadium before. I had no idea it had extra large foul zones. I didn’t realize that in a 160-odd game series the As would play around half, or nearly 80 games, at a stadium that had extra large foul zones.

I had no idea that a lot of players who had high on-base percentages got there because they hit balls that would normally end up in the stands at most other stadiums, but at the A’s home field it’d end up in the extra large foul zone. I had no idea that this meant those kinds of players would be extra valuable only on the Oakland A’s baseball team. I didn’t realize, as the demonstrator told us, Billy Bean was building a “pitching team”, not just a cheap on-base team (whatever that means).

This blew my mind. Maybe I just missed this in the book, and the movie. I am not a sports fan so maybe Lewis mentioned it and it wasn’t a detail that stuck out to me (which is actually another important lesson from all of this, but I digress…). Or maybe he didn’t. Maybe Lewis, the consummate story-teller, focused on the point he wanted to make from the story even though the reality, while related, was really determined by something else– the extra large foul zone at the Oakland A’s home stadium.

It reminded me of one of those situations with Warren Buffett. The first time you read Buffett’s biography and learn about his investments, you get the hokey “Just buy good businesses at fair prices!” schtick and you think, “Hey, that sounds simple, makes sense, that’s all there is to it!” Then you learn a few years later that what he was ACTUALLY doing was gaming the tax system, or creating synthetic leverage for himself, or whatever. You find the REAL angle, and it’s a bit more sophisticated and a bit harder for the average Joe to replicate by following the “invert, always invert” mantra of Charlie Munger.

What I took away from this is that people tell the stories they want to tell and you should never, ever take something at face value that involves a story of a person becoming wildly successful, wealthy, etc., just by figuring out some seemingly obvious, simple trick like buying cheap baseball stats.

There’s always an angle, like, he was buying cheap baseball stats that worked especially well in his home stadium.

That’s still genius, no doubt, but there’s less there that anyone operating outside that specific context can learn from it.

 

Notes – The Art Of Profitability: Time Profit (#profitability, #business, @CreditBubbleStocks)

(A multi-part co-blog series with CreditBubbleStocks.com about the book The Art of Profitability, by Adrian Slywotzky)

Chapter 5, Time Profit

Many of guru David Zhao’s profit models come with simple illustrations which capture the essential ingredient of the profit model. The image of the Time Profit model is an X-Y axis with “$/unit” on the Y-axis and “time” on the X-axis. Plotted across this chart is one line, which runs from the top left corner toward the bottom right corner at a 45-degree angle reading “Price”, and another line below that labeled “Cost” at a more mild angle, eventually intersecting with the “Price” line near the right side of the chart and then overtaking it.

The concept is simple: Time Profit is generated by being the first to market a new product or service because over time imitators will compete and eventually drive price toward cost. Time, therefore, is of the essence.

In TAOP, Zhao and Steve discuss Time Profit models in the context of firms without special legal protections (such as patents or copyrights) on their works which serve to shield them from competition. However, whether such legal protections are permanent or limited in duration, the Time Profit model principle is the same– only by being first to market would you even be afforded such legal protections in the first place, so there is an incentive to be first else you finish last.

Zhao and Steve discuss the Time Profit model within the context of an investment bank constantly innovating with new financial products. But this model could also easily apply to pharmaceutical and software development companies (which enjoy legal protections on their products), as well as a tech product manufacturer, such as a smartphone manufacturer, whose core product features are likely not subject to legal protections. Here, the Time Profit model is essential as the first firm to get a product to market with a valuable innovation that creates a consumer craze can capture a premium for their products while competing firms figure out how to duplicate this technology and make it standard in their follow-up product offerings. These “second place” firms are doomed to earn commodity returns on their products, only the first-mover gets to enjoy a profit premium.

Like the Customer Solution Profit model, the Time Profit model is more than just a specific business model, it is something of an essential feature to the competitive conditions of any firm in any industry facing innovative development which, practically speaking, is all firms in all industries. Whether a new product, a new service or a new internal or customer-facing process, all businesses seek to adopt one another’s best practices to save costs and increase profitability. The first firm to innovate something that is eventually imitable by others gets a profit advantage during the period of time between innovation and imitation by others. Time Profit models can be thought of as temporary competitive advantages due to periodic innovation.

As David Zhao teaches, a key component of the Time Profit model that is often overlooked is the role diligence in the innovative process plays:

Tedium is the single greatest challenge for a business that’s built on innovation

The first act of innovation is thinking, the arriving at of a brilliant new idea. The second act, and far more important, is the doing, the translation of an innovative idea into an innovative product, service or process. This part requires the same rigmarole of standard business practice: making phone calls, sending emails, training people, holding meetings, crunching numbers, keeping people on task and pulling in the same direction, etc.

Innovating, idea-making, is sexy and fun. But turning innovative ideas into real profit is often boring, common and time-consuming. The people and firms that are able to apply energy and determination to this part of the process are the ones who can most consistently capture the Time Profit. As innovator Paul Cook says, “What separates the winners and losers in innovation is who can master the drudgery.”

Ancillary Notes

Chapter 5 had a few other points worth mentioning, some of which were connected to carryover discussions from earlier chapters.

The first point concerns the power of critical numerical thinking. When working through a number problem, Zhao advises,

Getting the order of magnitude right is what matters, not the details

This is similar to Buffett and Munger’s “approximately right versus precisely wrong” dictum. Zhao also talks about using the numbers to ask and answer critical questions; the numbers of business (assumptions, projections, actual results, etc.) can tell us a story, but we have to be curious about the numbers. It’s not enough to wonder, “Why are the numbers what they are?” we have to be able to put forth some effort to attempt to answer such questions ourselves. As Zhao says,

Being able to take the measure of the world is one of the most crucial skills we can develop

The second point, which is arrived at in a discussion of business innovation, is the “paradox” Zhao observes in the semiconductor industry, which is that the firms involved “copy each other’s chips, but not each other’s business models.” It is the business model which is responsible for mastering the Time Profit concept and other models discussed in TAOP– why don’t more managements focus on copying successful business models rather than imitating successful products and services?

It brings to mind a question for potential investors, too. Which businesses could see their value dramatically improved by focusing the company’s efforts on copying the leading business model in the industry rather than engaging in the rat race of perpetual product innovation/imitation?

The final point has to do with the nature of learning. Steve the student asks Zhao for a copy of his notes from a previous meeting. Steve wants to see how Zhao solved a problem they both worked on. Zhao suggests,

you’ve got to learn how to solve these problems in your own way

the idea being that true knowledge means being able to solve a problem in your own way, not by imitating somebody else. This is why some firms are innovators while the rest are imitators. Innovators are capable of solving problems their own way; imitators just copy the innovator’s solution. But it’s a lesson that’s important to the budding business analyst, as well. How will you solve problems when there is no guru there to teach you? You have to find your own path and do your own thinking.

Until you can do that, though, as Steve says, copying a few “Picassos” to practice a known master technique can be helpful.

Videos – Toby Carlisle, Q&A Notes at UC Davis Talk on Quantitative Value (@greenbackd, #QuantitativeValue)

Click here to watch the video (wear earphones and bring a magnifying glass)

UC Davis/Farnam Street Investments presents Toby Carlisle, founder and managing partner of Eyquem Investment Management and author of Quantitative Value, with Wes Gray

Normally I’d embed a video but I can’t seem to do that with the UC Davis feed. Also, these are PARAPHRASED notes to the Q&A portion of Toby’s talk only. I ignored the “lecture” portion which preceeded because I already think I get the gist of it from the book. I was mostly interested in covering his responses to the Q&A section.

The video is extremely poor quality, which is a shame because this is a great talk on a not-so-widely publicized idea. I wish there was a copy on YouTube with better audio and zoom, but no one put such a thing up, if it exists. I hope Toby does more interviews and talks in the future… hell, I’d help him put something together if it resulted in a better recording!

I had trouble hearing it and only thought to plug in some earbuds near the end. Prior to that I was contending with airplanes going overhead, refrigerator suddenly cycling into a loud cooling mode as well as my laptop’s maxed out tinny speakers contending with the cooling fans which randomly decided to cycle on and off at often the most critical moments. I often didn’t catch the question being asked, even when it wasn’t muffled, and chose to just focus on Toby’s response, assuming that the question would be obvious from that. That being said, I often conjoined questions and responses when there was overlap or similarity, or when it was easier for me to edit. This is NOT a verbatim transcript.

Finally, Toby recently created a beta forum for his book/website, at the Greenbackd Forum and I realize now in reviewing this talk that a lot of the questions I asked there, were covered here in my notes. I think he’s probably already given up on it, likely due to blockheads like me showing up and spamming him with simpleton questions he’s answered a million times for the Rubed Masses.

Major take-aways from the interview:

Q: Could we be in a “New Era” where the current market level is the “New Mean” and therefore there is nothing to revert to?

A: Well that’s really like saying stocks will revert down, not up. But how could you know? You could only look at historical data and go off of that, we have no way to predict ahead of time whether this “New Mean” is the case. I think this is why value investing continues to work, because at every juncture, people choose to believe that the old rules don’t apply. But the better bet has been that the world changes but the old rules continue to apply.

Q: So because the world is unknowable, do you compensate by fishing in the deep value ponds?

A: I like investing in really cheap stocks because when you get surprises, they’re good surprises. I find Buffett stocks terrifying because they have a big growth component in the valuation and any misstep and they get cut to pieces; whereas these cheap stocks are moribund for the most part so if you buy them and something good happens, they go up a lot.

Q: (muffled)

A: If you look at large cap stocks, the value effect is not as prevalent and the value premia is smaller. That’s because they’re a lot more efficient. There’s still only about 5% of AUM invested in value. But the big value guys portfolios look very similar; the value you have as a small investor is you don’t have to hold those stocks. So you can buy the smaller stuff where the value premia is larger. The institutional imperative is also very real. The idea of I’d like to buy 20 stocks, but I have to hold 45. That pushes you away from the optimal holdings for outperformance.

Q: (muffled)

A: The easiest way to stand out is to not run a lot of money. But no one wants to do that, everyone wants to run a lot of money.

Q: (muffled)

A: The model I follow is a bit more complicated than the Magic Formula. But there are two broad differences. I only buy value stocks, I only buy the cheapest decile and I don’t go outside of it, and then I buy quality within that decile. ROIC will work as a quality metric but only within the cheapest decile. ROIC is something Buffett talks about from a marketing perspective but I think in terms of raw performance it doesn’t make much sense. There’s definitely some persistence in ROIC, companies that have generated high returns on invested capital over long periods of time, tend to continue to do that.  If you have Warren Buffett’s genius and can avoid stepping on landmines, that can work. But if you don’t, you need to come up with another strategy.

Q: (muffled)

A: Intuition is important and it’s important when you’re deciding which strategy to use, but it’s not important when you’re selecting individual stocks. We can be overconfident in our assessment of a stock. I wonder whether all the information investors gather adds to their accuracy or to their confidence about their accuracy.

Q: (muffled)

A: All strategies have those periods when they don’t work. If you imagined you ran 4 different strategies in your portfolio, one is MF, one is cheap stocks, one of them is Buffett growth and one is special situations, and you just put a fixed amount of capital into each one [fixed proportion?] so that when one is performing well, you take the [excess?] capital out of it and put it into the one that is performing poorly, then you always have this natural rebalancing and it works the same way as equal-weighted stocks. And I think it’d lead to outperformance. It makes sense to have different strategies in the fund.

Q: (muffled)

A: QV says you are better off following an indexing strategy, but which market you index to is important. The S&P500 is one index you can follow, and there are simple steps you can follow to randomize the errors and outperform. But if you’re going to take those simple steps why not follow them to their logical conclusion and use value investing, which will allow you to outperform over a long period of time.

Q: (muffled)

A: Not everyone can beat the market. Mutual funds/big investors ARE the market, so their returns will be the market minus their fees. Value guys are 5% of AUM, can 5% outperform? Probably, by employing unusual strategies. Wes Gray has this thought experiment where he says if we return 20% a year, how long before we own the entire market? And it’s not that long. So there are constraints and all the big value investors find that once they get out there they all have the same portfolios so their outperformance isn’t so great. There’s a natural cap on value and it probably gets exceeded right before a bust. After a bust is then fertile ground for investment and that’s why you see all the good returns come right after the bust and then it trickles up for a period of time before there’s another collapse.

Q: (muffled)

A: I think the market is not going to generate great returns in the US, and I am not sure how value will do within that. That’s why my strategy is global. There are cheaper markets in other parts of the world. The US is actually one of the most expensive markets. The cheapest market in the developed world is Greece.

Q: Did you guys ever try to add a timing component to the formula? That might help you decide how to weight cash?

A: Yes, it doesn’t work. Well, we couldn’t get it to work. However, if you look at the yield, the yield of the strategy is always really fat, especially compared to the other instruments you could invest the cash in, so logically, you’d want to capture that yield and be fully invested. I think you should be close to fully invested.

Q: What about position sizing?

A: I equal weight. An argument can be made for sizing your cheaper positions bigger. I run 50 positions in the portfolio. In the backtest I found that was the best risk-adjusted risk-reward. That’s using Sortino and Sharpe ratios, which I don’t really believe in, but what else are you going to use? If you sized to 10 positions, you get better performance but it’s not better risk-adjusted performance. If you sized to 20 positions, you get slightly worse performance but better risk-adjusted performance. So you could make an argument for making a portfolio where your 5 best ideas were slightly bigger than your next 10 best, and so on, but I think it’s a nightmare for rebalancing. The stocks I look at act a little bit like options. They’re dead money until something happens and then they pop; so I want as much exposure to those as I can. I invest globally so the accounting regimes locally are a nightmare. IFRS, GAAP to me is foreign. You have to adjust the inputs to your screen for each country as a result of different accounting standards.

Q: digression

A: Japan is an interesting market. Everyone looks at Japan and sees the slump and says it’s terrifying investing in Japan but if you look at value in Japan, value has been performing really well for a really long time. So, if the US is in this position where it’s got a lot of govt debt and it’s going to follow a similar trajectory, you could look at Japan as a proxy and feel pretty good about value.

Q: (muffled)

A: I’ll take hot money, I am not in a position to turn down anyone right now. It’s a hard strategy [QV] to sell.

Q: (muffled)

A: Special situation investing is often a situation where you can’t find it in a screen, something is being spun out, you have to read a 10-K or 10-Q and understand what’s going to happen and then take a position that you wouldn’t be able to figure out from following a simple price ratio. It’s a good place to start out because it’s something you can understand and you can get an advantage by doing more work than everyone else. It’s not really correlated to the market. I don’t know whether it outperforms over a full cycle, but people don’t care because it performs well in a bad market like this.

Q: What kind of data do you use for your backtests?

A: Compustat, CRISP (Center for Research Into Securities Prices), Excel spreadsheets. You need expensive databases that have adjusted for when earnings announcements are made, that include adjustments that are made, that include companies that went bankrupt. Those kinds are expensive. They’re all filled with errors, that’s the toughest thing.

Review – The Outsiders (#management, #capital, #business, @HarvardBiz)

The Outsiders: Eight Unconventional CEOs and Their Radically Rational Blueprint for Success (buy on Amazon.com)

by William N. Thorndike, Jr., published 2012

A “valueprax” review always serves two purposes: to inform the reader, and to remind the writer. Find more reviews by visiting the Virtual Library. Please note, I received a copy of this book for review from the publisher, Harvard Business Review Press, on a complimentary basis.

Capital allocation uber alles

“The Outsiders” rests on a premise, that the increase in a public company’s per share value is the best metric for measuring the success of a given CEO, which lends itself to the book’s major thesis: that superior capital allocation is what sets apart the best CEOs from the rest, and that most modern CEOs seem to be only partially aware, if at all, of its critical performance to their companies long-term business success.

Notice! This book is examining the efforts and measurements of CEOs of public companies, not all businesses (public and private), so as a result in comes up a bit short in the “universal application” department. Yes, capital allocation is still critical even in a private business, but you can not measure a private business’s per share value (because there isn’t a marketable security price to reference) and the CEO of a private company is missing one of the most powerful capital allocation tools available to public CEOs, the share buyback (because there is no free float for them to get their hands on at periodically irrational prices).

The CEO capital allocation toolkit

Thorndike describes five capital allocation choices CEOs have:

  1. invest in existing operations
  2. acquire other businesses
  3. issue dividends
  4. pay down debt
  5. repurchase stock

Along with this, they have three means of generating capital:

  1. internal/operational cash flow
  2. debt issuance
  3. equity issuance

With this framework, Thorndike proceeds to review the business decisions of 8 different “outsider” CEOs, so labeled because they tended to use these tools in a contrary fashion to the mainstream wisdom of their time and to much improved effect as per comparison to their benchmarks. Some of the CEOs are well known and oft mentioned and studied (Warren Buffett, John Malone, Kay Graham, Tom Murphy) and a few are known to the value cognoscenti but may have managed to escape notice of the wider public, academic or otherwise (Henry Singleton, Bill Anders, Bill Stirlitz and Dick Smith).

The author tries to tie together the various common threads, such as how,

All were first-time CEOs, most with very little prior management experience

and many of which (such as Singleton, Buffett and Graham) were large or majority equity holders in their companies, making them part of the vaunted owner-operator club with its resulting beneficial incentives.

Thorndike also tries to use the hedgehog vs. fox metaphor, claiming,

They had familiarity with other companies and industries and disciplines, and this ranginess translated into new perspectives, which in turn helped them to develop new approaches that eventually translated into exceptional results

Interestingly, the share buyback stands out as a particularly effective capital allocation tool for all and the author claims that during the difficult inflationary conditions and market depression of the 1974-1982 period,

every single one [emphasis in the original] was engaged in either a significant share repurchase program or a series of large acquisitions

In broad strokes, Thorndike’s efforts to paint these CEOs with a common brush works, but there are numerous times where his attempt to establish commonality  in genius comes across as forced and unworkable. Often, one of these CEOs will operate in a way inconsistent with Thorndike’s major thesis and yet he’ll end up praising the CEO anyway. In poker, we’d call this the “won, didn’t it?” fallacy– judging a process by the specific, short-term result accomplished rather than examining the long-term result of multiple iterations of the process over time.

Some quibbles

This is actually one of the things that rubbed me rather raw as I read the book. In every chapter, Thorndike manages to strike a rather breathless, hagiographic tone where these CEOs can do no wrong and everything they do is “great”, “fantastic,” etc. Unfortunately, this kind of hyperbolic language gets used over and over without any variety to the point it’s quite noticeable how lacking in detail and critical analysis Thorndike’s approach is at points.

Eventually, I reached a point where I almost wanted to set the book down, take a deep breath and say, “Okay… I get it, this guy is absolutely amazing… can we move on now?”

The editing seemed a bit sloppy, too. Thorndike is a graduate of Stanford and Harvard and runs his own financial advisory. He’s obviously an accomplished, intellectual person. Yet his prose often reads like an immature blog post. It’s too familiar and casual for the subject matter and the credentials of the author. I’m surprised they left those parts in during the editing process. I think it makes Thorndike’s thesis harder to take seriously when, in all likelihood, it’d probably be quite convincing if you happened to chat with the author on an airplane.

From vice to virtue

Something I liked about “The Outsiders” was the fact that there were 8 profiles, rather than one. It was reinforcing to see that the same principles and attitudes toward business and management were carried out by many different individuals who didn’t all know each other (though some did) and ALL had huge outperformance compared to their benchmark.

And I think for someone who is just jumping into the investing, management and agency problem literature, “The Outsiders” is a good place to start to get a broad outline of the major thesis which is that companies that are run by owner-operators, or by people who think like them, where the top management focuses on intelligently allocating capital to its highest use (which, oftentimes when the company’s stock remains stubbornly low compared to its estimated intrinsic value, makes buybacks in the public market the most intelligent option versus low margin growth) consistently outperform their peers and their benchmarks on a financial basis.

I think if this was one of the first books I had read on this theme, I would’ve found it quite illuminating and exciting, a real eye-opener experience. As it were, I read this book after reading a long train of other, often times significantly more comprehensive and detailed literature, so my personal experience was rather flat– I came away thinking I hadn’t learned much.

More to the story

There’s more to this story in two senses.

In the first sense, I actually highlighted many little comments or ideas throughout the book that are either helpful reminders or concepts I hadn’t fully considered myself yet, pertaining to best operational and management practices for businesses and the people who invest in them. In other words, the book is a little deeper than I bothered to share here. As a collection of anecdotes and principles for mastering the concept of capital allocation, it’s a good resource.

In the second sense, I think there’s a lot more to the success of the businessmen and their companies profiled (along with many others) than just good capital allocation. The text alludes to this with quotes from various figures about how they operated their businesses and managed people aside from the specific challenges of capital allocation. But it never goes into it because that isn’t in focus.

And as a business person myself, I know from my own reading, thinking and personal experience that capital allocation IS a critical factor in successfully managing and growing a business over the long-term — after all, if you can’t find good places to put your cash, you’ll inevitably end up wasting a lot of it — but you won’t have capital to allocate if you aren’t operating your business and managing your relationships with employees and customers well, in addition. The book just doesn’t do much in the way of explaining how it was that Ralston Purina, or General Dynamics or Teledyne or what have you, had so much capital to allocate in the first place.

Review – Quantitative Value (#valueinvesting, #quant, @greenbackd, @turnkeyanalyst)

Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors + website (buy on Amazon.com)

by Wesley R. Gray, PhD & Tobias E. Carlisle, LLB, published 2012

A “valueprax” review always serves two purposes: to inform the reader, and to remind the writer. Find more reviews by visiting the Virtual Library. Please note, I received a copy of this book for review from the publisher, Wiley Finance, on a complimentary basis.

The root of all investors’ problems

In 2005, renowned value investing guru Joel Greenblatt published a book that explained his Magic Formula stock investing program– rank the universe of stocks by price and quality, then buy a basket of companies that performed best according to the equally-weighted measures. The Magic Formula promised big profits with minimal effort and even less brain damage.

But few individual investors were able to replicate Greenblatt’s success when applying the formula themselves. Why?

By now it’s an old story to anyone in the value community, but the lesson learned is that the formula provided a ceiling to potential performance and attempts by individual investors to improve upon the model’s picks actually ended up detracting from that performance, not adding to it. There was nothing wrong with the model, but there was a lot wrong with the people using it because they were humans prone to behavioral errors caused by their individual psychological profiles.

Or so Greenblatt said.

Building from a strong foundation, but writing another chapter

On its face, “Quantitative Value” by Gray and Carlisle is simply building off the work of Greenblatt. But Greenblatt was building off of Buffett, and Buffett and Greenblatt were building off of Graham. Along with integral concepts like margin of safety, intrinsic value and the Mr. Market-metaphor, the reigning thesis of Graham’s classic handbook, The Intelligent Investor, was that at the end of the day, every investor is their own worst enemy and it is only by focusing on our habit to err on a psychological level that we have any hope of beating the market (and not losing our capital along the way), for the market is nothing more than the aggregate total of all psychological failings of the public.

It is in this sense that the authors describe their use of “quantitative” as,

the antidote to behavioral error

That is, rather than being a term that symbolizes mathematical discipline and technical rigor and computer circuits churning through financial probabilities,

It’s active value investing performed systematically.

The reason the authors are beholden to a quantitative, model-based approach is because they see it as a reliable way to overcome the foibles of individual psychology and fully capture the value premium available in the market. Success in value investing is process-driven, so the two necessary components of a successful investment program based on value investing principles are 1) choosing a sound process for identifying investment opportunities and 2) consistently investing in those opportunities when they present themselves. Investors cost themselves precious basis points every year when they systematically avoid profitable opportunities due to behavioral errors.

But the authors are being modest because that’s only 50% of the story. The other half of the story is their search for a rigorous, empirically back-tested improvement to the Greenblattian Magic Formula approach. The book shines in a lot of ways but this search for the Holy Grail of Value particularly stands out, not just because they seem to have found it, but because all of the things they (and the reader) learn along the way are so damn interesting.

A sampling of biases

Leaning heavily on the research of Kahneman and Tversky, Quantitative Value offers a smorgasbord of delectable cognitive biases to choose from:

  • overconfidence, placing more trust in our judgment than is due given the facts
  • self-attribution bias, tendency to credit success to skill, and failure to luck
  • hindsight bias, belief in ability to predict an event that has already occurred (leads to assumption that if we accurately predicted the past, we can accurately predict the future)
  • neglect of the base case and the representativeness heuristic, ignoring the dependent probability of an event by focusing on the extent to which one possible event represents another
  • availability bias, heavier weighting on information that is easier to recall
  • anchoring and adjustment biases, relying too heavily on one piece of information against all others; allowing the starting point to strongly influence a decision at the expense of information gained later on

The authors stress, with numerous examples, the idea that value investors suffer from these biases much like anyone else. Following a quantitative value model is akin to playing a game like poker systematically and probabilistically,

The power of quantitative investing is in its relentless exploitation of edges

Good poker players make their money by refusing to make expensive mistakes by playing pots where the odds are against them, and shoving their chips in gleefully when they have the best of it. QV offers the same opportunity to value investors, a way to resist the temptation to make costly mistakes and ensure your chips are in the pot when you have winning percentages on your side.

A model development

Gray and Carlisle declare that Greenblatt’s Magic Formula was a starting point for their journey to find the best quantitative value approach. However,

Even with a great deal of data torture, we have not been able to replicate Greenblatt’s extraordinary results

Given the thoroughness of their data collection and back-testing elaborated upon in future chapters, this finding is surprising and perhaps distressing for advocates of the MF approach. Nonetheless, the authors don’t let that frustrate them too much and push on ahead to find a superior alternative.

They begin their search with an “academic” approach to quantitative value, “Quality and Price”, defined as:

Quality, Gross Profitability to Total Assets = (Revenue – Cost of Goods Sold) / Total Assets

Price, Book Value-to-Market Capitalization = Book Value / Market Price

The reasons for choosing GPA as a quality measure are:

  • gross profit measures economic profitability independently of direct management decisions
  • gross profit is capital structure neutral
  • total assets are capital structure neutral (consistent w/ the numerator)
  • gross profit better predicts future stock returns and long-run growth in earnings and FCF

Book value-to-market is chosen because:

  • it more closely resembles the MF convention of EBIT/TEV
  • book value is more stable over time than earnings or cash flow

The results of the backtested horserace between the Magic Formula and the academic Quality and Price from 1964 to 2011 was that Quality and Price beat the Magic Formula with CAGR of 15.31% versus 12.79%, respectively.

But Quality and Price is crude. Could there be a better way, still?

Marginal improvements: avoiding permanent loss of capital

To construct a reliable quantitative model, one of the first steps is “cleaning” the data of the universe being examined by removing companies which pose a significant risk of permanent loss of capital because of signs of financial statement manipulation, fraud or a high probability of financial distress or bankruptcy.

The authors suggest that one tool for signaling earnings manipulation is scaled total accruals (STA):

STA = (Net Income – Cash Flow from Operations) / Total Assets

Another measure the authors recommend using is scaled net operating assets (SNOA):

SNOA = (Operating Assets – Operating Liabilities) / Total Assets

Where,

OA = total assets – cash and equivalents

OL = total assets – ST debt – LT debt – minority interest – preferred stock – book common equity

They stress,

STA and SNOA are not measures of quality… [they] act as gatekeepers. They keep us from investing in stocks that appear to be high quality

They also delve into a number of other metrics for measuring or anticipating risk of financial distress or bankruptcy, including a metric called “PROBMs” and the Altman Z-Score, which the authors have modified to create an improved version of in their minds.

Quest for quality

With the risk of permanent loss of capital due to business failure or fraud out of the way, the next step in the Quantitative Value model is finding ways to measure business quality.

The authors spend a good amount of time exploring various measures of business quality, including Warren Buffett’s favorites, Greenblatt’s favorites and those used in the Magic Formula and a number of other alternatives including proprietary measurements such as the FS_SCORE. But I won’t bother going on about that because buried within this section is a caveat that foreshadows a startling conclusion to be reached later on in the book:

Any sample of high-return stocks will contain a few stocks with genuine franchises but consist mostly of stocks at the peak of their business cycle… mean reversion is faster when it is further from its mean

More on that in a moment, but first, every value investor’s favorite subject– low, low prices!

Multiple bargains

Gray and Carlisle pit several popular price measurements against each other and then run backtests to determine the winner:

  • Earnings Yield = Earnings / Market Cap
  • Enterprise Yield(1) = EBITDA / TEV
  • Enterprise Yield(2) = EBIT / TEV
  • Free Cash Flow Yield = FCF / TEV
  • Gross Profits Yield = GP / TEV
  • Book-to-Market = Common + Preferred BV / Market Cap
  • Forward Earnings Estimate = FE / Market Cap

The result:

the simplest form of the enterprise multiple (the EBIT variation) is superior to alternative price ratios

with a CAGR of 14.55%/yr from 1964-2011, with the Forward Earnings Estimate performing worst at an 8.63%/yr CAGR.

Significant additional backtesting and measurement using Sharpe and Sortino ratios lead to another conclusion, that being,

the enterprise multiple (EBIT variation) metric offers the best risk/reward ratio

It also captures the largest value premium spread between glamour and value stocks. And even in a series of tests using normalized earnings figures and composite ratios,

we found the EBIT enterprise multiple comes out on top, particularly after we adjust for complexity and implementation difficulties… a better compound annual growth rate, higher risk-adjusted values for Sharpe and Sortino, and the lowest drawdown of all measures analyzed

meaning that a simple enterprise multiple based on nothing more than the last twelve months of data shines compared to numerous and complex price multiple alternatives.

But wait, there’s more!

The QV authors also test insider and short seller signals and find that,

trading on opportunistic insider buys and sells generates around 8 percent market-beating return per year. Trading on routine insider buys and sells generates no additional return

and,

short money is smart money… short sellers are able to identify overvalued stocks to sell and also seem adept at avoiding undervalued stocks, which is useful information for the investor seeking to take a long position… value investors will find it worthwhile to examine short interest when analyzing potential long investments

This book is filled with interesting micro-study nuggets like this. This is just one of many I chose to mention because I found it particularly relevant and interesting to me. More await for the patient reader of the whole book.

Big and simple

In the spirit of Pareto’s principle (or the 80/20 rule), the author’s of QV exhort their readers to avoid the temptation to collect excess information when focusing on only the most important data can capture a substantial part of the total available return:

Collecting more and more information about a stock will not improve the accuracy of our decision to buy or not as much as it will increase our confidence about the decision… keep the strategy austere

In illustrating their point, they recount a funny experiment conducted by Paul Watzlawick in which two subjects oblivious of one another are asked to make rules for distinguishing between certain conditions of an object under study. What the participants don’t realize is that one individual (A) is given accurate feedback on the accuracy of his rule-making while the other (B) is fed feedback based on the decisions of the hidden other, invariably leading to confusion and distress. B comes up with a complex, twisted rationalization for his  decision-making rules (which are highly inaccurate) whereas A, who was in touch with reality, provides a simple, concrete explanation of his process. However, it is A who is ultimately impressed and influenced by the apparent sophistication of B’s thought process and he ultimately adopts it only to see his own accuracy plummet.

The lesson is that we do better with simple rules which are better suited to navigating reality, but we prefer complexity. As an advocate of Austrian economics (author Carlisle is also a fan), I saw it as a wink and a nod toward why it is that Keynesianism has come to dominate the intellectual climate of the academic and political worlds despite it’s poor predictive ability and ferociously arbitrary complexity compared to the “simplistic” Austrian alternative theory.

But I digress.

Focusing on the simple and most effective rules is not just a big idea, it’s a big bombshell. The reason this is so is because the author’s found that,

the Magic Formula underperformed its price metric, the EBIT enterprise multiple… ROC actually detracts from the Magic Formula’s performance [emphasis added]

Have I got your attention now?

The trouble is that the Magic Formula equally weights price and quality, when the reality is that a simple price metric like buying at high enterprise value yields (that is, at low enterprise value multiples) is much more responsible for subsequent outperformance than the quality of the enterprise being purchased. Or, as the authors put it,

the quality measures don’t warrant as much weight as the price ratio because they are ephemeral. Why pay up for something that’s just about to evaporate back to the mean? […] the Magic Formula systematically overpays for high-quality firms… an EBIT/TEV yield of 10 percent or lower [is considered to be the event horizon for “glamour”]… glamour inexorably leads to poor performance

All else being equal, quality is a desirable thing to have… but not at the expense of a low price.

The Joe the Plumbers of the value world

The Quantitative Value strategy is impressive. According to the authors, it is good for between 6-8% a year in alpha, or market outperformance, over a long period of time. Unfortunately, it is also, despite the emphasis on simplistic models versus unwarranted complexity, a highly technical approach which is best suited for the big guys in fancy suits with pricey data sources as far as wholesale implementation is concerned.

So yes, they’ve built a better mousetrap (compared to the Magic Formula, at least), but what are the masses of more modest mice to do?

I think a cheap, simplified Everyday Quantitative Value approach process might look something like this:

  1. Screen for ease of liquidity (say, $1B market cap minimum)
  2. Rank the universe of stocks by price according to the powerful EBIT/TEV yield (could screen for a minimum hurdle rate, 15%+)
  3. Run quantitative measurements and qualitative evaluations on the resulting list to root out obvious signals to protect against risk of permanent loss by eliminating earnings manipulators, fraud and financial distress
  4. Buy a basket of the top 25-30 results for diversification purposes
  5. Sell and reload annually

I wouldn’t even bother trying to qualitatively assess the results of such a model because I think that runs the immediate and dangerous risk which the authors strongly warn against of our propensity to systematically detract from the performance ceiling of the model by injecting our own bias and behavioral errors into the decision-making process.

Other notes and unanswered questions

“Quantitative Value” is filled with shocking stuff. In clarifying that the performance of their backtests is dependent upon particular market conditions and political history unique to the United States from 1964-2011, the authors make reference to

how lucky the amazing performance of the U.S. equity markets has truly been… the performance of the U.S. stock market has been the exception, not the rule

They attach a chart which shows the U.S. equity markets leading a cohort of long-lived, high-return equity markets including Sweden, Switzerland, Canada, Norway and Chile. Japan, a long-lived equity market in its own right, has offered a negative annual return over its lifetime. And the PIIGS and BRICs are consistent as a group in being some of the shortest-lifespan, lowest-performing (many net negative real returns since inception) equity markets measured in the study. It’s also fascinating to see that the US, Canada, the UK, Germany, the Netherlands, France, Belgium, Japan and Spain all had exchanges established approximately at the same time– how and why did this uniform development occur in these particular countries?

Another fascinating item was Table 12.6, displaying “Selected Quantitative Value Portfolio Holdings” of the top 5 ranked QV holdings for each year from 1974 through 2011. The trend in EBIT/TEV yields over time was noticeably downward, market capitalization rates trended upward and numerous names were also Warren Buffett/Berkshire Hathaway picks or were connected to other well-known value investors of the era.

The authors themselves emphasized that,

the strategy favors large, well-known stocks primed for market-beating performance… [including] well-known, household names, selected at bargain basement prices

Additionally, in a comparison dated 1991-2011, the QV strategy compared favorably in a number of important metrics and was superior in terms of CAGR with vaunted value funds such as Sequoia, Legg Mason and Third Avenue.

After finishing the book, I also had a number of questions that I didn’t see addressed specifically in the text, but which hopefully the authors will elaborate upon on their blogs or in future editions, such as:

  1. Are there any reasons why QV would not work in other countries besides the US?
  2. What could make QV stop working in the US?
  3. How would QV be impacted if using lower market cap/TEV hurdles?
  4. Is there a market cap/TEV “sweet spot” for the QV strategy according to backtests? (the authors probably avoided addressing this because they emphasize their desire to not massage the data or engage in selection bias, but it’s still an interesting question for me)
  5. What is the maximum AUM you could put into this strategy?
  6. Would more/less rebalancing hurt/improve the model’s results?
  7. What is the minimum diversification (number of portfolio positions) needed to implement QV effectively?
  8. Is QV “businesslike” in the Benjamin Graham-sense?
  9. How is margin of safety defined and calculated according to the QV approach?
  10. What is the best way for an individual retail investor to approximate the QV strategy?

There’s also a companion website for the book available at: www.wiley.com/go/quantvalue

Conclusion

I like this book. A lot. As a “value guy”, you always like being able to put something like this down and make a witty quip about how it qualifies as a value investment, or it’s intrinsic value is being significantly discounted by the market, or what have you. I’ve only scratched the surface here in my review, there’s a ton to chew on for anyone who delves in and I didn’t bother covering the numerous charts, tables, graphs, etc., strewn throughout the book which serve to illustrate various concepts and claims explored.

I do think this is heady reading for a value neophyte. And I am not sure, as a small individual investor, how suitable all of the information, suggestions and processes contained herein are for putting into practice for myself. Part of that is because it’s obvious that to really do the QV strategy “right”, you need a powerful and pricey datamine and probably a few codemonkeys and PhDs to help you go through it efficiently. The other part of it is because it’s clear that the authors were really aiming this book at academic and professional/institutional audiences (people managing fairly sizable portfolios).

As much as I like it, though, I don’t think I can give it a perfect score. It’s not that it needs to be perfect, or that I found something wrong with it. I just reserve that kind of score for those once-in-a-lifetime classics that come along, that are infinitely deep and give you something new each time you re-read them and which you want to re-read, over and over again.

Quantitative Value is good, it’s worth reading, and I may even pick it up, dust it off and page through it now and then for reference. But I don’t think it has the same replay value as Security Analysis or The Intelligent Investor, for example.