Review – Invisible Wealth

Invisible Wealth: The Hidden Story of How Markets Work

by Arnold Kling, Nick Schulz, published 2011

Recently I found myself rooting around the in archives of sites like Let A Thousand Nations Bloom and Distributed Republic and I selected a few recommended titles about the frontier of economics, politics and soft institutions (culture, legal norms, etc.) looking for answers to these questions mentioned in an earlier post:

  • Why do political borders and different legal systems seem to have such disparate impacts on economic development?
  • Which follows which, the culture/political system or the economy?
  • How sound is the idea of “competition amongst governments” and why don’t we see more countries’ policies moving toward a “developed” mean?

Invisible Wealth proved helpful in thinking more deeply about the first two questions, but it really didn’t offer any insights on the third question. The book is a mixture of introductory lessons on concepts from “Economics 2.0” intermixed with interviews from numerous academic economists who have done research in the field of the interplay between economic development and social institutions. The strongest parts of the book are the interviews with the economists. The introductory lessons suffer from too many mixed metaphors (hardware/software layer, Malthusian meadow/food court, innovation as the heart of the economy) and the insistence on delineating economic ideas as part of 1.0 or 2.0 thinking seems contrived and forced, not only because there is no existing group of economic thinkers who so identify themselves as adhering to one system of ideas or the other, but also because there is an entire school of thought, the Austrian school of economics, which recognized the importance of both 1.0 and 2.0 concepts and successfully integrated them decades ago, but which gets no spotlight aside from the consistent mentions amongst the interviews of the importance of the work of FA Hayek as an exemplar.

Briefly, Economics 1.0 is supposedly Classical Economics, which sees all economic issues in terms of the three basic inputs of land (original, unprocessed resources), labor (the effort and ingenuity of human beings interacting with those resources) and capital (the factors of production generated by mixing land and labor for future production). E1.0 is obsessed with equilibrium and static economic models, which are amenable to mathematical and statistical analysis. In contrast, Economics 2.0 acknowledges the important role of entrepreneurs in managing change and dynamism in the economy. Sadly, the authors neglect the ultra-important dimension of TIME and the role this plays in production and the coordinating activities of entrepreneurs… which is why the Austrian school again seems incredibly advanced compared to this offering and might be categorized as Economics 3.0. But even ignoring time, E2.0 is a big advance on E1.0 in acknowledging change as not only a real phenomenon of economic systems that is neglected by E1.0, but also the central element of economic development and growth. For development to take place, change must occur, and for change to occur, there must be actors with an interest and incentive in causing the change.

This shifts the analysis from studying the mineral resources or accumulated capital of a community, to studying the existence and behavior of entrepreneurs as innovators improving economic outcomes for everyone. The question begged then is, “Why do some economies have a lot of entrepreneurs, or very talented ones, while others have none or poor ones (or corrupt ones who get wealthy making people worse off)?” And for an answer to that question, one must explore the role of institutions.

With institutions, whether we’re talking E2.0 or E3.0, it’s clear that the science is still developing on which institutions are important for development, what role they play and how they can be successfully built (a significant meta-problem, because often there is feedback between a poor economy and difficulty building strong institutions and so on). There are also so many potential institutions to consider that the analysis can quickly get complicated, for example:

  • Property rights (how to define, how to enforce, what can/can’t be owned and by whom)
  • Legal norms (ie, tendency to rule a certain way in a certain type of case)
  • Legislation (ie, “the law” that will be enforced, including civil, criminal and regulatory policies)
  • “Culture” (accepted behaviors, social expectations, traditions, ideals, even aesthetics)
  • History (this is an odd one because it is so intangible and uncontrollable, but the history that each community comes from has a real effect on shaping other institutions and thus economic outcomes)
  • IQ (more on summary findings from Hive Mind below)
  • Religion
  • The family

I think this is why the interview portions of the book really shine. It is here that we get a lot of competing theories of development and which institutional factors are most important and why. They not only highlight how unsettled this part of economic or social science is, but also they provide outstanding examples of how critical each of these factors can be. And there is a clear distribution of insight and intelligence demonstrated by these interviews as well– while almost all of the interviewees have earned numerous awards and accolades, including Noble Prizes, for their economic work, several stand out as innovative giants while others seem to trade in the same, tired old statist fallacies of yore. What follows are some of the quotes I thought were most fascinating.

Robert Fogel

RF emphasized the role of technology in development, because as he says, “technological advance is the basis for all economic growth.”

One measure of economic development he suggested was looking at life expectancy. A rising life expectancy implies that people are able to produce sufficient resources to protect themselves from basic environmental and health risks. However, in looking at the historical data, there is an interesting trend in early industrial European societies by which rural populations maintained higher life expectancies than urban dwellers until around the turn of the 20th century. He blamed this on changes in technology, because

when you walked around in New York City, you were breathing pulverized horse manure, a much worse pollutant than the exhaust of automobiles

That idea grabbed me, both because it is vivid and disgusting, but also because it highlights that economic development is fraught with risk and even though the “ultimate” destination of economic development might be a less toxic technology like automobiles, the “path” along the way might include way points with more toxic technology (pathogen-laden pulverized horse manure) which is worse for health outcomes than taking your chances with subsistence-level existence in the countryside. A question I had which wasn’t explored in the discussion is why a.) city municipal services failed to keep the volumes of horse manure out of the streets as part of a sanitation program or b.) why market entrepreneurs didn’t collect and sell this “fertilizer” back to the countryside? It could be a technological problem within a technological problem.

Fogel also emphasized that the rate of technological change appears to be increasing in industrial economies:

it took four thousand years to go from the invention of the plow to figuring out how to hitch a plow up to a horse… it took 65 years to go from the first flight in a heavier-than-air machine to landing a man on the moon

Now, the example is cherry-picked and there are probably still a lot of technologies we’re using that are 10,000 years old (for example, if we ever primarily grow crops indoors, one could say “It took us 10,000 years to go from growing crops outdoors, to figuring out how to grow them indoors”, which seems like a really long time to figure out what will at that point be a best practice idea) but it still has impact.

He also mentioned the importance of economic development for the well-being of the aged:

you need to have a successful and rapidly growing economy in order for standards of living for the elderly to improve

I think this is true because the savings of the elderly need to earn an increasing return in real terms for their standard of living to improve without being forced to consume their capital, which puts a fixed timeline on their survival once they run out of capital entirely. And the only way their savings can earn a greater real return over time is if the entire economic pie is growing. It’s an interesting example of the connection between economic growth and and humane conditions.

Robert Solow

RS highlighted the complexity of the problem of solving poverty in poor countries:

Without appropriate institutional infrastructure, without the right local incentives, without complementary human capital, aid and investment will be wasted… poor countries are not only poor in capital, they are poor in the factors that make for “total factor productivity”

This is a direct application of E2.0 thinking contrasted with E1.0 thinking. The E1.0 aid crowd believes that if you just redistribute enough of the world’s wealth to the poor countries, they’ll be able to escape poverty. But RS emphasizes that they’re not just poor in terms of resources but also in terms of institutions which allow them to manage and develop resources. If this is true (and I think it is), it certainly gives one pause before hitting the “Donate to Charity”-button.

Paul Romer

PR focused on changes in technological systems and the economic impact that comes from replacing an old technology with a new one:

We didn’t get that much more light by producing hundreds of thousands of candles per person, but by switching from candles to gas

He also discussed the way technological development may improve our capacity to make further discoveries,

it may be inherent in the process of discovery that the more we learn the faster we can learn

and the impact that improvements in institutional technology have allowed us to harness those discoveries with greater efficiency:

the modern university and research system was designed not to create property rights but to lead to the rapid dispersal of new information; academics were rewarded based on the priority with which they disclosed information, so that the first person to disclose gets all the professional credit for discovering something new

[…]

what we’ve done is created better institutions over time, so that we now exploit the opportunities for discovery much more effectively than we used to

The most important insight from his interview was that growth requires change, and change creates “winners” and “losers”, and it’s easy for the losers to become a special interest group and lobby the government to arrest the change:

everyone wants growth but nobody wants change, and you’ve got to have both or you’ve got to have neither… change accompanies growth… when you have change, there will inevitably be winners and losers… we can’t let a small group of losers — either absolute losers or relative losers — stop the process of growth that will benefit most people going forward

Incidentally, this is why countries pursuing socialist policies stagnate. Socialism is a policy that preserves the status quo and tries to equalize outcomes that are created by change. Inevitably, equalizing outcomes ends up stopping the change itself and thus stagnation sets in.

Joel Mokyr

JM was actually one of my favorite interviews, so I will quote him extensively.

First, he talked about the reasons why humanity has gotten increasingly technologically advanced over time:

inventions are made when there is a minimum epistemic base… you cannot build a nuclear reactor by accident… but you can invent aspirin quite serendipitously, without having the faintest clue about how it works

[…]

We invent something, and sometimes we know a little bit about how it works, sometimes we know nothing, sometimes we know quite a bit, but in all cases, as we use it more, the epistemic base gets wider.

This technological advancement requires time, and a bit of luck, because

the only way we can think about technology is in evolutionary terms… a kind of science that makes no predictions

That’s also a really interesting idea because some economists have claimed that “science is prediction” and thus any economics which does not concern itself with empiricism and making valid predictions is not scientific. But here we have two examples (evolution, and technology) of sciences where prediction is not possible. Does that mean they are not scientific?

Later, JM goes into an explanation of the way changing technology led to economic development, and the way economic development impacted institutions and social ideas, and then the way this fed back into attempts to limit technological development and, by extension, economic development:

If you look at Europe in 1650 or 1700, what you see is a very sophisticated set of economies. They have just basically finished exploring the rest of the world, and there has been great deal of commerce and trade — joint stock companies are emerging, insurance is emerging. This is a fairly sophisticated commercial economy. The problem is, there are lots of special interests trying to get exclusionary arrangements that are good for them but bad for the economy. This is a system in which property rights are well defined and enforced, as Douglass North loves to say, but also rather distortive in the sense that you have lots of exclusionary arrangements. In other words, for the economy to function well, you don’t just need good property rights, you also need what we could call, somewhat vaguely, “economic freedoms.” You need labor mobility; you need to get rid of guilds; you need to get rid of monopolies, both local and global; you need to get rid of all kind of regulations; and above all, you need free trade. And if you don’t have that, you’re going to end up in a society that will not be able to grow.

Nowadays we have a different term for this. We call it corruption. We always say, look at countries like Russia or the Central Asian nations — these countries will never have good economies because they are corrupt. But corruption is really just a special form of what we call, in economic jargon, “rent-seeking.” I argue in my book that one of the things that happens in eighteenth-century Europe is a reaction against what we today would call rent-seeking, and that this, to a great extent, is what the Enlightenment was all about. The Enlightenment wasn’t just about freedom of religion and democracy. It wasn’t to be about democracy at all, but never mind that. It was about freedom of religion, tolerance, human rights– it was about all of those things. But it was also a reaction against mercantilism, and you find that attitude in certain people who were very important in the Enlightenment. Above all, of course, the great Adam Smith.

[…]

when you look at the few places in Europe where the Enlightenment either didn’t penetrate or was fought back by existing interests, those are exactly the countries that failed economically [Spain, Russia]

This is definitely a different take on the Enlightenment than I have come across before, but it makes a lot of sense to me and seems to do a good job of integrating economic, technological and political phenomena of the time period!

nobody has held technological leadership for a very long time… technology creates vested interests, and these vested interests have a stake in trying to stop new technologies from kicking them out in the same way that they kicked out the previous generation

That is the feedback loop mentioned earlier, and why the Enlightenment might have been a reaction against a vested interest reaction.

Cardwell’s Law: the more open the world is, the more free trade, the more ideas and people can move from one country to another, the less likely it is that technological progress will come to an end

This idea gives hope that there is a case for rational optimism assuming liberal social institutions around the world.

if you change the institutions but don’t change the culture, you’re not going to change the institutions

[…]

the degree to which we hold fast to the wisdom of earlier generations is an incredibly important element in how innovative a society is, because if you think about it, every act of invention is an act of rebellion

This suggests that “conservativism” as a social policy might lead to stunted economic development, depending upon when marks the beginning of what traditions and systems one is trying to conserve. It also highlights the problem that RS mentioned, namely, that there is complex interactivity between social institutions which enable economic growth and it’s possible that a “backwards” culture could interfere with or limit the effectiveness of “progressive” social institutions as a whole, so it’s not as simple as, say, invading a country and giving them a modern political constitution (ignoring the obviously negative social impact of a war!)

And this might seem like a throwaway quote, but I thought it was interesting:

Over most of history people have not voted their pocketbooks — Marxists included.

Thankfully! Because if they did, or do, then it will be truly hopeless to expect any kind of reform ideology to take place in the face of billions of people who could “vote their pocketbook” and keep instituting handout systems that impoverish everyone.

William Easterly

WE focused on the appropriateness of specific institutions to solving specific problems, namely, the planner-mentality to solving poverty. He looks at poverty as a circumstance created by a lack of innovation, and he identifies planning as a practice which is antithetical to innovation. Thus, planning can not solve poverty:

Planners think that the end of poverty requires a comprehensive, administrative solution. They’re trying to do something that’s a lot like central planning in the old, Soviet-style economies, in the context of poverty reduction.

[…]

It’s as if central planning has been totally, mercifully extinguished everywhere else except [in the areas with] the world’s desperate, poorest people, who can least afford such a dysfunctional solution to their problems — [areas] where it would be much better to imitate the mentality of free markets, which are all about giving financial incentives and motivating people to meet consumer needs.

[…]

corporate planning is just about scaling up a solution after you find something that works… you can’t use planning to find what works

William Lewis

WL, like JM, emphasizes the way that institutions can be used to enable and unleash innovative forces, or to restrict and restrain them. He also talks about attitudes of people in the industrialized West who are trying to create panacea solutions for people in poor countries:

Just because people are not educated does not mean that they are incapable, which is a mistake educated people in the West often make.

He points out that if the opposite were true, poverty would be a necessary part of the social landscape for much of the world for at least the next 50 years while several generations of people are being educated. But this wasn’t the pattern of development in the industrial countries before they obtained their industrial development and he doesn’t think it’s a good assumption for the remaining non-industrial countries as well.

No producer – no producer – has ever asked for more competition. So these domestic producers are really the secret enemies of globalization and they are exerting a lot of influence against it.

There’s that feedback loop! And it gives us an insight into the truth of protectionist policies, which don’t enable development but rather enable special interest groups to profit patriotically.

[Gordon] Wood showed that at the time of the Revolution, consumerism exploded in the United States. And consumerism was associated with fundamental notions of individual rights. Prior to that, at least in the feudal societies of Europe, consumption was viewed as a luxury to which only the land-owning class was entitled.

I’ve got a Gordon Wood book on my stack right now so I am excited to explore this idea further, this is another example of integrating economic and political ideas holistically and applying them to the analysis of a historical period to yield an interesting result.

And of course, the way you make a plan happen is by having a plan for production, not for consumption. There is no way you can plan or affect the individual choices that people make as individuals when they buy things, but you certainly can affect strongly what they have to buy through production planning. So this whole producer orientation was aided and abetted in modern times by the planning idea. It’s easy to see where the idea came from in feudal times– basically, the landowners and the people who owned the capital could control what happens. They were the only ones who had the ability to do anything. This whole battle for individual rights, for the political philosophies based on individual rights, and for what immediately comes from those political philosophies — namely, the idea of consumer rights — has expanded around the world to a relatively small degree.

Earlier I had mentioned [amazon text=Hive Mind&asin=0804785961]. Here are some “institutional” effects of High IQ societies, according to the author.

High IQ:

  1. Correlated with higher savings, which means more capital which raises the productivity of all labor
  2. Correlated with more cooperation, which means less corrupt government and more productive businesses
  3. Correlated with social market orientation, a form of social organization key to widespread prosperity
  4. Better at using “weakest link” team-based technology

So one challenging idea from Invisible Wealth and some of these interviews is that poor countries, in so far as they demonstrate low average IQs, as well, may have a more difficult time creating the institutional arrangements necessary to allow for sustained economic development. That has many ramifications for social policy if it’s true!

I noticed also that this idea about the importance of institutions is exactly what Hernando de Soto was discussing in his The Mystery of Capital, which I read last year. His approach was to emphasize property rights and formal versus informal economies. His argument was that poor countries tend to have major urban areas centered around the political capital where the elites in power and their cronies have the benefit of property rights enforcement and thus are able to build and accumulate capital, whereas the squatters and poor folk in the outlying communities not only have no property rights but are actively prevented from developing them or having them recognized by the formal legal system. The result is an estimate of trillions of dollars of capital “frozen” in informal structures which limit their exchangeability and thus their value, usefulness and ability to be improved or accumulated over time.

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Review – Quantitative Value

Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors + website

by Wesley R. Gray and Tobias E. Carlisle, published 2012

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 principle), 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.

Undisturbing Birth

According to G. Kloosterman, Dutch professor of obstetrics,

Spontaneous labor in a normal woman is an event marked by a number of processes so complicated and so perfectly attuned to each other that any interference will only detract from the optimal character. The only thing required from the bystanders is that they show respect for this awe-inspiring process by complying with the first rule of medicine–nil nocere [do no harm].

I found this quote in Gentle Birth, Gentle Mothering and thought it was worth sharing.