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

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


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


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:


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.



Video – Hugh Hendry Visits The Milken Institute (#macro)

Hugh Hendry interviewed in a panel discussion at the 2012 Milken Institute Global Conference

Major take-aways from the interview:

  • Global economy is “grossly distorted” by two fixed exchange regimes: the Euro (similar to the gold standard of the 1920s) and the Dollar-Renminbi
  • China is attempting to play the role of the “bridge”, just as Germany did in the 1920s, to help the global economy spend its way into recovery
  • Two types of leverage: operational and financial; Germany is a country w/ operational leverage; Golden Rule of Operational Leverage, “Never, never countenance having financial leverage”, this explains Germany’s financial prudence and why they’ll reject a transfer union
  • Transfer of economic rent in Europe; redistribution of rents within Europe, the trade is short the financial sector, long the export sector
  • Heading toward Euro parity w/ the dollar, if not lower; results in profound economic advantage especially for businesses with operational leverage
  • “The thing I fear” is confiscation: of client’s assets, my assets; we are 1 year away from true nationalization of French banks
  • Theme of US being supplanted as global leader, especially by Chinese, is overwrought
  • Why US will not be easily overtaken: when US had its “China moment”, it was on a gold standard…
    • implication, as an entrepreneur, you had one chance– get it right or you’re finished
    • today is a world of mercantilism, money-printing, the  entrepreneur has been devalued because you get a 2nd, 3rd, 4th chance
    • when the US had its emergence on a hard money system, it built foundations which are “rock solid”
    • today, this robust society has restructured debt, restructured the cost of labor, has cleared property at market levels
    • additionally, “God has intervened”, w/ progress in shale oil extraction technology; US paying $2, Europe $10, Asians $14-18
  • Dollar is only going to go one way, higher; this is like early 1980/82
  • “I haven’t finished Atlas Shrugged, I can’t finish it”: it’s too depressing; it reads like non-fiction, she’s describing the world of today
  • The short sale ban was an attack on free thought; people have died in wars for the privilege to stand up and say “The Emperor has no clothes”; banned short selling because truth is unpalatable to political class; the scale and magnitude of the problem is greater than their ability to respond
  • We are single digit years away from a most profound market-clearing moment, on the order of 1932 or 1982, where you don’t need smarts, you just need to be long
  • Hard-landing scenario in Asia combined w/ recession in Europe would result in “bottoming” process, at which point all you need is courage to go long

Review – Fooling Some Of The People All Of The Time (#investing, #shortselling, #fraud)

Fooling Some Of The People All Of The Time, A Long Short (And Now Complete) Story, Updated with New Epilogue

by David Einhorn, published 2010

So much could be said about Einhorn’s “Fooling Some Of The People All Of The Time” that I’ll necessarily have to ignore much of it to keep this review to the point. And let me say up front that I believe the main point of Einhorn’s book is that frauds may not be transparent, but the people perpetrating and enabling them often are and on that note I believe it’s clear that Einhorn is the hero and the Allied Capital crowd are the villains. If the opposite be true, Einhorn certainly has me “fooled.”

For what amounts to a legal caper (not a crime caper, a legal caper) involving all kinds of humorless characters, including the liars at Allied Capital attempting to perpetrate a fraud, the duplicitous analysts and journalists seemingly working on their behalf to help cover it up and a menagerie of lawyers, government officials and SEC investigators — can you get any more humorless than that group? — “Fooling” is darned entertaining. Funny, too. I found myself chuckling at the outrageous prevarications of the guilty parties on more than one occasion.

It’s not just a good story, though, it’s something of an instructive modern parable, political, financial and even economic in nature.

Einhorn’s sojourn into the bowels of the Allied Capital fraud began before the current financial crisis but carried into it. Knowing this, it’s both fascinating to see the struggles of someone who had come upon the margins of the crisis before it had become a crisis as well as frustrating to see that the Allied Capital saga is yet another facet of that crisis and one which, despite Einhorn’s having published a whole book about it, has yet to see much coverage in the mainstream press. Three years into what is becoming a growing pile of frauds and wasted resources, many politicians and interest groups are unabashedly calling for the expansion of the Small Business Administration and its various loan programs, rather than the shutting down of a completely compromised institution.

Financially, “Fooling” tells two tales: one is of a bold, dedicated individual (Einhorn) and his small band of loyal followers (Greenlight Capital staff) and friends (private citizens like Jim Brickman) who, despite the odds and the constant doubting of the hoi polloi nevertheless persevered in their struggle for truth and were ultimately vindicated by the facts and their profitable short position; the other is the story of that same man and his merry band who put an ungodly amount of time and resources into investigating a fraud that ultimately represented only about 8% of their portfolio, begging the question, “How much of this was about ego-gratification versus responsibly representing the interests of Greenlight’s partners?”

Knowing that Einhorn and Greenlight continued to make other successful investments along the way, more than once you find yourself wondering if Allied Capital would prove to be some kind of a Pyrrhic Victory. Certainly it’s reasonable to question whether Greenlight wouldn’t have fallen victim to another fraud they had invested in at Tyco if they had spread their attention and energies more equally amongst their various positions.

In the end, it is the economic parable which reigns supreme, however. The Allied Capital case is one of those seeming empirical confirmations of free market economic tenets. One by one, the various watchdogs and regulators prove either useless, incompetent, disinterested or entirely corrupted, from the federal SBA, SEC and even FBI, to the ratings agencies, to the Wall Street establishment analysts to the sacred Fourth Estate itself. It is only Greenlight Capital, and finally the market place at large, motivated by the profit principle, which has any incentive to actually root out and expose the fraudulent financial activities at Allied.

Einhorn’s triumph demonstrates that it isn’t about people but processes, the fundamental and natural incentives of the two competing and mutually exclusive principles of profit versus welfare.

This book is not perfect but it’s enlightening in more ways than one. “Fooling” does an excellent job of revealing the way modern capital markets work and while Einhorn mostly manages to stay above the vulgarity of his opponents, the Allied feud proves that to win a confidence game it’s helpful to have both the truth, and some talented lawyers and public opinion-setters, on your side.


Video – Mohnish Pabrai On Forbes (#valueinvesting)

Intelligent Investing with Steve Forbes presents Mohnish Pabrai, managing partner, Pabrai Funds

Major take-aways from the interview:

  • Attitude is the most important attribute of any investor
  • The value investor’s attitude advantage is the ability to wait for the right opportunity
  • “All man’s miseries stem from his inability to sit in a room alone and do nothing” channeling Pascal into an investor appropriate format: “All investment managers’ miseries stem from an inability to sit alone in a room and do nothing”
  • Ideal investment industry: gentlemen of leisure who go about their leisurely tasks and when the world is severely fearful is when they put their leisurely tasks aside and go to work
  • People think entrepreneurs take risk; in reality, they do everything they can to minimize risk– low risk, high return bets
  • Pabrai Funds has a “moat” by mirroring Buffett’s 25% performance after 6% hurdle because it aligns his interests with his clients; total fund expenses are 10-15 basis points, with Pabrai’s salary and staff paid for out of performance fees
  • Shorting makes no sense because maximum upside is a double and maximum downside is bankruptcy
  • Do not talk to company management because they are high charisma sales people and will pitch you on optimism, not realism
  • Big fan of the Checklist Manifesto, has a checklist of 80 items he looks over before making an investment
  • Pioneers are the people who get filled with arrows

Review – More Money Than God (#hedgefunds)

More Money Than God: Hedge Funds And The Making Of A New Elite

by Sebastian Mallaby, published 2010

A veritable pantheon of masters of the universe

Mallaby’s book is not just an attempt at explaining and defending the beginning, rise and modern state of the hedge fund industry (the US-focused part of it, anyway), but is also a compendium of all of the hedge fund world’s “Greatest Hits.” If you’re looking for information on what hedge funds are, where they come from, what they attempt to do, why they’re called what they are and how they should be regulated (SURPRISE! Mallaby initially revels in the success “unregulated” funds have had and feints as if he’s going to suggest they not be regulated but, it being a CFR book and he being a captured sycophant, he does an about-face right at the last second and ends up suggesting, well, umm, maybe SOME of the hedge funds SHOULD be regulated, after all) this is a decent place to start.

And if you want to gag and gog and salivate and hard-to-fathom paydays and multiple standard deviations away from norm profits, there are many here.

But that wasn’t my real interest in reading the book. I read it because I wanted to get some summary profiles of some of the most well known hedgies of our time — the Soroses and Tudor Joneses and such — and understand what their basic strategies were, where their capital came from, how it grew and ultimately, how they ended up. Not, “What’s a hedge fund?” but “What is this hedge fund?” As a result, the rest of this review will be a collection of profile notes on all the BSDs covered by the book.

Alfred Winslow Jones – “Big Daddy”

  • started out as a political leftist in Europe, may have been involved in U.S. intelligence operations
  • 1949, launches first hedge fund with $60,000 from four friends and $40,000 from his own savings
  • By 1968, cumulative returns were 5,000%, rivaling Warren Buffett
  • Jones, like predecessors, was levered and his strategy was obsessed with balancing volatilities, alpha (stock-picking returns) and beta (passive market exposure)
  • Jones pioneered the 20% performance fee, an idea he derived from Phoenician merchants who kept one fifth of the profits of successful voyages; no mgmt fee
  • Jones attempted market timing as a strategy, losing money in 1953, 1956 and 1957 on bad market calls; similarly, he never turned a profit following charts even though his fund’s strategy was premised on chartism
  • Jones true break through was harvesting ideas through a network of stock brokers and other researchers, paying for successful ideas and thereby incentivizing those who had an edge to bring him their best investments
  • Jones had information assymetry in an era when the investment course at Harvard was called “Darkness at Noon” (lights were off and everyone slept through the class) and investors waited for filings to arrive in the mail rather than walk down the street to the exchange and get them when they were fresh

Michael Steinhardt – “The Block Trader”

  • Background: between end of 1968 and September 30, 1970, the 28 largest hedge funds lost 2/3 of their capital; January 1970, approx. 150 hedge funds, down from 200-500 one year earlier; crash of 1973-74 wiped out most of the remainders
  • Steinhardt, a former broker, launches his fund in 1967, gained 12% and 28% net of fees in 1973, 74
  • One of Steinhardt’s traders, Cilluffo, who possessed a superstitious eating habit (refused to change what he ate for lunch when the firm was making money), came up with the idea of tracking monetary data, giving them an informational edge in an era where most of those in the trade had grown up with inflation never being higher than 2% which meant they ignored monetary statistics
  • One of Steinhardt’s other edges was providing liquidity to distressed institutional sellers; until the 1960s, stock market was dominated by individual investors but the 1960s saw the rise of institutional money managers; Steinhardt could make a quick decision on a large trade to assist an institution in a pinch, and then turn around and resell their position at a premium
  • Steinhardt’s block trading benefited from “network effects” as the more liquidity he provided, the more he came to be trusted as a reliable liquidity provider, creating a barrier to entry for his strategy
  • Steinhardt also received material non-public information: “I was being told things that other accounts were not being told.”
  • In December 1993, Steinhardt made $100M in one day, “I can’t believe I’m making this much money and I’m sitting on the beach” to which his lieutenants replied “Michael, this is how things are meant to be” (delusional)
  • As the Fed lowered rates in the early 90s, Steinhardt became a “shadowbank”, borrowing short and lending long like a bank
  • Steinhardt’s fund charged 1% mgmt fee and 20% performance fee
  • Anecdote: in the bloodbath of Japan and Canada currency markets in the early 90s, the Canadian CB’s traders called Steinhardt to check on his trading (why do private traders have communications with public institutions like CBs?)

Paul Samuelson & Commodities Corporation – “Fiendish Hypocrite Jackass” (my label)

  • Paul Samuelson is one of history’s great hypocrites, in 1974 he wrote, “Most portfolio decision makers should go out of business– take up plumbing, teach Greek, or help produce the annual GNP by serving corporate executives. Even if this advice to drop dead is good advice, it obviously is not counsel that will be eagerly followed.”
  • Meanwhile, in 1970 he had become the founding backer of Commodities Corporation and also investing in Warren Buffett; he funded his investment in part with money from his Nobel Prize awarded in the same year
  • Samuelson paid $125,000 for his stake; total start-up capital was $2.5M
  • Management of fund resembled AW Jones– each trader was treated as an independent profit center and was allocated capital based on previous performance
  • Part of their strategy was built on investor psychology: “People form opinions at their own pace and in their own way”; complete rejection of EMH, of which Samuelson was publicly an adherent
  • Capital eventually swelled to $30M through a strategy of primarily trend-surfing on different commodity prices; in 1980 profits were $42M so that even net of $13M in trader bonuses the firm outearned 58 of the Fortune 500
  • Trader Bruce Kovner on informational assymetries from chart reading: “If a market is behaving normally, ticking up and down within a narrow band, a sudden breakout in the absence of any discernible reason is an opportunity to jump: it means that some insider somewhere knows information that the market has yet to understand, and if you follow that insider you will get in there before the information becomes public”

George Soros – “The Alchemist”

  • Soros had an investment theory called “reflexivity”: that a trend could feedback into itself and magnify until it became unavoidable, usually ending in a crash of some sort
  • Soros launched his fund in 1973, his motto was “Invest first, investigate later”
  • Soros quotes: “I stood back and looked at myself with awe: I saw a perfectly honed machine”; “I fancied myself as some kind of god or an economic reformer like Keynes”
  • Soros was superstitious, he often suffered from back pains and would “defer to these physical signs and sell out his positions”
  • Soros believed in generalism: know a little about a lot of things so you could spot places where big waves were coming
  • Soros had a “a web of political contacts in Washington, Tokyo and Europe”
  • Soros hired the technical trader Stan Druckenmiller, who sometimes read charts and “sensed a panic rising in his gut”
  • As Soros’s fund increased in size he found it harder and harder to jump in and out of positions without moving the markets against himself
  • Soros rejected EMH, which had not coincidentally developed in the 1950s and 1960s in “the most stable enclaves within the most stable country in the most stable era in memory”
  • Soros was deeply connected to CB policy makers– he had a one on one with Bundesbank president Schlesinger in 1992 following a speech he gave in Basel which informed Quantum fund’s Deutschemark trade
  • “Soros was known as the only private citizen to have his own foreign policy”; Soros once off-handedly offered Druckenmiller a conversation with Kissinger who, he claimed, “does know things”
  • Soros hired Arminio Fraga, former deputy governor of Brazil’s central bank, to run one of his funds; Fraga milked connections to other CB officials around the world to find trade ideas, including the number two official at the IMF, Stanley Fischer, and a high-ranking official at the central bank of Hong Kong
  • Soros was a regular attendee at meetings of the World Bank and IMF
  • Soros met Indonesian finance minister Mar’ie Muhammed at the New York Plaza hotel during the Indonesian financial crisis
  • Soros traveled to South Korea in 1998 as the guest of president-elect Kim Dae-jung
  • In June 1997, Soros received a “secret request” for emergency funding from the Russian government, which resulted in him lending the Russian government several hundred million dollars
  • Soros also had the ear of David Lipton, the top international man at the US Treasury, and Larry Summers, number 2 at the Treasury, and Robert Rubin, the Treasury secretary, as well as Mitch McConnell, a Republican Senator

Julian Robertson – “Top Cat”

  • Managed a portfolio of money managers, “Tigers”
  • Used fundamental and value analysis
  • Once made a mental note to never buy the stock of an executive’s company after watching him nudge a ball into a better position on the golf green
  • Robertson was obsessed with relative performance to Soros’s Quantum Fund
  • Called charts “hocus-pocus, mumbo-jumbo bullshit”
  • Robertson didn’t like hedging, “Why, that just means that if I’m right I’m going to make less money”
  • High turnover amongst analysts, many fired within a year of hiring
  • Tiger started with $8.5M in 1980
  • A 1998 “powwow” for Tiger advisers saw Margaret Thatcher and US Senator Bob Dole in attendance
  • Tiger assets peaked in August 1998 at $21B and dropped to $9.5B a year later, $5B of which was due to redemptions (Robertson refused to invest in the tech bubble)

Paul Tudor Jones – “Rock-And-Roll Cowboy”

  • Jones started out as a commodity trader on the floor of the New York Cotton Exchange; started Tudor Investment Corporation in 1983, in part with an investment of $35,000 from Commodities Corporation
  • “He approached trading as a game of psychology and high-speed bluff”
  • Superstition: “These tennis shoes, the future of this country hangs on them. They’ve been good for a point rally in bonds and about a thirty-dollar rally in stocks every time I put them on.”
  • Jones was a notorious chart reader and built up his theory of the 1987 crash by lining up recent market charts with the 1929 chart until the lines approximately fit
  • Jones was interested in Kondratiev wave theory and Elliott wave theory
  • “When you take an initial position, you have no idea if you are right”but rather you “write a script for the market” and then if the market plays out according to your script you know you’re on the right track
  • Jones made $80-100M for Tudor Investment Corp on Black Monday; “The Big Three” (Soros, Steinhardt and Robinson) all lost heavily in the crash
  • Jones, like Steinhardt, focused on “institutional distortions” where the person on the other side of the trade was a forced seller due to institutional constraints
  • Jones once became the catalyst for his own “script” with an oil trade where he pushed other traders around until they panicked and played out just as he had predicted
  • PTJ never claimed to understand the fundamental value of anything he traded
  • PTJ hired Sushil Wadhwani in 1995, a professor of economics and statistics at the LSE and a monetary policy committee member at the Bank of England
  • PTJ’s emerging market funds lost 2/3rd of their value in the aftermath of the Lehman collapse

Stanley Druckenmiller – “The Linebacker” (my title)

  • Druckenmiller joined Soros in 1988; while Soros enjoyed philosophy, Druckenmiller enjoyed the Steelers
  • He began as an equity analyst at Pittsburgh National Bank but due to his rapid rise through the ranks he was “prevented from mastering the tools most stock experts take for granted” (in other words, he managed to get promoted despite himself, oddly)
  • Survived crash of 1987 and made money in the days afterward
  • Under Druckenmiller, Quantum AUM leaped from $1.8B to $5B to $8.3B by the end of 1993
  • Druckenmiller stayed in touch with company executives
  • Druckenmiller relied on Robert Johnson, a currency expert at Bankers Trust, whose wife was an official at the New York Fed, for currency trade ideas; Johnson himself had once worked on the Senate banking committee and he was connected to the staff director of House Financial Services Committee member Henry Gonzalez
  • Druckenmiller was also friends with David Smick, a financial consultant with a relationship with Eddie George, the number 2 at the Bank of England during Soros and Druckenmiller’s famous shorting of the pound
  • Druckenmiller first avoided the Dot Com Bubble, then jumped aboard at the last minute, investing in “all this radioactive shit that I don’t know how to spell”; he kept jumping in and out until the bubble popped and he was left with egg on his face, ironic because part of his motivation in joining in was to avoid losing face; Druckenmiller had been under a lot of stress and Mallaby speculates that “Druckenmiller had only been able to free himself by blowing up the fund”

David Swensen & Tom Steyer – “The Yale Men”

  • Swensen is celebrated for generating $7.8B of the $14B Yale endowment fund
  • Steyer and his Farallon fund were products of Robert Rubin’s arbitrage group at Goldman Sachs; coincidence that Rubin proteges rose to prominence during the time Rubin was in the Clinton administration playing the role of Treasury secretary?
  • Between 1990 and 1997 there was not a single month in which Steyer’s fund lost money (miraculous)
  • Farallon somehow got access to a government contact in Indonesia who advised Bank Central Asia would be reprivatized soon and Farallon might be able to bid for it
  • Some rumors claimed Farallon was a front for the US government, or a Trojan horse for Liem Sioe Liong (a disgraced Indonesian business man); it is curious that Yale is connected to the CIA, Farrallon is connected to Yale

Jim Simons & Renaissance Capital – “The Codebreakers”

  • Between the end of 1989 and 2006, the flagship Medallion fund returned 39% per annum on average (the fund was named in honor of the medals Simons and James Ax had won for their work in geometry and number theory– named in honor of an honor, in other words)
  • Jim Simons had worked at the Pentagon’s secretive Institute for Defense Analyses (another possible US intelligence operative turned hedgie?)
  • Simons strategy was a computer-managed trend following system which had to be continually reconfigured due to “Commodities Corporation wannabes” crowding the trades by trending the trends
  • Simons looked to hire people who “would approach the markets as a mathematical puzzle, unconnected to the flesh and blood and bricks and mortar of a real economy” (this is distinctly different than the Graham/Buffett approach, and one wonders how this activity is actually economically valuable in a free market)
  • “The signals that we have been trading without interruption for fifteen years make no sense. Otherwise someone else would have found them.”
  • Renaissance treated employee NDAs like a wing of the CIA– anyone who joined could never work elsewhere in the financial industry afterward, and for this reason they specifically avoided hiring from Wall St in the first place; they were required to invest a fifth of their pay in the Medallion Fund and was locked up as bail payment for four years after they departed (money hostage)

David Shaw & D.E. Shaw

  • Began trading in 1988, the same year as the Medallion fund
  • Shaw was originally hired by MoStan in 1986 into their Analytical Proprietary Trading unit which aimed at beating Steinhardt at his block-trading game using predictive computer technology
  • In 1994, Shaw’s 135-member firm accounted for 5% of the daily turnover on the NYSE
  • Jeff Bezos, of Amazon, was originally a DE Shaw employee
  • The strategy was heavily reliant on pair-trade “arbitrage”, looking for securities in similar industries which were temporarily misaligned in price/multiple
  • Circle of competence: in 1995 the firm launched the ISP Juno Online, as well as FarSight, an online bank and brokerage venture

Ken Griffin & Citadel

  • Created in 1990, grew to $15B AUM and 1400 employees by 2008
  • Griffin’s goal was to develop an investment bank model that could compete with traditional, regulated ibanks, but which was actually a hedge fund
  • Flagship funds were down 55% at the end of 2008, losing $9B (the equivalent of two LTCMs)

John Paulson

  • Paulson graduated from HBS in 1980 and went to work for Bear Stearns; he launched his hedge fund in 1994 with initial capital of $2M which grew to $600M by 2003; by 2005 he was managing $4B
  • Paulson’s main strategy was capital-structure arbitrage
  • He looked for “capitalism’s weak spot”, the thing that would blow up the loudest and fastest if the economy slowed even a little; cyclical industries, too much debt, debt sliced into senior and junior tranches, risk concentrated
  • Paulson spent $2M on research related to the US mortgage industry, assembling a proprietary database of mortgage figures and statistics
  • Many of Paulson’s investors doubted him and threatened to pull capital in 2006
  • Paulson enlarged his bets against the mortgage market through derivative swaps on the ABX (a new mortgage index) and eventually acquired over $7.2B worth of swaps; a 1% decline in the ABX earned Paulson a $250M profit, in a single morning he once netted $1.25B
  • By 2007, he was up 700% net of fees, $15B in profits and made himself $3-4B


I’m actually even more bored with this book having finished typing out my notes than I was when I finished the book the first time I read it. The book actually has some great quotes in it, from the insane delusions of grandeur of government officials and central bank functionaries, to wild facts and figures about the statistical trends of the hedge fund and financial industries over the last 60 years. I am too exhausted to go back and type some of it out right here even though I kind of wish I had some of the info here even without an idea of what I’d use it for anytime soon.

My biggest takeaway from MMTG is that most of these masters of the universe have such huge paydays because they use leverage, not necessarily because they’re really good at what they do. Many of their strategies actually involve teasing out extremely small anomalies between asset prices which aren’t meaningful without leverage. And they’re almost uniformly without a meaningful and logically consistent understanding of what risk is– though many are skeptics of EMH, they seem to all see risk as volatility because volatility implies margin calls for levered traders.

There were so many displays of childish superstition. Many of these guys are chart readers. The government intelligence backgrounds of many was creepy. And it was amazing how many relied on informational asymmetries which are 100% illegal for the average investor. These people really travel in an elite, secretive world where everyone is scratching each other’s backs. How many one on one conversations have you had with central bank presidents? How many trips to foreign countries have you been on where you were the invited guest of the head dignitary of the country? Are you starting to put the picture together like I am?

Overall, it seems so arbitrary. The best word that comes to mind to describe these titans and their success is– “marginalism”. We have lived in an inflationary economy for the last 60+ years and these players all seem to excel in such an environment. But inflationism promotes marginalism; the widespread malinvestment of perpetual inflation confuses people looking to engage in real, productive economic activity, and paper shuffling necessarily becomes a high value business.

The author himself is incredibly ignorant of economic fundamentals and the role monetary intervention plays in the economy. All of the various crises these hedgies profited from seem to come out of nowhere according to his narrative. The incredible growth in volumes of money managed by the hedge fund industry over time goes without notice, as if it was just a simple, unexceptional fact of life. Shouldn’t that be interesting? WHY ARE THERE HUNDREDS OF FIRMS MANAGING TENS OF BILLIONS OF DOLLARS EACH? Where did all this money come from?!

That makes the book pretty worthless as it’s key.

One thing that does strike me is that many of the most successful, most levered trades of Soros, Druckenmiller and others were related to currencies. These guys are all Keynesians but they probably don’t fully believe their own economic theories. However, they do understand them well enough to make huge plays against the dope money managers who DO put all their credence into what they learned at university. I should think an Austrian econ-informed large cap macro fund would have quite a time of it playing against not only the dopes, but the Soroses of the world– they’ll get their final comeuppance as this system of artificial fiat exchange finally unwinds over the next decade.

And, little surprise, the guy with the nearly perfect trading record for almost a decade (Farrallon) was involved in arbitrage trades.

Trend following is for slaves. It may have proven to be a profitable strategy (with gobs of leverage) for the contemporary crop of hedgies but I feel fairly confident in saying most of these guys will get hauled out behind the woodshed in due time if they keep it up, to the extent their strategies truly are reliant on mystic chart reading and nothing more.

Bon voyage!


Lessons in Short Selling: Why Jim Chanos Targeted Enron

I saw this testimony, delivered to Congress February 6, 2002, by Jim Chanos on his decision to short Enron before it collapsed, posted over at John Chew’s Case Study Investing. I enjoyed reading it and thought it was worth commenting on as a kind of basic guide to short-selling– why and how. This testimony is a Warren Buffett-style (and quality) lesson on short-selling fundamentals.

How To Identify A Short-Sell Opportunity

Kynikos Associates selects portfolio securities by conducting a rigorous financial analysis and focusing on securities issued by companies that appear to have (1) materially overstated earnings (Enron), (2) been victims of a flawed business plan (most internet companies), or (3) been engaged in outright fraud.

Three key factors to look for in a short-sell:

  1. Overstated earnings
  2. Flawed business model (uneconomic activity)
  3. Fraud

As with the Enron fiasco, Chanos first became interested when he read a WSJ article that discussed Enron’s aggressive accounting practices. Aggressive, confusing, archaic or overly technical accounting practices are often a potential red-flag that could identify a company which is not actually as profitable as it appears to be to other market participants. When this profitability if revealed to be illusory later on, a catalyst is in place to galvanize investors into mass selling.

Another factor which can create an opportunity for a short is when the company has a flawed business model which essentially means the company is engaged in uneconomic activity. Short of government subsidies and other protective regulations, the market place tends to punish uneconomic (wasteful, that is, unproductive) activity with the tool of repeated and mounting economic losses until the offending individual or firm’s resources are exhausted and they must declare bankruptcy and liquidate their assets into the hands of more able owners. Chanos gives the example of tech bubble companies which never managed to achieve operating profitability– their business models were nothing more than exciting ideas, unable to overcome the reality check of achieving business profit.

The last type of short Chanos describes is general fraud– a company claims to own assets it does not own, or it is subject to liabilities and debts it has not disclosed, or there is an act of corruption or embezzlement amongst employees or managers of the business. Recent examples could be found in the growing “China short” sub-culture of financial research and hedge fund activity, such as the Sino Forest company which did not have thousands of acres of productive timberland it claimed to own.

The Enron “Case Study”

Returning to the Enron example, Chanos discloses three suspicious facts he and his firm uncovered through perusal of public financial disclosures that got them thinking about shorting Enron:

The first Enron document my firm analyzed was its 1999 Form 10-K filing, which it had filed with the U.S. Securities and Exchange Commission. What immediately struck us was that despite using the “gain-on-sale” model, Enron’s return on capital, a widely used measure of profitability, was a paltry 7% before taxes. That is, for every dollar in outside capital that Enron employed, it earned about seven cents. This is important for two reasons; first, we viewed Enron as a trading company that was akin to an “energy hedge fund.” For this type of firm a 7% return on capital seemed abysmally low, particularly given its market dominance and accounting methods. Second, it was our view that Enron’s cost of capital was likely in excess of 7% and probably closer to 9%, which meant, from an economic cost point-of-view, that Enron wasn’t really earning any money at all, despite reporting “profits” to its shareholders. This mismatch of Enron’s cost of capital and its return on investment became the cornerstone for our bearish view on Enron and we began shorting Enron common stock in November of 2000.

Chanos essentially did a competitive analysis on Enron and concluded that Enron was underperforming its competitors in the energy trading arena, despite large size and market dominance. He also concluded that its returns appeared uneconomic because they did not cover costs (capital), implying the company was  consuming capital rather than generating it.

We were also troubled by Enron’s cryptic disclosure regarding various “related party transactions” described in its 1999 Form 10-K as well as the quarterly Form 10-Qs it filed with the SEC in 2000 for its March, June and September quarters. We read the footnotes in Enron’s financial statements about these transactions over and over again but could not decipher what impact they had on Enron’s overall financial condition. It did seem strange to us, however, that Enron had organized these entities for the apparent purpose of trading with their parent company, and that they were run by an Enron executive. Another disturbing factor in our review of Enron’s situation was what we perceived to be the large amount of insider selling of Enron stock by Enron’s senior executives. While not damning by itself, such selling in conjunction with our other financial concerns added to our conviction.

Importantly, Chanos notes that it was not the insider selling alone, but within the context of other suspicious activity, that concerned him. Often executives and insiders sell for personal liquidity reasons (buying a new home, sending kids to college, buying a boat, etc.) and some observers necessarily conclude this means foul play or that the insider knows the Titanic is about to hit an iceberg.

More common with smaller companies where management and ownership are often synonymous, related-party dealings are always something to be skeptical about and almost never are harmless in the context of multi-billion dollar public corporations.

Finally, we were puzzled by Enron’s and its supporters boasts in late 2000 regarding the company’s initiatives in the telecommunications field, particularly in the trading of broadband capacity. Enron waxed eloquent about a huge, untapped market in such capacity and told analysts that the present value of Enron’s opportunity in that market could be $20 to $30 per share of Enron stock. These statements were troubling to us because our portfolio already contained a number of short ideas in the telecommunications and broadband area based on the snowballing glut of capacity that was developing in that industry. By late 2000, the stocks of companies in this industry had fallen precipitously, yet Enron and its executives seemed oblivious to this! Despite the obvious bear market in telecommunications capacity, Enron still saw a bull market in terms of its own valuation of the same business — an ominous portent.

Again, Chanos and his firm were able to see the Enron picture more clearly by comparing it to the competitive landscape as a whole. How much validity does a firm’s claims possess when looked at in the context of the wider industry (or economy), rather than just its own dreams and/or delusions?

Throughout the rest of the testimony, we learn a few other interesting details about the development of his short thesis concerning Enron: the use of Wall Street analysts for sentiment feedback, the analysis of additional qualitative data for confirming target company statements and the use of conferences and investor communications networks to spread an idea and generate critical investor momentum.

Chanos also shares this helpful Wall Street axiom:

It is an axiom in securities trading that, no matter how well “hedged” a firm claims to be, trading operations always seem to do better in bull markets and to struggle in bear markets.

An important reminder for considering all business strategies which require positive momentum (ie, Ponzi schemes) to work.

More telling than insider selling, in Chanos’ mind, is management departures, change ups and board reshufflings:

In our experience, there is no louder alarm bell in a controversial company than the unexplained, sudden departure of a chief executive officer no matter what “official” reason is given.

In the case of Enron, the executive to depart was Enron CEO Jeff Skilling who was considered to be the “chief architect” of the company’s controversial trading program. His absence meant not only that Enron was potentially a ship without a rudder, but that the captain had found a leak and was jumping overboard with the rats before everyone else figured it out.

In Summary

To summarize the lessons of the Enron case, good shorts usually involve at least one or more of the following: questionable earnings, uneconomic business models and/or fraud.

Accomplished short-sellers look for clues suggesting the presence of the above factors by reading between the lines in public financial disclosures and major news stories. They use social signaling clues like surveying Wall Street analysts and other market participants to gauge sentiment, which is a contrarian tool for discovering whether controversial information they are aware of is likely priced into the market or not. They engage in competitive analysis to judge whether the target firm’s claims are credible and reasonable. They watch the activity of insiders, specifically unanticipated departures of key staff, for confirmation of their thesis. They anticipate stressors to a firm’s business model which might serve as catalysts for revealing the precarious state of a firm’s business to other market participants.

Finally, and perhaps most importantly, they never take the price of the shorted security going against them as evidence that they are wrong and they add to their position as their conviction rises with new evidence of weakness or trouble for the target firm.

As Ben Graham would observe, in the short term the market is a voting machine and it’s common for those who are responsible for a fraud or dying business to cheerlead the market out of desperation. And as Chanos himself observed,

While short sellers probably will never be popular on Wall Street, they often are the ones wearing the white hats when it comes to looking for and identifying the bad guys!