Category Archives: Investor Psychology

Quantitative Blow-Ups: We are all doing the same thing.

“We Are All Doing The Same Thing”

I recently listened to a podcast with some all-star [there are awards for everything now] “Black Box” equity trader. It was quite a “telling” interview & I thank him for his insights but I’d heard it all before. His confidence was staggering considering the general unpredictability of the future and, of course, the equity markets. He explained how he had completely converted from a generally unsuccessful, discretionary technical trading style to a purely quantitative and scientific trading mode. He seemed to be so excited that his models, according to him, were pretty much “bullet proof”. Having had more than just some tangential experience with black box modeling and trading myself I thought … you know … some people will just never learn.



Image via


You see some years ago I was particularly focused on quantitative investing. Basically, “screw” the fundamentals and exclusively concentrate on price trends/charts and cross security/asset correlations [aka “Black Box” trading]. I was fascinated with the process and my results were initially stellar [high absolute returns with Sharpe Ratios > 2.0]. And, after looking at the regression data many others agreed. I was in high demand. So I “made the rounds” in Manhattan and Greenwich to a group of high profile hedge funds. It was a very exciting time for me as the interest level was significant.

As it turned out I had the good fortune of working with one of the world’s largest and best performing hedge funds. Their black box modeling team had been at it for years … back-testing every conceivable variable from every perceived angle … twisted/contorted in every conceivable/measurable manner … truly dedicated to the idea that regression tested, quantitative trading models were the incremental/necessary “edge” to consistently generate alpha while maximizing risk-adjusted, absolutely positive returns. We worked together for some time and I became intimately involved with their quantitative modeling/trading team … truly populated with some of the best minds in the business.

While in Greenwich Ct. one afternoon I will never forget a conversation I had with a leading quantitative portfolio manager. He said to me that despite its obvious attributes “Black Box” trading was very tricky. The algorithms may work for a while [even a very long while] and then, inexplicably, they’ll just completely “BLOW-UP”. To him the most important component to quantitative trading was not the creation of a good model. To him, amazingly, that was a challenge but not especially difficult. The real challenge, for him, was to “sniff out” the degrading model prior to its inevitable “BLOW-UP”.

And I quote his humble, resolute observation:


because, you know, eventually they ALL blow-up


… as most did in August 2007.

It was a “who’s who” of legendary hedge fund firms that had assembled “crack” teams of “Black Box” modelers: Citadel, Renaissance, DE Shaw, Tudor, Atticus, Harbinger and so many Tiger “cubs” including Tontine [not all strictly quantitative but, at least, dedicated to the intellectual dogma] … all preceded by Amaranth in 2006 and the legendary Long Term Capital Management’s [picking up pennies in front of a steam-roller] demise one decade earlier.

Years of monthly returns with exceedingly low volatility were turned “inside out” in just 4-6 weeks as many funds suffered monthly losses > 20% which was previously considered highly improbable and almost technically impossible … and, voilà … effectively, a sword was violently thrust through the heart of EVERY “Black Box” model. VaR and every other risk management tool fell victim to legitimate liquidity issues, margin calls and sheer human panic.

Many of these firms somehow survived but only by heavily gating their, previously lightly-gated, quarterly liquidity provisions. Basically, as an investor, you could not “get out” if you wanted to. These funds changed the liquidity rules to suit their own needs … to survive … though many did fail.

Anyway … to follow up on my dialogue with the esteemed portfolio manager … I asked why do they all “BLOW-UP”? What are those common traits that seem to effect just about every quantitative model despite the intellectual and capital fire-power behind them? And if they all eventually “BLOW-UP” then why are we even doing this?”

He answered the second part of the question first … and I paraphrase …


“We are all doing this because we can all make a lot of money BEFORE they “BLOW-UP”. And after they do “BLOW-UP” nobody can take the money back from us.” He then informed me why all these models actually “BLOW-UP”. “Because despite what we all want to believe about our own intellectual unique-ness, at its core, we are all doing the same thing. And when that occurs a lot of trades get too crowded … and when we all want to liquidate [these similar trades] at the same time … that’s when it gets very ugly.“.


I was so naive. He was so right.


Exactly What Were We All Doing?

We all knew what the leaders wanted and, of course, we wanted to please them. Essentially they wanted to see a model able to generate 4-6% annual returns [seems low, I know, but I’ll address that later] … with exceptionally low volatility, slim draw-down profiles and winning months outweighing the losing months by about 2:1. They also wanted to see a model trading exceptionally liquid securities [usually equities].

Plus the model, itself, had to be completely scientific with programmable filtering and execution [initiation and liquidation] features so that it could be efficiently applied and, more importantly, stringently back-tested and stress-tested . Long or short did not really matter. Just make money within the parameters. Plus, the model had to be able to accommodate at least $100M [fully invested most of the time as cash was not an option] and, hopefully, much more capital. This is much easier said than done but, given the brainpower and financial resources, was certainly achievable.


This is what all the “brainpower” learned … eventually.

First of all, a large number of variables in the stock selection filter meaningfully narrowed the opportunity set … meaning, usually, not enough tickers were regularly generated [through the filter] to absorb enough capital to tilt the performance meter at most large hedge funds…as position size was very limited [1-2% maximum]. The leaders wanted the model to be the hero not just a handful of stocks. So the variables had to be reduced and optimized. Seemingly redundant indicators [for the filter] were re-tested and “tossed” and, as expected, the reduced variables increased the population set of tickers … but it also ramped the incremental volatility … which was considered very bad. In order to re-dampen the volatility capital limits on portfolio slant and sector concentration, were initiated. Sometimes market neutral but usually never more than net 30% exposure in one direction and most sectors could never comprise more than 5% of the entire portfolio. We used to joke that these portfolios were so neutered that it might be impossible for them to actually generate any meaningfully positive returns. At the time of “production” they actually did seem, at least as a model, “UN-BLOW-UP-ABLE” considering all the capital controls, counter correlations and redundancies.

Another common trait of these models that was that, in order to minimize volatility, the holding periods had to be much shorter than a lot of us had anticipated. So execution [both initiation and liquidation] became a critical factor. Back then a 2-3 day holding period was considered acceptable, but brief, although there were plenty of intra-day strategies … just not at my firm … at least not yet. With these seemingly high velocity trading models [at the time], price slippage and execution costs, became supreme enemies of forecasted returns. To this day the toughest element to back-testing is accessing tick data and accurately pinpointing execution prices. Given this unpredictability, liberal price slippage was built into every model … and the model’s returns continued to compress … not to mention the computer time charges assessed by the leadership [which always pissed me off].

So, given all of this, what types of annual returns could these portfolios actually generate? A very good model would generate a net 5-7% … but 3.5-5% was acceptable too. So how then could anybody make any real money especially after considering the labor costs to construct/manage/monitor these models … which … BTW … was substantial?

Of course there was only one real way … although it would incrementally cut into performance even more, in the near term, but ultimately pay off if the “live” model performed as tested. The answer = LEVERAGE … and I mean a lot of it … as long as the volatility was low enough.

Back-tested volatility was one thing and live model volatility was another thing so leverage was only, ever so slowly, applied … but as time passed and the model performed, the leverage applied would definitely increase. Before you knew it those 5% returns were suddenly 20-25% returns as the positive beauty of leverage [in this case 4-5x] was unleashed.

Fast forward 10 years and the objectives of hedge funds are still the same. Generate positive absolute returns with low volatility … seeking the asymmetrical trade … sometimes discretionary but in many cases these “Black Box” models still proliferate. And BTW … they are all “doing the same thing“ as always … current iteration = levered “long” funds.

What has changed though is the increased dollars managed by these funds [now > $3.5T] and the concentration, of these dollars, at the twenty largest funds [top heavy for sure]. What has also considerably changed is the cost of money … aka leverage. It is just so much cheaper … and, of course, is still being liberally applied but, to reiterate, in fewer hands.

Are these “hands” any steadier than they were ten years ago? I suppose that is debate-able but my bet is that they are not. They are still relying on regression-ed and stress tested data from the past [albeit with faster computers & more data]. They may even argue that their models are stronger due to the high volatility markets of ’08/’09 that they were able to survive and subsequently measure, test and integrate into their current “Black Boxes” … further strengthening their convictions … which is the most dangerous aspect of all.

Because …

  1. Strong Conviction … aka Over Confidence +
  2. Low Volatility +
  3. High Levels/Low Costs of Leverage [irrespective of Dodd-Frank] +
  4. More Absolute Capital at Risk +
  5. Increased Concentration of “At Risk” Capital +
  6. “Doing the Same Thing”

… Adds up to a Combustible Market Cocktail.

Still a catalyst is needed and, as always, the initial catalyst is liquidity [which typically results in a breakdown of historic correlations as the models begin to “knee-bend” … and the perceived safety of hedges is cast in doubt] followed by margin calls [the ugly side of leverage … not to mention a whole recent slew of ETF’s that are plainly levered to begin with that, with the use of borrowed money, morph into “super-levered” financial instruments] and concluding with the ever ugly human panic element [in this case the complete disregard for the “black box” models even after doubling/trebling capital applied on the way down because the “black box” instructed you to]. When the “box” eventually gets “kicked to the curb” … that is when the selling ends … but not after some REAL financial pain.

So can, and will, this really occur once again? It can absolutely occur but it is just impossible to know exactly when … although there are plenty of warning signs suggesting its likelihood … as there have been for some time.

However, I am quite confident of the following:

  1. The Fed Is Clueless On All Of This
    [just too many moving parts for them … they cannot even coordinate a simple “cover-up” of leaked monetary policy … so no way the academics can grasp any of this … just liked they missed LTCM. Nor it seems…do they have any interest in it as it is too tactical for a strategically inclined central banker].
  2. Eventually They ALL Blow-Up
  3. The Only Real Question Left To Answer =
    HOW BIG CAN THIS POSSIBLE “BLOW-UP” ACTUALLY BE? I think … if the pieces simultaneously shudder … then pretty BIG


Blame Management and Human Sacrifice


wmc150615b   Never in market history has the MEDIAN stock been so highly priced relative to valuation metrics.












Buy the Accused, Sell the Acclaimed

Going back to behavioral finance and what causes cheapness: Blaming Management and Human Sacrifice

Buying Distress and Falling Knives or the “No Hope” Portfolio.

Symbol Company Price Shares Amount Comm. Total Date
CLD Cloud Peak Energy 4.66 1071  $ 4,991 8  $ 4,999 6/14/2015
BTU Peabody Energy 2.47 2020  $ 4,989 8  $   4,997 6/14/2015
CNX Consol Energy 25.51 196  $5,000 8  $   5,008 6/14/2015
Completed 1:14 EST  $15,004.00 6/14/2015

I am buying a basket of coal miners today with different characteristics. CNX is not a pure coal company. Peabody is more of a stub stock since its market cap is about 1/10th its debt–scary!  I will add on a further 25% decline.  Holding period: five years.  I recommend that anyone reading this NOT follow me. Do your own thinking or mis-thinking as the case may be.

We also don’t want to confuse low nominal prices or declining prices with “cheapness.” There are plenty of junior resource stocks trading for pennies that are worth $0.  Microcap_stock_fraud

Lot’s of hope:

Better when there is no hope:btu

Catch a Falling Knife

half full

We left off at Regression to the Mean Part II here:

We skip Chapter Six (for now) and focus on Chapter 7 in DEEP VALUE: Catch a Falling Knife: The Anatomy of a Contrarian Value Strategy

In Search of Un-Excellence

The authors identified 36 publicly traded “excellent companies” on the basis of out-performance in six criteria, measured from 1961 to 1980.

  1. Asset growth
  2. Equity growth
  3. Return on total capital
  4. Return on equity
  5. Return on sales
  6. Market to book value

Then an investment analysts, Michelle Clayman, identified 39 publicly traded “un-excellent companies” which ranked in the bottom third of all Peters and Waterman’s criteria from 1976 to 1980.   These “in search of disaster” companies outperformed 24.4% pa over five years vs. 12.7% for the “excellent” companies.

The good companies under-perform because the market overestimates their future growth and future return on equity and, as a result, accords the stocks overvalued price-to-book ratios; the converse is true of the poor companies.

Over time, company results have a tendency to regress to the mean as underlying economic forces attract new entrants to attractive markets and encourage participants to leave low-return businesses. Because of this tendency, companies that have been good performers in the past may prove to be inferior investments, while poor companies frequently provide superior investment returns in the future.”

Note pages 128 to 136 in DEEP VALUE: Tables 7.1 to 7.9

Stocks in the Contrarian Value portfolios were cheaper than the comparable Glamour portfolios on every metric but on a Price-to-Earnings basis, possibly because the earnings in those portfolios were so weak.

First, valuation is more important than growth in constructing portfolios.

Cheap, low growth portfolios systematically outperform expensive, high-growth portfolios, and by wide margins.  It seems that the uglier the stock, the better the return, even when the valuations are comparable. Oppenheimer found in a study on Ben Graham Net/Nets that loss making and non-dividend paying net/nets outperform profitable, dividend-paying net/nets. Ben Graham Net Current Asset Values A Performance Update

In almost any study, the cheap, hated, ugly, least-admired, and poorly performing stock outperforms the high-growth, glamour stocks.

What these studies demonstrate is that mean reversion is a pervasive phenomenon, and one that we don’t intuitively recognize. Our untrained instinct is to pursue the glamorous stock, the high-growth stock, the story stock, the excellent stock, the admired stock, the A+ stock, or even the profitable net net, but study after study shows that this instinct leads us to under-perform. Buying well-run companies with good businesses at bargain prices seems to make even more sense. The research shows , however, that the better investment–rather than the better company–the value stock, the scorned, the unexcellent, the Ds, the loss-making net nets.  And the better value stock, according to Lakonishok, Shleifer, and Vishny’s research is the low-no-growth value stock, what they describe as “contrarian value,”

What is clear is that value investing in general, and deep value (buying the ugliest of ugly) in particular, is exceedingly behaviorally difficult. It is counter-intuitive and against instinct, which is why many investors shy away from it.

Lecture by Toby Carlisle on Deep Value Investing

Next Lesson:

We will finish up this chapter by covering The Broken-Leg Problem. Please give this chapter a close study–the conclusions are extremely COUNTER-INTUITIVE and the opposite of what most investors look for.  We are at the heart of deep value investing.

Mean Reversion Part II


Roman Orgy

Last post on Part 1:

Why does high growth seem to depress stock market returns and low growth seem to generate high stock market returns? It is not the growth destroys returns, but that the market already recognizes the high-growth nation’s potential, and bids the price of its equities too high. Market participants become overly optimistic during periods of high growth, driving up the prices of stocks and lowering long-term returns, and become too pessimistic during busts, selling down stocks and creating the conditions for high long-term returns.   Jay Ritter says that irrationality generates volatility “and mean reversion over multi-year horizons.” Graham would agree (p. 88, DEEP VALUE).

The implications for mean reversion in stocks are counter-intuitive. Stocks with big market price gains and historically high rates of earnings growth tend to grow earnings more slowly in the future, and underperform the market. Stocks with big market prices losses and historically declining earnings tend to see their earnings grow faster, and out perform the market. Undervalued stocks with historically declining earnings grow earnings faster than overvalued stocks with rapidly increasing earnings. This is mean reversion, and, as Ben Graham said, it’s the phenonmenon that leads value strategies to beat the market.

The update to Lakonishok’s research Contrarian Investments Extrapolation and Risk demonstrates that, aside from short periods of under-performance, value stocks generate a consistent value premium, and beat both the market and glamour stocks over the long haul. ….Researchers believe the reasons are because they are contrarian to overreaction and naive extrapolation. Efficient market academics Eugene Fama and Ken French, counter that value strategies outperform because they are riskier.  However, Lakonishok found that while value strategies do disproportionately well in good times, its performance in bad times is also impressive.  Value strategies are also outperform during “bad” states for the world such as recession and extreme down markets.

When Lakonishok compared the growth rates implied by the market price to the actual growth rates appearing after the selection date, they found a remarkable result–one that supports Graham’s intuition–value stocks grow fundamentals faster than glamour stocks.  The high prices paid for glamour stocks imply that the market expects them to generate high rates of growth. Contrary to this expectation, however, the growth rates do not persist.  Growth stocks;s growth rates mean-revert from fast growth to slow growth.


If you read all the links and research papers in the past two posts for this chapter in DEEP VALUE, you know that:

  • Out-of-favor value stocks beat glamour stocks because….
  • the actual growth rates of fundamental sales and earnings of glamour stocks relative to value stocks after selection are much lower than they were in the past, or as the multiples on those stocks indicate the market expects them to be.
  • Value strategies appear to be less risky than glamour strategies.

So why do investors persist in buying glamour? For behavioral reasons like anchoring and “overreaction bias.”   We will next explore chapter 6 in Deep Value.


Autos a Bad Business Damordaran Blog


Lesson on Franchises in Cyclical Markets




Money Managers Are Price Chasers

Markets can do ANYTHING in the short-term, so the following is not a prediction that miners will rise in price. However, what comes first–the price rising or the buying? :) Miners chop around in a trading range as money managers flee the sector and now sit with record low allocations to this sector. * How good are money managers (on the whole) picking the right sector to invest in?  I leave it to you to find that out.

Wedgewood Partners: Franchises in Cyclical Market and a Lesson on Diversification

wedgewood partners fourth quarter 2014 client letter Look at pages 12-20 where David Rolfe, the manager, discusses NOV, SLB and CLB–high-quality companies in the cyclical oil sector.

  1. clb_vl
  2. slb_vl
  3. nov_vl

He points out diversification may mean the sources of profitability can be different among companies within a particular sector.  (Refer to Competition Demystified by Bruce Greenwald for a course on this distinction). Note the high revenue conversion to free cash flow (page-14) for those companies compared to other companies in the oil services sector.

Now move on to wedgewood partners first quarter 2015 client letter crude realities.  Note on page 13 how he looks at the oil services market–the structural attribute to focus on is drilling intensity.  Interesting…. Look at pages 18 and 19 for a further discussion on NOV and CLB.

To learn, you might download those company’s recent annual reports and try to figure out their revenue to free cash flow conversion.  Look at what the companies use for maintenance capex.  Note how Core Labs is a free cash flow gusher (Charlie Munger would smile on this).  Core Labs is a different business than SLB and NOV, but is grouped in the same industry/index.  When sellers of ETF sell, they don’t distinguish among companies and therein lies opportunity for us. Yeah!

I do the opposite of this:

**Merrill Lynch Fund Managers Survey May 4, 2015

Today’s chart of the day focuses on sentiment in the basic materials sector. Regular readers of the blog already know that I have been closely following Merrill Lynch’s Fund Manager Survey for years now. This months survey was conducted in a period between 2nd to 9th April 2015 with a total of 177 panellists, with $494 billion of assets under management. The survey should be used as a very good contrarian indicator.

According to the recent survey, global fund manager allocation towards global materials declined sharply in the month of April to net 27% underweight from net 16% underweight the previous month. As we can clearly see from todays chart of the day, sentiment is very depressed right now. Merrill Lynch states that the current allocation is 1.8 standard deviation below its long term average.

Furthermore, the overall commodity and natural resources theme is very much disliked by global money managers. Commodity allocation is unchanged for the third straight month and remains at net 20% underweight. That is 1.2 standard deviations below its long term average and even more interestingly, fund managers remain underweight commodities for the 28th month in the row.


Tim McElvaine: Kissing a lot of frogs to find a prince or Portrait of a Deep Value Investor

“Value investing is about praying on the emotions of the seller,” McElvaine said, noting that he loves to be a buyer of un-loved securities when their owners need out at any cost.

McElvaine pointed to a Globe and Mail headline about beat-up mining stocks being great tax-loss sale candidates this past December. He bought up shares in Sprott Resource Corp and Anglo American recently for trading at considerable discounts to NAV (more info at

Six years into the global bull-market and McElvaine’s funds are about 25% in cash to provide an opportunity to buy assets if prices return to Tim’s liking.

Is the US bull-market over? McElvaine talked about what could go right in the United States, and suggested that a great way to stimulate the US Economy would be to wipe out student loan debt, which is $1 trillion of $1.3 trillion owned by the US Government, according to McElvaine. That move could put $1 trillion back in the hands of the most aggressive consumers.

There was a brief moment before Tim’s speech that my dad and I got to share a word with him, and I asked how do they know if a cheaply priced security represents a value gap, meaning it’s undervalued and going higher, or is it a value-trap, as so often cheap stocks get cheaper.

“You don’t know,” Dad and McElvaine agreed, which reminded me of something Tim taught me 6-7 years ago:

“You’ve got to kiss a lot of toads in this business to find your prince.”

Take the time to read his annual reports and transcripts, then go the extra mile and look at the annual reports of the companies he mentions–do you see what he sees?  For example, in the chat of his presentation for 2014 (see bold index and then the link) he mentions that Sprott Resource Corp is trading for about $1.00 Cdn while its NAV is above $3.00 or “It’s not pretty, but it’s cheap.”  Can you learn from his approach and analysis? What would you do differently? You have to be a contrarian with a calculator to buy what is hated.

Some reports below:

Buying dollar bills for fifty cents Recent talk on his investments.

2013-Annual-Report and 2013-Partners-Confererence-Transcript

2012-Annual-Report and 2012-Transcript-Partners-Conference-website-version

Go deeper:

Tomorrow: I will post a reader’s list of great annual reports.

I love reading Warren Buffett’s letters and I love contrasting his words with his actions…I love how he criticizes hedge funds, yet he had the first hedge fund,” Mr. Loeb said. “He criticizes activists, he was the first activist. He criticizes financial services companies, yet he loves to invest in them. He thinks that we should all pay taxes, yet he avoids them himself.  – Business Insider LINK     (A bit over the top but I like to present the contrasting view whether I agree or not).

Commodities Carnage; Reversion to the Mean and the Growth Illusion; Net/Nets


CRBSearch Strategy: Go where the outlook is bleakest (John Templeton). Keep his wisdom by your side: Sixteen Rules for Investment Success_Templeton

Commodities (CRB Index) fall back to a 40-year support zone ($185/$205)


As global commodities prices plummet, it’s incredibly convenient to pronounce the commodities super-cycle dead, isn’t it?  Yet banks from Goldman Sachs to Citigroup to Deutsche Bank are on record as saying it’s over.

The point is not to follow the “experts” but search where there is carnage. I am looking at Templeton’s Russian and Eastern Europe Fund TRF Semi Annual Report because:

  • Hated Countries (Russia, Ukraine)
  • Currencies Down,
  • Commodity Exporters and
  • trading at a 10% discount so the 1.4% management fee is covered for six years.
  • Poor performance for the past few years

Things can and will probably get worse. So please don’t follow the blind (me) off the cliff. This is meant as an example of a SEARCH STRATEGY.

More on Reversion to the Mean and the Growth Illusion

We are beating this subject to death but you can’t understand how investing in bargains works without grasping these concepts.

Contrarian Strategy Extrapolation and Risk  Abstract: Value strategies yield higher returns because these strategies exploit the sub-optimal behavior of the typical investor and not because these strategies are fundamentally riskier.  Yes, this is an academic paper, but worth reading to understand WHY and HOW value (buying stocks with low expectations/and low price to business metrics like earnings, cash flow, EBITDA, etc.) provide better returns.

Growth Illusion

The Two Percent Dilution It is widely believed that economic growth is good for stockholders. However, the cross-country correlation of real stock returns and per capita GDP growth over 1900–2002 is negative. Economic growth occurs from high personal savings rates and increased labor force participation, and from technological change. If increases in capital and labor inputs go into new corporations, these do not boost the present value of dividends on existing corporations. Technological change does not increase profits unless firms have lasting monopolies, a condition that rarely occurs. Countries with high growth potential do not offer good equity investment opportunities unless valuations are low.

value-vs-glamour-a-global-phenomenon (Brandes Institute)

Thick as a Bric by Efficient Frontier

Does the Stock Market Over React

Discussion of Does the Stock Market Over React

Criticism of the Over Reaction Theory

The above is meant to supplement your reading in Deep Value Chapter 5, A Clockwork Market


Ben Graham’s Net-Net Strategy Revisited

Ben Graham Net Current Asset Values A Performance Update

R-T-M, Gross Profitability, Magic Formula

Our last lesson was in Mean Reversion (Chapter 5 in Deep Value) discussed  View this video on a very MEAN Reversion.

We must understand full cycles and reversion to the mean.  Let’s move on to reading Chapter 2: A Blueprint to a better Quantitative Value Strategy in Quantitative Value.

Investors should be skeptical of history-based models. Constructed by a nerdy-sounding priesthood using esoteric terms such as beta, gamma, sigma and the like, these models tend to look impressive. Too often, though, investors forget to examine the assumptions behind the symbols. Our advice: Beware of geeks bearing formulas. -Warren Buffett, Shareholder Letter, 2000.


Greenblatt defined Buffett’s definition of a good business as a high Return on Capital (ROC) – EBIT/Capital

Capital is defined as fixed asses + working capital (current assets minus current liabilities) minus excess cash.

ROC measures how efficiently management has used the capital employed in the business. The measure excludes excess cash and interest-bearing assets from this calculation to focus only on those assets actually used in the business to generate the return.


High earning yield = EBIT/TEV

TEV + Market Cap. + Total debt – minus excess cash + Preferred Stock + minority interests, and excess cash means cash + current assets – current liabilities.EBIT/TEV enables and apples-to-apples comparison of stock with different capital structures.

Improving on the Magic Formula?

ROC defined as Gross profitability to total assets.

GPA = (Revenue – Cost of Goods Sold)/Total Assets

GPA is the “cleanest” measure of true economic profitability.

See this study Gross Profitability a Better Metric and see pages 46-49 in Quant. Value. (the book was sent to deep-value group on Google)

The authors found GPA outperformed as a quality measure the magic formula.  Note on page 48, Table 2.3: Performance Stats for Common Quality Measures (1964 – 2011) that most simple quality measures do NOT provide any differentiation from the market!

FINDING PRICE, Academically–Book value/Market Price

The authors found that analyzing stocks along price and quality contours using the Magic Formula and its generic academic brother Quality and Price can produce market beating results 

The authors: “Our study demonstrates the utility of a quantitative approach to investing. Relentlessly pursuing a small edge over a long period of time, through booms and busts, good economies and bad, can lead to outstanding investment results.”

Ok, let’s come back to quality and avoiding value/death traps in the later chapters (3 and 4) in Quantitative Value.  We are just covering material in Chapter 2. 


Investors and the Magic Formula

Adding Your Two Cents May Cost a Lot Over the Long Term by Joel Greenblatt
01-18-2012  (Full article: Adding Your Two Cents

Gotham Asset Management managing partner and Columbia professor Joel Greenblatt explains why investors who ‘self-managed’ his Magic Formula using pre-approved stocks underperformed the professionally managed systematic accounts.

So, what happened? Well, as it turns out, the self-managed accounts, where clients could choose their own stocks from the pre-approved list and then follow (or not) our guidelines for trading the stocks at fixed intervals didn’t do too badly. A compilation of all self-managed accounts for the two-year period showed a cumulative return of 59.4% after all expenses. Pretty darn good, right? Unfortunately, the S&P 500 during the same period was actually up 62.7%.

“Hmmm….that’s interesting”, you say (or I’ll say it for you, it works either way), “so how did the ‘professionally managed’ accounts do during the same period?” Well, a compilation of all the “professionally managed” accounts earned 84.1% after all expenses over the same two years, beating the “self managed” by almost 25% (and the S&P by well over 20%). For just a two-year period, that’s a huge difference! It’s especially huge since both “self-managed” and “professionally managed” chose investments from the same list of stocks and supposedly followed the same basic game plan.

Let’s put it another way: on average the people who “self-managed” their accounts took a winning system and used their judgment to unintentionally eliminate all the outperformance and then some! How’d that happen?

1. Self-managed investors avoided buying many of the biggest winners.

How? Well, the market prices certain businesses cheaply for reasons that are usually very well-known (The market is a discounting mechanism). Whether you read the newspaper or follow the news in some other way, you’ll usually know what’s “wrong” with most stocks that appear at the top of the magic formula list. That’s part of the reason they’re available cheap in the first place! Most likely, the near future for a company might not look quite as bright as the recent past or there’s a great deal of uncertainty about the company for one reason or another. Buying stocks that appear cheap relative to trailing measures of cash flow or other measures (even if they’re still “good” businesses that earn high returns on capital), usually means you’re buying companies that are out of favor.

These types of companies are systematically avoided by both individuals and institutional investors. Most people and especially professional managers want to make money now. A company that may face short-term issues isn’t where most investors look for near term profits. Many self-managed investors just eliminate companies from the list that they just know from reading the newspaper face a near term problem or some uncertainty. But many of these companies turn out to be the biggest future winners.

2. Many self-managed investors changed their game plan after the strategy under-performed for a period of time.

Many self-managed investors got discouraged after the magic formula strategy under-performed the market for a period of time and simply sold stocks without replacing them, held more cash, and/or stopped updating the strategy on a periodic basis. It’s hard to stick with a strategy that’s not working for a little while. The best performing mutual fund for the decade of the 2000’s actually earned over 18% per year over a decade where the popular market averages were essentially flat. However, because of the capital movements of investors who bailed out during periods after the fund had underperformed for a while, the average investor (weighted by dollars invested) actually turned that 18% annual gain into an 11% LOSS per year during the same 10 year period.[2]

3. Many self-managed investors changed their game plan after the market and their self-managed portfolio declined (regardless of whether the self-managed strategy was outperforming or underperforming a declining market).

This is a similar story to #2 above. Investors don’t like to lose money. Beating the market by losing less than the market isn’t that comforting. Many self-managed investors sold stocks without replacing them, held more cash, and/or stopped updating the strategy on a periodic basis after the markets and their portfolio declined for a period of time. It didn’t matter whether the strategy was outperforming or underperforming over this same period. Investors in that best performing mutual fund of the decade that I mentioned above likely withdrew money after the fund declined regardless of whether it was outperforming a declining market during that same period.

4. Many self-managed investors bought more AFTER good periods of performance.

You get the idea. Most investors sell right AFTER bad performance and buy right AFTER good performance. This is a great way to lower long-term investment returns.

Luck-versus-skill-in-mutual-fund-performance by Fama

….We will finish the chapter with a study of checklists in the next post.

Interesting reading: The Crescent Fund (note reversion to the mean)  Oil Crash Pzena and

Go-where-it-is-darkest-when-company.html (Vale-Brazilian Iron Ore Producer).   Prof. Damordaran values Vale and Lukoil on Nov. 20, 2015.  I am looking at Vale because they have some of the lowest cost assets of Iron Ore in the world.  They have good odds of surviving the downturn but where the trough is–who knows. 

Valuing Cyclical Companies:

Valuing Cyclical Commodity Companies

CS on a Cyclical Business or Thinking About Cypress Stock

Letter to Cypress Shareholders about Price vs Value





I think the author at least knew of the risks, but underestimated the extent of the cycle due to massive distortions caused by the world’s central banks.  It did get iron prices fell another 10% and still falling. 

Month Price Iron Ore Change
Aug 2014 92.63
Sep 2014 82.27 -11.18 %
Oct 2014 80.09 -2.65 %
Nov 2014 73.13 -8.69 %
Dec 2014 68.80 -5.92 %
Jan 2015 67.39 -2.05 %
Feb 2015 62.69 -6.97


Damodaran: I have not updated my valuation of Vale (as of Feb. 20th), but I have neither sold nor added to my position. It is unlikely that I will add to my position for a simple reason. I don’t like doubling down on bets, even if I feel strongly, because I feel like I am tempting fate. 

Prof. Damodaran is responding to a poster who is asking about Vale’s plummeting stock price.  If you are a long-term bull you want declining prices to bankrupt weak companies in the industry so as to rationalize supply.




An early sign of a turn: Gold vs. the Commodity Research Bureau Index (CRB). Since gold is commodity money it is much more sensitive to changes in financial conditions than consumable commodities.

gold crb






Rate of change of dollar


I like to take note of extremes. However, the amount of central bank monetary distortion is so huge (note negative interest rate on government debt, huge deficits, QE, etc.) that extremes can go to new levels but when investors are on one side of the boat–be careful.

Also, visit

PS: Once I clear my backlog, I will return to our lessons in Deep Value and Quantitative Value.  Plus post the links to the videos of valua investing classes.

Dollar Panic; Valuation Ratios; Buyback Mania, CEFs


If you think nobody cares about you, try missing a couple of payments.- Wright.

Long-term view of the Dollar (DXY)

Oil service, oil producers, mining companies etc. are being hammered by a dollar “shortage.”  Opportunity may be knocking. Remember what Klarman said about forced selling.

An overview of the situation: Dollar Shortage. With money supply rising in the US there is no dollar “shortage”, but there is a fear of inter-bank lending.

Dollar Leverage BIS Report

Dollar Crisis 2009

JPM-dollar-shortage funding

A Guide to the Swap Market

Now the “experts” say confidently cnbc Dollar Euro Parity. Perhaps a bit late in a trend!   If you are to follow a trend then The Whipsaw Song

A Reader’s Question on Valuation Ratios.  This sheet may be good as a guide to go through an annual report, but none of those ratios means anything without context.   Is growth good? It depends. Only profitable growth within a franchise.  How about asset turnover?   For some companies like Costco asset turnover is critical but not for Boeing (gross margin).   Why not take those ratios and work through the financials of these trucking companies.  Which company is doing the best? Why? Follow the money!   Those ratios may help you structure the information you pull out from the financials. But first focus on how does the company provide a service to its customers and then trace the financial effects back to your returns as an investor.

  1. HTLD VL
  2. JBHT VL
  3. KNX_VL

Buy-Back Mania (a yellow light of caution) Stock Buybacks Hit RecordTotal 2 Trillion Since 2009

Emultate Henry Singleton

Case Study in capital allocation: Dr. Singleton and Teledyne A Study of an Excellent Capital Allocator (must read!)

Gold is in a hyper bubble……………….

Gold Bubble

But now not so much…………………Gold Bubble Not Quite as much

Gold is stupid-cheap compared to all the money out there…………………Gold hyper undervalued

What determines the price of gold

2010-06-21 IE Special Report GOLD

A Case Study in investing in Closed-End Funds

Prof. Greenblatt once said that sometimes people just go crazy.

A Lesson in CEF Investing TRF


Investors ran to pay a 90% PREMIUM to NAV AFTER a six-year boom and now after a seven-year decline they sell at a 10.5% DISCOUNT. Go figure.

Interesting video on China–a country brimming with centrally planned mal-investment. Is China Already in a Hard Landing?

Read more at reality-check-how-fast-is-china-growing.

We will get back to Deep Value next week and I will post links to valuation class videos.  Have a great weekend and if you do try to emulate someone, then: