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Archive for January, 2010

CXO Advisory Group has uncovered a superb paper Stocks of Admired Companies and Spurned Ones by Deniz Anginer and Meir Statman, which finds that the most admired companies on Fortune Magazine’s annual survey of list of “America’s Most Admired Companies” had lower returns, on average, than stocks of spurned companies from April 1983 through December 2007. Further, Anginer and Statman find that increases in admiration were followed, on average, by lower returns.

Anginer and Statman describe their methodology as follows:

Fortune has been publishing the results of annual surveys of company reputations since 1983 and the survey published in March 2007 included 587 companies in 62 industries. Fortune asked more than 3,000 senior executives, directors and securities analysts to rate the ten largest companies in their own industries on eight attributes of reputation, using a scale of zero (poor) to ten (excellent): quality of management; quality of products or services; innovativeness; long-term investment value; financial soundness; ability to attract, develop, and keep talented people; responsibility to the community and the environment; and wise use of corporate assets. The rating of a company is the mean rating on the eight attributes. The list of admired companies in the 2007 survey includes Walt Disney, UPS and Google, with ratings of 8.44, 8.37 and 8.07. The list of spurned companies includes Jet Blue, Bridgestone and Stanley Works, with ratings of 5.25, 5.34 and 5.37.

The mean rating of companies in some industries, such as the 6.53 of the Communications industry, are higher on average than those of other industries, such as the 5.26 of the Agricultural Production industry. Our focus is on companies and we distinguish company effects from industry effect by using industry adjusted ratings of companies. They are the difference between the rating of a company and the mean rating of companies in its industry.

Consider two portfolios constructed by Fortune ratings; each consisting of one half of the Fortune stocks. The admired portfolio contains the stocks with the highest Fortune ratings and the spurned portfolio contains the stocks with the lowest. We construct the portfolios on April 1st of 1983, based on the Fortune survey published earlier that year1. We calculate the returns of the portfolios during the 12 months from April 1st 1983 to March 31st 1984 from daily returns. We reconstruct each portfolio on April 1st of subsequent years based on the Fortune survey published earlier that year and calculate returns similarly during the following 12 months.

CXO summarize the findings as follows:

  • Over the entire sample period, the mean annualized equally-weighted (value-weighted) return for the unadmired (lower half) portfolio is 18.3% (16.1%), compared to 16.3% (13.8%) for the admired (upper half) portfolio.
  • Risk-adjusted alphas of an annually reformed hedge portfolio that is long (short) the unadmired (admired) stocks is sometimes positive and sometimes insignificant, depending on whether the risk adjustment is beta only or multi-factor.
  • Increases in admiration generally indicate lower future returns. For example, the mean annualized equally-weighted return of the stocks in the most unadmired quartile for which reputation decreased (increased) relative to the median is 18.8% (13.2%).
  • The dispersion of returns is higher within the unadmired portfolio than the admired one. Among the 12 stocks with the worst (best) annual returns, 11 (9) come from the unadmired portfolio. Investors seeking to exploit “unadmiredness” should therefore diversify widely among unadmired stocks.
  • The effect is non-linear. The annualized return of an equally-weighted portfolio of the 10 least (most) admired stocks is 13.4% (16.6%). The next ten most and least admired stocks have about the same annualized return. However, for rankings 21-30, 31-40 and 41-50, unadmired stocks substantially beat admired stocks.
  • In summary, the stocks of companies unadmired by the ostensibly well-informed may well outperform the stocks of the companies admired.

Why might this be so? I’d like to venture a guess. Anginer and Statman’s findings would seem to accord with the findings of Josef Lakonishok, Andrei Shleifer, and Robert Vishny in Contrarian Investment, Extrapolation and Risk (and the The Brandes Institute update Value vs Glamour: A Global Phenomenon. Those two papers found that value stocks (defined as the lowest decile of stocks by price-to-book) outperformed glamour stocks (and by a wide margin).Recall that glamour stocks are those that “have performed well in the past,” and “are expected by the market to perform well in the future.” Value stocks are those that “have performed poorly in the past and are expected to continue to perform poorly.” LSV say value beats glamour because investors don’t fully appreciate the phenomenon of mean reversion, which leads them to extrapolate past performance too far into the future. It’s possible that “admired” can be a proxy for “glamourous” and therefore Anginer and Statman have identified another aspect of this phenomenon. Admired companies are bid up like glamour stocks, and scorned companies are ignored like value stocks, which creates the opportunity for contrarian bet. I love a counter-intuitive strategy.

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In “Black box” blues I argued that automated trading was a potentially dangerous element to include in a quantitative investment strategy, citing the “program trading / portfolio insurance” crash of 1987. When the market started falling in 1987 the computer programs caused the writers of derivatives to sell on every down-tick, which some suggest exacerbated the crash. Here’s New York University’s Richard Sylla discussing the causes (care of Wikipedia).

The internal reasons included innovations with index futures and portfolio insurance. I’ve seen accounts that maybe roughly half the trading on that day was a small number of institutions with portfolio insurance. Big guys were dumping their stock. Also, the futures market in Chicago was even lower than the stock market, and people tried to arbitrage that. The proper strategy was to buy futures in Chicago and sell in the New York cash market. It made it hard — the portfolio insurance people were also trying to sell their stock at the same time.

The Economist’s Buttonwood column has an article, Model behaviour: The drawbacks of automated trading, which argues along the same lines that automated trading is potentially problematic where too many managers follow the same approach:

[If] you feed the same data into computers in search of anomalies, they are likely to come up with similar answers. This can lead to some violent market lurches.

Buttonwood divides the quantitative approaches to investing into at three different types and their potential for providing a stabilizing influence on the market or throwing fuel on the fire in a crash:

1. Trend-following, the basis of which is that markets have “momentum”:

The model can range across markets and go short (bet on falling prices) as well as long, so the theory is that there will always be some kind of trend to exploit. A paper by AQR, a hedge-fund group, found that a simple trend-following system produced a 17.8% annual return over the period from 1985 to 2009. But such systems are vulnerable to turning-points in the markets, in which prices suddenly stop rising and start to fall (or vice versa). In late 2009 the problem for AHL seemed to be that bond markets and currencies, notably the dollar, seemed to change direction.

2. Value, which seeks securities that are  cheap according to “a specific set of criteria such as dividend yields, asset values and so on:”

The value effect works on a much longer time horizon than momentum, so that investors using those models may be buying what the momentum models are selling. The effect should be to stabilise markets.

3.  Arbitrage, which exploits price differentials between securities where no such price differential should exist:

This ceaseless activity, however, has led to a kind of arms race in which trades are conducted faster and faster. Computers now try to take advantage of arbitrage opportunities that last milliseconds, rather than hours. Servers are sited as close as possible to stock exchanges to minimise the time taken for orders to travel down the wires.

In arguing that automated trading can be problematic where too many managers pursue the same strategy, Buttonwood gives the example of the August 2007 crash, which sounds eerily similar to Sylla’s explanation for the 1987 crash above:

A previous example occurred in August 2007 when a lot of them got into trouble at the same time. Back then the problem was that too many managers were following a similar approach. As the credit crunch forced them to cut their positions, they tried to sell the same shares at once. Prices fell sharply and portfolios that were assumed to be well-diversified turned out to be highly correlated.

It is interesting that over-crowding is the same problem identified by GSAM in Goldman Claims Momentum And Value Quant Strategies Now Overcrowded, Future Returns Negligible. In that presentation, Robert Litterman, Goldman Sachs’ Head of Quantitative Resources, said:

Computer-driven hedge funds must hunt for new areas to exploit as some areas of making money have become so overcrowded they may no longer be profitable, according to Goldman Sachs Asset Management. Robert Litterman, managing director and head of quantitative resources, said strategies such as those which focus on price rises in cheaply-valued stocks, which latch onto market momentum or which trade currencies, had become very crowded.

Litterman argued that only special situations and event-driven strategies that focus on mergers or restructuring provide opportunities for profit (perhaps because these strategies require human judgement and interaction):

What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.

As we’ve seen before, human judgement is often flawed. Buttonwood says:

Computers may not have the human frailties (like an aversion to taking losses) that traditional fund managers display. But turning the markets over to the machines will not necessarily make them any less volatile.

And we’ve come full circle: Human’s are flawed, computers are the answer. Computers are flawed, humans are the answer. How to break the deadlock? I think it’s time for Taleb’s skeptical empiricist to emerge. More to come.

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Daniel Rudewicz, the managing member of Furlong Samex LLC, has provided a guest post today on Paragon Technologies (PGNT.PK). Furlong Samex is a deep value investment partnership based on the principles of Benjamin Graham. Daniel can be reached at rudewicz [at] furlongsamex [dot] com.

Anyone Need a (Sanborn) Map?

In his 1960 partnership letter, Warren Buffett described his investment in Sanborn Map. At the time of his investment, Sanborn Map was selling for less than the combined value of its cash and investment portfolio. Additionally, the operating portion of the company was profitable. Opportunities like Sanborn Map are a dream for value investors.

The market downturn of 2008 had created some similar opportunities. But by early 2010 the market price of most of those companies had converged to at least the value of their cash and investment portfolio. One company that has managed to stay under the radar is Paragon Technologies. It was trading below its cash level when the company elected to be listed on the Pink Sheets. This also removed the requirement to file with the SEC and now the company is no longer on many of the databases and stock screens.

It’s a fairly illiquid company whose most recent quarter was profitable. As of 9/30/2009, Paragon had just over $6 million in cash, or $3.88 per share.

Cash and cash equivalents $6,094,000
Shares outstanding 1,571,810
Cash per share $3.88

Year to date, its stock has traded between $2.20 and $2.55, quite a discount from its cash. The Board and the interim CEO are looking at strategic alternatives and will consider shareholder proposals. Unfortunately, what we had hoped was a 1960 Buffettesque proposal was turned down. In the proposal we outlined the benefits of the company offering a fixed price tender at $3.88 per share. Maybe next time. To the Board’s credit, they have authorized a large share buyback and have increased the amount authorized several times. The problem is that authorizing an amount and buying back an amount is not the same thing.

While the interim CEO searches for opportunities, the company could conceivably end up buying back enough shares in the open market so that we’re the only shareholder left. The downside is that I’m not sure that we would want that. Even though it was profitable last quarter, the long term earnings record is not that impressive. Looking back at Buffett’s Sanborn Map investment, it seems like Sanborn’s Board should have encouraged Buffett to stay on and manage its investment portfolio. Our hope is that Paragon moves in the direction of becoming a tiny Berkshire or Fairfax by putting a great capital allocator in charge of the cash. It would be a great way to use some of the company’s operating losses to shield future investment gains. So if you’re the next Buffett — or even ‘Net Quick’ Evans — send them your resume. Maybe they’ll hire you (I doubt it).

Our firm’s portfolio is relatively small and we have purchased as much of Paragon as we would like to at this time. If you would like a copy of our letter to the Board or any of our research, feel free to contact us and we’d be happy to share it with you. There are risks involved with this company so do your own research before investing.

Disclosure: Long Paragon Technologies (PGNT.PK). This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.

[Full Disclosure: I do not hold PGNT. This is neither a recommendation to buy or sell any securities. All information provided believed to be reliable and presented for information purposes only. Do your own research before investing in any security.]

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One of the major concerns with quantitative investing is that the “black box” running the portfolio suddenly goes Skynet and destroys the portfolio. It raises an interesting distinction between “quantitative investing” as I intend it and as it is often perceived. For many, the word “quantitative” in relation to investing suggests two potentially dangerous elements:

  1. A very complex investment methodology, the understanding of which is beyond the minds of mere mortals, requiring a computer to calculate its outputs, ala Long-Term Capital Management.
  2. Automated trading, where the black box interacts directly with the market, without human supervision, which some have suggested caused the “program trading” crash of 1987.

The Wall Street Journal has a florid article, The Minds Behind the Meltdown, by Scott Patterson, which speaks directly to these two fears in the context of the recent credit crisis:

PDT, one of the most secretive quant funds around, was now a global powerhouse, with offices in London and Tokyo and about $6 billion in assets (the amount could change daily depending on how much money Morgan funneled its way). It was a well-oiled machine that did little but print money, day after day.

Then it achieved sentience (which, adds Wikipedia, is not to be confused with sapience).

That week, however, PDT wouldn’t print money—it would destroy it like an industrial shredder.

The unusual behavior of stocks that PDT tracked had begun sometime in mid-July and had gotten worse in the first days of August. The previous Friday, about half a dozen of the biggest gainers on the Nasdaq were stocks that PDT had sold short, expecting them to decline, and several of the biggest losers were stocks PDT had bought, expecting them to rise. It was Bizarro World for quants. Up was down, down was up. The models were operating in reverse.

The models were operating in reverse. How do we know? Instead of making money, they started losing money.

The market moves PDT and other quant funds started to see early that week defied logic. The fine-tuned models, the bell curves and random walks, the calibrated correlations—all the math and science that had propelled the quants to the pinnacle of Wall Street—couldn’t capture what was happening.

The market, under the thrall of sentient black boxes, defies logic, fine-tuned models, bell curves, random walks and calibrated correlations, leaving only fresh Talebian epistemic humility in its wake.

The result was a catastrophic domino effect. The rapid selling scrambled the models that quants used to buy and sell stocks, forcing them to unload their own holdings. By early August, the selling had taken on a life of its own, leading to billions in losses.

The selling had taken on a life of its own. Even the selling turned sentient (or sapient).

It was utter chaos driven by pure fear. Nothing like it had ever been seen before. This wasn’t supposed to happen!

Nothing like it had ever happened before. Of course the “program trading / portfolio insurance” crash of 1987 would be a total mystery to a newly sentient black box, given that it happened a full 20 years earlier.

As conditions spun out of control, Mr. Muller was updating Morgan’s top brass. He wanted to know how much damage was acceptable. But his chiefs wouldn’t give him a number. They didn’t understand all of the nuts and bolts of how PDT worked. Mr. Muller had kept its positions and strategy so secret over the years that few people in the firm had any inkling about how PDT made money. They knew it was profitable almost all the time. That was all that mattered.

The models were so complicated, and so secret, not even the guys running them knew know they worked.

That Wednesday, what had started as a series of bizarre, unexplainable glitches in quant models turned into a catastrophic meltdown the likes of which had never been seen before in the history of financial markets. Nearly every single quantitative strategy, thought to be the most sophisticated investing ideas in the world, was shredded to pieces, leading to billions in losses. It was deleveraging gone supernova.

The bizarre computer glitches, still unexplained, led to big losses, rendering billions of dollars into nothing but dust and  Talebian epistemic humility.

Sounds pretty scary. Putting aside for one moment the melodrama of the article, it does highlight the potential problems for a quantitative investment strategy. It also presents some pretty obvious solutions, which I believe are as follows:

  1. The investment strategy should be reasonably tractable and the model should be simple. This avoids the problem of not understanding the “nuts and bolts” of the fund and should reduce the “bizarre, unexplainable glitches”.
  2. The strategy should be robust. If it can’t survive a crisis the magnitude of at least 1987, or 2007-2009, it’s not robust. The problem is probably too much leverage, either in the fund or baked into the security. What’s the “right” amount of debt? Little to none. What’s that mean? As Charlie Munger might say, “What don’t you understand about ‘no debt’?”
  3. To the extent that it is possible to do so, a human should enter the trades. Of course, human entry of trades is not the panacea for all our ills. It creates a new problem: Fat fingers are responsible for plenty of trades with too many or too few zeros.

Perhaps the best solution is a healthy skepticism about the model’s output, which is why simplicity and tractability are so important.

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The New Yorker has John Cassidy’s interview with Richard Thaler, Chicago School economist and co-author (along with Werner F.M. DeBondt) of Further Evidence on Investor Overreaction and Stock Market Seasonality, a paper I like to cite in relation to low P/B quintiles and earnings mean reversion. Thaler is also the “Thaler” in Fuller & Thaler Asset Management, which James Montier identifies in his 2006 research report Painting By Numbers: An Ode To Quant as being a “fairly normal” quantitative fund (as opposed to being “rocket scientist uber-geeks”) with an “admirable track [record] in terms of outperformance.” I diverge from Thaler on a number of issues, but on these two I think he’s right:

On the remnants of efficient markets hypothesis:

Well, I always stress that there are two components to the theory. One, the market price is always right. Two, there is no free lunch: you can’t beat the market without taking on more risk. The no-free-lunch component is still sturdy, and it was in no way shaken by recent events: in fact, it may have been strengthened. Some people thought that they could make a lot of money without taking more risk, and actually they couldn’t. So either you can’t beat the market, or beating the market is very difficult—everybody agrees with that. My own view is that you can [beat the market] but it is difficult.

The question of whether asset prices get things right is where there is a lot of dispute. Gene [Fama] doesn’t like to talk about that much, but it’s crucial from a policy point of view. We had two enormous bubbles in the last decade, with massive consequences for the allocation of resources.

On stock market bubbles:

[Cassidy] When I spoke to Fama, he said he didn’t know what a bubble is—he doesn’t even like the term.

[Thaler] I think we know what a bubble is. It’s not that we can predict bubbles—if we could we would be rich. But we can certainly have a bubble warning system. You can look at things like price-to-earnings ratios, and price-to-rent ratios. These were telling stories, and the story they seemed to be telling was true.

And I love this line in relation to the impact of the recent crisis on behavioral economics:

I think it is seen as a watershed, but we have had a lot of watersheds. October 1987 was a watershed. The Internet stock bubble was a watershed. Now we have had another one. What is the old line—that science progresses funeral by funeral? Nobody changes their mind.

Science progresses funeral by funeral. Nobody changes their mind. It seems to me it’s not the only discipline that proceeds by funeral.

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The Official Activist Investing Blog published its list of activist investments for November (My apologies. I’m a little tardy with it):

Ticker Company Investor
ADPT Adaptec. Inc. Steel Partners
AEPI AEP Industries KSA Capital
AGYS Agilysys Inc MAK Capital One
BKS Barnes & Noble Inc Ronald Burkle
CITZ CFS Bancorp Inc. Financial Edge Fund LP
CLCT Collectors Universe Marlin Sams Fund
CNO Conseco Inc. Paulson & Co
COBR Cobra Electronics Corp Timothy Stabosz
CWLZ Cowlitz Bancorporation Crescent Capital
DEST Destination Maternity Corporation Crescendo Partners
DGTC.OB Del Global Technologies Steel Partners
DVD Dover Motorsports Marathon Partners
EDAC Edac Technologies Corp Resilience Capital
EQS Equus Total Return, Inc. Sam Douglass
FCM First Trust/Four Corners Senior

Floating Rate Income Fund

Bulldog Investors
FFHS First Franklin Corp Lenox Wealth Management
FGF SunAmerica Focused Alpha Growth Inc Bulldog Investors
FGF SunAmerica Focused Alpha Growth Fund, Inc. Bulldog Investors
FGI SunAmerica Focused Alpha Large-Cap Fund, Inc. Bulldog Investors
GLA Clark Holdings, Inc. Cherokee Capital Management
GRNB Green Bankshares, Inc. Scott Niswonger
GSIG.PK GSI Group Stephen Bershad
GSIG.PK GSI Group JEC II Associates LLC
IMMR Immersion Corp Ramius Capital
IPCS iPCS, Inc. Greywolf Capital Management
JTX Jackson Hewitt Tax Service Inc. Discovery Capital
KONA Kona Grill Inc BBS Capital Management
LEGC Legacy Bancorp Inc. Sandler O’Neill Asset Management
LNY Landry’s Restaurants Inc. Pershing Square Capital
MACE Mace Security International Inc Lawndale Capital
MEG Media General Inc GAMCO Investors
MGYR Magyar Bancorp Inc. Financial Edge Fund
MYE Myers Industries Inc GAMCO Investors
OMPI Obagi Medical Products Discovery Group
ORCC Online Resources Corp Tennenbaum Capital Partners
OSTE Osteotech Inc. Kairos Partners
PLFE Presidential Life Corp Herbert Kurz
PTEC Phoenix Technologies Ramius Capital
PTFC.PK Penn Traffic Co Foxhill Opportunity Fund
SONA Southern National Bancorp of Virginia Inc Patriot Financial Partners
SRO DWS Rreef Real Estate Fund, Inc Susan Ciciora Trust
SSTI Silicon Storage Technology Inc Lloyd Miller
TICC TICC Capital Corp Raging Capital Management
TRA Terra Industries Inc CF Industries Holdings
TXCC Transwitch Corp Brener International Group
UAHC United American Healthcare Corp. Strategic Turnaround Equity Partners
UAHC United American Healthcare Corp John Fife
USAT USA Technologies Inc. Brad Tirpak; Craig Thomas
WWVY Warwick Valley Telephone Co Santa Monica Partners

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Continuing the quantitative value investment theme I’ve been trying to develop over the last week or so, I present my definition of a simple quantitative value strategy: net nets. James Montier, author of the essay Painting By Numbers: An Ode To Quant, which I use as the justification for simple quantitative investing, authored an article in September 2008 specifically dealing with net nets as a global investment strategy: Graham’’s net-nets: outdated or outstanding? (Edit: It seems this link no longer works as SG obliterates any article ever written by Montier). Quelle surprise, Montier found that buying net-nets is a viable and profitable strategy:

Testing such a deep value approach reveals that it would have been a highly profitable strategy. Over the period 1985-2007, buying a global basket of net-nets would have generated a return of over 35% p.a. versus an equally weighted universe return of 17% p.a.

An annual return of 35% over 23 years would put you in elite company indeed, so Montier’s methodology is worthy of closer inspection. Unfortunately he doesn’t discuss his methodology in any detail, other than to say as follows:

I decided to test the performance of buying net-nets on a global basis. I used a sample of developed markets over the period 1985 onwards, all returns were in dollar terms.

It may have been a strategy similar to the annual rebalancing methodology discussed in Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update. That paper demonstrates a purely mechanical annual rebalancing of stocks meeting Graham’s net current asset value criterion generated a mean return between 1970 and 1983  of “29.4% per year versus 11.5% per year for the NYSE-AMEX Index.” It doesn’t really matter exactly how Montier generated his return. Whether he bought each net net as it became a net net or simply purchased a basket on a regular basis (monthly, quarterly, annually, whatever), it’s sufficient to know that he was testing the holding of a basket of net nets throughout the period 1985 to 2007.

Montier’s findings are as follows:

  • The net-nets portfolio contains a median universe of 65 stocks per year.
  • There is a small cap bias to the portfolio. The median market cap of a net-net is US$21m.
  • At the time of writing (September 2008), Montier found around 175 net-nets globally. Over half were in Japan.
  • If we define total business failure as stocks that drop more than 90% in a year, then the net-nets portfolio sees about 5% of its constituents witnessing such an event. In the broad market only around 2% of stocks suffer such an outcome.
  • The overall portfolio suffered only three down years in our sample, compared to six for the overall market.

Several of Montier’s findings are particularly interesting to me. At an individual company level, a net net is more likely to suffer a permanent loss of capital than the average stock:

If we define a permanent loss of capital as a decline of 90% or more in a single year, then we see 5% of the net-nets selections suffering such a fate, compared with 2% in the broader market.

Here’s the chart:

This is interesting given that NCAV is often used as a proxy for liquidation value.

Very few companies turn out to have an ultimate value less than the working capital alone, although scattered instances may be found.

Montier believes this may provide a clue as to why the net net strategy continues to work:

This relatively poor performance may hint at an explanation as to why investors shy away from net-nets. If investors look at the performance of the individual stocks in their portfolio rather than the portfolio itself (known as ‘narrow-framing’), then they will see big losses more often than if they follow a broad market strategy. We know that people are generally loss averse, so they tend to feel losses far more than gains. This asymmetric response coupled with narrow framing means that investors in the net-nets strategy need to overcome several behavioural biases.

Paradoxically, it seems that what is true at the individual company level is not true at an aggregate level. The net net strategy has fewer down years than the market:

If one were to frame more broadly and look at the portfolio performance overall, the picture is much brighter. The net-net strategy only generated losses in three years in the entire sample we backtested. In contrast, the overall market witnessed some six years of negative returns.

Here’s the chart:

And it seems that the net net strategy is a reasonable contrary indicator. When the market is up, fewer can be found, and when the market is down, they seem to be available in abundance:

The main drawback to the net net strategy is its limited application. Stocks tend to be small and illiquid, which puts a limit on the amount of capital that can be safely run using it. That aside, it seems like a good way to get started in a small fund or with a individual account. Montier concludes:

…In various ways practically all these bargain issues turned out to be profitable and the average annual return proved much more remunerative than most other investments.

Good old Benjamin Graham. What a guy.

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