Archive for December, 2009

Happy holidays, folks

I’m taking a two-week break. See you back here January 11th, 2010.

In the interim, please feel free to comment or drop us a line at greenbackd [at] gmail [dot] com. I’ll be checking email and the comments on the site throughout the break, and will respond sporadically.

Thank you for supporting Greenbackd in 2009.

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I find it interesting to see which posts on Greenbackd attract the most attention and I thought you might too. To that end, here are the 10 most popular Greenbackd posts of 2009:

  1. The best unknown activist investment of 2009
  2. Seth Klarman on Liquidation Value
  3. Tweedy Browne updates What Has Worked In Investing
  4. Marty Whitman’s adjustments to Graham’s net net formula
  5. Walter Schloss, superinvestor
  6. Sub-liquidation value ten baggers
  7. VXGN gifted to OXGN; VXGN directors abandon shareholders, senses
  8. Valuing long-term and fixed assets
  9. Where in the world is Chapman Capital?
  10. Counterintuition

Why was The best unknown activist investment of 2009 the most popular post of 2009, attracting 5 times the traffic of the Seth Klarman on Liquidation Value post, which is number 2 on the list? Who knows? It seems you guys like stories about idiosyncratic investors who trade in odd securities found off the beaten track.

Here are four near misses:

  1. The end of value investing?
  2. Buffett on gold
  3. Marty Whitman discusses Graham’s net-net formula
  4. John Paulson and The Greatest Trade Ever

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In his 2006 research report Painting By Numbers: An Ode To Quant (via The Hedge Fund Journal) James Montier presents a compelling argument for a quantitative approach to investing. Montier’s thesis is that simple statistical or quantitative models consistently outperform expert judgements. This phenomenon continues even when the experts are provided with the models’ predictions. Montier argues that the models outperform because humans are overconfident, biased, and unable or unwilling to change.

Montier makes his argument via a series of examples drawn from fields other than investment. The first example he gives, which he describes as a “classic in the field” and which succinctly demonstrates the two important elements of his thesis, is the diagnosis of patients as either neurotic or psychotic. The distinction is as follows: a psychotic patient “has lost touch with the external world” whereas a neurotic patient “is in touch with the external world but suffering from internal emotional distress, which may be immobilising.” According to Montier, the standard test to distinguish between neurosis or psychosis is the Minnesota Multiphasic Personality Inventory or MMPI:

In 1968, Lewis Goldberg1 obtained access to more than 1000 patients’ MMPI test responses and final diagnoses as neurotic or psychotic. He developed a simple statistical formula, based on 10 MMPI scores, to predict the final diagnosis. His model was roughly 70% accurate when applied out of sample. Goldberg then gave MMPI scores to experienced and inexperienced clinical psychologists and asked them to diagnose the patient. As Fig.1 shows, the simple quant rule significantly outperformed even the best of the psychologists.

Even when the results of the rules’ predictions were made available to the psychologists, they still underperformed the model. This is a very important point: much as we all like to think we can add something to the quant model output, the truth is that very often quant models represent a ceiling in performance (from which we detract) rather than a floor (to which we can add).

The MMPI example illustrates the two important points of Montier’s thesis:

  1. The simple statistical model outperforms the judgements of the best experts.
  2. The simple statistical model outperforms the judgements of the best experts, even when those experts are given access to the simple statistical model.

Montier goes on to give diverse examples of the application of his theory, ranging from the detection of brain damage, the interview process to admit students to university, the likelihood of a criminal to re-offend, the selection of “good” and “bad” vintages of Bordeaux wine, and the buying decisions of purchasing managers. He then discusses some “meta-analysis” of studies to demonstrate that “the range of evidence I’ve presented here is not somehow a biased selection designed to prove my point:”

Grove et al consider an impressive 136 studies of simple quant models versus human judgements. The range of studies covered areas as diverse as criminal recidivism to occupational choice, diagnosis of heart attacks to academic performance. Across these studies 64 clearly favoured the model, 64 showed approximately the same result between the model and human judgement, and a mere 8 studies found in favour of human judgements. All of these eight shared one trait in common; the humans had more information than the quant models. If the quant models had the same information it is highly likely they would have outperformed.

As Paul Meehl (one of the founding fathers of the importance of quant models versus human judgements) wrote: There is no controversy in social science which shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one… predicting everything from the outcomes of football games to the diagnosis of liver disease and when you can hardly come up with a half a dozen studies showing even a weak tendencyin favour of the clinician, it is time to draw a practical conclusion.

Why not investing?

Montier says that, within the world of investing, the quantitative approach is “far from common,” and, where it does exist, the practitioners tend to be “rocket scientist uber-geeks,” the implication being that they would not employ a simple model. So why isn’t quantitative investing more common? According to Montier, the “most likely answer is overconfidence.”

We all think that we know better than simple models. The key to the quant model’s performance is that it has a known error rate while our error rates are unknown.

The most common response to these findings is to argue that surely a fund manager should be able to use quant as an input, with the flexibility to override the model when required. However, as mentioned above, the evidence suggests that quant models tend to act as a ceiling rather than a floor for our behaviour. Additionally there is plenty of evidence to suggest that we tend to overweight our own opinions and experiences against statistical evidence.

Montier provides the following example is support of his contention that we tend to prefer our own views to statistical evidence:

For instance, Yaniv and Kleinberger11 have a clever experiment based on general knowledge questions such as: In which year were the Dead Sea scrolls discovered?

Participants are asked to give a point estimate and a 95% confidence interval. Having done this they are then presented with an advisor’s suggested answer, and asked for their final best estimate and rate of estimates. Fig.7 shows the average mean absolute error in years for the original answer and the final answer. The final answer is more accurate than the initial guess.

The most logical way of combining your view with that of the advisor is to give equal weight to each answer. However, participants were not doing this (they would have been even more accurate if they had done so). Instead they were putting a 71% weight on their own answer. In over half the trials the weight on their own view was actually 90-100%! This represents egocentric discounting – the weighing of one’s own opinions as much more important than another’s view.

Similarly, Simonsohn et al12 showed that in a series of experiments direct experience is frequently much more heavily weighted than general experience, even if the information is equally relevant and objective. They note, “If people use their direct experience to assess the likelihood of events, they are likely to overweight the importance of unlikely events that have occurred to them, and to underestimate the importance of those that have not”. In fact, in one of their experiments, Simonsohn et al found that personal experience was weighted twice as heavily as vicarious experience! This is an uncannily close estimate to that obtained by Yaniv and Kleinberger in an entirely different setting.

It is worth noting that Montier identifies LSV Asset Management and Fuller & Thaler Asset Management as being “fairly normal” quantitative funds (as opposed to being “rocket scientist uber-geeks”) with “admirable track records in terms of outperformance.” You might recognize the names: “LSV” stands for Lakonishok, Shleifer, and Vishny, authors of the landmark Contrarian Investment, Extrapolation and Risk paper, and the “Thaler” in Fuller & Thaler is Richard H. Thaler, co-author of Further Evidence on Investor Overreaction and Stock Market Seasonality, both papers I’m wont to cite. I’m not entirely sure what strategies LSV and Fuller & Thaler pursue, wrapped as they are in the cloaks of “behavioural finance,” but judging from those two papers, I’d say it’s a fair bet that they are both pursuing value-based strategies.

It might be a while before we see a purely quantitative value fund, or at least a fund that acknowledges that it is one. As Montier notes:

We find it ‘easy’ to understand the idea of analysts searching for value, and fund managers rooting out hidden opportunities. However, selling a quant model will be much harder. The term ‘black box’ will be bandied around in a highly pejorative way. Consultants may question why they are employing you at all, if ‘all’ you do is turn up and run the model and then walk away again.

It is for reasons like these that quant investing is likely to remain a fringe activity, no matter how successful it may be.

Montier’s now at GMO, and has produced a new research report called Ten Lessons (Not?) Learnt (via Trader’s Narrative).

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In mid November I ran a post on Convera Corporation (NASDAQ:CNVR) (see the CNVR post archive here), which was in the process of liquidating and planning to pay distributions valued in the range of $0.26 to $0.45 per share. The stock was then trading at $0.221. The distributions consisted of three cash payments with a value of $0.26 per share ($10M on liquidation, and a $2M payment on each of the 6 and 12 month anniversaries of liquidation) and a share in a newly created company, VSW, worth between nothing and $0.14 on pretty heroic assumptions. The most recent 10Q is a little troubling because it doesn’t mention the two $2M distributions, which account for about $0.07 of value in the liquidation. They are still included in the original plan of liquidation and therefore by reference in latest 10Q. We have not, however, been able to contact the CFO to confirm that the distributions are still payable. I believe that some distribution is still payable, but not in the quantum originally estimated by the company. My rough estimate, based on the accounts as at October 31, places the total cash distributions slightly lower than the company’s last estimate at ~$13.0M or $0.24 per share.

The original information statement

Here’s the description from the 14(c) information statement:

We plan to distribute $10,000,000 shortly after the closing of the Merger, with the remaining $4,000,000 to be distributed in $2,000,000 increments at six months and 12 months after the closing of the Merger, subject to possible holdbacks for potential liabilities and on-going expenses deemed necessary by our board of directors in its sole discretion.

The present value of this cash distribution, assuming a discount rate of 10%, is estimated at $0.26 per share.


Hempstead assessed the value indication associated with a one-third equity interest in VSW based upon the discounted cash flows methodology. Specifically, under a discounted cash flows methodology, the value of a company’s stock is determined by discounting to present value the expected returns that accrue to holders of such equity. Projected cash flows for VSW were based upon projected financial data prepared by our management. Estimated cash flows to equity holders were discounted to present value based upon a range of discount rates, from 25% to 35%. This range of discount rates is reflective of the required rates of return on later-stage venture capital investments. The resultant value indications for the VSW component of the transaction, on a per-Convera share basis, are as follows:


Based upon the above analyses, the value indications for the cash and VSW stock to be received by our stockholders in exchange for their current Convera shares are within a range of $0.37 to $0.45 per Convera share.

The most recent 10Q

This is the position according to the most recent 10Q:

On June 1, 2009, we announced our plans to merge our search business with Vertical Search Works, Inc. and our expectation to adopt a plan of dissolution with orderly wind down and liquidation of Convera before the closing of the merger. The merger with VSW contemplates the transfer of all the business assets and obligations of the search business, including $3.0 million in cash and a $1.0 million line of credit to VSW, subject to certain adjustments. The plan of dissolution contemplates a $10.0 million dividend to shareholders of record at the close of the transaction and an orderly wind up of Convera’s remaining obligations over the twelve months after closing. We believe that we have sufficient cash resources on hand to complete the merger and the plan of dissolution. We expect the conditions for the closing of the VSW transaction will be met early in 2010. However, we make no assurances that either the merger or the plan of dissolution will be completed.

Here’s my rough estimate of the state of the balance sheet here after a further 12 months of cash burn and professional fees:

According to my back-of-the-envelope calculations, the distributions estimated by management seem slightly high, but my estimate is sensitive to the quantum of the cash burn and professional fees. At the present stock price, there’s no upside in the cash distributions. The share in VSW may present some value, but no sensible estimate can be made as to that value. The range is likely nil to $0.14 per share, and I believe nil is the more likely end of the range. I’m going to maintain Greenbackd’s position in CNVR because I think the worst case scenario – which is probably the most likely scenario – is that the position is a wash, but there is some small chance that there is value in VSW.

Hat tip Rodrigo.

[Full Disclosure:  I do not hold CNVR. 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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Update: I’ve removed SIG from the list.

In Ben Graham’s Net Current Asset Values: A Performance Update Professor Henry Oppenheimer examined the return on stocks selected using Benjamin Graham’s net current asset value strategy over the period 1970 to 1983. Oppenheimer’s conclusion about the returns from such stocks was nothing short of extraordinary:

The mean return from net current asset stocks for the 13-year period was 29.4% per year versus 11.5% per year for the NYSE-AMEX Index. One million dollars invested in the net current asset portfolio on December 31, 1970 would have increased to $25,497,300 by December 31, 1983. By comparison, $1,000,000 invested in the NYSE-AMEX Index would have increased to $3,729,600 on December 31, 1983. The net current asset portfolio’s exceptional performance over the entire 13 years was not consistent over smaller subsets of time within the 13-year period. For the three-year period, December 31, 1970 through December 31, 1973, which represents 23% of the 13-year study period, the mean annual return from the net current asset portfolio was .6% per year as compared to 4.6% per year for the NYSE-AMEX Index.

Oppenheimer’s methodology was to acquire all stocks meeting Graham’s investment criterion on December 31 of each year, hold those stocks for one year, and replace them on December 31 of the subsequent year. I’m introducing a new portfolio to track the performance of Graham NCAV stocks in real time. I’ll roll it over annually, like Oppenheimer did. Here’s the Greenbackd 2010 Graham NCAV Portfolio (extracted from the Graham Investor screen):

You can track the performance of the Greenbackd 2010 Graham NCAV Portfolio throughout 2010 with Tickerspy.

[Full Disclosure:  No positions. 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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The Wall Street Journal’s Deal Journal blog has an article, The Secret to M&A: It Pays to Be Humble, about a KPMG study into the factors determining the success or otherwise of M&A deals over the period from 2002 to 2006. Some of the results are a little unexpected. Most surprising: acquirers purchasing targets with higher P/E ratios outperformed acquirers of targets with lower P/E ratios, which seems to fly in the face of every study I’ve ever read, and calls into question everything that is good and holy in the world. In effect, KPMG is saying that the relationship of value as a predictor of investment returns broke down for the period studied. I think it’s an aberration, and I’ll be sticking with value as my strategy.

In the study, The Determinants of M&A Success What Factors Contribute to Deal Success? (.pdf), KPMG examined a number of variables to determine which had a statistically significant influence on the stock performance of the acquirer. Those variables examined included the following:

  • How the deal was financed—stock vs. cash, or both
  • The size of the acquirer
  • The price-to-earnings (“P/E”) ratio of the acquirer
  • The P/E ratio of the target
  • The prior deal experience of the acquirer
  • The stated deal rationale
  • Whether or not the deal was cross-border

KPMG found that some factors were highly correlated with success (for example, paying with cash, rather than using stock or cash and stock) and others were not statistically significant (surprisingly, market capitalization). Here are KPMG’s “key findings”:

  • Cash-only deals had higher returns than stock-and-cash deals, and stock-only deals
  • Acquirers with low price-to-earnings (P/E) ratios resulted in more successful deals
  • Those companies that closed three to five deals were the most successful; closing more than five deals in a year reduced success
  • Transactions that were motivated by increasing “financial strength” were most successful
  • The size of the acquirer (based on market capitalization) was not statistically significant

The P/E ratio of the target is correlated with success, but not in the manner that one might expect:

The P/E ratio of the target was also statistically significant. In contrast to our previous study, acquirers who were able to purchase companies with P/E ratios below the industry median saw a negative 6.3 percent return after one year and a negative 6.0 percent return after two years. Acquirers who purchased targets with P/E ratios above the median, including those with negative P/E ratios, had a negative 1 percent return after one year and a negative 3.5 percent return after two years. These results are very different from the ones we found in our last study for deals announced between 2000 and 2004. Those earlier deals demonstrated the more anticipated results: acquirers who purchased targets with below average P/E ratios were more successful than acquirers who purchased targets with higher P/E ratios.

It is probable that in the deals announced between 2002 and 2006, acquirers who purchased targets with high P/E ratios were buying businesses that were growing and where the acquirer was able to achieve greater synergies. Deals announced between 2000 and 2004 included deals from the “dot-com” era, where high P/E ratios were often associated with unprofitable ventures that were not able to meet future income expectations.

Here’s the chart showing the relative returns to P/E:

Now, we value folk know that, in any given instance, the P/E ratio alone tells us little about the sagacity of an investment. In the aggregate, however, we would have expected the lower P/E targets to outperform the more expensive acquisitions. That’s not just wishful thinking, it’s based on the various studies that I am so fond of quoting, most notably Lakonishok, Shleifer, and Vishny’s Contrarian Investment, Extrapolation and Risk. Lakonishok, Shleifer, and Vishny found that “value”determined on the basis of P/E consistently outperformed “glamour”. That relationship seems to have broken down over the period 2002 to 2006 according to the KPMG study.

There are several possible explanations for KPMG’s odd finding. First, they weren’t directly tracking the performance of the stock of the target, they were analysing the performance of the stock of the acquirer, which means that other factors in the acquirers’ stocks could have influenced the outcome. Second, five years is a relatively short period to study. A longer study may have resulted in the usual findings. Third, it’s possible that 2002 to 2006 was a period where the traditional value phenomenon broke down. It was a big leg up in the market, and a bull market makes everyone look like a genius. Perhaps it didn’t matter what an acquirer paid. That seems unlikely, because the stocks of the acquirers were generally down over the period. Finally, KPMG might have taken an odd sample. They looked at acquisitions “where acquirers purchased 100 percent of the target, where the target constituted at least 20 percent of the sales of the acquirer and where the purchase price was in excess of US$100 million. The average deal size of the transactions in this study was US$3.4 billion; the median was US$0.7 billion.” Perhaps that slice of the market is different from the rest of the market. Again, that seems unlikely. I think KPMG’s finding is an aberration. I certainly wouldn’t turn it into a strategy.

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I’m setting up a new experiment for 2009/2010 along the same lines as the 2008/2009 Net Net vs Activist Legend thought experiment pitting a little Graham net net against activist investing legend Carl Icahn (Net Net vs Activist Legend: And the winner is…). This time around I’m pitting a small portfolio of near Graham net nets against a small portfolio of ultra-low price-to-book value stocks. The reason? Near Graham net nets are stocks trading at a small premium to Graham’s two-thirds NCAV cut-off, but still trading at a discount to NCAV. While they are also obviously trading at a discount to book, they will in many cases trade at a higher price-to-book value ratio than a portfolio of stocks selected on the basis of price-to-book only. I’m interested to see which will perform better in 2010. The two portfolios are set out below (each contains 30 stocks). I’ll track the equal-weighted returns of each through the year.

The Near Graham Net Net Portfolio (extracted from the Graham Investor screen):

The Ultra-low Price-to-book Portfolio:

The Ultra-low Price-to-book Portfolio contains a sickly lot from a net current asset value perspective. Most have a negative net current asset value, as their liabilities exceed their current assets. Where that occurs, the proportion of price to NCAV is meaningless, so I’ve just recorded it as “N/A”. The few stocks that do have a positive net current asset value are generally trading a substantial premium to that value, with the exception of NWD and ZING, which qualify as Graham net nets.

While the Net Net vs Activist Legend thought experiment didn’t amount to (ahem) a formal academic study, there are two studies relevant to the outcome in that experiment: Professor Henry Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update, which found “[the] mean return from net current asset stocks for the 13-year period [from 1970 to 1983] was 29.4% per year versus 11.5% per year for the NYSE-AMEX Index.” Also relevant was Hedge Fund Activism, Corporate Governance, and Firm Performance, by Brav, Jiang, Thomas and Partnoy, in which the authors found that the “market reacts favorably to hedge fund activism, as the abnormal return upon announcement of potential activism is in the range of [7%] seven percent, with no return reversal during the subsequent year.”

This experiment is similar to the Net Net vs Activist Legend thought experiment in that it isn’t statistically significant. There are, however, several studies relevant to divining the outcome. In this instance, Professor Oppenheimer’s study speaks to the return on the Near Graham Net Net Portfolio, as Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986), Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987), Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s Contrarian Investment, Extrapolation and Risk (1994) as updated by The Brandes Institute’s Value vs Glamour: A Global Phenomenon (2008) speak to the return on the Ultra-low Price-to-book Portfolio. One wrinkle in that theory is that the low price-to-book value studies only examine the cheapest quintile and decile, where I have taken the cheapest 30 stocks on the Google Finance screener, which is the cheapest decile of the cheapest decile. I expect these stocks to do better than the low price-to-book studies would suggest. That said, I expect that the Near Graham Net Net Portfolio will outperform the Ultra-low Price-to-book Portfolio by a small margin. Let me know which horse you’re getting on and the reason in the comments.

[Full Disclosure:  I hold RCMT and TSRI. 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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