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Posts Tagged ‘Value Investment’

In A Crisis In Quant Confidence*, Abnormal Returns has a superb post on Scott Patterson’s recounting in his book The Quants of the reactions of several quantitative fund managers to the massive reversal in 2007:

In 2007 everything seemed to go wrong for these quants, who up until this point in time, had been coining profits.

This inevitably led to some introspection on the part of these investors as they saw their funds take massive performance hits.  Nearly all were forced to reduce their positions and risks in light of this massive drawdown.  In short, these investors were looking at their models seeing where they went wrong.  Patterson writes:

Throttled quants everywhere were suddenly engaged in a prolonged bout of soul-searching, questioning whether all their brilliant strategies were an illusion, pure luck that happened to work during a period of dramatic growth, economic prosperity, and excessive leverage that lifted everyone’s boat.

Here Patterson puts his finger on the question that vexes anyone who has ever invested, made money for a time and then given some back: Does my strategy actually work or have I been lucky? It’s what I like to call The Fear, and there’s really no simple salve for it.

The complicating factor in the application of any investing strategy, and the basis for The Fear, is that even exceptionally well-performed strategies will both underperform the market and have negative periods that can extend for three, five or, on rare occasions, more years. Take, for example, the following back-test of a simple value strategy over the period 2002 to the present. The portfolio consisted of thirty stocks drawn from the Russell 3000 rebalanced daily and allowing 0.5% for slippage:

(Click to enlarge)

The simple value strategy returns a comically huge 2,450% over the 8 1/4 years, leaving the Russell 3000 Index in its wake (the Russell 3000 is up 9% for the entire period). 2,450% over the 8 1/4 years is an average annual compound return of 47%. That annual compound return figure is, however, misleading. It’s not a smooth upward ride at a 47% rate from 100 to 2,550. There are periods of huge returns, and, as the next chart shows, periods of substantial losses:

(Click to enlarge)

From January 2007 to December 2008, the simple value strategy lost 20% of its value, and was down 40% at its nadir. Taken from 2006, the strategy is square. That’s three years with no returns to show for it. It’s hard to believe that the two charts show the same strategy. If your investment experience starts in a down period like this, I’d suggest that you’re unlikely to use that strategy ever again. If you’re a professional investor and your fund launches into one of these periods, you’re driving trucks. Conversely, if you started in 2002 or 2009, your returns were excellent, and you’re genius. Neither conclusion is a fair one.

Abnormal Returns says of the correct conclusion to draw from performance:

An unexpectedly large drawdown may mark the failure of the model or may simply be the result of bad luck. The fact is that the decision will only be validated in hindsight. In either case it represents a chink in the armor of the human-free investment process. Ultimately every portfolio is run by a (fallible) human, whether they choose to admit it or not.

In this respect quantitative investing is not unlike discretionary investing. At some point every investor will face the choice of continuing to use their method despite losses or choosing to modify or replace the current methodology. So while quantitative investing may automate much of the investment process it still requires human input. In the end every quant model has a human with their hand on the power plug ready to pull it if things go badly wrong.

At an abstract, intellectual level, an adherence to a philosophy like value – with its focus on logic, discipline and character – alleviates some of the pain. Value answers the first part of the question above, “Does my strategy actually work?” Yes, I believe value works. The various academic studies that I’m so fond of quoting (for example, Value vs Glamour: A Global Phenomenon and Contrarian Investment, Extrapolation and Risk) confirm for me that value is a real phenomenon. I acknowledge, however, that that view is grounded in faith. We can call it logic and back-test it to an atomic level over an eon, but, ultimately, we have to accept that we’re value investors for reasons peculiar to our personalities, and not because we’re men and women of reason and rationality. It’s some comfort to know that greater minds have used the philosophy and profited. In my experience, however, abstract intellectualism doesn’t keep The Fear at bay at 3.00am. Neither does it answer the second part of the question, “Am I a value investor, or have I just been lucky?”

As an aside, whenever I see back-test results like the ones above (or like those in the Net current asset value and net net working capital back-test refined posts) I am reminded of Marcus Brutus’s oft-quoted line to Cassius in Shakespeare’s Julius Caesar:

There is a tide in the affairs of men,

Which, taken at the flood, leads on to fortune;

Omitted, all the voyage of their life

Is bound in shallows and in miseries.

As the first chart above shows, in 2002 or 2009, the simple value strategy was in flood, and lead on to fortune. Without those two periods, however, the strategy seems “bound in shallows and in miseries.” Brutus’s line seems apt, and it is, but not for the obvious reason. In the scene in Julius Caesar from which Brutus’s line is drawn, Brutus tries to persuade Cassius that they must act because the tide is at the flood (“On such a full sea are we now afloat; And we must take the current when it serves, Or lose our ventures.”). What goes unsaid, and what Brutus and Cassius discover soon enough, is that a sin of commission is deadlier than a sin of omission. The failure to take the tide at the flood leads to a life “bound in shallows and in miseries,” but taking the tide at the flood sometimes leads to death on a battlefield. It’s a stirring call to arms, and that’s why it’s quoted so often, but it’s worth remembering that Brutus and Cassius don’t see the play out.

* Yes, the link is to classic.abnormalreturns. I like my Abnormal Returns like I like my Coke.

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In a post in late November last year, Testing the performance of price-to-book value, I set up a hypothetical equally-weighted portfolio of the cheapest price-to-book stocks with a positive P/E ratio discovered using the Google Screener, which I called the “Greenbackd Contrarian Value Portfolio“. The portfolio has been operating for a little over 4 months, so I thought I’d check in and see how it’s going.

Here is the Tickerspy portfolio tracker for the Greenbackd Contrarian Value Portfolio showing how each individual stock is performing:

(Click to enlarge)

And the chart showing the performance of the portfolio against the S&P500:

[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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In the Introduction to my 2003 copy of Philip A. Fisher’s Common Stocks and Uncommon Profits and Other Writings, his son, Kenneth L. Fisher, recounts a story about his father that has stuck with me since I first read it. For me, it speaks to Phil Fisher’s eclectic genius, and quirky sense of humor:

But one night in the early 1970’s, we were together in Monterey at one of the first elaborate dog-and-pony shows for technology stocks – then known as “The Monterey Conference” – put on by the American Electronics Association. At the Monterey Conference, Father exhibited another quality I never forgot. The conference announced a dinner contest. There was a card at each place setting, and each person was to write down what he or she thought the Dow Jones Industrials would do the next day, which is, of course, a silly exercise. The cards were collected. The person who came closest to the Dow’s change for the day would win a mini-color TV (which were hot new items then). The winner would be announced at lunch the next day, right after the market closed at one o’clock (Pacific time). Most folks, it turned out, did what I did – wrote down some small number, like down or up 5.57 points. I did that assuming that the market was unlikely to do anything particularly spectacular because most days it doesn’t. Now in those days, the Dow was at about 900, so 5 points was neither huge nor tiny. That night, back at the hotel room, I asked Father what he put down; and he said, “Up 30 points,” which would be more than 3 percent. I asked why. he said he had no idea at all what the market would do; and if you knew him, you knew that he never had a view of what the market would do on a given day. But he said that if he put down a number like I did and won, people would think he was just lucky – that winning at 5.57 meant beating out the guy that put down 5.5 or the other guy at 6.0. It would all be transparently seen as sheer luck. But if he won saying, “up 30 points,” people would think he knew something and was not just lucky. If he lost, which he was probable and he expected to, no one would know what number he had written down, and it would cost him nothing. Sure enough, the next day, the Dow was up 26 points, and Father won by 10 points.

When it was announced at lunch that Phil Fisher had won and how high his number was, there were discernable “Ooh” and “Ahhhh” sounds all over the few-hundred-person crowd. There was, of course, the news of the day, which attempted to explain the move; and for the rest of the conference, Father readily explained to people a rationale for why he had figured out all that news in advance, which was pure fiction and nothing but false showmanship. But I listened pretty carefully, and everyone he told all that to swallowed it hook, line, and sinker. Although he was socially ill at ease always, and insecure, I learned that day that my father was a much better showman than I had ever fathomed. And, oh, he didn’t want the mini-TV because he had no use at all for change in his personal life. So he gave it to me and I took it home and gave it to mother, and she used it for a very long time.

Common Stocks and Uncommon Profits and Other Writings is, of course, required reading for all value investors. I believe the Introduction to the 2003 edition, written by Kenneth Fisher, should also be regarded as required reading. There Kenneth [Edit:, an investment superstar in his own right,] shares intimate details about Phil from the perspective of a son working with the father. As the vignette above demonstrates, Phil understood human nature, but was socially awkward; he understood the folly of the narrative, but was prepared to provide a colorful one when it suited him; and he understood positively skewed risk:reward bets in all aspects of his life, and had the courage to take them, even if it meant standing apart from the crowd. What is most striking about this sketch of Phil Fisher is that it could just as easily be a discussion of Mike Burry or Warren Buffett. Perhaps great investors are like Leo Tolstoy’s happy families:

Happy families are all alike; every unhappy family is unhappy in its own way.

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Yesterday I highlighted an investment strategy I first read about in a Spring 1999 research report called Wall Street’s Endangered Species by Daniel J. Donoghue, Michael R. Murphy and Mark Buckley, then at Piper Jaffray and now at Discovery Group, a firm founded by Donoghue and Murphy. The premise, simply stated, is to identify undervalued small capitalization stocks where a catalyst in the form of a merger or buy-out might emerge to close the value gap. I believe the strategy is a natural extension for Greenbackd, and so I’m going to explore it in some depth over the next few weeks.

The idea is reminiscent of “Super” Mario J. Gabelli’s Private Market Value with a Catalyst methodology, the premise of which is the value of a company “if it is acquired by an informed wealthy family, or by another private or public corporation, as opposed to the price it is trading at in the stock markets. Simply put, it is the intrinsic value of a company plus the control premium:”

To calculate PMV, Gabelli first takes into account the free cash flow (after allowing for depreciation), deducts debt and net options (stock options) and adds back the cash. To this, he then applies an ‘appropriate’ multiple to arrive at the PMV. It sounds simple enough, but where you can go completely wrong is the multiple. Gabelli says he either looks at recent valuations of similar acquisitions or applies an appropriate historical industry acquisition multiple to arrive at the PMV.

“Some of the factors that we look at while deciding multiples to apply are: what the business is going to be worth in five years from now, what kind of return on equity can we get over time, how much further debt can be put on the company, the tax rate and what the company would be worth if there was no growth or at some particular rate (4 or 8 per cent for instance),” he explains. Of course, the multiple – and the PMV – changes over time, as it is a function of interest rates, the capitalisation structure and taxes, all of which have an indirect impact on the value of the franchise.

Donoghue, Murphy and Buckley followed up their initial Wall Street’s Endangered Species research report with two updates, which I recall were each called “Endangered Species Update” and discussed the returns from the strategy. It seems that those follow-up reports are now lost to the sands of time. All that seems to remain is the press release of the final report:

For the last few years, Piper Jaffray has been reporting on the difficulties that small public companies face in today’s equity markets. Since the late 1990s many well run, profitable companies with a market capitalization of less than $250 million have watched their share prices underperform the rest of the stock market. With limited analyst coverage and low trading liquidity, many high-quality small companies are “lost in the shuffle” and trade at significantly lower valuation multiples than larger firms. Since our 1999 report “Wall Street’s Endangered Species,” we have held the position that:

This is a secular, not cyclical, trend and the undervaluation will continue. The best strategic move to increase shareholder value is to pursue a change-of-control transaction. Company management and the Board should either sell their company to a large strategic acquirer with the hope of gaining the buyer’s higher trading multiple, or take the company private.

In the last few of years, many small public companies identified this trend and agreed with the implications. Executives responded accordingly, and the number of strategic mergers and going-private transactions for small companies reached all-time highs. Shareholders of these companies were handsomely rewarded. The remaining companies, however, have watched their share prices stagnate.

Since the onset of the recent economic slowdown and the technology market correction, there has been much talk about a return to “value investing.” Many of our clients and industry contacts have even suggested that as investors search for more stable investments, they will uncover previously ignored small cap companies and these shareholders will finally be rewarded. We disagree and the data supports us:

Any recent increase in small-cap indices is misleading. Most of the smallest companies are still experiencing share price weakness and valuations continue to be well below their larger peers. We strongly believe that when the overall market rebounds, small-cap shareholders will experience significant underperformance unless their boards effect a change-of-control transaction.

In this report we review and refresh some of our original analyses from our previous publications. We also follow the actions and performance of companies that we identified over the past two years as some of the most attractive yet undervalued small-cap companies. Our findings confirm that companies that pursued a sale rewarded their shareholders with above-average returns, while the remaining companies continue to be largely ignored by the market. Finally, we conclude with our third annual list of the most attractive small-cap companies: Darwin’s Darlings Class of 2001.

Piper Jaffray did follow up the reports in a 2006 article called Is There a Renewed Prospect of Going-Private Transactions? Their conclusion:

Small-Cap Stocks Outperform

Small-cap stocks have experienced a dramatic resurgence over the past five years. With weak performances from large-cap stocks, small-caps have become more favorable investments with better returns and stronger trading multiples. Here is what we have seen:

  • Over the last one-, three- and five-year periods, companies in the Russell 2000 have offered average returns of 21%, 227% and 240%, respectively, compared to S&P 500 companies with average returns of 16%, 89% and 57%, respectively.
  • The valuation gap that we saw five years ago between the bottom two deciles of companies in the Russell 2000 and the S&P 500 no longer exists, with the last two deciles in the Russell trading at only a 3% discount to the median EBIT multiple of S&P 500 companies and a 9% premium over the median P/E multiple.

(Click to embiggen)

Despite the rebound in valuations, small-cap stocks continue to face the same capital market challenges:

  • For companies with market caps between the $50 million and $250 million range, there are approximately 1.3 analysts covering each stock versus 7.7 analysts for companies with market caps of more than $250 million.
  • Trading volumes are slightly higher, with the last three deciles trading an average 202,276, 176,092 and 223,599 shares, respectively, per day, but still significantly below the volume of S&P 500 companies, which trade an average of 4.0 million shares per day.

More to come.

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Michael Burry is a value investor notable for being one of the first, if not the first, to short sub-prime mortgage bonds in his fund, Scion Capital. He figures prominently in the Gregory Zuckerman’s book, The Greatest Trade Ever, and also in The Big Short, Michael Lewis’s contribution to the sub-prime mortgage bond market crash canon. In Betting on the Blind Side, Lewis excerpts The Big Short, which describes Burry’s short position in some detail, how he figured out that the bonds were mispriced, and how he bet against them (no small effort because the derivatives to do so didn’t exist when he started looking for them. He had to “prod the big Wall Street firms to create them.”).

Here Lewis describes Burry’s entry into value investing:

Late one night in November 1996, while on a cardiology rotation at Saint Thomas Hospital, in Nashville, Tennessee, he logged on to a hospital computer and went to a message board called techstocks.com. There he created a thread called “value investing.” Having read everything there was to read about investing, he decided to learn a bit more about “investing in the real world.” A mania for Internet stocks gripped the market. A site for the Silicon Valley investor, circa 1996, was not a natural home for a sober-minded value investor. Still, many came, all with opinions. A few people grumbled about the very idea of a doctor having anything useful to say about investments, but over time he came to dominate the discussion. Dr. Mike Burry—as he always signed himself—sensed that other people on the thread were taking his advice and making money with it.

Once he figured out he had nothing more to learn from the crowd on his thread, he quit it to create what later would be called a blog but at the time was just a weird form of communication. He was working 16-hour shifts at the hospital, confining his blogging mainly to the hours between midnight and three in the morning. On his blog he posted his stock-market trades and his arguments for making the trades. People found him. As a money manager at a big Philadelphia value fund said, “The first thing I wondered was: When is he doing this? The guy was a medical intern. I only saw the nonmedical part of his day, and it was simply awesome. He’s showing people his trades. And people are following it in real time. He’s doing value investing—in the middle of the dot-com bubble. He’s buying value stocks, which is what we’re doing. But we’re losing money. We’re losing clients. All of a sudden he goes on this tear. He’s up 50 percent. It’s uncanny. He’s uncanny. And we’re not the only ones watching it.”

Mike Burry couldn’t see exactly who was following his financial moves, but he could tell which domains they came from. In the beginning his readers came from EarthLink and AOL. Just random individuals. Pretty soon, however, they weren’t. People were coming to his site from mutual funds like Fidelity and big Wall Street investment banks like Morgan Stanley. One day he lit into Vanguard’s index funds and almost instantly received a cease-and-desist letter from Vanguard’s attorneys. Burry suspected that serious investors might even be acting on his blog posts, but he had no clear idea who they might be. “The market found him,” says the Philadelphia mutual-fund manager. “He was recognizing patterns no one else was seeing.”

Lewis discusses Burry’s perspective on value investing:

“The late 90s almost forced me to identify myself as a value investor, because I thought what everybody else was doing was insane,” he said. Formalized as an approach to financial markets during the Great Depression by Benjamin Graham, “value investing” required a tireless search for companies so unfashionable or misunderstood that they could be bought for less than their liquidation value. In its simplest form, value investing was a formula, but it had morphed into other things—one of them was whatever Warren Buffett, Benjamin Graham’s student and the most famous value investor, happened to be doing with his money.

Burry did not think investing could be reduced to a formula or learned from any one role model. The more he studied Buffett, the less he thought Buffett could be copied. Indeed, the lesson of Buffett was: To succeed in a spectacular fashion you had to be spectacularly unusual. “If you are going to be a great investor, you have to fit the style to who you are,” Burry said. “At one point I recognized that Warren Buffett, though he had every advantage in learning from Ben Graham, did not copy Ben Graham, but rather set out on his own path, and ran money his way, by his own rules.… I also immediately internalized the idea that no school could teach someone how to be a great investor. If it were true, it’d be the most popular school in the world, with an impossibly high tuition. So it must not be true.”

Investing was something you had to learn how to do on your own, in your own peculiar way. Burry had no real money to invest, but he nevertheless dragged his obsession along with him through high school, college, and medical school. He’d reached Stanford Hospital without ever taking a class in finance or accounting, let alone working for any Wall Street firm. He had maybe $40,000 in cash, against $145,000 in student loans. He had spent the previous four years working medical-student hours. Nevertheless, he had found time to make himself a financial expert of sorts. “Time is a variable continuum,” he wrote to one of his e-mail friends one Sunday morning in 1999: “An afternoon can fly by or it can take 5 hours. Like you probably do, I productively fill the gaps that most people leave as dead time. My drive to be productive probably cost me my first marriage and a few days ago almost cost me my fiancée. Before I went to college the military had this ‘we do more before 9am than most people do all day’ and I used to think I do more than the military. As you know there are some select people that just find a drive in certain activities that supersedes everything else.” Thinking himself different, he didn’t find what happened to him when he collided with Wall Street nearly as bizarre as it was.

And I love this story about his fund:

In Dr. Mike Burry’s first year in business, he grappled briefly with the social dimension of running money. “Generally you don’t raise any money unless you have a good meeting with people,” he said, “and generally I don’t want to be around people. And people who are with me generally figure that out.” When he spoke to people in the flesh, he could never tell what had put them off, his message or his person. Buffett had had trouble with people, too, in his youth. He’d used a Dale Carnegie course to learn how to interact more profitably with his fellow human beings. Mike Burry came of age in a different money culture. The Internet had displaced Dale Carnegie. He didn’t need to meet people. He could explain himself online and wait for investors to find him. He could write up his elaborate thoughts and wait for people to read them and wire him their money to handle. “Buffett was too popular for me,” said Burry. “I won’t ever be a kindly grandfather figure.”

This method of attracting funds suited Mike Burry. More to the point, it worked. He’d started Scion Capital with a bit more than a million dollars—the money from his mother and brothers and his own million, after tax. Right from the start, Scion Capital was madly, almost comically successful. In his first full year, 2001, the S&P 500 fell 11.88 percent. Scion was up 55 percent. The next year, the S&P 500 fell again, by 22.1 percent, and yet Scion was up again: 16 percent. The next year, 2003, the stock market finally turned around and rose 28.69 percent, but Mike Burry beat it again—his investments rose by 50 percent. By the end of 2004, Mike Burry was managing $600 million and turning money away. “If he’d run his fund to maximize the amount he had under management, he’d have been running many, many billions of dollars,” says a New York hedge-fund manager who watched Burry’s performance with growing incredulity. “He designed Scion so it was bad for business but good for investing.”

Thus when Mike Burry went into business he disapproved of the typical hedge-fund manager’s deal. Taking 2 percent of assets off the top, as most did, meant the hedge-fund manager got paid simply for amassing vast amounts of other people’s money. Scion Capital charged investors only its actual expenses—which typically ran well below 1 percent of the assets. To make the first nickel for himself, he had to make investors’ money grow. “Think about the genesis of Scion,” says one of his early investors. “The guy has no money and he chooses to forgo a fee that any other hedge fund takes for granted. It was unheard of.”

Hat tip Bo.

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As I’ve discussed in the past, P/B and P/E are demonstratively useful as predictors of future stock returns, and more so when combined (see, for example, LSV’s Two-Dimensional Classifications). As Josef Lakonishok, Andrei Shleifer, and Robert Vishny showed in Contrarian Investment, Extrapolation, and Risk, within the set of firms whose B/M ratios are the highest (in other words, the lowest price-to-book value), further sorting on the basis of another value variable – whether it be C/P, E/P or low GS – enhances returns. In that paper, LSV concluded that value strategies based jointly on past performance and expected future performance produce higher returns than “more ad hoc strategies such as that based exclusively on the B/M ratio.” A new paper further discusses the relationship between E/P and B/P from an accounting perspective, and the degree to which E/P and B/P together predict stock returns.

The CXO Advisory Group Blog, fast becoming one of my favorite sites for new investment research, has a new post, Combining E/P and B/P, on a December 2009 paper titled “Returns to Buying Earnings and Book Value: Accounting for Growth and Risk” by Francesco Reggiani and Stephen Penman. Penman and Reggiani looked at the relationship between E/P and B/P from an accounting perspective:

This paper brings an accounting perspective to the issue: earnings and book values are accounting numbers so, if the two ratios indicate risk and return, it might have something to do with accounting principles for measuring earnings and book value.

Indeed, an accounting principle connects earnings and book value to risk: under uncertainty, accounting defers the recognition of earnings until the uncertainty has largely been resolved. The deferral of earnings to the future reduces book value, reduces short-term earnings relative to book value, and increases expected long-term earnings growth.

CXO summarize the authors’ methodology and findings as follows:

Using monthly stock return and firm financial data for a broad sample of U.S. stocks spanning 1963-2006 (153,858 firm-years over 44 years), they find that:

  • E/P predicts stock returns, consistent with the idea that it measures risk to short-term earnings.
  • B/P predicts stock returns, consistent with the idea that it measures accounting deferral of risky earnings and therefore risk to both short-term and long-term earnings. This perspective disrupts the traditional value-growth paradigm by associating expected earnings growth with high B/P.
  • For a given E/P, B/P therefore predicts incremental return associated with expected earnings growth. A joint sort on E/P and B/P discovers this incremental return and therefore generates higher returns than a sort on E/P alone, attributable to additional risk (see the chart below).
  • Results are somewhat stronger for the 1963-1984 subperiod than for the 1985-2006 subperiod.
  • Results using consensus analyst forecasts rather than lagged earnings to calculate E/P over the 1977-2006 subperiod are similar, but not as strong.

CXO set out Penman and Reggiani’s “core results” in the following table (constructed by CXO from Penman and Reggiani’s results):

The following chart, constructed from data in the paper, compares average annual returns for four sets of quintile portfolios over the entire 1963-2006 sample period, as follows:

  • “E/P” sorts on lagged earnings yield.
  • “B/P” sorts on lagged book-to-price ratio.
  • “E/P:B/P” sorts first on E/P and then sorts each E/P quintile on B/P. Reported returns are for the nth B/P quintile within the nth E/P quintile (n-n).
  • “B/P:E/P” sorts first on B/P and then sorts each B/P quintile on E/P. Reported returns are for the nth E/P quintile within the nth B/P quintile (n-n).

Start dates for return calculations are three months after fiscal year ends (when annual financial reports should be available). The holding period is 12 months. Results show that double sorts generally enhance performance discrimination among stocks. E/P measures risk to short-term earnings and therefore short-term earnings growth. B/P measures risk to short-term earnings and earnings growth and therefore incremental earnings growth. The incremental return for B/P is most striking in low E/P quintile.

The paper also discusses in some detail a phenomenon that I find deeply fascinating, mean reversion in earnings predicted by low price-to-book values:

Research (in Fama and French 1992, for example) shows that book-to-price (B/P) also predicts stock returns, so consistently so that Fama and French (1993 and 1996) have built an asset pricing model based on the observation. The same discussion of rational pricing versus market inefficiency ensues but, despite extensive modeling (and numerous conjectures), the phenomenon remains a mystery. The mystery deepens when it is said that B/P is inversely related to earnings growth while positively related to returns; low B/P stocks (referred to as “growth” stocks) yield lower returns than high B/P stocks (“value” stocks). Yet investment professionals typically think of growth as risky, requiring higher returns, consistent with the risk-return notion that one cannot buy more earnings (growth) without additional risk.

(emphasis mine)

The paper adds further weight to the predictive ability of low price-to-book value and low price-to-earnings ratios. Its conclusion that book-to-price indicates expected returns associated with expected earnings growth is particularly interesting, and accords with the same findings in Werner F.M. DeBondt and Richard H. Thaler in Further Evidence on Investor Overreaction and Stock Market Seasonality.

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I’ve just finished Ian Ayres’s book Super Crunchers, which I found via Andrew McAfee’s Harvard Business Review blog post, The Future of Decision Making: Less Intuition, More Evidence (discussed in Intuition and the quantitative value investor). Super Crunchers is a more full version of James Montier’s 2006 research report, Painting By Numbers: An Ode To Quant, providing several more anecdotes in support of Montier’s thesis that simple statistical models outperform the best judgements of experts. McAfee discusses one such example in his blog post:

Princeton economist Orley Ashenfleter predicts Bordeaux wine quality (and hence eventual price) using a model he developed that takes into account winter and harvest rainfall and growing season temperature. Massively influential wine critic Robert Parker has called Ashenfleter an “absolute total sham” and his approach “so absurd as to be laughable.” But as Ian Ayres recounts in his great book Supercrunchers, Ashenfelter was right and Parker wrong about the ‘86 vintage, and the way-out-on-a-limb predictions Ashenfelter made about the sublime quality of the ‘89 and ‘90 wines turned out to be spot on.

Ayers provides a number of stories not covered in Montier’s article, from Don Berwick’s “100,000 lives” campaign, Epagogix’s hit movie predictor, Offermatica’s automated web ad serving software, Continental Airlines’s complaint process, and a statistical algorithm for predicting the outcome of Supreme Court decisions. While seemingly unrelated, all are prediction engines based on a quantitative analysis of subjective or qualitative factors.

The Supreme Court decision prediction algorithm is particularly interesting to me, not because I am an ex-lawyer, but because the language of law is language, not often plain, and seemingly irreducible to quantitative analysis. (I believe this is true also of value investment, although numbers play a larger role in that realm, and therefore it lends itself more readily to quantitative analysis.) According to Andrew Martin and Kevin Quinn, the authors of Competing Approaches to Predicting Supreme Court Decision Making, if they are provided with just a few variables concerning the politics of a case, they can predict how the US Supreme Court justices will vote.

Ayers discussed the operation of Martin and Quinn’s Supreme Court decision prediction algorithm in How computers routed the experts:

Analysing historical data from 628 cases previously decided by the nine Supreme Court justices at the time, and taking into account six factors, including the circuit court of origin and the ideological direction of that lower court’s ruling, Martin and Quinn developed simple flowcharts that best predicted the votes of the individual justices. For example, they predicted that if a lower court decision was considered “liberal”, Justice Sandra Day O’Connor would vote to reverse it. If the decision was deemed “conservative”, on the other hand, and came from the 2nd, 3rd or Washington DC circuit courts or the Federal circuit, she would vote to affirm.

Ted Ruger, a law professor at the University of Pennsylvania, approached Martin and Quinn at a seminar and suggested that they test the performance of the algorithm against a group of legal experts:

As the men talked, they decided to run a horse race, to create “a friendly interdisciplinary competition” to compare the accuracy of two different ways to predict the outcome of Supreme Court cases. In one corner stood the predictions of the political scientists and their flow charts, and in the other, the opinions of 83 legal experts – esteemed law professors, practitioners and pundits who would be called upon to predict the justices’ votes for cases in their areas of expertise. The assignment was to predict in advance the votes of the individual justices for every case that was argued in the Supreme Court’s 2002 term.

The outcome?

The experts lost. For every argued case during the 2002 term, the model predicted 75 per cent of the court’s affirm/reverse results correctly, while the legal experts collectively got only 59.1 per cent right. The computer was particularly effective at predicting the crucial swing votes of Justices O’Connor and Anthony Kennedy. The model predicted O’Connor’s vote correctly 70 per cent of the time while the experts’ success rate was only 61 per cent.

Ayers provides a copy of the flowchart in Super Crunchers. Its simplicity is astonishing: there are only 6 decision points, and none of the relate to the content of the matter. Ayers posits the obvious question:

How can it be that an incredibly stripped-down statistical model outpredicted legal experts with access to detailed information about the cases? Is this result just some statistical anomaly? Does it have to do with idiosyncrasies or the arrogance of the legal profession? The short answer is that Ruger’s test is representative of a much wider phenomenon. Since the 1950s, social scientists have been comparing the predictive accuracies of number crunchers and traditional experts – and finding that statistical models consistently outpredict experts. But now that revelation has become a revolution in which companies, investors and policymakers use analysis of huge datasets to discover empirical correlations between seemingly unrelated things.

Perhaps I’m naive, but, for me, one of the really surprising implications arising from Martin and Quinn’s model is that the merits of the legal arguments before the court are largely irrelevant to the decision rendered, and it is Ayres’s “seemingly unrelated things” that affect the outcome most. Ayres puts his finger on the point at issue:

The test would implicate some of the most basic questions of what law is. In 1881, Justice Oliver Wendell Holmes created the idea of legal positivism by announcing: “The life of the law has not been logic; it has been experience.” For him, the law was nothing more than “a prediction of what judges in fact will do”. He rejected the view of Harvard’s dean at the time, Christopher Columbus Langdell, who said that “law is a science, and … all the available materials of that science are contained in printed books”.

Martin and Quinn’s model shows Justice Oliver Wendell Holmes to be right. Law is nothing more than a prediction of what judges will in fact do. How is this relevant to a deep value investing site? Deep value investing is nothing more than a prediction of what companies and stocks will in fact do. If the relationship holds, seemingly unrelated things will affect the performance of stock prices. Part of the raison d’etre of this site is to determine what those things are. To quantify the qualitative factors affecting deep value stock price performance.

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Aswath Damodaran, a Professor of Finance at the Stern School of Business, has an interesting post on his blog Musings on Markets, Transaction costs and beating the market. Damodaran’s thesis is that transaction costs – broadly defined to include brokerage commissions, spread and the “price impact” of trading (which I believe is an important issue for some strategies) – foil in the real world investment strategies that beat the market in back-tests. He argues that transaction costs are also the reason why the “average active portfolio manager” underperforms the index by about 1% to 1.5%. I agree with Damodaran. The long-term, successful practical application of any investment strategy is difficult, and is made more so by all of the frictional costs that the investor encounters. That said, I see no reason why a systematic application of some value-based investment strategies should not outperform the market even after taking into account those transaction costs and taxes. That’s a bold statement, and requires in support the production of equally extraordinary evidence, which I do not possess. Regardless, here’s my take on Damodaran’s article.

First, Damodaran makes the point that even well-researched, back-tested, market-beating strategies underperform in practice:

Most of these beat-the-market approaches, and especially the well researched ones, are backed up by evidence from back testing, where the approach is tried on historical data and found to deliver “excess returns”. Ergo, a money making strategy is born.. books are written.. mutual funds are created.

The average active portfolio manager, who I assume is the primary user of these can’t-miss strategies does not beat the market and delivers about 1-1.5% less than the index. That number has remained surprisingly stable over the last four decades and has persisted through bull and bear markets. Worse, this under performance cannot be attributed to “bad” portfolio mangers who drag the average down, since there is very little consistency in performance. Winners this year are just as likely to be losers next year…

Then he explains why he believes market-beating strategies that work on paper fail in the real world. The answer? Transaction costs:

So, why do portfolios that perform so well in back testing not deliver results in real time? The biggest culprit, in my view, is transactions costs, defined to include not only the commission and brokerage costs but two more significant costs – the spread between the bid price and the ask price and the price impact you have when you trade. The strategies that seem to do best on paper also expose you the most to these costs. Consider one simple example: Stocks that have lost the most of the previous year seem to generate much better returns over the following five years than stocks have done the best. This “loser” stock strategy was first listed in the academic literature in the mid-1980s and greeted as vindication by contrarians. Later analysis showed, though, that almost all of the excess returns from this strategy come from stocks that have dropped to below a dollar (the biggest losing stocks are often susceptible to this problem). The bid-ask spread on these stocks, as a percentage of the stock price, is huge (20-25%) and the illiquidity can also cause large price changes on trading – you push the price up as you buy and the price down as you sell. Removing these stocks from your portfolio eliminated almost all of the excess returns.

In support of his thesis, Damodaran gives the example of Value Line and its mutual funds:

In perhaps the most telling example of slips between the cup and lip, Value Line, the data and investment services firm, got great press when Fischer Black, noted academic and believer in efficient markets, did a study where he indicated that buying stocks ranked 1 in the Value Line timeliness indicator would beat the market. Value Line, believing its own hype, decided to start mutual funds that would invest in its best ranking stocks. During the years that the funds have been in existence, the actual funds have underperformed the Value Line hypothetical fund (which is what it uses for its graphs) significantly.

Damodaran’s argument is particularly interesting to me in the context of my recent series of posts on quantitative value investing. For those new to the site, my argument is that a systematic application of the deep value methodologies like Benjamin Graham’s liquidation strategy (for example, as applied in Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update) or a low price-to-book strategy (as described in Lakonishok, Shleifer, and Vishny’s Contrarian Investment, Extrapolation and Risk) can lead to exceptional long-term investment returns in a fund.

When Damodaran refers to “the price impact you have when you trade” he highlights a very important reason why a strategy in practice will underperform its theoretical results. As I noted in my conclusion to Intuition and the quantitative value investor:

The challenge is making the sample mean (the portfolio return) match the population mean (the screen). As we will see, the real world application of the quantitative approach is not as straight-forward as we might initially expect because the act of buying (selling) interferes with the model.

A strategy in practice will underperform its theoretical results for two reasons:

  1. The strategy in back test doesn’t have to deal with what I call the “friction” it encounters in the real world. I define “friction” as brokerage, spread and tax, all of which take a mighty bite out of performance. These are two of Damodaran’s transaction costs and another – tax. Arguably spread is the most difficult to prospectively factor into a model. One can account for brokerage and tax in the model, but spread is always going to be unknowable before the event.
  2. The act of buying or selling interferes with the market (I think it’s a Schrodinger’s cat-like paradox, but then I don’t understand quantum superpositions). This is best illustrated at the micro end of the market. Those of us who traffic in the Graham sub-liquidation value boat trash learn to live with wide spreads and a lack of liquidity. We use limit orders and sit on the bid (ask) until we get filled. No-one is buying (selling) “at the market,” because, for the most part, there ain’t no market until we get on the bid (ask). When we do manage to consummate a transaction, we’re affecting the price. We’re doing our little part to return it to its underlying value, such is the wonderful phenomenon of value investing mean reversion in action. The back-test / paper-traded strategy doesn’t have to account for the effect its own buying or selling has on the market, and so should perform better in theory than it does in practice.

If ever the real-world application of an investment strategy should underperform its theoretical results, Graham liquidation value is where I would expect it to happen. The wide spreads and lack of liquidity mean that even a small, individual investor will likely underperform the back-test results. Note, however, that it does not necessarily follow that the Graham liquidation value strategy will underperform the market, just the model. I continue to believe that a systematic application of Graham’s strategy will beat the market in practice.

I have one small quibble with Damodaran’s otherwise well-argued piece. He writes:

The average active portfolio manager, who I assume is the primary user of these can’t-miss strategies does not beat the market and delivers about 1-1.5% less than the index.

There’s a little rhetorical sleight of hand in this statement (which I’m guilty of on occasion in my haste to get a post finished). Evidence that the “average active portfolio manager” does not beat the market is not evidence that these strategies don’t beat the market in practice. I’d argue that the “average active portfolio manager” is not using these strategies. I don’t really know what they’re doing, but I’d guess the institutional imperative calls for them to hug the index and over- or under-weight particular industries, sectors or companies on the basis of a story (“Green is the new black,” “China will consume us back to the boom,” “house prices never go down,” “the new dot com economy will destroy the old bricks-and-mortar economy” etc). Yes, most portfolio managers underperform the index in the order of 1% to 1.5%, but I think they do so because they are, in essence, buying the index and extracting from the index’s performance their own fees and other transaction costs. They are not using the various strategies identified in the academic or popular literature. That small point aside, I think the remainder of the article is excellent.

In conclusion, I agree with Damodaran’s thesis that transaction costs in the form of brokerage commissions, spread and the “price impact” of trading make many apparently successful back-tested strategies unusable in the real world. I believe that the results of any strategy’s application in practice will underperform its theoretical results because of friction and the paradox of Schrodinger’s cat’s brokerage account. That said, I still see no reason why a systematic application of Graham’s liquidation value strategy or LSV’s low price-to-book value strategy can’t outperform the market even after taking into account these frictional costs and, in particular, wide spreads.

Hat tip to the Ox.

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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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As I foreshadowed yesterday, there are several related themes that I wish to explore on Greenbackd. These three ideas are as follows:

  1. Quantitative value investing
  2. Pure contrarian investing
  3. Problems with the received wisdom on value investment

Set out below is a brief overview of each.

A quantitative approach to value investment

I believe that James Montier’s 2006 research report Painting By Numbers: An Ode To Quant presents a compelling argument for a quantitative approach to value investing. Simple statistical or quantitative models have worked well in the context of value investing, and I think there is ample evidence that this is the case. (Note that simple is the operative word: I’m not advocating anything beyond basic arithmetic or the most elementary algebra.) Graham was said to know little about the businesses of the net current asset value stocks he bought. It seems that any further analysis beyond determining the net current asset value was unnecessary for him (although he does discuss in Security Analysis other considerations for the discerning security analyst). Perhaps that should be good enough for us.

As Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update paper demonstrates, a purely mechanical application of 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.” Oppenheimer puts that return in context thus, “[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.” That’s a stunning return. It would have put you in elite company if you had been running a fund blindly following Oppenheimer’s methodology from the date of publication of the paper. Other papers examining the returns over different periods and in different markets written after Oppenheimer’s paper have found similar results (one of the papers is by Montier and I will be discussing it in some detail in the near future). The main criticism laid at the feet of the net net method is that it can only accommodate a small amount of capital. It is an individual investor or micro fund strategy. Simple strategies able to accommodate more capital are described in Lakonishok, Shleifer, and Vishny’s Contrarian Investment, Extrapolation and Risk. In that paper, the authors found substantial outperformance through the use of only one or two value-based variables, whether they be price-to-book, price-to earnings, price-to-cash flow or price-to-sales.

I believe these papers (and others I have discussed in the past) provide compelling evidence for quantitative value investing, but let me flip it around. Why not invest solely on the basis of some simple value-based variables? Because you think you can compound your portfolio faster by cherry-picking the better stocks on the screen? This despite what Montier says in Painting By Numbers about quant models representing “a ceiling in performance (from which we detract) rather than a floor (to which we can add)”? Bonne chance to you if that is the case, but you are one of the lucky few. The preponderance of data suggest that most investors will do better following a simple model.

Pure contrarian investing

By “pure” contrarian investing, I mean contrarian investing that is not value investing disguised as contrarian investing. LSV frame their Contrarian Investment, Extrapolation and Risk findings in the context of “contrarianism,” arguing that value strategies produce superior returns because most investors don’t fully appreciate the phenomenon of mean reversion, which leads them to extrapolate past performance too far into the future. LSV argues that investors can profit from the market’s (incorrect) assessment that stocks that have performed well in the past will perform well in the future and stocks that have performed poorly in the past will continue to perform poorly. If that is in fact the case, then contrarian strategies that don’t rely on value should also work. Can I simply buy some list of securities at a periodic low (52 weeks or whatever) and sell some list of securities at a periodic high (again, say 52 weeks) and expect to generate “good” (i.e. better than just hugging the index) returns? If not, it’s not contrarianism, but value that is the operative factor.

It is in this context that I want to explore Nassim Nicholas Taleb’s “naive empiricist.” If contrarianism appears to work as a stand alone strategy, how do I know that I’m not mining the data? I also want to consider whether the various papers written about value investment discussed on Greenbackd and the experiences of Buffett, Schloss, Klarman et al “prove” that value works. Taleb would say they don’t.  How, then, do I proceed if I don’t know whether the phenomenon we’re observing is real or a trick? We try to build a portfolio able to withstand stresses, or changes in circumstance. How do we do that? The answer is some combination of employing Graham’s margin of safety, diversifying, avoiding debt and holding an attitude like Montaigne’s “Que sais-je?”‘ (“What do I know?”). It’s hardly radical stuff, but, what I believe is interesting, is how well such a sceptical and un-confident approach marries with quantitative investing.

Problems with the received wisdom on value investment

Within the value investment community there are some topics that are verboten. It seems that some thoughts were proscribed some time ago, and we are now no longer even allowed to consider them. I don’t want delve into them now, other than to say that I believe they deserve some further consideration. Some principles are timeless, others are prisoners of the moment, and it is often impossible to distinguish between the two. How can we proceed if we don’t subject all received wisdom to further consideration to determine which rules are sound, and which we can safely ignore? I don’t believe we can. I’ll therefore be subjecting those topics to analysis in any attempt to find those worth following. If I’m going to make an embarrassing mistake, I’m betting it’s under this heading.

There are several other related topics that I wish to consider, but they are tangential to the foregoing three.

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