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Posts Tagged ‘Value Investment’

Welcome back for 2010

Posted in About, Greenbackd, Stocks, tagged , on January 11, 2010| 8 Comments »

Welcome back to Greenbackd for 2010. I hope the holidays were as good to you as they were to me.

The break has afforded me the opportunity to gain some perspective on the direction of Greenbackd. Away from the regular posting schedule I found the time to write some Jerry McGuire The Things We Think and Do Not Say treatises, quickly consigning most of them to trash so that they couldn’t come back to haunt me at a later, more lucid and, perhaps, sober moment (I did say the holiday was good to me). Some (heavily edited) remnants of those rambling essays will filter through onto this site over the coming weeks. I’m charged up about several topics that I want to explore in some depth, which is a change from the net net ennui that was starting to creep in before the break.

The beauty of the Graham net net as a subject for investment is its simplicity. Conversely, that same characteristic makes it a poor subject for extended contemplation and writing. There is a limit to which the universe of Graham net nets, even those entwined in activist or special situations, can be subject to analysis before the returns to additional analysis diminish asymptotically to approaching zero. Note that in this context I don’t mean investment returns, but returns to the psyche, good feelings, the avoidance of boredom…in other words, the really important stuff. The investment returns in that area are good, but we all already know that to be the case. What am I contributing if I keep digging up undervalued net nets? Not much. Graham invented it. Oppenheimer proved it. Jon Heller writes about it better than anyone else. The rest of us are just regurgitating their work.

Really, this is old news. Greenbackd passed the point some time ago at which it was possible to hold off the tedium of net nets and evolved organically to embrace several related topics. I still love the activist dogfight for control or influence and I think a well-written 13D makes for excellent copy. I also still love finding blatantly misplaced securities, each one a little slash at the heart of the EMH. Greenbackd will continue to study individual securities and follow interesting activist situations, however, it will not be the sole focus of the site. For me, there are more interesting problems to tackle. My concern has been whether Greenbackd can contain the new topics or whether I’ll just annoy old Greenbackd readers with the new direction. My favorite blogger wrestled with same issue several years ago, and so I’m using her experience as a guide.

I think the smartest thinker and most lucid writer in the financial and political (in the broadest sense of the word) sphere is Marla Singer at Zero Hedge and occasionally Finem Respice (formerly Equity Private at Going Private). Marla, then writing as Equity Private, started out with a narrowly focussed blog about the “sardonic memoirs of a private equity professional,” but gradually expanded to cover only tangentially related topics like the role of government, economics, philosophy, literature, art, duelling, card sharping and cargo cults (the implications of which won’t be lost on most readers). For me, it was a thrilling departure, but Marla must have felt that Equity Private was too limited, and created Finem Respice before moving on to Zero Hedge. I was only too happy to follow, but I would have been equally happy for Equity Private to keep posting as Equity Private. (As an aside, I recommend following Marla at Zero Hedge. Her ability to tease out the hidden story from some granular detail in legislation or data is simply breathtaking and unmatched in the mainstream media.)

I’ll persist with Greenbackd because I like this boat, but it will be embarking for new shores. Pure net net investors are well served by other sites, so it’s probable that some readers will depart. This site will always be dedicated to deep value, but I want to find some uncharted territory. The voyage might not yield any new land, but I think it will be more fun than continuing to orienteer on Graham’s old maps. I have an inkling there is something interesting out there at the intersection of Montier, Montaigne, Taleb and Graham. Tomorrow, I’ll start to sketch out the new world. It also coincides with a personal change for me. Working in someone else’s fund has been enjoyable, but I feel it’s time to graduate to principal. I’m presently considering entering into an established partnership or starting my own fund. Whichever direction I go will likely have some influence on Greenbackd.

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What Montier’s Painting by Numbers can offer to value investors

Posted in About, Stocks, Value Investment, tagged , , , on December 22, 2009| 16 Comments »

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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KPMG says glamour outperformed value in mergers and acquisitions

Posted in About, Stocks, Value Investment, tagged on December 17, 2009| 3 Comments »

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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Miguel Barbosa interviews Joe Calandro, Jr. author of Applied Value Investing

Posted in About, Austrian Economics, Net Current Asset Value, Stocks, Value Investment, tagged , , on December 9, 2009| Leave a Comment »

The wonderful Miguel Barbosa of Simoleon Sense has interviewed Joe Calandro, Jr., author of Applied Value Investing. The interview is first class. Joe Calandro, Jr. is an interesting guy. Deep value? Check. Activist investing? He’s for it. Austrian School of Economics? Check. I think I just wet myself.

Here’s Joe’s take on value:

Q. Can you give me an example of some of your best investments?

A. In the book I profile a value pattern that I call “base case value” which is simply net asset value reconciling with the earnings power value. Firms exhibiting that pattern, which sell at reasonable margins of safety have proved highly profitable to me. In the book, I show examples of this type of investment.

…

“Value investing” in general has 3 core principles:

(1) The circle of competence, which essentially relates to an information advantage and holds that you will do better if you stick to what you know more about than others.

(2) The principle of conservatism. You will have greater faith in valuations if you prepare them conservatively.

(3) The margin of safety. You should only invest if there is a price-to-value gap: when the gap disappears you exit the position.

Here’s Joe on activist investing:

Q. You’re primarily in the corporate sector now; in your book you touch upon the failure of corporate M&A groups to apply value investing.  Why do you think this is the case? What is your take on activist value investors?

A. That’s a good question and I don’t have a definitive answer for it. My take on it is that Corporate America hasn’t been trained in Graham and Dodd. For example, if you get away from Columbia and some of the other top schools you really don’t have courses of study based on Graham and Dodd. I think this lack of education carries over to practice. If educational institutions aren’t teaching something, then executives are going to have a difficult time applying it. And if they do try to apply it, their employees and boards may not understand it. Hopefully, my book will help to rectify this over time.

Regarding activist investors, I think every investor should be active. If you allocate money to a security (either equity or debt) you have the responsibility of becoming involved in the respective firm because, as Benjamin Graham noted, you invested in a business, not in a piece of paper or a financial device. This is real money in real businesses so there is a responsibility that comes with investing.

And Joe’s view on economics:

Q. Can you give us a tour of the major insights you obtained from the Austrian School of Economics.

A. I have two big academic regrets: I did not study Graham and Dodd or Austrian economics until I was in my mid 30’s. One of the major theories of Austrian economics is its business cycle theory. Just the other day (11/6/2009), that theory was mentioned in the WSJ by Mark Spitznagel, who is Nassim Taleb’s partner, in his article “The Man Who Predicted the Depression.” As you know, Taleb has also spoken highly of Austrian economics as have other successful traders/practitioners such as Victor Sperandeo, Peter Schiff and Bill Bonner.

Austrian economics finds success with practitioners because, I think, Austrian economists are truly economists; they do not try to be applied mathematicians. Therefore, Austrians tend to see economics for what it is; namely, a discipline built around general principles that can be applied broadly to economic phenomena. As a result, Austrian economics is generally very useful in areas such as the business cycle and the consequences of government intervention, which are very pertinent topics today.

Read the rest of the interview while I run out to buy the book.

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Piotroski’s F_SCORE

Posted in About, Stocks, Value Investment, tagged , , , , on November 30, 2009| 4 Comments »

The second method for boosting the performance of book value as a predictor of future investment returns is Joseph D. Piotroski’s elegant F_SCORE. Piotroski first discussed his F_SCORE in 2002 in Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. In the paper, Piotroski examines whether the application of a simple accounting-based fundamental analysis strategy to a broad portfolio of high book-to-market firms can improve the returns earned by an investor. Piotroski found that his method increased the mean return earned by a low price-to-book investor “by at least 7 1/2% annually” through the “selection of financially strong high BM firms.”

In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23% annual return between 1976 and 1996, and the strategy appears to be robust across time and to controls for alternative investment strategies.

With a return of that magnitude, it’s well worth a deeper look.

Piotroski’s rationale

Piotroski uses “context-specific financial performance measures to differentiate strong and weak firms:”

Instead of examining the relationships between future returns and particular financial signals, I aggregate the information contained in an array of performance measures and form portfolios on the basis of a firm’s overall signal. By focusing on value firms, the benefits to financial statement analysis (1) are investigated in an environment where historical financial reports represent both the best and most relevant source of information about the firm’s financial condition and (2) are maximized through the selection of relevant financial measures given the underlying economic characteristics of these high BM firms.

F_SCORE

On the assumption that the “average high BM firm is financially distressed,” Piotroski chose nine fundamental signals to measure three areas of the firm’s financial condition: profitability, financial leverage/liquidity, and operating efficiency:

In this paper, I classify each firm’s signal realization as either “good” or “bad,” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). I define the aggregate signal measure, F_SCORE, as the sum of the nine binary signals. The aggregate signal is designed to measure the overall quality, or strength, of the firm’s financial position, and the decision to purchase is ultimately based on the strength of the aggregate signal.

F_SCORE component: Profitability

On the basis that “current profitability and cash flow realizations provide information about the firm’s ability to generate funds internally,” Piotroski uses four variables to measure these performance-related factors: ROA, CFO, [Delta]ROA, and ACCRUAL:

I define ROA and CFO as net income before extraordinary items and cash flow from operations, respectively, scaled by beginning of the year total assets. If the firm’s ROA (CFO) is positive, I define the indicator variable F_ROA (F_CFO) equal to one, zero otherwise. I define ROA as the current year’s ROA less the prior year’s ROA. If [Delta]ROA [is greater than] 0, the indicator variable F_[Delta]ROA equals one, zero otherwise.

…

I define the variable ACCRUAL as current year’s net income before extraordinary items less cash flow from operations, scaled by beginning of the year total assets. The indicator variable F_ ACCRUAL equals one if CFO [is greater than] ROA, zero otherwise.

F_SCORE component: Leverage, liquidity, and source of funds

For the reason that “most high BM firms are financially constrained” Piotroski assumes that an increase in leverage, a deterioration of liquidity, or the use of external financing is a bad signal about financial risk. Three of the nine financial signals are therefore designed to measure changes in capital structure and the firm’s ability to meet future debt service obligations: [Delta]LEVER, [Delta]LIQUID, and EQ_OFFER:

[Delta]LEVER seeks to capture changes in the firm’s long-term debt levels:

I measure [Delta]LEVER as the historical change in the ratio of total long-term debt to average total assets, and view an increase (decrease) in financial leverage as a negative (positive) signal. By raising external capital, a financially distressed firm is signaling its inability to generate sufficient internal funds (e.g., Myers and Majluf 1984, Miller and Rock 1985). In addition, an increase in long-term debt is likely to place additional constraints on the firm’s financial flexibility. I define the indicator variable F_LEVER to equal one (zero) if the firm’s leverage ratio fell (rose) in the year preceding portfolio formation.

[Delta]LIQUID seeks to measure the historical change in the firm’s current ratio between the current and prior year, where Piotroski defines the current ratio as the ratio of current assets to current liabilities at fiscal year-end:

I assume that an improvement in liquidity (i.e., [Delta]LIQUID [is greater than] 0) is a good signal about the firm’s ability to service current debt obligations. The indicator variable F_[Delta]LIQUID equals one if the firm’s liquidity improved, zero otherwise.

Piotroski argues that financially distressed firms raising external capital “could be signaling their inability to generate sufficient internal funds to service future obligations” and  the fact that these firms are willing to issue equity when their stock prices are depressed “highlights the poor financial condition facing these firms.” EQ_OFFER captures whether a firm has issued equity in the year preceding portfolio formation. It is set to one if the firm did not issue common equity in the year preceding portfolio formation, zero otherwise.

F_SCORE component: Operating efficiency

Piotroski’s two remaining signals seek to measure “changes in the efficiency of the firm’s operations:” [Delta]MARGIN and [Delta]TURN. Piotroski believes these ratios are important because they “reflect two key constructs underlying a decomposition of return on assets.”

Piotroski defines [Delta]MARGIN as the firm’s current gross margin ratio (gross margin scaled by total sales) less the prior year’s gross margin ratio:

An improvement in margins signifies a potential improvement in factor costs, a reduction in inventory costs, or a rise in the price of the firm’s product. The indicator variable F_[Delta]MARGIN equals one if [Delta]MARGIN is positive, zero otherwise.

Piotroski defines [Delta]TURN as the firm’s current year asset turnover ratio (total sales scaled by beginning of the year total assets) less the prior year’s asset turnover ratio:

An improvement in asset turnover signifies greater productivity from the asset base. Such an improvement can arise from more efficient operations (fewer assets generating the same levels of sales) or an increase in sales (which could also signify improved market conditions for the firm’s products). The indicator variable F_[Delta]TURN equals one if [Delta]TURN is positive, zero otherwise.

F_SCORE formula and interpretation

Piotroski defines F_SCORE as the sum of the individual binary signals, or

F_SCORE = F_ROA + F_[Delta]ROA + F_CFO + F_ ACCRUAL + F_[Delta]MARGIN + F_[Delta]TURN + F_[Delta]LEVER + F_[Delta]LIQUID + EQ_OFFER.

An F_SCORE ranges from a low of 0 to a high of 9, where a low (high) F_SCORE represents a firm with very few (mostly) good signals. To the extent current fundamentals predict future fundamentals, I expect F_SCORE to be positively associated with changes in future firm performance and stock returns. Piotroski’s investment strategy is to select firms with high F_SCORE signals.

Piotroski’s methodology

Piotroski identified firms with sufficient stock price and book value data on COMPUSTAT each year between 1976 and 1996. For each firm, he calculated the market value of equity and BM ratio at fiscal year-end. Each fiscal year (i.e., financial report year), he ranked all firms with sufficient data to identify book-to-market quintile and size tercile cutoffs and classified them into BM quintiles. Piotroski’s final sample size was 14,043 high BM firms across the 21 years.

He measured firm-specific returns as one-year (two-year) buy-and-hold returns earned from the beginning of the fifth month after the firm’s fiscal year-end through the earliest subsequent date: one year (two years) after return compounding began or the last day of CRSP traded returns. If a firm delisted, he assumed the delisting return is zero. He defined market-adjusted returns as the buy-and-hold return less the value-weighted market return over the corresponding time period.

Descriptive evidence of high book-to-market firms

Piotroski provides descriptive statistics about the financial characteristics of the high book-to-market portfolio of firms, as well as evidence on the long-run returns from such a portfolio. The average (median) firm in the highest book-to-market quintile of all firms has a mean (median) BM ratio of 2.444 (1.721) and an end-of-year market capitalization of 188.50(14.37)M dollars. Consistent with the evidence presented in Fama and French (1995), the portfolio of high BM firms consists of poor performing firms; the average (median) ROA realization is –0.0054 (0.0128), and the average and median firm saw declines in both ROA (–0.0096 and –0.0047, respectively) and gross margin (–0.0324 and –0.0034, respectively) over the last year. Finally, the average high BM firm saw an increase in leverage and a decrease in liquidity over the prior year.

High BM returns

The table below (Panel B of Table 1) extracted from the paper presents the one-year and two-year buy-and-hold returns for the complete portfolio of high BM firms, along with the percentage of firms in the portfolio with positive raw and market-adjusted returns over the respective investment period. Consistent with the findings in the Fama and French (1992) and Lakonishok, Shleifer, and Vishny (1994) studies, high BM firms earn positive market-adjusted returns in the one-year and two-year periods following portfolio formation:

Perhaps Piotroski’s most interesting finding is that, despite the strong mean performance of this portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. Piotroski concludes, therefore, that any strategy that can eliminate the left tail of the return distribution (i.e., the negative return observations) will greatly improve the portfolio’s mean return performance.

High BM and F_SCORE returns

Panel A of Table 3 below shows the one-year market-adjusted returns to the Piotroski F_SCORE strategy:

The table demonstrates the return difference between the portfolio of high F_SCORE firms and the complete portfolio of high BM firms. High F_SCORE firms earn a mean market-adjusted return of 0.134 versus 0.059 for the entire BM quintile. The return improvements also extend beyond the mean performance of the various portfolios. The results in the table shows that the 10th percentile, 25th percentile, median, 75th percentile, and 90th percentile returns of the high F_SCORE portfolio are significantly higher than the corresponding returns of both the low F_SCORE portfolio and the complete high BM quintile portfolio using bootstrap techniques. Similarly, the proportion of winners in the high F_SCORE portfolio, 50.0%, is significantly higher than the two benchmark portfolios (43.7% and 31.8%). Overall, it is clear that F_SCORE discriminates between eventual winners and losers.

Conclusion

Piotroski’s F_SCORE is clearly a very useful metric for high BM investors. Piotroski’s key insight is that, despite the strong mean performance of a high BM portfolio, a majority of the firms (approximately 57%) earn negative market-adjusted returns over the one- and two-year windows. The F_SCORE is designed to eliminate the left tail of the return distribution (i.e., the negative return observations). It succeeds in doing so, and the resulting returns to high BM and high F_SCORE portfolios are nothing short of stunning.

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Testing the performance of price-to-book value

Posted in About, Stocks, Value Investment, tagged , , on November 23, 2009| 16 Comments »

This site is dedicated to undervalued asset situations, but I haven’t yet spent much time on undervalued asset situations other than liquidations and Graham net current asset value stocks. Two areas worthy of further study are low price-to-book value stocks and low price-to-tangible book value stocks. I’ve found that it is difficult to impossible to find any research examining the performance of stocks selected on the basis of price-to-tangible book value. That may be because book value alone can explain most of the performance and removing goodwill and intangibles from the calculation adds very little. Tangible book value is of interest to me because I assume it more closely describes the likely value of a company in liquidation than book value does. That assumption may be wrong. Some intangibles have value in liquidation, although it’s always difficult to collect on the goodwill. If anyone knows of any study explicitly examining the performance of stocks selected on the basis of price-to-tangible book value, please shoot me an email at greenbackd at gmail or leave a comment in this post.

Book value has received plenty of attention from researchers in academia and industry, starting with Roger Ibbotson’s Decile Portfolios of the New York Stock Exchange, 1967 – 1984 (1986) and  Werner F.M. DeBondt and Richard H. Thaler’s Further Evidence on Investor Overreaction and Stock Market Seasonality (1987). In Value vs Glamour: A Global Phenomenon, The Brandes Institute updated the landmark 1994 study by Josef Lakonishok, Andrei Shleifer, and Robert Vishny Contrarian Investment, Extrapolation and Risk. All of these studies looked at the performance of stocks selected on the basis of price-to-book value (among other value metrics). The findings are uniform: lower price-to-book value stocks tend to outperform higher price-to-book value stocks, and at lower risk. On the strength of the findings in these various studies I’ve decided to run a handful of real-time tests to see how a portfolio constructed of the cheapest stocks determined on a price-to-book value basis performs against the market.

Constructing a 30-stock portfolio

The Ibbotson, LSV and Brandes Institute studies created decile portfolios and Thaler and DeBont created quintile portfolios. I propose to informally test the P/B method at the extreme, taking the cheapest 30 stocks in the Google Finance screener (I use the Google Finance screener because it’s publicly available and easily replicable) and creating an equally weighted portfolio. Here is the list of stocks generated as at the November 20, 2009 close:

Symbol Market cap Price to book Last price P/E ratio Book value/share
TOPS 33.97M 0.11 1.15 0.92 9.77
CEP 76.78M 0.15 3.38 4.76 23.23
SVLF 28.99M 0.17 0.76 2.48 5.09
BXG 77.47M 0.23 2.38 33.81 12.24
SGMA 13.46M 0.29 3.52 14.12 11.88
KRG 190.16M 0.3 3.02 28.97 10.3
BDR 5.88M 0.31 0.95 9.99 3.14
FREE 34.30M 0.31 1.62 1.61 5.71
IOT 22.51M 0.32 5.4 3.75 16.98
WPCS 20.69M 0.35 2.98 16.61 8.53
SSY 8.71M 0.35 1.83 14 5.19
CUO 19.18M 0.36 12 7.81 32.93
ONAV 73.53M 0.37 3.84 6.83 10.87
SBLK 202.11M 0.37 3.46 1.93 9.59
CHMP 17.68M 0.38 1.77 6.64 5.19
XFN 17.64M 0.39 0.96 5.22 2.34
HTX 966.12M 0.39 3.01 29.92 7.68
KV.A 178.31M 0.4 3.57 2.18 9.27
ULTR 144.94M 0.4 4.91 4.75 12.6
MDTH 145.35M 0.42 7.4 30.63 18.86
HAST 42.70M 0.43 4.42 23.92 10.45
TBSI 255.08M 0.44 8.53 4.67 20.01
GASS 129.40M 0.44 5.8 6.51 14.25
CONN 145.52M 0.44 6.48 6.88 14.89
BBEP 596.46M 0.44 11.3 3.39 25.7
CBR 230.18M 0.45 3.31 11.57 7.53
PRGN 222.19M 0.48 5.15 2.29 11.37
EROC 352.29M 0.48 4.59 1.25 9.76
JTX 119.45M 0.5 4.15 6.55 8.47
INOC 23.93M 0.5 1.94 5.68 3.77

For the sake of comparison the S&P500 closed Friday at 1,091.38.

Perhaps one of the most striking findings in the various studies discussed above was made by DeBondt and Thaler. They examined the earnings pattern of the cheapest companies (ranked on the basis of price-to-book) to the most expensive companies. They found that the earnings of the cheaper companies grew faster than the earnings of the more expensive companies over the period of the study. DeBondt and Thaler attribute the earnings outperformanceof the cheaper companies to the phenomenon of “mean reversion,” which Tweedy Browne describe as the observation that “significant declines in earnings are followed by significant earnings increases, and that significant earnings increases are followed by slower rates of increase or declines.” I’m interested to see whether this phenomenon will be observable in the 30 company portfolio listed above.

It seems counterintuitive that a portfolio constructed using a single, simple metric (in this case, price-to-book) should outperform the market. The fact that the various studies discussed above have reached uniform conclusions leads me to believe that this phenomenon is real. The companies listed above are a diverse group in terms of market capitalization, earnings, debt loads and businesses/industries. The only factor uniting the stocks in the list above is that they are the cheapest 30 stocks in the Google Finance screener on the basis of price-to-book value. I look forward to seeing how they perform against the market, represented by the S&P500 index.

Update

Here’s the Tickerspy portfolio tracker for the Greenbackd Contrarian Value Portfolio.

[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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Can relative price-to-asset values be used to determine which countries are likely to provide the best investment returns?

Posted in About, Stocks, Value Investment, tagged , , on October 5, 2009| 3 Comments »

In an August post, Applying value principles at a country level, we discussed The growth illusion, an article appearing in a Buttonwood’s notebook column of The Economist. In that article, Buttonwood argued that valuation, rather than economic growth, determined investment returns at a country or market level. Buttonwood highlighted research undertaken by Elroy Dimson, Paul Marsh and Mike Staunton from the London Business School, which suggested that chasing growth economies is akin to chasing growth stocks, and generates similarly disappointing results. Buttonwood concluded that higher valuations – determined on an earnings, rather than asset basis – led to lower returns:

What does work? Over the long run (but not the short), it is valuation; the higher the starting price-earnings ratio when you buy a market, the lower the return over the next 10 years. That is why buying shares back in 1999 and 2000 has provided to be such a bad deal.

It raised an interesting question for us: Can relative price-to-asset values be used to determine which countries are likely to provide the best investment returns? It took some time, but we’ve tracked down some research that answers the question.

In Fundamental Determinants of International Equity Returns: A Perspective on Conditional Asset Pricing (9.17MB .pdf) Journal of Banking and Finance 21, (1997): 1625-1665. (P42), Campbell Harvey and Wayne Ferson examined, among other things, the relationship between price-to-book value and future returns from a global asset pricing perspective. Harvey and Ferson found that “the price-to-book value ratio has cross-sectional explanatory power at the country level,” although they believe that its use is mainly in determining “global stock market risk exposure.”

An earlier – and slightly more readable – study by Leila Heckman, John J . Mullin and Holly Sze, Valuation ratios and cross-country equity allocation, The Journal of Investing, Summer 1996, Vol. 5, No. 2: pp. 54-63 DOI: 10.3905/joi.5.2.54, also examined the link between equity returns at a market level and valuation measures. Heckman et al found that, despite the substantial accounting differences across countries, price-to-book measures are useful for predicting the “cross-sectional variation of national index returns.”

The results are perhaps unsurprising given the various studies demonstrating the relationship between valuation determined on a price-to-earnings basis and country level returns. We believe they are useful nonetheless given the ease with which one can invest in many global markets and our own predisposition for assets over earnings valuations.

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Applying value principles at a country level

Posted in About, Stocks, Value Investment, tagged on August 31, 2009| 5 Comments »

In The growth illusion, an article appearing in the most recent Buttonwood’s notebook column of The Economist, Buttonwood argues that valuation, rather than economic growth, determines investment returns at the country level. In support of this thesis, Buttonwood highlights research undertaken by Elroy Dimson, Paul Marsh and Mike Staunton from the London Business School, which suggests that chasing growth economies is akin to chasing growth stocks, and generates similarly disappointing results. Buttonwood explains Dimson, Marsh and Staunton’s findings thus:

Over the 17 countries they studied, going back to 1900, there was actually a negative correlation between investment returns and growth in GDP per capita, the best measure of how rich people are getting. In a second test, they took the five-year growth rates of the economies and divided them into quintiles. The quintle of countries with the highest growth rate over the previous five years, produced average returns over the following year of 6%; those in the slowest-growing quintile produced returns of 12%. In a third test, they looked at the countries and found no statistical link between one year’s GDP growth rate and the next year’s investment returns.

Buttonwood posits several possible explanations for the phenomenon:

One likely explanation is that growth countries are like growth stocks; their potential is recognised and the price of their equities is bid up to stratospheric levels. The second is that a stockmarket does not precisely represent a country’s economy – it excludes unquoted companies and includes the foreign subsidiaries of domestic businesses. The third factor may be that growth is siphoned off by insiders – executives and the like – at the expense of shareholders.

William J. Bernstein discussed the phenomenon on his Efficient Frontier website in a 2006 article, Thick as a brick, in which he wrote:

In even simpler terms, just as growth stocks have lower returns than value stocks, so do growth nations have lower returns than value nations—and they similarly get overbought by the rubes.

Buttonwood discusses other research supporting Dimson, Marsh and Staunton’s findings:

Paul Marson, the chief investment officer of Lombard Odier, has extended this research to emerging markets. He found no correlation between GDP growth and stockmarket returns in developing countries over the period 1976-2005. A classic example is China; average nominal GDP growth since 1993 has been 15.6%, the compound stockmarket return over the same period has been minus 3.3%. In stodgy old Britain, nominal GDP growth has averaged just 4.9%, but investment returns have been 6.1% per annum, more than nine percentage points ahead of booming China.

Buttonwood concludes that higher valuations – determined on an earnings, rather than asset basis – lead to lower returns:

What does work? Over the long run (but not the short), it is valuation; the higher the starting price-earnings ratio when you buy a market, the lower the return over the next 10 years. That is why buying shares back in 1999 and 2000 has provided to be such a bad deal.

It raises an interesting question for us: Can relative price-to-asset values be used to determine which countries are likely to provide the best investment returns? If anyone is aware of such research, please leave a comment or contact us at greenbackd [at] gmail [dot] com.

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