Category: Stocks

Joseph Piotroski and You

As I have mentioned in previous articles, there is much to be gained by quantifying fundamental information that we use in the analysis of companies. I have begun the long and painstaking work of deriving from theory a quantitative investment formula. However, I am no rare genius; others before me have thought of the same idea. One particular person came up with an investment formula that is quite successful. This is Joseph Piotroski and he is a professor at the University of Chicago. His paper, “Value Investing: The Use of Historical Financial Information to Separate Winners from Losers”, available as a PDF here, was published in 2000. In that paper, Piotroski showed that by using a set of nine different fundamental signals to screen among low P/B stocks, an investor could separate the winners from the losers. By buying only those stocks that had the highest scores, an investor could have outperformed the market by an average of 10% per year from 1976 to 1996.

Piotroski started by screening for the stocks with the lowest 20% P/B ratios that were non-negative. This limits the strategy to true value companies. After the price to book ratio, nine other pieces of information were used, as follows:

  1. positive earnings

  2. positive cash flow from operations

  3. increasing ROA

  4. increasing cash flow from operations

  5. decreasing long-term debt as a proportion of total assets

  6. increasing current ratio, indicating increasing ability to pay off short-term debts

  7. decreasing or stable number of shares outstanding

  8. increasing asset turnover ratio, indicating increasing sales as a proportion of total assets

  9. increasing gross margin

Each company is given either a one or a zero on each variable. The strategy calls for buying every company with the requisite low P/B ratio and a score of either eight or nine. As you can see by looking at table 3 in Piotroski’s paper, the composite score does a very good job in discriminating between the stocks that perform well and those that do not perform well. On average, those companies scoring either zero or one saw their stock rise by only about 8% annually. On the other hand, those companies with scores of either eight or nine saw their stocks rise by on average 32% per year. For comparison, over the study time period (1976 – 1996) the stock market as a whole gained about 20% annually.

These results are incredible, but Piotroski does a good job of making them more credible. A priori, it would be reasonable to believe that fundamental analysis would be most beneficial for those stocks that were not well followed (e.g., stocks with small market caps). That is exactly what Piotroski shows in table 4 in his paper. While the strategy does outperform the market for all sizes of companies, it is much more effective with small companies. As table 5 shows, companies with no analyst following and high rank scores outperform the market by 18% per year.

What is perhaps most exciting about this paper is that the strategy of using fundamental analysis to find the strongest value stocks works with many different measures of fundamental strength. As shown by table 8 in the paper, a measure of financial distress or decreasing earnings or decreasing profitability can also discriminate between better and worse investments among low P/B stocks.

I cannot say with surety why this strategy works (ie, why investors do not take this information into account already). However, I think that the reason does not matter. Consider this analogy: you are the manager of a professional baseball team. Your goal is to find and hire the best baseball players you can for the least amount of money. You scout out all the lowest earning free agents in professional baseball. You then hire four or five of the most talented of those. In the long-run, you’ll be able to build a fairly solid baseball team for relatively little money. Assuming that you have a good eye for talent, you’ll be able to pick up many players for much less than they are worth. Why are they available for so little? It doesn’t really matter to you. Perhaps certain among them have been known to have a temper, perhaps others are perceived in the league as being injury prone and you judge that perception to be wrong. Perhaps other teams just overlooked certain players. It doesn’t matter much to you as long as you can get good players without paying too much.

It is much the same with finding value stocks: maybe some good companies are in boring industries, maybe they have suffered from some bad publicity, or maybe they are in a difficult industry. As long as you look for the best of the cheap stocks you’ll likely do well. Piotroski’s strategy ensures that this is what you are doing, by using quantitative variables such as profitability, asset turnover, cash flow from operations, and other variables that are correlated with future profitability and future earnings.

This is probably a good point to remind you of the unpredictability of future earnings. David Dreman as well as others have shown repeatedly that it is very hard to predict future earnings. I have previously mentioned the dangers of regression to the mean in trying to buy stocks based on projections of future growth that are unreliable. So, in buying cheap stocks (stocks with low price to book ratios) we ensure that we are not paying too much for growth that may never occur. Also importantly, we reduce our risk by buying stocks with improving fundamentals. As Piotroski points out, low P/B stocks with high rankings are less likely to go bankrupt or to fall drastically in price than are those with low rankings.

In a world where mutual fund managers only rarely can beat the market by one or two percentage points over 10 or 15 years, such performance is astounding. However, one problem with this or with any similar research is that these returns were not actually obtained. Anytime an investment strategy is tested on past data, there is the risk of optimizing the strategy for that past data and by doing so changing the strategy in such a way that it will be worthless in the future. An example is certainly an order: let’s say I have developed a technical trading system that simply buys stocks that are at their 52-week lows. I test this system over market data from the past 10 years and I find that the system leads to an annualized 5% return. This is not good, so I try to improve the system. I add information on stock’s P/E ratios, their market cap, and analyst ratings for those stocks. I retest the system and find that over the past 10 years it would have given me an annual return of 10%. That is better but still not great. So I try to improve the system even more. At this point, I have run out of ideas so I try plugging in random things to see if they improve the system. I find that only buying on certain days of the week and selling on other days improves the system. Also, I find that buying stocks only after a Chicago sports team has won a game almost doubles returns.

After all this work, my system yields theoretical annual returns of 30%. Confident, I try out my system and lose half of my money in one year. What went wrong? By testing factors that are almost certainly of no relation to stock returns, I over-optimized the trading system. While the system worked well in the past, the system was developed for that past. There is thus no reason that it should work well in the future.

Therefore, one of the keys to developing a trading or investment system is to ensure that it is both relatively simple and that it is theoretically derived. If the system is derived from theory then it is not vulnerable to the problems of data mining. One way to ensure that a strategy has not been over optimized is to test it on data that was not used to form the strategy. This has been done with Piotroski’s strategy and the results are impressive. Paul Sturm of SmartMoney.com wrote about the strategy three times in 2001, 2002, and 2004. Over that period of time, the stocks he chose using Piotroski’s strategy rose 50%, whereas the S&P 500 lost over 10%.

Another concern in developing a quantitative investment strategy is that it should be robust. A robust system will tend to work even if you use unreliable data or if circumstances change. An example of a non-robust system would be DCF analysis. In DCF analysis, if your estimate of future growth is even slightly off your estimate of a company’s true value will be way off.

Piotroski’s strategy is quite robust because it involves weighting each of nine variables equally. So if two of those nine variables turn out not to be related to a stock’s performance, the strategy will perform worse but will still likely outperform the market. Or, if there is an error in our database and a number is off by an order of magnitude, it will only slightly reduce the performance of the strategy.

Harry Domash has written an article at MSN Money about implementing this strategy. While he describes how you can implement a similar strategy using MSN’s stock screener, that stock screener does not contain all the necessary information to fully implement Piotroski’s strategy. (Also, MSN’s advanced stock screener only works with Internet Explorer. Other than that, however, it is one of the most powerful free stock screeners available).

Lighting Science Group: Yet Another Overvalued, Overhyped OTC BB Company

Lighting Science Group (OTC BB: LSCG) is a step above the everyday vermin that inhabit the OTC BB. It has two real businesses, one of which manufactures and distributes LEDs, and the other of which installs LED and other lighting systems. The one problem with Lighting Science is that its value as a real company is dwarfed by its market cap. In this way it resembles some other companies I have criticized in the past, including Continental Fuels (OTC BB: CFUL) and Noble Roman’s (OTC BB: NROM).

First, the market cap: with a total of 26.524 million shares (fully diluted) outstanding after its recent 1-for-20 reverse split, and a recent price of $9.90, Lighting Science has a fully diluted market cap of $263 million. The company has some sales and is a real business, but the thing it is best at selling is its shares: its share count has doubled in the past year alone (see the 10Q on page F-3 for details; this calculation excludes the 1-for-20 reverse split).

Book Value

Book value is something that cannot easily be faked. While different industries have different capital requirements, book value is a very good way to compare the size and intrinsic value of different companies in the same industry. As of September 30, 2007 (still according to the company’s 10Q), the company had a book value of $3.5 million. However, after a recent reverse merger with a private company, LED Holdings, the combined company has much greater book value of $24 million, including $16 million in cash (see the pro-forma financial statements).

Sales

The combined company (which will keep the name Lighting Science) had $5.7 million in sales for the first eight months of 2007. This is equal to an $8.5 million annual revenue run rate. This leaves the company with a stratospheric price to sales ratio (P/S) of 31, as compared to a P/S of just about 2 for GE [[ge]], one of the best and most consistent manufacturers, and a P/S of 6.5 for CREE [[cree]], a much larger manufacturer of LEDs.

I should note that LED Holdings has had a poor 2007 in terms of sales. Sales for all of 2006 were $8.9 million, but they fell to $3.7 million (a run rate of $7.4 million) in the first half of 2007.

Losses

While quickly increasing revenues is normally a good thing, it is not good to increase revenues and at the same time increase losses. Lighting Science’s (not including LED Holdings) loss over the first nine months of 2007 ($8.1 million) was 4.8 times greater than the loss over the same nine months in 2006. The third quarter 2007 loss of $4.8 million was 3.7 times larger than the 3rd quarter 2006 loss. LED Holdings, while being profitable over the last few years (with a profit of just over $1 million in 2006), has seen much slower growth in sales, and its profits in 2007 will be a lot lower than in 2006. The combined company would have had a pro-forma loss of $5 million over the first eight months of 2007.

PIPE Dreams

Longtime readers know of my disdain for PIPEs, or private investments in public equity. With penny stocks, these usually mean that a well-heeled investor gets shares at a deep discount to the market price and as soon as a six-month or year-long lockup period is over that investor will flip the shares onto the public for a tidy profit, even if the stock price of the company decreases.

Note 11 in the 10Q details a PIPE from a year ago in which the shares were placed for $0.30 ($6 per share, post-split). In addition to 0.667 million post-split shares, the investors also received (for free) 0.5 million post-split Class A Warrants and 0.667 million post-split Class B Warrants. The Class A Warrants give the holders the right to by the post-split stock at $7 per share. The Class B Warrants give the holder one full share and the right to buy 3/4 of a share at $6 (post-split) per share. Given a stock price of $9 (below the current $9.90), the the “PIPE-fitters” would have garnered themselves $8.5 million on an investment of $3.6 million ($9 per share times 0.667 million shares plus $2 per share times 0.5 million Class A Warrants plus $3 per share times 0.500 million Class B Warrants).

When PIPE investors do so well (more than doubling their investment in under a year), the company and its public shareholders do poorly. In return for a measly $3.6 million in cash (plus an extra $6.5 million when the warrants are exercised), shareholders were diluted by 1.667 million post-split shares with a market value of $15 million. If Lighting Science’s business were truly doing well, it surely could have found more advantageous funding sources.

La Revanche de David Gelbaum

Perhaps a good investment strategy would be to short sell any stock in which David Gelbaum invests. His Quercus Trust showed up as a large holder in Octillion (OTC BB: OCTL) and he is a large holder of Lighting Science.

[Edit 3/31/08: upon further review, I think it is more accurate to state that Gelbaum invests fairly indiscriminately in ‘green’ companies. So his investment does not mean much of anything, good or bad.]

Comparables

Perhaps the most comparable company to Lighting Science is CREE [[cree]]. It manufacturers LEDs and has some good technologies and patents. It trades at a P/S of 6.5 and a price to book ratio (P/B) of 2.7. If CREE is fairly valued and both it and Lighting Science deserve similar multiples, Lighting Science is overvalued by 500% according to the P/S ratio and by 430% according to P/B ratio. Being generous to Lighting Science, I would call it 400% overvalued and give my fair value estimate as $2.35 per share, one quarter the current price of $9.40. Of course, because Lighting Science is not profitable (unlike CREE) and is much less established, it deserves to trade at a significant discount to CREE. A 50% discount would be reasonable and would result in a fair value of around $1.20 per share (88% below the current market price).

A Bit of Positive Press

Perhaps you caught Jeff Bishop’s positive review of the company on SeekingAlpha. His article is entirely fluff. Furthermore, his company, Beacon Equity Research, has a nasty habit of covering a ton of OTC and Pink Sheets stocks and rating them “speculative buy” or “outperform”. I looked at over half of their reports, and all of the companies that were rated (one was not rated) were rated either “speculative buy” or “outperform”. Highly rated companies included such utter dreck as Rocket City Automotive and Universal Property Development and Acquisition Corp. Beacon is paid to cover most of the companies it covers, either directly by the companies or indirectly by large shareholders. That explains why Beacon is so overwhelmingly positive about the companies it covers.

Reverse Stock Split

Lighting Science recently completed a 20 for 1 reverse stock split, increasing the market price from around $0.48 to around $9.60. Academic research (pdf) has consistently found that companies that undergo reverse stock splits underperform the stock market drastically.

Conclusion

Lighting Science Group Co. is at best a halfway decent company that could eventually become profitable. At worst, it will continue to lose money for the foreseeable future. Either way, it is way overvalued. While LEDs will be a great market, there is little reason to believe that Lighting Science will be a leader in that market. It has too many competitors with more resources. If anything, my target price of $1.20 for Lighting Science is too high. I would argue that the comparable I used for the valuation, CREE, is overvalued as well. In fact, CREE is one of the most highly shorted companies on the NASDAQ.

Lighting Science Group Corp. is overvalued by any means. Smart investors should head for the exits and watch this company’s stock collapse from the sidelines.

For More Information

3rd Quarter 2007 10Q
Pro-Forma Financial Statements
LED Holdings Financials (and those of its predecessor company, LED Effects)

Disclosure: I am short LSCG. I have a full-disclosure policy.

Paye Tes Dettes!

The title of this post comes from the Charles Trenet song about the importance of paying off one’s debts (full lyrics).

In the search of good value we must be willing to take necessary risks. We must be willing to bet on struggling companies, sometimes with bad management, sometimes in struggling industries. We must never combine those three, however. Most important of all, we must shun excessive debt like the plague. While I prefer to avoid companies with significant debt, in cases in which the company has consistent earnings and the ability to maintain those earnings (because of strong brands or monopoly status), debt is forgivable.

For companies with tough competition and little competitive advantage, debt is a very, very bad idea. Two great cases in point are Movie Gallery (MOVI) and General Motors (GM). Both companies have historically strong brands and decent business models. They are both extraordinarily cheap. If they had less debt they would be great companies to buy. Saddled with debt, however, they lack the ability to survive their cut-throat industries.

Movie Gallery is a great example of stupid management harming a company. The company’s stock traded as high as $30 in 2005; it now trades at $4. Movie Gallery runs a chain of video rental stores. They have historically been profitable. However, early in 2005 the company took on much debt to buy Hollywood Video. The company now has a market cap of $130 million and debt of $1.1 billion.

I would argue that the movie rental business is one of the best businesses to be in. People like watching movies, new movies cost a lot to see at movie theaters, and the competition (satellite and cable movies on demand) are not that great. While Netflix (NFLX) has made it harder for bricks and mortar stores, I feel its impact has been drastically over-rated. I subscribe to the Netflix service, but there are plenty of people who do not. A bricks and mortar store can do good business because those that rent less frequently will not subscribe to Netflix.

Therefore, I think that Movie Gallery’s two chains, Movie Gallery and Hollywood Video, will still be around in one form or another fifteen years from now. The problem is that the debt of the current company will likely result in bankruptcy and leave the stock worthless.

General Motors faces much the same problem. Unfortunately for the careless investor, their full debt his hidden in details in their financial statements about their union contracts and the number of retirees for whom they provide pensions. Some have estimated that GM will have to pay out over $70 billion in pensions and health care benefits to its current and future retirees. For a company that has consistently lost a few billion dollars per year over the last few years, this is a problem.

In addition to its debt, GM has too many brands, too much production capacity, an unfavorable union contract, and shrinking sales. Without such a sizable debt, GM would stand a chance of restructuring and saving its stockholders. As it stands, it has no room to maneuver. Unless it can become highly profitable within a year or at most two years, it will go bankrupt.

So does debt matter for stock returns? Yes, at least according to “Predictability of UK Stock Returns by Using Debt Ratios” by Muradoglu and Whittington (scroll down on the page to which I link to download the PDF). While the data are from the UK, the results are logical and should apply in the USA as well. While the correlation of debt ratio with stock returns is lower than the correlation of P/E with stock returns, there is still a definite negative correlation: the stock of those companies with the least debt did the best. The 30% of companies with the lowest debt showed a consistent advantage over those with higher debt. Those companies had gearing ratios (leverage ratios for you Americans) of under 20%, meaning that total debt represented less than 20% of enterprise value. There are other ways of reporting leverage but I like this one. For comparison, MOVI has a gearing ratio of 89%, while GM has a gearing ratio of 96%.

Disclosure: I have no position in any company mentioned. This was originally written two years ago and published elsewhere. Movie Gallery has since declared bankruptcy. I have a disclosure policy.

The Weighting Game: Proper Weighting of Evidence in Making Investment Decisions

This article should not surprise you, but I think it important to emphasize points I have made earlier about the predictable irrationality of investors. I recently came across a paper written by Dale Griffin and Amos Tversky, entitled The Weighing Of Evidence and the Determinants of Confidence (no full-text version available online). This article gives evidence as to why investors ignore regression to the mean and why they do not pay attention to the reliability of certain kinds of financial information.

The research behind the article is not brilliant nor even very interesting. What is exciting is the theory that Griffin and Tversky put forth. They also review relevant prior research. It is best to start with their theory.

Their theory is that people pay too much attention to the strength or extremity of evidence and not enough attention to its weight or reliability. They do not put forth a theory as to why people do this, but such a theory does not matter to us. What is important is what this theory predicts and how we can use it to predict how other investors will behave so that we may profit from it. One of the most important predictions of this theory is that people will be overconfident when information strength is high and information weight is low but they will be underconfident when weight is high and strength is low.

What do I mean by information weight and strength? The strength of information would be its extremity. For example, when hiring from among a pool of job applicants, a job interview that goes very poorly is strongly negative information. Information weight is the reliability of the information. A 20 minute job interview is not a reliable indicator of a potential employee’s demeanor and character and thus can be characterized as low-weight information.

The simplest test of this theory involves having people guess about a spinning coin. Unbeknownst to most, coins that are spun will tend to land on either one side or the other, due to imperfections in the manufacture of said coins. Griffin and Tversky told their subjects about a series of coins. They gave their subjects results of coin spinning experiments that varied in both the strength of evidence (the percentage of spins that landed on either side) and the weight of that evidence (the number of times the coin had been spun). Subjects had to guess whether the coin was weighted towards heads or tails. Subjects also had to give their confidence for each decision they made. Keep in mind that even if a coin lands on head 60% of the time, it would not be implausible for it to wind up on heads four out of 10 times or 10 out of 20 times, due to random error.

If people were perfectly logical, their confidence would increase as a function of both the strength of the evidence (the percentage of heads) and the weight of the evidence (the number of spins). There is a way to calculate the statistically correct inference and the confidence one can have in that inference given a certain weight and strength. This can be done by using Bayes’ theorem, which I will mercifully avoid describing here. While it would not be reasonable to expect people to always draw the statistically correct conclusion, it would be reasonable for them to be consistent. That was not the case.

In fact, the researchers’ theory was confirmed: given strong evidence with little weight (e.g., a coin that lands on heads 80% of the time, after five spins), people tend to be overconfident. Given weak evidence with a high weight (e.g., a coin that lands on heads 60% of the time, after 17 spins), people tend to be less confident than they should be. Overall, the researchers found that people relied over twice as much on the strength of the information as they did on its weight.

What does this mean for us as investors? First, if we pick our stocks using our intuitive judgment, we are at risk of becoming overconfident in our stock picking when our decision is heavily influenced by strong information of low weight (low reliability). A good example of this would be the novice investor’s choice to invest in a company primarily because he likes their product. Unless that investor is an expert in that type of product, such information is rarely useful. Conversely, we are at risk of being underconfident in our stock picking when the evidence is rather weak but highly reliable. I think Wal-Mart [[wmt]] is a great example of an investment with weak but reliable information in its favor: its PE ratio is average, its recent growth has been steady but unremarkable, and its brand name is well-known. These pieces of information are all highly reliable, but they indicate that Wal-Mart is a pretty good investment, not a great investment.

Oftentimes, conflicting information about a company will be of different strengths and weights. Whether by using a quantitative investment method or by just being aware of how much weight you should give to each piece of information, you must always consider the reliability or weight you should put on a piece of information when you are making investment decisions.

Besides the implications for costs and our personal investment decisions, this research has important implications for us because other investors will make these mistakes. They will not pay enough attention to the reliability of information (its weight). This will lead to consistent mispricing of stocks. This explains why value stocks outperform growth stocks: investors put too much weight in unreliable estimates of future growth, which leads them to bid up the price of growth stocks too high.

Other consistent inefficiencies in the market are also likely caused by investors not paying enough attention to the reliability of information. Companies in exciting sectors or industries are valued more highly (have higher PE ratios) than companies in less exciting industries, despite evidence that hot sectors do not outperform the market and may under-perform the market as a whole. While some might say that investors buying into the latest hot sector are just being stupid, I would argue that they are just over-weighting the importance of the industry’s future and under-weighting the importance of value (as measured by the PE ratio).

There are probably other stock market inefficiencies that this can explain. I will address these in future articles.

Disclosure: I have no position in WMT. I have a disclosure policy. This article was originally written two years ago and published elsewhere. I have a disclosure policy.

Cytocore: Management by Hype and Distortion

It is one thing for a speculative company low on cash to get more money in a PIPE at a discount to the stock’s market price. It is quite another for such a company to give that opportunity to insiders and then to shamelessly announce in a press release that it was good news. Yet this is exactly what Cytocore (OTC BB: CYOE) just did. Daniel Burns (a director) and Robert McCullogh (CFO and CEO) each purchased a large number of shares from the company for $2 per share on January 22. This was an 18% discount to the stock’s close that day. And still the fools who “invest” in the company’s stock rejoiced by pushing the share price up 51% in the three weeks since then.

At the end of the quarter ended September 30, 2007, Cytocore had under $1 million in cash and a negative cashflow from operations of about $1.5 million per quarter. So despite what the press release said, this was not an investment to “assist in the scale up” of the company’s manufacturing, but rather a necessary investment to keep the company up and running.

I have written about Cytocore’s travails before and I have received some kind comments in response to my previous article.

Disclosure: I have no position in CYOE, although I do confess to a visceral hatred of a few of the company’s investors. I have a disclosure policy. An earlier version of this article referred to the cash flow from operations as the cash burn rate. This was incorrect and I regret the error (there was negligible cash burn over the last 9 months due to the sale of stock and the exercise of warrants).

Kiplinger’s recommends target date funds

Long-time readers will know that I recommend these funds for people who don’t want to spend too much time on investing. See Kiplinger’s article.

I like Vanguard’s target-date funds because of their low costs. While they tend to be more conservative than other company’s target-date funds, an investor willing to take on more risk could easily do so by investing only in the longest-date fund, no matter when that investor plans to retire, or by supplementing the target-date fund with a pure-stock index fund.

A game of risk or a risky game?

The traditional thinking in the world of finance is that to increase returns you need to increase risk. This view is quite logical. Let’s consider an investor who wants a minimum of risk. She can buy certain blue-chip stocks such as Wal-Mart, Home Depot [[hd]] , and GE [[ge]]. The stocks offer low risk because they are all giant companies with dominant market positions; I think it is a fair bet that all three companies will still be around in one form or another 50 or 100 years from now. For such low risk our investor will get a relatively low return because these companies are so huge and have less ability to grow than they had in the past. Now, with such great blues chip stocks available why would our investor choose to buy shares in a company such as DayStar technology [[DSTI]] or Cheniere Energy [[LNG]]? Neither of these companies currently makes a profit nor has any significant revenue. In owning such companies an investor has significantly more risk of losing her capital. Therefore, no rational investor would buy the stock of such companies without being assured that those investments offer the potential of very great reward.

This thinking underlies the Capital Asset Pricing Model (CAPM). Now, for the most part this works quite well. However, there are some problems with the capital asset pricing model. One huge problem is that in this model risk is defined as the stock’s volatility. Volatility is of course a measure of how much a stock’s price changes each day, week, and year. For those of us who are long-term investors, however, volatility is an inadequate measure of risk. What matters more for us is the stability of the future earnings of a company.

For financial analysts and portfolio managers, volatility is most commonly measured by something they call beta. Simply put, beta is a measure of the correlation of the stock’s price to the broader stock market as a whole. Therefore, an index fund would have a beta of 1.0. Let’s say we have a stock that has a beta of 2.0; this means that in general, when the market goes up 10% the stock will go up 20%, conversely, when the market goes down 10% the stock will go down 20%.

Since we’re already talking about beta and financial analysts, I might as well mention alpha. Alpha is a measure of a portfolio’s performance. An alpha of zero indicates that a portfolio matched the market’s return. An alpha of one would indicate that a portfolio outperformed the market by 1% annually.

The goal of a professional portfolio manager, at least according to the capital asset pricing model, is to construct a portfolio with the desired amount of alpha in order to maximize returns without exceeding a certain level of risk (beta). According to the model, it is impossible to consistently beat the market because the market is efficient. This aspect of the model is known as the efficient market hypothesis and I obviously believe it to be wrong. I will deal with why this is wrong in a later article. For now let’s return to risk.

One finding that has been problematic both for the efficient market hypothesis and for beta is the finding that low P/B stocks outperform high P/B stocks. According to the CAPM, this could not happen without low P/B stocks being more risky than high P/B stocks. However, low P/B stocks do not have higher betas than high P/B stocks. The creator of the efficient market hypothesis himself, Eugene Fama, realized then that beta do not adequately measure risk. He and his collaborator Eric French argued that low P/B stocks are more risky than high P/B stocks. I disagree with this but the important point is that as of their paper in 1992 (unfortunately not available free online), beta was officially dead or at least dying.

So how can we measure risk? There are no easy ways to do so. We must rely on sound fundamental analysis. Risk obviously decreases the more products the company makes and the more customers to which it sells. Thus, GE and Berkshire Hathaway are less risky than almost all other companies because their revenue streams are so diverse. Conversely, ExpressJet [[xjt]] and the other regional airlines are very risky because they all have only one or two customers. Similarly, small defense contractors are risky if they sell only a few major products and to only one major customer: the United States government.

Another risk factor is debt. Companies with more debt are much less likely to be able to survive a recession or industry downturn because they would be unable to meet their debt obligations if their revenues drop more than slightly. For this reason, companies such as Fortune Brands [[fo]], Blockbuster [[bbi]], and Ford [[f]] have elevated risk due to high debt loads.

Another risk factor is obviously competition. Companies and highly competitive industries have greater risk than companies with monopolies or that for other reasons do not have much competition. There are many ways that a company can avoid too much competition, including patents, trade secrets, operating in a niche market, and effective branding. Ceteris paribus, a dollar of earnings that is at lower risk from competition is worth more than a dollar of earnings that comes from a highly competitive industry.

This is not to say that companies in highly competitive industries cannot be great investments. Those companies that are wildly successful in competitive industries usually have some key advantage that gives them an edge in that is not easily copied. This advantage is not always easy to identify. For example, Southwest Airlines [[luv]] had the important advantage over legacy carriers of not having an established business prior to airline deregulation. This meant that Southwest was not burdened with the same costs that hindered the larger airlines. In addition, Southwest’s fares were simple and did not penalize travelers for arbitrary reasons such as not staying over a weekend. Another good example of a successful company in a competitive industry is Wal-Mart [[wmt]]. What did Wal-Mart offer that Kmart [[shld]] and other discounters could not? One thing it offered was everyday low prices. By avoiding sales Wal-Mart gained both the reputation as the low-price leader and it gained more consistent profitability. Wal-Mart has also been known for some time for the effectiveness of its distribution system. In an industry with low profit margins and high inventory requirements, any improvement in logistics drops straight to the bottom line.

The last important risk factor is the elasticity of demand for a company’s products. The business cycle is a fact of life; any company that suffers less during recessions, whether because it sells products that are always in demand or because it sells to people who are not greatly affected by recessions, has lower risk than the average company. Big industrial companies such as auto manufacturers and aircraft manufacturers are usually very cyclical. Cyclicality of earnings is not in and of itself a black mark against a business. However, combined with high debt and stiff competition, a cyclical company in an industry downturn can be a very risky bet. See, for example, General Motors [[gm]] or Northwest Airlines [[nwa]].

While it is not possible to exactly quantify risk, it can still be approximated. Any decision to invest in a company should come only after carefully weighing the possibility for reward against the risk that company presents. In certain circumstances, adequate calculation of risk and reward cannot be made, such as with development stage companies with no revenues. In such cases, the conservative investor would do well to watch from the sidelines unless she is an expert in the field and is sure that she is not paying too much.

On a related note, I urge you to read Richard Russell’s article on the perfect business, which discusses the ideal business from the standpoint of a small-business owner. The points Russell brings up are also important to large publicly traded companies.

Disclosure: I have no position in any stock mentioned above. I have a full disclosure policy.

Document Security Systems Getting Desperate for Cash

I just ran across an interesting article on SeekingAlpha on my old friend Document Security Systems [[dmc]]. The author of the article is short DMC, so take it with a grain of salt, but he does bring up some good points, including some past failures for the company’s new Chairman of the Board. Shareholders will be very disappointed if the company does not actually collect any cash from its patent lawsuits. If that happens, his estimation that the stock could fall 85% seems conservative.

When I last wrote about Document Security Systems, I was decidedly negative. The stock price has since fallen by 50%.

For more information, see DMC’s most recent 10Q.

Disclosure: I have no position in DMC. I have a disclosure policy.