Quand HSOA Fait “Boum”

With apologies to Charles Trenet, Home Solutions of America just went “Boum”. Allegations of fraud by journalists, contractors, and short sellers are one thing. But to have a board member quit and list details of numerous fraudulent activities–well, that usually spells the end of a company. Following is Stephen Sewell’s resignation letter (from the SEC filing here):

The Board of Directors
Home Solutions of America, Inc.
1500 Dragon Street, Suite B
Dallas, Texas 75207

Dear Gentlemen:

This letter is delivered to you for filing by Home Solutions of America, Inc. pursuant to the requirements of the Securities Exchange Act of 1934. I am resigning from the Board of Directors of Home Solutions of America, Inc., effective upon your receipt of this letter and its filing in a Current Report on Form 8-K.

On Friday, December 14, 2007, Frank Fradella handed me a letter that purported to terminate my employment at Home Solutions Restoration of Louisiana, Inc. (“HSRLA”) for “cause.” Since there in fact was no “cause” pursuant to the definition of that term in my contract, any termination could only have been effective “without cause” on January 13, 2008, thirty days from delivery of the termination letter. Further, only five days before my improper termination, HSRLA and Mr. Fradella had reaffirmed my position with a purportedly unanimous Board vote and even directly informed all employees that my position with the Company was not in jeopardy and was unchanged. While the contract does not require me to resign from the board of Home Solutions of America, Inc. until at least January 13, I do so now for, among other things, the reasons set forth below.

Sometime before I was informed that an outside law firm had been engaged to conduct an investigation of the actions of HSOA’s management in exchange for a deferral on an SEC inquiry, I became concerned about the accounting and business practices of CEO Frank Fradella, Executive Vice President Brian Marshall, CFO Jeff Mattich, and certain members of their staffs. It began when I was approached by CEO Fradella and a prior HSRLA officer with a proposal to use corporate resources to renovate their houses at Company expense. They apparently intended to bill the costs incurred by the Company on their homes, which were later reported to me as equaling approximately $95,000, to an existing job for a corporate employee, and then the Company would later write those charges off. The employee was offered $30,000 in similar “free” work for cooperating. The employee, however, had rightly refused the offer, and I demanded restitution for the corporate assets already expended, but Mr. Fradella refused. Thus began a contentious relationship with Mr. Fradella, and the first of various threats by him to terminate me.

This period also started my status as a whistleblower. I uncovered what I believe was improper revenue recognition for non-existent work, which HSOA used to fund a bank line of credit. I fought with management over the recognition of revenues that were anticipated only in later quarters. I noted abuse of officer expense accounts, vehicle

acquisitions and more, to the detriment of the operating capital and cash position of the Company. I discussed with officers the information I had received to the effect that a Fireline employee was demanding kickbacks from vendors. For all these matters, HSOA and Mr. Fradella failed or refused to take action. Finally, without other recognizable alternative, and to protect the company and its shareholders, I initiated conversations with the Federal Bureau of Investigation. When I told Mr. Fradella that I had done so, he apparently notified others who were involved, and threats on my life followed.

Another incident involved the acquisition of RG America. It was reported to me that the revenues and income opportunity we in fact were acquiring were not as beneficial as had been presented by management, but actually were only a small fraction of the amount represented. Mr. Fradella stated to me that the Company had already spent funds advanced as part of the transaction, and that we could not repay those funds if we did not proceed with the acquisition. This was bad enough, but what was not disclosed until much later was the fact that management already had recognized contemplated revenue in the quarter prior to the actual acquisition, and that the Company already had borrowed against those revenues. I also recently was informed that, although many receivables were old — unpaid for many months and even years — the Company had put them on its books as current (apparently on the ground that the Company had just acquired RG America) so as to appear to be usable for bank borrowing under the terms of the Company’s line of credit. The Company apparently used these “recognized revenues” in its borrowing base calculations.

The business arrangement of the Company with American Renaissance Homes (“ARH”), a modular home business and a personally directed project of Mr. Fradella, also was questionable. Subsequently discovered but originally undisclosed was the fact that the ARH arrangement was in effect a related party transaction funded by HSOA, that ARH was a start-up business whose officers had no direct experience in the industry, that HSOA continued to fund the venture which purportedly continued to lose money, and that ARH now is reportedly owned by the Company. The Company spread costs throughout its subsidiaries and refused to provide financials on this venture for review, even to me as a member of the Board.

In the last two months, I received oral reports from an investigation by a law firm engaged by the Company to the effect that certain Company management engaged in significant undisclosed related party transactions, that work reported to have been done by the Company may not actually have occurred, that phantom receivables were reported in the Company’s SEC filings and used in the corporate bank line of credit borrowing base, that false documents were presented to auditors to substantiate those receivables, and that false public statements and press releases had been made. When I asked for a copy of the report in writing so I could study it, follow up and comply with my fiduciary duties as a director, my request inexplicably was denied.

As a member of the HSOA and HSRLA Boards, I have been deprived of my ability to make reasoned decisions and to help guide corporate policy. I have been denied requested information, including data regarding corporate finances. I was not timely

notified of all Board meetings or critical information. I was excluded from certain meetings, and was provided apparently incorrect information about others. My complaints and comments were unheeded. Independent directors did not act independently. Serious reform of corporate governance is required.

The Company also intentionally has refused to pay its clear contractual obligations. For example, the former owners of Associated have never received full payment for their interests. Mr. Fradella instructed that required payments not be made. In June 2007, management apparently wrote off the obligation to pay for Associated, thus indicating that the intent was to take that company without paying. Citing cash flow and the need to fund legal expenses, the Company attempted to seize the cash flow of Associated without considering its obligations to its subcontractors, vendors or the monies owed to the former owners. Just last month, secretly and without proper Board resolution, Mr. Fradella attempted to convince Associated’s bank to give him control over that corporation’s checking account. When the bank on its own followed appropriate procedures and initially prevented him from looting corporate coffers, Mr. Fradella then convened a meeting that he said was a valid Board meeting of HSRLA (although all directors did not receive notice, while others who were not directors did receive notice, attended, and voted), and proceeded to pass banking resolutions that would give him the right to completely control and remove HSRLA funds.

Confronted with the probability that the former owners now would never be paid, and fearful of the waste of HSRLA’s funds, I acted. Rather than pay the former owners of Associated the money to which I believed they clearly were entitled by contract, I sought to protect all concerned and give everyone an opportunity to state their positions fairly. As the chief executive officer of HSRLA, which authority had just again been ratified by its purported Board, I deposited the money in the registry of the court through a concursus proceeding, which I understand is an appropriate legal procedure. Further, and although the former owners were entitled to more, I placed in the registry only the amount the CFO of HSRLA reported would not be needed by that company in the immediate future; in other words, although the former owners are owed far more than HSRLA put in the court registry, I had HSRLA retain enough funds to make payroll and continue operations. Interestingly, Mr. Fradella does not appear as concerned as I about HSRLA and its continued operations, or about fairness: I understand he has opened new bank accounts, that he has failed to comply with an order of the court to make deposits in the registry of that court, that monies were removed from HSRLA accounts, and that creditors are not being paid in accordance with the terms of their agreements with HSRLA.

As an officer of HSRLA, and as a member of the Board of HSRLA and HSOA, I have attempted, but been impotent, to halt the inappropriate actions of management. The majority of the Board has not supported me, and some members instead appear to have effectively defaulted to management, thus failing to satisfy their fiduciary duties. While I do not assert that each and every Board member has acted improperly or fraudulently, I have been unable to break through the shield management has erected for those directors who may honestly seek to perform their fiduciary duties. If you do no more as a result of

my resignation, I demand that, at a minimum, you remove Mr. Fradella and the others involved in wrongdoing, assert claims on behalf of the Company to recoup what HSOA management wrongfully has taken, and act to preserve the Louisiana company intact. Do not permit yourselves to be swayed by Mr. Fradella rather than by the facts, and do not give Mr. Fradella authority to act or take action regarding employees, former employees or corporate funds; look independently at all facts, and exercise the fiduciary duties you owe to the shareholders and for which you were elected.

Sincerely Yours,

/s/ Stephen Scott Sewell

I have previously written about HSOA’s troubles and its battles with short seller Andrew Left.

Disclosure: I have no interest in HSOA, long or short.

What Every Investor Needs to Know About Regression to the Mean

Perhaps one of the most widely disseminated and most widely misunderstood statistical concepts is that of regression to the mean. It is also one of the most important concept for investors to understand.

The simple definition of regression to the mean is that with two related measurements, an extreme score on one will tend to be followed by a less extreme score on the other measurement. This definition will not suffice for us as it is incomplete. Regression to the mean only happens to the extent that there is a less than perfect correlation between two measures. Thus, as a technical definition, let us use that of Jacob Cohen: whenever two variables correlate less than perfectly, cases that are extreme on one of the variables will tend to be less extreme (closer to the average or mean) on the other variable.

For those of you who have been away from math for too long, a correlation is simply a measure of how well one thing can predict another. A correlation of 0 indicates that two things are unrelated, while a correlation of 1 or -1 indicates that they are perfectly related. See this website for a nice graphical presentation of what different correlation coefficients mean. For example, the price of a restaurant is correlated with its quality at about .60 (this is just my rough guess)—more expensive restaurants tend to be higher quality than less expensive restaurants, but there are plenty of exceptions.

On the other hand, I would estimate that price correlates more strongly with the quality of chocolate—probably around .80. Except for exceptions such as Candinas and Sees, most really good chocolates are horribly expensive, while cheap chocolates (such as Russell Stover) are invariably bad. An example of a near-perfect correlation would be the correlation between altitude and temperature at any given time in any given place—as the altitude increases, the temperature drops.

Some people refer to regression to the mean as a statistical artifact. It is not. It is a mathematical necessity. Let us start with a very simple example. Suppose that people who have more money tend to be happier than those with less. This is actually true, but the correlation is weak—money really matters to happiness only to the extent that people can afford the basic necessities. If we were to predict the happiness of both 100 billionaires and 100 people who live on welfare, we might expect that the billionaires would be significantly happier. In fact, billionaires are only slightly happier than those on welfare. Because the correlation is so weak, we would be better off ignoring the correlation of wealth and happiness and just guessing that everyone was of average happiness.

Let’s try another example. Suppose that you work as an admissions officer for Harvard. You have two main sources of information in order to decide whether or not to admit prospective students. You have the candidates’ SAT scores and you have the results of their admissions interview. Suppose that one student has an SAT score of 1550 (out of 1600 possible points) and a very bad interview—the interviewer considered the student to be uninteresting and not very bright. Another student had an SAT score of 1500 and an outstanding interview. Assuming there is only one spot left, which student should you admit and which should you reject?

Take a moment to think and make your decision. You most likely chose the student with the lower SAT score and better interview, because the SAT score was only slightly lower, while the interview was much better than that of the first student. However, this is the wrong decision. Repeated studies have shown that admissions interviews have no correlation whatsoever with college student performance (as measured by graduation rate or college grades). SAT scores, on the other hand, do correlate (albeit less strongly than most believe) with college grades. Thus, you should completely ignore the interview and make a decision purely based upon SAT scores.

I admit that this example is unfair—truth be told, SAT scores are only correlated moderately well with college grades: about .60. That means that there is little difference between a score of 1550 and a score of 1500. However, a small, meaningful difference is still more informative than a large, meaningless difference.

To make this a little more clear, we can do this without an interview, since the interview is useless. Rather, we throw a die (as it is equally useless). For the student with the 1500 SAT, we roll a 6. For the student with the 1550 SAT, we roll a 3. Would you decide to admit the student with the 6 because of his higher die roll? Obviously not, because the die roll is pure chance and does not predict anything. The same reasoning applies to the interview, since its relation to school performance is just chance.

Suppose we selected students based on a roll of the die—how would they fare? The students with the best scores would tend to do average, while those with the worst scores would also do average. This is perfect regression to the mean. Simply put, the die roll adds nothing.

Regression to the mean only happens to the extent that the correlation of two things is less than perfect (less than 1). If the correlation is 0, then there will be perfect regression to the mean (as with the die). If the correlation is between 0 and 1, then there will be partial regression to the mean. Let us now look at example of this.

There is a correlation between income and education level. I cannot find the actual data, so I will make it up—I will say that it is around .60. Therefore, level of education (as measured numerically by highest grade level or degree completed) is a fairly good predictor of a person’s income. More educated people tend to make more money. Let’s look at a sample of the top 10% of money-earners. If education perfectly predicted income, then those top money earners would be the top 10% most educated. Whereas education imperfectly predicts income, we will find regression to the mean. Those earning the highest incomes will tend to be well educated, but they will be closer to the average education level than they are to the average income level.

One of the beautiful things about regression to the mean is that if we know the correlation between two things, we can exactly predict how much regression to the mean will occur. This will come in handy later.

If all we had to worry about when two things are not perfectly correlated was regression to the mean, we would be fine. It is fairly simple to calculate a correlation coefficient and then figure out how much of some effect is caused by regression. Unfortunately, there is one more complicating factor: measurement error.

Imagine you have a bathroom scale that has 100% error. In other words, the weight it shows is completely random. One morning you weigh yourself at 12 pounds, while the next morning you weigh 382 pounds. Whereas height is normally correlated strongly with weight, your weight as measured by your scale will not correlate with your height, since your measured weight will be random. If we make the bathroom scale just a little more realistic and say that its measurement has 2% error (quite normal for bathroom scales), the same problem applies—the measurement error reduces the apparent correlation between height and weight and increases regression to the mean.

This is exactly the problem that we see in the stock market, although the errors are much larger than with your bathroom scale. The value of a company is a function of only one thing: the net present value of its future cash flows. That, in turn, is determined by two things: the company’s current price (as measured most typically by P/E or P/CF) and its future earnings growth. The measurement of P/E has very little error. The estimation of future growth has much error, though.

For the moment let’s assume that P/E and future growth each account for half of the current value of a company. (This is actually wildly inaccurate—as the growth of a company increases the growth will become much more important than the current P/E in determining the net present value of the company. Conversely, if growth is zero, then P/E will completely determine the net present value of a company.)

Since P/E accounts for half of present value, it is correlated at r=.71. (R2 is the proportion of variance explained, which is .50 in this case, so the square root of this is the correlation coefficient r). This is a fairly strong correlation. Nevertheless, it is far from perfect. Regression to the mean will ensure that companies with the most extreme P/E ratios will be less good values than is purely indicated by their P/E ratios. When you think about it, this makes perfect sense—some companies deserve low P/E ratios because their prospects are poor.

Now for the other half of the equation: growth. Growth is correlated at r=.71 with the net present value of the company. However, that is assuming that we can accurately predict future growth. This is simply not true. Analyst predictions of company earnings less than one year ahead are on average off by 17% of reported earnings (meaning that near-term estimates have a .83 correlation with actual earnings*). Their estimates of growth years in the future are of course much worse. So while the correlation between future growth and present value of a company is fairly strong, .71, the correlation between predicted growth and present value is very much less than that (about .28).

Due to this reduced correlation, there will be much greater regression to the mean for growth as a predictor of value than there is for P/E. The one problem is that investors do not take this into account. Investors and analysts put faith in projections of high growth for years in the future. However, the chances are only 1 in 1,250 that a company will go for 5 consecutive years without at least one quarter of earnings over 10% less than analysts’ estimates. This even understates the problem, because in the above calculation, the estimates can be updated until just before a company actually announces earnings. Estimating earnings five years in the future is impossible.

Remember how I earlier mentioned that as a company’s growth rate increases, its current P/E has less and less relation to its true value? The true value of these companies (such as Google [[goog]]) is determined primarily by their growth rate. So in effect, when the growth investors say that P/E does not matter if the growth is fast enough, they are correct.

There is one problem with this: because of regression to the mean, those companies that grow the fastest are also most likely to under-perform analyst and investor expectations. So the predictions of growth will be least accurate for those companies whose value most depends on their growth rate!

Investors do not realize this and they thus bid up the prices of growth stocks in proportion to the anticipated future growth of a company. Because of regression to the mean caused primarily by the lack of reliability of analyst estimates of earnings, earnings for the best growth companies (as measured by anticipated future growth rates) will tend to disappoint more often than other stocks. The converse will actually happen with the most out of favor stocks: analysts and investors are too pessimistic and thus they will underestimate future earnings and cash flow growth. See “Investor Expectations and the Performance of Value Stocks vs. Growth Stocks” (pdf) by Bauman & Miller (1997) for the data.

Some converging evidence for my regression to the mean hypothesis would be useful. According to my hypothesis, earnings growth for the lowest P/E or P/BV (Price/Book Value) stocks should increase over time relative to the market, while earnings growth for the highest P/E or P/BV stocks should decrease relative to the market. The value stocks in the following data are those with the lowest 20% of P/BV ratios, while the growth stocks are those with the highest P/BV ratios. Ideally, I would look not at P/BV, but at projected earnings growth, but these data will do.

The value stocks have earnings growth of 6.4% at the point in time when they are selected for their low P/BV ratio. After 5 years, their earnings growth increases to 11.6%. Their increase in earnings growth rate was thus 5.2%. The growth stocks, on the other hand, see their earnings growth rate fall from 24.6% to 12.1% (decrease of 12.5%), while the market’s rate decreases from 14.2% to 10.6% (decrease of 3.6%). The figures for cash flow growth are similar: value stocks increase their growth rate by 2.3%, while the market decreases its growth rate by 3.3% and the growth stocks see a decrease in growth rate of 10.3%. Changes in sales growth rates are not as convincing, but do not contradict my hypothesis: value stocks do as well as the market (seeing a 3.6% decrease in sales growth), while growth stocks see a whopping 6.5% decrease in sales growth rate.

The icing on the cake is in return on equity (ROE) and profit margin. In both cases there is no such benefit for value stocks over growth stocks. Why? Both ROE and profit margin are primarily determined by the industry a company is in: commodity industries will see lower ROE and lower profit margins, while industries with a possibility of long-lasting competitive advantage will see higher ROE and profit margins. ROE and profit margins tend to remain relatively stable (but generally decreasing over time for every company), meaning that they are reliable measurements. More reliable measurements means less regression to the mean.

So what does this all mean? Investors do not overreact to good or bad news. Or at the very least, it is not some sort of emotional overreaction—rather, they predict that current (either negative or positive) trends will continue. They do not take the unreliability of their estimates into account. Thus, they do not anticipate nor do they understand regression to the mean.

For this reason, value stocks will out-perform growth stocks until people understand how faulty earnings predictions are. Given the complexity of the topic, I doubt many investors will ever figure out that they should not trust predictions for earnings growth. Besides helping to ensure that value stocks will outperform and giving us a reason to avoid high P/E stocks no matter the projected growth rate, how can we improve our investing based on this data?

Perhaps the best way to use this knowledge would be to seek out all the most reliable predictors of future growth. P/E and other measures of current value are pretty darn reliable (excepting for Enron-style accounting), so we only need to look for more reliable predictors of growth and profitability.

Luckily, much of this work has already been done for us. I described one important piece of information in the review of Joel Greenblatt’s Little Book That Beats the Market. That information is ROC, or return on capital. ROC is a good measure of how easy it is for a company to expand, and thus should be highly correlated with future earnings growth. It is also more reliable than analysts’ predictions of future earnings growth, though it is less reliable than measures of current value.

In addition to ROC, academics have done many studies on all sorts of variables that correlate more or less with stock returns. I have previously highlighted one study, for example, that showed that stocks with high short ratios tend to do poorly. This correlation is not very strong, however, so it should not be very important to us in deciding upon an investment. Rather than just use one or two types of data, however, we can use every piece of data that correlates with stock returns. The key is to use all these variables together—by looking first at P/E, then at ROC, then at insider ownership and short interest, as well as some subjective measures such as management quality, we can more accurately predict a company’s true value. The key is to correctly weight each component.

People in general tend to make decisions based on only a few pieces of data. They often ignore important data and concentrate on salient data. By using a formula as our investing guide, we will be able to avoid making that mistake.

*This is not true. I am not sure how to calculate the correct number, though, so I will use this as an approximation.

Book Review: The Theory of Investment Value

John Burr Williams wrote The Theory of Investment Value as his dissertation. First published in 1938, this book is one of the classics of investing. I will not say that the book is a fun read, for it is not. It is dry and difficult. Half the pages are filled with equations. However, this book was a landmark and it remains relevant. This book is far too large and detailed for me to describe in detail, so I will present but a few of the highlights.

John Burr Williams invented the dividend discount model of stock valuation. Previous economists and stock analysts had only guessed at what the proper P/E valuation was for a company or what the proper dividend yield was. Also, most previous analysts ignored the sustainability of the dividend. In his book, Williams made the point that a company could be valued by calculating the present value of the future dividends (discounting those future dividends at the risk-free interest rate).

However, companies sometimes pay dividends that are unsustainable or that are below their true dividend-paying ability. Williams thus showed how to calculate the sustainable dividend payout. This is also known as owner earnings—it is a measure of the earnings after subtracting necessary reinvestment.

Williams also shows that this can be applied even to companies that do not pay a dividend. He made the point that a company increases in value once it has made money, and thus dividends are not necessary (the stock will increase in value proportional to how much would have been paid out in dividends). (As history has since shown, though, companies that do not pay dividends tend to do worse than those that do, simply because they may reinvest the money unwisely.) Williams thus laid the groundwork for what has later become discounted cash flow (DCF) models of valuation. For some types of companies, dividend discount models are still useful today.

Besides this, the last section of the book is a series of examples, ranging from Phoneix Insurance to GM and US Steel. Even if you only read this section, the book is worth the price. The problems facing investors 70 years ago remain today. We would be wise to learn from the past. By all means buy this book and read it.

Disclosure: This article was originally written two years ago and published elsewhere.

How to accidentally commit mortgage fraud

Back in late 2006 I was looking to purchase a small rental property. I initially agreed to purchase a 4-unit building but then backed out after an inspection revealed it to be in poor condition. I signed up with a mortgage broker and would have used her had I gone through with the purchase. We got so far as to look over and sign the good faith mortgage estimate. What I found there surprised me. The broker had pre-filled our answers, which was fine, as I had given her all the correct information. However, I found that our income had magically jumped from $35,000 for the prior year to $83,000. My wife and I were both in graduate school at the time so this was obviously not true. Also, although it was to be a rental building, the broker had checked the box saying that we would live there, contrary to what I had said multiple times.

If I had not read the document carefully I would have inadvertently committed felony mortgage fraud.

See the offending documents (personal info redacted).

The mortgage broker I worked with was Home Loan Experts (now defunct), which was a subsidiary of Golden West Financial, now part of Wachovia.

A short book review of much importance

I could have spent a few pages extolling the virtues of David Dreman and his book, Contrarian Investment Strategies: The Next Generation. Fortunately for you, I did not do that. Instead, I tell you simply to buy the book. It deserves a spot on your library shelf adjacent to Ben Graham’s Intelligent Investor. It is one of the most important investment books you will read. In the book, Dreman discusses at length the problems with estimating future earnings and psychological impediments to effective investing. He also lays out the key reasons why value investing works so well and he gives much data that support his arguments. Much of the data in my forthcoming article on regression to the mean is taken from this book.

Buy this book.

A day in the life of a short seller

What is it like to be a short seller? What is it like to be reviled? What is it like to be feared utterly? It is the most wonderful experience known to man. To give you a glimpse into my wonderful life as a short seller, I give you here a glimpse of what my days look like.

I wake up every morning at 5AM and run 10 miles. I get home and punch a punching bag until my fists are bloody. Each day I pretend it is someone different. Yesterday it was William Telander (of US Windfarming). Today it was Richard Altomare of Universal Express. Tomorrow it will be the despicable Judd Bagley.

My breakfast is the same every morning: oatmeal flavored with ox blood, followed by a banana. I then peruse my favorite blogs before the market opens. There’s Gary Weiss, Herb Greenberg, David Milstead, David Baines, Sam Antar (blah blah blah former CFO of Crazy Eddie’s and convicted felon; okay, we get it Sam, now shut up!), Tracy Coenen, the SEC litigation releases, and the Forbes Informer.

I usually spend my mid-morning tweaking my stock positions. I run a few quantitative screens to search for new stocks to target. I also search all SEC filings for certain phrases that indicate bad companies (such as “Our CEO is a convicted felon who is also a registered sex offender”). I do not stop for lunch. Rather, I grab a few Ks and Qs and a bottle of whiskey and settle down to find weaknesses in the companies I have targeted. Depending upon the companies I am short my midday reading may also include patent applications, scientific articles, and policy papers.

If my energy starts to flag during the day I rip off my shirt, stand in front of the mirror, and shout the following:
“I am a wolf among sheep!”
“I am a master among slaves!”
“I am a god among men!”
“I shall not only destroy my enemies, but I shall annihilate them, wipe them from existence. When I am done they will be gone, forgotten, they will cease to ever have been!”

To really get the blood boiling, I pump my fists and shout, “I am MICHAEL GOODE and I am a SHORT SELLER. I AM ALL POWERFUL! I CANNOT BE STOPPED!” After this motivational interlude I can face the market even if I am down seven digits on the day.

In the afternoon I always call up Patrick Byrne to harass him. I actually get through to him once in awhile. I once pretended I was his father and he believed me for 10 minutes into my diatribe against his conspiracy theories. Late afternoon there are usually more phone calls to many of the big short-selling hedge funds. So far I’ve got a better IRR than Jim Chanos and I like to shove that in his face. He is too much the gentleman to point out that he manages 10,000 times more money than I do. I then call up the Sith Lord, Patrick Byrne’s best friend. I had a bet with him about Overstock.com’s [[ostk]] inventory turns and it looks like he won that.

Sometimes I call up my journalist buddies. I have several journalists on the take, although I do not use their services too often. Anyway, when I focus on truly deplorable companies I have no need of any help. After I am done with phone calls I will usually look up the new financial research (shout out to my buddy Dr. Sloan! Booya!).

By the time I am done with all this it is usually 7pm and I stop for a quick bowl of rice and steamed vegetables. I then settle into my easy chair for another 4 hours of reading financials. And that is a day in the life of a short seller.

Disclosure: If you cannot guess what I should disclose about the above article then I cannot tell you.

Book Review: The Little Book That Beats the Market

Despite an audacious, even pretentious, title, Joel Greenblatt’s Little Book that Beats the Market is a worthwhile book. It seems absurd to pay so much for such a little book, but do not let that get in your way.

There are two keys to any successful investment strategy: having a winning strategy and sticking to that strategy. To stick with a strategy takes guts and determination. Beyond that, you must also be utterly convinced that your strategy will work. That is why I am a value investor and I pay no attention to many things that other investors fancy, such as new technology, paradigm shifts, and exciting products. Because so many investors before me have successfully used the basic strategy I use, I can rest assured that at worst I will do no better than the market as a whole. I fully expect that I will outperform the market by about 5% per year over the next 10 years.

Greenblatt’s book is useful first because it reaffirms much of what we already know and gives us data to increase our resolve. Second, his book can give us a good way to screen stocks.

The magic formula that Greenblatt mentions in his book is this: take the stocks with the combination of the best earnings yields (the inverse of P/E) and the best ROC (return on capital). Invest in each stock for a year and then repeat the procedure.

This is different from a true Graham-style value investor, who cares more about finding companies that are purely undervalued in terms of the P/E ratio or earnings yield. Overall, I agree with Greenblatt more than Graham, especially with pure value stocks seemingly overvalued now.

Now, one of the keys of Greenblatt’s magic formula is that for earnings yield he does not use just the inverse of P/E. He uses EBIT / Enterprise Value. For an explanation of why this is a good thing, see the forthcoming article, “Accruals & EBITDA,” which includes a great example I blatantly took from Greenblatt’s book.

The other key to the secret magic formula for immortality and enlightenment (or something like that), is ROC. No, not the bird. Not even receiver operating characteristics. ROC is Return on Capital. It is not to be confused with ROA (return on assets) or ROE (return on equity). It is harder to calculate than either ROE or ROA, but it is more precise and more useful.

All three terms measure the efficiency of a company’s use of capital. Each is calculated by taking net income and dividing by a different measure of capital. ROE uses the amount of stockholders equity; this is flawed because stockholder equity bears little relation to a company’s assets. ROA uses the total assets of a company; this is flawed because it includes non-earning assets such as goodwill.

ROC includes only earning assets; it uses EBIT as the measure of net income and net working capital plus net fixed assets as the measure of capital. It is thus a more true measure of how much money would need to be expended to achieve a certain increase in earnings. A ROC of 30% would indicate that for each dollar spent on new capital equipment, 30¢ of EBIT would be produced.

Why is ROC important? Any business, no matter how bad its ROC, can be a good deal if bought cheaply enough. However, if the ROC is 3%, it would be foolish to expand the business—it would make more sense to invest that money in bonds and earn 4.5% with no risk. Thus, the higher the ROC, the more profit will accrue in expanding the business. Investing in such great businesses is a way to achieve investing success.

The magic formula simply ranks companies on ROC and EBIT / EV and produces a list of the companies with the best average rank. While this is a very simple concept, it is a good one. I heartily recommend screening for stocks using this method. Greenblatt has even made it easy for us at his website (free registration required).

Buy the book. It is a good introduction to investing and thus makes a great gift.

Disclosure: This review was originally written two years ago and published elsewhere.

Just One Thing

I just finished John Mauldin’s new book, Just One Thing. It took me only two days to read. I cannot enthusiastically recommend this book even thought there are some nuggets of wisdom in it. In the book, twelve investment writers each give their one best investment idea.

Some of the authors rambled and others (Bill Bonner, George Gilder, John Mauldin) did not have anything useful to say that you could not have already picked up from reading Mauldin’s email newsletter or other sources. For those who are not familiar with Dennis Gartman, James Montier, Gary Shilling, and Richard Russell, their chapters make good reading.

The two best chapters were Ed Easterling’s chapter on the capital asset pricing model (CAPM) and its faults and Rob Arnott’s chapter on non-market-weighted index investing. Easterling does a good job of explaining problems with how we look at risk. Arnott makes a good case for avoiding index investing in market-weighted indexes such as the S&P 500. In a market-weighted index, companies that are selling above their true value will be overweighted while companies that are selling below their true value will be under-weighted.

The solution is to invest equal amounts in all different companies. By investing equal amounts in the stocks in the S&P 500, you can average a return of 2% more per year over the market-cap weighted S&P 500. Of course, that is what investors in individual stocks should do. By putting the same amount of money into each stock, regardless of market-cap or price, investors lower their risk while increasing our returns.

Overall, Just One Thing is a decent book and a quick read. Consider buying it.

Disclosure: This review was originally written two years ago and published elsewhere.