invest(igations)

The Paradox of Stock Screeners

Let’s say I screen the stocks in the S&P 1500 by the following criteria (all for the last twelve months): sales greater than enterprise value; sales growth between 0% and 15%; net profit margin greater than 5%. Tested on Portfolio123 since 1/1/2000 with a four-week holding period and a slippage of 0.25% per transaction, this screener gets a pretty nice annualized return of 14.99% with an average portfolio of 48 stocks.

Now let’s say I run another screener for S&P 1500 stocks. I want stocks with falling earnings: the current quarter’s EPS estimate should be lower than the GAAP EPS in the same quarter last year. I want stocks with a trailing twelve-month unlevered free cash flow of less than zero, just to make sure they’re completely worthless by DCF analysis. And I want stocks with decreasing volume: last week’s average volume has to be lower than the average volume three weeks ago. This is a horrible group of stocks. Tested since 1/1/2000 with a four-week holding period and a slippage of 0.25%, this screener gets a terrible annualized return of –1.65% with an average portfolio of 36 stocks.

Now let’s take the case of General Motors (GM). It passes the first screener with flying colors. It has sales of $170 billion and an EV of only $117 billion, making it a real bargain. It has a nice healthy profit margin of 5.92%. Its sales this year went up by 10.66% compared to last year.

GM, however, also passes the second screener with flying colors. The EPS estimate for the second quarter is only $1.69, less than its GAAP EPS of $1.81 this time last year. Its capital expenditures are almost $10 billion more than its cash flow from operations. And its average volume last week (10 million shares per day) is significantly lower than it was three weeks ago (16 million shares per day).

Have you ever wondered why, when you create a stock screener, your results are never nearly as rosy as your backtested results? This is why. Every stock can be chosen by an infinite number of stock screeners that screen for different criteria. Some will show that the stock is a sure-fire winner; others will show that it’s a sure-fire loser. There’s no way to evaluate a stock--whether it be GM or any other--solely on the basis of a screener. This is what I call the paradox of stock screeners.

The lesson? Don’t use stock screeners to pick stocks.

What you want to do is to evaluate every stock you choose from as many angles as possible. Screeners can’t do that. If you use more than five or six criteria in your screener, you’re only going to get five or six stocks that fit them all. What about all the other criteria that are important to stock market performance? No screener can cover them all.

But there is a way to solve this problem. Instead of binary screening criteria, use ranking systems.

Here’s the difference. Let’s say you have six equally weighted factors. A screener will accept or reject each stock depending on how you set the limits for each factor. A ranking system, on the other hand, will assign a percentile rank to each stock, and then average those ranks to get a final number. The stocks with the highest ranks will have the highest average percentiles of the factors. A stock screener can only look at a few factors; a ranking system can take into account a huge number of them.

Let’s look, for example, at a ranking system based on the criteria I used to set up the two screeners above. I’m looking for stocks with high ratio of sales to enterprise value; an annual sales growth that’s pretty close to average; a high net profit margin; high earnings growth, based on this quarter’s estimate compared to the same quarter last year; a high ratio of unlevered free cash flow to enterprise value; and increasing volume. So I’ll set up a ranking system that weighs each of these things equally. Here’s the result of investing in this ranking system by decile:

And if you just bought the top ten stocks in the S&P 1500 from this ranking system, rebalancing monthly since 1/1/2000 and paying 0.25% slippage, you’d make a nice annualized return of 17.47%. That’s not bad. But just imagine how much you could improve your results if you added other factors to your ranking system--and unlike with screeners, there’s no reason not to. You can load your ranking system with absolutely everything you consider important in a stock, and it will serve as a de facto stock evaluator. I recently created a ranking system for S&P 1500 stocks whose top ten gets an eighteen-year annualized return of 37%. And if you broaden your universe to include microcaps and nanocaps, ranking systems can be far more profitable than that.

I’m not saying that you should never use stock screeners. In fact, I screen out stocks with low liquidity, companies from corrupt countries, certain industries, and firms that are filing late. But if I can rank them on a factor, I don’t screen them on that factor.

I am not a wizard at financial accounting. I have not trained to be a stock analyst. I just know that if I’m going to spend tens of thousands of dollars on something, that something better be good. I treat every investment like a precious object, evaluating it from forty different angles before I buy it, and selling it, usually at a profit, only when I need the cash to buy an even more precious object.

Using stock screeners, I not only lagged my benchmark, I lost tens of thousands of dollars of my hard-earned money. But since November 2015, when I suddenly understood that stock screening relied on a fallacy and started using ranking instead, I’ve made an annualized return of 48%. Throw out your screeners and evaluate your stocks. You won’t regret it.

My ten largest holdings right now: ARIS (which was just acquired by a private equity firm), CAMT, MEIP, BASI, FNJN, RVSB, EGY, ATTO, YUME, ELMD.

CAGR since 1/1/2016: 56%. One-year performance: 70%.

Sustainable Yield

A few months ago I was wondering how I could synthesize the following considerations when buying stock in a company: a) its indicated dividend; b) its price; c) its payout ratio (i.e. how much of the company’s earnings does it pay out in dividends); and d) its projected dividend growth. I wanted to buy stock in companies with a high and growing dividend yield but also with high earnings retention. I knew that companies that paid out more than they earned were not promising investments.

So I developed a formula I’m calling sustainable yield. Essentially, it’s the dividend yield divided by the payout ratio. Take the indicated annual dividend per share (IAD), divide that by the price, multiply by the last twelve months’ net income, and divide by the last twelve months’ dividends paid, adding one to that last number so that you avoid dividing by zero. Then multiply by 100.

For a company that pays dividends but only to common shareholders and whose dividend payout is not going to change in the next year, the sustainable yield will approximate the earnings yield (earnings per share divided by price). A company that doesn’t pay dividends will have a sustainable yield of zero. A company that pays dividends despite having a negative income will have a sustainable yield below zero. A company that is increasing its dividend gets a higher sustainable yield than a company that is decreasing it. I call this the sustainable yield because it gives low scores to companies whose dividend payments are unsustainable due to their low or negative income or as signified by their shrinking dividends.

"Sustainable yield" is not a concept one normally applies to finance, but instead to "natural capital," especially in the areas of forestry and fishery management. The maximum sustainable yield is the maximum amount that can be harvested without reducing the productivity of the remaining stock. My application of it to the stock market is not really parallel, but the same principle applies: I’m looking for dividend payments that do not harm a company’s productivity.

Let’s see how this works by using a few companies as examples.

The highest scores are given to undervalued companies whose dividends are growing. A good example is Bed Bath and Beyond (BBBY), which ranks second out of the S&P 500 in sustainable yield. It has a dividend yield of 1.68%, its indicated annual dividend is 58% higher than the dividend paid over the last year, and it has a price-to-earnings ratio of 7.6. So its sustainable yield is a relatively high 20.37%.

The other ten highest-ranked stocks are Cognizant Technology Solutions (CTSH), Affiliated Managers Group (AMG), General Motors (GM), Gilead Sciences (GILD), Goodyear Tire & Rubber (GT), McKesson (MCK), Corning (GLW), Navient (NAVI), and Synchrony Financial (SYF).

All of these have sustainable yields between 12 and 20, with the dubious exception of CTSH, which earns the number-one spot (with a sustainable yield of 1,510%) simply because it paid no dividend last year but is paying one this year. Its dividend yield is only 0.9%, so it might be best to ignore this one as a fluke.

Right in the middle of the ranks are companies like General Electric (GE), United Parcel Service, (UPS), and Coach (COH), with sustainable yields of 4%. These are reasonably priced companies (p/e’s in the mid-twenties) with very little change in dividend payout.

At the very bottom are companies with negative incomes who are planning to pay dividends anyway. Right now, six of the bottom ten are energy companies, two are utilities, and two are pharmaceuticals.

If you were to buy the stocks in each decile of the S&P 500 according to sustainable yield and rebalance monthly since 1999, here’s what you’d get:

I’ve also played around with a slight variation, which has some appeal. Instead of multiplying by net income, multiply by retained earnings, i.e. net income minus dividends paid. This gives companies whose dividend payments exceed their net income a negative score.

Now I don’t suggest using this formula as the sole basis for an investment strategy. There are plenty of other things to take into account when choosing which stocks to buy. But it’s the best way I could find of taking dividend payments into account.

My ten largest holdings right now: CAMT, BASI, MEIP, ARIS, ELMD, FNJN, ATTO, ULBI, VVUS, TGB.

CAGR since 1/1/16: 52%. One-year return: 71%.

Why Are Stocks So Expensive?

There have been countless discussions of the overvaluation of stocks these days. But one metric has been getting short shrift. And that is supply.

The demand for stocks is going to be high as long as alternative investment vehicles are not as appealing as stocks. Right now the perceived return on stocks is higher than that for most other investment instruments, especially because of the low-interest rate environment. This demand may shift, which I'll get to later. But for now let's table that end of the equation, as demand is difficult to predict and hard to measure.

What I can measure is supply. I'm going to look at float because the conventional measure of how expensive the market is involves earnings per share. Right now the total float of equity stocks in the three major exchanges is 765 trillion shares. Two years ago it was 754 trillion shares. Twelve years ago it was 598 trillion shares. The number of shares available to buy has risen by only 28% over twelve years, which works out to 2.07% per year, and for the last two years by only 1.46%, or 0.73% per year. While the float per company listed has increased, the number of companies on the exchanges has fallen drastically, from about 7,500 companies in 1999 to fewer than 5,000 today. Simply put, US companies as a whole are buying back almost as many shares as they issue.

Now let's compare this lackluster growth to the amount of money that people are investing. It's hard to put a finger on that number, but I'll offer a few points of comparison. The amount of money in circulation has gone up 104% in the last twelve years, or 6.14% per year. The amount of money in institutional money funds has gone up 58% in the last twelve years, or 3.87% per year. The total financial assets of households and non-profits has gone up 80% in the last twelve years, or 5.06% per year. The total amount in IRAs has gone up 141% in twelve years, or 7.61% per year. With demand growing two or three times as fast as supply, it's no wonder the price-to-earnings ratio has risen so high.

The situation is even more extreme if we just look at the float of the S&P 500, which has remained almost exactly the same for over a decade. Between January 2006 and today, the total float has varied between 281 trillion and 320 trillion shares, and its size today--294 trillion--is almost exactly what it was ten years ago--295 trillion. Now consider this: right now the amount of money invested in the S&P 500 amounts to 60% of all equity investments in the three major exchanges. Ten years ago, it was only 47%. By this measure, the supply of shares, relative to the amount invested, has shrunk drastically. And because the Shiller CAPE, which measures the 10-year price-to-earnings ratio of the S&P 500, is the most common measure of how expensive the market is, perhaps the float of the S&P 500 is the best comparable measure of supply.

What are the prospects that stocks will get cheaper in the near future? Here are the possibilities that I can see, most of them unlikely:

1. Changes in regulation could make going private less attractive and going public more, thus increasing the number of companies available for investment.
2. Companies could stop buying back their shares and instead pay dividends, decrease debt, and invest in their futures.
3. Interest rates could rise enough to make bonds more competitive with stocks as investment vehicles.
4. Investors could lose faith in the US stock market and shift their investments into alternatives--bonds, commodities, real estate, collectibles, futures, certificates of deposit, and foreign stocks.
5. Changes in regulation and trading practices could make stocks in foreign companies much easier to trade, and investment in them could become more attractive than investing in US companies.

I don't see any signs of any of these things happening in the near future. And, being a long-only equity-only investor, I'm glad of that.

But there is a sixth possibility: that earnings per share could grow to a level commensurate with investment growth. The median EPS of the S&P 500 has grown 44% in the last twelve years, from $1.95 to $2.82; it hasn't yet regained its peak of $3.00 in March 2015, but it's getting close to doing so, and could surpass it in the next year or two. That would make the stock market somewhat less expensive.

At any rate, considering all this, it's a wonder that stocks aren't more expensive than they are. If one judges by the Shiller CAPE, they're significantly more expensive than they were ten to twelve years ago, but if one looks at other measures--the median price to book value or price to free cash flow of all the stocks on the major exchanges (not just the S&P 500)--stocks were more expensive during the period 2004 to 2007 than at any time since. There's room for prices to rise yet farther and for stocks to get even more expensive, since the low growth in shares shows no signs of accelerating.

My ten largest holdings right now: CAMT, BASI, ULBI, MEIP, ELMD, ATTO, FNJN, VVUS, YUME, NHLD.

CAGR since 1/1/2016: 53%. One-year return: 71%.

Why I Invest in Microcaps

A lot of investors avoid microcaps. They’re hard to trade, their prices are volatile, slippage and market impact present major headaches, and there’s just not enough information out there about them to enable investors to easily judge their worth.

I love microcaps. Here are the seven reasons why.

1. Inefficiencies. The bigger the company, the more efficiently its stock is going to be priced. The only way I can make alpha is by exploiting market inefficiencies, and there aren’t as many in the large-cap space.
2. Measurability. I call things that affect the price of a stock but don’t show up in financial statements or analyst estimates “unmeasurables.” They include the pipe dreams of Elon Musk, the customer-is-always-first philosophy of Jeff Bezos, and the various reasons Yahoo is always playing catch-up to Google. This is the stuff of water-cooler conversation, and when it comes to large companies, they are some of the biggest influences on stock prices. I can’t take any of this into account by looking at financial statements. With microcaps, though, there are few unmeasurables. Almost everything I need to know is in their financial statements.
3. Expectations. The only way to make significant money in the stock market is to invest in stocks that beat the expectations of other investors. It’s easier to do so with microcaps and small caps than with large caps because most large-cap companies are studied and pored over. Low-volume stocks can be primed to outperform without anyone noticing.
4. Growth. The great investor Thomas Rowe Price Jr. believed that companies, like all living things, go through a period of growth and a period of decline. The most profitable form of investing is in companies with strong potential for growth. Those are mostly (but not exclusively) found in the small and microcap arenas. (So, of course, are companies that have gone through a long decline.) If you look for small companies with strong earnings growth and high profit margins, you’re following the path that Price blazed.
5. Value. Value investors believe in buying low and selling high. As for buying low, large caps, by definition, aren’t cheap. It’s harder to find stocks with very low price-to-earnings, price-to-sales, or price-to-book value among large caps than among small caps. For example, 4.8% of the S&P 500 has a price-to-sales ratio of under 0.5, while 8.9% of the Russell 3000 does. As for selling high, large caps’ potential for large price increases in a short time period is much lower. 4.1% of the Russell 3000 has doubled in price in the last year, while only 0.8% of the S&P 500 has.
6. Ethics. Small businesses are, on the whole, not destroying the environment, exploiting their workers, sheltering their profits overseas, engaging in monopolistic practices, or undercutting their competitors in unethical ways. The same can’t be said of large companies.
7. Curatorship. I’ve always enjoyed finding obscure things of value and celebrating them. Now I can do so not just in the music I listen to, the books I read and publish, the films and art I see, and the subjects I write about, but in the companies I invest in.

So here are my favorite microcaps at the moment: MEI Pharma (MEIP), an oncology company; Finjan Holdings (FNJN), a cybersecurity company; YuMe (YUME), which provides digital video advertising solutions; Vivus (VVUS), a wide-ranging small biotech firm; Taseko Mines (TGB), which digs for copper, gold, and other metals; and InTest (INTT), which makes products for testing integrated circuits. All of them reported strong earnings growth last quarter compared to the same quarter last year; and in terms of value, all of them have high ratios of EPS to price and unlevered free cash flow to enterprise value, two of my key value metrics.

My ten largest holdings right now: CAMT, ABCD, INTT, BASI, MEIP, ULBI, ATTO, VVUS, FNJN, NHLD.

CAGR since 1/1/2016: 57%. One-year return: 80%.

My ten largest holdings right now: CAMT, ABCD, INTT, BASI, MEIP, ULBI, ATTO, VVUS, FNJN, NHLD.

CAGR since 1/1/2016: 57%. One-year return: 80%.

Do Price Trends Exist?

In technical analysis, a trend is defined as the general direction of a security’s price over a period of time.

In order for a trend to be useful, it has to have a greater chance of continuing than of reversing. If it has a greater chance of reversing than of continuing, it’s no longer a trend.

The stock market as a whole follows an upward trend. It’s important to remember that individual stocks don’t. A stock’s price is more likely to fall than to rise. (I’m talking about ex-dividend prices and I’m including penny stocks and companies that go bankrupt.) The upward trend in the stock market as a whole is due in part to the fact that the stocks that have gained in value outweigh, in terms of the size of their increase, the stocks that have lost value. So to determine whether trends exist in individual stocks, we have to compare their performance to the market as a whole.

I recently performed some experiments using the S&P 1500 (the 1500 largest stocks on the major exchanges), and ran them over the past eighteen years (because the data I’m using, on Portfolio123, only goes back to early 1999). I wanted to see whether a stock that was outperforming or underperforming the S&P 1500 as a whole had a better chance to continue or to reverse direction.

I tested all stocks that were beating the benchmark for one, two, three, four, and five weeks in a row—the five-week ones seemed to be exhibiting a very strong trend. In every single case, a stock that was beating the benchmark had a greater chance of not beating the benchmark the following week than of continuing to beat the benchmark, and an underperforming stock had a greater chance of outperforming than of continuing to underperform. In every single case, stocks were more likely to “mean-revert”—to reverse direction—than to continue along their “trend.”

Now if all price movements were random, the chance of continuing and failing to continue would be equal; if price trends existed, the chance of continuing would be higher than failing to continue. But those were not the case.

My conclusion is that so-called price trends in stocks—at least those that are measured in days or weeks—are simply an optical illusion.

How is this illusion created? Let’s create a benchmark that is the median stock return, and let’s assume stock prices are purely random. At any one time, half the stocks will outperform the benchmark for the past week. One-quarter will outperform the benchmark for two weeks running. One-eighth will outperform it for three weeks running and one-sixteenth for four weeks.

Look at the price charts of the stocks in that one-sixteenth (or of the one-sixteenth that have underperformed the benchmark for four weeks running). You’ll definitely see what looks like a trend. But it’s not really a trend, is it? It’s just a happenstance driven by random numbers.

Now let’s look at the real-world stocks that have been outperforming for four weeks in a row. Statistically, how many stocks out of the S&P 1500 should be doing this? One-sixteenth, or 6.25%, if stock prices were random. If trends exist, though, it should be greater than one-sixteenth. In fact, however, if you look at the last eighteen years, only 6.04% of S&P 1500 stocks have outperformed the benchmark for four weeks in a row, and only 5.95% have underperformed it for four weeks in a row.

Now let’s look at long-term trends. Take, for example, the 30% of stocks with the highest and the lowest Sharpe ratio over the last ten months (defined as the average monthly return divided by its standard deviation). One would think, if trends were real, that the stocks with high Sharpe ratios would outperform the benchmark the following month while those with low Sharpe ratios would underperform it. Well, the proportion of high-Sharpe-ratio stocks that outperform the benchmark the following month is lower than the proportion of low-Sharpe-ratio stocks that outperform it. And the proportion of medium-Sharpe-ratio stocks—those in the middle four deciles—that outperform the benchmark is the same as that of the low-Sharpe-ratio stocks. A stock that has been trending downward or exhibiting no trend at all is more likely to outperform the benchmark the following month than a stock that has been trending upward. In other words, if you want to outperform next month, you should choose stocks that have not outperformed over the past ten months. What if we look at the top 20%? The really trending stocks? Well, the numbers are the same: the lowest 20% outperform the top 20%.

A lot of people use simple moving averages to gauge trends. One of the most popular is the ratio of the 20-bar SMA to the 200-bar SMA. So let’s do the same test. We’ll rank all stocks in the S&P 1500 by how far above the 20-bar SMA is from the 200-bar SMA, and see how the top and bottom 30% perform over the next month. The result is the same as for the Sharpe ratio: more of the bottom-trending stocks outperform the benchmark over the next month than the top-trending stocks. Once again, you’re more likely to outperform if you’re trending downward than if you’re trending upward.

I’ve been racking my brains for months now, trying to prove the persistence of a trend in stocks—some sort of trend, somewhere, somehow. But everything I try—the up-down ratio, the RSI, the rate of change—only proves the opposite. No matter how you look at things, a “trend” is more likely to reverse than to continue. (I have not examined very short-term trends—price movements within a day’s trading—so I can’t reach any conclusion about those.)

To me, that implies that trends in individual stocks for the most part don’t exist. Obviously, it depends on how you define a trend. And the technical analysts and trend-followers out there will probably argue that it’s somehow possible to separate persistent trends from non-persistent ones, to identify the former, and to follow them. I’d be glad to perform further tests if someone suggests a plausible one.

Now some people may think there’s a flaw in my method because the tests I’ve performed have all been on the S&P 1500, which is a momentum index by definition. All the stocks in the S&P 1500 are there because they have “trended upward.” The S&P 1500 excludes stocks whose market cap has fallen out of the top 1500. But I got exactly the same results whether I was testing upward or downward trends. In every case, a stock that exhibited any kind of trend was more likely to reverse than to continue that trend.

Does this mean that the so-called “momentum effect” is non-existent? Or, perhaps, dead?

First, it’s important to distinguish between momentum studies that look at time-series momentum, which applies to markets as a whole, and those that look at individual stocks. I have no beef with the former: trends do exist in markets as a whole. The classic example of the latter is Jegadeesh and Titman’s 1993 study “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” It’s worthwhile reading this paper because of the authors’ profound skepticism of the momentum effect that they document.

Jegadeesh and Titman’s conclusion was based on buying and selling stocks in the top decile of past performance vs the bottom decile, using the stocks in the NYSE and AMEX (notice they excluded NASDAQ stocks). They then measured the total gains of each portfolio. What they did not do was measure the percentage of stocks bought that gained or lost money relative to the benchmark. If, say, only 49% of the stocks in the top decile outperformed the median but their performance was particularly strong, and 51% of stocks in the bottom decile outperformed but their performance was somewhat weak, their results would be consistent with mine. In other words, the “momentum anomaly” might be explained by the outperformance of a few particularly strong-performing stocks that keep beating the market month after month and year after year.

Jegadeesh and Titman’s best strategy was to invest in the top decile and short the bottom decile of stocks based on their twelve-month performance and hold for three months. That strategy would not have worked since 1999 on the S&P 1500. Here is the performance of investing in each decile for three months since then: 

The top decile (in green) underperforms every other decile besides the bottom decile, and outperforms that one just by a hair. If we break it down into quintiles instead of deciles, it’s even worse: the top quintile (in light blue) underperforms all the other quintiles.

I won’t speculate on why Jegadeesh and Titman’s strategy seems to have worked prior to 1993 and hasn’t worked for the last eighteen years. Maybe momentum has been arbitraged away. Maybe NASDAQ stocks make a difference. Maybe it has to do with high-speed and computerized trading. There could be any number of reasons. But it’s a fact.

Now let’s get to an important question: why don’t trends exist? Why is mean reversion more common than relative price momentum?

It’s because the prices of stocks are more variable than the underlying performance of companies. Investors often overreact to some sort of news about a company. That overreaction creates increases and decreases in prices that are not sustainable and are smoothed out by subsequent, more rational behavior on the part of investors. This explanation is not original to me; it has been around a long time.

My conclusion that trends don’t exist makes trading a lot easier. I like to buy stocks when they’re cheap and sell them when they’re not. Since trends don’t exist, I don’t have to watch for them. I don’t have to wait until a stock is “finished trending upward” before I sell it, and I don’t have to wait until it’s “finished trending downward” before I buy it. I don’t have to use trailing stops or look at daily price fluctuations. I know that any direction a stock moves in is more likely to reverse than to persist. So I buy low, wait, and sell high.

My ten largest holdings right now: CAMT, ABCD, INTT, BASI, MEIP, ULBI, ATTO, VVUS, FNJN, NHLD.

CAGR since 1/1/2016: 57%. One-year return: 80%.