invest(igations)

Alphanomics: The Study of Security Mispricing

I just finished reading Alphanomics by Charles M. C. Lee and Eric So, published by a small academic press in 2015. The subtitle is The Informational Underpinnings of Market Efficiency, but that doesn’t really give a sense of the book, which essentially summarizes the last five decades of academic research into market pricing mechanisms. I’d like to give readers a summary of what the book accomplishes.

I: Is the Market Efficient?

There are a lot of arguments against the efficient market hypothesis (the belief that the price of a stock reflects all available information and therefore approximates the intrinsic value of a company), and Lee and So seem to marshal practically every one of them. But my favorite is their surfing analogy.

Market efficiency, or so argue its proponents, is “an inevitable outcome of continuous arbitrage,” where arbitrage is defined as “trading aimed at profiting from imperfections in the current price.” Because so much arbitrage is going on, prices rapidly adjust to reflect the information available.

Lee and So point out that this belief “is akin to believing that the ocean is flat, simply because we have observed the forces of gravity at work on a glass of water. No one questions the effect of gravity, or the fact that water is always seeking its own level. But it is a stretch to infer from this observation that oceans should look like millponds on a still summer night. . . . If we are in the business of training surfers, does it make sense to begin by assuming that waves, in theory, do not exist? A more measured, and more descriptive, statement is that the ocean is constantly trying to become flat.”

Because price discovery is an ongoing process, there is always going to be a lot of adjustment as the market searches for some sort of equilibrium, and this adjustment involves lots of mispricing and correction of that mispricing even as new information and signals come in. “In fact,” Lee and So write, “it strikes us as self-evident that arbitrage cannot exist without some amount of mispricing. Therefore, either both mispricing and arbitrage exist in equilibrium, or neither will.” (What Lee and So mean by “mispricing” is essentially the divergence between the market price and the net present value of a security.)

Lee and So then take on the idea that abnormal returns (pricing anomalies) are a compensation for risk factors. This doesn’t fit with empirical observations. There is a great deal of evidence that “healthier and safer firms, as measured by various measures of risk or fundamentals, often earn higher subsequent returns. Firms with lower beta, lower volatility, lower distress risk, lower leverage, and superior measures of profitability and growth, all earn higher returns.” In other words, these companies all appear to be less risky than comparable companies.

Let’s take the opposite assumption (please note that Lee and So don’t go here) for a moment. Let’s hypothesize that the market is fundamentally inefficient and that while arbitrage makes it slightly more efficient, it doesn’t even begin to make it fully efficient. Let’s hypothesize that mispricing is ever-present in part because information is always imperfect. Let’s hypothesize that arbitrage—a industry that costs, in management fees, $600 billion per year, as Lee and So estimate—succeeds only occasionally and is mostly making stabs in the dark. Under this model, investing in less risky assets is more likely to succeed than investing in highly risky assets, and there’s no need to worry about “compensation for risk factors.”

II: Who Are “Noise Traders”?

In 1984, Robert J. Shiller came up with the idea that you can classify investors into two types, smart-money investors and ordinary investors. Fischer Black quickly characterized these ordinary investors as “noise traders.” These are basically people who do not trade on the basis of information about fundamentals; they ignore arbitrage methods and instead trade on the basis of other beliefs, fads, or impulses. If noise traders are more predominant than smart-money investors, you end up with a model that can explain asset pricing very well. It can explain why the price of most stocks is far more volatile than the stock’s intrinsic value could possibly be, why closed-end funds veer so far from their net asset values, why the same stock can trade in two different markets with wide differences in price, and why investor inflows and outflows are inversely correlated with subsequent returns. (The efficient market hypothesis can explain none of these “anomalies.”) With the burgeoning field of behavioral economics, we can understand better the various psychological forces that influence stock pricing, and we can recognize that successful arbitrage can be based not just on the disparity between a stock’s price and its intrinsic value, but on the patterns of (mis)behavior that shape that disparity. However, as Lee and So conclude, “In a noisy market, we are all noise traders.”

III: Why Does Value Investing Still Work?

Lee and So ask the big question: why hasn’t the value effect been arbitraged away? Why has it persisted for so long?

The answer isn’t that value stocks are riskier. If you incorporate quality (or growth potential, which is really the same thing) into your definition of value, as Benjamin Graham did and Lee and So do, value stocks are actually less risky than expensive/growth stocks.

The answer is behavioral. Ordinary investors prefer glamor stocks, stocks with exciting stories; value stocks are pretty boring. Ordinary investors look at recent price performance of a stock and think it will persist; value stocks are usually low-priced. Ordinary investors overweight far-future earnings and underweight present earnings due to overconfidence when information is highly uncertain; wise value investors concentrate on known/present revenue, earnings, and cash flow when judging a stock’s worth.

IV: The Practical Limits of Arbitrage

“Information complexity matters,” write Lee and So. Financial markets have a hard time quickly processing information about stocks, and when subtle signals arise (for example, between the lines of quarterly earnings reports or among “boring” or low-volume stocks that are not subject to much attention), they tend to be ignored for a while, resulting in persistent mispricing.

Another limitation is due to whether the information is right or not. Let’s say you identify a large discrepancy between the market price of an asset and its estimated value. As Lee and So write, “there are two possibilities. The first is that the market price is wrong and the discrepancy represents a trading opportunity. The second is that the theoretical value is wrong and the discrepancy is due to a mistake in the valuation method. How can an active investor tell the difference?” Arbitrage is limited by this kind of doubt.

In addition, capital constraints come in many forms. One is high transaction costs, which tend to minimize trading, which in turn tends to diminish arbitrage. Another is that near market tops, when money can be put to little good use, come the heaviest cash inflows. But around market bottoms, outflows tend to congregate and funding constraints are severe, and that is precisely when money can be put to best use.

With all these limits to effective arbitrage, it’s no wonder that securities are so often mispriced. As Lee and So sum it up, “smart money seeking to conduct rational informational arbitrage faces many costs and challenges. These challenges can dramatically limit the deployment of arbitrage capital in financial markets, leading to significant price dislocations. Such effects are pervasive across asset groups.”

V: How Can We Tell If Excess Returns Are a Result of Increased Risk-Taking or Market Mispricing?

If you’re a smart investor, you’d want to capitalize on market mispricing and avoid risk-taking as much as possible. But academics and professional investors have come to opposite conclusions about the source of excess returns. Warren Buffett and his myriad followers talk about “margins of safety” and espouse value investing as far safer than any other kind. At the same time, a number of Nobel Prize–winning economists say that the excess returns from value investing, like those from momentum investing and other factor-based investing (including even low-volatility investing!), is based on compensation for additional risk-taking. One couldn’t come up with more opposed conclusions.

The orthodox position among academics has been that an asset’s expected returns are determined by its sensitivity to a given “risk factor,” as measured by a linear asset-pricing model. But as Lee and So point out, referring to a large number of more recent academic studies, these models fail to distinguish between mispricing and risk; many of the “risk factors” do not conform to any common-sense ideas of what is actually risky; the models can’t explain post-earnings announcement drift or the fact that firms with strong accounting fundamentals outperform firms with weak ones; looking at the returns of the various “risk factors” on their own predicts future returns better than their factor-loading (sensitivity) on the factor portfolio; and the regression-based model is based on the assumption that the returns of a given firm are uncorrelated over time (independently and identically distributed), a violation of which leads to an over-rejection of the null hypothesis. In other words, there’s no good reason to use the complicated linear multi-factor asset-pricing model that so many use these days.

What Lee and So do not challenge is the idea that there is a linear relationship between risk and reward. As I have shown, the relationship is by no means linear; past a certain point, the riskier the asset, the lower the reward, and the relationship is therefore best characterized by a curve whose shape looks something like this:

VI: Conclusion

It’s hard not to come away from this book with mixed feelings about academic research on investing. On the one hand, highly questionable conclusions have become orthodoxy, and the basic models taught to students have very little, if any, empirical foundations. On the other hand, academics are challenging these models, and their research has been excellent and very useful.

The book, I think, should be an essential read for anyone who wants to understand how current asset-pricing models work (or don’t). But I think the authors don’t go quite far enough.

My own conclusion after reading Alphanomics is that markets are fundamentally inefficient, that arbitrage is mostly ineffective, and that mispricing is seldom risk-related. There are plenty of very good behavioral reasons for investors to be more attracted to risky investments than to safe ones, thus presenting plenty of opportunities for investors who take advantage of the consequent mispricing of safer securities.

And, practically speaking, how do you do that?

The first step is to find mispriced safe securities. Take all the factors that academics have shown work—in Lee and So’s words, quoted above, “lower beta, lower volatility, lower distress risk, lower leverage, and superior measures of profitability and growth”—and combine them with value factors, which, as Buffett and other value investors have shown, create a margin of safety. Use a powerful screen or, better yet, a ranking system such as those available from www.portfolio123.com (design your own, or rely on the seven pre-built “core” ranking systems).

Here are eight stocks that I think are “safe” by almost any measure. In alphabetical order, they are: Barrett Business Services (BBSI), Mix Telematics (MIXT), National General Holdings (NGHC), Points International (PCOM), Perion Network (PERI), Pilgrim’s Pride (PPC), TTEC Holdings (TTEC), and WNS (WNS). But that’s my list. Make your own!

The next step is to wisely invest in safe securities and sell them when and only when they begin to seem unsafe. (Or, conversely, short those securities that are the riskiest.)

The great investors have long recognized that you can get superior returns at the same time as minimizing your risk. An inefficient market is ripe for successful arbitrage, and it’s up to smart investors to take advantage of that.

CAGR since 1/1/16: 33%.

My top ten holdings right now: MIXT, RMNI, PCTI, SPNS, PALDFF, MGIC, QMCO, PRPL, CO, TTEC.

How to Be a Great Investor, Part Four: Compare Effectively

This article is the third in a ten-part series loosely based on Michael J. Mauboussin’s white paper “Thirty Years: Reflections on the Ten Attributes of Great Investors.” See Part 1: Be Numerate, Part 2: Understand Value, and Part 3: Properly Assess Strategy for previous installments. And please keep in mind that although I quote Mauboussin a great deal, I am departing from his ideas on occasion.

Part I: Fundamentals Versus Expectations

Mauboussin begins:

Investors compare all day: stocks versus bonds, active versus passive, value versus growth, stock A versus stock B, and now versus later. Humans are quick to compare but not very good at it. Perhaps the most important comparison an investor must make, and one that distinguishes average from great investors, is between fundamentals and expectations. Fundamentals capture a sense of a company’s future financial performance. . . . Expectations reflect the financial performance implied by the stock price. Making money in markets requires having a point of view that is different than what the current price suggests. Michael Steinhardt called this a “variant perception.” Most investors fail to distinguish between fundamentals and expectations. . . . But great investors always distinguish between the two.

Let’s pretend that you had to choose between investing in Intel (INTC) and Advanced Micro Devices (AMD).

Intel’s ROE is 27% and its margins are massive. It has low debt, plenty of cash, pays a nice and growing dividend, and has solid growth in both sales and EPS.

AMD, on the other hand, has an ROE of 14% and its margins are pretty poor. Its sales and EPS growth have been in a steep decline lately. Its debt-to-EBITDA ratio is close to 3, which is pretty high; it doesn’t look like it’ll be paying any dividends in the foreseeable future.

Let's look a little deeper. Both Intel and AMD are spending plenty of money on R&D, which is good. AMD has huge projected EPS growth for the next quarter while Intel is projected to have negative growth. AMD has significantly better asset turnover, another point in its favor; on the other hand, it has the highest share turnover of any company in the S&P 500, which is a point against it. AMD’s cash conversion cycle is growing too fast for my liking; on the other hand, their net operating assets are only 40% of their total assets, which is a good thing. Lastly, AMD’s cash-flow accruals are appallingly high, while Intel’s are quite low.

What are the market’s expectations for these two firms? As of market close on 9/4, Intel is selling at an 18% discount to its 52-week high, while AMD is only 13% from its high. AMD’s price has risen 10% in the last year; Intel’s has risen 2%. Intel’s enterprise value is $239 billion, which matches my estimate of its intrinsic value based on its free cash flow and EBITDA, $246 billion. AMD’s enterprise value is $35 billion, which is more than five times higher than my estimate of its intrinsic value, $5.8 billion. Institutions are, overall, selling shares in Intel and buying shares in AMD. Clearly the market has significantly higher expectations for AMD than for Intel.

The reason is clear. With its 80% to 90% market share, Intel seems like a dinosaur, with nowhere to go but down, while AMD is on the cutting edge, producing brilliant CPUs that outperform Intel’s. They’ve just inked several valuable partnership deals, while Intel has been labeled a “chronic underperformer.”

The average investor will look at these signs and put their money into AMD. The great investor will see that there’s a huge divergence between fundamentals and expectations, and will keep their money out of AMD and in solid, safe, boring, inexpensive firms like Intel.

Why? AMD certainly seems to have better prospects than Intel. Cutting-edge technology stocks can really pay off. If this were a race, AMD seems to have much better odds.

Mauboussin continues:

One vivid analogy is pari-mutuel betting. Horse racing is a good example. The amount bet on a horse gets reflected in the horse’s odds, or probability, of winning the race. The goal is not to figure out which horse will win but rather which horse has odds that are mispriced relative to how it will likely run the race.

(I’ve written about this gambling analogy here.) AMD may have better odds, but they’re mispriced. Even if AMD performs spectacularly well over the next few years, its risk of failing to meet expectations is far higher than that of Intel.

Basically, the expectations, based on their market price, for these two companies are that one will remain roughly the size it currently is and that the other will grow to be five or six times its current size. Now which, I ask you, is a safer bet?

Part II: Instinctive Versus Reasonable Comparisons

Mauboussin writes,

Humans tend to think by analogy, which can create some cognitive trouble. One issue is that a single analogy, or even a handful of analogies, may fail to reflect a full reference class of relevant cases. For example, rather than asking whether this turnaround is similar to a prior turnaround, it is useful to ask for the base rate of success for all turnarounds. Psychologists have shown that properly integrating the outcomes from an appropriate reference class improves the quality of forecasts.

The price of Apple, after adjusting for dividends and splits, has gone up by over 8,000% in the last twenty years. And how many times have you heard someone say of a company, “This could be the next Apple!” That is a simple analogy, easy to grasp.

What people don’t say is that twenty years ago there were stocks quite similar to Apple. The proper response when someone says “This could be the next Apple!” is “Yes! And this could be the next Gateway!” or “Yes! And this could be the next Lexmark!” or “Yes! And this could be the next ATI Technologies!” Stretch it a little and this could also be the next Comverse, or Altera, or Unisys, or BMC Software, or Teradyne! All of these companies showed some strong similarities to Apple in 1999.

As Mauboussin writes, “What you are looking for is what you see.” Choosing companies to compare a potential investment with is a daunting task. If you do so on a purely intuitive level, you’re bound to choose companies you’re familiar with rather than companies that sank without a trace. If you try to use a more data-driven approach, using screens to narrow down your options, you’ll come up with a better class of comparisons, but only barely. Every company is truly unique.

Moreover, this method can lead to another trap. Mauboussin writes,

A final challenge is considering causality from the point of view of attributes versus circumstances. Attributes are features that allow for categorization. For instance, animals with wings and feathers can fly. Circumstances capture causal mechanisms. Since the physics of lift causes flight, animals or objects that can create lift will fly, including most birds and airplanes, and those that can’t create lift won’t fly. To learn from history, you need to understand causality. We commonly limit our comparisons to attributes and hence miss essential insights.

What caused Apple’s price to rise over 8,000%? Clearly it was not the fact that its market cap in 1999 was $10 billion or that its P/E was 18 or that it was in the technology sector. Was it the fact that it had a visionary at its helm? In that case, let's take a quick look at people who were called “visionary” in 1999 and see where they and their companies ended up.

• Marc Collins-Rector was the visionary founder of Digital Entertainment Network, one of the first Internet media content delivery companies, a precursor of Netflix. The company was valued at $58.5 billion in 1999, and was slated for a $75 billion IPO. Collins-Rector and his cofounders were accused of sexual assault and the company went bankrupt in 2000.
• Noboyuki Idei was the visionary CEO of Sony; if you had bought shares in Sony in 1999, they would now be worth 2% more than what you paid for them.
• Mark Weiser was the visionary chief technology officer of Xerox, and the father of “ubiquitous computing.” But then he died at the age of 46. If you had bought shares in Xerox in 1999, you would have lost 68% of your money by now.
• Robert B. Shapiro was the visionary CEO of Monsanto. In 2000, Monsanto was merged with Pharmacia and Upjohn, and Shapiro stepped down in 2001. If you had invested in Monsanto once it was spun off in 2002, you would have made a boatload of money, but by that time Shapiro was no longer working for them.

Clearly, following visionary CEOs won’t make you rich.

What kinds of comparisons will, then? Data-driven ones, perhaps.

The main thing is not to get lost in analogies. Rather than looking for what made Apple’s price rise 8,000%, one might better ask what factors will at least somewhat consistently induce a future rise in a stock’s price. And there is data that will answer that question.

To stick with comparisons, let’s say you’re faced with the choice of investing in two companies, one with low accruals and the other with high accruals, but they’re otherwise pretty similar. If you look at the data, companies in the Russell 3000 with the lowest accruals have gained, on average, 7.04% in any given six-month period over the last twenty years (I ascertained this fact using a screen on Portfolio123 that chose companies that ranked among the lowest 25% on two very different and largely uncorrelated methods of measuring accruals); companies with the highest accruals have gained, on average, 3.09% (which is lower than the average six-month gain of the S&P 500). Now are low accruals simply an attribute of a stock or a cause of the price gain? There are some very good arguments to be made for the latter position.

And that’s how factor-based investing works. We look for factors that seem to result in price increases. We then compare thousands of companies and choose those that rank the best on each factor or on all of them combined. We should not only compare each company to all others, but perform industry-specific comparisons too. We can focus on causal factors and let attributional factors take a back seat.

This goes back to Mauboussin’s initial point in this part: “rather than asking whether this turnaround is similar to a prior turnaround, it is useful to ask for the base rate of success for all turnarounds.” I just did that for low accruals, and if I had a data-driven definition of a turnaround, I could get such a base rate.

Using data, backtesting it, discovering properties and relationships, looking for causal factors—that is the best way to compare effectively.

My CAGR since 1/1/16: 33%.

My top ten holdings right now: RMNI, MIXT, PERI, PALDF, SPNS, PCTI, VISI, TTEC, QMCO, GTS.

How to Be a Great Investor, Part Three: Properly Assess Strategy

This article is the third in a ten-part series loosely based on Michael J. Mauboussin’s white paper “Thirty Years: Reflections on the Ten Attributes of Great Investors.” See How To Be A Great Investor, Part 1: Be Numerate and How To Be A Great Investor, Part 2: Understand Value. Please note: in this article, I’m departing from Mauboussin’s ideas at a number of junctures.

Part I: Assessing a Business Strategy

Every business has a strategy, a plan for beating its competitors. It’s a good idea for an investor to assess that strategy before investing in the company. But doing so is complicated.

One obvious way to do so is to look at its numbers. Mauboussin primarily looks at the difference between a company’s CFROI, or cash flow return on investment (free cash flow divided by net operating assets), and its cost of capital, and compares that to other companies in its industry.

Arriving at CFROI and cost of capital is not an easy job. I covered some of the pitfalls in cash flow and cost of capital measures in How To Be A Great Investor, Part 2: Understand Value, and also discussed how to get around them. (Also see my article on net operating assets: My Favorite Balance Sheet Ratio.) In addition, it’s probably best to use the cost of capital for the industry or industry group rather than for the firm in question.

So, for example, let’s look at two chemical companies. Celanese (CE) has about $1.5 billion in annual free cash flow and $6.7 billion in net operating assets, giving it a CFROI of 22%. Albemarle (ALB) has under $200 million in annual free cash flow and $5.6 billion in net operating assets, so its CFROI is 3%. Assuming the cost of capital for both companies is around 8.5%, Albemarle is what Mauboussin calls a “value destroyer” while Celanese is a “value creator.”

If you look only at this measure, the best “value creators” in the S&P 500 (excluding the financial and real estate sectors and excluding firms that are less than five years old) are Verisign (VRSN), Qualcomm (QCOM), Fortinet (FTNT), Starbucks (SBUX), and Boeing (BA), while the worst “value destroyers” are Netflix (NFLX), GM (GM), Carmax (KMX), Alliant Energy (LNT), and Advanced Micro Devices (AMD).

And value creators do tend to outperform value destroyers: over the past ten years, the top decile (value creators) in the S&P 500 have beat the bottom decile (value destroyers) by 4.2% in annualized returns (if rebalancing monthly with no transaction costs), and in the past twenty years that spread has been 7.0%. (I use Portfolio123 to get these numbers.) That’s quite a remarkable spread for a quality factor—the stock’s market price and its growth rate don’t come into the calculation.

But obviously, a business can change its strategy, and one number can’t tell you that much about a strategy’s future viability. Assessing strategy is a complicated and rich subject, and I can’t possibly give you better pointers than Mauboussin can. His papers on the subject are well worth reading, and can be found here and here. At the end of each of those papers is a checklist you can use to assess each company you investigate. There are close to a hundred useful questions in those checklists, and you can learn a lot about a company while trying to answer them.

Part II: Assessing an Investment Strategy

Mauboussin does not cover assessing your own investment strategy in his “Ten Attributes of Great Investors,” but I think it’s just as important as assessing the strategy of the companies you invest in—if not more so.

Before you can properly assess your strategy, you need to properly articulate it. You can begin doing so by asking yourself a number of questions. There are no right or wrong answers here: this is just a way to get to know what kind of strategy is right for you.

1. Are you interested in beating the market over the short term, investing wisely for long-term portfolio growth, or both?
2. Are you interested in maximizing your returns, minimizing your risk, or both?
3. Are you interested in finding a single strategy that suits you or developing a number of different strategies for portions of your portfolio?
4. Are you interested in making money primarily from stock price appreciation, dividend income, or both?
5. Are you interested in stocks, ETFs, options, or all three?
6. Do you consider yourself a gambler, a trader, an investor, a speculator, or some combination of those?
7. Would you rather use a fully automated strategy or get stock ideas which you’d then accept or reject on a discretionary basis?
8. Do you believe that backtesting will guide you to better strategy development or do you believe that backtesting offers illusory results and that it’s best to limit its use?
9. Do you consider yourself financially skilled enough to come up with new, untried methods of beating the market, or do you think you’d do better to follow established paths?
10. What is most important to you when considering a stock purchase: the company’s quality, price, growth potential, or category (size, industry, etc.)?
11. Are you most comfortable with approaches to investing based on the ideas of professional investors such as Warren Buffett, Peter Lynch, James O’Shaughnessy, and Joel Stern, or do you prefer to take a scholastic approach, using sophisticated statistical analysis and keeping up with the latest academic research?
12. Do you place your faith primarily in fundamental analysis, technical analysis, or sentiment indicators?

(I myself would choose the first option in all these questions.)

Once you’ve articulated your strategy, try to think of what chance it has to succeed. Everybody overestimates their chances. It’s very rare to encounter someone who estimates her chances of success at a particular endeavor as below average.

To avoid this pitfall, the first thing to consider is your base rate. In other words, try to assess not your own skill at achieving your strategy goal but the average skill. Pretend you come across another investor with exactly the same strategy, and you have no idea whether she’s good or not. You know nothing about her except her articulated plan. What chance would you give her of success? That’s your base rate.

So, for example, my own strategy is to use an automated system based on fundamentals (primarily quality, secondarily value and growth) to choose stocks, trading frequently, concentrating primarily on microcaps, doing a lot of backtesting, placing big bets, and trying to beat the market every year. If I were to meet someone else implementing this strategy, knowing nothing about her particular skill set, I would give her a 40% chance of beating the market. That’s my base rate, and I need to acknowledge that my own chances aren’t a whole lot better, even if my performance so far has been quite good.

Speaking of performance, the standard way to assess an investor’s past strategy is to look at her alpha. Some would prefer to look at her Sharpe ratio, or use another risk-adjusted return measure, but in my opinion, of all the conventional measures of strategy performance, alpha is the best. Here’s why:

1. It shows the relationship of an investor’s performance to the performance of the market as a whole; CAGR and certain other performance measures are market neutral.
2. The Sharpe ratio, Sortino ratio, and information ratio divide average performance/outperformance by the standard deviation of the returns. But standard deviation is a flawed measure of volatility that has little persistence and—in the final analysis—makes little difference in terms of out-of-sample performance.
3. High alpha correlates with low beta, and a low-beta strategy is less subject to market-induced volatility than a high-beta strategy.

Estimating the future alpha of a strategy is a very tricky business. Even measuring past alpha is tricky. The first thing to do is to choose the right benchmark. If you have access to good research tools, you can construct your own benchmark made up of the type of stocks that you’re likely to invest in (look at size, industry, minimum price, and, if you have other requirements, such as limiting yourself to stocks listed on the major exchanges, include those). But there are also a wide variety of ETFs available that may be suitable benchmarks.

The conventional way of measuring alpha is extraordinarily susceptible to outliers. There are lots of ways around this—you could Winsorize your returns, for example. I calculate beta by using the Theil-Sen estimation (this is the median slope of all pairs of points) and then calculate the median difference between my returns and the product of that beta and the benchmark returns. (I subtract the risk-free rate from both my returns and those of the benchmark before calculating any of this.) By this measure, and using the microcap ETF IWC as my benchmark, my strategy has gotten an annualized alpha of 26% since its inception in November 2015, which is 3% lower than if I’d used the conventional calculation.

Once you have past alpha—or even if you don’t—estimating future alpha has certain pitfalls you should be aware of. First, if your strategy is successful and you don’t make regular withdrawals, your assets will grow, and the more assets you have, the harder it will be to maintain a good alpha, because either the market impact of your trades will be greater or you’ll be investing in more stocks and thus underweighting your best ideas compared to what you used to do. Second, if you use backtests to optimize your strategy, reduce your backtested returns by at least a third in order to compensate for the fact that out-of-sample returns are almost never as good as optimized backtested returns.

If your strategy has certain inherent advantages over other folks’ strategies and you’re getting high alpha, you’re likely doing a good job. If, however, your strategy is just one out of many, with few or no inherent advantages over that of others, then a high performance may indicate a low performance in the future simply due to the axiom of regression to the mean (uncorrelated performance has, by definition, a high probability of relative reversal; if that weren't the case, the performance would be correlated).

Even if your base rate is high, your skill set is strong, and your performance is exemplary, ask yourself some additional questions about your strategy.

1. Are you willing to stick with your strategy, making only minor tweaks, even if you underperform for an extended period? If so, give yourself a point.
2. Are you liable to change your strategy radically during a market downturn? If not, give yourself a point.
3. Are you juggling several different equal but uncorrelated strategies, and are you choosing those that have performed the best in the recent past? If not, give yourself a point; if so, regression to the mean will doom you.
4. Pretend your strategy has utterly failed and you have lost not only a good portion of your savings but all faith in your strategy. What will you do then? If you have a good answer, give yourself a point; if you have no idea, try to come up with one.
5. How much research is involved in your strategy? If a lot, give yourself a point. Doing your homework should be an essential part of your investment strategy.

This last point is perhaps the most important part of assessing your strategy. A strategy that involves little to no research is doomed to eventually fail. And that brings us back to the first part of this article. Assessment is at the very core of successful investment: assessing the investments themselves, and assessing your ability to take advantage of them. Without spending the time and effort to assess, your investment success will be no better than that of a dart-throwing monkey.

CAGR since 1/1/16: 34%.

My top ten holdings right now: RMNI, MIXT, PERI, PALDF, SPNS, VISI, PCTI, TTEC, GTS, GSL.