invest(igations)

How to Be a Great Investor, Part Ten: Read (and Keep an Open Mind)

This article is the last 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 One: Be Numerate,” “Part Two: Understand Value,” “Part Three: Properly Assess Strategy,” “Part Four: Compare Effectively,” “Part Five: Think Probabilistically,” “Part Six: Update Your Views Effectively,” “Part Seven: Beware of Behavioral Biases,” “Part Eight: Know the Difference Between Information and Influence,” and “Part Nine: Position Sizing” for previous installments. And please keep in mind that although I’m basing my work on Mauboussin’s, I am departing from his ideas on occasion.

As Michael Mauboussin relates, not too long ago the Columbia Business School sent a group of students to meet with Todd Combs, the investment manager at Berkshire Hathaway and (currently) CEO of Geico. He recommended that they read 500 pages a day. The students were dumbfounded. Combs’s colleague at Berkshire, Vice Chairman Charlie Munger, has said, “In my whole life, I have known no wise people (over a broad subject matter area) who didn’t read all the time—none, zero.” And Warren Buffett himself has suggested that he devotes 80% of his working day to reading.

All this might seem extreme. But, as Munger says, Einstein pointed out that “success comes from curiosity, concentration, perseverance and self-criticism. And by self-criticism, he meant the ability to change his mind so that he destroyed his own best-loved ideas.”

(All of the above quotes are taken from Mauboussin’s “Thirty Years” paper.)

The question, then, is: what should we, as investors, read?

I’ve broken the answer down to nine basic categories.

1. Books on investing in general. These can range from classics like Graham and Dodd’s Security Analysis to contemporary overviews like Frederik Vanhaverbeke’s Excess Returns. It’s best if they were written by actual investors.
2. Financial, accounting, and statistics textbooks and courses. Good investors must understand generally accepted accounting principles, be able to read financial statements, understand how to get the most out of those statements, and be familiar with basic statistical analysis. Textbooks and courses (in-person or online) may be the best way to get a good grasp of this material.
3. Analyses of individual stocks and companies. Even if, as I do, you use a quantitative approach, you should be familiar with how analysts—whether certified ones who work for an investment bank or amateur ones who write for sites like Seeking Alpha—approach evaluating companies to invest in.
4. White papers and academic articles. One needs to be discriminating here, but many valuable insights can be gleaned from the research done by investment firms and university professors.
5. SEC filings and earnings call transcripts. Being able to read balance sheets, income statements, and cash flow statements—along with all the footnotes—is invaluable; transcripts of earnings calls can be very informative as well.
6. Memoirs by and interviews with great investors. Putting investing success into a personal narrative can be inspiring; but often the most helpful bits in such reading are the accounts of investing mistakes.
7. Investment/trading blogs and forums. Some of these are wise, some are foolish, and I doubt any are indispensable. But perusing them is a good way to keep abreast of current thinking, and their interactive components can help you get answers to your investing questions.
8. History. Understanding how economics, markets, and politics worked in the past is key to having a grasp on the future.
9. Philosophy, poetry, and advice. Sometimes stepping away from the markets and reading about life in general can produce unexpected insights.

The next question is, of course, what not to read:

1. Computer-generated stock news. If you Google a small, obscure stock looking for news about it, the large majority of what you’ll come up with are “stories” written by an algorithm. These are garbage at best.
2. Technical analysis literature. Most of this will cause you to lose money faster than any other financial advice you’ll come across. Remember: the price you paid for a stock has nothing to do with its prospects, and its recent price history matters very little. A few technical metrics are valuable, but most of the literature out there is stock-market astrology.
3. Econometrics, EMH theory, and CAPM theory. If you don’t know what these are, that’s OK. And while these theories have produced some amazing insights, they ended up flouting empirical observations and using seriously flawed assumptions about market behavior. Before approaching this material, I suggest reading Alphanomics by Charles Lee and Eric So, which I wrote about here. On the other hand, though, Mauboussin counsels, “make a point of reading material you do not necessarily agree with. Find a thoughtful person who holds a view different than yours, and then read his or her case carefully. This contributes to being actively open-minded.”
4. Market speculation. Spending more than a few minutes a day reading what pundits think is going to happen next in the markets can poison your mind. Remember that more than 50% of their predictions will be wrong.
5. Get-rich-quick pitches. These come in a variety of forms, ranging from printed newsletters to high-tech websites. If anyone promises you excess returns of over twenty percent, either steer clear or contact the SEC.
6. Stocktwits. This website showcases content-free time-sucking garbage. There may be a few diamonds among the trash, but going through it all is exhausting. When researching a stock, steer clear of robo-advice and uninformed opinion and stick to actual financial analysis.

What does all this reading (and not reading) do for us as investors? It keeps us sharp and inquisitive; it makes it more likely we’ll find an edge; it increases the chance that we’ll stumble upon an investing idea that will not only be profitable but will set us apart from other investors; it increases our ability to question our own beliefs and to modify them as necessary. And reading has one additional benefit: it keeps us from obsessing about the performance of our portfolio. Psychologically, especially in bear markets such as these, nothing could be better.

CAGR since 1/1/2016: 20%.

My top ten holdings right now: III, ELMD, DL, ESALY, PDEX, CO, ECOM, ACIU, PCTI, PCOM.

Low-Volatility Stock Picking for High-Volatility Markets: A Multifactor Approach

In high-volatility markets like the one we’re in now, low-volatility investing can offer considerable comfort. But it can also offer excess returns. In this article, I’m going to single out six basic factors (and their variations) that investors should explore when designing a low-volatility model, and I’m going to present an actual model on Portfolio123 that can form a good basis for your investments.

1. Low price volatility. This is simply the standard deviation of daily (or weekly) returns. Stocks that are stable and steady in terms of price volatility tend to remain that way.

2. Low volume volatility. Once again, choose stocks with low standard deviation of volume to be really safe.

3. Beta. Beta is a rough measurement of how closely a stock’s movements resemble those of the market as a whole. Alpha is a measurement of excess returns after beta is taken into account. Beta is the slope of the linear regression between the stock’s weekly or monthly price movements and those of the market, and alpha is the y-intercept. If a stock’s beta is 1, then one should expect that when the market goes up or down 2%, the stock will go up or down 2% plus its alpha. If its beta is 2, when the market goes up or down 2%, the stock will go up or down 4% plus its alpha. If its beta is close to 0, the stock will act completely independently of the market. And if the stock’s beta is –1, when the market goes up 2%, the stock will, on average, go down 2% plus its alpha. The lower the beta, the more uncorrelated the stock is to the market, and portfolios with low correlations between stocks have much less variance than those with high correlation. As a bonus, beta and alpha are negatively correlated (I’ve written a mathematical proof of this), so low-beta approaches tend to produce high alpha.

4. Low share turnover. This is the number of shares traded divided by the number of shares outstanding. I have written on this factor at length, but if you don’t want to read my article, here’s a nutshell version.

Older, established, and somewhat boring companies (widows and orphans), often with high-priced shares and low short interest, have low share turnover; and cutting-edge, exciting, controversial, highly hyped, turnaround, high-momentum, and/or heavily shorted companies have high share turnover. Stocks with lower share turnover are less subject to market shocks, and the higher their turnover, the more they’ll be moved by the market. In other words, low-turnover stocks have lower beta and high-turnover stocks have higher beta.

Why? Prices and quantities traded are driven by the same forces. And it almost goes without saying that without a large increase in volume, you simply can’t have a large increase or decrease in price.

If you multiply both the top and bottom of the share turnover formula by the price per share, in the numerator you get dollar volume—the amount of money actually exchanging hands—while in the denominator you get market cap. Put everything together and you can see how the price movements in the cap-weighted market as a whole are going to be much more heavily reflected in stocks that experience a high dollar volume compared to their market cap than in stocks that experience a low dollar volume, simply because volume amplifies market-related price movements. Share turnover—average dollar volume divided by market cap—is essentially a measure of investor participation in a company. And higher investor participation will be associated with a larger amount of capital responding to common shocks. Thus, other things being equal, a larger amount of capital responding to a common shock will result in a stronger price movement.

5. Low fundamentals volatility. I have found that a number of measures of stability can help reduce volatility and increase returns. In particular, look for stocks with stable accounts receivables, assets, cash conversion cycle, cash flow, employees, income, margins, sales, and so on.

6. Large size. Large companies tend to be far more stable than small companies. The measure of size with the lowest negative correlation to volatility is market cap, with sales coming a close second, so companies with large market caps and high sales are good low-volatility investments.

I’ve designed a relatively simple ranking system that combines all these factors for a comprehensive low-volatility approach. If you’re a Portfolio123 subscriber, you can link to it here. If you’re not, a screen shot of it is below.

I’ve put in two fundamental factors as examples of how to minimize fundamentals variance, but feel free to add others. I haven’t assigned weights to any of the factors: please feel free to copy this system and assign weights as you see fit. You can backtest the system with this screen. You’ll see that a 50-stock portfolio of Russell 1000 stocks gets solid excess returns over the last five, ten, and fifteen years, with much higher Sharpe ratios and much lower beta and standard deviation than the S&P 500. I’m sure you can improve these results by adding screening rules and varying the weights of the ranking system.

What kind of stocks does this system recommend? Right now, the top five stocks are Republic Services (RSG), CNA Financial (CNA), Kimberly-Clark (KMB), Ecolab (ECL), and Procter & Gamble (PG). Boring companies all. And safe, too.

Academics have long puzzled over the fact that low-volatility stocks have higher returns than high-volatility stocks. It goes completely against their theory that higher risk should be compensated with higher returns.

But in fact, high risk does not correlate with high returns. Risk and return do have a relationship, but it’s not linear, and is shaped more like the top of a right-facing baseball cap. If you’d like to read more about this, I’ve written a short article here. By using a low-volatility approach, you can both reduce your risk and increase your returns.

My CAGR since 1/1/2016: 26%.

My top ten holdings right now: MTBC, MHH, ELMD, III, PCTI, ESALY, ACU, PDEX, CO, ECOM.

Break Your Strategy: How to Stress Test Your Quantitative Models

Building and Breaking Models

If you’re a quantitative investor or trader, you build a model and then backtest it to see if it has worked in the past; if you’re like most people, you try to improve your model with repeated backtests. You’re operating under the assumption that there will be at least some modest resemblance between what has worked in the past and what will work in the future. (If you didn’t assume that, you wouldn’t backtest at all.)

But what few backtesters do after building their model is to try to break it by subjecting it to stress tests. A truly robust model should withstand every moderate attempt to break it. Only then should it be put into practice.

This article will outline some techniques for stress testing quantitative models. I use Portfolio123 to build and backtest my models. If you model using a different platform, most of what follows won’t apply, but I’ll try to explain my techniques in language that can be adapted to other platforms.

General Guidelines for Stress Tests

Each model that one designs on Portfolio123 essentially consists of a ranking system and a universe to which it applies. One can add a lot of complexity to the model, but those are perhaps the two most important foundations. The universe consists of a root universe and then incorporates a variety of screening rules to eliminate stocks with low liquidity, high risk, low growth, high price, or whatever else you want to put in there. In order to do these tests, you must put as many of your screening and buy rules into your universe as you can. Each model then buys the top-ranked stocks according to its ranking system, and sells them when their ranking falls to a certain point, buying new stocks to replace them. The number of holdings and the sell rules are some of the things we’re going to vary to see if the models will break.

Portfolio123 offers four very different ways of testing a particular ranking system and universe. We can use:

• a screen backtest, which rebalances to equal weight every rebalance period;
• a rolling screen backtest, which holds stocks for a certain period, and one can see overlapping returns that way;
• ranking system performance, which separates the universe into quantiles according to the ranking and shows how each would have performed; and
• an actual simulation of the model, with buy and sell rules.

To try to break a model, we should try all four of these tests, varying parameters. We should vary the number of stocks held at one time; we should vary the universe rules, testing on subsets of the universe or on an altogether different root universe; we should vary the factor weights in our ranking system; and we should vary the time period tested, including, if possible, testing it on a time period that hasn’t been tested before.

A test can be said to fail if the results have a negative alpha or a negative excess return when compared to a benchmark that consists of all of the stocks in the root universe of the one that’s being tested (or if, in a rank performance test, the top bucket is lower than the middle buckets).

So let’s take a closer look at these stress tests.

Four Sets of Variations

• First, vary the number of stocks being held. Test it on the top ten, top twenty, top fifty, and top one hundred.
• Second, vary the root universe. Test it on the S&P 500, the Russell 1000, the S&P 1500, the Russell 3000, Canadian stocks, international stocks only, and universes consisting of only certain sectors. Be sure to adjust the slippage accordingly: testing on the Russell 3000 will require higher transaction costs than testing on the S&P 500. Change your screening rules by varying your hard limits by 10% or 15%.
• Third, vary the factor weights in your ranking system to a moderate degree. Add, say, 3% to five factors and subtract 3% from another five factors, or add 3% to five factors and normalize them all. Then go back to your original weights and do that again.
• Fourth, vary the time period. Test it on the last ten years, fifteen years, and three years; go back and test it on other discrete time periods as well.

If you do all this using a screen backtest, a rolling screen backtest, a ranking system performance test, and a simulation, you’ll be doing thousands of stress tests. This is obviously impractical, so it’s best to design a subset of a dozen or so stress tests that will help you try to break your system.

Practical Stress Tests

For the purposes of this article, I designed five very different systems that backtested quite well, and then put them through a dozen stress tests. I won’t describe all these in detail, as it would be tedious in the extreme. Suffice it to say that three out of the five systems I tested failed a test. The two toughest tests were as follows:

• I ran a ten-bucket rank performance test on one variation of my ranking systems over the last fifteen years with a rebalance period that matched my actual average holding period and my root universe being the S&P 1500. I then compared the top bucket to the middle buckets. In one case, the top bucket was lower than the average of the two middle buckets.
• I ran a rolling screen backtest of the top 100 stocks, using the Russell 3000 as my base universe, over the last four years only, using another variation of my ranking systems, with a holding period that matched my actual average. In two cases, my backtest failed to exceed that of the benchmark, in part because of realistic slippage costs and in part because the last four years have been pretty terrible for small-cap value stocks.

Conclusion

The natural impulse for a quantitative investor or trader is to try to create a system by tweaking various inputs so that when backtested it shows excellent returns. But unless the system is subjected to stress tests such as the ones I’ve discussed, it has a higher chance of breaking down when actually implemented with real money. Backtesting for failure may be just as important as backtesting for success.

My CAGR since 1/1/2016: 26%.

My top ten holdings right now: MTBC, ELMD, MHH, PCTI, ESALY, III, ACU, ECOM, PDEX, DL.