Building and Putting Capital Markets Technology Into Practice

By now you know the Three Questions work together, helping you identify what you can know that others don’t. While this book demonstrates how to debunk some common myths and uncover some surprising truths via the Three Questions, don’t stop with the few examples the limited space in these pages allow. The point isn’t to garner a few useful investing tidbits but to use the questions always with every decision. Stop and ask, “Why am I buying or selling this stock, sector, fund, whatever? Why do I think this is a good idea? What do I know that others don’t? What do I believe that is false here? What can I fathom?” Simply asking the questions puts you ahead of most investors. Then ask, “Is my brain just messing with me?” This isn’t a static how-to book. Its aim is to give you a dynamic process and a tool set to serve you for your entire investing life.

The answers to the Three Questions, either one by one or together, provide you with a new way of approaching markets—one amounting to a technology you can repeatedly test and apply. Your goal in repeatedly asking the Three Questions is to build, over time, a dynamic arsenal of capital markets technology. In effect, with each answer to a question, you’ve created new capital markets technology. Some capital markets technologies can be big game-changers. Others can be smaller, simpler tools. This book is chock full of examples of them.

We don’t know much today about how capital markets work compared to what we will know in 10, 20 or 50 years. One way to know something others don’t is to build capital markets technology of the future now. If you can know something now others won’t know for 5, 10 or 20 years, you have a long lead. Capital markets technology will help explain parts of the investing world never before understood and, like any other piece of technology, give you a dependable, usable tool. The technology you create allows you to make more accurate forecasts and learn to make bets that are right more often than wrong. What’s more, technology allows you to discover and create even more unique technology.

History as a Research Lab

If approaching investing correctly (like a scientist, not a blacksmith), you must test your capital markets technology. And there’s no better laboratory for testing new technology than history. Far too many of our investing myths, the ones comprising the documented and accepted ways of thinking, are based on ideology, theoretical whim, political inclination or, worse, cognitive bias. When tested against historical data, they often simply fall apart—just like the myth about high P/Es and the misplaced fear about federal budget deficits. Proving something is true takes more rigor than proving something isn’t true. To prove something isn’t true, you just need to show consistently lousy correlation. History can teach us if something is beyond reasonable to expect.

If, throughout history, X wasn’t tightly linked to Y, and, in fact, X is linked to a bunch of stuff other than Y, you have no basis to bet X will suddenly start causing Y consistently. Stubbornly clinging to a popular causal theory without supporting evidence is how myths become firmly entrenched in our culture. But it takes a long time, and the longer the myth runs, the less people are prone to verify its validity.

The good news is you needn’t own a pricey Bloomberg terminal to have access to data. Vast quantities of neatly organized data in varying levels of granularity are available for free on any number of websites. A sampling of websites that might be useful can be found in Table 4.1.

Table 4.1 Data Sources

If you can’t figure out how to download or analyze the data using Excel, see the example in Chapter 1 or find a high school student to show you how. By 2011, I assume most readers are at least generally comfortable on the Internet.

That said, the data in your proof can be either quantitative or qualitative. The high-P/E myth was debunked with quantitative data. You saw how easy it was to disprove a very widely held theory using standard data by doing simple tests based on asking the questions.

But what if the data are either hard to come by or measure? Can your capital markets technology be qualitative in nature? Sure, as long as you have plenty of examples to analyze and it makes economic sense. A great example is the presidential term cycle from Chapter 2. The cycle is difficult to measure numerically but thus far is still powerful. In terms of data, you can examine all the election cycles going back to 1926. It’s not exactly quantitative, but there’s a clear pattern with underlying fundamentals. And, of course, an important reason it works is the fundamentals aren’t well understood, the pattern isn’t well accepted and most often, when articulated, it’s ridiculed.

But your proof must make basic economic sense. If you find a reliable pattern but don’t have a good causal explanation for it, don’t bet on it. Did you know every year ending in a 5 since 1926 has been positive for US stocks?1 You might feel thus justified betting on the next 5 year. Don’t! Simple numerology! There’s no known economic reason why every 10th year should always be positive. For that matter, years ending in 5 since 1955 have seen more fierce land-falling hurricanes (as evidenced by the number of hurricane names retired).2 So what? I doubt the National Oceanic and Atmospheric Administration is relying on the “year-5” theory in any way whatsoever in its forecasting. And you sure as heck can’t argue heavy hurricane incidence causes years ending in 5 to have good stock markets. What we have here is a statistical anomaly—a freak of nature. The lucky guy flipping 50 heads in a row. They happen all the time, so be cautious. Again, correlation without causation is no basis for a bet.

Then again, maybe you can uncover a sound economic reason why 5 years are always positive, and that can be your personal capital markets technology. Good for you. Then you’ve done a Question Two and fathomed what is otherwise unfathomable to the rest of us. Fair game—if you can do it, you have the basis for a bet.

Also, once you’ve tested and put a new piece of capital markets technology to good use, don’t become overconfident and assume you have a sure winner every time you make the same bet. Nothing is perfect or works all the time. Suppose X causes Y 70% of the time. That’s pretty good. It’s likely worth betting on. And yet some other thing or things cause Y 30% of the time. So while it’s worth betting on X to cause Y, you will still likely be wrong 30% of the time. No one thing is perfect.

What’s more, even the best capital markets technology can fade, which is why you must keep testing your hypotheses. Following are two examples of great technologies that were, in many ways, groundbreaking—but their effectiveness, at least as initially intended, has largely passed.