The only sure thing about the economy is this: Past performance does not equal future returns.
By Bo Billeaud
July 26, 2012
Edward Lorenz was a meteorologist who accidently made one of the most important scientific discoveries ever. In 1961, the MIT researcher was testing weather forecasting models on what were then advanced computers. It was an exciting time in the world of science, as new computing technologies opened the door to testing and modeling exercises as never before. Scientists were quite confident that their ability to peer into the future was about to explode. They were looking forward to predicting weather, earthquakes and many other natural phenomena with the accuracy that they were able to do so with eclipses and tides. One day, as Lorenz’s computer was busy grinding out a calculation, he stopped it halfway through the modeling exercise. Later, when he was ready to resume the computations, he reentered the data that the computer has earlier produced, and left it to run. So far, so good, except for one thing: The forecast created this time by the computer on this run could not have been more different than that created just a few days earlier. Something went wrong. The computer seemed to be working fine, so what could have happened?
The problem turned out to be this: Lorenz had originally fed the computer raw data that was carried out to six decimal places. On the second run, he entered that data, but it was only carried out to three decimal places. And that tiny, insignificant change in the input — invisible to the casual observer — produced a result that had a radically different outcome. Lorenz then realized that definitively forecasting a complex system such as weather would forever be elusive, as the tiniest changes in data going into the forecast could drastically alter the outcome. He discovered what has come to be called “Chaos Theory.” We laymen may know it by its more common name, the butterfly effect. That is, the idea of miniscule changes making a big difference in the outcome — like the flapping of a butterfly’s wing in Brazil that might ultimately lead to a tornado in Texas.
We are surrounded by complex systems. The economy is one of them. And the butterfly effect explains exactly why economists and market analysts have such a dismal forecasting record. The slightest change for a data input in their forecasting models can produce a radically different forecast.
Look at the color boxes in the accompanying chart. What you see is the last 12 years of annual market returns for eight separate asset classes including domestic and foreign stocks, real estate, bonds, gold and oil. I like colors — it makes it easy to illustrate the point about future returns.
For example, look at calendar year 2000. We can see that the asset class that gave the highest return that year was oil. It rose 65.4 percent that year. The category that did the worst was emerging markets, which lost 30.61 in 2000. Now look at the very next year, 2001. Did oil give a repeat performance? Not even close. In fact, 2000’s winner, oil, became 2001’s dog, with a decline of 16.3 percent.
The chart illustrates an investment truism. Past performance simply does not equal future returns. And forecasting which asset class will lead or lag is a fool’s errand. If it were not, the future of the colored patterns would have a sense of order to them. But, they don’t. Do you see any patterns from one year to the next in terms of best to worst performance? Look closely. Nope. It seems to be random. In fact, it is. WARNING: Do not be fooled by the randomness of returns.
So remember this when you’re tempted to chase last year’s hot story. In fact, here’s an idea: Don’t chase anything. As I’ve long suggested in this column, don’t play the loser’s game. Instead — once again — hold a broadly diversified investment portfolio that covers all bases. Put some thought into the design. As the color chart illustrates, no one area leads the pack for long. A well-designed and fully diversified portfolio gives you a fighting chance. It is the only free lunch in town.