One instance of this detachment from reality is the “random walk” theory of financial markets. This model of market behavior was developed in the 1960s and still holds sway in the retail research departments of most U.S. brokerage firms. It’s based mainly on this:
What the chart shows is the Dow Jones Industrial Average from its creation in 1896 to the present (monthly closing prices; my apologies for the unreadability). The greenish line is a “linear regression,” that is, the straight line that is the best fit to the actual data points. Linear regression was a cutting-edge tool back in the ’60s, when computing power was rather limited. But there’s an unargued underlying assumption in applying it to a market, which makes the “random walk” theory an exercise in circular logic.
The assumption is that the graph of a stock index like the Dow in a way “wants” to be a straight line but can’t manage it. What economists actually say is that the market “seeks equilibrium,” by which they mean that it wants to go up continuously and at a consistent rate. But instead, there are “perturbations” that cause “fluctuations” above or below the idealized rate of gain. Those fluctuations are by nature random (hence “random walk”) and therefore are unpredictable.
Thus, investors shouldn’t try to guess when the market is going to have one of those “perturbations” – in other words, they shouldn’t try to practice “market timing” – but instead should simply buy stocks and hold them for the long term, because the underlying trend always goes higher. And here the logical circle is closed.
Since the 1980s, the random walk theory has come under increasing criticism, and many economists outside the Wall Street houses acknowledge that it’s not a realistic model. Many still want to cling to some variation of randomness, however, and have developed hybrid models that include some degree of self-recursiveness along with the randomness, giving us things like the exotically named GARCH model: “generalized auto-regression with conditional heteroskedacity.” However, scientific studies have shown that these models have zero value in predicting market movements.
Another argument in favor of “buy and hold” investing is the claim that the stock market consistently over the years has provided an average annual percentage return that is higher than other kinds of investments. But this depends very much on how you calculate the annual return. The usual figure is 8 percent, compared with half that return or less from interest-bearing investments such as bonds.
It’s true that if you take the closing price of an index like the Dow on a given day and calculate the percent change from the same day the year before, and you average that calculation over the past 100 years, you’ll get a figure something like that 8 percent number. But the calculation doesn’t bear any resemblance to how people actually invest: You can’t “buy the Dow” every day and sell it a year later.
I’ve constructed a model that I think represents more accurately how people really invest. I had to make some simplifying assumptions, but I believe the result is still more indicative of the kind of average returns investors can expect.
Here’s the idea: Let’s suppose that a worker sets up a program in which he or she invests a set percentage of his or her income each month. This program continues for 30 years, at which point the worker retires and cashes out. I’ve also assumed that the worker gets a cost-of-living raise at the beginning of each year, based on the nominal inflation rate (based on the Consumer Price Index) for the previous year. (That’s something few workers are actually seeing today, so the model may actually overstate the investor’s returns somewhat.)
The following chart shows the average annual return a worker would have received by following this investment strategy:
The time scale (if you can read it; right-click to open it bigger in a new window) indicates the date upon which the worker began the monthly investment program, and the vertical scale shows the percentage return at the end of 30 years for a worker who started investing on the date shown. For example, a worker who started an investment program in the very first month that Charles Dow calculated his industrial average in 1896 (i.e., the very beginning of the blue line) would have earned an average annual return of 2.86 percent over the next 30 years.
The very highest average return, 18.11 percent, would have been earned by a worker who began a monthly investment program in December 1969 and cashed out in December 1999. Obviously, the average annual return for someone who started 10 years later and cashed out last year would have been quite a bit less, just 3.92 percent at the low point in February 2009.
Worse yet, people who started investing in late 1901 to mid-1903 would have lost money, as would almost everyone who started in 1912. Perhaps most surprisingly, anyone who embarked on this kind of program in the late 1940s to mid-1950s -- which we're used to thinking of as boom times -- would have earned a fairly paltry annual return of about 2 percent or less when they cashed out in the late 1970s-early 1980s -- which were not so booming.
Overall according to this scenario, investors who have cashed out to date have earned an average annual return of 5.05 percent or a median annual return of 4.44 percent. Those figures aren’t all that much above the long-term average or median returns on interest-bearing investments. As I said earlier, the results may overstate the average returns because of my assumption about annual salary increases. In addition, the numbers don’t include any taxes or transaction costs such as brokerage commissions.
However, the real lesson of this exercise isn’t about long-term average returns, it’s about the wide variability of the real-time returns. What it boils down to is that even for a long-term investor, your results still depend entirely on when you start investing and when you cash out. If we relate that to our entry into the career world, it shows how much the decision is out of our hands: We can’t choose what year we’re born. Whether we like it or not, we’re all market-timers.
