BUNK 42
STOCK RETURNS ARE TOO HIGH AND MUST FALL
Life would be simpler if there were just one, easy-to-diagnose problem causing us to think wrong about investing. Instead, a whole field of behavioral psychology—behavioral finance—evolved to deal with the myriad ways human brains go haywire when contemplating something as unintuitive and unnatural as investing in capital markets.
One common theme that pops up, though, is fear of heights. It’s what makes high P/Es so scary when you look at them in the wrong context (Bunk 26) even though overall P/Es, no matter their level, aren’t predictive of future stock returns over any reasonable time frame, despite persistent myth to the contrary. “Too high” can be framed any number of ways that lead your brain to market ruin. And one is how we look at long-term stock returns.

Scary Scaling

You’ve probably seen Figure 42.1—it’s a simple chart of the S&P 500 Total Return Index going back to 1926. This chart scares the pants off some folks, particularly those who think stocks are unreasonably high now (and at any future point in time) and must crash down to earth. Just look at it! It looks like through history, stocks have had pretty steady returns—then, starting about in 1990, stocks took off and had truly unsustainable returns. Too high! Scary! (On this chart, 1929 doesn’t even show as a blip—which gives you the first clue to its reality.)
Figure 42.1 US Stock Returns, Linear—Looks Are Deceptive
Source: Global Financial Data, Inc., S&P 500 total return from 12/31/1925 to 12/31/2009, graphed on a linear scale.
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Looks deceive. That “scary” run-up is simply the impact of compounding returns over an ultra-long period. Still, many can’t shake the notion that stocks have come too far, too fast, and a big crash must be coming—bigger and more lasting than the 2007-2009 bear market, which by historical standards was in fact a monster.
Now consider Figure 42.2. It looks like a reasonable, long-term rate of return—not scary and top-heavy. Except Figures 42.1 and 42.2 show the exact same data—S&P 500 total return from 1926 through year-end 2009. The only difference is the scale.
What gives? Figure 42.1 shows returns on a linear scale. Linear scales are fine and used all the time in statistics. Even for stock returns they’re fine for shorter periods. The problem with a linear scale for stocks longer term is every point move takes up the same amount of vertical space.
Figure 42.2 US Stock Returns, Logarithmic—Same Stats, Different Perspective
Source: Global Financial Data, Inc., S&P 500 total return from 12/31/1925 to 12/31/2009, graphed on a logarithmic scale.
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Envision it this way: An increase from 100 to 200 looks the same as a move from 1,000 to 1,100—both 100 points. But that’s not how you experience market growth. A price move from 100 to 200 is a huge 100 percent increase—double! But 1,000 to 1,100 is only a 10 percent rise. So plotting the S&P 500 since 1926 makes more recent gains seem stratospheric because the index level itself is higher. But average annualized returns from 1990 to 2009 were actually smaller than from 1926 to 1989 (7.8 percent and 10.2 percent respectively)1—thanks to the two big bear markets during the 2000s. Yet the graph makes those returns look infinitely huger, compared with the totality of earlier decades. Ironic, isn’t it?
Figure 42.2 shows market returns on a logarithmic scale—a better way to consider long-term market returns. On a logarithmic scale, percent changes look the same even if absolute price changes are vastly different. This is the right way to “see” stock returns in the long term. This way, an increase in index price from 100 to 200 (a 100 percent change) looks the same as a rise from 1,000 to 2,000—also 100 percent. And that’s just how you and your portfolio experience market changes.
This is a scaling issue—learning to look at data the right way. Scaling is a classic debunkery tactic. Once you get scale, that inherent fear of heights most people feel goes away. And, you’ll see that over long periods, market returns are a lot steadier than you think.
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