Almost all professional investors look at the P/E ratio
of the stock market, but rather than smoothing the earnings as we do, they look
at either the past 12 months of earnings or the estimates of earnings over the
next year. We can’t believe how many
sophisticated investors use the P/E ratio based on the past year or following year. The reason we attempt to smooth the earnings is
to dampen the volatility that one year of earnings has had over the past
decades. This will make it easier to
make longer term decisions.
An example of the volatility of one year of earnings over
the past 14 years would be that “reported earnings” (or GAAP—Generally Accepted
Accounting Principles) were $50.00 in 2000, $24.69 in 2001 and came back to
$48.74 in 2004. The earnings “estimates”
over that period of time also had tremendous volatility and it boiled down to
almost a guess. So how could you
possibly base an intelligent investment decision during that time? Also earnings were $66.18 in 2007, $14.88 in
2008, and $50.97 in 2009. Earnings are among
the most mean-reverting statistics in investing, and therefore there has to be a
better way of determining the “true” earnings.
Earnings always return to the trend and the long term trend of earnings growth
over time has consistently averaged about 6% despite the year-to-year volatility.
The idea of smoothing earnings over a period of more than
a single year was first proposed by Graham and Dodd in their classic book “Security
Analysis” published in the early 1930’s.
Cyclically—smoothed earnings have proven to be a much more accurate
predictor of long-term market returns than any method that uses earnings over
the past year or following year. In
fact, we used this method in late 1999 when we published a report forecasting
that the normalized S&P 500 would be about 1260 ten years from then. That was significantly below the then
prevailing price. It was a projection we
made near the peak of the dot-com bubble.
Almost everybody thought the forecast was crazy although it eventually
proved to be highly accurate. [More]