# P/E forecasting, from Steve Ellison

January 3, 2011 |

You can get as-reported earnings for the S&P 500 from 1988 on at Standard & Poor's website.

Using 12-month trailing earnings for each year's September quarter (the last that would be known by Dec. 31) to calculate an E/P ratio for the S&P 500 as of Dec. 31, I get a somewhat positive correlation of trailing E/P to year-ahead returns with t=1.10, R sq=0.057, p=0.29, and N=22.

## Larry Williams writes:

My model for the DOW suggests a 12.25% growth for the year, slightly above the long term average growth.

For the S&P, I get 10.6 % barely above the long term average.

## Bruno Ombreux writes:

There are two things I don't like in P/E or E/P studies.

1) Your regression is in the form:

-1 + P(t)/P(t-1) = f[P(t-n)/E(t-n)] + e we have the same variable on both sides, and even if it is lagged I am not sure standard regression is OK to handle this type of formula. Just to give you an idea, multiplying both sides by P(t-1), it is actually P(t) = P(t-1) + P(t-1)* f[P(t-n)/E(t-n)] + P(t-1)*e

This is certainly amenable to study, but not with the standard regression toolbox.

2) Price is more volatile than earnings. There is a subtle bias introduced by the fact that over the estimation sample, high P/E will be naturally followed by lower P/E, and vice-versa. This is a bit like regression to the mean but more subtle. This can lead to spurious mean-reversion.