by Jeff Miller
Editor’s note: This was first published at A Dash of Insight August 31, 2010
Regular readers of “A Dash of Insight” know that I like to present sports analogies to help investors escape their biases about the market. Since we are at the start of a new football season, let us engage in some football forecasting.
Suppose that you have an opportunity to invest in a season-long football pool. You can take one of two approaches, one looking at last year’s records (Plan A) and the other looking at scouting reports (Plan B):
Plan A. Look at the exact records and statistics for each of the teams over the past ten years. You have perfect data for this period, so you cannot be criticized for your analysis. Your method of prediction is to choose the teams in each division that did the best over the last decade.
In short, you are using past performance over a set time frame to predict the future. You pick a time period long enough to escape short-term fluctuations and find long term team strengths.
Plan B. Look at how each team did last year. Look at scouting reports for each team for the upcoming season. Take the predictions from a group of scouts and use their average forecast as the starting point for your analysis. You are allowed to tweak it a bit, since some of the scouts may be biased locals.
In short, you rely mainly on interpretation of current information — coaching changes, injuries, retired players, new acquisitions, strength of schedule — to make a forecast for each team’s record.
The Contrast
In one case you are using known data — rock-solid information. The problem is that you must then make guesses about how these trends may have changed. In the other case you use micro-level data which includes estimates and speculation. You are relying upon the expertise of the sources.
Please note that you cannot avoid making an estimate as part of your forecast. It is only the nature of the estimate that has changed.
The Market Application
There is a constant drumbeat of criticism about market valuation using forward earnings. The most common criticism, that estimates are too optimistic, is open to challenge. If the estimates are too high, why is the beat rate consistently in the 65% range?
The key choice is the same as our football example. The fans of the Shiller 10-year past earnings method take pride in having solid data. Then they make a wild guess about whether the trend will continue. Those praising this method point to a few notable successes, mostly times when P/E ratios were very low since interest rates were very high.
Those interested in forward earnings are taking the aggregate work of dozens of specialists. If you think they are a little high, you can feel free to add an error range. If you do so, you should look at past data — especially that of recent years.
Time Frame for Testing
Pundits choose time frames to suit their purpose.
Many critics of forward earnings use data from before 1980, mostly because that suits their purpose. Since we did not have forward earnings data before then, all of those sources are misleading at best and deceptive at worst.
There is also a logical question about comparisons from ancient times. Here is a surprise. The same critics who now complain about the effects of high frequency trading think it is completely appropriate to cite data from the 1930’s or the 1970’s.
Markets have changed dramatically as information became more widely available to all and the bid-ask spreads narrowed. In the 1970’s, the average investor had no idea what the sell-side earnings estimates were and had to pay a huge commission to act even if he did. It is different now.
Conclusion
These points are blindingly obvious, yet widely ignored.
There will be more posted on this topic in a few days.
