By now, some of you may have noticed that I am none too fond of the average Wall Street forecaster.

This isn't because of some random prejudice, but rather, a view that has evolved based on long experience. It is backed up by solid statistical evidence that forecasters are not very good at forecasting.

It bears repeating that: 1) almost all forecasting is folly, and 2) forecasting is marketing. However, with a few small tweaks forecasting could be more useful, or at least more honest. Here are a few suggestions:

No. 1. Share the underlying model’s past performance: We're all familiar with those who trumpet the accuracy of their forecasts. And now, you too can partake of their unique genius for X, for the low, low price of . . . whatever.

But let's restrain our enthusiasm for the anecdotes cited as proof. Now, if the forecaster has an audited track record showing how the prognostications stacked up versus reality during the past five years, and can demonstrate how these made clients some money, that might be worth notice.

But probably not.

There is a reason the standard disclaimer -- past performance is no guarantee of future results -- is provided. It's designed to protect people (largely from themselves). It serves as a reminder that a good track record may not be repeatable; that those winning outcomes could have been the result of luck or that specific era or some other random element.

No. 2. Acknowledge the unknown variables: Reading a Politico column about Yale economist Ray Fair’s economic models pointing to a Donald Trump re-election blowout in 2020, I was impressed.

Not because of the landslide forecast, but because of the very smart use of caveats: “Fair and other analysts who use economic data and voting history to make predictions also note that a sharp decline in growth and an increase in the unemployment rate by next fall could alter Trump’s fortunes.”

Not locking oneself into any single outcome because things might change is simply common sense. Unfortunately, that is a rare characteristic in too many forecasters.

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