How To Make Better Predictions
By Tis the season of Wall Street predictions. How will US, European and Asian stocks do next year? How many times will the Fed increase interest rates? Where will the 10-year Treasury trade in 2022? All this got us to thinking about the process of making predictions, and that is the subject of this week’s Story Time Thursday.
We’ll start with some wisdom from Daniel Kahneman and Amos Tversky, who coauthored a paper they called “On the Psychology of Prediction” in 1973 (link here). Despite the title, their approach to making useful predictions is math- (not psychologically-) based:
- Any prediction should start with the statistical base rate associated with a given outcome. For example, if we’re trying to predict where the S&P 500 will be at year-end 2022 then the index’s long run annual rate of return should be our starting point. Let’s call that 10 percent.
- Then we ask the question “how representative will 2022 be of the general environment that created that base rate?” In the case of the S&P, we might make a list of plusses and minuses with “strong earnings” in the first camp and “possible Fed policy mistake” in the latter. Let’s say the bias is to the bearish by 2-3 points.
- That would leave us with a predicted return of 7-8 percent for the S&P 500 next year, which is what our DataTrek Look Ahead Survey reported was the plurality of readers’ expectations. Given the answers to several other questions in that survey (notably the coin-flip odds respondents gave to a Fed policy mistake), the “what” and the “why” of the survey’s output lines up very well.
Now, if one has a profoundly bearish outlook, this approach also allows you to consider that view in a statistical framework:
- Let’s say we want to predict that the S&P 500 will be down 10 percent or more next year.
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