In one of famous investor Howard Marks’ memos to clients of Oaktree Capital, the eccentric and successful fund manager hits on an interesting aspect of prediction markets and probability alike. In 1993 Marks wrote:

Being ‘right’ doesn’t lead to superior performance if the consensus forecast is also right. […] Extreme predictions are rarely right, but they’re the ones that make you big money.

Let’s unpack this.

In economics, the recent past is often a good indicator for the present: if GDP growth was 3% last quarter, it is likely around 3% the next quarter as well. Similarly, since CPI growth was 2.4% last year and 2.1% the year before, a reasonable forecast for CPI growth for 2019 is north of 2%.

If you forecast extrapolation like this, you’d be right most of the time – but you won’t make any money, neither in betting markets nor financial markets. That is, Marks explains, because the consensus among forecasters are also hoovering around extrapolations from the recent past (give or take some), and so buyers and sellers in these markets price the assets accordingly. We don’t have to go as far as the semi-strong versions of the Efficient Market Hypothesis which claim that the best guesses of all publicly available information is already incorporated into the prices of securities, but the tendency is the same.

if you forecasted 5% GDP growth when most everyone else forecasted 3%, and the S&P500 increased by say 50% when everyone estimated +5%, you presumably made a lot more money than most through, say, higher S&P500 exposure or insane bullish leverage.
If you forecasted -5% GDP growth when most everyone else forecasted 3%, and the S&P500 fell 40% when everyone estimated +5%, you presumably made a lot more money than most through staying out out S&P500 entirely (holding cash, bonds or gold etc).

But if you look at all the forecasts over time by people who predicted radically divergent outcomes, you’ll find that they quite frequently predict radically divergent outcomes – and so they would be spectacularly wrong most of the time since extrapolation is usually correct. But occasionally they do get it right. In hammering the point home, Marks says:

the fact that he was right once doesn’t tell you anything. The views of that forecaster would not be of any value to you unless he was right consistently. And nobody is right consistently in making deviant forecasts.

The forecasts that do make you serious money are those that radically deviate from the extrapolated past and/or current consensus. Once in a while – call it shocks, bubble mania or creative destruction – something large happens, and the real world outcomes land pretty far from the consensus predictions. If your forecast led you to act accordingly, and you happened to be right, you stand the make a lot of money:

Predicting future development of markets thus put us in an interesting position: the high-probability forecasts of extrapolated recent past are fairly useless, since they cannot make an investor any money; the low-probability forecasts of radically deviant change can make you money, but there is no way to identify them among the quacks, charlatans, and permabears. Indeed, the kind of people who accurately call radically deviant outcomes are the ones who frequently make such radically deviant projections and whose track record of accurately forecasting the future are therefore close to zero.

Provocatively enough, Marks concludes that forecasting is not valuable, but I think the bigger lesson applies in a wider intellectual sense to everyone claiming to have predicted certain events (market collapses, financial crises etc).

No, you didn’t. You’re a consistently bullish over-optimist, a consistent doomsday sayer, or you got lucky; correctly calling 1 outcome out of 647 attempts is not indicative of your forecasting skills; correctly calling 1 outcome on 1 attempt is called ‘luck’, even if it seems like an impressive feat. Indeed, once we realize that there are literally thousands of people doing that all the time, ex post there will invariably be somebody who *predicted* it.

Stay skeptical.

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The Paradox of Prediction