UK Prime Minister Theresa May’s gamble to call an early general election has backfired, dealing Britain an unexpected outcome that’s dealt another blow to the so-called experts who anticipated that the Conservatives would gain seats in Parliament. Instead, the electorate confounded the polls and forecasting models and delivered a major setback to May’s government. Once again, there are lessons to be learned about the hazards of forecasting, although assuming that all efforts at diving the future are wrong isn’t one of them.
If every prediction is reliably wrong, managing risk would be easy since dependably wrong is effectively the equivalent of dependably right. Imagine that a stock market seer issued a new forecast every Saturday for the week ahead. Further, assume that this forecast always got it wrong. Whenever Mr. Oracle ORCL told you the market would rise in the following week, it fell, and vice versa. Embarrassing for the analyst, but a gold mine for everyone else.
If you knew in advance that a given forecast was destined to be wrong, you would simply take the opposite side of the trade. Mr. Oracle tells you to buy? That’s a signal to sell. The next time he predicts that the market will slide, it’s time to buy.
Forecasts are equally valuable if they’re reliably wrong or right. In reality, of course, forecasting – like the world around us – is messy. Forecasts can be wrong – spectacularly so in some instances. But assuming that forecasts are always wrong is no less dangerous than assuming that every forecast is right.
Instead, there’s a gray area in the dark art/science of prognosticating. Many if not most forecasts are worthless, but some forecasts are better than others. But even the failed forecasts may turn out be useful. Indeed, some investors have successfully turned forecasts and theories upside down in a bid to exploit the stock market in surprising ways.
Perhaps the most famous example is bound up with the risk metric known as beta, which is a core component of the capital asset pricing model (CAPM). Initially, it was widely assumed that beta was a reliable predictor of return: stocks with higher betas are expected to have higher returns. But in the 1970s, researchers began to find evidence that beta wasn’t a reliable predictor after all — the slope of the line between risk and return tended to be flatter than expected, which is to say that higher betas don’t necessarily lead to higher returns, or at least not as high as CAPM predicts.
Some researchers soon decided that CAPM was worthless, beta in particular. But Fisher Black famously observed in “Beta and Return,” a 1993 essay in The Journal of Portfolio Management, that “Beta is a valuable investment tool if the line is as steep as CAPM predicts. It is even more valuable if the line is flat.”
Fast forward a quarter century and we find that investing in low-volatility stock – a.k.a. low-beta stocks – is all the rage. ETFdb.com lists dozens of low-beta ETFs in existence, holding billions of dollars under management. The underlying logic is the assumption that CAPM is wrong and that low-beta stocks outperform high-beta stocks – with less volatility.
Philip Tetlock and Dan Gardner, in their book Superforecasting: The Art and Science of Prediction
Foresight isn’t a mysterious gift bestowed at birth. It is the product of particular ways of thinking, of gathering information, of updating beliefs. These habits of thought can be learned and cultivated by any intelligent, thoughtful, determined person… And never forget that even modest improvements in foresight maintained over time add up. I spoke about that with Aaron Brown, an author, a Wall Street veteran, and the chief risk manager at AQR Capital Management, a hedge fund with over $100 billion in assets. “It’s so hard to see because it’s not dramatic,” he said, but if it is sustained “it’s the difference between a consistent winner who’s making a living, or the guy who’s going broke all the time.” A world-class poker player we will meet soon could not agree more. The difference between heavyweights and amateurs, she said, is that the heavyweights know the difference between a 60/40 bet and a 40/60 bet.”
Forecasts aren’t always right, but they’re not always wrong either. The challenge is separating the wheat from the chaff, which starts by recognizing that demonizing all forecasts as worthless is the intellectual equivalent of arguing that all forecasts are right. As usual, the truth lies somewhere between the two extremes.