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The writer is co-founder and co-chair of Oaktree Capital Management and author of ‘Mastering the Market Cycle: Getting the Odds on Your Side’
In the investment management business, it’s standard practice to come up with macro forecasts and bet clients’ money on them. And these days it seems as if investors hang on forecasters’ every word. While I’ve long expressed my disregard for this, I believe it’s now important to consider why making helpful macro forecasts is so difficult.
Forecasters have no choice but to base their judgments on models, be they complex or informal, mathematical or intuitive. Models, by definition, consist of assumptions: “If A happens, then B will happen.” In other words, relationships and responses. When I think about modelling an economy, my first reaction is to consider how incredibly complicated this task is.
To predict the path of the US economy, you have to forecast the behaviour of hundreds of millions of consumers, plus millions of workers, producers and intermediaries. A real simulation would therefore have to deal with billions of interactions, including those with suppliers, customers and other market participants around the globe.
Clearly, this level of complexity necessitates the use of simplifying assumptions. For example, it would make modelling easier to be able to assume that consumers won’t buy B in place of A if B isn’t either better or cheaper (or both). But what if consumers are attracted to the prestige of B despite (or even because of) its higher price?
Further, a model will have to predict how each group of participants in the economy will behave in a variety of environments. But consumers may behave one way at one moment and a different way at another similar moment. That’s largely because participants’ behaviour is influenced by their psychology, which can be affected by qualitative, non-economic developments. How can those be modelled?
Additionally, how can a model be comprehensive enough to deal with things that haven’t been seen before or in modern times? Consider the Covid-19 pandemic. What aspect of a pre-existing model would have enabled it to anticipate the pandemic’s impact?
Next, think about the limitations inherent in any attempt to predict something that can’t be expected to remain unchanged. “Stationarity” — the belief that the past is a statistical guide to the future — might be fairly assumed in the realm of the physical sciences. But few processes can be counted on to be stationary in the world of economics and investing.
Even if investors somehow manage to get an economic forecast correct, that’s only half the battle. They still need to anticipate how that economic activity will translate into a market outcome. This requires an entirely different forecast, also involving innumerable variables, many of which pertain to psychology and thus are practically unknowable.
So is it possible to create valuable macro forecasts? We can’t answer this without first deciding whether we think the economic world is one of order or of randomness. Put simply, is it entirely predictable, entirely unpredictable or something in between? I believe it’s in between.
Thus, I believe that the output from an economic model may point in the right direction much of the time. But it can’t always be accurate, especially at critical moments such as inflection points . . . and that’s when accurate predictions would be most valuable.
As I’ve long said, we have two types of forecasts: extrapolation forecasts, most of which are correct but unprofitable — as extrapolations are already reflected in market prices — and deviation forecasts, which are potentially very profitable but are rarely correct and thus generally unprofitable. The bottom line for me is that forecasts can’t be right often enough to be worthwhile.
Yet macro forecasting goes on. I don’t think of forecasters as crooks or charlatans. Most are bright, educated people who think they’re doing something useful. But self-interest causes them to act in a certain way, and self-justification enables them to stick with it in the face of evidence to the contrary. Many will blame unsuccessful forecasts on having been blindsided by random occurrences or exogenous events. But that’s the point: why make forecasts if they’re so easily rendered inaccurate?
Ultimately, we can’t know the future, so the proper goal of the investor is to do the best possible job in the absence of that knowledge. This means focusing on areas where one can gain a potential knowledge advantage — such as companies, industries and securities — and recognising the difference between forecasting where we’re going and knowing where we are.
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