If we were in pursuit of the crown for “world’s most cliched lead sentence,” we would be hard-pressed to go past, “We live in turbulent times.” For extra credit, we might couch it within a proverb or quotation of dubious provenance.

This illustrates the difficulty of saying anything original or interesting about the world of macroeconomics. When speaking about rapidly developing situations with lots of variables at play, it is tempting to fall into one of two traps—either making bold, faux-prophetic, rifle-shot predictions about how the future will play out, and in the process giving a hostage to fortune; or hedging interminably with a lot of “on the one hand this” and “on the other hand that” puffery to avoid ever coming to a falsifiable conclusion of any kind.

Bain’s Macro Trends Group seeks to avoid these traps by taking a systematic approach to exploring the known and unknown, unpacking implicit assumptions to make them explicit, and using scenarios to test how wrong a management team would have to be about those assumptions in order to act differently. We believe executives navigating uncertainty should take stands, not for the sake of proving how brave they are but for the sake of understanding how much they really believe in them.

Our role in the Macro Trends Group is to help our clients make sure they aren’t blindsided by external macro forces. Most of the time macro doesn’t matter, but when it does, it’s decisive. We don’t want our clients to get punched in the face by an unexpected macro force (or anything, really, but the rest isn’t our purview).

"We believe executives navigating uncertainty should take stands, not for the sake of proving how brave they are but for the sake of understanding how much they really believe in them."

It’s not surprising that executives find it daunting to keep track of every new trend. Underneath all the noise from headlines, the world really is in the middle of a structural transition, one that we’ve been following and outlining for more than a decade, at times at the risk of sounding like demented Cassandras. During structural transitions, it becomes much harder to see what’s coming.

We call this period The Great Transformation, because it’s a large-scale secular shift where the macro forces that shaped the last 30 years or longer are reversing. Globalization is giving way to post-globalization. Capital superabundance is becoming capital rationalization. Plentiful labor is turning into pressure to automate and the pressure to manage aging populations. Urbanization is being replaced by ex-urbanization.

These aren’t surface changes; these are foundational shifts in the operating environment. When the environment is this unstable, when the relationships between critical variables are in flux, relying on trend data from the last decade to make decisions becomes deeply problematic. The patterns upon which you have historically been able to rely on may not be relevant at all in the future. Our view is that the current US administration is pursuing a controlled demolition of the global system of the past 30 or even 50 years, so defaulting to expecting that structure will be misguided at best.

For that reason, among others, many companies have given up altogether on any explicit process of prediction. We think that’s a mistake, and not just because we enjoy full employment. And we say that with all humility, recognizing that we identified two out of the last one recession and that forecasts as a rule are a great way to be wrong. But forecasts are not the same as predictions.

Exposures are predictions

Even companies that think they’ve opted out of prediction are still making predictions, because they can’t avoid making choices, and every choice is an implied prediction.

Every market you’re in is a bet on that market. Every product, every customer segment you serve is a long position. And the markets you’ve chosen not to enter? Those are short positions.

Most companies don’t frame it that way, but hedge funds do.

"Even companies that think they’ve opted out of prediction are still making predictions, because they can’t avoid making choices, and every choice is an implied prediction."

If a hedge fund looked at your business, how would they map your exposures? Where are you long; where are you short? What positions are you holding—intentionally or otherwise?

Because make no mistake: Your business is a portfolio of exposures. Whether you articulate them or not, they’re there. And each exposure is a prediction. Unless a business wants to be frozen in place, it’s making forward-looking bets. Doing nothing is no more than doubling down on your current bets, which in a transition like today’s could be even more risky than change.

As we think about bets, take the example of an airline. Laying out their route network, choosing fuel hedging strategies, changing pricing structures—all of those actions imply predictions about regional demand, oil prices, customer price sensitivity, and so on.

A hedge fund knows exactly where its exposures lie and actively manages them. But most companies don’t even see them, let alone manage them. The bets are implicit. The predictions go unstated and, therefore, untested.

We think this is a missed opportunity.

The right way to use scenarios 

Some firms believe that they can let themselves off the hook when it comes to predictions by focusing on scenarios instead. Their goal is to transcend prediction by making choices that are resilient in multiple scenarios.

This is, of course, a great thing to do, when done well, but scenarios are not substitutes for predictions; they should instead work hand in hand.

An easy trap to fall into—and one we see quite often—is “the overbuilt central case.” This involves a tremendous amount of energy going into the “base case,” often without much thought as to whether it is supposed to be the most likely case, or merely the one in the middle. Or worse, the one that makes everyone comfortable and validates present strategy.

Then they torture variables up or down by 10% or 20% and call that the high case and the low case. Is the high case also the best case? Is the low case the worst case? Who knows?

We have worked with clients to get very specific about “corner scenarios,” or extreme but plausible scenarios that help clients think about “no-regrets” moves under any future, and what the paths to different futures could look like. This can be incredibly valuable. In addition to that exercise, however, it’s important to define what you believe right now. Otherwise, you risk the trap of thinking you and your team have a common perspective, but it’s not built on a shared understanding of the set of predictions that actually underlie your current actions.

Even during these valuable future-oriented scenario planning sessions, people are at work investing resources based on some implicit beliefs. They’re still making decisions, just without acknowledging the underlying view those decisions reflect. When these assumptions go unstated, they may not be shared. When this happens, risks can pile up.

For example, around the middle of the last decade, we saw a number of companies that were very long on China because of a collection of exposures, not just directly to China but to sectors like agriculture in Brazil that were also China-dependent. It might have looked like a hedge, but it was essentially doubling down on the same bet.

In sum, we’re not opposed to scenarios: They have a time and place, and that place is at the end of the process, not the beginning. We suggest:

• Start by surfacing the predictions you’re already making—aka mapping your exposures—with a focus on the ones with the biggest material impact on your business.
• Test your conviction in those predictions.
• Then use scenarios to specifically push extreme but plausible cases to test what would make you change your mind or actions.

If you’re wrong, what are you most likely to be wrong about? How soon could you go wrong? How would you know? And what could you put in place to catch that error so that it doesn’t become existential? 

"If you’re wrong, what are you most likely to be wrong about? How soon could you go wrong? How would you know?"

Do your exposures match your beliefs?

Mapping the exposures themselves is fairly straightforward, though getting the right level of abstraction is important.

When we do this work with a client, we aim to put together a two-to-three-page memo, not a massive deck. This will discuss the major markets but also major assumptions about how the world is working.

Going back to our airline example, the choice of how to manage fuel cost exposure implies some beliefs about the future. If an airline has chosen not to hedge fuel, it’s making a bet—implicitly—that fuel prices will stay flat or fall. Our role in this process is not to evaluate those choices but to make them explicit, to define them from an outside-in perspective like a hedge fund would.

Once the choices are on a page, the next conversation is whether they match the company’s actual beliefs or predictions (and if those beliefs are shared among the leadership and the company as a whole). Where there are gaps, why is that?

Some of the gap is about conviction. What do we mean by that? We mean how strongly you believe your prediction. Note that conviction is not the same as confidence, particularly not in the classic statistical sense.

Statistical confidence suggests the existence of sample data and testability. In macro, you rarely get either. There’s no “n” to speak of, just a series of often binary events: Will Brexit happen or not? Will the Fed cut rates? Probabilities are meaningless ahead of time and unverifiable even after the fact.

In contrast, conviction is personal. It’s based on experience, worldview, context, and judgment. Two leaders looking at the same facts may arrive at different levels of conviction. That’s normal, and it’s worthy of debate. It also acknowledges what all of our clients understand, which is that there is as much art as science involved in making decisions.

Critically, you don’t need complete conviction to act. The biggest difference between underperforming hedge fund managers and outperforming ones tends not to be their ratio of winning bets to losing ones. The difference is that great fund managers cut their losses early and let their winners ride.

In the same spirit, CEOs and management teams cannot expect to get every prediction and decision right, not in this moment of uncertainty and probably not ever (if they do, they make us look bad, so we hope they don’t). They must, however, understand the difference between a decision they are reaching with low conviction and one they are reaching with high conviction.

The former suggests an exploratory approach, some small bets and experiments that can help us test and learn our way to higher conviction; the latter calls out for going “all in.”

Famous investor Stanley Druckenmiller captures this insight well: “When I’ve looked at all the investors (that) have very large reputations—Warren Buffett, Carl Icahn, George Soros—they all only have one thing in common. And it’s the exact opposite of what they teach in a business school. It is to make large, concentrated bets where they have a lot of conviction.”

Companies need to have a shared set of predictions and an understanding about their conviction around those predictions to develop their strategies. Once you understand your predictions and conviction, then you can ask, “What assumptions would have to be way off for me to reach a different conclusion and how off would they have to be? Is that plausible?”

In other words, you don’t want a low, base, and high case; you want a case that you think is probably right and some stress tests to understand the consequences of being wrong. 

Accepting the inevitability of error

Even with the best intentions and clearest views, you’ll get things wrong. We certainly do!

That brings us to the qualities of adaptability and resilience.

Where your business is able to quickly change as circumstances do—because of things like technology, inventory, or organizational design—you can rely on adaptability to shift quickly when the call goes sideways.

Where your business isn’t as agile—because of footprint, regulatory regime, industry dynamics, or a multitude of other reasons—you’ll need resilience. It’s a form of insurance, and it’s expensive, which is why it’s the last action but a critical one in a narrow set of areas.

For example, setting up a second supply chain in a domestic market is a long and challenging process, but one that might pay handsomely in a world of increasing trade barriers.

None of that works if you don’t know what your bets are in the first place, or what would make you change them.

Rather than dismissing prediction, we think it’s a necessary first step—know what you are predicting, test those predictions for conviction, and then plan for mistakes through adaptability and resilience.

It’s not about being omniscient—it’s about being prepared. You won’t get every call right, but you can know which calls you’re making, why you’re making them, and how you’ll respond when the world moves differently than expected.

In times of transformation, clarity is a superpower. Use it.

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