Until recently, it seemed like stock prices were beginning to look a bit frothy. Concerns about the strength of company profits sent the market tumbling in May, while commodity prices continued their recent slide.
A two-year sell-off on commodities, in turn, raised fears that global growth may be slowing—even though commodity prices are now essentially back to where they were at the beginning of 2017. The supply/demand imbalance for commodities, we were told, will eventually force prices higher—but only when the long run requires higher prices. Now it turns out that we were wrong: the long run has an inflationary bias that's basically fixed, and prices may rise even without significant demand growth.
We knew all this already, in fact. This is not new news. Oil prices moved down in late 2016 and into early 2017, partly because the global economy was slowing and partly because President Donald Trump was taking a very hard line on sanctions on Iran. Once President Trump stepped back from his threats to yank the United States out of the Iran nuclear agreement, oil prices turned upward. The real concern was that a trade war with China and Europe would hurt both the U.S. and global economies. In fact, the combination of higher oil prices and, in particular, retaliation from China and Europe—also caused in part by Trump—turned out to be much more costly than anyone had expected.
It's true that the economy has continued to grow, despite concerns about inflation and trade. But when U.S. manufacturers complain about strong competition and insufficient exports, they are basically complaining about the much stronger dollar, which is now approaching levels last seen a decade ago. And that means a lower dollar—which would make American exports cheaper, and make U.S. firms more competitive—is almost certainly overdue.
It wasn't that economists were blind to this. Even a few months ago, most—including the International Monetary Fund—gave a better-than-50 percent chance of a trade war. Yet the key variable that made the difference between continuing to argue that this would not cause a trade war, and raising the alarm is the fact that the risks of such a trade war appear to be higher than before.
Which brings us to Jeremy Grantham, manager of GMO, a $150 billion asset management firm that specializes in asset allocation. The American economy is, even now, dominated by low-wage workers and households that keep a big chunk of their income in savings, not income from work, Grantham argues. And while such households may be able to withstand small changes in living standards—greater availability of safe-deposit boxes, say—it's hard to imagine how they can survive a big slump in the middle class.
What's more, while the U.S. is quite dependent on export markets, it is also quite dependent on imports, in any case: "Farms, with their enormous inputs of capital, farmers get foreign markets very much, never mind the resulting incentives they have to improve productivity. Consumer goods producers also need markets outside the U.S."
Grantham points out that, when you start adding in the cost of imported finished goods, foreign-made items seem even more competitive. That's largely true, and you don't have to look very far to see the evidence. Just yesterday, we learned of Caterpillar, which has been a huge beneficiary of the Trump administration's pro-agriculture policy, announcing that sales will fall 25 percent in 2019. Like a number of other big American companies, Caterpillar will have to try harder to get the U.S. to pay a fair price for its products. This has two implications: First, the administration's trade actions—which are under sustained attack in Congress—may have weakened its leverage. Second, the possibility of a recession suggests that the discount in domestic production that has been depressed by the dollar is beginning to revert to the norm, which can't be good for the national economy.
Of course, predicting the future is fraught with peril. But if you believe in a dynamic economy that adapts both to domestic and foreign conditions, and has a rudimentary understanding of how capital flows, the signs are all there. Whether you agree or disagree with those signals—and some of those signals are very good, by the way—the market is probably ready to change direction.
This week, stock analysts at New York brokerage firm JPMorgan Chase & Co. released a study of a stock that hadn’t been publicly traded in two years. As my Bloomberg View colleague Tobin Harshaw noted, it did not in fact exist at all.
Truth be told, I didn’t fully grasp the complexity of this question until I looked into it a bit more. Although there’s a great deal of stock analysis that will never cross my desk because there’s no stock to analyze, I’ve long been fascinated by something I’ll call “stock theory” and has informed a lot of my writing about stocks, economics and finance. It’s basically the practice of asking a bunch of questions and trying to derive a plausible conclusion.
The problem is, you never quite know what you’re looking at. Stock theory, as I understand it, has two parts:
The first is taking stock information about a company and about the business environment in which it operates -- and gauging how this relates to actual financial performance. The second is extrapolating this to a more concrete (in hindsight) estimate of the future.
This is usually done by looking at a very large sample of existing stock data, or at the earnings reports of large firms that use a model with a high correlation with the firm’s future prospects. As a basic example, you might think it helpful to look at the corporate balance sheets of any two companies in the S&P 500 index. Then you might do the same for all the large bank companies. Then perhaps the construction and shipping companies in the Dow Jones Industrial Average, and finally another few hundred companies.
The idea is that you can give some objective approximation of what a company in a given sector -- say, oil and gas drilling companies -- might look like, assuming that the firm’s management has done everything within its power to make that possible. But everything isn’t necessarily the same.
For one thing, management does not strive for any particular level of efficiency. If an oil company needs a certain amount of rigs or wells in order to run efficiently, then that is what it will get. (Unless, of course, you make a bunch of money betting that the companies involved are about to overbuild.) If companies cost a lot to operate, that might make up for the high costs of locating and operating new wells. If they need a lot of employees, that is the price they can pay.
All in all, I’d say you have a pretty good idea of what the underlying cost is -- if the company operates a particular way -- but the actual cost will depend to some extent on what it can do with the money, and to a considerable extent on which firms compete for that money.
A second point is that you cannot use the costs associated with building oil and gas wells to derive the likely profits of a company in that sector. Suppose, for example, that you want to look at Chevron Corp. versus a smaller firm that has only one successful well. Chevron will have to build a lot of wells to come close to making money; it will depend on others to do the same. Another way to look at it is that both oil and gas wells cost a lot to build, and most large, profitable companies need a large number of wells in order to make money.
Some useful aspects of this approach can be taken away from it. It is possible to estimate the firm’s future profits. But understanding that is not the same as being able to calculate the exact net present value of the profits the firm is expected to generate. (At the moment, for example, Chevron pays about $7 billion a year to pay dividends, while it earns about $20 billion.) A final drawback is that this approach can be very badly calibrated. A well under-performs; the firm gets way overpaid, or poorly paid; it meets a default.
Aspects of this approach are clearly more sophisticated than others. But all of them, in their own way, are akin to looking at a company’s financial statements. All of them assume that the company is rational and does its own thing, and all of them depend in part on a strong correlation between the income reported and what the company actually does. None of them are very satisfying.
Yet this hasn’t stopped economists and investors from doing it every day. And it hasn’t stopped them from promoting a model of stock analysis that perfectly embodies stock theory.
What we need is a broader view of the financial markets that doesn’t rely on financial statements or, more broadly, on a definition of a stock that defines what makes it interesting to invest in.
Stocks are going gangbusters. This happened over the weekend. But what is it, exactly? Does it matter?
First, the basics: As the chart above shows, indexes have done remarkably well this year, particularly the S&P 500 Index (pictured at left). The big forces at work are the U.S. economy -- and its president -- and global central banks, in particular the European Central Bank and the Bank of Japan.
GDP is also going well, driven mainly by private consumption, with a little help from government spending. Meanwhile, with short-term interest rates still super-low, investors have borrowed money cheaply and put it into stocks. That doesn’t mean there isn’t real money out there, but it does mean there is a lot more of it.
What the investment landscape really means is that, barring a collapse in economic activity, financial markets will ride out the very slow growth in incomes that will likely be the rule of the day for the foreseeable future.
Why do that? For one thing, stocks are basically an alternative currency. Companies are simply earning less and less in real terms as the economy shifts toward consumption and away from investment, and there’s a lot less room to move other ways than there is in cash. For another, though real interest rates are super-low, inflation continues to rise, making fixed-income investments unappealing.
Falling rates, in other words, have a lot of utility as an inflation hedge, making stocks attractive to very rich people (when they’re not trying to trade into stuff that will go down in value) and everything else. And so stocks do exceptionally well at a time when people are willing to invest a lot of their money in anything.
Today, of course, this is happening even as financial regulation is being loosened. It’s also happening at a time when there’s a reassessment of the usefulness of stocks as a store of value.
Although stocks have performed quite well lately, as Yglesias points out, they’ve done a lot worse in the recent past. And that compares to … most other forms of wealth-holding.
So how does that match up with technical analysis? The basic theory of technical analysis is that stocks are going up as a function of the stock-market capitalization. That’s a sign that a lot of investors are piling into stocks.
As Yglesias notes, this makes sense if the idea is that this is a speculative boom. But the measured rise in the stock market (shown above in red) has been modest compared to economic growth (in blue), and monetary policy (in red) has been very accommodative.
The stock market might be going up faster than the economy, but it’s not much faster than “normal” economic growth. And when compared to value, there’s not much more reason to think that the rally is speculative. Indeed, according to Andrew Baggarly’s analysis, the market is way, way overvalued, which means there is not much more to buy here.
As a result, you don’t want to buy much. You might want to consider selling, but most sensible observers expect stocks to go down further before reaching any meaningful sustainable support. What’s more, you need to realize that there’s something weirdly risky about trying to make money in stocks -- people mostly need some meaningful correction to reduce their exposures to this asset class, so a healthy correction might push the market lower, even as investors continue their cheerleading.
So technical analysis is what is going on right now. But it’s not very useful. The question is whether something else is going on, and whether that is also going to play out.
There’s a new wrinkle in interest-rate policy that, if it were accepted as the gospel truth, would be excellent news for the stock market. No, I’m not talking about Jerome Powell’s verbiage on “forward guidance.” (Although that’s pretty good.) Rather, I’m talking about what has become a new standard of central-bank analysis for financial markets: the so-called “fundamental analysis” of big data.
For years, central bankers have been more or less fascinated with the ways in which information technology will change the way we allocate capital -- not just the use of data analysis but also how markets work. In recent years, they’ve been about as captivated as a child with a child’s robot. They’ve used a variety of tools -- including big data -- to find new ways to explain how asset prices interact, and how economic models are basically the same whether the markets use information technology, or in the absence of it.
But they’ve never really applied this technology to prices in an age in which there’s just a ton of information available about all sorts of things, many of which haven’t been thought through in a systematic way. A generation ago, more or less the same approach would have meant extreme caution: You’d find little else but accurate and reliable economic data. That’s changed radically over the last two decades, but as yet we’re still not in a condition where that approach is practical.
Thus the trouble central bankers have had with interest rates. Central bankers used to be stuck in the old model: Prices move very slowly, so every big change in the way the economy worked had to be considered backward-looking rather than forward-looking. Until the Internet revolution blew all that up, and until the fake-news movement became so robust that actual economic news often made little news and the networks couldn’t resist promoting it on their air.
In essence, the mainstream consensus for central-bank policy has been based on a quasi-transactional: “Why would prices actually move in the way the price-makers say they do?” Again, the problem is not with the most recent research: It’s just that the model no longer works.
And so, according to the consensus view, “forward guidance” is essential. It’s called “forward guidance” for a reason: It requires the use of some of the more basic theories that are supposed to work in the real world. If this theory were wrong -- and most of it is -- it could throw the current situation into disarray.
The increasing skepticism over forward guidance has become virtually obligatory in some circles. Even Janet Yellen made it clear in recent testimony that she’s looking for “evidence that what interest-rate policy does is really relevant.” The last thing she wants to do is to be in any way associated with decisions that sound too close to “irrational exuberance” or “irrational exuberance” 2.0.
But the critique of forward guidance is also a critique of fundamental analysis -- which has always made itself heard in monetary policy. In 2008, I wrote an article about how many of the classic central-bank theories had been wrong about the consequences of collapsing real estate prices. And now I’m seeing similar work being done about forward guidance.
I think a very rough and early reading of the evidence suggests that the fundamental approach works. It has a better track record than monetary policy in general, since forecasting that’s preceded by a careful evaluation of historical trends and other factors in the real world is often better than it sounds.
But the problems with economic forecasting aren’t just theoretical. They’re also practical problems. And so even if central bankers have to abandon the very old belief that prices move slowly, in practice we’re probably not going to use that new normal. Economists are probably just going to have to settle for “normal” -- with respect to FOMC interest-rate decisions, that is.
Each trade in the stock market is very closely linked to the overall market -- that is, to the sentiment of investors. So how can one understand the meaning of various market statistics, in which correlation is of course negative?
Quantitative analysis would probably not even attempt to calculate a "measure of correlation," which, of course, there is none. But there’s now a fascinating article from Ryan Avent at Vox explaining how various market stats are impacted by political factors -- which is exactly what you’d expect.
Why does political analyst Nicholas Bala help determine the future trajectory of the stock market? As always, it all has to do with forecasting, and in particular, future government policy -- in his thinking, the three-month rolling average of unemployment and median income growth offers the best measure of “economic health,” as opposed to GDP growth, or something else, which he calls “total final demand.” So he turns to an ancient trading axiom: When the economic outlook in general and the outlook for government policy in particular are both at best somewhat bad, and in two separate ways deteriorating, optimism is warranted. (Hence “bets in the market”.) If they get better, all is well. If they don’t, we’re back to bad stuff.
Still, what’s really remarkable is that Bala was able to figure out the data needed to convert the data into an alternative vision of economic health, based on current political conditions:
Last year, I started noticing changes in the daily reactions to a few domestic economic indicators that were quite telling. They were broken down by month. The percentage of positive readings was falling. The percentage of negative readings was rising. And when I averaged the averages, they were moving in the wrong direction: They were moving in the direction of the average of negative readings. I also noticed that within months, two of these economic variables — average wage growth and average unemployment rates — were going to change dramatically, going up and down, respectively.
The article goes on to explain how Bala came to base his readings on political trend, and that these political indicators turned out to be much more accurate than those based on past trends. Bala is now an economist at Vanguard.
One problem: these charts show only seasonal effects -- June isn’t “bad” the month before September, nor is September “good” the month before July. On the other hand, Bala is talking about the long-term trend, so the charts aren’t just weakly seasonal.
I think Bala is probably right, and one has to assume that eventually he will be proved right. Or perhaps not: If Bala is right, we’ll be experiencing a very negative period. But that is not a one-way street, and we might have been in the midst of some truly unhappy times if the charts had suggested we were in the midst of something much worse than this.
The most useful thing about such charts is the opportunity to think about forward-looking indicators. But given the somewhat haphazard data collection and measurement methodologies used, one might be tempted to treat such graphics as merely useful for more strictly seasonal, directional purposes.
There’s only one surer path to investing success and big returns than to join the charmed circle of professional stock analysts.
So much so that, when the market crashes, it often defies Wall Street forecasts. This was the case in 2000, 2008, and again earlier this year.
Now, with Wall Street back at full strength, using machines to buy and sell billions of dollars of stocks a second, there is a new challenge: How can non-scalpers figure out the future of the market?
“Put simply, the dawn of artificial intelligence has spawned a huge number of new fund managers and hedge funds in the last two years,” Danny Rabin, a finance professor at Yale, said at a recent conference. “There are a lot of new players in the market with a ton of money to invest. The brainpower is just as enormous as the market capitalization, which means anybody who makes a prediction on where the market is going right now is facing a real challenge.”
That challenge is even more pronounced for those trying to divine the future of market predictions. AI and machine learning can accurately diagnose data, but that’s different from accurately predicting the future of stocks.
The basic problem here is that stocks are complex, interdependent systems that can move in a dizzying array of directions at different times. Each prediction is “derived” from the underlying assumptions and forecasting models of the preceding one.
Overlaying all of that analysis and polling produces just one forecast that, if it comes true, ought to produce a good profit. But the past record just doesn’t support that.
For all the knowledge at their disposal, investors can’t always get the only forecast, the market reaction to that forecast, to go right. The reason is that forecasts are built in the context of all the previous forecasts, and investors are notoriously poor prediction takers.
The reasons are complicated, but they boil down to greed and fear. During bull markets, investors are usually much more wary of miss-reading the market than they are of correctly betting against it. That helps explain why, with a few minor exceptions, the housing bubble looks like most people’s best-run bet of the 21st century.
The same condition explains the historical preeminence of the past-tense model (FRM), where investors make subjective choices based on historical patterns. Of course, we can’t live in the past forever, and the fact that a future market does often defy predictions now partly reflects how much financial markets have improved over the past 50 years.
Much of the newfound skepticism is also connected to a rejection of the idea that stock prices should be the starting point for the budget debate. Instead, proponents of Keynesian fiscal policy generally emphasize the global economy and the dollar (less often inflation), allowing stock prices to fall and taxes to rise. The fact that CBO projections and other measures of the economy are typically just a smidge less rosy than those of the Stock Market Journal makes Keynesians even more reluctant to use tax policy to break the debt ceiling.
That reluctance is now the default explanation for the constant movement in the yield curve. In conventional thinking, bonds have nowhere to go but up and stocks have nowhere to go but down. Today, of course, such backward-looking reasoning is as unlikely as a Top of the Flats jumper leaping toward the lobby. If nobody really knows the future, why should we worry about forecasting, even for Wall Street pros?
The problem is that the bond market is far more responsive to economic shocks than the stock market is. All this means that the bond market carries far more weight in determining the long-term direction of interest rates, the budget deficit, and the future behavior of the dollar. But the stock market is also the lodestar for the transition from consumption to investment, and this too is difficult to predict.
What’s a non-scalper to do? One solution would be to try to predict something that happens less than a third of the time—much less than the last economic expansion.
Finding this kind of change as a permanent sign of prosperity would, in theory, make forecasting all the easier. If only there were something else to predict—like the how-much-cash-exhausted-your-credit-card-earner ratio.
As a general rule, economic analysis is happiest in books. Investing in particular needs more pages per page. Too many models take their human companions for granted, neglecting those most capable of interpretation.
So even with all the new data coming out from stocks, we turn to Terry Haines, co-founder of Serra Capital, for an explanation of what he sees coming. From this archive post:
I should state my own humble contribution to the debate.
I have often said that the best way to forecast the path of the stock market is to predict the average earnings growth rate in the next year. If the earnings growth rate improves over time, the stock market is going up. If it worsens, then sell stocks. It's an odd stroke of luck, but I think it works.
Some market gurus have said the "EPS is the market," so there is some truth to that. But the logic of this approach is a bit odd. You would think that investors would prefer a model that suggests that earnings growth is now terrible, and that the future is likely to be worse, than one that suggests that it will be better. In any case, this approach, while laudable in theory, does not always have significant implications for the current market.
Data is fundamental, but there’s nothing wrong with taking technical and anecdotal data into account as well. That’s the lesson of the U.S. stock market, where numerous factors -- some well known, some less so -- drive the path of the markets.
One example is John Flannery’s resignation as CEO of General Electric Co. A key reason for Flannery’s woes was that he had failed to understand that the company, the sector it operates in, and the dynamics of it can’t be analyzed as if through a simple set of lenses.
QuickTake The Wall Street Fleecing
It is worth remembering that the stock market is a real market. It’s a negotiation, not a pure market in which people generally sell and buy for all sorts of reasons. In addition, investors exchange information about stocks and asset classes all the time, as they check market information and try to make sense of it. This is not a place where hedges against failure (the exact opposite of the expectations-based trading strategies that have dominated modern finance) and game theory about whether a stock goes up or down really come into play.
For all of these reasons, predicting the direction of stock prices involves some combination of facts and intuition. Not every person who operates in the stock market has the resources, knowledge or inclination to sift through every single data point available, assess what it means, and decide on a course of action. That’s true for people who buy and sell shares of companies directly -- but, by definition, it’s also true for most participants in the broader market.
Since market participants can’t agree on the best way to invest in a given area, various theories, projects and foibles emerge, producing variations on the standard market rule. Sometimes people take things out of proportion; sometimes they overreappraise or misinterpret some aspect of the underlying market. And sometimes some sort of overconfidence emerges that leads some groups of investors, or market segments, to be more sensitive to an unusual event than others.
Sometimes the elements combining to give rise to “market overconfidence” are visible as well. Maybe, for example, an overconfident group of investors starts buying a certain stock instead of some else. Or maybe one group of investors, not an entire market, determines that a particular sector is about to go on a rampage. Or maybe an ahistorical breakdown in data reporting leads to wildly optimistic predictions about an expected stock-market boom.
There are many things that can go wrong; you and I both know that. But it’s not possible to say in advance how markets are going to respond to such indicators, and it’s not right to conflate bet-the-company strategy with things such as good corporate earnings.
Many people who believe stocks are in the midst of a long-term decline have good reasons to do so. These reasons have nothing to do with “greater-than-average probability” -- market overconfidence is just an easy convenient explanation for trouble. Those who stand to benefit from a coming bull market should pay attention to macroeconomic indicators such as unemployment, interest rates, real GDP, and the yield curve, but they shouldn’t place any heavy reliance on them.
Lindsey Bever, a market strategist at Haverford Trust, an asset management firm, seems to think it’s foolish for anybody to buy stocks based on fundamental analysis alone. In fact, you can’t even buy a stock that is based on fundamentals alone.
Obviously, this is nonsense. Buyers of stocks are rational and, by definition, any such rationality comes from their “intrinsic value,” which is really the most basic psychological equation. Besides, people cannot yet define fundamental analysis, for better or worse.
But understanding the sources of this nonsense is a lot easier if you accept a basic premise: that most fundamental analysis is wrong.
Take the prospectus for Twitter (TWTR), as one example. Twitter has tried to launch a line of financial stocks, both in the U.S. and in Hong Kong. The idea is to pretend that Twitter (which actually sells stock itself) isn’t a company at all, just a service that exists to feed you information. So, for example, the prospectus doesn’t mention the company’s margins, profits, revenue or market value — it just says that “Twitter’s revenue growth rate has been trending higher.”
All this might be true, but is it relevant? Real money wants stocks that are growing at a good clip — perhaps up to 30 percent a year, perhaps much more — and even though Twitter doesn’t have those kinds of metrics, they’re supposedly worth more than just another big-name service. So, people see those numbers and decide that Twitter is worth much more than it actually is.
You can buy Twitter’s stock in the U.S. and Hong Kong. But that requires a bad understanding of financial statements. The financial statements tell you how much a company makes and reports those profits and sales in the way that people understand them.
The interesting thing about these comparisons is that there really isn’t a good explanation for why a company can sell itself for more than the advertised value. If the company is really growing by 30 percent, and its market capitalization is $1 billion (and it’s generating $100 million in profits), it’s certainly not trading for $1,000.
Some simplistic, I won’t deny it, rule for valuing companies (which this isn’t) is that they are worth the sum of all their parts — or, to put it another way, that if they have a value, that value would be somewhere between the number on the current balance sheet and $100 million profit. But that’s not a universally recognized guide to valuing companies, and it doesn’t explain why you could plausibly buy Twitter’s stock.
So, fundamentally, that prospectus should give you no idea of how much of its value you can use as an approximation when making a purchase. You simply can’t buy any stock based on the possibility that any part of the balance sheet might be ignored.
Even if you could, another key problem is that nothing in the prospectus actually tells you where the public can buy shares or how it would trade. Some investors might prefer to buy individual stocks, and they could do so, but they can’t trade them. If you were worried about what the underlying public perceptions were about Twitter, it would be useful to have access to its financial statements. At least, it would be worth making an effort to access them.
Buyers of stocks often create artificial demand. This works for good reasons — if we have individual stocks in our 401(k)s, it can be advantageous to buy them for diversification’s sake — but it can also make companies look overvalued.
Another problem is that many companies withhold key information, like market share or revenues, even from their own stockholders. As an asset manager, you really ought to know how your portfolio of stocks is doing. But a balance sheet doesn’t make a great-looking investment prospectus, and well-meaning investors who want good information about stock movements can trade individual stocks without ever hearing how your own money is doing.
Are the markets any less foolish for this fact? No, and they are getting worse. It’s really not too complicated to see that most fundamentally related concepts are wrong. We should be talking about better predictions of what the future will bring. But that’s not even close to a new paradigm.
I've spent the past few days at the summit of international scholars and thinkers on social and economic policy issues held in Aspen, Colorado. The top officials from the globe's largest economies are gathering in what passes for June in America -- well, not quite in America; although the gathering has been somewhat more substantive than in years past, discussions of trade are still dominated by the personalities of their respective presidents.
However, important stuff was going on across the board, as one of the other speakers pointed out: Empowering citizens -- progressives, middle-class Americans -- means empowering international economic discourse, too. "At the end of the day, it’s the economy that dictates politics," said C. Fred Bergsten, director of the Peterson Institute for International Economics, "and it’s politics that determine economic policy. But economics has always played a large role in those debates."
Put another way, policymakers of the United States and Europe are making the right policy choices, but only if international policy experts are able to explain to policymakers why. That's why some of the most interesting conversations this week were on questions of how to explain to policymakers the realities of global production.
There was a lot of discussion about a new standard for the division of labor that relies on some kind of uniform analysis of the components of output, rather than explicitly taking into account the economic effects of labor costs in various countries. (It's one reason why so many Americans are so impressed when they travel abroad; almost all of us are now on the other side of the globe.)
Even better, the talk hinted at an early stage of agreement in Europe that the dominance of high-tech industries in the twenty-first century is going to require a more complex and transparent global economic order. That could be a positive development for the economic and social welfare of the world's poorest regions, because it would mean that the kind of regulations designed to protect emerging and frontier markets would have to be easier to ignore and eviscerated.
The problem, of course, is that the opponents of international cooperation, as manifested in the Trump administration and more broadly in European politics, are still too powerful. That's not an exaggeration: This week in Aspen, one panelist, a Harvard economist, got this sentiment across with an analogy drawn from the realm of the absurd:
"The bottom line is, I don’t think I have to tell you that the social institutions that support strong public institutions are crumbling all around us," he said. "I don’t even think I have to mention the fact that we’re doing it with an increasing arsenal of tools, from politics to propaganda to mass media, to limit debate and flatten out the public realm.”
The problem is so bad that your guess is as good as mine as to whether this economist is right or wrong. If Donald Trump becomes, after Brexit, the president of Europe -- and Europeans do seem to think that's a future that they might relish -- he'll face the same pressures on his ability to function properly within that structure.
The fact is, it's never too late to revive international cooperation, even if a narrow group of leaders (of varying ideologies) remains vehemently opposed. After years of cutting their own deals and doing their own thing, it's possible that these people are just now realizing they should, given their diminished power, defer to the rules of trade and the rules of economics. And that's good news.