Posts Tagged ‘uncertainty’

06-11-mcfadden

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The business press is having a hard time figuring out this one: the combination of unrelenting drama in and around Donald Trump’s White House and the stability (signaled by the very low volatility) on Wall Street.

As CNN-Money notes,

One of the oldest sayings on Wall Street is that investors hate uncertainty. But that adage, much like other conventional wisdom, is being challenged during the Trump era.

Despite enormous question marks swirling around the fate of President Trump’s economic agenda and his political future, American financial markets have remained unusually calm.

What’s going on?

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What investors actually hate is not uncertainty but, rather, threats to profits. And corporate profits have been growing spectacularly during the recovery from the Second Great Depression. Between the fourth quarter of 2008 and the first quarter of 2017, corporate profits rose more than 150 percent. Meanwhile, U.S. stocks (as measured by the Standard & Poor’s 500) increased by more than 200 percent. The rise in stock prices stems both from the growth in corporate profits and from gains in the stock market itself, which together have fueled further increases in the stock market with steadily declining levels of volatility.

As Ruchir Sharma admits,

Mr. Trump’s mercurial ways may be a source of great concern or indifference, depending on your ideological leanings. But Wall Street doesn’t seem to care one way or other.

What Wall Street cares about is not uncertainty but profits.

That’s the bottom line.

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Mark Tansey, “Coastline Measure” (1987)

The pollsters got it wrong again, just as they did with the Brexit vote and the Colombia peace vote. In each case, they incorrectly predicted one side would win—Hillary Clinton, Remain, and yes—and many of us were taken in by the apparent certainty of the results.

I certainly was. In each case, I told family members, friends, and acquaintances it was quite possible the polls were wrong. But still, as the day approached, I found myself believing the “experts.”

It still seems, when it comes to polling, we have a great deal of difficult with uncertainty:

Berwood Yost of Franklin & Marshall College said he wants to see polling get more comfortable with uncertainty. “The incentives now favor offering a single number that looks similar to other polls instead of really trying to report on the many possible campaign elements that could affect the outcome,” Yost said. “Certainty is rewarded, it seems.”

But election results are not the only area where uncertainty remains a problematic issue. Dani Rodrik thinks mainstream economists would do a better job defending the status quo if they acknowledged their uncertainty about the effects of globalization.

This reluctance to be honest about trade has cost economists their credibility with the public. Worse still, it has fed their opponents’ narrative. Economists’ failure to provide the full picture on trade, with all of the necessary distinctions and caveats, has made it easier to tar trade, often wrongly, with all sorts of ill effects. . .

In short, had economists gone public with the caveats, uncertainties, and skepticism of the seminar room, they might have become better defenders of the world economy.

To be fair, both groups—pollsters and mainstream economists—acknowledge the existence of uncertainty. Pollsters (and especially poll-based modelers, like one of the best, Nate Silver, as I’ve discussed here and here) always say they’re recognizing and capturing uncertainty, for example, in the “error term.”

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Even Silver, whose model included a much higher probability of a Donald Trump victory than most others, expressed both defensiveness about and confidence in his forecast:

Despite what you might think, we haven’t been trying to scare anyone with these updates. The goal of a probabilistic model is not to provide deterministic predictions (“Clinton will win Wisconsin”) but instead to provide an assessment of probabilities and risks. In 2012, the risks to to Obama were lower than was commonly acknowledged, because of the low number of undecided voters and his unusually robust polling in swing states. In 2016, just the opposite is true: There are lots of undecideds, and Clinton’s polling leads are somewhat thin in swing states. Nonetheless, Clinton is probably going to win, and she could win by a big margin.

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As for the mainstream economists, while they may acknowledge exceptions to the rule that “everyone benefits” from free markets and international trade in some of their models and seminar discussions, they acknowledge no uncertainty whatsoever when it comes to celebrating the current economic system in their textbooks and public pronouncements.

So, what’s the alternative? They (and we) need to find better ways of discussing and possibly “modeling” uncertainty. Since the margins of error, different probabilities, and exceptions to the rule are ways of hedging their bets anyway, why not just discuss the range of possible outcomes and all of what is included and excluded, said and unsaid, measurable and unmeasurable, and so forth?

The election pollsters and statisticians may claim the public demands a single projection, prediction, or forecast. By the same token, the mainstream economists are no doubt afraid of letting the barbarian critics through the gates. In both cases, the effect is to narrow the range of relevant factors and the likelihood of outcomes.

One alternative is to open up the models and develop a more robust language to talk about fundamental uncertainty. “We simply don’t know what’s going to happen.” In both cases, that would mean presenting the full range of possible outcomes (including the possibility that there can be still other possibilities, which haven’t been considered) and discussing the biases built into the models themselves (based on the assumptions that have been used to construct them). Instead of the pseudo-rigor associated with deterministic predictions, we’d have a real rigor predicated on uncertainty, including the uncertainty of the modelers themselves.

Admitting that they (and therefore we) simply don’t know would be a start.

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Leicester City was not going to win the Premiership—not by a long shot. Nor was the Republican nomination supposed to be handed to Donald Trump. And Bernie Sanders, well, there was no chance he was going to give Hillary Clinton a serious run for her money (and machine) in the Democratic primaries.

And yet here we are.

Leicester City Football Club, as anyone who has even a fleeting interest in sports (or reads one or another major newspaper or news outlet) knows, were just crowned champions of the Premiership, the highest tier of British football, after starting the season at 5000-1 odds. There really is no parallel in the world of sports—any sport, in any country. (By way of comparison, Donerail, with odds of 91-1 in 1913, is the longest odds winner in Kentucky Derby history.) And the bookies are now being forced to pay up.

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Similarly, Donald Trump was not supposed to win the Republican nomination. Instead, it was going to go to Jeb Bush and, if he failed, to Marco Rubio. (And certainly Ted Cruz, the candidate most reviled by other members of the GOP, was not supposed to be there at the end.)

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Finally, Bernie Sanders’s campaign for the Democratic nomination was written off almost as soon as it was launched. And yet here is—winning the Indiana contest by 5 points (when it was predicted he would lose by the same number of points) and accumulating enough pledged delegates to be him within a couple of hundred of the presumptive nominee.

What’s going on?

In all three cases, the presumption was that the “system” would prevent such an unlikely occurrence, and that the pundits and prognosticators “knew” from early on the likely outcome.

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So, for example, the winner of the Premiership was supposed to come from one of the perennial top four (Manchester United, Chelsea, Arsenal, and Manchester City)—not a club that were only promoted from the second division of British football in 2014 and last April were battling relegation (they finished the season 14th).

Pretty much the same is true in the political arena: neither Trump nor Sanders was taken particularly seriously at the start, and along the way the prevailing common sense was that their campaigns would simply implode or wither away. The idea was that the Republican and Democratic parties and nominating contests were structured so that their preferred nominees would inexorably come out on top.

There are, I think, two lessons to take away from these bolts from the blue. First, the “system,” however defined, is much less complete and determined than people usually think. There are many fissures and spaces in such systems that make what are seemingly unlikely outcomes real possibilities. Second, our presumably certain “knowledges” are exactly that, knowledges, which are constructed—in the face of radical uncertainty—out of theories, presumptions, blind spots, and much else. The fact is, we simply don’t know, and no amount of probabilistic certainty can overcome that epistemological gap.

So—surprise, surprise—Leicester City and Trump won, while Sanders has put up a much more formidable challenge than anyone expected from a socialist presidential candidate in the United States.

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Uncertainty, as we wrote years ago, is a real problem. Not a problem in and of itself. But it’s certainly a problem for modernist thinking.

That’s why, time and gain, neoclassical economists have attempted to reduce uncertainty to probabilistic certainty. It also seems to be why a team of scientists (neurobiologists and others) [ht: ja] have devised an experiment to show that we’re hardwired to experience stress under uncertainty.

So what’s the big deal? Everyone knows that uncertainty is stressful. But what’s not so obvious is that uncertainty is more stressful than predictable negative consequences. Is it really more stressful wondering whether you’ll make it to your meeting on time than knowing you’ll be late? Is it more stressful wondering if you’re about to get sacked than being relatively sure of it? De Berker’s results provide a resounding “yes”.

There are two problems with this approach. First, it ignores the possibility that uncertainty is a discursive phenomenon—that the stories we tell about uncertainty affect how we experience it. Second, uncertainty in and of itself need not be stressful. There are plenty of instances in which the outcome is simply unknown—from sitting down to write a paper to starting a new investment project, from starting a new relationship to participating in a political movement—when our uncertainty about what might happen is precisely what propels us forward.

Sure, turning over rocks that might have snakes hidden under them would probably induce stress. But that’s not because of the uncertainty; it’s because they’re snakes! (And, even then, I have herpetologist friends who would be delighted to find those snakes.)

Let’s just say I’m not convinced of the project to domesticate and control uncertainty, either by reducing it to a probabilistic calculus or to locate it in the brain (as part of some evolutionary process).

There’s lots of uncertainty out there but what it is and how we respond to it depend on the stories we tell (as I have written about many times on this blog). Uncertainty, in other words, is always and everywhere a discursive phenomenon.

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One of the most studied issues in contemporary economics is the effect of an increase in the minimum wage. But here we have a panel of so-called experts composed of mainstream economists who are uncertain—about whether employment will decrease or output will increase.

Ordinarily, I would applaud a health dose of uncertainty among economists, especially mainstream economists.

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But, of course, mainstream economists show themselves to be quite certain about things other than the minimum wage, such as the idea that the median American household, notwithstanding the small increase in household income, is actually much better off.

Just sayin’. . .

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I’ve been listening to and reading lots of financial pundits over the course of the past week—all of whom use the same lingo (the U.S. economy as the “cleanest shirt in the hamper,” the “deterioration in risk appetite” around the globe, and so on) and try to explain the volatility of the stock markets in terms of economic “fundamentals” (like the slowing of the Chinese economy, the prospect of deflation in Europe, and so on).

Me, I’m much more inclined to think of terms of uncertainty, unknowability, and “shit happens.”

Let’s face it: stock markets are speculative markets, in the sense that individual and institutional investors are always speculating (with the aid of computer programs) about how others view the market in order to make their bets—with fundamental uncertainty, unknowability, and the idea that shit happens. That is, they have hunches, and they have no idea if their hunches are correct until others respond—with the same amount of uncertainty, unknowability, and the idea that shit happens. And then all of them make up stories (using the lingo of the day and often referring to changes in the “fundamentals”) after the fact, to justify whatever actions they took and their advice to others.

That’s pretty much the view outlined by Robert Shiller. It’s all about stories characterized by uncertainty, unknowability, and shit happens.

In general, bubbles appear to be associated with half-baked popular stories that inspire investor optimism, stories that can neither be proved nor disproved. . .

the proliferation of such stories is a natural part of economic equilibrium. Successful people who value their careers rely on an instinctive sense for what pitch will sell. Who knows what the truth is, anyway?

As time goes on, the stories justifying investor optimism become increasingly shopworn and criticized, and people find themselves doubting them more and more. Even though people are asking themselves if prices are too high, they are slow to take action to sell. When prices make a sudden drop, as they did in recent days, people tend to become fearful, even if there is a subsequent rebound. With the drop they suddenly realize that their views might be shared by other people, and start looking for information that might confirm their belief. Some are driven to sell immediately. Others are slower, but they are all similarly motivated. The result is an irregular but large stock market decline over a year or more. . .

It is entirely plausible that the shaking of investor complacency in recent days will, despite intermittent rebounds, take the market down significantly and within a year or two restore CAPE ratios to historical averages. This would put the S. & P. closer to 1,300 from around 1,900 on Wednesday, and the Dow at 11,000 from around 16,000. They could also fall further; the historical average is not a floor.

Or maybe this could be another 1998. We have no statistical proof. We are in a rare and anxious “just don’t know” situation, where the stock market is inherently risky because of unstable investor psychology.

I would only add one correction: we always “just don’t know”—not just in anxious situations of volatility (such as during the past week), but also in more stable periods. In fact, we don’t even know if we’re in a volatile or stable period (until a new story becomes the common sense that what we’ve been through was volatile or stable) and we certainly don’t know how a stable situation becomes volatile (and vice versa).

Really, all we can say, when it comes to bubbles, is: shit happens.