An Economist Walks Into a Brothel: And Other Unexpected Places to Understand Risk

by Allison Schrager

If you believe that the range of what can happen looks normal, you can make a quick estimate of risk. This is called a standard deviation, or volatility. Volatility tells you the range in which stock returns will vary most of the time.

Suppose you drove to the airport nine hundred times and estimate airport-travel volatility: the range of time it usually takes to get there is twenty to forty minutes. You’d also notice that a three-hour airport trip caused by a major traffic accident is less probable. It only happens 1–2 percent of the time. The traffic accident is called a “tail risk” because a three-hour trip is so unlikely it is in the tail of normal distribution.

These measurements are how people in finance define risk: they often assume a normal distribution and use volatility as the standard measure of risk.

But it does not tell you much about tail risk, which, though improbable, could be a catastrophic outcome, like the stock market falling 40 percent.

Typically, it is hard to measure risk in the movie business because it is nearly impossible to pin down a reasonable range.

The figure above shows the ratio of box office revenues (foreign and domestic) to production costs for all movies released and shown in at least one hundred U.S. theaters between 2008 and 2017. Any value less than 100 percent means ticket sales did not cover the costs of production. To cover marketing and additional costs not related to production, a good rule of thumb is that a movie must make double its production costs to be profitable.*

The figure is known as a skewed distribution.

The asymmetric shape shows how risky and unpredictable the movie business is. If there’s a normal distribution and the center falls on breaking even, there are an equal number of profit-making and money-losing scenarios, and most movies fall within a close range of breaking even. With a positive-skewed distribution, like the previous one illustrating the movie business, the range of possibilities is large; there are far more profitable scenarios than money-losing ones. Notice the long tail on the right, which covers the range of potential positive profits. A movie in this range could barely break even or return more than 1000 percent, or anything in between. All the profitable scenarios are equally unlikely. The odds are a movie will lose money because most are clustered in the smaller, loss part of the curve. Fifty-three percent of movies shown don’t earn back production budgets at the box office, and that’s assuming they are shown in many theaters (most movies aren’t). And even if they do make a profit at the box office, their earning potential looks like a complete crapshoot with only a few big winners. “Skewness” poses problems for measuring risk. For volatility to tell what will happen most of the time, you need a normal, symmetric distribution. If your distribution is skewed, volatility underestimates risk; it might only tell you what will happen 30–40 percent of the time. The long tail contains a huge range of possibilities; all are nearly equally probable and unlik...

It’s common for risk to be skewed; the symmetric distribution is called normal, but it is not always common.

Another reason measuring risk in Hollywood is precarious is that the data gets stale fast.

the thing about predicting the future based on the past. It works until it doesn’t, because the market (especially for movies) keeps changing, and estimates based on old data no longer tell you much of anything; what is difficult is knowing when you need to update your data. Often we don’t realize the world is changing until long after it has changed.

Most pictures aren’t worth much, but that great shot of a new baby, a celebrity kissing a new paramour, or a wedding can change fortunes on a dime. And that mostly comes down to being in the right place at the right time. The element of luck and timing means a paparazzo’s income is extremely risky, because it is so variable and unpredictable. They face the risk that one day they won’t get any pictures at all and on another they might stumble upon a celebrity eating breakfast with a new lover. The paparazzi have various ways of managing these risks, but even the best strategies are undermined by infighting and a changing industry.

Since the best shots come down to being in the right place at the right time, photogs often form teams or alliances to share tips and sometimes royalties to increase the odds or payoffs they’ll be in that place.

But the temptation to cheat on the alliance, to withhold an especially good tip or not share the money evenly, means any alliance is as fragile as a celebrity marriage. After all, getting a picture no one else has means more money. If a paparazzo is the only one who gets that life-changing shot, he or she has every incentive to cheat on the alliance, which in turn leads to bitterness among the photographers.

Financial economists separate risk into two broad categories: the first is idiosyncratic risk, or the risk unique to a particular asset. Suppose Facebook changes management; the future of the company is unclear, and the price of the stock might drop based on factors unique to Facebook that don’t impact any other stock.

The second kind of risk is systematic risk, or risk that affects the larger system instead of an individual asset. Systematic risk is when every stock rises or falls together because the entire market surges or crashes, as it did in 2008. Systematic risk events often happen because of a big economic disruption like a recession or an election result that people think will impact business. Systematic risks are harder to manage than idiosyncratic risks, and the downsides are potentially more dangerous. If the entire stock market tanks, you risk losing your job and stock portfolio at the same time.

Being able to spot the difference is important because it determines what the best risk strategy is (we’ll cover this topic in later chapters). For example, when you are looking to buy a house, the price might be driven by idiosyncratic risk (a new trendy feature like concrete countertops in the kitchen) or systematic risk (the whole market is hot and driving up prices). Discerning different types of risk can tell you if you are overpaying or if now is the right time to buy.

The way to manage idiosyncratic risk in finance is to buy lots of stocks.

You shouldn’t own stock in the company you work for because you’re exposed to a great deal of your employer’s idiosyncratic risk. For example, if you had worked at Enron and owned stock, you would have lost your job, your income, and your retirement savings all in one fell swoop when a major accounting scandal led to its insolvency.

The paparazzi also manage their idiosyncratic risk by spreading it around. This is what they are doing when they form an alliance or work in teams. Each photographer bears lots of risk based on how lucky he is that day: if he catches a celebrity with her latest paramour in public; if the celebrity leaves a trendy restaurant through the back door where the photographer happens to be standing. A paparazzi alliance pools their luck, reducing their idiosyncratic risk. This means more stable income because it increases the odds of getting the right shot. However, the incentive to cheat constantly undermines a paparazzo’s ability to reduce idiosyncratic risk.

Systematic risk is even harder to manage. To measure systematic risk, finance professionals look at the history of stock prices and see how much one stock price moves with the rest of the market. That produces a single number, based on this correlation, called market beta.

Idiosyncratic risk can be reduced easily by owning lots of different stocks—any other stock. But a stock that reduces systematic risk is especially valuable, because it is more rare and has the power to reduce risk for your entire portfolio. A stock that moves in a different direction or less strongly than the rest of the market has a low beta, which reduces your systematic risk and makes you safer, so it offers a lower expected return. Conversely, a stock sensitive to the rest of the market, one that goes up 15 percent when the rest of the market only goes up 5 percent, has a high beta. It amplifies systematic risk in your investment portfolio, so you’d only want to buy that stock if you are compensated for taking on more risk. It should promise you a much higher return. If you want to reduce risk in your portfolio, you need lower-beta stocks; if you want higher returns and are comfortable with lots of non-diversifiable risk, you want high-beta stocks.

Our lives are full of high-beta risky decisions.

say you are offered a job in construction; it has a high beta because construction jobs pay off when the economy is booming, but workers in the industry are the first to lose their jobs when there’s a recession.

To find out which Americans face the greatest systematic income risk, economists measured trends in American earnings when the economy was up or down since the 1950s using Social Security records. They noticed a U-shaped pattern with income and systematic risk, which indicates that systematic risk is more severe for both very low-paid and very high-paid workers, while people in the middle face less risk. It is not surprising that the lowest earners are also the most vulnerable when the economy falters. They work in industries with high betas, like retail, that tend to crash when the economy goes south. As a general rule, the more you earn, the safer you are. But there is an exception: when it comes to the highest earners, the relationship changes. For example, people who work in finance make ridiculous amounts of money, but they also often expect to lose their jobs when the economy sours (the more systematic risk you take on, the bigger your expected gains). Of course, few people feel sorry for them. Their income is so high, they can survive a layoff better than minimum-wage employees who live paycheck to paycheck and also risk being laid off in a downturn. But at least some of the high pay in the finance industry is compensation for the fact that the income of its workers is more sensitive to economic conditions.

If you take a job in the government, your income has a low beta; it is fairly stable, no matter what the state of the economy is. High-skilled people who work in government are often paid less than their private sector counterparts in exchange for assuming less systematic income risk.

When we face a risky situation, the desire to avoid loss can lead us astray from what financial economics predicts we should do. And this behavior sometimes means we make decisions we regret and lose even more.

“Playing good poker means only playing 12 percent of your hands. You can’t make money if you play more than 30 percent, and if you play 100 percent you’ll go broke every day.”

Winning at poker comes down to luck and skill. Luck is being dealt a winning hand. Skill is knowing how and when to bet, and having the discipline and ability to infer what other players are doing.

When we calculate risk, the rational way to estimate it is to think through all possibilities and weight them by how probable they are. If the odds add up to something we want, it is a risk worth taking.

Suppose you are deciding whether or not to go on a date with someone you met on Tinder. Based on your extensive dating experience, you figure there’s a 5 percent chance the date will be a total bust. You will spend the whole time being lectured about some politically motivated conspiracy theory, alternating with subtle put-downs about your age and appearance. There is a 60 percent chance your date will be perfectly nice, but there will be no chemistry and awkward, strained conversation. There is a 30 percent chance you’ll like each other enough to date for three months and then break up and feel indifferent about it. And there’s a 5 percent chance the date will be amazing and you’ll walk away thinking you met the love of your life. Deciding whether or not to go on the date isn’t just a matter of 35 percent good versus 65 percent bad. If you look at it that way, you’ll never leave home. We have strong emotions attached to each potential outcome. How we feel about, or value, each possibility is what economists call utility. The prospect of a good relationship gives us so much joy, or lots of utility, we are willing to go on what will probably be a bad date.

the observation that when we make a risky decision and weigh the different possibilities, how we feel about each outcome, not just its value (even if it is money), is what matters. In most situations, economists, like Bernoulli, assume we place less value on money as we get more of it. One thousand dollars is nothing to a millionaire, but it is serious money for someone on welfare. This diminishing utility of money explains why people are risk averse. They prefer certainty to a risk.

Prospect theory says when we weigh different options, the value we place on them depends on how much money we have when we start and if there is the possibility of loss. Humans aren’t just risk averse; they hate losing anything—from a $20 bill to a free T-shirt.

Amateur poker player Bob’s total wealth is $1 million. Phil’s total wealth is $10 million. Both are offered a gamble that changes the value of their net worth: A: a 50 percent chance to end up with $1 million or a 50 percent chance to end up with $10 million, or B: end up with $5 million for sure Kahneman and Tversky argue that Bob will happily take option B because there’s A: a 50 percent chance Bob gets nothing, and a 50 percent chance of increasing his wealth tenfold, or B: a 100 percent chance of increasing his wealth fivefold Just like Bernoulli predicted, the incremental value of another $5 million once Bob already has $5 million isn’t worth taking the risk; he is risk averse to this menu of possible gains. But when Phil, who has ten times what Bob has, is offered the same bet, he views it differently. From Phil’s perspective, his options are A: a 50 percent chance of losing $9 million and a 50 percent chance of losing nothing, or B: a 100 percent chance of losing half his net worth Faced with the possibility of certain loss or a 50 percent chance of losing nothing, Phil will take the risky bet. Prospect theory argues that humans are risk seeking, or willing to take a chance on even bigger losses and forgo certainty when offered a menu of loss scenarios.

Before prospect theory, economists assumed we were risk averse all the time. But in this case, Bob is risk averse because he only has money to gain, and Phil is risk seeking because most of his choices involve losing and none involve gaining. Reference points, or how much we have to s...

Even classical economics assumes it is worse to have been rich and lost it, than never to have been rich at all.

One extension of loss aversion that often results in needless risk taking and more loss than we’d normally tolerate is called the break-even effect. It was first identified by the behavioral economists Richard Thaler and Eric Johnson, who argue that when we lose money and have a chance to win it back right away, or even come out ahead, not only do we take more risk, but we take even bigger risks and thereby subject ourselves to even bigger losses. If we want to avoid loss, it would be better to just walk away. The break-even effect explains your conviction that the next hand of blackjack or the next coin in the slot machine will make you whole or win back your money and then some.

A later study of online poker gamers found similar results. Players were observed taking bigger risks when they were down and playing tighter, or betting less than 20 percent of the time, when they were up. The researchers also noticed that more experienced gamers, like Hellmuth (who only plays 12 percent of the time), were able to overcome this pattern and play consistently, whether up or down.

The more we see and deal with risk, the better we get at handling it. But our natural biases are always present. Overcoming them sometimes just comes down to self-control.

To be successful at poker or in any risky situation, you must not get too emotional or aggressive when losing.

You can also hone the skills you need so that when it really matters you stay calm and wait for the right hand.

Never Have Too Much of Your Own Money at Stake

He gives up some of his potential winnings to avoid having too much on the line that he can lose. We can all do this by tempering the risks we take, otherwise known as hedging (explained in more detail in chapter 9). It might be balancing a stock portfolio with bonds or not taking a bigger salary instead of stock options at work. The principle is the same: when you have less at stake to lose, you stay more rational.

Eliminate Extreme Downside Risk

In everyday life we can follow Hellmuth’s example by buying insurance.

We can buy insurance in case our house burns down, we are robbed, or we get in a car accident. And just like Hellmuth’s strategy, it offers peace of mind because there is a smaller cost to loss.

Remind Yourself, “This Is Just One Hand Out of Many”

Think of broad framing as playing the long game. For example, you shouldn’t look at your stock portfolio too often. If you are investing for the long term, a bad day on the markets, or even a few bad months, is only a blip. It is not the time to sell your stocks. Framing an individual risky decision as part of a larger gamble will help you think clearly and avoid overreacting to temporary loss.

Avoid Overconfidence to Maintain Focus

Hellmuth’s strategy illustrates why we might want to seek out different viewpoints and be open to friendships with people who don’t always share our opinions. Avoid group think by forming a team of people who approach problems from different perspectives at work or attempting a civil political discussion with someone who doesn’t share your opinion. This strategy can make you more aware of downside risks you need to protect yourself against so that you can see them more clearly.