Harry Markowitz won a 1990 Nobel Prize in economics for efficiently passing the buck — make that bucks. He was honored for developing a mathematical formula that helps investors maximize profit and minimize loss in their portfolios. After an exhaustive analysis of financial information, Markowitz’s procedure allocates a person’s stash of cash to an array of assets, with more money going to better bets.
Many banks rely on this or similar investment approaches, warning customers to avoid picking investments intuitively. Yet Markowitz, now at the University of California, San Diego, followed a hunch in 1952 when he split paycheck contributions to his retirement account equally between stocks and bonds.
Economists call this simple approach “1 over N,” distributing money evenly among the number of available investment options, the Ns. The 1/N strategy is also called “naïve diversification,” a presumably second-rate alternative to crunching the numbers and calculating gain and loss probabilities for each potential investment. Nonetheless, many people with stock-and-bond retirement accounts opt for an even split.
As a young economist, Markowitz just wanted to avoid future regrets about fouling up his nest egg. “I thought, ‘You know, if the stock market goes way up and I’m not in it, I’ll feel stupid. And if it goes way down and I’m in it, I’ll feel stupid,’ ” he recalls. “So I went 50–50.”
Still, the gut-level appeal of that uncomplicated tactic hasn’t stopped him from investing nonretirement funds according to a modified version of his more complex, Nobel-winning formula.
Welcome to the two-sided world of economics, where complexity and simplicity, like star-crossed lovers, can’t get along and can’t leave each other alone — even within the same person. In the wake of worldwide financial turmoil that blindsided most financial specialists, economists’ mathematically brawny decision formulas now face a determined challenge from 1/N and other upstart rules of thumb. If David’s scrawny tactics fell the academic Goliath in this brawl, economics could fundamentally change.
Markowitz and other traditional economists want to harness huge amounts of information to find optimal solutions to problems such as how to invest money. These academic prophets of profit regard people as members of a logically consistent, selfish species dubbed Homo economicus. From this perspective, people try their darndest to solve complex risk-benefit equations in their heads when making money decisions. Mental shortcuts are often used to avoid such high-level math, but the shortcuts usually mean less money.
Lobbing a conceptual grenade into the ring are champions of Homo heuristicus, a harried creature who makes quick, surprisingly effective choices with sparse information (SN: 7/5/08, p. 22). Such “fast and frugal” tactics are now giving traditional economists’ sophisticated decision models a run for their money, research suggests.
From investors vying for a financial edge, to business managers trying to target loyal customers, to high-powered entrepreneurs looking for profitable business locations, new studies celebrate the efficiency and power of mental shortcuts. In uncertain economic situations where time is precious and information incomplete (of which there are a lot these days), heuristics can quickly narrow down a bunch of choices to a few good bets, says economist Nathan Berg of the University of Texas at Dallas.
“The economic environment constantly changes, and the whole menu of choice options that economists traditionally study isn’t immediately visible,” he asserts. “People can make better economic decisions by using simple rules of thumb to limit the number of choices they consider.”
Keep it simple
A major international insurance company found that lesson out the hard way. The head of the firm’s investment department attended a recent lecture about the power of distributing money evenly among assets, given by psychologist Gerd Gigerenzer, director of the Max Planck Institute for Human Development’s Center for Adaptive Behavior and Cognition in Berlin. Skeptical but intrigued, the insurance honcho returned home and reanalyzed his company’s investments from 1969 to 2009.
To his surprise, a 1/N portfolio would have made more money in that time than any of the complex strategies that his department had employed. Naïve diversification requires periodic reassessments of which stocks and other assets to include in a portfolio. Yet regardless of how the insurance official realigned the portfolio, 1/N came out ahead.
He told Gigerenzer about the results, asking that his identity and his company’s name be kept secret.
Many firms pay dearly for complex investment packages that calculate risks and benefits of an array of potential assets based on each one’s previous performance. Others devise secret investment formulas. “These approaches do well at predicting the past,” Gigerenzer says. “But they have problems predicting the future.”
In an economic world in perpetual flux, elaborate investment models that explain a bevy of historical trends can identify some genuine moneymaking opportunities but mistake many accidental and random financial patterns for good bets, Gigerenzer argues. By ignoring past financial information, 1/N misses some profitable buys but rebounds nicely by spreading out investment risks without throwing money at a lot of illusory prospects.
Gigerenzer was inspired by a 2009 study led by economists Victor DeMiguel of the London Business School and Lorenzo Garlappi of the University of Texas at Austin. Using 40 years of data from the U.S. stock market, DeMiguel and Garlappi found that 1/N portfolios consisting of either 25 or 50 stocks usually generated greater returns than 14 complex models for investing in the same stocks.
Given a portfolio of the same 50 stocks, an investor would need to wait 500 years before Markowitz’s Nobel-winning formula yielded superior returns to 1/N, the researchers estimated.
Markowitz remains skeptical of the findings and plans to closely evaluate the team’s calculations.
Naïve diversification works well for large portfolios but often backfires when applied to a few assets, says Richard Thaler of the University of Chicago, a leading behavioral economist and critic of Gigerenzer’s approach. Financial disaster looms when people put equal amounts of their life savings into a handful of stocks, especially if those people invest in a company they work for, Thaler says. Think Enron or Bear Stearns.
Thaler, who advises a British government agency that provides economic policy suggestions to Prime Minister David Cameron, says that simple rules of thumb such as 1/N lead to thinking blunders.
“In many cases, individuals make pretty bad decisions that they would not make if they paid full attention and possessed complete information, unlimited cognitive abilities and complete self-control,” says Thaler, coauthor of Nudge, a 2008 book arguing that institutions and governments should steer people’s choices in directions that would boost personal health and wealth.
Less effort, more money
Yet when given the opportunity to peruse a lot of financial information before choosing investments, affluent customers of an Italian mutual bank say “no, grazie.” These savvy investors consider only a few key pieces of information when buying assets, Berg and his colleagues contend. In their view, Italians seeking a good return on the euro need a complex investment analysis like they need a plate of overcooked pasta.
“If rationality means you have to assess all possible trade-offs before making an investment decision, then that’s a nuts definition of rationality,” Berg says.
His team randomly recruited 15 bank customers who completed computer-administered investment tasks at mutual bank branches in and around Trento, Italy. Participants held bank deposits of at least 40,000 euros and consulted with bank-employed financial advisers.
Investors rank three or four investment features that, based on experience, can be used to pick the better of two potential assets, his team found. A pair of investment possibilities gets compared on the top-ranked feature first and, if possible, a choice is made. If not, the second-ranked feature gets considered, and so on.
In a typical case, an individual will choose the asset deemed least risky, because risk of possible losses represents that person’s most valued investment characteristic. If neither choice is excessively risky, the investor will select based on characteristic No. 2, taking the asset considered likely to yield returns more quickly. If that doesn’t produce a clear winner, feature No. 3, the asset with lower brokerage fees, receives consideration. If fees are comparable, the choice is random.
In another computer task, participants similarly considered risk and a few other investment features when deciding how to allocate money to six investment categories, including stocks and government bonds.
Bank clients said that brief, nontaxing deliberations informed their real-life investment verdicts as well. No one tried to calculate probable profits and losses for every potential asset when putting cold, hard euros on the line.
Customer managers in large businesses take a similarly straightforward approach to crucial money decisions, apparently for good reason. Rules of thumb guide forecasts of which customers will remain loyal and which won’t. That’s a big deal, since flyers, special offers and other expensive marketing efforts are targeted at customers who are “active,” meaning likely to buy more products.
One popular forecasting tactic in the business trenches is called the recency-of-last-purchase, or hiatus, heuristic. Managers tag a customer who hasn’t made a purchase within a certain time window, say the past nine months, as inactive.
Researchers have developed complex statistical models, fed by exhaustive data on customers’ purchase patterns, to improve on managers’ intuitions. Yet customer-finding formulas in the business world have proven about as popular as pay cuts.
Customer managers have every right to hold on tight to their heuristics,says marketing researcher Florian von Wangenheim of Munich Technical University. To von Wangenheim’s surprise, he and a colleague found that the hiatus heuristic predicted a range of customers’ buying practices for three large businesses at least as well as, and sometimes better than, two complex models did.
In a 2008 paper, the two investigators called the hiatus heuristic’s unexpected power “a devastating result” for purveyors of complicated forecasting methods.
“It’s important to understand when speedy, simple heuristics are the right way to go,” von Wangenheim says.
Situations infused with uncertainty that require original thinking bring out the best in heuristics, Berg suggests. Dallas business entrepreneurs offer a prime example. In a pressure-packed milieu where millions of dollars ride on determinations of where to build a high-rise office building or a new grocery store, the entrepreneurs hitch their wagons to heuristics.
When searching for locations suitable for development or expansion, major business players could scan all affordable properties in Dallas, or even the world. Standard economic theory calls for assessing potential costs and benefits of every possible choice, meaning that low property prices or enticing amenities could compensate for erecting a new mall somewhere other than Dallas.
In interviews with 49 Dallas business owners and senior managers in charge of locating new building spots, Berg heard nothing about exhaustive property searches. Most business people considered no more than three possible sites, and often only one, in the Dallas vicinity before signing off on a project. Locations were usually discovered by chance, Berg reports in an upcoming Journal of Business Research.
In one case, a real-estate developer driving to a suburban golf course noticed an undeveloped tract of land that struck him as promising. He took a detour and drove around to get a feel for the area. His entrepreneurial radar went off. Over the next few days, the developer determined that a building project on the vacant land would produce at least a 20 percent annual return on initial cost within two or three years. That financial prospect sealed the deal.
Nearly all business owners chose expansion sites using a version of the simple real estate formula: “If I can get at least x percentage of return on my initial expense within y years, then I’ll do it.”
New ventures that resulted from this limited search-and-analysis strategy tended to be profitable, participants said. A smaller number of failures provided valuable lessons for evaluating new expansion sites.
Berg’s sample of entrepreneurs scoffed when asked whether they weighed costs and benefits of many potential sites to calculate the best possible choice. No one has the time or data to do that, the business people replied. In a volatile business environment, there’s no way to know whether such a determination would hold up in six months or a year, they added.
Like Dallas business big shots, experts in any endeavor tune out tons of informational noise and focus on a few powerful but imperfect cues to get the job done, Gigerenzer proposes. For them, rationality consists of creative experimentation that leads to the discovery of useful shortcuts, in his view.
Dropping an econ bomb
Gigerenzer and a colleague, Nobel laureate economist Reinhard Selten of the University of Bonn in Germany, took that sacrilegious argument to a high cathedral of financial orthodoxy last year. The two were invited to speak, along with a pair of business entrepreneurs, at a prestigious annual gathering of the economics and business departments at Germany’s Bielefeld University.
Gigerenzer and Selten bluntly told the assembled professors and marketing researchers to scrap their curriculum. Trash their textbooks, too. Start teaching about how people in the real world make successful, profitable decisions based on informed shortcuts, without having to consult mathematical formulas dropped out of ivory towers.
“You should have seen their faces,” Gigerenzer says.
Although the audience reacted as if a couple of drunken longshoremen had crashed a faculty mixer, the entrepreneurs invited to speak at the event defended Gigerenzer and Selten’s heresy. Everything the two businessmen learned as MBA students was of no use to them, they insisted. Each had amassed a fortune by trusting gut decisions that often worked but were difficult to explain.
At other lectures on investment heuristics, heads of large firms have told Gigerenzer the same thing. Often, he says, masters of the business universe sheepishly acknowledge having hired a staff member to generate complex mathematical covers for their heuristic-based choices, so that clients and investors wouldn’t worry.
Equally troubling, Gigerenzer adds, is a tendency for complex forecasting formulas to encourage corporate complacency. Many businesses aim to predict financial crises with these tools rather than prepare to react flexibly to the next unforeseeable calamity, he says.
It remains to be seen whether Homo heuristicus triggers an unforeseeable calamity for economics-as-usual. David used a slingshot to floor Goliath, but shortcuts for taking down academic giants are hard to come by.
By studying consumers’ rankings of phones, researchers recently tried to determine what rules of thumb the buyers used. The scenarios outlined below portray hypothetical rankings based on two features: brand and operating system.