For many years, a conventional assumption in economic models was that each person made financial decisions that would be most profitable for them. Former Cornell University professor Richard C. Thaler rocked the boat when he observed that not all financial decision making was so rational—that decision makers often acted against their own interests. Thaler, who won the Nobel Prize in Economics in 2017, and others established the field of behavioral economics, which looks to psychology and sociology to try to explain how preference, bias, and social behavior impact economies.
Scott E. Yonker, The Charles H. Dyson School of Applied Economics and Management, studies these deviations from rationality—which he calls the human element—in finance. Yonker witnessed the human element firsthand while working at an investment advisory firm before completing his PhD. When he started research as a student, much of the literature focused on individual investors and how their biases influenced decisions.
“I was more interested in managers,” Yonker says. “Because in my experience, they were also very biased. And the decisions they make impact a lot of people’s lives.” The Lynn A. Calpeter Sesquicentennial Faculty Fellow in Finance, Yonker has since uncovered key variables that could lead to improved models and best practices for the most powerful decision makers in the financial industry.
Using Public Records to Infer CEO Bias
Yonker says a lot of his research begins with what he calls, “this creepy data on people,” which he uses to understand managers’ decision making. In his dissertation, he asked how the backgrounds of CEOs impact an important decision—how much debt to take on to finance a firm, called the corporate leverage decision. Yonker looked up public records of CEOs’ personal habits, how they paid for or financed their homes.
“There’s a psychological theory called behavioral consistency,” Yonker says. “People tend to make similar decisions in similar situations. So I became a bit of a snoop and looked all of these people up.”
Yonker was able to show that the personal preferences of these CEOs did correlate to their professional decisions. “Some CEOs just don’t like leverage, even though there are tax benefits. By acting on their bias, they’re not maximizing the value of the firm,” Yonker says.
“There’s a psychological theory called behavioral consistency. People tend to make similar decisions in similar situations. So I became a bit of a snoop and looked all of these people up.”
In subsequent projects, Yonker used the social security numbers of CEOs to determine where they grew up. He then examined both the CEO labor market and CEO bias. He found that companies, even really large companies, are more likely to hire CEOs who grew up in the state where the company is headquartered. “It’s something like 35 or 40 percent of CEOs, so it’s a huge local bias. If it were random, it would be about six percent,” Yonker says.
Yonker also found that after distress in their industries, CEOs exhibited bias toward their home areas. “They tend not to lay off the workers in their home plants or cut their wages relative to everyone else,” Yonker says. “This finding—that where the CEO was born is going to help determine whether a community is successful or not—shows the randomness that can determine the success or failure of communities.”
Investments of Mutual Fund Managers, Correlated with Where They Live
Along similar lines, Yonker has shown that mutual fund managers, another of his research interests, over-invest in companies in their home states, concentrating their holdings geographically and raising risk for their investors. He’s also identified what he calls neighbor trades, by mapping where mutual fund managers live and how closely correlated their trades and holdings are to each other. Regardless of what firms they work for, Yonker found that trades and holdings of mutual fund managers in a concentrated geographic area were more closely correlated.
“So managers living in this one area of Chestnut Hill in Boston, for example, are more likely to have more correlated trades and holdings than with other fund managers in Boston,” Yonker says. “They run into people in the neighborhood, their kids go to the same school, that kind of thing, and they share information. There’s nothing wrong with that. If I buy something, and you then buy it, it pushes the price up, so it’s symbiotic.”
For economists, however, it’s an additional, previously untracked variable. “These social interactions, this human element, is influencing decisions in a way we wouldn’t have predicted,” Yonker says.
Studying the Impact of Registered Investment Advisers (RIAs), Bernie Madoff’s Line of Work
Yonker and his co-authors made headlines when they were able to assign a monetary value to the broader impact of the Bernie Madoff scandal, the elaborate Ponzi scheme which defrauded thousands of investors of billions of dollars. Yonker and his team determined the indirect but significant effects that spread beyond Madoff’s actual clients, amounting to investment withdrawals of $363 billion.
“He broke the trust of a lot of clients and then other people started pulling money from financial advisers who didn’t do anything wrong,” Yonker says. “The indirect costs were huge on the institutions, our trust in the SEC [Securities and Exchange Commission], and their ability to regulate.”
Madoff was an independent registered investment adviser (RIA), operating independently from large banks and firms. The number of independent RIAs has spiked in recent years, and their role in our economy is next on Yonker’s research agenda. He says these mom-and-pop kinds of companies manage 25 to 30 percent of the country’s wealth, but there’s virtually nothing in the finance literature about them.
So Yonker is at it again—scouring for data. He’s sent the SEC numerous Freedom of Information Act requests. “We’re using these data to write a paper that’s more descriptive than anything. What do these firms do? What do their fees look like? Just to start people thinking,” he says.
He is also looking broadly at the market for financial advice, analyzing the effects of a specific policy change that occurred in 2004, which allowed brokers to take their clients with them when they switched firms. “What we do is estimate. Is this a good thing? Is client choice a good thing?”
The answer is yes and no. Now that brokers are linked to their clients, they are worth more and have more power. A firm may offer a million-dollar bonus to a broker to switch companies, which isn’t disclosed to the broker’s clients. Then fees for those clients go up to pay for the bonus. “Brokers start moving more, and shenanigans by these brokers goes up as well. If they engage in bad behavior, they’re less likely to get fired,” Yonker says.
Any policy change, Yonker continues, is going to have pros and cons. “You can push one agenda, and there’s a good outcome. Clients can move. But now there are all these other problems that emerge because of the same policy.” The change has also contributed to the boom in RIAs, as independent advisers break away from firms to start their own companies, bringing clients with them. “There are 5,000 of these RIAs in the U.S.,” Yonker says, “and not a single paper about them.”
Trading Industry for Academia
Yonker went to get his PhD twice. He entered a program in economics straight out of college and excelled in his classes. “I was used to school, but then when it came time to do research, I didn’t know what to do,” he says.
Yonker completed a master’s degree and took a job in an investment advisory firm, where research ideas sprang up all around him. Even now, his experience in the industry informs his research. “I’ve interacted with clients, and I sort of know how they think and that leads to research ideas,” he says. “I understand the mentality of the average investor.”
After five years, Yonker left industry to earn his PhD, with a host of ideas and a yearning to return to academia. “Managing people’s money, you don’t have that much control. You can’t control the stock market, and hard work doesn’t always correlate to doing well,” he says. “In academia, the correlation between hard work and output is much higher. And I’m surrounded by smart, curious people. We’re all asking interesting questions and trying to solve them.”