Chief financial officers believe they’re far better at calling markets than they actually are — even after markets prove them wrong, according to a new study.
Researchers from Duke University and Ohio State looked at thousands of finance executives’ S&P 500 projections over time to examine one behavioral bias: excess conviction. Their work followed up on a major study from several years prior that observed this phenomenon among experts.
Not only did the CFOs exhibit overconfidence bias, they did so to a greater degree than even the original landmark research found. They also proved to be learning creatures — somewhat.
“These people are supposed to be very good at risk management, and have jobs where an important part is not to be overconfident. You want this person to be well-calibrated when they say, ‘This is what we think is going to happen, and we’ve got 80 percent confidence it will, within these bounds.’ As it turns out, that’s just not the case,” said Campbell Harvey, one of the paper’s authors and a Duke finance professor, in a Monday interview with Institutional Investor.
Year after year, CFOs projected the S&P 500’s returns, which they got right, on average. Where they erred was the precision. Each respondent provided a range of outcomes with 80 percent likelihood, or the upper- and lower-limits of what the S&P 500 would deliver 80 percent of the time. But only 36 percent of realized stock market returns fell within the range executives provided, the study found.
After being off-base one year, CFOs did tend to widen their bands the next year.
“CFOs do learn,” Harvey said. “But — and this is in boldface — they don’t learn enough. They are still severely miscalibrated. It’s over precision. Their conviction is so strong that it skews their view of reality.”
The findings likely carry over to asset management, he suggested. In particular, overconfidence may skew highly concentrated portfolios run by discretionary managers towards excessive risk-taking. “This something asset owners need to be aware of,” he said. “As researchers, we’re really questioning whether the calibration of risk is realistic, because of this inherent bias on the part of experts.”