Research by Chicago Booth’s Alex Imas and Samuel Hartzmark suggests that ownership has an important effect on how people process information. This finding has implications in the stock market, as it may explain how investors react to news and why people trade as much as they do.People Overreact to News about Stocks (and Other Things) They Own
Born in Bender, Moldova, professor Alex Imas is an award-winning behavioral economist who joined Booth’s faculty in 2020. His research spans a wide range of topics across the fields of economics and psychology, with a focus on the dynamics of decision-making. He’s explored the role of incorrect beliefs in discrimination, the prevalence of behavioral biases among investors, and the ways to better motivate performance by incorporating psychology into incentives. In Spring Quarter 2021, he taught a research class for PhD students, Behavioral Economics: Theory and the Lab. Recently, he participated in a tweet chat with Chicago Booth to share key insights from his research and discuss why they matter. Read an edited version of the conversation below.
Chicago Booth: Why did the field of behavioral economics capture your interest as a researcher?
Alex Imas: As a behavioral economist, I came to be interested in finance for a couple of reasons:
- It is a setting where people make repeated, consequential decisions, with ample scope for learning. This makes it a great environment to test predictions of behavioral economics outside the lab. One of the biggest pushbacks against behavioral econ are the low stakes and one-shot decisions. In finance, these critiques have less bite.
- I’m also interested in the field because the implications of biases for people’s welfare are substantial. For example, participating in financial markets (e.g. holding diversified portfolios over time) has yielded big returns, but only a fraction of US households participates. Figuring out why that participation rate is so low is important for better understanding decision-making, and for policy more broadly.
Booth: What are some common biases that typical, individual investors show when making trading decisions?
Imas: Individual investors often display the same types of biases as participants in the lab, only manifested in financial settings. They appear loss averse, overweigh low probability events, chase after realized gains, and avoid realizing losses.
One example is investors’ sensitivity to prior returns, which manifests in several ways. The most famous and well-replicated phenomenon is the disposition effect, where investors are more likely to sell their winners than their losers.
Investors are net buyers of attention-grabbing stocks, and my colleague [associate professor of finance and the Fujimori/Mou Faculty Scholar] Sam Hartzmark has a really nice paper showing that investors are much more likely to trade assets that have either gone up or gone down a lot. For an overview of individual investor biases, the paper “The Behavior of Individual Investors” is great.
“Participating in financial markets ... has yielded big returns, but only a fraction of US households participates. Figuring out why that participation rate is so low is important for better understanding decision-making, and for policy more broadly.”
Booth: What did you learn through your research about biases among expert investors?
Imas: My coauthors and I studied the buying and selling habits of institutional portfolio managers, and find that they were indeed very good at picking which stocks to buy, but were terrible (read: worse than random) at selling.
On the buying side, the managers beat the benchmark consistently, but when selling, they earned less than a counterfactual of throwing darts at the portfolio and selling that instead.
When looking at what was going on, we found that these experts were devoting the majority of their attention to buying, and sold the assets that drew the most attention (stocks with the highest/lowest returns). Barry Ritholtz and Matt Levine each wrote really nice columns about the paper for Bloomberg.
Booth: What kinds of data do you use in your research?
Imas: I use a mixture of lab experiments and observational data. For example, the study on expert investors was from a large observational data set that included managers’ buying and selling decisions over time.
For other projects, I combine observational data with a lab experiment that aims to replicate the phenomenon in a more controlled environment. For example, Zwetelina Iliewa, Rawley Heimer, Martin Weber, and I demonstrate that investors deviate substantially from their planned choices when seeing gains and losses: they start out planning to exit early after losses and stay after gains, but do the exact opposite.
Booth: You and your coauthors find that constraints can actually improve financial decision-making. Can you give an example?
Imas: Heimer and I looked at how access to leverage impacts financial decisions. Leverage essentially expands an investor’s opportunity set by letting them borrow to buy instead of having to sell. Standard theory implies leverage.
We exploited variation in access to leverage to demonstrate that those with less of it actually made better decisions. In an observational dataset with real investors, traders held on to losers for longer with more leverage than with less. This led to lower returns.
Intuitively, without leverage, investors who find a new opportunity would have to sell something in order to buy it. With leverage, they can instead borrow and avoid selling anything. Access to leverage thus decreases the opportunity cost of holding on to losers. We replicated this effect in the lab with student participants. The same behavioral phenomenon—a reluctance to realize losing positions—manifested itself in both settings.
Booth: How can average investors become more aware of their own biases?
Imas: This is the million-dollar question. Investor decisions are prone to contextual changes to their environment. For example, Cary Frydman and Baolian Wang have this great paper demonstrating that how returns are displayed can eliminate disposition effect.
At the same time, most of the evidence we have on “de-biasing” programs is that they have a limited effect, if any (an exception being this great paper: “Debiasing Decisions: Improved Decision Making with a Single Training Intervention”).
My sense is that the “de-biasing” will come from investors understanding their biases and adopting decision aids (like the one from the paper linked above) that help them overcome them, rather than being able to overcome these biases on their own.
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