MARK RIEPE: Decisions are a big part of investing, and one of the most fundamental is what assets to buy, hold, or sell.
Some investors are "set it and forget it" types who make a buy decision and never review that trade again.
Most of us though will periodically log in into our accounts, scan the list of holdings, and then either explicitly or implicitly make judgments about what to do with them as well as any cash that's in the account.
We've mentioned on this podcast many times that successful investors know that choosing what to sell is just as important as choosing what to buy.
In fact, our very first episode was about something called the disposition effect.
It's a decision-making bias that causes individuals to be much more likely to sell their winning stocks than their losers. In other words, if a stock sale will result in a gain, that stock is more likely to be sold than a stock whose sale will result in a loss.
I'm Mark Riepe, and I head up the Schwab Center for Financial Research, and this is Financial Decoder, an original podcast from Charles Schwab.
It's a show about financial decision making and the cognitive and emotional biases that can cloud our judgment.
If you've been listening to our show for the last six months or so, you may have heard some of our What's New segments where we highlight a recent finding or study that relates to behavioral finance.
We came across one study destined for the What's New slot that was so compelling, we decided it deserved its own episode.
The study examines the psychology behind buying and selling stocks, particularly around selling, which is interesting because the sell decision hasn't been studied as much as the buy decision.
Another facet that's interesting is that the study concentrates on professional portfolio managers instead of individual investors.
And it appears that even they are subject to psychological forces while making buying and selling decisions.
The title of the study is "Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors,"[1] and I'd like to welcome Alex Imas, one of the authors of the study, to the podcast.
Alex is a professor of behavioral science and economics and Vasilou Faculty Scholar at the University of Chicago's Booth School of Business.
He's received many prestigious awards and fellowships, including the 2023 Alfred P. Sloan Research Fellowship, the Review of Financial Studies Rising Scholar Award, and the New Investigator Award from the Behavioral Science and Policy Association.
Alex studies behavioral economics with a focus on cognition and mental representation in dynamic decision-making.[2]
Alex, welcome to the show.
ALEX IMAS: Great to be here. Thanks for having me.
MARK: Before we get into the study that we're going to be talking about today, you're a professor of behavioral economics or behavioral science and economics. You know that economics is a big field. How did you find your way into it? And what kind of drew you to kind of the behavioral part of it?
ALEX: I've always really been interested in behavioral economics. I was interested in behavioral economics before economics. So I was an undergraduate. I was basically interested in psychology and neuroscience and things like that. And then I kind of stumbled on economics as, I mean, frankly, a way to boost my GPA because the science classes are actually really hard. And I ended up really liking it, and I didn't really know how to connect the two.
And then I learned about behavioral economics and decided, you know, this is what I really want to do. This is what I want to do research on. So I applied to programs that had behavioral economists on the faculty, got lucky enough to get into UC San Diego, and kind of took it from there. But I've always really been interested in kind of the more psychological side of economic decision-making.
MARK: That's really interesting. And what kind of drew me to this particular study was, in fact, I think it was our first episode. We did a study on the disposition effect about how individual investors, you know, when they hold individual stocks, are more likely to sell the winners rather than the losers. When I saw your study talking about professional investors and their different behavior around the buy and sell decision, it was just super interesting.
So maybe you should talk about the data, because I don't think people appreciate that so much of research is just getting, you know, really good, really rich datasets. Describe the data that you were using and how you came across it.
ALEX: Well, this is really my co-author Rick Di Mascio's data. He has spent decades as essentially kind of a consultant for institutional investors is how I want to put it. So they would come to him, you know, pension funds and things like that, and to say, look, how well is our money doing? And he would take their data. And the data is really, really rich. In order to be able to say, "How well are we doing?" you need really good data as far as what is the trade every single day that these people are making, what is there in their portfolio, what are they looking at, and things like that. So he's got a very rich dataset, and then he could then say, "Look, in your peer group, here's what's going on. You know, you're doing kind of in the median, or you're doing better, you're doing worse."
And this is really what they're paying him money to do. And so at the end of the day, he ended up with this incredible dataset. And how did I become involved? It was actually very serendipitous.
I was giving a talk on a completely unrelated topic, and one of his clients was interested in behavioral economics and thought that the talk was cool, and he knew Rick was interested in behavioral economics, and he was like, "Hey, can I put you guys in touch? And maybe there is a collaboration there." And we started talking, and the project really went from there.
The dataset, as I said, is very rich. We have data on what each individual is doing at any given time as far as what they're holding, what they're selling, and what they're buying. So we have the full picture of the portfolio and what's going on to that portfolio over a pretty long time span. I think 13, 15 years for some of the traders. And the thing to note about this is that it's not a kind of representative kind of subset of institutional investors.
These are investors who chose to be in this dataset, which could be an issue for some analyses, but it wasn't for ours because this is a positively selected sample. These are people who are confident that they're good, which is why they want to be evaluated. They're basically asking the question, "How good am I? I know I'm good, but am I like the best, or am I pretty good?"
And so for the type of questions that we're asking, it's almost like a lower bound. If we see some behavioral biases in this sample who tend to be very good, then this means probably the rest of the people doing this sort of activity would probably display this sort of bias even more.
I mean, I can't say that that's true, but that's kind of the assumption that we're going on.
MARK: So maybe kind of dive in that on the … what were the primary findings of the study?
ALEX: Yeah. So as I said, this is positively selected. So what we can do is we can say, "How well are these folks performing on their buy decisions and on their sell decisions?" And the way that we can make that sort of inference is because we know exactly what they bought, and we know what they could have bought, and we know what they sold, and we know what they could have sold.
So on the buy side, we say, "Look, I observe this individual buying this stock or this asset. So this could be a top-up. This already exists in his portfolio, and he's just kind of purchasing more shares. Or this could be a brand-new position that they're opening." On the sell side, it's the same thing. We see what they sell.
Is it kind of cutting something a little bit, selling a couple of shares, but mostly keeping the position, or are they getting rid of that entire asset? And the reason that having the entire portfolio is so important is we can construct really detailed counterfactuals, and we can say, "Look, I see you buying Apple or Samsung. Well, instead of buying that, why don't you buy something else for the same amount of money?"
And we can match these things based on risk characteristics, based on the number of shares, based on the dollar amount, have a really rich set of counterfactuals to keep basically everything constant and to say, "Was that a good buy given what other things you could have bought?" And on the buy side, we just see that they're doing really well.
That's one of the findings. On their buying we can control for risk factors, everything like that, and they're just adding value what they're buying. So there's something there. There is signal in the noise, excellent performance on the buys. But on the sell side, what we found was a very different picture. What we did is we kind of created these what we viewed as conservative counterfactuals.
We say, "Look, we saw you generated a dollar by selling something. Well, we're going to throw a dart to your portfolio and sell something at random from your portfolio and see if that does better than what you actually did." And we saw that this random selling strategy actually did better than their actual selling strategy, which to us was really wild, especially given how good they were doing on their buys.
The fact that, you know, a dart can do better than them on selling was really kind of incredibly surprising for us. And that's basically the headline result is that they're really good at buying but terrible at selling. And we really threw the boat at this thing trying to figure out maybe they're doing … they're constrained in some way that we're not picking up as far as what they can sell and what they can't sell.
And we couldn't figure out anything. They're just bad at selling.
MARK: So when you kind of started this out, were you expecting to see, it sounds like you were obviously not expecting the sell results. Were you expecting the skill level that was demonstrated on the buy side?
ALEX: No, we didn't really expect either.
MARK: Interesting.
ALEX: There's a lot of literature in finance suggesting that kind of like actively managed portfolios aren't beating the market. And we found that at least in our sample, these guys were doing really well as far as their buying behavior. And again, there's been some kind of qualifications to this research.
So there's some recent papers showing that if you actually kind of do the full bootstrap analysis that there is a small subset of funds that are consistently beating the market. So there is some skill there. And we found this skill and, you know, the median trader in our sample is beating the market on their buys. On the sell side, we thought that they would be doing worse—that was our hypothesis—than buying. We did not at all expect that they'd be doing worse than random, that was shocking to us
MARK: Got it. Got it. Interesting. Yeah, I would have guessed that active managers have skill, but sometimes, maybe many times, their skill isn't worth the fees that are actually being charged. So to see this asymmetry, I thought that was just fascinating. What do you think is driving the results? Clearly, given the time period, given the number of different managers, something's going on here. What do you think's the cause?
ALEX: So we spent a lot of time in the paper trying to figure this out and, you know, we can't definitively say what's driving it because, again, we can't run an experiment or anything like that.
But we have pretty good data showing that they basically spend a lot of attention and a lot of thinking trying to decide what to buy, which is, you know, they're smart folks, and they're doing well in that, but they're really neglecting their selling decisions to the point where they're not only not paying attention to them, they're doing something even worse.
They're kind of displaying a sort of endowment effect in their selling. And the reason that they're … can be doing worse than random in their selling is almost a function of being better than average in buying, because the only way you can kind of lose relative to the market on selling is being better than the market in buying.
There's kind of value in the buy, and if you sell too quickly or sell something that you haven't kind of held on for long enough to get that return, you're going to be doing worse than the counterfactual, as in you should be selling the stale positions rather than the positions you've kind of recently bought and are still earning you money.
And that's kind of what we're finding is basically this big trace of limited attention. So they're selling things that have gone up or gone down in price. So things that are really, really, really salient to them. So if I'm not really paying attention to something, what am I going to sell? I'm going to sell something that's kind of lighting up on my screen, something that's gone up a lot or gone down a lot.
So from the tails, it's like 200 or 100% the probability of selling something in the tails relative to the middle. So this is kind of a telltale sign of limited attention.
And then from those tails, they're not selecting at random either. They're selecting the things that they're not really kind of committed to.
So they're selling things that are still earning them value, things that they've recently bought or they still haven't fully developed in their portfolio.
And this is what's driving this worse-than-random. What they should be selling is their stale positions.
MARK: So our audience is, and Schwab, you know, our clients are primarily individual investors. That's the people who kind of listen to this podcast. Do you think these results would apply to individuals as well, if you had kind of a comparable data set?
ALEX: So for individuals, the only way you can do worse than random, as I said, is if you're doing better than random on the buying. Right? So if you're a skilled investor, the takeaway is pay attention to selling, because you could be leaving a lot of money on the table just by thinking, "I'm doing super well at buying. Selling is kind of a cash-raising exercise. I don't need to pay attention to it." You should pay attention to it because you're basically leaving … we kind of did kind of a back-of-the-envelope analysis, and they're leaving something like double their management fees on selling poorly. It's a lot. So if you're doing pretty well on the buying decisions, pay attention to selling.
MARK: Yeah. At the end of the day, right. I mean, it's about, you know, where you put your attention. You know, the buy decision is as important as the sell decision, and you've got to make sure you're balancing that out.
What about other asset classes? I know this was focused on individual stocks. Is there a reason to believe that you might see the same sort of results for someone who is managing a portfolio of mutual funds or ETFs or individual bonds or other asset classes?
ALEX: Yeah, we've replicated this sort of result with mutual funds too. The problem with that is we don't have as rich of a dataset with mutual funds.
That's the simple answer. So, you know, we could replicate kind of suggestively that sort of result. But we can't say like, look, we can generate a tight, tight counterfactual and say, look, you're underperforming. That's only possible if you have a rich enough dataset.
MARK: Yep. Yeah, makes sense. How do you overcome now that this is out there? You've kind of documented this finding. Aside from paying lot more attention to the sell decision, any other advice that you would have to either professionals or individuals to help kind of mitigate this tendency?
ALEX: I guess my advice would be to … I don't have any quote-unquote investment advice, but I would say that you have limited attention.
Everybody's got limited attention. Try to find out what are the important decisions that you're making. Usually, as you said earlier, selling and buying are equally important. You should be devoting equal amounts of attention to both. And so if something you feel like is more important as part of your kind of research process in terms of trying to find out what you're buying and selling, make sure you're paying attention to it. Make sure that you're not kind of swayed by news or by flashy things that are popping up on the screen and are just going through your process and sticking through your process through the entire kind of investing episode.
And I think that sort of advice shows up not just for the sort of institutional environment that we've been exploring, that sort of attention-driven buying. So Terry Odean and Brad Barber have this paper called "All That Glitters Is Gold" about kind of retail investors and other traders kind of going for these attention-grabbing stocks, trying to overcome these sorts of biases that are driven by basic cognitive constraints as much as possible by setting out a process ahead of time and just following through.
MARK: Last question here, you'd mentioned and described some of these heuristics that people follow that are kind of driven by biases. I'm wondering, are there any other instances that you can think of where maybe this kind of short-term, quick, heuristic-driven thinking is actually beneficial, or no, being kind of slower, more deliberative, that's always the way to go?
ALEX: Heuristics are beneficial when you are resource-constrained. So if you're trying to drive, and you see a sharp turn, and you want to assess all of the sort of angles and things like that in order to make the right turn, you're going to go off the road, right? You want to use a heuristic, and you want to kind of turn right.
Heuristics have evolved for a reason. We're constrained in all sorts of dimensions, and heuristics are basically shortcuts in order to kind of get almost optimal in a given spot without using too many resources. Nothing wrong with heuristics. But in financial markets, people can typically take a day to think. Unless they can't, in which case, you know, that's a different situation.
But typically, when you're buying something with the intention of holding, you can take an extra day of research. You can think about it overnight, especially for an individual trader, for a retail trader. And so to me, a lot of these heuristics are detrimental in some way. You know, you can't put a dollar value in a lot of contexts.
In our case, we can put a dollar value on those heuristics. In some cases, you can't just because of data limitations. But in general, in these sorts of contexts where you're not really cognitively constrained as far as the number of minutes that you have to make a decision, I don't really see a reason to be following heuristics rather than going through your process.
MARK: Yeah, I think that makes a lot of sense. And I think those are kind of good words of wisdom, even for more the financial-planning part of your life, where often cases there is even less kind of time pressure, and there's time to study things and get them right. So excellent. Very good. Alex Imas is a professor of behavioral science and economics at University of Chicago's Booth School of Business.
Alex, thanks for being here today.
ALEX: Thanks so much. It was a pleasure.
MARK: That's it for this episode, and thank you for listening to not only this episode, but to this entire season of Financial Decoder.
We're taking a break, but we'll be back in June with more episodes.
In the meantime, you can follow me on my LinkedIn page or at X @Mark Riepe: M-A-R-K-R-I-E-P-E.
If you enjoyed the show, a rating or review on Apple Podcasts is a great way to convey that message.
We always like new listeners, so if you know someone who might like the show, please tell them about it and how they can follow us for free in their favorite podcasting app.
For important disclosures, see the show notes and schwab.com/FinancialDecoder.
[1]Akepanidtaworn, Klakow et al., "Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors," The Journal of Finance, Vol. LXXVIII, No. 6, December 2023
[2]Aleximas.com
