Trading Insights: Behavioral biases and their impact on investing decisions

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Michelle Baddeley: So behavioral biases emerge from times when we have to make decisions quickly, when decisions are complex, when we're facing quite fundamental uncertainty. So when it comes to something like a meme stock, it comes back to these social influences again. People can make their decisions on the basis of information that's very easy to recall and that information that's at the top of their mind is not necessarily the best information.

Eloise Goulder: Hi, I'm Eloise Goulder, head of the Data Assets and Alpha Group here at J.P. Morgan. And today I'm so delighted to be joined by Michelle Baddeley, who is Professor in Economics at the UTS Business School at the University of Technology, Sydney, and is visiting us in London here today. So Michelle, thank you so much for joining us here.

Michelle Baddeley: It's lovely to see you, Eloise. Thank you for inviting me along.

Eloise Goulder: And Michelle, you've done so much work and in fact published several books on behavioral economics and behavioral finance and their impact on investing decisions and the economy, all of which is so relevant for us in markets, in trading, and in the investing world. So Michelle, could we go back to basics and start with behavioral biases as a whole? What are they and why do they exist?

Michelle Baddeley: So behavioral biases emerge from times when we have to make decisions quickly, when decisions are complex, when we're facing quite fundamental uncertainty. The standard model in economics that everyone's a rational decision maker, that they take in lots of information, they process that information accurately and efficiently, and then they churn out an absolutely right answer or an answer that is at least correct on average, allowing for random mistakes. Well, we all know intuitively that that's not how people think and decide. And so in behavioral economics, it's bringing together that analytical rigor with insights from psychology about the sorts of biases to which people are prone. And this arises from a tool that we all use in our everyday decision making, heuristics, which are simple rules of thumb that enable us not to look at all the information and think really carefully about everything, but just come to a quick decision when we need to. The trouble with heuristics is that they work quite well, but often they send us down the wrong path. And you see that a lot in economic and financial decision making. People decide, if you like, too quickly. And that not only affects the individual decision maker, but also those mistakes can be multiplied and aggregated within a large complex system. And in terms of the different sorts of biases Kahneman and Tversky they're so prolific. One article they wrote in 1974 in the journal Science, put a structure around them. And they identified three broad categories, which isn't going to catch everything, but the availability heuristics. That's about when we decide based on the information that we can recall really quickly and easily, anchoring and adjustment. We make our decisions according to reference points, where we are now, and we adjust our decisions accordingly, and representativeness. And that's when we make comparisons across different types of decisions and we draw analogies between one decision and one choice and another. And often, these heuristics might work quite well, but they can also send us into mistakes.

Eloise Goulder: These heuristics, these rules of thumb, how do they relate to Kahneman in thinking fast and slow, referring to type one and type two thinking?

Michelle Baddeley: Yeah the idea is that you've got these very fast, quick decision-making systems that get there first it's a form of impulsivity. And in evolutionary terms, that can be very helpful. And that's your system one but your system two will churn through the information, will think quite carefully, but it's a bit slow to respond. And so usually system one gets there first, unless system two can be engaged. And so the system one, system two is more about the neuropsychology of the brain functions that drive the behaviors, whereas the heuristics are actually the behaviors.

Eloise Goulder: So these three key heuristics that Kahneman had identified, availability, bias, anchoring, and representativeness. How do you believe they manifest the most clearly in financial markets?

Michelle Baddeley: One of the most established and well-tested example is loss aversion that if you look at a certain reference point, people care much more about the losses than they care about any gains and it also explains the equity premium puzzle, that the returns on equities are so much greater than the returns on bonds. And Thaler and Bernazzi explained this in terms of a combination of this loss aversion, And the phenomenon of myopia, that people are very short-termist. That every day we wake up and we worry that our portfolio is losing money. And so we respond and readjust our portfolios too frequently, And so it's this interaction of myopia and loss aversion that explains this equity premium puzzle. Another one that is my particular area of expertise, herding and social influence. Herding in financial markets and particularly the evolution of speculative bubbles. So the most colorful episode, which is the tulip mania speculative bubble. At the height of tulip mania, people were buying and selling tulip bulbs for the equivalent of a three-story house in central Amsterdam. Those social learning heuristics, the herding heuristic, which isn't something that Kahneman Tversky talked about particularly, but there's quite a big literature in behavioral finance about herding, financial herding. And I think that's a really important one as well. A third example of a bias that is highly relevant in financial markets is confirmation bias. So people's beliefs are self-reinforcing. It connects up with anchoring in that people are anchoring their opinions on their own opinions and thus confirmation bias takes hold.

Eloise Goulder: Yes. And doesn't it also link back to herding in the sense that if there's a prevailing financial market wisdom like U.S. exceptionalism, then there may have been herding and in those assets that are exposed to that trend. But then data points that support that thesis might be disproportionately discussed in financial markets.

Michelle Baddeley: And where does this initial belief come from? It's socially driven. So you acquire this belief from the people around you, and then it becomes self-reinforcing not only within yourself, but within the crowd as well.

Eloise Goulder: Absolutely. So all of these biases you're referring to, they feel so real and so present. Right now, we are going through a resurgence again in the meme phenomena. And the retail investor as a whole in the U.S. and in Asian markets have really grown their share of volumes particularly post-COVID. Michelle, how do you think about the behavior of the retail investor and what this significant presence means for financial markets?

Michelle Baddeley: So when it comes to something like a meme stock, it comes back to these social influences again, that they're picking up on information that's out there in the press and it starts to interact with an availability heuristic. People can make their decisions on the basis of information that's very easy to recall and that information that's at the top of their mind is not necessarily the best information.

Eloise Goulder: So for what it's worth, we have a lot of interest from our investing clients in tracking what the retail investor is doing, i.e. trading and saying via social media in order to have a lens on that, because that herding phenomena can be so powerful, as we witnessed most clearly in January 2021, when we saw that that retail induced short squeeze.

Michelle Baddeley: Yeah and again, it gets back to the system one, system two, and the quick decision making that social media is so ephemeral, generated by what's happening from one minute to the next. And so any new little bit of information can just take off so quickly.

Eloise Goulder: Things go viral very quickly. Disseminating very quickly across many different market participants.

Michelle Baddeley: Yeah. And it's the parallel of a liquid financial market with a liquid information market. If you like, via social media.

Eloise Goulder: And one of the so-what’s for the institutional investor, I think, given the presence of all of these biases from many market participants, is how can I capitalize on these biases in order to make profitable investment decisions? And a few thoughts come to mind. One of them is isn't there value in looking to the longer term? Because to the extent that myopia is a behavioral bias and a tendency for so many investors, taking a longer term view could set you apart from the crowd. The other thought I have is, isn't there value in being contrarian? And being contrarian from a behavioral perspective is so uncomfortable for so many of us and probably goes against so many of these behavioral biases. And yet if you're willing to wait for the longer term and you're willing to be contrarian, you could potentially capitalize on quite a lot.

Michelle Baddeley: So when I was writing my book, Copycats and Contrarians, I was very interested in the fact that we copy one another, we're social animals, we're evolved to learn from one another and so the contrarian is a rather rare beast. So it comes back to the sort of standard financial theory risk reward trade off, that it's risky to be a contrarian, it's risky to stand out from the crowd and be doing something completely different. But if you can pull it off you would have to know something more about the fundamentals perhaps.

Eloise Goulder: I guess another way that many of our investing clients tell us they're capitalizing on behavioral biases is through trend following. And we've spoken to many of them on this podcast series. In fact, very recently we heard from Marty Lewick, the co-founder of AHL and of Aspect Capital, talking about the presence of herding and crowding and momentum as a feature of financial markets. And following trends globally and cross-asset over the prior period, can be incredibly powerful because then you can be relatively more early onto a trend that then magnifies as herding behavior continues. And perhaps the retail investor is helping those trends get magnified even further.

Michelle Baddeley: Yeah if you're a trend follower, get in early. Don't be a late trend follower.

Eloise Goulder: Yes. Could we just turn to the economy, the underlying economy, and whether there are key behavioral biases you also see playing out from an economic standpoint?

Michelle Baddeley: So present bias is when people are disproportionately focused on the short term. And it's not just that they're impatient. If you're consistently impatient, that to an economist, can be a rational, stable preference. But it's when people are disproportionately less patient in the short term and patient in the long term. And there's been quite a lot of research about the way in which this has impacts the macro economy. And in the conduct of monetary policy, which, of course, is all very topical at the moment. What if the policymakers suffer from this present bias? So a lot of models that underlie monetary policy and inflation targeting, for example, and central bank independence based on the idea that policymakers are rational and have stable time preferences. But what if you've got a policymaker who is disproportionately impatient and suffering from this present bias problem? And you have got that interacting with the world at large. How do these forms of present bias interact to generate disproportionately high inflation outcomes, for example? And what do central banks do about it?

Eloise Goulder: Couldn't you say the same thing about government deficits? And fiscal discipline?

Michelle Baddeley: Yes, exactly. And that's a relatively new area of macroeconomics, is trying to get the behavioral stuff in there. Because in a micro context, if you're thinking about one representative agent, behavioral economics takes that on. But if you're thinking about everyone in a complex system, the whole is no longer equal to the sum of the parts. And similarly for a financial system, if you've got millions, of people all suffering these biases, how do you figure out how that's going to unfold on the global scale?

Eloise Goulder: Maybe some of them cancel out, but others don't presumably, particularly given the bias towards herding.

Michelle Baddeley: It doesn't cancel out, yes.

Eloise Goulder: Yes. So Michelle, it's so fascinating to hear your lens on all of these biases, the fact that they are so instinctive, so ingrained in us in humans, and so evident in the economy at large, but also in financial markets. From an investing perspective, how can we possibly go about mitigating them?

Michelle Baddeley: In terms of what can be done to mitigate it at different levels, I think making good financial advice easily accessible, and whether that comes via institutions or properly trained financial advisors, I think that is a very important thing. There's quite a large literature on emotional trading as well, that emotions are an example of a system one thing, that the emotions get there first, they operate really quickly. And if there are ways to make people more mindful about what they're doing to slow things down a little bit, I think that could be really helpful in sort of stabilizing the system.

Eloise Goulder: One can force friction on one's own trading ideas by making you think twice, by making you stop. One example that I'm very familiar with is writing down your investment thesis, your price target, your rationale, what would make you change your rationale, what would make you change your price target. Writing that down first, and then holding yourself to it, or ideally even having someone else hold you to that, so that you just cannot, no matter how much your emotions want it, you cannot just run away with your behavioral biases.

Michelle Baddeley: And it connects up with a pre-commitment strategy, that you pre-commit to doing things in a certain way. And so, the trouble is, if your impulsive self wants you to throw your pre-commitment strategy out the window, then you've got a problem. But at least if you form that intention to do a certain sort of thing, that will have a certain power and will provide a form of anchor.

Eloise Goulder: It's this accountability. I mean, in the investing world, it is more common to see on the one hand, coaches, coaches to help investors understand both the reasons behind their prior winning trades and the reasons behind their prior losing trades. Are there some consistent behavioral biases that they are falling foul of? And how can they train themselves to be aware of them and then overcome them? And also, with greater data availability and toolkits, it's also increasingly possible to track one's historic trades and quantify insights from one's past trading behavior.

Michelle Baddeley: Yeah, you're putting a brake on your system one by doing any of those sorts of things. And I also like the way that you refer to it as coaching, because in the process of coaching, you're not necessarily telling someone what to do, but you're encouraging a person to reflect and build insight within themselves.

Eloise Goulder: Well, we've heard many of our investing clients talk about leveraging AI tools to really try and get a better handle on the history of portfolio managers and really learn more from their historic trades. And on the topic of AI and biases, I mean, this is a very difficult one. Do you have a view on how growth in use of AI and machines will impact biases, and could it help us as humans mitigate our biases?

Michelle Baddeley: I imagine it comes down to how it's trained. So if you trained your AI on the fact that there are biases and told it to watch out for those biases, yeah, that would work. But if it wasn't told to do that, it could just magnify the biases.

Eloise Goulder: And if you don't train it to watch out for the biases, if it's trained on social media data that is subject to herding, and 90% of the information is going one way on a given topic, then of course the AI will reflect that. So I think that's a really interesting point. So Michelle, we always ask our guests, what's next? You also have an unusually multidisciplinary background spanning economics, which is in fact where we first met. I had the real privilege of being taught by you at Cambridge more than 20 years ago now. But your background also spans psychology, neuroscience. You've recently trained as a lawyer. Michelle, where do you expect this to take you?

Michelle Baddeley: Yes, I've discovered the law, which I find completely fascinating and I had another look at Richard Thaler's book, Misbehaving, in which he talks about how he first came across the law. And of course, many of your listeners will know about Nudge, the book that Richard Thaler wrote with Cass Sunstein who is a lawyer. And it was very interesting to read that chapter because it really cemented some ideas I had about these overlaps between behavioral economics and the law. So for example, a lot of the legislation that underlies competition and consumer regulation in England and Wales, in Australia, in the U.S., has been founded on this idea that markets are comprised of rational players and rational agents. And once you start to bring in heuristics and biases and how they operate, particularly, for example, in the area of consumer law, that consumers aren't making rational decisions in their own best interests, then that's going to have big implications for consumer law, consumer regulation, also financial regulation as well, to the extent that it affects consumers.

Eloise Goulder: Well, thank you, Michelle. This has been such a wonderful conversation. The topic of behavioral biases and behavioral finance it's so important to not only be aware of them, but also to think about how we can mitigate them or capitalize on biases being present in human intermediated markets. So thank you so much, Michelle, for taking the time to come in and talk to us about this today.

Michelle Baddeley: Thank you, Eloise. It's been an absolute pleasure.

Eloise Goulder: Thank you also to our listeners for tuning into this biweekly podcast from our group. If you'd like to learn more about Michelle's work, then please do see the links to her books and research in the show notes. Otherwise, if you'd like to be in touch with our team, then please do go to our website at jpmorgan.com/market-data-intelligence. And with that, we'll close. Thank you.

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