What's Your Prior?

Behavioral Finance in Practice: using its lessons to improve investment outcomes.

June 23, 2021 Damian Handzy Season 1 Episode 10
What's Your Prior?
Behavioral Finance in Practice: using its lessons to improve investment outcomes.
Chapters
What's Your Prior?
Behavioral Finance in Practice: using its lessons to improve investment outcomes.
Jun 23, 2021 Season 1 Episode 10
Damian Handzy

How can we use all the fascinating lessons about our hidden biases to actually make better investment decisions? What are the most common psychology and bias-based mistakes investors make, and how can behavioral economics help us avoid them?

Join me and Clare Flynn Levy, Founder and CEO of Essentia Analytics, as we discuss how her firm helps traders and portfolio manager identify and then reverse their worst habits. 

Mentioned in the show:

Show Notes Transcript

How can we use all the fascinating lessons about our hidden biases to actually make better investment decisions? What are the most common psychology and bias-based mistakes investors make, and how can behavioral economics help us avoid them?

Join me and Clare Flynn Levy, Founder and CEO of Essentia Analytics, as we discuss how her firm helps traders and portfolio manager identify and then reverse their worst habits. 

Mentioned in the show:

Speaker 1:

I am simply fascinated by behavioral economics, understanding how we make decisions and basically how our brains work. I'm especially interested in all of the biases that we carry without recognizing them and hopefully learning what we can do about it. Now, Dan Kahneman and Amos Tversky, these two are considered kind of the godfathers of behavioral economics. These two psychologists ran groundbreaking behavioral experiments in the seventies that led to Conaman being awarded the Nobel prize in economics. He won the Nobel prize for the far reaching surprising and really unbelievable effects and biases that the two of them uncovered these biases in how we humans make decisions and what influences us without us, even knowing it years after learning about their work. The one bias that still haunts me, it's called anchoring and condiment diversity observed that a person's estimate of anything is highly influenced by whatever number they referred to moments before. Even if that first number has nothing to do with what they're estimating. And even if that first number is completely random in one of their original experiments, condiment diversity had their subjects, their participants, they watched a roulette wheel. Now the roulette wheel will secretly rigged to stop either at 10 or at 65. Only those two numbers. Everyone was then asked to estimate what percentage of countries in the United nations are physically in Africa, the participants who saw the roulette wheel stop on the number 10, they guessed much lower values for what portion of the UN's members are in Africa. They guessed about 25% on average, and that was way lower than the people who saw it stop on 65. Those people, they averaged the guests around 45%. That was just the first one, right? The first study, many other studies have reconfirmed this anchoring phenomenon and I'll say many good negotiators have exploited it for their own benefit. Obviously the bottom line is hearing, seeing, even saying a small number. It primes our brains to make small estimates or to accept small offers just as the reverse is true. Now, if you're wondering how many times you've been manipulated just in the past week with somebody using the anchoring bias, or if you're beginning to wonder about the existence of free, will you see why I find this topic? Absolutely fascinating. Wouldn't it be great to use this knowledge to invest better?

Speaker 2:

Hello and welcome to what's your prior the podcast, pretty adaptable investor with your host Damian Hanzi .

Speaker 1:

My guest today is clear Flynn levy who runs an amazing firm, but I'll let her tell you about it. Clear. Can I ask you to let the listeners know about you and about essential analytics?

Speaker 3:

I'm Clare, Flynn levy. I am the founder and CEO of a company called the essential analytics. And what essentially does is solve a problem that I had in my previous career as a fund manager, which is run data analytics to make it possible for a fund manager, to look in the mirror and really understand what's working and what's not working about his or her investment process.

Speaker 1:

One of the things that really strikes me is a lot of people are reluctant to get into their own biases. Who are the types of people who are willing to go through this kind of real look in the mirror?

Speaker 3:

Well, you know, it's funny when thinking fast and slow came out, that was not long before I found it essential so that I started the company in 2013. So it's been a while now. And behavioral finance has been a topic that has gained popularity amongst investment teams all over the world. It's become part of the CFA curriculum. It's a thing. And Nobel prizes keep being granted for work in that space and the world has embraced it. And yet it's all well and good to read about other people's biases, but actually putting that information to work in your own decision-making is a whole nother story. And there's sort of two challenges to that. One is just practically, how do you do it? And there've been really interesting books that have come out since then that have lots of different approaches. I think any duke came out with a book earlier this year called how to decide, which is probably the best one I've seen in terms of giving people an actual practical set of approaches to mitigating their own bias in making decisions. But what we do at essentially is take that one step further. We automate the process so that you're being pushed the opportunity to ask yourself objective questions, and in the moment, stop yourself from making a biased decision. So there's that practical challenge, and we're doing a lot to tackle that, but the other challenge is bravery. You know, people naturally are not inclined to seek out evidence of their own imperfections, no matter how intellectually correct. That seems, you know, that logically they know that they should do that. And if they want to be the best fund manager or any other skilled activity, really, if they want to be the best at it, then they really need to be honest with themselves and capture data about every decision they're making. In the case of fund managers, do the analysis and face up to the facts. You're good at some things you're not good at other things. Some people cross their arms and say, this one doesn't apply to me and come up with like a million, but we whole yeah. But yeah, but you know, my strategy is very unique. Yeah . But you know, there are a million reasons why you can not go there, but the reason you're not going there usually is because emotionally you can't quite bring yourself to do it. It takes us as you alluded to, it takes a certain type of person. So

Speaker 1:

Claire went on to describe a profile of the kind of person who that certain type of person who will actually take advantage of what her firm has to offer. The analogy she made, I think is very apt. She said that if you're the kind of person who wears a Fitbit, then you're already into, and you've accepted the idea of measuring things about you, about your behavior and using that to improve how you do things. So her firm does that for professional traders and portfolio managers. So I asked her to get into the guts of it is to describe what exactly her firm does and how they do it. And here's Claire describing

Speaker 3:

That. It's about asking yourself a set of questions at a certain moment. And the challenging part is actually doing that. You know, you could have a million books on yourself about which questions you should ask and which moments you should ask them. But that doesn't mean that when the moment comes, you're going to do it. So what we do at Ascensia is that we nudge, but we nudge our clients in not in the way of the book, nudge if , uh, I think our listeners have read that one. What we do is like it very blatantly tap you on the shoulder via an email or a text and say, you said, you wanted me to ask you these questions. The next time you're in this situation. Now you're in the situation again. And I'm asking you these questions. And so the situation might be as simple as you've just entered a new position in your portfolio, how will you know, you're right? How will you know, you're wrong? But even just thinking about how will I know I'm wrong and documenting that at the outset means that down the road, you can look back and what our system does is pin that to the share-price craft . So when you down the road are looking at, how did I end up in this situation again? Or why did I get, you know, maybe it's a position that's made a lot of money, but you, it's very easy to lose sight of the original thesis. When you know, you're making a lot of money, we're losing a lot of money, but to be able to look back, you say, not have to rely on your memory because your brain is always going to tell you a story about what happened. That makes sense to you now. But if you can look back at the facts and say, okay, I said, I would know I was wrong. If you know, it missed two quarters of earnings in a row, or if I don't know some other specific thing. And unfortunately that has happened, I am wrong. You know? And then it's not so like, painful to say, well, I don't want the analyst to be upset with me. Or, you know, I'm going to look stupid in front of my investors. No, I have a very clear reason. I'd said this at the beginning. I'm gonna stick with it. It's not, it's not an emotional decision. That's very liberating actually, when people do engage with it .

Speaker 1:

Okay. So Claire, are there kind of common biases that, you know , happen quite often that all traders should be aware of that you and your firm can help them overcome?

Speaker 3:

Well, there , you know, it's different for everybody. I should point out. We did an analysis across every portfolio that we have glue the ongoing back as far as we could and found that, you know, we thought, okay, they're going to be the most common biases. These ones that affect everybody. There isn't anything that affects everybody. And, and yet what we did find was that each one of the managers did have at least one pattern that could be associated with, you know, a documented behavioral finance bias. So everybody's got something it's just not always the same thing that said, there's some very popular, you know, some clusters that appear in that data. One of them is about having too many small positions that bleed the portfolio. You know, so people make a lot of the bulk of their alpha out of their big positions. And that's great. But then often have large numbers of small positions, just sort of sitting there and it's death by a thousand cuts. And it eats away all the alpha you're generating from these big bets you took, which is so disturbing. And when you asked me the portfolio manager, you know, here's all the small positions, let's talk about a word . Like, why are these here? Yeah . It wasn't really that convicted amaze . I didn't really feel the strength of belief. And so I stuck in a little bit, we'll dip my toe in there. We would say, well, that's about, you know , the fear of future regret or the fear of sometimes a fear of missing out, you know, where you're just not, you're afraid not to hold it. Um, another very common one is about loss aversion and how people behave when a stock has been underperforming for a long period of time, you know, there's a tendency to just kind of sell your winners and hold onto the losers. Right . You know , professional fund managers tend to be better at running winners, then retail investors, but they still hate taking losses. You know, it makes them feel stupid and look bad. So no wonder they hate taking losses and yeah, in the analysis can see it over and over again. Where if only you dig in this little sooner, you would have saved yourself so much more pain, stop

Speaker 1:

Loss limits aren't popular anymore.

Speaker 3:

We'll be maybe surprised at how few people actually use stop-loss limits . Not very many people actually, you know , maybe in the hedge fund space, but not in the more traditional fundamental stock fricking space. It's not to say they don't have , they don't have awareness when something hits in a particular trigger point, but that doesn't mean that they do anything specific at that point. So something can have a draw down that's brief and then bounce back. So what we're interested in actually, analytically is stocks in the portfolio that have been in a drawdown state , a long period, so many weeks in a row. And so we back test historically how many weeks in a row would have to been in this draw down state before it would have made sense for you to do something about it. Every stock that had been in had a draw down from its peak alpha point in your portfolio of greater than 20%, let's say, and just sat there. Always know , never recovered more than a third of that for one week in a row, two weeks in a row, three weeks in a row, you know, up to a hundred weeks in a row. Where's the point where if you had just cut it at this point, you would have been right more often than wrong. And you would have been more right when you're right than wrong, when you're wrong and would have generated alpha versus hanging on. And then we find that point and then that's what we then nudge the client about on the go-forward basis and say, okay, this week, here are the names in your portfolio that have been in this draw down state for this many weeks in a row. I'm going to ask you some questions that you said you wish you'd asked yourself less time. You know, would I be a buyer of this dog today, maybe? And so it's just about putting them through that their own process, really at that point, when emotionally, you know, cognitively they're , they're kind of stuck in this bias state where they're not doing anything, probably the next most common thing that we see is people who hold onto winners for too long. You taught to run your winners as a fund manager, and yet you can run them all the way back down into the ground. And in fact, you we've done research on alpha decay that shows that that's exactly what happens on average. People make a lot of alpha and then they give it all back. And yet, if you could figure out where's the alpha tend to start to run out. The juice tends to start to run out after a certain point for a given manager, if you can send them a nudge at that point and say, here are all the positions you're currently holding that are at that sort of sell by date. Not actually saying you have to sell by the way, just saying here's some questions you said you wanted to ask yourself the next time you were in one of these. It's amazing how just prompting them to ask hard questions at that moment, causes them to make measurably better decisions. And we can see that because we can see, and we can look at all of the trading decisions and in particular, all the trimming decisions or exiting decisions that they make on the back of that alpha decay nudge versus all the other selling decisions that they made at the same, you know , not on the back of the alpha decay nudge at the same time. And you can see the stats on the ones that happen on the back of the nudge or actually, so

Speaker 1:

Have you been able to measure the impact of using your nudges or not using your nudges? Right? Is there, is there some quantification of that this stuff actually makes a difference?

Speaker 3:

What we've been able to do is quantify the correlation between engagement with nudges and outperformance . So I mean, what we did was look at 75 portfolio managers over a hook . It was July, 2018 to March, 2021. So relatively recent. And some of those portfolio managers answered nudges. And some of them didn't the ones who did out performed by 160 basis points versus the ones who didn't and the ones who didn't, they outperform their index by lik that what it was, but like two basis points or something like that. So this is your fund managers who don't outperform net of fees. Whereas the ones that did engage with nudges had an extra 160 basis points, which is more than enough to outperform net of fees. Okay. Can you

Speaker 1:

Give us a real life example or two, obviously without naming names, but can you kind of describe here's something that's actually happened and what we did and what they did to change the , the trading patterns

Speaker 3:

It's been about first identify using the analytics to identify a pattern, right? So it might be the , the case of one where the manager is doing what I was describing before, like generating all their alpha out of their highest conviction positions and destroying value through their lowest conviction positions. What we can do in that case is implement a set of nudges that are designed to just get them to start recording what they're thinking. You know, it might be about recording what your conviction level is at the outset. And if it's not above a certain level asking you, are you sure you want to do that? Or it might be about nudging you on losers? You know, that I've been losing for a long time, or it might even be nudging you about how long you've been holding on to a position. But what we're doing in doing that is getting you to revisit these positions and ask yourself in this, in the case of that, that manager that had lots of small positions where they didn't have enough conviction constantly be asking, is your conviction greater than this? If it's not, and we don't even have to finish the sentence because the manager already knows, gosh, I did say, if my conviction wasn't greater than this, and I'm going to get rid of this position,

Speaker 1:

I gotta say everything you're describing is kind of a no brainer, right? It's almost like, you know, if you want to get in shape, don't try to do it yourself, use a trainer, they will motivate you more. They'll , they'll push you more and they'll find your mistakes that you have a more efficient workout. So when do you think this is going to become standard kind of accepted practice in the industry? I've

Speaker 3:

Always thought for for eight years, I have thought that this would become standard in the bone management industry any day now, but it is finally starting to happen where allocators have heard of us managers are being asked about it. You know , managers are being asked for evidence of how they're mitigating their own biases in their investment processes. And I think we're going to start to see asset owners become more and more sort of interested in this and potentially use it as a requirement, as a due diligence point before they give people money. Okay.

Speaker 1:

Claire , last question. Under what circumstances would you change your mind? Would you, what evidence would it take for you to say, you know what, this isn't working, this approach simply doesn't work.

Speaker 3:

If we didn't find any patterns in a given fund managers data, if it was just like noise then, okay, this is, this approach is not going to be very helpful. I've never actually seen that, but I suppose it's possible.

Speaker 1:

So that last question that I asked Claire , I've also asked just about every other guest I've had on the podcast, because it kind of summarizes the Basie and approach to things, right. It's Hey, what if I'm wrong? What would it actually take to convince me that I should change the way I'm doing things? And that is exactly what Claire's business does. I told her that after she gave her answer. Oh, so far, I think only two people have really answered the question. Well, and by, well, I mean, accepting the idea that yeah, you might be wrong and finding some rational way to estimate that. And I was really impressed with Claire's with Claire's clarity of Clara's answer and with that, we wrapped up the conversation. Great.

Speaker 3:

All right . Thanks . Bye . All right . Take care. Bye bye . So if

Speaker 1:

You liked the podcast, I have a request , please go out and actually share it and like it on LinkedIn. Tell your friends and colleagues about it and spread the word. The more listeners we have, the better it is for everybody. So thanks. [inaudible] .