The behavioral biases behind our investments with Clare Flynn Levy

Loss aversion

"Are you good at exit-timing decisions when the price is going up, but not when the price is going down? Probably. Once you have access to this sort of data and you can do this sort of analysis, you can see behavioral bias right in the mirror, like this is what it looks like. Here's what loss aversion looks like, it's late exit-timing."

The endowment effect…or the 'Round Trip' as they call it in the investment world! 

"The round trip is when you buy something and it starts working and it's making you good money and you get very invested in the story. And if you're a professional fund manager, maybe you meet the CEO and the management team, and you'll have a relationship. So when the cracks start forming, you don't notice, not in the same way you would if you didn't have this sort of romance going on. And so suddenly you wake up one day and you've almost lost all the money you made in that stock and you didn't even appreciate that."

Investors get FOMO too!

"And there's the fear of missing out. And it plays to all your emotions, all these exciting stories. "This could turn into your tenbagger," which is what you're always after. And each time it ramps you up, you get sucked into the story and you do it. But when you look at it in aggregate, you see that, "Oh God, we're so rarely right about that. Ugh. All right."

Using nudges to overcome bias

"We use a nudge, we call it the 'alpha decoy nudge', to ping you at the moment where historically this would have been where the alpha generation or the juice of your return, would've run out. We just suggest that you ask yourself some extra questions at this point."

Our gut instinct might be right, but only if the data says so

"We're all walking around assuming that our guts are correct and that just is not true. That is not true. But if you do the data analysis, who knows what it'll say? It might say that when you have a certain feeling, when the hair's on the back of your neck stand up, that is correlated with the biggest wins of your life."

Brooke: Hello everyone, and welcome to the podcast of The Decision Lab, a socially-conscious applied research firm that uses behavioral science to improve outcomes for all of society. My name is Brooke Struck, research director at TDL and I'll be your host for the discussion. My guest today is Clare Flynn Levy, Founder and CEO of Essentia Analytics. Before Essentia, Clare spent a decade as a fund manager and in leading roles at other FinTech companies. In today's episode, we'll be talking about the behavioral alpha and omega, how cognitive biases cause investors to lose money, how behavioral interventions can help, and the role of the modern fund manager. Clare, thanks for joining us.

Clare: Thanks for having me.

Brooke: I'm excited to dig into this. The relationship between biases and investing is one that's been around for a long time in this space, but there never seems to be a lack of fresh insights about the renewed depth of this challenge. So let's dive in. How do cognitive biases end up hitting the bottom line of investments? Let's start there.

Clare: Sure. Well, so you heard already I spent the first half of my career as a professional investor, as a fund manager. And so when I'm talking about the investment bottom line, the investment returns that you and I see from our pension funds and our 401ks, or whatever construct you have, that money is typically being managed by someone else, right? And it might be in index funds or it might be in mutual funds or other things, but ultimately there are humans who are making some decisions behind the scenes that affect the money that we're going to get out the other end. And some of the time, those decisions are being made by us. e are the humans. So it's important for all of us to consider ourselves as investors, when we talk about this stuff, but who I work with are professional fund managers.

So these are the people whose job it is to make investment decisions on behalf of you and me. And my backstory really is that I was a tech fund manager during the internet bubble in the late 90s, which was fantastic and a lot of fun. And I won all the awards and I was great because my performance was really good. And I was very up for adopting new technologies and so I did, to the extent that they existed in that space at the time. It was a long time ago now, but the bubble burst one day or one month or one quarter, depending on how you look at it, and my performance started to deteriorate. And so I started asking myself questions about "What can I do to improve this?"

You don't ask yourself a lot of questions when it's going well, but as soon as it stops going well, now you're asking questions. "Why is this happening? What should I be doing differently? Did I screw up?" I am a process-driven person. And so I was interested in taking a step back and saying, "All right, my job is to make investment decisions. Here's my process for making those decisions. Which aspects of this process are working and which are not working?" And what I found was that I couldn't answer the question. The technology didn't exist at the time to answer that question. All that existed was the ability to look at the performance outcome and then work backwards to try to explain why did this happen? All that does is tell you stories. It doesn't help you actually improve.

Brooke: Yeah. So you stir the tea leaves, you come up with some coherent picture that seems to make sense of what you're seeing.

Clare: Exactly.

Brooke: But there are lots of competing explanations of how it might have come to pass. How do you figure out?

Clare: And it doesn't matter.

Brooke: What's the truth? Yeah.

Clare: I mean the storytelling piece appeals to the investor's brain, but it doesn't help you on a go-forward basis to know these things. The question really is what is it that I am doing that is consistently helping or consistently hurting my performance? And that starts with a data point on every decision that I'm making. And so that's what I set out to do with Essentia, is build a service really.t's a piece of technology, but technology takes in data about trading decisions and looks for patterns in those decisions. What do the ideas that have made you good money have in common?hat do the ones that have lost you money have in common?nd then decompose them into each type of decision that you're making.

Is it exit-timing decisions you're really good at, or are you really bad at that? Are you good at exit-timing decisions when the price is going up, but not when the price is going down? Probably. Once you have access to this sort of data and you can do this sort of analysis, you can see behavioral bias right in the mirror, like this is what it looks like. Here's what loss aversion looks like, late exit-timing.

Brooke: Yeah. So let's dive into that. Let's talk about loss aversion and exit-timing. Tell us a story about how loss aversion affects the choice of when to exit and the impacts that that has.

Clare: I mean, anybody who's invested has presumably lost at some point. That typically goes with the territory. Sometimes you win, sometimes you lose. When you're losing, it feels really terrible and people do all kinds of irrational things. Take COVID, for example. That first few weeks when prices were tanking, plenty of people would have run for the hills and sold all their positions and thought, "The world is ending," or "The market's going down." They've been waiting for the market to go down for years now. "This is it. This is what's going to happen. I'vegot to get out." That's more of a retail investor phenomenon, I think. A professional investor is less likely to do that because they spend all day, every day dealing with market volatility. But even they have emotions that get triggered by watching money go away.

And the question is, do they know that, first of all? Do they recognize that that's what's going on? And then do they act on these emotions or do they not? And we saw even amongst some professional fund managers that sort of snap reaction where they sell a whole bunch of things and in that particular situation, it bounced back so fast right after that it looks, in retrospect, like terrible decision-making if you're the one who did that. And it's pretty mortifying actually as a professional investor to see yourself do that. And yet some people did. That's not the trend that you see the most. That's sort of an unusual thing to have happened, but more often what happens is people own a stock, it starts losing money maybe from day one, or maybe it makes money and then it starts losing money, and they just wait. They hope that it's going to go back up or that things are going to get better.

And they say, "I'm going to give it this amount of time." And then when that amount of time passes, they say, "Oh, I'm going to give it a little bit more time." And it keeps going down and down. And sometimes it's like a very slow bleed so you don't even really notice that that's what's happening. But when you start to do analysis at the aggregate level of every position you've ever held, and your job is to hold hundreds or thousands of these positions, you start to see, "Oh gosh, this is what it looks like for me when the price is going down and down and down, and I wait way too long." And then you can use data analytics to figure out what would've been the point historically, where you should have pulled the plug.

Brooke: Right.

Clare: You could take that forward and say, "Okay, that's going to be my point where I pull the plug in the future maybe," but actually most professional investors don't like to use that strict of a rule because the past is the past and the past doesn't predict the future of the stock market. We know that in the fine print of every ad, right? So well, I don't want a rule, but even just knowing that that's the point to ask yourself some questions is pretty helpful.

Brooke: Great. So have you noticed some patterns in there, like certain rules, even if they're applied with a measure of kind of critical judgment, certain rules that come out as being particularly helpful or perhaps certain personas? There's the investor who's around for the slow bleed and there's investor who sells too early and doesn't stick around long enough to enjoy the upswing later. Are there some patterns that you've noticed?

Clare: Absolutely, and we've looked at so many different portfolios over the years. We did a study actually, where we were looking for clusters and particular "Is there a type? Is everybody a type?" And what we found is that there's no one thing that everybody does badly or well, every professional investor, but they all have at least one thing that they do consistently that affects their performance. And in some cases it's a good thing, but the biggest clusters existed around holding on to losers for too long. And some people have a very interesting characteristic of only ever selling losers, never selling winners. And that sort of lines up with the other most popular sort of bias that we run into. We attribute to the endowment effect, but what's called in investing, the round trip when you buy something and it starts working and it's making you good money and you get very invested in the story.

And if you're a professional fund manager, maybe you meet the CEO and this management team, and they have a relationship, but it's a whole thing. And when the cracks start forming, you don't notice, not in the same way you would if you didn't have this sort of romance going on. And so suddenly you wake up one day and you've almost lost all the money you made in that stock and you didn't even appreciate that. And now you're about to look really stupid. The round trip is for a fund manager just so embarrassing and so much of this is about sense of embarrassment I think. This goes on all the time. So what you can do, what we do do to help people with that is we look at every single position they've ever held and how did the performance accumulate over the life of the position, and then we find the point again, historically, when would it have made sense for you to, if not get out, at least ask yourself some harder questions.

Because we get the daily trade data from these portfolio managers we work with, we can detect when that moment has arisen for any given position and ping you a message that says, "FYI, you know that stock? Ask yourself these three questions that you said you wanted to be asked the next time you were at this point, because you're there." We call them nudges. But it's a different kind of nudge. It's a very blatant, explicit... It's more like a shout than a nudge. But people find that helpful because it pitches that objective checklist type of mentality to them at a moment where in the past we've seen them passively just make the wrong decision by sticking around.

Brooke: Yeah. So let's talk about the past. How have problems like these traditionally been viewed and handled?

Clare: I mean, they haven't been viewed is the reality and therefore they haven't been handled. The closest thing to it, if you read any of the investment greats, Warren Buffett or George Soros, all of these different people, they will say, "I kept an investment journal and when I looked back through it, I found that there was a correlation between losing money and my back hurting." I think it was George Soros. So certain things that they took note of and every textbook or course about being a good investor will tell you, "Keep an investment journal," but that literally is being done on paper, spreadsheets, post-it notes, not consistently. These are not structured data sets typically that are then being used for analysis. And in fact, people don't necessarily even go back and reread their journal.

I mean, that's never really an urgency and so they just don't do it. So in the past, all the fund management industry had to rely on was performance attribution, analysis that we were talking about before where it was just an explanation of, "Oh, you lost a lot of money last quarter. That's because you were heavily weighted towards Argentina." "Okay. Well, thanks for letting me know." That's not really that helpful. Or if you're a diligent, disciplined person, maybe you keep a journal, but then you've got to build a process where you're going back through that journal and try to discern what the relevant patterns are in your own behavior. And a human is just not well-equipped to do that clearly. So this is where we've automated that process.

And some of the other kinds of nudges that we do are really just email notifications to ask for context about what you're thinking about. But once you have that data, you can connect the dots between what was going on in the person's head, what was the decision they made to trade or not to trade, and then what was the P&L outcome of that that affected their performance for better or for worse. And if you do that enough time, because these people trade that much, you can start to see really interesting and statistically significant patterns.

Brooke: So it seems like there are two potentially separate things that are going on here, or at least they're related to each other, but they're not one and the same. One of them you've been describing up until now, which is very much around data and analysis. So you mentioned that your old notebook that you keep kind of jammed under your keyboard, that you pull out every once in a while and scribble a few notes in and potentially never go back and reread, there's a mile of gap between that and a structured data set that's getting assessed on an ongoing basis and has all kinds of different data feeds automatically being brought into it.

So that's one, this kind of piece around analysis and effective data creation, but the second piece is around biases. So the patterns that we discover in that data set, being non-random, that there are specific and at times, very predictable ways in which people fall down in trying to make investment decisions. Let's shift onto that second track and start talking about some of those patterns that you're discovering and how it is that you're using behavioral nudges or shoves, as the case may be, to try to get people back on the straight and narrow there.

Clare: Sure. So the round trip is the first example. You've already heard that but holding onto a stock for too long such that a winner becomes a loser and we use a nudge, we call it the alpha decay nudge, to ping you at the moment where historically this would have been where the alpha generation, the juice of your return would've run out. Just suggest that you ask yourself some extra questions and here are those questions. And then the user answers the questions and the data goes back into the system and now you've got a feedback loop going. But not everybody has that problem. Everybody's different. So some people, what we notice is that they might get their stock picking right, but they destroy a lot of value through adding and trimming too much.

We attribute that to overconfidence in the sense that if you're making a trade, you're saying that you think there's a better than 50% chance that this is going to work, right? Otherwise, you should not be doing that, ethically. So therefore, if more of your trades lose money than make money, your calibration of what has a more than 50% chance of working is wrong. And so if we could just detect that by looking at every adding and trimming trade that you do and see, "Do they work out more often than they don't? And when they do work out, do they win big and the losers only lose a little?" That's what you really want to see. And for those people, it's usually not something that they didn't think of before. They've probably had other people in their life say, "Oh, we think your portfolio turnover is too high. Investors don't like to see that."

This sometimes is all they need to see to be like, "All right, fine. Okay. I'm going to give myself a set of questions that I'm going to ask myself before I trade. If I want to add, it needs to meet these criteria. If I want to trim, it needs to meet those criteria." So kind of simple concept, but it's about committing to going through that in that moment. So that's another one. Sometimes you see things where it's the type of stocks that people are getting involved with, like small cap. Some fund managers who are not specialists in small companies, but are investing across the market capitalization spectrum have a tendency to get sucked into small company ideas because they're sexy and exciting.

And there's the fear of missing out. And it plays to all your emotions, all these exciting stories. "This could turn into your tenbagger," which is what you're always after. And each time it ramps you up, you get sucked into the story and you do it. But when you look at it in aggregate, you see that, "Oh God, we're so rarely right about that. Ugh. All right." Usually there's somebody on the team who's been saying this the whole time, that we should not be touching those stocks, but it takes seeing it in the data mirror to actually say, "Okay, either we're going to make a rule that says, "No, no more small cap. We're done." Or more realistically, we're going to set some stronger criteria that this idea has to pass through for us to actually put money in it." Yeah. That's the idea.

Brooke: I'm very curious about those criteria. So how do you calibrate the types of questions right? So you mentioned that fund managers don't like to have these very, very firm rules. They want to be applying some critical judgment. So these questions are kind of the pre-commitment, right? It's like, "Okay. So I kind of get this idea in my head that when I get this shove, this set of questions, I'm committed to going through a structured thought process to help to guide me towards a better decision here." What does that thought process look like? And how do you manage to make it, on the one hand, palatable enough for the fund managers to get on board with it? But on the other hand, painful enough that if someone's already really committed to a bad decision, this is going to pull them back from the edge?

Clare: Yeah. It's a great question. And it also has to be very relevant. That's part of the palatable piece, I guess, but it can't be that we're just asking questions that they think they should be asked. We want to ask them questions they think they should be asked and empower them. You come up with a question so it's you talking to yourself. And if you're open to solving a problem, a behavioral problem, whether it's to do with trading or could be about your diet or your exercise or how you behave with your family. I mean, it could be all sorts of things. I started experimenting on myself with this originally, and I don't trade. So this isn't even about trading for me, but I created this thing called the Nudge Experiment and it exists online.

I mean, it really is an experiment, so not a commercial entity, but it's just a little thing that says, "Give me three goals that you have," and they might be goals about anything. And then for each goal, it says, "What's one question you could ask yourself every day that would make you more likely to achieve that goal?" And the answer probably isn't, "Did you use your Peloton today?" It's probably something more subtle than that. I mean the most successful one I've asked myself was about, "Have you said something kind to your spouse today?" Because it's important to me to have a good relationship with my spouse. And actually, if all you did was say one kind thing to your spouse every single day, I guarantee you're going to end up having a better relationship. It's kind of common sense.

So it's a similar ethos with the investors when we're creating questions for them. I mean, the service that we're providing is not just a software product that says stuff to them, because they would never listen to that. There's a human. Crucially, a human that has been a fund manager before because also they don't want to listen to somebody who hasn't sat in their seat, but that human comes in and sits down and really probes them and says, "All right. So you have a tendency to hold onto your positions, that we're winners all the way until they're losers. Let's look at some specific examples of that. In this one, what do you wish you'd asked yourself at this point in its life?"

And they'll start talking, "Well, yeah. What went wrong was this. We should have asked ourselves at this point, do you still trust the management team? Or do you think there is a greater than 50% chance of them beating their earnings forecast next quarter? Or something like that." Great. That's your question. But you have to elicit these questions from them and then they're more likely to engage is the hypothesis there, that because they hear their own voice and they're asking a question that they've already deemed relevant and worth asking. So that's how it works.

Brooke: Yeah. That's very interesting. And you mentioned that this process is not just kind of a, "Sit down on the corner of your desk and answer this." But in fact that feeds back into the system. Can you tell us a little bit about that? So how are you using these shoves, these mega nudges as a way to create valuable data to kind of have the system learn dynamically over time?

Clare: Well, so it goes back to the point of asking the right questions, right? And it has to be the right question from the point of view of the person who's going to answer it, but it also needs to capture some interesting data that's going to be useful in analysis because if it's just free text, that might be helpful to the user in the moment. And there's an argument for that. But at the end of the day, particularly, although natural language processing has come a long way, investor jargon has not. Fund managers are not speaking the same language that these AIs have been trained on yet. So it's hard to do that, but if you can get them to answer questions, sometimes it'll be a question like, "Was this decision unanimous amongst the team? Or was it contentious or mixed?"

And if you can get them to start answering that sort of question every time they make a decision, then it doesn't take that long before you've got some interesting data about, "Wow, have you noticed that the decisions that work out the best tend to be the most contentious ones? That's interesting." Which is what we have found. I think it gives a team permission to have a contentious conversation without it being the end of the world, because you know, "Actually this is good for us, because when this happens, it typically works out well." So yeah, I mean it is all about what you're asking and making sure that you can get a structured data answer out of it. And we do. We have some clients who get a nudge every single day that ask them questions about the state of them.

You've got a market context for the decisions you're making, but what about your emotional context, your physical context, all of that? How'd you sleep last night? Have you been exercising? Are you hungover? These are questions... We're never going to force on anyone, but some people are very curious and they want to know, "What's the connection between my sleep and my investment performance or the quality of the decisions I make?" If I knew that I made better decisions when I got nine hours versus 12 hours or six hours versus eight hours or whatever, fine, I'll do that. That's of interest to them. And same with being hungover and some people don't want to ask that outright. And we had one client, he wanted to track how much he was drinking, alcohol he was drinking, and how that related to his trading decisions the next day. But he didn't want to say that that's what he was tracking because his employer actually is the one paying us, not him.

Brooke: Right.

Clare: And who knows what could happen? That was his view. And so the question we would ask him was, "How many servings of fruit did you have yesterday?"

Brooke: Right.

Clare: And so the correlation between his fruit intake and his decision-making was markedly negative, surprisingly.

Brooke: But I keep hearing that fruit is so healthy.

Clare: That's so funny. Anybody would've been like, "Wow, that guy eats a lot of fruit." Yeah. Turns out it's not so healthy.

Brooke: Yeah. So given that you're collecting data in this structured way, I imagine you've probably seen patterns of the kinds of questions that seem to be the most helpful predictors of performance in the future. Are there certain types of questions that you see coming back over and over again? So in terms of ones own physiological and psychological state, it's like, "Did you get more than three hours of sleep yesterday?" I'm going to say that question's probably highly correlated with performance.

Clare: Right.

Brooke: Did you have more than four drinks yesterday? Did you have an awful fight with your spouse before leaving the house this morning? There are going to be some things that are really high up there on the list, but predictably so. What are some of those questions that kind of came out of left field and provided some really interesting and beneficial surprises? Like we didn't expect this thing to be such a strong predictor of outcomes?

Clare: Well, the consensus versus contentious one is a good example of that. I wasn't expecting to see that the most contentious decisions were the ones that resulted in the best outcomes, but I can see it in retrospect. It's like, "Oh, I can see how that could be the case." Actually most recently the one that has come out of left field, it's the opposite. It's the most obvious question to ask that you assume must be correlated, turns out is not. So in the world of a fund manager, a lot rides on your conviction level. How convinced are you about this idea? And I mean, we can spend an hour talking about conviction and what does that even mean? And how do you measure it? And everybody's percept of what that is is different and so how could you have a consistent process around it? But nevertheless, conviction is a keystone of how fund managers work. And usually they use it as the way they size position.

So, "The most of my portfolio is the thing I'm the most convinced about. And if I have a small position, it's typically because I'm not quite there yet with my conviction." Yeah. Well, one thing we found that is very common is the tendency to make all your money out of your big positions and bleed a lot of money out of your tiny little positions. And when you show that to a fund manager, they'll say, "Oh, those small ones are the ones where we're not that convinced. We should never have had those in the first place." Okay. I mean, there could be other reasons that they're small positions, but we don't want to assume anything, but that's usually what they say. But we have a client actually who did a webinar with us recently where he, to his credit, was very upfront about this.

He had us using nudges to track his conviction level every time he made a decision and then look at, "Is our conviction level actually predictive of success? Do we make the most money out of the things we've the highest conviction in?" Which may or may not be the same as our biggest positions because he felt that that wasn't always the case. And the answer was, "No, your conviction is not predictive of anything." And to some fund managers, that would be like a really big dissonance moment of, "What? My whole existence is wrapped up in my conviction and how I make decisions is completely based on this. What do you mean it's not predictive?"

That would call into question their entire reason for being a fund manager. But for this guy, actually it didn't. His view was, "You know what? Fine, I'm going to have an equally weighted portfolio where every position's the same size. I'm not going to try and waste my energy trying to say which one I'm more convinced about than the others. And I'll use that bandwidth for other things," which is exactly the right answer. But it takes an unemotional mind to be able to come to that conclusion and then broadcast it and not be embarrassed, not have your identity be wrapped up in that assumption that your conviction is what makes you a good investor.

Brooke: Yeah. It's quite poetic. That's the real punch in the gut, right?

Clare: Yeah.

Brooke: Everyone who's just kind of branded themselves, not only in terms of their professional worth, but also their reputation, in terms of the quality of their gut, what you're showing them is your gut is worth zero.

Clare: Yeah. And that might not stop you having a gut feeling, but it should stop you from acting on it. And don't beat yourself up. This is natural, this is normal, and it's kind of crazy that we ever thought that your conviction level was predictive of anything.

Brooke: Right.

Clare: Why did you think that?

Brooke: Well, I mean, only because we have an entire society and industry around us that tells us that we should, right? But I mean, beyond that reason, there's no reason.

Clare: Yeah. But if you could just be objective about these things and appreciate that, "Okay. That's a waste of my energy. I'm not going to spend time translating my conviction into decisions. I'm going to spend it on picking these stocks in the first place," or whatever it is, that's very liberating actually.

Brooke: Yeah.

Clare: If you knew you could make fewer decisions and get a better outcome than all the running around making a zillion decisions you're making that's getting you nowhere, life would be way better. And that actually goes back to why did I start this company? Because I was that person running around getting... I was running in one place for years, getting nowhere and thinking, "This is not a good return on energy expended. How do I improve on that? I want to make fewer, better decisions."

Brooke: Yeah. And you talked earlier about the round trippers who end up getting to know the executives in the company and this kind of thing. And through this lens, what we're saying is they invest all of that time, money, energy in training their gut or in trying to inform their gut feeling. But the gut feeling actually doesn't deliver any value. So all of the energy that you're pouring into trying to give your gut all the things that it needs to come to a decision, you're putting that energy in the wrong place. You should be putting that energy towards informing your brain.

Clare: Yeah, exactly. And there are plenty of things you can do, if you're thinking about things in terms of probabilities and upside versus downside and that sort of thing. There's more you could be doing to get those numbers right. And then you will have a higher probability of success.

But that's a rude awakening for somebody who has been doing it like this for 30 years.

So it is very much a generational shift that's going on in the finance industry right now. And even within asset management itself, amongst the fund managers. Then the next gen people completely get this. At least they understand that they shouldn't be afraid of acknowledging that their gut isn't necessarily right. And they should be open to looking at evidence and data and trying to keep an open mind. So yeah, it's interesting to watch that crossover happening.

Brooke: Yeah. So if not the gut, then what? I mean, there are so many companies out there who are trying to use artificial intelligence to discern which market signals are really robust predictors of future success. And it turns out that the market's just a really, really noisy place. So if you're not using your gut... Whether your gut is right or wrong, it can be a very strong signal. You really feel it. Your gut really tells you you should or you should not. Whether you trust it and follow that advice is a different matter. But your gut is loud. Once you've taken care of kind of dialing down the volume on your gut, or at least training yourself to put in ear plugs when it starts to scream at you, how do you dial up the volume of signal, knowing that there's so much noise out there, such that even very sophisticated AI algorithms really struggle to pull the needle out of the haystack?

Clare: I mean, that's the game of the stock market. It's like if you think that you can beat the market, then become a professional investor, know that the average person cannot, and therefore you better be above average, right? We know how people feel about their above averageness, in general. And everybody has a different approach. All investors have a different approach. You have AI ones who are trying to take lots of data and use that to predict the future. It can work for a little while and then things change. The market is a changing beast. And so the algorithms have to be constantly refactored and updated. And it's very hard to consistently outperform, but some people do. There are certainly examples of firms that have made huge... I mean, even a Bridgewater who, they have had bad years as well as good years, but in general, they've been very, very good.

So whatever they're doing, they have a process that's much more quantitative than sort of human-focused in decision-making. But it seems to have worked for a long period of time. I mean, I have clients who are human investors who have their own way of deciding what to buy and what to sell. And it might be about how a stock behaves around its earnings announcements, or it might be about picking companies of a certain type with a certain long-term time horizon. And they feel that the market hasn't really understood the potential yet of this company. And they're willing to wait and see it out. And these people can consistently outperform, but they have to be very disciplined about how they're doing that process. And they have rules, just like an algorithm would have rules. They figured out the ones that work for them and the smart ones will retest those rules on an ongoing basis, just like a quant fund manager would be refactoring the algorithm because things change. And it's not possible that the same rules that worked 20 years ago are going to work today.

Brooke: Right.

Clare: So it's not for the faint of heart. If you aren't prepared to spend your entire career focused on this, then you're really gambling. Most of the time that's what's going on and we call it investing.

Brooke: Yeah, that's just more structured sometimes.

Clare: It's more expensive than gambling, I think, in the end. There's a lot more activity involved.

Brooke: So if I can distill a number of things that have come up through the course of this conversation, the first is that, and probably the foremost, is that your gut is not a very reliable data point when it comes to investing decisions. That's the first frontier that must be met, is to just stop trusting your gut.

Clare: I would say or your gut is predictive, maybe. You don't know this until you capture data about it and do the analysis. And we're all walking around assuming that our guts are correct and that just is not true. That is not true. But if you do the data analysis, who knows what it'll say? It might say that when you have a certain feeling, when the hair's on the back of your neck stand up, that is correlated with the biggest wins of your life. Okay, great. It would be great to know that so that you don't act on every gut thing, but when the hairs stand up on the back of your neck, that's the time. Okay, great.

Brooke: Yeah. Yeah. That's interesting. There's a body of research that really digs into that, which I find quite compelling, which is to think that your emotions and your physical responses, like the hairs on the back of your neck standing on end and this kind of thing, should not be confused for rational thought processes. But that doesn't mean that they should be entirely chucked either. Your body is giving you a piece of evidence, but you need to know how to process that evidence in order to be able to get value out of it.

Clare: Exactly. It's data and your emotional state, that's data. And if you can capture it and reflect on it later, yeah, you can see patterns in your own behavior that get in your way, or that are predictive of what the outcome is going to end up being. And we do have intuition, I think. I mean, I know in my own life, there's certain things where my intuition is horrible. Horrible. Like what direction is north? I have no idea. I can't tell you. The opposite of what I think would be the right answer, except that I don't even know what to think at this point. But if you put me in front of a person and my Spidey-Sense goes off with them, and I try so hard to not be biased and to be objective, and so I've overridden that intuition a million times and regretted it every single time. It's uncanny. So now I've learned to like, "That one, just trust it. And you've been through this too many times, just back away from that person."

Brooke: Yeah. So taking this structured approach, so if you want to win in your investments, you need to have a strategy. You need to have discipline. That strategy needs to be pressure tested with data, and it needs to be refined over time as more data comes in. Your strategy evolves, there are new things that you learn, new things that you learn to pay attention to. And at the end of the day, it's just very hard to do that effectively with post-its and pieces of paper and notebooks and journals and this kind of thing, not only from the data capture and creation perspective, but especially from an analytical perspective, to actually start to identify those patterns that are not necessarily easily visible to the naked eye.

Clare:  It takes work. This is not a do-it-yourself. That's why we exist because doing it yourself is very hard. I mean, that said, if all you had was an Excel spreadsheet and your brain, you can write down what your strategy is going to be and what your sort of initial algorithm is going to be for how you make decisions, and what questions you're going to ask yourself in the run up to deciding to buy something, and that's a great starting point. If you could just set all of that before you're in it, not while you're reviewing an idea, before you've got any ideas, what are the questions that are going to matter, and then be able to revisit those questions down the road and say, "Was I asking myself the right questions at the right points or not? And if not, I need to adapt that."

Brooke: And so I think in closing, there's nothing more appropriate than pointing out the sign that's on behind you, which, of course, none of the listeners will be able to see, but the sign says, "Know thyself." And given my background, I was particularly drawn to that sign. I now appreciate there's a much deeper kind of meaning to that than was apparent to me at first glance. Like what you're helping people to do is to understand the signals that they are giving themselves and to understand how to fit that into an algorithm that helps them to make better decisions about how to invest.

Clare: Exactly. And to just become more mindful in every decision they make and more self-aware, quicker to be able to process their own foibles when they are foibles. I mean, even myself, I am on Zoom most of the day and I can see that sign in the background. I'm reminded to know myself all day long every day, and I have emotions like anybody else, but I've become very quick to be able to identify when I'm acting on emotion or when some emotion is leading me in a particular direction, and not act on it. It's about not trying to stop yourself from experiencing the gut and the emotions and all of that, but rather to work with that much faster, much more efficiently and not get trapped by it. And I think that life is better for everybody if they can do that.

Brooke: Yep. I think that's a great point to end on. Thank you very much for this conversation today. It's been great.

Clare: It's a pleasure. Great to talk to you.

We want to hear from you! If you are enjoying these podcasts, please let us know. Email our editor with your comments, suggestions, recommendations, and thoughts about the discussion.