Ep 6 - Beyond Diversification: Insights from Sebastian Page on Finance, Leadership, and Resilience

In Part two of this insightful interview, Alan sits down with Sebastian Page, author of "Beyond Diversification," to unravel the complexities of financial forecasting and portfolio construction. 

Sebastian challenges the conventional reliance on averages, revealing how they can obscure critical extremes and mislead investors. Through engaging anecdotes and expert analysis, he explains why understanding the nuances of current conditions and historical data relevance is crucial for making informed investment decisions. This conversation is a must-listen for anyone looking to deepen their understanding of asset allocation and the dynamic nature of financial markets.

Transcript

SPEAKER_01 0:00

This might be one of the greatest mistakes that we make as an industry is to rely on averages. Averages can hide extremes. They can hide different regimes.

SPEAKER_00 0:16

Welcome to the most important thing. The podcast where leading voices in finance, economics, investment, and geopolitics share the one idea they believe matters most. Renowned broadcaster Ivan Yeats and finance expert Dr. Alan O'Sullivan will uncover for you what actually matters. In a noisy world, clarity is power. Here, we focus on the principles and insights that endure long after the headlines fade. This is the most important thing.

SPEAKER_04 0:45

Hello, you're very welcome to part two of the Sebastian Page interview on the most important thing. So we covered a lot of ground in part one, and I am aware that it was a bit technical, okay? So we're trying to push the learning curve here in the most important thing. From now, from time to time, we'll have some leading experts and talking about some complex topics. Hopefully, we'll make it digestible. Follow-up to this series is going to be the truth series, where we are going to offer some educational material for investors that they can improve their own knowledge. But in part one, we looked at Sebastian's background. So trying to look around corners as such. Naive diversification is this diversification which says that we think we're diversified, like in 2008, but really all we have is a a collection or a basket of different assets that fall together during a stressed environment. And we need to be a bit more thoughtful, I suppose, in terms of how we build portfolios. We talked about fat tails. So what is a fat tail? So if you're looking at a distribution of outcomes, so-called normal distribution, but what we know in life, in markets, in everything we do, it isn't normal. It's it's uh random, it is dynamic, it's non-normal. So fat tales talk about the extreme. We say fat tail is where you get an extreme event, but it has a disproportionate impact on the outcome. Uh so famously Nassim Taleb, the black the author of the black swan, talked about um these black swans, and essentially these are these fat tail events, like 9-11 perhaps could be described as a fat tail event, two planes hitting two tall buildings. In terms of averages, this was the m this was Sebastian's most important thing. So this is part two. We're going to talk into why over reliance on the average can be really uh dangerous, especially when we look at all the investment literature says past performance is no guarantee of future performance, but yet we still hone in on the average performance of funds or the average risk or volatilities. So we discussed that, but we also finally then look at leadership, and it's another big area for Sebastian. His focus on leadership skills, and I know that would be a of keen interest to this audience, but also he has a new book out, which I'll put some links to the show notes as well. Okay, enough for me. I hope you enjoy this part two of my interview with Sebastian Page on the Most Important Thing podcast. Thank you. Your book also talks a lot about the stock bond correlation or lack thereof now, perhaps in a different regime. In terms of the stock bond correlation, but also this notion of negatively correlating assets. So we talked to our clients about the importance of having assets in the portfolio that behave differently during a stressed environment. That's very easy to say, but quite difficult to implement. Can you maybe speak to that challenge?

SPEAKER_01 4:18

One client told me once if you can't diversify hedge, of course, hedging is costly, but in some of our portfolios for people that are near retirement, we have a slice of our equities that is hedged and we manage the hedge, we manage to downside explicitly with different methodologies to reduce the cost overall. We recognize there's a return drag, but for that part of the portfolio, we know we're going to do well if the market crashes, or we expect we're going to do well if the market crashes, and bonds don't show up as the diversifier, like in 2022, right? If it's an interest rate or an inflation shock, then U.S. treasuries might not be your diversifier. And it's extremely difficult to find assets that will act as a hedge or diversifier in the market sell-off, because that's when investors panic. That's when you have liquidity issues. We talked earlier about my father, a finance professor. He loved to use analogies. And he had an analogy to describe liquidity crises to students. I think is relevant when we think about why things all tend to fall together and what happens when things go bad. Tell the students, imagine the building's on fire. Of course, everybody's rushing for the door. The difference, he would say, in financial markets is that in order to get out of the building, you need to convince someone to take your place. If you're holding an illiquid bond in a market sell-off, the only way you get out is by finding a buyer. And that creates gaps in prices. It creates assets that go down together just because they have similar liquidity characteristics. It creates panics, and that's why fear is more contagious, or it's related to the observation that fear is more contagious in financial markets than optimism. What do you do about it in portfolio construction? Well, I talk about in Beyond Diversification how you can construct portfolios using optimization models that are more sensitive or more calibrated to the tail risk of different asset classes. You know, instead of just running a sharp ratio maximization and loading up on credit and hedge funds or liquid assets that all have negative SKU, they're all sort of selling options. If you calibrate your model using full-scale optimization, then you can control the trade-offs with much more directionality and much more optimality ultimately. Alan, I started working on this early in my career. It was a really interesting moment. I got a call from my mentor, Mark Kritzman. And at the time I was doing work for him, we would schedule our meetings. He was pretty busy. I was a quant running numbers and building models. And when we met, it was scheduled, and I had just a limited time to explain my findings. Then he would help me interpret and think about next steps. This was a random call. So I'm sitting at my desk, phone rings, I get a random call, and Mark says, Seb, can you just walk over? His office was next door. I walk over. Mark is sitting there in his office with a sheet of paper, and he's frowning a little bit. And he hands the sheet over to me and he goes, Do you understand what this is? It was a handwritten fax, Alan. And the fax was from Paul Samuelson, Nobel Prize economist. He's passed away since then, one of the most famous financial economists of all time. And Paul was writing a handwritten fax to Mark to complain because Mark had just published something about mean variance optimization, explaining how useful it can be to deal with different problems like currency hedging, understanding risk tolerance, and so on. So Paul Samuelson was saying, you're forgetting fat tails. And here's how in portfolio construction you address fat tails. You optimize over the entire probability distribution. You don't need to model it with parameters. It's called full scale optimization. This Alan kicked off a project for me to start running full-scale optimizations to build portfolios of hedge funds, comparing those with mean variance optimization. And it became a framework that we eventually started using in practice and writing about and publishing about. But it's a story to say there are ways to address fat tails in portfolio construction that have been published over the last couple decades, and they work quite well if you know how to use them. Now at the time, what did Mark do? Because at the end of the facts it was written on the margin by Paul Samuelson to Mark. And Mark, therefore, you and Markowitz are both wrong. And when Mark tells this story, he likes to say, well, then I called Markowitz and I asked him, Harry, what did you do wrong? But it was a debate of the Titans, right? A debate of the academic Titans between Markowitz and Samuelson about fat tails. And it's an important debate. It's part of the debate I cover in Beyond Diversification, part of the debate we're having today.

SPEAKER_04 9:35

You talk a lot as well in the book about sample size in terms of I thought it was fascinating that you reveal that the opposite of what we believe may be true in terms we as researchers, as practitioners, we're told large sample size is better because we've more observations, more information. But you actually say no. And the reasons you give are quite interesting in terms of how relative is 1926 uh to what's going on today in this economy. And that actually is very intuitive. Can you speak to that?

SPEAKER_01 10:09

Look, uh, from a statistical perspective, it's a fact that more data leads to more statistical confidence. It's a mathematical fact. In finance, though, if we want to add more data, we have to go further back in time to a time when maybe the world was different. And going back to what I talked about earlier relevance, maybe the data is not that relevant. In the 90s, there's some people that were still going around on a horse, you know, or with a horse and buggy that was part of the transportation mode. Now there was no computer, 30% of the jobs were in agriculture. Now we're at 0.8% of the jobs in that. So it's like why is that data would be relevant to the volatility of the asset I'm looking at? Now there's a broad discussion about that in the book because asset classes change over time. The high yield asset class now, if you look at the spreads, they're really tight as we record this, but the quality of the asset class is much higher. The ratings are higher on average, the companies are better capitalized, and it's less sensitive to the boom and bust of the energy sector. So does it make sense to say the spread is the tightest it's ever been? Well, maybe not if you quality adjust, because the asset class itself has changed. The SP at some point was much less in technology, much more in financials and energy. Now it's a tech index, basically, the SP 500. So maybe those high valuations are just reflecting the fact that it's a high margin tech index. Asset classes change over time. I'm not making the hard statement, Alan, that you should always use short-term recent data for forecasting, but I'm just casting doubt on the general assertion that more data is always better. That's not the case. And you have to account for is the data I'm adding relevant to today or not? And then you can go one step further and ask, what are the conditions today? And what's the data that's relevant to similar conditions in the past? It's an important topic. I had this thing with risk forecasting, Alan, where I looked at different look back windows to forecast volatility and different forecasting horizons. What do we do for all of us who do strategic asset allocation? What's the first thing we do? Oh, we have a, we'll use the five-year volatilities and correlations or the 10-year volatilities and correlations. Why? Because it's strategic asset allocation. It's for the next three years or five years, right? But what I found, for example, is that when I used higher frequency, more recent data, like a couple months of daily data, for example, was more predictive of volatility for the next year than using more data or longer data set. So I kind of uncovered this rule of thumb where higher frequency, sort of more rat more recent data had more forecastability over longer time horizon risk models and metrics than you might expect. And that's why a lot of us use exponential weights, for example, where we put more weight on recent data. Andor, again, going back to what we were talking about earlier, different probabilities relevant to the current environment applied to different risk regimes that we've pre-modeled.

SPEAKER_04 13:33

This is like a portfolio construction masterclass. Just in terms of the theme of this podcast, Sebastian is the most important thing. It's supposed to be evergreen in nature. So my aim really is that a graduate, a young professional, looks at this maybe in four or five years and says, Yeah, I could take something from this. This is really valuable. So I know it's a high, that's a high bar for finance though, because everything is so dynamic. But if you were to say your most important thing from a portfolio construction perspective, what would you lean towards?

SPEAKER_01 14:06

Beware of averages. They can be misleading. When you look at 10 years of monthly data to build a risk model, you're essentially averaging equally 10 years, but you're blending very different correlation environments, very different risk-on, risk-off regimes. I like to use the analogy of the statistician who had their head in the freezer and their feet in the oven, and they made this statement. Hey, on average, I feel awesome, right? With the head and freezer and the feet in the oven. Well, if you build a risk forecast, say for the stock-bond correlation, and you use the average correlation based on equal weighted time intervals, you're blending times when bonds were great hedges and times when bonds were horrible hedges where inflation spiked, like in 2022. What does the average tell you? Not much. So there's an effort to be made to recognize the environment we're in to assign probabilities to regimes and reweight our risk forecasts accordingly. Sometimes using our judgment is kind of either taboo or nerve-wracking for quantitative analysts. But you know what? It's part of how Markowitz set up the whole science of portfolio construction. It actually allows for judgment in the process in different ways. That's kind of why I wrote the book. Where and how can you incorporate judgment with quantitative methods? Reweighting your risk regimes to reflect current conditions, to make them more of a forecast, to make your risk analysis more of a forecast. I think it's just one way to do that. That's illustrative.

SPEAKER_04 15:46

Listening to you there, I was thinking about my own kind of journey from, you know, the mean variance, which is looking at the first two moments of distribution, the mean and the variance. And your book is covered in uh you know references to skewness, cortosis, the third and fourth moments of the distribution. For people who are lost and don't know what I'm speaking about, we'll have some of the more information on this in the notes. But you also reference views a lot. Actually, this is the alpha, perhaps, where you know, if we just want the market, we can buy the index. But perhaps what is alpha? It's either stock selection, superior stock selection, or market timing. We know market timing doesn't really work, so maybe it's in the asset selection then. So some of my research has traded into the kind of Bayesian approach in relation to asset allocation where you're starting with your you're using some mean variance, but then you're imposing your views. And that's back to our what we started talking about at the start. The views are conditions on what's happening today. So it sounds to me like you have a Bayesian view of asset allocation. That'd be right.

SPEAKER_01 16:55

That's one of the best ways to blend judgment with views because if you use something like Black Litterman portfolio construction or related Bayesian methods, and you want to incorporate views in your process with the data, the process is going to force your views to be consistent with your assumptions for correlations and volatilities, for example. It imposes structure. It also accounts for the level of confidence you have in different views. Some of your views you might express with high confidence, others with less confidence. That's a wonderful framework of blending judgment into practice. But, you know, I'm reminded of the opening story in the book, Alan, about Bern Sharer. He's a very well-respected practitioner. He had a great career managing money and academic. He's right now in academia, he's very well published, and he's written books about portfolio construction. You might recall the story, Alan, when you read the book. I was sitting at a quant conference, and you know, it was kind of a sleepy conference. Bernd Scherer showed up to make a presentation, and things got interesting. First of all, he was jet lagged. He was off the plane, a little tired, so a little short. And he makes presentation about portfolio optimization models. I think it might actually have been Bayesian model. And there was uh someone in the audience who was more of a fundamental investor, not a quant. And I remember they raised their hand and they brought up the Geigo critique. Now we all know the Geigo critiqued, right? Garbage in, garbage out. And the point was you're presenting us this fancy portfolio optimization model, but it's all wrong because we can't estimate expected returns with any certainty or the risk models. And I always remember because Bert was a bit jet lag, right? He looked at the person, I always remember what he said. He looked at them in the eye and he said, if you don't think you can estimate expected returns, you shouldn't be in the investment business. And mic drop. And I think you have to recognize that when you're investing, you're making decisions under uncertainty. It's unavoidable that you're actually implicitly or explicitly making a forecast. You could say, oh, risk parity is agnostic. It's not making any forecasts. Of course it's making a forecast. It's making plenty of forecasts. You're actually making a forecast that all asset classes will basically have the same sharp ratio, which is quite a bold view. I mean, if valuations are super high in an asset class and super cheap in another one. I'm not sure they should have the exact same sharp ratio even in the long run. So all of us, when we invest, when we construct portfolios, we have to remember guy go critique or not, garbage in, garbage out or not. We have to live in a world where we're making forecasts in one way or another.

SPEAKER_04 19:50

That was a light bulb moment for me in the book as well. You know, we make assumptions about expected returns, but those assumptions are are forecasts that we're assuming ourselves, whether they're right or wrong. Um I'd like to maybe touch on your other passions, Sebastian, in terms of leadership. Okay, but before I do, is there anything that uh we haven't touched upon that you think would be important for, say, a young graduate? I I I was at a conference recently, an AI conference, and there's a lot of concern at the moment. Young people, you know, white-collar jobs, can't get work, trainee jobs. What would you say to a young person today that is interested in markets, interested in finance and economics?

SPEAKER_01 20:32

I mean, in terms of career advice, I would say a few things. I would say even if you're extremely skilled as an individual, remember to emphasize communication. If you're entering the corporate world, you might be underestimating how it's important for you to make a contribution to an organization. To do that, you need to hone your communication skills. I think Warren Buffett said if I had one piece of advice to give to a new graduate that would increase their career earnings prospect by 50%, it would be improve your communication skills. I would also say take care of yourselves. It's a long journey. We're in a business that requires a ton of resilience. You might have gotten all A's in class. If you start investing and managing risk, you're not going to get all A's. No one does. No one has a crystal ball. Take care of yourselves, make sure you get enough sleep, make sure you eat okay, and make sure you exercise because it's a long journey. It can be extremely fulfilling, but it's challenging. And if you're not taking care of yourself fundamentally, everything else becomes harder. So it might not be the advice you expected, right? Communication, taking care of oneself. A lot, a lot of people are entering. They'll they'll have the skills. And you know, AI is they're gonna learn how to use AI, they're gonna be more efficient with AI. AI is not gonna fully replace all the jobs, there's gonna be new jobs that are gonna be AI related. AI optimist here. I think it's gonna create productivity gains, but not kill all the jobs. That's how I think about it.

SPEAKER_04 22:07

I love the Warren Buffett um piece because I I taught an MBA class for years in in Dublin. And my advice was communication. And I I I embedded a presentation at the end of every semester because I wanted them to get a little bit. And I told a story about Warren Buffett where he has honorary doctorates from all over the world, but he has one diploma hanging in his office. And it's a diploma from Carnegie, I think he got in 1968, about communication. Because when he learned how to communicate, his life changed. He says that. He was a genius, but he couldn't couldn't sell it. You know what I mean? Couldn't communicate it. So that's a great story. I really think people would take a lot of value from your other passion in terms of leadership. And I'd love to know where that where that comes from. Obviously, Jean Paul was was important there, but you seem to have a very deep interest in it, insofar as you published through your book, and we put information and notes about that as well. But where did that come from?

SPEAKER_01 23:05

I've been managing teams for about 23 years. I've been in the business for about 25. So it's an imperative for me to always improve as a leader. I became more interested in this from the resilience perspective. I was stressed at work, I was stressing about stressing, and I started exploring sports psychology, which I think is remarkably relevant to investment management and decision making under uncertainty. As an example, Alan, I love what Roger Federer told the students at Dartmouth during his viral commencement address. Some of our listeners might recall this. It's easy to find online because it went completely viral. It was about a year or a year and a half ago. And Federer said to the crowd of students, eager to hear his advice, one of the top tennis players of all time. He said, I have played 1,500 matches and I've won 80% of them. Not surprising, top tennis player of all time. But then he asked, what percentage of the matches, what percentage of the points that I played do you think I won? So Roger Federer, one of the top tennis players of all time, what percentage of the points he ever played did he win? And the answer is remarkably low. It's 54%. His point was, you know, you're going to lose. No matter what game you play in life, you're going to lose. And so he then said to the students, the best in the world aren't the best in the world because they never lose. They're the best in the world because they know they're going to lose repeatedly. They've learned to deal with it. And therefore, I'm asking you students to learn to adapt and grow. So in the context of my leadership journey, the ups and downs of a corporate career, I became interested in the sports psychology, the resilience aspect. And then I started digging into positive psychology, setting long-term goals, which is super important for leadership. And ultimately, what makes a great leader? It's absolutely not what we think. And I did this for self-improvement. I write in the intro to the this is the follow-up, this is not a finance book. This is my latest psychology of leadership book. I wrote in the intro to my reader, you're reading this book for self-improvement. I wrote it for the same reason, right? So this is my learning journey as well. And it's backed by research. But you think of a great leader as someone who's, we just talked about communication, right? Who's super inspiring and an outstanding communicator. She never quits, she's decisive, she's not stressed. Guess what? It's basically most of the time for top-level leadership, the opposite of these things. The opposite. Leadership at the highest levels is much more about your listening skills counterintuitively. The more you climb the ladder, the more you need, must become a better listener. It's about not about never, never quitting. The toughest decision to make in business is knowing what to quit when you've invested a lot and you realize it's not going to work. Annie Duke has a great book on this. She titled it Quit. So it's not really about never quitting. The prized skill in business is knowing when to quit. Like if you want to climb Everest, you got a 4% chance of dying in the process. But there's someone who's reached a summit 30 times and you know what they're good at? They know exactly when to come back and not attempt the summit, avoid the goal-induced blindness. And this thing about being decisive, yeah, making decisions, it's a big part of leadership. But the top leaders I've worked with exhibited unbelievable strategic patience, very rare in business. Knowing when you need to make a quick decision, knowing when you need and should wait it out. That's a more difficult skill, more attached to high performance leadership. You know, stress, stress is part of life. So if you imagine a leader is always calm, we're all going to feel stress. So how do you embrace it and use it for performance? So it kind of flips a lot of traditional things we think about leadership on its side.

SPEAKER_04 27:04

It's really fascinating. And listening to you, it's ego. I think ego was the big thing there, like being able to be quiet, being able to listen, and also be able to admit not defeat, but being able to walk walk back. I think that's uh just a couple of more questions to finish, Sebastian, if you don't mind. Um the leadership side of things, you're a very humble guy, right? I know that because I've watched a lot of your previous interviews, and repeatedly you tell a story about uh a bad review you got on Amazon and how it hurt you. And I was intrigued by this. This here is this uh super successful, you know, senior person leading a company with billions under management. Yet a review on Amazon was very impactful, right? But I think that speaks a lot to the type of leadership you have in terms of I'm gonna take that criticism and I'm gonna channel it positively.

SPEAKER_01 28:02

Is that is that a fair kind of there were two or three reviews that essentially said the same thing about the book Beyond Diversification, yeah. That it was too complex. And one was like, one star, I don't understand anything that this guy's saying. And it hurt because I wrote the book to be accessible. We were talking about skewness and ketosis before. I actually explain all of these things. There's no equations in the book, and I was writing it for a financial advisor. So it prompted some reflection of how we pitched the book and how it was received, and it did it did hurt my feelings. But, you know, Alan, I made something out of this because I was reading this during the pandemic, and my daughter was 10 years old at the time, was hanging out around the house, and I had a light bulb moment, and I thought, okay, this is what I'm gonna do with this criticism that my book is too wonky, too complicated. I'm gonna turn on the webcam and teach my daughter the basics of finance. She's 10 years old. If I can do that, I know I will, as an individual, have improved my communication skills and simplification skills. It was so much fun. I started teaching her about interest rates. I started teaching her about what's a stock, what's a bond, how compound interest works. And we published the videos on LinkedIn. They actually went viral by LinkedIn standards and by the standards of my social media presence, which is very small. It was a remarkably well received because Alan, ultimately, we don't really teach this in high schools or middle schools. And when we do teach it, because there's an effort to increase financial literacy, we just focus on budgeting and spending. What's really interesting, I think, for high schoolers and middle schools as they grow up is to understand the how awesome the investment part of it is, especially the compound interest part. I have a few series of those videos now. The last series, she's 14. She's like at some point started wearing makeup, like becoming, and it's so fun to in some of the videos we compare her responses from when she was 10 to when she was 15. But it really resonated. And I got so many people telling me I'm showing this to my kids because I want them to understand money and finance and investing. And so I had a lot of fun, but it started with not one, but two or three reviews that hurt my feelings.

SPEAKER_04 30:20

That is just a great way to finish because that's a good example of channel channeling energy into a positive place.

SPEAKER_03 30:27

And I think that's wonderful.

SPEAKER_04 30:29

I also want to say that um, you know, all the proceeds of the book are donated to charity as well, which is fantastic and well done on that. Sebastian or Seb, I will I just want to really thank you. This has been absolutely fantastic. A masterclass in portfolio construction. Um, we will share links to the books. If people want to follow you, Sebastian, follow your work. I know you're pretty active on LinkedIn. Is that the best place to get you?

SPEAKER_01 30:56

Definitely. Sebastian, page on LinkedIn, easy to find. The books are easy to find. Amazon, probably the best place. Alan, you gave us a masterclass in resilience today with your slight laryngitis. You you plowed powered through the podcast like a pro. Like a best example of stoicism. You know, I I have this condition, I can't control it. What am I gonna do with it? I'm gonna do the podcast anyways. So kudos to you, Alan.

SPEAKER_03 31:24

When you've got Sebastian Page on the other end, you're gonna do what you gotta do. Thanks, Sebastian.

SPEAKER_01 31:30

Thank you.

SPEAKER_02 31:36

That was Alan in deep conversation with Sebastian Page, one of the largest uh chief investment officers in the world, actually taking real decisions, investment decisions of potentially your pension fund uh and and and many takeaways. Um I like the story he told about in the early days of making a presentation, and he was asked what topic would he speak about, and he had a convoluted answer about uh asymmetric correlations. Uh, and the guy who was organizing the talk said uh maybe we'll put that in another language. So tell us tell us about what is USP in terms of his area of expertise.

SPEAKER_04 32:20

It really is portfolio construction because he has studied it deeply, he has wrote academic papers as we got into he's a CFA, as I said. So, I mean, the bottom line is this stuff isn't easy. You know, trying to generate returns, trying to extract outperformance alpha from this market is going to be challenging. So, in an environment where equities have had an enormous run-up post the global financial crisis, in an environment where bonds are operating in an inflationary.

SPEAKER_02 32:51

Because there used to always be a thing about a bull in a bear market that there was a max run on a bull market of seven years. Is that kind of uh an old adage?

SPEAKER_04 33:01

Again, we talked about averages at the start of this, uh Ivan, and that again, you know, noting is is uh the same. Average is useful for maybe giving a ballpark, but it doesn't tell you the truth because you know, we've had just different economic expansions, different terms. The long we had the longest economic expansion was the end of the financial crisis, where believe it or not, the SP 500 was at 666. That's a very infamous number, okay? At the bottom of the uh April 2000, April 2009, the SP 500 was 666. Do you know what it is today? It's it's well over six six thousand. Okay, so it's tenfold. Tenfold, more than tenfold, and that has been an enormous period. So that uh COVID put a stop to that, but that's the longest expansion on on history, I think. Uh, but then you go to something like 1980s, where you had recessions every five minutes because you had massive problems with geopolitical uh events, rising oil prices. So this stuff isn't easy, you need a scientific approach, and that is his approach.

SPEAKER_02 34:05

Also, I did detect from him that he has plenty to say about self-care leadership beyond investing. He he seems to have a slightly wider remit.

SPEAKER_04 34:14

Yeah, so he's a senior person in a large organization, he's managing a big team, so a lot of his job is day-to-day, managing people, spotting talent, hiring. And he has another book coming out about leadership. Uh, we'll put the link to that as well. Uh, I it'll be on my buy list uh also. But he wants to pass on a lot of his uh the learnings and the of his of his experience, I suppose, at the top of the game in big organizations. And what he what he considers a leader is not what people think. So, for example, uh our view of a leader, leader might be you know, coming in, uh banging the table, uh giving orders. He his definition of a leader is is actually somebody who does a lot of listening, says very little, and has this quite confidence approach. So I thought I found I found that interesting. What's a fat tail? Okay. A fat tail. So when we talked about T A I L instead of T A I L. Okay. So when we talked about averages, or we talked, you know, if people go back to their you know, elementary or primary school maths, they will learn that there's a this normal distribution or this bell curve of distributions. So the best way to explain it would be if we were to look at the average uh score or the average height of a student in first year in university. You know, you're gonna have somebody that's five foot knotten and you're gonna have somebody that's six foot six. But on average, it's gonna be five foot eight, five foot nine. So most of the volume will be in around the average. That makes sense, okay? But you cannot assign that type of statistical approach to financial markets because they're not rational, right? Financial markets can go widely one way, widely another way for no uh explainable rational reason at all. A fact tail is something where you get a massive drawdown negative event, like a 1987, where huge largest one-day crash in in history, 1929, uh, 2008. So, what's the statistics tell us is that these events, fat tail events, are very rare, but they're not rare. Okay. They're they're much more common than people realize. And what we need to do in the context of what Sebastian Page is saying is prepare our portfolios for fat tail events.

SPEAKER_02 36:40

And he's saying that the horizon is more than 10 years?

SPEAKER_04 36:43

Absolutely. I mean, anybody looking at trying to get an understanding of expected returns into the future needs to pull back their time series, needs to pull back their analysis. I would argue 100 years. Okay. 10 years is is like five minutes because you could be you could be looking at 10 years in an environment where growth is strong, no inflation. How useful is that if you're going into a structurally higher inflationary environment and a structurally higher, yeah, I mean Putin's invasion of Ukraine was once in 80 years.

SPEAKER_02 37:16

There had been no war in Europe. Uh uh, there hasn't been Brexit more than one in a hundred years. Uh and and you know, take the pandemic. Yeah. Uh more than one in a hundred years and go back to the plague, you know?

SPEAKER_04 37:30

Yeah, you're right. I mean, because uh, you know, if we looked at the the the the basic statistical models, these things should happen once in a lifetime. They're happening every couple of years. So if there's a basic problem with how you're modeling risk, you've got a big problem with how you allocate assets.

SPEAKER_02 37:46

So that's that's what I was gonna ask you in conclusion about. His main job as a a global investment officer is to assess risk. And he speaks about true risks being obscured. Can you give examples or explain what he means by that? Looking forward.

SPEAKER_04 38:04

I mean, the main job of somebody like Sebastian Page and his team is to try as best as possible, look around corners. Okay, you can take a long-term view on most assets like equities, uh, corporate bonds, and property, and say over time they'll be they'll be okay. But they don't have the luxury of that. If you're retiring next year and you've got your two million in a portfolio, or whatever you have, you need to understand that if there's a big shock, I'm affected here. So the true risk is the risk we don't see, the latent risk under under the surface. Okay, it's the two planes hitting uh two towers. It's what happens. The two most important words in finance are what if, and I would suggest in investing. What if this happens? What if that happens? What you do is you go into a room and uh go through all these possibilities and then make sure that the adequate hedges are in place for that. It's not perfect.

SPEAKER_02 39:07

Okay. So finally, what is the most important thing for Sebastian Page?

SPEAKER_04 39:12

In my opinion, the most important thing from Sebastian the Page's page is to uh integrate proper risk metrics, risk analysis in your portfolio construction process. Be aware that uh have a bit have a bit more intelligence around how you build assets, be aware of the changing secular regimes, uh, and be conscious that markets are dynamic, stochastic, they're unpredictable in nature, and you have to prepare for the worst.

SPEAKER_02 39:41

And maybe at this particular moment in time, the greatest risk is actually geopolitics more than economics, and a time of great uncertainty. Well, you have the best brains there in Sebastian Page. I hope you found that there were lots of learnings for you from that. My thanks to Alan, and until the next episode of The Most Important Thing. And I'd ask you also to check out the Truth Series, which is the knee learning module which Alan has curated and developed, which is now available for people who would like to learn more about investing, finance, economics, and so on. So until uh the next time, thank you for joining us and goodbye.