Ep4 - Portfolio Construction Masterclass: Unveiling Hidden Risks with Sebastien Page CFA

Unlock the secrets to superior portfolio construction with Sebastian Page, a leading expert blending cutting-edge academic insights with practical investing wisdom. Discover why traditional models fall short in today’s unpredictable markets and learn how to navigate fat tails, regime shifts, and asymmetric correlations—all while managing uncertainty like a pro. 

If you're a young professional, investor, or finance enthusiast eager to deepen your understanding of risk, return, and strategic decision-making, this episode is your masterclass in staying resilient under real-world conditions.

Transcript

SPEAKER_02 0:00

How do you deal with uh fat tales or forecasting fat tales?

SPEAKER_03 0:04

I mean, look, the holy grail is to try to forecast fat tales, but it really is kind of impossible. Because what makes a market event extreme? What makes a market event extreme? It's the fact that no one saw it coming. So by definition, fat tales are gonna be very difficult to predict. I give it a shot in the book.

SPEAKER_00 0:31

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_01 1:03

Today's conversation of the most important thing features a guy from America who's been with uh investment manager for State Street, a company called Pimco, and is now the main man, the chief investment officer of the global multi-asset firm T-Row Price, and they manage a fund of over$2 trillion of other people's money. So, Alan, who is Sebastian Page?

SPEAKER_02 1:33

Okay, so Sebastian Page again is a global expert, very well known, huge following. Um wrote a book very recently uh called Beyond Diversification. So that title really explains a lot of why I was speaking with uh Sebastian. He's a French Canadian, um, obviously working in the US, very high-profile guy, managing a lot of money, particularly ahead of multi-asset solutions, uh, 500 billion. So this is a this is another big hitter here.

SPEAKER_01 2:04

Okay, so one of the things that I've learned in my idiot's guide to investing is that there's a kind of basic formula of funds, which is 60% equities, stocks and shares, the SP, the FTSE, or whatever, whether you buy the market or individual stocks, and 40% bonds. And he is saying, actually, when he talks about diversification, it's way beyond equities and bonds. Explain that.

SPEAKER_02 2:29

Yeah, so he takes a scientific approach. So Sebastian is a chartered financial analyst, a CFA. He has published along with uh some of his colleagues, uh peer-reviewed uh academic work in some of the best uh academic journals. So he knows a thing or two about portfolio of construction, the scientific uh application of it. And what Sebastian says crucially is this notion of naive diversification is something we need to be very, very careful of. What's naive diversification? Let's look at 2022, most recently, when inflation was spiking up. As you said, Ivan, people in bonds and equities, 60% equities, 40% bonds, bonds fell, bond prices fell as interest rates went up, and stocks tanked as well. So, where was the diversification?

SPEAKER_01 3:18

Naive is the is the kind of word to to to So when he talks about diversification, and so off the top of my head, I'm thinking of gold, I'm thinking of commodities, I'm thinking of property, I'm thinking of cash. Uh is that what he means?

SPEAKER_02 3:31

Some of that, not all of it. Okay, you mentioned uh property, you gotta be careful with property because property is a it's an illiquid asset, but it's also tied, the red that income stream could be tied to economic growth, okay? So gold for sure. If you I my some of my own research looked at gold in terms of a stressed environment. What's a stressed environment? It's a 9-11, it's a 2008, it's a COVID, it's an inflation shock. How will the asset perform during that stressed period? And gold it's the haven for security. It's yeah, it's an insurance, it's an insurance policy, okay. But again, you've got to be careful with gold. We get into some of that because gold is volatile. Gold can be volatile, it moves around.

SPEAKER_01 4:09

And has been a great performer for the last decade.

SPEAKER_02 4:11

It has. I mean, it's been one of the best performers, okay? And some people are getting a little bit nervous about that. But there's a lot of reasons to hold gold. But just in terms of our conversation with Sebastian, really was getting into the science of portfolio construction. He's a really credible voice, Ivan, okay, and some really good insights in it.

SPEAKER_01 4:29

No, I I noticed some things like uh when you look at a sales catalogue for bangers, they talk about the average and they talk about the median, which is if you piled all the lots up, you know what you know, it's actually better than the average. He's pretty anti-averages. Discuss that.

SPEAKER_02 4:46

So the average tends to hide a lot of information, Ivan. Okay. So if you've looked at statistics, studied statistics in any way, you need to understand that there's other metrics that we get into quite a bit in the conversation that you need to look at that don't uh hide the truth, is what we call, which we cover in the truth series. So, no, he's not a fan of averages because the averages can be misleading.

SPEAKER_01 5:08

All right, so let's get into it. Here is Alan in conversation with Sebastian Page.

SPEAKER_02 5:20

I'm delighted to say that joining me now is Sebastian Page. And I want to start, Sebastian, by maybe talking about the young sung hero in all this, um, Monsieur John Paul Page and your your upbringing and how influential he has been on you.

SPEAKER_03 5:40

Alan, John Paul Page is my father. And in my asset allocation book, Beyond Diversification, I have a mystery author that I quote at the beginning of every chapter. And it just says, JPP. Only at the end of the book do I reveal that JPP is my father. He was a finance professor for 40 years. So you can imagine, Alan, I grew up around finance. He never pushed me to say, you have to study finance, this is your destiny, or actually, he was quite hands-off. But growing up, I would witness discussions at the dinner table between him and other academics, his colleagues, and they would have a glass of wine and discuss the capital asset pricing model and debate. There was so much passion around the table. And I was a 13, 14, hearing this, not knowing what it was about. But it's certainly Alan, part of my DNA. Ultimately, it was somewhat predictable that I would grow up and study finance. And here we are, 25 years into a money management career.

SPEAKER_02 6:59

And did you did you sit and listen to those conversations? Because I have uh an eight-year-old. No, she's not 13 quite yet, but I can't imagine her actually staying around too long if I started talking about academic theory.

SPEAKER_03 7:13

You know, not really, because I didn't know what it was about. But but it really matters because what I learned was the passion they all had for it. They could be anywhere else on a Friday or Saturday night, late in the night after dinner eating dessert, but they decided to sit together and debate these ideas. So even though I didn't really know what was going on, I wasn't participating, I grew up around it. My father had an unbelievable work ethic. That's the number one thing I learned from him. He wrote textbooks, and they were very intuitive textbooks focused not on publishing academic papers, but on teaching students the basics and then the advanced topics in finance and economics and money management. Alan, if you were to stack the textbooks he published in his academic career, it would go higher than your dinner table. That's the work ethic he had. Not only that, but he ran a working farm basically by himself. Farming is incredibly hard. It's a full-time job. He was a full-time tenure professor on top of that and kept publishing academic textbooks. There was no doubt at some point when I started studying the topic and participating in those dinner discussions with those academics that I would end up in that field. But again, just for the record, he never actually said, Sebastian, you should study finance. He was just teaching by example, working hard by example, and having passion for what he did, which is the best way to teach by example.

SPEAKER_02 8:59

Yeah, and I know you you have a lot of passions, leadership being one of them, and I hope we have time to discuss that maybe at the end. But I read your book, uh Sebastian, Beyond Diversification. I have a copy of it here, and I have copious amounts of notes for people that are watching it. But I really devoured it. It was a fabulous book, and I loved the way you started kind of sequentially talking about forecasting returns, uh, expected returns, then looking at risk, and then at the end, you kind of brought it all together talking about building blocks in the portfolio construction. Can you just talk to me, please, about why you chose that kind of workflow for your book?

SPEAKER_03 9:46

I reflected on what I learned over my entire career and realized that the best way to organize the content was in line with what we do in the practice of asset allocation. It begins with forecasts, and some people forget that. We're enamored with the historical averages and volatilities and correlations, but it begins ultimately with a forecast of returns, then a forecast of risk. Then you put it all together in portfolio construction. The structure reflects how we do asset allocation in practice. For each of these, there are countless debates to be had because while asset allocation, portfolio construction has a scientific component, Alan. A lot of it is judgment. A lot of it is integrating quantitative methods with judgment. That's one of the contributions I want to make with the book. And sometimes integrating bottom-up insights with top-down macro insights. That's also another contribution I'm trying to make. So there's a lot in there, but the organization is all about how we do this in practice.

SPEAKER_02 11:11

You've published in some of the best financial journals in the world, but you're also a practitioner. I mean, you've got skin in the game in terms of the billions that uh you manage with T Roll Price and your team. But uh in terms of uh investing, the best description I ever or definition I heard about the investing was decision making under uncertainty. Um, the decision making we can understand that, but the uncertainty is where it gets a bit complicated. Can you speak to that, please?

SPEAKER_03 11:43

That's what we do in investment management. We make decisions under uncertainty, and uncertainty is not necessarily the same as risk. Risk you can theoretically model. Uncertainty, much more difficult to model. There are plenty of biases when we as human beings make decisions under uncertainty. For example, how do you frame the information? Every day, Alan, money managers get presented with information that's framed in one way or the other. To be bullish, to be bearish. There's a famous study that goes back to the early 80s on decision making under uncertainty, where Thversky, who ended up being co-author with Daniel Kahneman, who ended up winning a Nobel Prize, and co-authors, asked a group of about 400 people to make decisions under uncertainty, and they found that the frame greatly influenced the responses people would give. It was, to summarize, a choice between how do you solve a deadly pandemic and assume there's a deadly disease and you're in a room with 300 people, and you give people a choice. Do you want to save 100 people or take a one-third chance that you're going to save everybody? Now, if that doesn't go through, though, you won't save anybody. So it's basically a choice between a certain thing, 100 people saved, versus a gamble. And there's no right or right or wrong answer. Is are you risk averse or not? And people chose over 70% of the people chose the certain thing. Then they gave people another choice, two other cures. And they said, okay, the first one is gonna kill 200 people for sure. Or you can take a gamble that uh one-third chance that you won't kill anybody, and two-thirds chance that you'll kill everybody. Now, for our audience, I know your podcast, Alan, you've probably all realized already that the first two choices and the second two choices are exactly the same. It's just a different frame. One that it's expressed in terms of killing people, one it's expressed in terms of saving people. When you offered people the chance to save 100 people for sure out of 300, 70% will say, let's do it. Let's save 100 people for sure. When you offer them, you killed 200 people for sure, then it dropped to below 50% of the respondents. So decision making under uncertainty involves framing in the information. And every day in financial markets, whether we look at quantitative models, outputs, or just turn on CNBC, we're looking at information that's been framed around a certain narrative in a certain way, sometimes by cherry-picking data, sometimes simply by showing data in a different light. I can say, Alan, the unemployment rate is up by 90 basis points, which it is right now, and make the claim that there's no time in history when that happened that it didn't continue to go up and end in a recession. So that's a strong recession signal. I can also say, however, that the unemployment rate right now is 4.3%, and that the long-run average is 5.7%. So we're actually running at full employment. Now, if I look at the level to frame the employment situation, I wouldn't expect a recession anytime soon. If I look at the change, that's a clear recession signal. Again, framing. So, Alan, it's super important, sometimes underrated. Most of us don't think, oh, what is the frame here that I'm looking at? And that's super important.

SPEAKER_02 15:43

I interviewed Professor Campbell Harvey of Duke University a couple of months back. And when I was doing my own research, Sebastian, I was I watched one of his old clips on YouTube. I don't know if you saw it. He did a course on uh asset allocation on YouTube. And it's like 30 years ago, and he was talking about expectations, conditional and unconditional expectation. And I just found it it was kind of a light bulb moment for me because I realized the unconditional expectation is just historical data. It's just we we're we're our expectation of the future is based on the past. Whereas what you brought into the book as well was that uh everything in finance and markets is time variant, everything is moving dynamically all the time. So, what we need is a conditional frame, if to use your frame analogy. Is that correct?

SPEAKER_03 16:39

Absolutely. 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. Now, mind you, historical data is all we have. I remember early in my career, I was presenting to clients models for expected return and ultimately asset allocation recommendations. And sometimes the clients would say, Well, okay, but your framework uses past data, so I'm not sure it's valid. And at one point I was jet lagged and a little short with a client, and I said, Look, uh, I don't have future data. All I have available is past data. I can I cannot find future data on Bloomberg. But here enters conditionality. What are the conditions now that are relevant for investing? The valuations, the macro, the fundamentals. And can we find periods in history, either in terms of returns or risks, that resemble those conditions, to make our forecast more relevant to current conditions? The best simple example you can think of is expected bond returns. You can say, here's the average 100-year bond return, I'm going to use that. Past data, it's all I have. But if you know the current yield, it's actually a remarkable predictor of future bond returns. And we just went through a period where yields were essentially zero for the front end of the curve and zero for most of the curve after inflation. Therefore, current conditions, conditional expected returns, should account for current levels of yields. This sounds overly simplified. It is, I'm just trying to make an example. But every time in finance, when we do forecasts, we have to think about current conditions and how we map those current conditions to make the historical data that we have more relevant. Recent work published after my book by my old mentor, Mark Kritzman, and his co-author, David Turkington, is focusing on what they call relevance. And this is super important for our industry. I would encourage your listeners to look into it. But what they do, what Kritzmann, Turkington, Magensis Unis, what they do is they look at current conditions, macrovaluation, fundamentals, and then they look back at the data and look for similar periods in history. They then re-weight their prediction models, say a linear regression, to emphasize those data points because presumably they're more predictive because they resemble current conditions better. They wrote an entire book on this. It's much more complicated than I make it out to be. But these are advances in quantitative methods for return and risk forecasting that are continuing to evolve. They're as important as anything else right now. This is the cutting edge that we're looking at.

SPEAKER_02 19:57

As I was listening to you there, Sebastian, I was thinking of a quote from the bond king Jeffrey Gunlock recently, and where he said that what if everything we know about financial markets is based on a set of economic relationships that no longer exist? And what he was kind of hinting at is since 1982, Volcker raised interest rates. We've had this declining, secular declining interest rate environment, secular declining inflation. And what if we're going into a more normalized interest rate environment and and and inflation is no longer very low either? Perhaps it's more normalized as well. And that speaks exactly to your point that we have to be cognizant of the conditions uh today relative to our asset allocation decisions.

SPEAKER_03 20:50

Yes, we've just been through up until pre-pandemic a 30-year bull market in bonds with constantly declining interest rates. I don't think that's the prevailing environment anymore. This has implications for how we think of strategies like risk parity, where you use leverage to increase the contribution to your risk budget from bond returns. It changes asset allocation decisions ultimately. I also think we're in a regime with greater inflation volatility. And the ability to capture a regime shift is it's actually very difficult. But it's one of the most important things a financial forecaster can do is understand what are the regime probabilities. You don't need to predict exactly we're shifting from regime A from declining rates to rising rates, but you can say generally the probability that we're in a rising rate environment is much higher than the embedded probability. In my 30-year historical data.

SPEAKER_02 22:05

Yeah, I remember when I was doing my own research, uh I I I got a quote about what a Markov property is, that kind of turning point. And it was a line that said, the future is independent of the past given the present. So really to use a very simple analogy, if it's raining today, it's more than likely going to be raining tomorrow. So that type of analogy. If you've got a a low interest rate environment, it's likely going to be a low interest rate environment. And you also speak in your book about this clustering uh the way volatility clusters, the way uh economic variables cluster. In terms of forecasting, Sebastian, how do you forecast? Is it leading indicators? Um, what what what what do you use to kind of look around the corners as such?

SPEAKER_03 22:52

Valuations are important, although from a cross-asset perspective, they haven't worked as well as they have in the past. You also need to account for macro factors, fundamentals, earnings growth, margins across asset classes, and sentiment, which I would also include, to which I would also include positioning. You put these variables together and then you use your judgment, quantitative models, dashboards, frameworks, capital markets assumptions. It's a mosaic. On the risk side, I think the clustering is very, very important. There is predictability in risk much more than in return. I talk about this in Beyond Diversification. If you look at daily volatility from one month to the other, you actually get a correlation of about 60%. If you look at average returns from one month to the other, it's essentially uncorrelated. So risk is more predictable in that framework than returns. You can say, look, I recognize that markets behave differently when we're at extremes, whether extreme positive or negative returns. If you use monthly data, a very simple thing you can do is cut it up into subsamples and say, I'm going to look at correlations, exposure to loss for periods when the monthly return was minus 10 or below, for periods when the monthly return was plus 10 or below. And that's a good way to look at conditional expectations or conditional realized volatilities and returns and tail risks. However, that's not perfect, Alan, because you don't really know like how to classify the regime you're in. I can have three months that are very extreme in a row or three days that are very extreme in a row, but then get a quiet day or a quiet month in my data sample. And let's say this is today. Well, what is it? Am I now all of a sudden in a quiet regime? I just had three really volatile days. And the Markov models and clustering models and regime models tried to say, based on the path of volatility and the observations, even though you might be looking at a quiet day today, if you've had lots of turbulence leading up to it, it's much more likely that you're still in that volatile regime, that risk-off regime, than otherwise. I like to use this analogy. I use a Garmin watch to keep track of my heart rate, my sleep, and all that stuff. You know, a data person is going to always be a data person in all aspects of their lives. Well, let's say you don't know me, see me, you don't know what's going on, but you get my heart rate data. So the heart rate data actually clusters and you have regimes. You have the asleep regime where I might get up, my heart rate might go up, but just temporarily. And then you have the awake regime. And you probably have a third regime where it's exercise, where the heart rate looks even more different. But there are periods within those regimes that look like I'm awake but I'm really sleeping. And there are periods when during the day, if I'm just sitting watching Netflix, it kind of looks like I'm sleeping. But if you have a statistical model that recognizes the clustering and the patterns in the heart rate, you can tell without seeing me, without knowing anything else, most likely whether I'm awake or asleep or exercising. Same thing works with volatility market.

SPEAKER_02 26:55

It's very interesting listening to you there. And what I was thinking of is that you don't necessarily have to get the bottom or the top, but you once you can get the the turning point, you know, uh correct, then, and and you identify the clustering, you identify a trend.

SPEAKER_03 27:13

Just look at it. And Alan, if if I can jump in, because you just mentioned forecasting as well. You you don't have to say, I'm in a high risk-on, risk-off, I'm going from this regime to that regime. If you have models, say a risk-on and a risk-off regime, you can just reassign the probabilities. So a risk-off regime might represent five or 10% of your historical data sample. But if something just happened like Lehman just defaulted, and you have a qualitative probability that, you know, if we just had uh April Liberation Day, well, you know, market's going to be volatile for the next few days. So you might say, okay, I'm going to reweight my risk forecast to put an 80% probability on the 10% most volatile periods, right? So it's more about a sliding rule of subjective probabilities that you use to reweight your historical regimes.

SPEAKER_02 28:16

Yeah, and and that lends into your or leans into your fat tails, because I mean you've dedicated an awful lot of the book to the truth. I I would describe it as the truth. I mean, there's lots of issues we have with you know finance and perhaps the way it's taught, perhaps the way we assess markets, uh normal distributions, you know, um averages, but you dedicate so much of the book to to the truth around fat tails is what matters. These outliers in the probability distribution that you know Taleb calls black swans. But how do you deal with uh fat tails or forecasting fat tails? Very, very tough question. Sorry.

SPEAKER_03 29:03

I mean, look, the holy grail is to try to forecast fat tails, but it really is kind of impossible. Because what makes a market event extreme? What makes a market event extreme? It's the fact that no one saw it coming. So by definition, fat tails are going to be very difficult to predict. I give it a shot in the book. When I show that volatility is predictable, I have different trailing windows to build the forecast and then different forecast horizon. Then I do the same with skewness. Is the distribution symmetrical? Or kurtosis, how fat are the tails? And unfortunately, while I find that the volatility month to month is 70% autocorrelated, I get, you know, basically zero for kurtosis and skewness, which is kind of expected. However, this is key. You know that certain asset classes have fat tail properties fundamentally. Credit, for example, in its fundamental form, involves selling a put option. If you're a credit bondholder according to the Merton model, you're selling a put option. So an asset class that loads up on credit or other types of risk premia where you're essentially selling a liquidity option or another form of option, then while you can't predict when you're going to hit the fat tail event, you know that that asset is prone to fatter tails. You can model that. And what you want is build the plane to be solid enough to withstand the turbulence, not knowing when it's going to occur. And that is key. And it's well known. I'm not saying anything new. Academic finance has talked about this. Even Markowitz, even in 1952, mean variance optimization. Even back then, he recognized that if you read the original paper. So it's nothing new. But there's effort to be made in practice to better handle the risk return trade-off with a focus on am I getting paid not just for the volatility, not just the Sharp ratio, which uses the volatility as this measures of risk, but that for the actual exposure to loss. And am I using scenario analysis not just after the fact to convince myself that I've constructed the right portfolio, but before the fact to make sure that I'm going to get paid for the exposure to loss in a way that's sensitive to realistic scenarios. So I got to pontificate a bit, but it's so important. It's part of risk management in practice. We all know this in our industry. I think Taleb's a great writer, but he kind of makes it sound like it's a discovery and practitioners are completely naive and don't know anything about fat tails. No, not at all. But the tools to handle those and to maximize your risk return trade-off, they continue to evolve, and practice continues to evolve in a way that is becoming much more attuned to how markets really behave.

SPEAKER_02 33:05

That would I say asymmetric correlation, they've no idea what I'm talking about. What is that? Because it's a crucial point.

SPEAKER_03 33:12

That's a great question. I started working on that before the great financial crisis. I started with US versus non-US stocks. I looked at the correlation using monthly data going back to the 70s when US stocks were down by 15, 20% or more in a month. And I saw a correlation for that sub-sample that was in the range of 90 plus percent. That's not that surprising. But importantly, as you hinted in your question, the correlation absolutely cratered when U.S. stocks were rallying. So diversification, not only did it leave you when you didn't want it, but it also showed up uninvited. Um sorry, it left you when you needed it, and it showed up uninvited when you didn't want it. Right? So it was the exact opposite. So Alan, I was working on this and I got it when I got a call around 2007 from Charlie Henneman, who ran the conferences at the CFA Institute. And I was, you know, a young sort of quantitative analyst. I didn't make a lot of presentation, didn't give a lot of presentations at the time. And Charlie said, Sebastian, what are you working on? We might have a spot, a speaking spot for you. And I said, to your point, Alan, um, the title of my talk would be Undesirable Asymmetries and Conditional Correlations. There's a silence on the other end, right? Someone's organizing a conference. They don't want a nerdy talk. Charlie, like after just a pause, Charlie Hanneman said, okay, I have a better title for you, because he actually understand what I was talking about. He suggested the title, The Myth of Diversification, which is a wonderful title. We ended up writing a paper because we found those asymmetries across almost all asset class spares. And the more research we did on this, controlling for what should happen under a normal distribution, using different ways of slicing the data, the more we found that we kind of know this asymmetry is undesirable. But we thought the industry didn't realize how widespread this was across asset classes and how consequential it could be for risk management and portfolio construction. Alan, the fascinating thing is A, the title of the paper became the myth of diversification, same as the title of the talk. B, it came out right after the fall of 08. And it was just good timing because we actually used data from before, and then everything that we describe in the paper happened in the fall of 2008. So we had a great title and we had remarkable timing on the paper. So it won a Best Paper of the Year award. I'm convinced most people didn't read it but voted for it because it has a great title, it had a great title and it was great timing. Now there's a there's an asterisk to this story. We found out years later, actually recently, that there's a better way to slice the data to look at these how correlations change in function of what the returns are, conditional correlations. And we kind of corrected the original paper. And that paper, correcting our original paper, won the award as well. So no matter what you do, if you talk about asymmetrical correlations, you have a shot at an award.

SPEAKER_02 37:02

Okay, so we look back at 2008 and we say widespread losses, huge uh drawdowns, correlations didn't work. This concept of naive diversification. Um, as you said, the native diversification. So 20 years, fast forward nearly 20 years, Sebastian. Are we making the same mistakes?

SPEAKER_03 37:27

That's a tough question. I don't think the amount of leverage in financial markets is nearly as high as it was back then. We're worried now about high valuations on the stock market, kind of like comparable to the dot-com bubble. I don't think I think we will make those mistakes. The history of humanity and financial markets is that there are bubbles. At the time as we're recording this, I don't think we're nowhere near the level of speculation that we have in the housing market in 2008 and the irrationality of it. And the fact that financial institutions package those loans with tranches and derivatives, and it became a house of cards, and all the banks held those assets on their balance sheet. So, in hindsight, it's what an what a massive amount of systemic risk. I don't think we're in that situation now. We might get there. Uh, but right now, US corporations, public companies have actually solid balance sheets. The financial system is resilient, and of course there's speculation. Of course, there's speculation, there's meme stocks, there's a lot of liquidity. But I don't think the conditions are that right now, as we speak, there's impetting doom and massive global financial crisis like we had back then. Again, we'll have some of those crises over time. I just don't think we're here right now.

SPEAKER_02 39:06

Your book also talks a lot about the stock-bond correlation or lack thereof now, perhaps in a different regime. Um in terms of the stock-bond correlation, but also uh this notion of negatively correlating assets. So we talk 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. Um can you can you maybe speak to that challenge?

SPEAKER_03 39:45

One client told me once, if you can't diversify hedge.

SPEAKER_02 39:50

So that concludes part one of the Sebastian Page interview. There's a lot in it, and I'm conscious that some of the concepts that we discussed are quite technical. In part two, we go a bit more into portfolio construction and also some very interesting leadership insights from Sebastian's wonderful career. I hope you'll stay tuned for part two of Sebastian Page's interview in the most important thing. Thank you.