The Biggest Lie in Investing Is Diversification

Join us for an insightful highlight reel discussion with Sebastian Page,  Head of Global Multi-Asset at T. Rowe Price, as he shares his journey from growing up around finance to becoming a leading voice in asset allocation and risk management. Discover practical insights into forecasting, fat tails, regime shifts, and the importance of judgment and humility in investing.

Main Topics:

• The influence of upbringing and family on Sebastian’s career in finance
• The organization of asset allocation around forecasts of returns and risks
• Decision-making under uncertainty and framing effects, with references to Kahneman and Tversky
• The significance of conditional forecasts and relevance in market modeling
• Strategies for managing fat tails and tail risks through portfolio construction techniques
• Asymmetric correlations and the myth of diversification
• The pitfalls of relying on averages in risk modeling and the importance of regime-aware analysis
• Incorporating judgment through Bayesian frameworks and regime probabilities
• Lessons from sports psychology and leadership principles for resilience, humility, and effective communication
• A personal reflection on handling criticism and the importance of simplifying finance for broader audiences

Resources & Links:

• Beyond Diversification: Strategic Asset Allocation for a Changing World (Book)
• Sebastian Page on LinkedIn
• Markowitz’s Original Mean Variance Paper (1952)
• Kahneman & Tversky’s Studies on Framing and Decision Making
• Full-Scale Portfolio Optimization Approaches
• Positive Psychology Resources
• Roger Federer’s Commencement Address

Connect with Sebastian Page:

• LinkedIn

This episode offers a mix of technical insights, practical portfolio management, leadership principles, and ways to navigate uncertainty—valuable for early-career professionals and seasoned investors alike.

Transcript

SPEAKER_00 0:07

Welcome to the Most Important Insights, the short form series from the Most Important Think podcast. Each episode is still an hour-long conversation with some of the world's leading investors, economists, and thinkers. It's the essential ideas you need to know in just 10-15 minutes. Our aim is simple. Separate the signal from the noise, identify what matters most, and leave you with insights that can improve the way you think about markets, investing, business, and the world around you. I'm Alna Sullivan. Thank you for joining me. Welcome back to the podcast. One of the biggest challenges every investor faces isn't just predicting markets, it's making good decisions when the future is uncertain. Every day we're bombarded with headlines, market commentary, economic data, and expert opinions. But how much of what we believe is actually influenced by the way that information is presented to us. In this highlight, I'm joined by Sebastian Page, one of the world's leading investment strategists and the author of Beyond Diversification. Sebastian shares fascinating insights into how behavioral finance shapes investment decisions, why averages can often be misleading, and how traditional diversification may not protect investors when they need it most. What I particularly enjoyed about this conversation is that it challenges some of the assumptions many investors take for granted. From Nobel Prize winning research on framing effects to practical lessons on portfolio construction and risk management, Sebastian explains why uncertainty isn't the same thing as risk and why recognizing that distinction can make you a better investor. If you've ever wondered why smart investors can look at exactly the same data and arrive at completely different conclusions, or why markets often behave very differently during periods of stress than our models predict, then I think you really enjoy this discussion. Let's jump into this highlight with Sebastian Page of T Raw Price.

SPEAKER_01 1:57

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 a hundred 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 at 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 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 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. 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, I don't have future data. All I have available is past data. I could 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. 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 subsample 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 US stocks were rallying. So diversification, not only did it leave you when you didn't want it, but it also showed up uninvited. You know, it left you when you needed it, and it showed up uninvited when you didn't want it. We ended up writing a paper because we found those asymmetries across almost all asset class pairs. 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. 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 the 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, even 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 fact 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. 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 bill 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_00 15:02

That was a fascinating insight from Sebastian Page, and I think there are several lessons worth reflecting on. One of the biggest takeaways is that successful investing isn't simply about having more information. It's about interpreting information correctly. The way data is framed can significantly influence our decisions. Averages can hide the risks that matter most, and diversification doesn't always provide the protection investors expect during periods of market stress. The bastion also reminds us that markets operate in different regimes. Rather than relying solely on long-term averages, investors should consider current conditions, understand tail risks, and recognize that uncertainty requires both quantitative analysis and sound judgment. That's a valuable perspective, whether you're managing institutional portfolios or just investing your own money. Of course, this highlight only scratches the surface of our conversation. In the full long form episode, we explore these ideas in much greater depth, including how Sebastian approaches asset allocation today, the practical applications of behavioral finance, portfolio construction techniques, and the research behind many of the concepts discussed here. If you found this clip valuable, I'd strongly encourage you to watch or listen to the complete episode. You'll gain much more context here, additional examples and stories from Sebastian's wonderful career. I come away with an even deeper understanding of how some of the world's leading investors think about risk, uncertainty, and building resilient portfolios. I'll see you in the next episode. Welcome to the Most Important Insight, the short form series from the Most Important Thing podcast. Each episode is still an hour-long conversation with some of the world's leading investors, economists, and thinkers. It's the essential ideas you need to know in just 10-15 minutes. Our aim is simple. Separate the stigma from the noise, identify what matters most, and leave you with insights that can improve the way you think about markets, investing, business, and the world around you. I'm Allah Sullivan. Thank you for joining me.