Victor Haghani has 40 years’ experience working and innovating in the financial markets, and has been a prolific contributor to academic and practitioner finance literature. He founded Elm Wealth in 2011 to help clients, including his own family, manage and preserve their wealth with a thoughtful, research-based, and cost-effective approach that covers not just investment management but also broader decisions about wealth and finances.
Victor started his career at Salomon Brothers in 1984, where he became a Managing Director in the bond-arbitrage group, and in 1993 he was a co-founding partner of Long-Term Capital Management. He lives in London and Jackson Hole, Wyoming.
James White has spent two decades working in finance, covering the gamut of quantitative research, market-making, investing, and wealth management. He is currently the CEO of Elm Wealth, and previously has held research, trading, and executive roles at PAC Partners, Citadel, and Bank of America. He lives in Philadelphia.
If the wealthiest families of the past century spent a reasonable amount of their wealth, invested in the stock market, and paid taxes, there would be thousands of billionaires today. But there aren’t. So, what happened? To answer this question, we are joined by authors and finance professionals, Victor Haghani and James White. Their recently released book, The Missing Billionaires: A Guide to Better Financial Decisions, uses the missing billionaires puzzle to explore how and why most investors fail to capture the returns offered by the market. Victor was a founding partner of Long-Term Capital Management (LTCM), the multi-billion-dollar hedge fund that famously collapsed in 1998 and nearly took the global financial markets down with it. His participation in the downfall of LTCM led him to reassess much of the way he thought about investing, and in this episode, he shares some simple but powerful frameworks and personal finance recommendations. We also receive accessible explanations of the Merton model and expected utility theory from James, take a deep dive into dynamic asset allocation, discuss optimal solutions for lifetime spending, and learn more about the certainty equivalent return and Sharpe ratios, plus so much more. Whether you’re an entrepreneur invested in your own business or simply focused on building long-term wealth, Victor and James’ book (and this conversation about it) will be a valuable resource for better financial decision-making, so be sure to tune in today!
Key Points From This Episode:
(0:05:19) The puzzle of the missing billionaires (and why it matters to Victor and James).
(0:09:45) Some common but critical financial decision-making problems most people face.
(0:12:39) Unpacking the coin-flipping experiment in their ‘What's Past is Not Prologue’ paper.
(0:19:57) What investors should aim to maximize when sizing positions in risky assets.
(0:24:22) An example that illustrates how the Merton model relates to bullish bets.
(0:29:04) What the Merton share tells us about dynamic asset allocation if it is or isn't possible to estimate expected equity returns.
(0:35:29) How real expected returns affect optimal risky shares for long-term investors.
(0:37:29) Different ways to forecast volatility to determine the optimal risky share.
(0:42:00) Easy-to-understand definitions of the utility curve and expected utility theory.
(0:50:20) Using the certainty equivalent return and Sharpe ratio to evaluate investments.
(0:57:56) Whether or not options belong in the portfolios of typical retail investors.
(0:59:01) If expected utility is a good model for normative personal finance recommendations.
(1:05:16) How Victor’s experience with LTCM affected him, both professionally and personally.
(1:09:08) What optimal solutions for lifetime investing and spending look like.
(1:22:22) Questions to ask yourself to work out your own utility function and risk aversion.
(1:28:19) Victor and James’ parting financial advice and respective definitions of success.
Read The Transcript:
Ben Felix: This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision-making from two Canadians. We are hosted by me, Benjamin Felix, and Cameron Passmore. Portfolio managers at PWL Capital.
Cameron Passmore: Welcome to episode 270. And this week we have a couple of authors and unbelievably experienced people in the finance profession join us. And what prompted it, Ben, was a book that just released, actually, called The Missing Billionaires: A Guide to Better Financial Decisions. And this is written by Victor Haghani and James White.
For those who read the book When Genius Failed, you might recognize that first name, Victor Haghani. Was a partner at Long-Term Capital Management. But the point of the book is that, over the past century, if the wealthiest families had spent a reasonable fraction of their wealth, paid taxes, and invested in the stock market, there would be thousands of billionaires today. But there aren't. There's virtually none that came from a hundred years ago. And that inspired these guys to write this book as to how can you make better financial decisions. With that, Ben, why don't you tell the story of how we met Victor and James?
Ben Felix: Yeah, sure. That's a good introduction to the book though. Where did all the billionaires go? And a lot of it is spending policy and sizing decisions on how much risk to take in your portfolio. But, yeah. How do we get connected with them?
I knew who Victor was because I'd read When Genius Failed. It's a great book, a great story about Long-Term Capital Management. I knew him as this sort of character in that book. And then I was doing research on options for a video idea that I have, and he's got a paper. He's got a paper on what other options make sense in the portfolios of retail investors in The Journal of Derivatives, I think. I'm reading that paper. And I thought it was kind of cool that Victor had this paper because I knew who he was from the story.
And then you and I both got a message from him on LinkedIn and he's saying that he enjoys our podcast and that he's got this book coming out and he'd love to come on to talk about it. And I'm like, "Victor Haghani? Is this the same guy that is in When Genius Failed and the same guy whose paper I'm reading right now?" And it was.
Cameron Passmore: Who was a partner of Bob Merton.
Ben Felix: Right. At Long-Term Capital Management. Anyway, it was kind of a crazy thing. And it was just a crazy coincidence that I had his paper open when he sent us that message. And then they sent us the advanced copy of the book and started flipping through it. And on the praise on the cover of the book, you've got a note from John Campbell, Bob Merton. I thought I saw in there somewhere too.
Cameron Passmore: Yes. He's there.
Ben Felix: And then you start – Myron Scholes, Michael Mauboussin. Yeah, just like pretty serious people to get praise from. And then start flipping through the content of the book and it's like, "Okay. Yeah, this is incredible." Because what they've done is they've taken pretty hardcore theory, expected utility theory, and packaged it up in a way that – I mean, personally, from reading their book, my understanding of expected utility and how it's useful and why it's important for making good financial decisions is on a whole different level. It's a tough topic. You see it all the time when you're reading papers and stuff like that. But really understanding why it's important and how it can be applied is not so easy.
But anyway, they did a great job with that in the book. But it was pretty obvious, to me at least, once we've got a copy of the book and started flipping through it, that it made sense for them to be guests in the podcast.
Cameron Passmore: And we get a couple of questions into Victor about his time at LTCM.
Ben Felix: Yeah, which is just fascinating, right? That's the story. And James is no slouch either. His academic background is in math from the University of Chicago, but he's worked in finance for I think 20 years. But one of the ones that jumps out at me is that he spent time as a portfolio manager at Citadel, which is like I don't know what you'd call that. The MIT of hedge funds or something. I don't know. Not an easy place to get into.
Cameron Passmore: And they both work at Elm Wealth, which is a wealth advisory firm in the US. They're in the business, which is also interesting. Because they can apply what they're learning to real-life situations.
Ben Felix:Yep. Definitely. I mean – I don't know. Do we want to give any more background on who they are?
Cameron Passmore:I think we've covered. I mean, Victor's been in the industry for over 40 years. Started at Solomon Brothers in '84. '93, joined Long-Term Capital Management. Had an incredible career in research since then. Co-founded Elm Wealth. James pretty much went through it. He's had positions in research trading, executive roles at Citadel and Bank of America. James is in Philly. Victor's in Wyoming. If you watch the video, you'll see Victor's got the quintessential Wyoming background in the video. But they're really good, interesting, really smart guys. It was a fun conversation.
Ben Felix: Yeah, fun conversation. I think it's a worthwhile listen. And we hope people enjoy it. I think with that, we can go ahead to the episode with Victor Haghani and James White.
***
Ben Felix: Victor Haghani and James White, welcome to the Rational Reminder Podcast.
Victor Haghani: Thank you. Great to be here.
Ben Felix: Victor, to start off, based on the title of your book, what is the puzzle of the missing billionaires?
Victor Haghani: Well, the puzzle of the missing billionaires, which ties back to a TEDx Talk that I gave six or seven years ago, is that if we go back and look at the year 1900 and the US census of wealth in America, there were about 4,000 families that had more than a million dollars.
Now, if those families had been able to invest in a way that more or less matched the return of the stock market and if they had children at the average rate of other families, then what we would have found is that today, just based on those 4,000 families back then, that we would have – I don't know. 120,000 billionaire families today that trace back to those four thousand families.
And that even if we make assumptions for spending and taxes, that we still should see thousands of billionaire families today. And if we look at the rich list published by Forbes, we don't really see one family today that traces their wealth back to the year 1900 to those families.
And the puzzle is how is it possible that not even one family, let alone thousands, weren't able to get that kind of result in terms of investing, spending, and distributing wealth down the generations? That's the puzzle. And it really got us thinking about the different headwinds that individuals and families face in making financial decisions.
Cameron Passmore: Why is the puzzle important to you guys?
Victor Haghani: As I was starting to say there, that it's just remarkable that we don't have any kind of successful outcomes or even these normal outcomes from all of those families. And it just really highlights how difficult it is to make sound financial decisions over long periods of time. And that really got us thinking how valuable it would be if we could help people to think about the different errors and missteps that families and individuals make in their financial decision-making. It really helped to spur us to see that there was a really big need for people getting direction on making better financial decisions.
James White: If I might add, even if you don't particularly value the very long periods of time part in your own investing, given how amazing an investing environment it was the last hundred years in America, a lot of investing mistakes were made to generate the outcome that happened. And there's just a lot that can be learned. Whether you're investing for one year or you're investing for the next 100 years of a family dynasty, a lot of the lessons about what investing mistakes to avoid are the same.
Ben Felix: Okay. I mean, we've talked about in sort of general terms, financial decision-making is hard, especially over the long term and people make investing mistakes. More specifically, what do you think explains the puzzle of the missing billionaires?
Victor Haghani: Well, we face all kinds of headwinds in terms of trying to grow and maintain our wealth. I mean, there's taxes. There's the fact that we're spending our money. But we think that those sorts of obvious problems don't really fully explain the puzzle at all. And so, what we think is happening – and as we've taken a closer look at some particular families, we really see this, is that, over time, people make poor risk decisions.
As James was saying, this has been a really – the last 120 years has been a super positive investment environment. It's not that returns have been really low. It's that people take either too much risk or too little risk at different times with their investment portfolio and also with their consumption or spending decisions. These two things go together. We're going to talk more about risk and volatility in your spending decisions and how that could help or hurt your outcomes over time.
But on both the investing and the spending side, we think that people weren't making good decisions under uncertainty. And that's really what our book is primarily about, is these “how much?” decisions, these risk decisions. It's much more that than the choosing of your investments.
Cameron Passmore: Can you talk about the features of the common but important financial decision-making problems that most people face?
Victor Haghani: Sure. I think that the most important common feature is uncertainty, that we just don't know what the future holds. And building uncertainty into our decisions is really important and it's kind of subtle. That you don't want to just think about “what's the most likely thing to happen?” and then make your decision based on that. Or make your decision based on a probability of a certain outcome. You want to take account of all the outcomes and you want to have an objective function. You want to have a benchmark that you're using to translate all these financial outcomes into what's important for you to come up with the best decisions under uncertainty. And we'll expand on that more.
Ben Felix: What do you think are the most consequential financial errors that people commonly make?
Victor Haghani: Our belief is that their decisions around risk. And as I was saying earlier, you could be taking too much risk or too little risk over time. Also, I think that sometimes people kind of believe that you can get return without much risk. And I think that that leads people down some very bad paths that, at the centre of thinking about investing and your decisions, that you have to really build in that there shouldn't be a way to get more return without taking more risk. And so, I think sometimes people will take risk thinking that they're not taking risk and they're just getting kind of returned for free.
Another really big error that I think people have a tendency to make is to extrapolate the future from too little past data. What we call return chasing. Thinking that the future is going to be very much like the immediate past. And that this extrapolation error that we are prone to make I think is another really harmful one over time.
James White: I would add, and this is something we see and work with clients a lot in our investment management practice, is people failing to connect their investment policies with their spending policies. And that lack of connection, especially not being able to withstand a lot of spending volatility but having a lot of investment volatility, is what leads to not just once but sometimes many times for people having to de-risk when markets are on their lows then they re-risk when they're on their highs and they ride that several times. And that can be really wealth-destroying for people. And I think the ultimate, ultimate cause of that is people not recognizing that their spending and investing policies need to be determined jointly rather than separately.
Cameron Passmore: Victor, how do you describe the coin-flipping experiment that you documented in a paper in The Journal of Portfolio Management?
Victor Haghani: That was a really fun experiment that we did – I don't know. Maybe seven years ago. And Rich Dewey was the co-researcher on it with me. We basically presented people that we thought should be pretty financially sophisticated with an opportunity to bet real money on a coin that we programmed. A digital coin that we programmed to land on heads 60% of the time. Tails, 40%. And they could bet on it.
We told them that we programmed the coin to be 60% likely to land on heads. We gave them $25 and a half an hour to keep playing the game and that we would pay them whatever money they had grown that $25 into at the end of a half an hour subject to a maximum limit that we would tell them about if they got close to it. And that maximum limit was $250.
We gave them $25. They could just sit there for a half an hour and we'd give them the $25 at the end of a half an hour. Or they could play the game and however much money they wound up with at the end, we paid them. And that was our experiment. We did that.
Ben Felix: You mentioned that the players were financially sophisticated. Who were they?
Victor Haghani: About two-thirds of them were university students in various finance and mathematical finance programs. Mostly graduate students. But some undergrads too. And then a bunch of them about, a third or so were people working in the financial industry who also had relatively quantitative backgrounds.
Cameron Passmore: So, how did they do?
Victor Haghani: Well, we were pretty surprised that I think 25% of them went bust and only about 25% of them got close to the cap of $250. And the reason that that's surprising is that just playing the game in a fairly sensible, disciplined way should give you a 90% or a 95% chance of hitting that $250 cap. But very few people did play the game in a coherent way.
Ben Felix: What were they doing that caused them to underperform?
Victor Haghani: Boy, they were just doing all kinds of things. I mean, one of the most surprising things that they did is that, occasionally, most of them would bet on tails at different times. They just thought tails was due. After three heads, it was like, "Okay, it's going to be tails." And they would bet on tails. But they did all kinds of things.
Some people just bet a dollar every time and never change their dollar bet. Just, "I'm going to spend a dollar each time." And they just did that for half an hour. Other ones did a doubling-down strategy. Some of them just changed the amount they were betting really dramatically over time. We saw all kinds of crazy betting strategies, but very little that seemed coherent to us.
Cameron Passmore: What is the optimal strategy?
Victor Haghani: Well, the near-optimal strategy is some sort of constant fractional betting, where you bet between 10% and 20% of your bankroll on each flip. You start off – let's say we're going to go with 20%. The first time, you bet $5. If you lose money, then you have $20 left. You bet 20% of that or $4. If you lose again, you have $16, you bet $3.20. And you just keep betting like that. Or you could do it with 10% or 15%. But just a constant fractional amount you keep betting.
If your bankroll is up to $50 and you're using a 20% fractional betting rule, then you bet 10% on the next one. Just something that's fractional that's sort of in that zone of 10% to 20% is going to give you a 90-plus percent chance of getting to $250 at the end of the half an hour assuming that you're betting pretty fast.
Ben Felix: Unreal. And so, I mean, these relatively financially sophisticated people in a lot of cases failed. What do you think of the main lessons coming out of the experiment for investors?
Victor Haghani: Well, I think really the main lesson is that people are not being trained in risk-taking strategies in the sizing question, right? After we did the experiment, we were really sort of puzzled. And we went and we looked at the curricula of various finance program master's degrees at different business schools and there was no mention of the concepts of how to size your bets. When you're faced with a favourable investment opportunity, how big should you be? What should you be trying to maximize? There was no training. It wasn't surprising that these people didn't really know what to do because they hadn't been trained.
James White: Not only has there been no training, but that what we see both from this experiment and from our day-to-day interactions is that it's not very intuitive to people either. Once you know the answer, it seems really intuitive.
But when even we talk to our clients, almost all of whom are finance professionals, and ask them something like, "Okay, you're happy betting 10% of your bankroll on a 60-40 coin. Now it's an 80-20 coin. How do you want your bet size to change?" Some people say lower. Some people say higher. Some people don't change. Absent the training, it’s not intuitive for most people, even very financially knowledgeable people, what to do.
Ben Felix: Yeah. Super interesting. I guess that speaks to the missing billionaire puzzle.
Victor Haghani: It does. I mean, it shows us that people are not well-equipped to think about this “how much?” decision. And as James was saying, the risk of your portfolio sort of can be compounded by how you treat your spending policy as well.
Ben Felix: Okay. So, we have this experiment, which is super interesting. But they were betting $25 to start. How is investing real, impactful, meaningful-to-your-future kind of money in financial assets as opposed to a biased coin? How is that different?
James White: We tend to think it's more similar than it's different. But there is one way in which it's really different. Focusing on the $25. For most people, losing $25 or winning $25 dollars is a pretty symmetric outcome and not a very impactful outcome either way. Whereas in investing real money, you can have outcomes that are really impactful on your life, up to and including losing all your money.
As the outcomes become larger relative to your wealth, the asymmetry in impact becomes more evident. I think almost everybody would agree that whatever your wealth is, going broke versus doubling your wealth are not symmetric. They're radically, radically different outcomes.
That's the big difference between a really narrow experiment like this, where there's just not much money at stake. But if instead you look at betting multiple coin flips over time and the distribution of results like that, yeah, no financial asset follows a binomial distribution. But the kinds of results you can get relative to your bankroll even in that very simple thought experiment are a lot closer to the kinds of results that you see in major financial assets than one might at first think from such a simple setup.
Cameron Passmore: What should investors be aiming to maximize when they do size their positions in risky assets?
Victor Haghani: We haven't touched on it quite yet. There's one thing they should not be trying to maximize, and that is they should not be trying to maximize their expected wealth.
A lot of people sort of think that maybe that's what I should be trying to do, is maximize my expected wealth. But, no. Go ahead, James. Take it from there.
James White: Maximizing expected wealth turns out to be not very satisfying. Because if you want to maximize expected wealth, you would bet all of your wealth on any opportunity with a positive edge. And it really surprises people. People regularly say to me things like, "Are you sure that's right? How can that be right? That can't be right."
But if you do the math, you can satisfy yourself that that is right. And that even though that strategy has an extraordinarily high chance of losing all your money and a tiny, tiny chance of making a huge amount of money, that is what optimizes your expected wealth. But that's not a strategy anyone would choose to follow.
A more sensible strategy, not strictly optimal depending on individual circumstances, but a more sensible strategy is to maximize median wealth. As we write about lovingly in the book, we think the full-credit answer is to maximize expected utility contingent on an individual or an agent first having defined and knowing your individual utility function.
Victor Haghani: To translate maximizing expected utility, we could substitute in maximizing your risk-adjusted wealth or your risk-adjusted return where the risk adjustment is basically coming out of your utility preferences. It's how risk-averse you are. You want to maximize your risk-adjusted wealth or risk-adjusted return. And that is equivalent to maximizing the expected utility of your wealth.
Ben Felix: Utility is a tough concept for people to grab. And I do want to come back to that later. We do have some more questions on that. But I want to move to the Merton share. Can you talk about how the Merton share for sizing positions and risky assets works?
James White: Conceptually, you can think of it as you want to size positions proportionally to expected returns and inversely to variance. For any given kind of opportunity, be it investing in a stock, a coin flip, a bet in Vegas, or whatever it may be, if the expected return doubles, you want to double your bet size. If the volatility doubles, you want your bet size to be cut in four.
And there's a lot of interesting things in there. But one of the more interesting things in there is the non-linearity between risk and return, that you want your bet to be inversely quadratic in risk in linear and expected return.
Victor Haghani: I think it's also a good time to just say a few words about Bob Merton. That's Bob C. Merton. And the Merton share, the reason we call it the Merton share is from a paper that Bob wrote in 1969. He actually wrote a paper. He was a student of Paul Samuelson at the time. And Samuelson wrote a paper that also had the same finding. Bob wrote his paper and sort of did all the work in continuous time, math. And Paul Samuelson wrote sort of the same paper using discrete-time analysis.
But this 1969 sort of watershed paper that Bob wrote came up with this formula from a pretty advanced, complicated sort of rocket science type of application of mathematics. And in some ways, we're going to talk about it more as we go on. But somehow, this 1969 paper, that was a really, really big deal. And it predated Black-Scholes, which used the same technology eventually to come up with its option pricing results. But Bob and Paul's work was like all the rage for quite a while from 1969 onwards but kind of went quiet. And we'll talk about that more.
James White: What we call the Merton share is really a special case of the result from that paper assuming that returns normally distributed and other major standard assumptions.
Ben Felix: I got to ask. And maybe this speaks more to my memory than anything else. But I don't remember reading about the Merton share. Why is your book the first time I'm seeing this?
James White: I suspect that even though you've never read about the Merton share specifically, you have actually seen related things. For instance, anybody who's read about the Kelly criterion or using the Kelly criteria has read about a special case of the Merton share. And if you look at the form of the Kelly criteria or you work out how the Merton share works for a single binomial bet and with the Merton share assuming log utility, they work out to the same thing.
A lot of people have seen this special case of the Merton share but probably haven't seen the more general result. And the reason I think is the reason why we wrote the book, which is that there hasn't been much, if anything, out there that takes all of this really interesting work from academia but writes about it for a non-academic audience.
Ben Felix: I think I remember from reading the book that I'm not alone in that. You've given talks, I think, Victor to business schools. And not a whole lot of people are familiar with this concept.
Victor Haghani: Yep. Correct.
Ben Felix: Using the Merton share concept, how bullish – this is just a great example from your book. How bullish would an investor need to be to go all-in on a bet on Tesla?
Victor Haghani: Right. That is an example from our book. We say, well, if Tesla has a standard deviation of outcomes over a year of about 60%, which is roughly how risky it's been, which kind of says that it's moving around by 5% daily standard deviation of the Tesla share price, which for a long time has been that level of volatility. As you get with a lot of stocks that have very, very high PEs. Then for a typical investor with sort of a typical amount of risk aversion, you would have to expect that Tesla's expected return is 72% per year to make having 100% of your wealth in Tesla the optimal thing to do.
And, yeah, there were some years where Tesla sort of got close to those returns. But that's an incredibly bullish viewpoint for somebody to have to justify having that much of their wealth in Tesla. And we do know from Twitter and so on that there are a fair number of all-in Tesla investors out there. Either they're very, very bullish, they're very, very risk tolerant, or they're not making a great decision.
Ben Felix: Yeah. I mean, it speaks to one of the comments you made earlier, Victor, about people extrapolate from the recent history far into the future.
Cameron Passmore: What role does Merton share thinking play in the success of firms like Renaissance Technologies?
Victor Haghani: It's hard to say. I mean, we don't know much about what's going on there. One thing that I would say though from reading the Greg Zuckerman book about the story of Ren Tech and Jim Simons is that, at least in the early days, it seemed that they were not thinking that much about maximizing risk-adjusted return. They seem to be taking an awful lot of risk.
And I would almost say that in the early days of Ren Tech – again, I don't know what the truth of it is, but just based on reading the book that, in the early days of Ren Tech, they took a lot of risks and they took a lot of risks with most of their own money. I think that the kinds of ideas that we talk about in the book would say that that was awfully bold. Either represented some very strong set of assumptions or was probably overly bold.
Today, now that the principles are super, super wealthy and investing in their Medallion fund represents a relatively small fraction of their wealth. I mean, now I think they've sort of grown themselves out of a dangerous and probably sub-optimal position that they might have been in 20 years ago or 25 years ago. But, again, that's all kind of speculation.
In the book, we talk about my experience at LTCM, which has some similarities to Ren Tech. It had a different outcome than Ren Tech. But in those early days, there were some similarities of thinking. And Ren Tech sort of grew themselves past that dangerous period of time and LTCM didn't.
Ben Felix: I read that book too. I don't remember the specifics, but I think there were stories in there about very obviously lucky outcomes early on that contributed to the ultimate success.
Victor Haghani: Yeah. And taking a lot of personal risk. And the fund was running a lot of risk. But they figured it out and they got past it and they had good all of the above. Been really smart guys. I'm certainly not taking anything away from them. Today, I think that their main constraint is just how much of the stuff they can do. Rather than – risk is just not a constraint for them anymore, it seems.
Ben Felix: Interesting. What does the Merton share tell us about asset allocation over time if it's possible to estimate expected equity returns?
Victor Haghani: As James was saying earlier that the Merton share is helping us to realize that, when expected returns are higher, we should allocate more to the asset. When risk is higher, we should allocate less.
And so, if it is the case that we can estimate the expected return of stocks at different points in time and estimate the risk of stocks at different points in time, and if those risk and return numbers are changing over time, then it's very likely that we're going to want to change our allocation to equities over time too. When the risk and return is more favourable, we should want to have more invested in equities and vice versa.
Now it is possible that risk and return could move in exactly a particular way where our asset allocation wouldn't change. But most people believe that the risk of the equity market is probably, at least over longer horizons, a little bit more stable. And that the expected return is moving around quite a bit.
And when I say expected return, what I really mean is expected return in excess of whatever the safe asset is. And I think that's important to get out there. It's not just the expected return of equities, but expected return minus the safe asset expected return.
Cameron Passmore: And how confident do you think investors should be in estimating expected stock returns?
Victor Haghani: Confident enough to act on it. Again, we're talking about estimating the expected return. We're talking about estimating the centre of the distribution. Of course, the outcome is never going to be exactly the expected return. It's going to be above or below that for each period of time. But we think that using something like the cyclically-adjusted earnings yields of equities is a coarse but sufficiently good measure of expected long-term returns that we think that investors should be changing their asset allocation based on that relative to the expected or offered return by the safe asset.
James White: I'd like to caveat that we think that measures like one over CAPE are sufficiently good measures of expected return to be useful for broad markets, for things like the S&P, US equities, European equities, things like that, is you go from broad aggregates like that to individual sectors. From individual sectors to single stocks, the quality of the metrics degrades really quickly. If you ask the question about US equities, I would say people should be confident enough that it's quite useful. If you ask the question about a single stock, I would say not useful at all.
Ben Felix: What about styles? Value is cheap relative to growth right now.
James White: Yeah. The core logic behind why measures like one over CAPE do a reasonable job forecasting long-term returns is because, at a high enough level of aggregation, earnings look not totally bond-like, but more bond-like than stock-like. This comes back to an observation that Shiller has written extensively about, that stocks have dramatically more volatility than earnings do. It is sufficiently high-level.
And so, when you're dealing with large aggregates, where if you look at total corporate earnings in the US and the history of that over time, there's little squiggles in it, but the squiggles are really little. It looks surprisingly bond-like. And so, just like you would for a bond, looking at the earnings you're getting divided by the price you're paying does a pretty good job. As you start going towards aggregates where the earnings look less and less bond-like, that logic holds less and less.
Even for individual styles like value stocks or growth stocks, anything where there's really significant regime shifts over time or where if you look at the earnings and the squiggles aren't kind of de minimis, that's a pretty good suggestion that that metric is not going to help very much.
Victor Haghani: Right. And by the way when we talk about the squiggles, we're saying looking at blocks of earnings. Like, 10-year blocks of earnings, that in quarterly earnings or even annual earnings, they're big squiggles you can get. But looking at cyclically adjusted earnings, they're just not bouncing around all that much. That was the Shiller paper. Or maybe it was Shiller-Campbell actually.
Ben Felix: Yeah. Back to market-level dynamic asset allocation, how does that change if we decide that, hey, we actually reject the ability to estimate differences in expected returns over time?
Victor Haghani: Well, if we reject the ability to estimate expected returns, then in some ways I think you just can't invest in that asset at all. That's the first thing, right? I mean, at a high level. If you look at the equity market and just say, "I have no idea what the expected return is of equities." Then, I’d say, "Well, you really shouldn't be investing there." You need to estimate it somehow.
Now if your estimate is just going to be, "I always believe that equities have a five percent long-term real expected return." Well, if that's your belief that the expected return is always the same, then your asset allocation would change. Because we know that the safe asset return is changing over time. The risk premium would be changing and you'd want to change your asset allocation.
If, however, you said, "I believe that equities always are going to give me 5% above the risk-free asset and that their risk is constant," then your asset allocation wouldn't change over time. It's probably better than other things you could do, but it's not great. But if that leads you to just always having 65% of your money in equities come rain, come shine, come anything, that's better we think than not having any money in equities over time. And it's better than being two times leveraged in equities over time. But we think that it's substantially sub-optimal. Not just a little bit, but substantially.
James White: I should add for listeners that whenever we talk about risk premium, what we mean by that is the expected return of an asset minus the risk-free rate.
Ben Felix: Yeah. I want to dig more into that. Can you talk about how the real return offered by long-term TIPS, which as John Campbell and Cochran I think both explained to us, is the risk-free asset for long-term investors, how does that real expected return affect the optimal risky share in assets for long-term investors?
James White: It's interesting to think about a thought experiment where let's say your expected real return for equities was 10%. Amazingly good but. But in that environment, you could get 10% risk-free from TIPS. Let's say from a 30-year tip or some very, very long-dated tip. Now, you have two long-dated assets with the same expected return. Equities, let's say they have 20% annual volatility. How much equities would you want in that environment?
And in contrast, imagine an environment where the equity real return is much lower. Now let's say it's 5%. But in this second environment, the real return on TIPS is zero. Which environment would you want more equities in? We think it's clear that you would want dramatically more equities in the second environment because one interesting thing about investing that we think doesn't get talked about as much as maybe it should, is that all investing is relative. You have to invest your money in something. Even if it's in cash, you're really investing in overnight deposits.
And so, whenever making an investment decision, it almost never makes sense to look at one investment in isolation. You're always investing in to something and out of something. And so, you have to look at the relative risks and returns. And in theory, you could do that relative to any benchmark asset. But an asset that feels like a risk-free asset to you is kind of the natural baseline to use when comparing to other investments.
Cameron Passmore: How do you forecast volatility for determining the optimal risky share?
Victor Haghani: There's lots of observations that we can look to in coming up with that. And in practice, we think that blending some of the different sources of information makes sense. I think realized historical volatility, giving more weight to long-term realized volatility than to short-term is one good input.
The options market is a really good input. It's not perfect. There could be risk premium that's also sort of built into the options market. But that's a good metric too. Although in general, we're making long-term investment decisions. We kind of want to think about longer-term volatility forecasts. And the long-term options market isn't that liquid or transparent to people.
But I think some combination of implied volatility from the options market, historical volatility, and also just thinking a little bit about a bottoms-up approach to estimating volatility in terms of, "Well, how volatile are earnings? How volatile are discount rates?" I think all of those things together are important.
And what's really important also that it's not just the volatility that's important, but it's the full distribution. And in the book, we go into this, that the Merton share is for this very specific set of assumptions that are very ideal and non-realistic assumptions. We know that the stock market can gap down. And we know that we can get a Black Monday in 1987 or in the 1920s.
When we're really thinking about the distribution, we really want to also build in probabilities of large tail events as well. And so, it's important to get a really full distribution and not to assume that stocks are this sort of continuously trading brownie in motion sort of thing. But that there are real tails. There are fat tails. There are gaps down. All of those things. And we really need to build that into the distribution and then think about maximizing our risk-adjusted return based upon that.
It's not just you know thinking about the centre of the distribution. But we really care about more than anything almost is this small probability of these big tail events. This expected utility framework is great for thinking about how much weight to put into those events and how much that affects your asset allocation.
Ben Felix: I do want to get into utility. But real quick. Because, Canada, we've decided – or the country is phasing out real return bonds. How would dynamic asset allocation work in a country that doesn't have an equivalent to TIPS in the US?
Victor Haghani: What you want to do in making your decision is try to imagine if TIPS existed in Canada or in some market, where would they be trading? And then it's like, "Okay. Unfortunately, we don't have that to invest in. So, now my choice is I can invest in these different assets that have different amount of risk."
Let's say I have now equities and I have nominal bonds, but I don't have TIPS. I use tips kind of as my numeraire in some ways. And now I have to make a decision between how much I want to put into equities and how much I want to put into these nominal bonds which are not really that safe. And this framework can easily handle that.
We wrote a paper about that. What to do in a world where there is no safe asset? And even TIPS are not totally safe, right? I mean, there's default risk. There's tax risk on these things. There's the fact that the index of CPI is not going to be exactly your index.
It's quite nice that you don't need a real investable risk-free asset. You just need a risk-free asset to benchmark everything to and then decide what's the optimal risk-return portfolio and size for you to have. You don't need TIPS to be able to use this risk-adjusted return framework to do your asset allocation and sizing.
James White: To maybe give a slightly more concrete answer. What most people would do to kind of get that proxy TIP yield is look at the return on long-dated nominal bonds that do exist and then look at inflation expectations, inflation forecasts, historical inflation, and take nominal yield. Subtract out some arbitration of inflation metrics and then you get a forecast real yield. That's not as good as having a trading inflation market but is probably the best you can do.
Ben Felix: All right. We've touched on this idea of expected utility. To start this more detailed discussion on that topic, can you describe the concept of expected utility?
James White: Yeah. I think it's useful to back up and talk about utility first before we get to expected utility. And my impression is that a lot of people have a bad experience of utility from kind of Econ 101 a long time ago, where it felt like this very abstract, squirrelly, unreasonable thing. But we actually think it's something that's really intuitive and that people feel really intuitively.
When I talk about it with people I usually go back to talking about gummy bears. I have this irrational – I don't even have much of a sweet tooth. But I really like gummy bears. And I think most people have had the experience, let's say you buy a pack of gummy bears and you're eating them, that the last one in the pack is still good. But it's not nearly as good as the first one was. The first one is like really good. And then you know by the time you get to the last one, I'm still going to eat it. I'm not going to turn it down. But it's slightly grudging.
That phenomenon is true in almost everything we consume. Economists would call it diminishing marginal benefit of consumption. And I would argue that this isn't an accident. It's not something that's culturally contingent. It's not something that some people feel and others don't. It's a really deep part of normal psychological functioning.
Imagine the opposite. Imagine that, for anything we had. Like, as I eat the gummy bears, the more I eat, the more I want. Controlling your desires would just be impossible. And so, this phenomenon that consumption isn't symmetric and that there's this diminishing marginal value of consumption is a really innate part of human nature. And utility is really just a recognition of that. And the utility curve and the shape of the utility curve, something like a logarithm, is really just trying to mathematically capture this phenomenon.
In consumption terms, the way it tends to manifest is that more consumption is always better, but it gets less and less better as you consume more, and then, dynamically, the way it tends to manifest is that, from a given amount of consumption, losing 10 units of consumption and gaining 10 units of consumption are asymmetric. Losing is worse than winning in its impact on me. As those amounts get more extreme, the asymmetry gets more extreme.
Going back to the first example I talked about. No matter what your wealth is, if you compare going broke to doubling your wealth, the monetary end values involved are identical. But the impact isn't identical. There is a massive difference for almost everybody experientially in going broke versus doubling your wealth, and the utility curve is just recognizing that. The degree to which that asymmetry exists is different for different people. And so, the exact shape and curvature of a utility function is going to be different for different people in different contexts. But the core idea is the same.
Now, expected utility. Again, we think this is just a mathematical expression of something that's super intuitive for people. Let's say you have a choice to make and the outcome of the choice is uncertain. There's two possible outcomes. Well, what are you going to do? In some sense, you're going to intuitively or explicitly make a judgment about what the probabilities are of the different outcomes and what the impact on you is of the different outcomes. And then you're going to weigh them. And that's just what expected utility is doing.
It's taking the probability-weighted average of different outcomes. Except you're not probability-weighting the – let's say we're talking about financial decisions where there's dollars and cents involved. You're not taking the weighted average of the dollars involved because if I look at my going broke versus doubling my wealth, if that was 50-50, well, the weighted average of the dollar is zero. It looks like no big deal.
By calculating expected utility, you're taking the weighted average of the impact on you, which is your change in utility, not of the dollars involved, and that's the core insight.
Ben Felix: Everything that you're just saying matches up so well with a lot of the literature from psychology on the relationship between money and happiness where some research finds there's a plateau in happiness. Some research finds that happiness increases with log income, but it's never a linear increase of happiness with increasing income.
James White: Yeah. And I think it can't be. For people who have that kind of profile, or even worse, a profile where it's more valuable as you get more, that really verges into what we call addiction.
Cameron Passmore: What is the certainty equivalent return?
James White: One challenging thing about utility, expected utility especially, is that it's a yardstick, but it doesn't have a very natural sense of scale. It's really nice to be able to talk about something that has a scale that we're used to dealing with. And in making financial decisions return is the obvious sense of scale, right?
The certain equivalent return is the risk-free return, which delivers the same expected utility as a given risky investment. I'm considering some risky investment. It has different outcomes with different probabilities. I can figure out its expected utility. But I can also figure out the expected utility of a risk-free investment. The nice thing about certainty equivalent return is that it says, "Okay, I have this risky investment. Maybe it has a 20% expected return, but it has a ton of risk." That's equivalent, in the utility it will deliver to me, of making a 3% risk-free investment.
Another word we use for certain equivalent is risk-adjusted return. And that probably makes the sense of it even clearer, that you can think of the certainty equivalent return as an expected return on the asset with a built-in risk adjustment.
Victor Haghani: We did a survey where we went to people and we said, "Okay. Imagine that you're not allowed to invest in anything risky ever again for the rest of your life. How much would you need to be paid to forego investing in anything risky for the rest of your life?"
What we found was how would you go about trying to answer that question? Well, if you sort of have this – if you read our book and have this expected utility framework, it would give you a way to answer it. But people really intuitively come to an answer. They realize that, "Well, maybe equities are going to give me an extra 4% or 5% return over time. But they're risky. I would be indifferent between being able to invest in equities with this 4% or 5% risky return. I would be indifferent between that and just getting paid an extra 2.5% risk-free. Give me 2.5% risk-free and that would be about equivalent to 5% risky sort of scaled down for how much equities I want to own."
When we did this survey, we really found that people were answering the question. Now these are all – our client base tends to be a lot of finance-y people and there's professors in their finance and all that, so it’s not super surprising. But it's just interesting that when the question is framed like that, you really kind of need an expected utility framework. But at the same time, you don't. Because you kind of get the idea. And that risk-adjusted return is a pretty cool thing. Like if you really stop people from investing in equities, you're really taking away something very valuable to them.
Ben Felix: Yeah, that's interesting. It may be obvious from the description that you just gave, but can you talk about how you'd use the certainty equivalent return to evaluate two alternative investments?
James White: Yeah. The full-credit answer is to find portfolio weights which generate the optimal certain equivalent return. Optimizing certain equivalent return or optimizing risk-adjusted return is basically the same as optimizing expected utility. And so, for almost any portfolio question, the most general full-credit answer is just, find weights that maximize risk-adjusted return.
But heuristically, the nice thing about thinking about risk-adjusted return is that it just lets you compare assets head to head. If I have one asset that has a 5% expected return with 5% volatility and another that has a 30% expected return with 100% volatility, I can't just compare their returns. It's not obvious at all how those assets relate to each other in terms of quality on a standalone basis. Whereas if I had two assets, one has a higher risk-adjusted return. If I'm going to do A or B, I'm going to do the one with the higher risk-adjusted return. That's a higher-quality investment at that size.
Or another kind of interesting way to think about it is that if you look at a given investment at a given size and its risk-adjusted return is lower than the risk-free rate, you have too much of it. Do less of it. If it has a positive expected return, you'll want to do some of it. Anything with a positive expected return in isolation you should want some of. But if at a given size its risk-adjusted return is lower than the risk-free rate, you have too much.
Cameron Passmore:How does the certainty equivalent return deal with skewed expected return distributions?
James White: Yeah. The nice thing about the whole expected utility, risk-adjusted return framework is that it's not making any built-in assumptions about the nature of the return distribution. Your utility function is intrinsically skewed. In that the same magnitude, negative outcomes hurt more than positive outcomes help.
And then when you compute expected utility, you're just taking probabilities and outcomes. The machinery of it can really deal with any kind of distribution. You'll be at normal distribution, heavily skewed, kurtotic, whatever.
The outcome you see is that, for normal shapes of utility, if you fix the expected return and you fix the Sharpe ratio, the framework will naturally prefer positively-skewed to negatively-skewed distributions. Which is to say, if all the other characteristics are the same, the same expected returns, the same variance, you would prefer a skewness where there's a high chance of losing a small amount of money and a small chance of making a lot to a negatively skewed investment where there's a high chance of making a little bit but a small chance of losing a bundle.
Ben Felix: Okay. Interesting. If we take the certainty equivalent return and the Sharpe ratio and we're using them to evaluate something like covered calls or something else with an asymmetric return profile, what's the main difference in our decision going to be?
James White: Yeah. Sharpe ratio really breaks down, is you start looking at heavily asymmetric payoffs. For instance, I can construct a portfolio or an investment that has normally distributed returns in a given Sharpe ratio or I can get the same Sharpe ratio with a heavily, heavily asymmetric payoff.
And so, the Sharpe ratio doesn't really tell you anything about that. It's not useful in distinguishing between those two. But what the utility framework tells us is that people with utility-like preferences should distinguish between those two. You should really prefer, let's say, normally distributed to negatively skewed and you should prefer positively skewed to negatively skewed payoffs. And so, using expected utility or risk-adjusted return will naturally deal with that whereas the Sharpe ratio won't.
And one of the famous papers that kind of talks about this with respect to the Sharpe ratio is the Goetzmann-Ingersoll paper Sharpening Sharpe Ratios, where they basically show that various option combinations can really be used to manufacture Sharpe ratios for people who are only sensitive to Sharpe ratio and don't care about other qualities of the return distribution.
Victor Haghani: Let me just mention that we have a whole chapter in the book on the use of options within the expected utility framework. And I think we give options a bit of a hard time. And this is actually the 50th Anniversary right now of the Black-Scholes model and two of our friends and partners are – well, the Black-Scholes-Merton we could call it. Two of the three initials are friends. And I'm going to see them soon. I'm kind of hoping they haven't read that chapter. Although, Myron has read the chapter and objected somewhat.
But in general, we kind of feel like that options are not super useful for individual investors in general. Or there's a hurdle there. And we decided to put this chapter in the book because there's a ton of books out there that are about trading options to get rich but there's not anything that we could find out there that really addressed options within the context of individual personal finance and investing. Even great books like Jack Bogle's books or others don't even address options at all.
In Common Sense Investing, there's no mention of options. Or in Charlie Ellis's book or whatever, or even William Bernstein. Authors that we like a lot, they just don't really get to talking about options. We thought it would be useful to bring them in there and talk about them within this risk-adjusted world.
The other thing I just want to add briefly here also is all of the expected utility that we're talking about, we're talking about it with one particular utility function. What we call constant relative risk aversion utility. We like that utility function. There are other utility functions that people could have. Other shapes. Other preferences. But for background, we're sort of making a lot of our statements based on that sort of function and shape. But again, we think it's a good one. And we also think in most other plausible ones, you get the same sorts of results out, as long as you have this diminishing marginal benefit of more wealth or more consumption that James talked about.
James White: I'll also add that this question about skewed distributions arguably is the question that ultimately resulted in our writing this book. Because the first note Victor and I ever wrote together back in January of '17 was called ‘A Sharper Lens for Sizing up Nickels and Steamrollers’. And it was exactly about the differing perspective you get between Sharpe ratio and a utility analysis and looking at investments that have a lot of tail risk.
Ben Felix: Incredible. I'm glad you brought that section of the book up. I'm going to ask a little bit more about it now, Victor, because, actually, when you sent Cameron and I a message on LinkedIn, it was the craziest thing because I knew you as a character from a book that I'd read, right? About LTCM. And then I had your paper open on whether options make sense in the portfolios of retail investors. I'm sitting there reading your paper and I get a message from you on LinkedIn. Anyways, it's crazy. I'm going to ask about that a little bit more explicitly. You touched on it a little bit. Do you think that options generally make sense in the portfolios of retail investors?
Victor Haghani: The answer to that is no for a yes and no answer. I mean, could make sense sometimes. But generally speaking, no. James and I don't use options in our personal investing. We don't think it's a great fit for most individual investors in under most circumstances.
And we were talking here about options on financial assets. Home insurance is kind of an option. Home insurance against fire or whatever, that can make sense. Sometimes it doesn't. I don't actually carry home insurance. But don't tell my wife. But for most people, home insurance is going to make sense. And that's a form of an option. But in general, we think that for individual investors, that options don't make sense in general. Not always. But in general.
Ben Felix: Yeah. That's a great paper. And I think that the section of your book on that is similar to the paper, right?
Victor Haghani: Yeah, The Journal of Portfolio Management or The Journal of Derivatives led us base that chapter on that.
Ben Felix: Nice. Okay. When we started talking about utility, James, you kind of mentioned that people might not remember it so fondly. And I think it does get criticized sometimes. How do we know that expected utility as a framework is actually a good model for normative personal finance recommendations when so many people don't behave the way that the model predicts?
James White: Yeah. I think it's worth distinguishing behavior and preferences, and also between important and non-important situations. A lot of the experimental evidence violating people optimizing expected utility is really in very small dollar situations where the relevant asymmetry is just not as big.
But more importantly, I think I want to distinguish between people's preferences violating expected utility and people's behavior violating it. As investment advisors, researchers working with our friends and clients and other people, our overwhelming experience is that when we really focus on people's preferences, reasonable utility functions do a pretty good job of representing people's preferences.
And again, I think that's not an accident. That's really innate. That comes down to the fact that human nature would look totally different if we didn't experience some kind of diminishing marginal benefit of consumption.
And so, one of the first things we say in the book is this book is meant to be normative, not descriptive. What we're trying to do is help people behave better in line with their preferences, not just reinforce the behaviour they have. And I think seeing that gap between preferences and behaviour shouldn't be so surprising. Because the real world is so complicated. There's taxes. There's longevity uncertainty. There's returns uncertainty. There are so many different outcomes that, intuitively, mapping your preferences onto a consistent set of choices given all the choices we have to make is really difficult.
There may be some people out there who are just intuitively amazing at that. Warren Buffett, I don't think he has an expected utility framework. But he seems really naturally good at incorporating the same kinds of information that a utility framework would. Risk, and return, and the asymmetry of benefits and things like that. Most people aren't that naturally good at it.
And so, if I thought that there were a lot of people whose underlying preferences were really different from utility assumptions, I would be more worried. But that is not our experience at all.
Ben Felix: That's really interesting. Again, in the psychology literature, there're lots of it. I talked about how when you actually look at whether money makes people happier, there are diminishing returns. But then there's also research showing that people predict that more money will make them a lot happier even though that's not actually the case. I think that kind of speaks to what you're talking about.
Cameron Passmore: Why hasn't this framework caught on more widely?
James White: We think there are a few reasons in different domains. If you look at Wall Street, for example, I think the big reason is that Black-Scholes came along. This kind of work was really hot. People were really interested in it. And then there was the derivatives pricing revolution and a lot of the people who had the mathematical and technical backgrounds to read Merton and Samuelson and read this kind of literature just went into derivatives pricing and risk neutral evaluation. And just the whole risk-neutral world kind of took over everything.
Outside of Wall Street, I would say I feel confident that the reason it hasn't caught on is not due to lack of utility. No pun intended. One piece of evidence I have for that is that if you look at a group of people who are the most dedicated to making consistently good financial decisions; successful professional gamblers.
Successful professional gamblers overwhelmingly use variants of the Kelly criterion, utility-based decision-making, and decision-making that integrates risk and return in some consistent way. And I think the experience of professional gamblers who don't is that they don't remain professional gamblers for very long. As for why it hasn't penetrated the broader investing world more widely, I think that's a big part of why we wrote the book.
For something to penetrate widely, you really need not just great, but really, really heavy technical papers, you need a broader conversation for a broader audience. And that's really what we hope our book to be.
Ben Felix: It really is incredible. I agree with you. We did an episode on covered calls a while ago. That's why I asked about that. And that's a topic where it's like they get evaluated with Sharpe ratios and you get people selling ETFs or mutual funds going around showing how high the Sharpe ratios are with no mention of expected utility. I totally agree with you that it's something that people need to understand better. I think you guys have done a great job in the book doing that.
James White: And I think maybe another reason is that, like we talked about up front, that there's just a lot of people who have this bad experience with dealing with utility from taking Econ 101 or conflate this kind of utility-based decision making with Benthamite utilitarianism and have this feeling or have, again, from Econ 101, tried to apply utility to non-financial things where it becomes much, much squirrellier and just have this kind of gut reaction that like, "Oh, this stuff isn't very useful."
But our experience is that, when we really take people regardless of their priors. When we really take people through this framework and its assumptions, people actually find it really natural and intuitive.
Ben Felix: All right. Victor, I mentioned that I knew you prior to this as a character in the book When Genius Failed. I got to ask, how was your experience? Because I know the story from the author's perspective, but not yours. How did your experience with LTCM affect you professionally and personally?
Victor Haghani: First of all, it was very, very painful. I mean, I can sort of smile about it now 25 years later. But it was painful. It was embarrassing. Yeah. I mean, it really hurt. I think that I'm incredibly grateful that, as a group of partners, we sort of had each other and maintained our friendships till today, throughout. And that helped it somewhat. But it was just really, really painful.
But over time – I don't know if you guys have read the book by Dan Gilbert, Stumbling on Happiness, is he and others in this sort of happiness field point out that, over time, no matter how bad a thing happens to us, we kind of drift back to our normal state of happiness. And that took a little while. But I think, eventually, I got there.
Professionally, it marked the change in direction for me where I kind of wanted to take time to understand what had happened. To learn from it. And I stopped in that field. And I kind of feel a little bit fortunate, because in 2007 and 2008, this bigger storm came through the markets and I feel good that I didn't sort of devote those interim years to going back to the cold face of relative value trading. Yeah, I think it gave me a good feel for the range of my utility function, you could say.
Cameron Passmore: How did you decide how much of your wealth to invest in LTCM?
Victor Haghani: Well, we have a chapter on this in the book about my own personal perspective and experience. It's not about trying to rewrite or set the record straight or anything on LTCM. It's just about how I experienced it, and dealt with it and thought about it.
And by the time we started LTCM, we had this long track record of doing relative value trading that went well beyond my time going back into the late 70s with John Meriwether and the earlier people in the group. Seemed like this highly profitable, high Sharpe ratio type of activity with a really good sense of where the money was coming from. It wasn't like a black box sort of thing at all. And we had a lot of market experience. We really thought about what was causing things to be out of line and what would be a catalyst to bring them back.
We didn't just look at valuations. We didn't just use models. We used a lot of experience looking at things. And it looked very attractive. As a partner of LTCM, my approach to begin with was to say, "Well, I'm going to just take a certain amount of money that I'm going to be happy with if I lose everything else. What's an amount of money that I'll just be happy with having in safe assets on the side?" And I put some money aside. And then with the rest of my money, I put it into the fund.
I don't remember exactly, but I would say that probably 80% or so of my liquid financial net worth I had invested in LTCM one way or the other. And as we discuss in a chapter in the book, I think in hindsight – and without knowing what the outcome turned out to be. I mean, with perfect hindsight, I wouldn't have invested anything. I would have invested and then taken it out at the end of 1997.
But sort of I think that there's a reasonable case to be made on an ex-ante basis that, using an expected utility framework, I would have reached a different decision and and that other people can sort of benefit from those experiences and mistakes now.
Ben Felix: Yeah. Super interesting to hear you talk about it. And I'm sure that our listeners will find the same thing. Because that's not an everyday event to have lived through as a person.
Victor Haghani: No. It wasn't. Learned a lot from it. It's actually also roughly the 25th anniversary. I think some people would say September 12th or something like that, 1998. A couple more weeks and it'll be exactly 25 years. But yeah, the fact that the book is coming out now is a coincidence. The book is not about LTCM. Even though the experience of LTCM certainly has shaped. And James had his own mini-LTCM sort of experiences too.
Ben Felix: All right. We spent the last little while talking about sizing positions in risky assets and the expected utility framework. I want to move on to spending, which you also cover in the book. What do optimal solutions for lifetime spending from a portfolio look like?
James White: There's two characteristics that I really want to focus on. The first is that spending should be proportional to wealth. This is probably the most important one. And, obviously, nobody is going to follow a spending policy that every day is literally proportional to wealth.
But in some big picture with a lag, or with some averaging, or whatever, for a spending policy to be close to optimal, it has to be proportional to wealth. And that falls out of the math. But I think it's also pretty intuitive. If you have any spending policy that's not, your wealth drops a lot. Your spending doesn't change. You're going to run out of money pretty quickly. In some big – or the other way, if your wealth increases a lot and your spending doesn't change, then you're just massively underutilizing your wealth.
The most important characteristic is that spending should be proportional to wealth. The proportion can change over time. Depending on your other preferences, it might be optimal for you to increase the proportion over time, decrease it over time. But at any given time, it should be proportional to wealth.
This creates a really interesting relationship, which is that imagine you are – you're following it perfectly and your spending is perfectly proportional. Well, then, the volatility of your spending is going to equal the volatility of your investment portfolio. And that connection is both a major constraint and also really useful in helping figure things out. That's what creates the necessity to not kind of think about your spending and your investing separately, which is in practice what we see often happens. But to set your spending and investing policies jointly. Because you have to be proportional to follow even a reasonable spending policy.
Let's say you can tolerate 10% of spending volatility, but then you go invest in a portfolio with 30% volatility, you’re going to have a huge problem.
Cameron Passmore: How did Merton and Samuelson solve the lifetime investing and spending problem?
James White: They solved it differently. In both cases, their solutions were really interesting and innovative. But I actually think this is a case in which the bigger part of the genius was imposing the question rather than the specific way in which they solved it.
And the way they posed it was, every period, say every year, I have two questions to ask and answer. How much should I spend and how much risk should I take? Specifically, how much – I have some risky portfolio and I have a risky asset. How much should I invest in the risky portfolio versus the risky asset? And how much should I spend? I have that same question every year.
What is the joint spending investing policy that maximizes my lifetime utility of consumption? That was their setup. And that setup really unlocked a huge amount of interesting results. Now in some cases, with a lot of starting assumptions, you can get to analytic solutions for that, which Merton showed. These days, we tend to solve that system numerically rather than analytically because we want to include a lot of real-world hair like taxes, longevity, uncertainty.
There's a lot of real-world situations that make the analytics solution unworkable. The analytics solution still gives us a lot of intuition about the character of what's going on. But I really think the number one innovation was just posing the problem like that.
One of the first things I talk about with clients now is that I still think the two most important questions clients should focus on are how much to spend as your wealth changes? And how much risk to take? And a lot of people, even highly financially sophisticated people, almost never explicitly think about, "How much total risk am I taking?" They think about individual investments. How much they have in individual stock or individual portfolios. But it's rare to encounter somebody who has explicitly framed the question for themselves as, "This year and next year, how much risk should I be taking?"
Ben Felix: What are the inputs? If we're talking about optimizing lifetime spending and asset allocation, what are the inputs that people should be thinking about?
James White: Yeah. In a really highly stylized world, the major inputs are your personal risk aversion, your personal time preference, which is basically the discount rate you apply to your utility of spending over time. The characteristics of the risky portfolio. So, highly, highly stylized world into the expected return volatility of the risky portfolio. In a less stylized world, just the distribution of returns of the risky portfolio and the risk-free rate.
Then if I add a little bit more real-world context, I would add to those your age, your longevity distribution, the priority you place on bequeathing wealth versus consuming wealth, or another way to think about that is your utility of bequest separate from your utility of personal consumption. Your subsistent spending, meaning, for most people, they get to a really, really, really bad place before spending literally goes to zero.
And for most people, there's something economists call subsistence level of spending at which – that's basically as bad as it can get. I'm not personally. But let's say I'm spending a million dollars a year and my spending drops to $10,000 a year. Well, there's really no difference between ten thousand and zero at that point.
Identifying subsistent spending tax rates. Not just the absolute rates, but the structure of taxes. Those are all things that are inputs to our process and inputs to a less stylized process that involves a lot of real-world things.
Ben Felix: You've presumably got this in a model. Can you talk us through the mechanics of how you would use this framework with like a client, for example?
James White: We start off by sending them a lot of questions. And some of the questions have factual answers. What is your net worth? Things like that. And some of the questions are less factual. Like, how do you think about the value of spending on yourself versus leaving money to your family, to philanthropies? Things like that.
First, we collect a lot of information from them. And that has personal factors. Their age. Their wealth. It has questions related to calibrating their utility functions. We don't treat those as kind of final answers. But I think of it more as the calibration questions give me a starting guess as to how risk-averse somebody is. By risk-averse, what I really mean is how much curvature is there in their utility function?
The calibration questions also give me a sense of what is there that's consistent or inconsistent in how they're thinking about it. Sometimes people answer calibration questions very consistently. And sometimes they answer them quite inconsistently and then you have to drill down into that and understand where that comes from.
All of the answers are kind of starting guesses. We take all of those. We put them into a big optimizer we have which stimulates a risky portfolio. It has taxes. It knows about the actuarial tables. It has stochastic longevity built into it. Then we apply what is called the general method, which is optimized utility of consumption over time.
We find the spending rule and the risk rule that maximizes their lifetime utility of consumption. We have to do that numerically by simulation rather than analytically. And then what we show people is basically what those policies look like. Both in terms of, "Okay. I spend 4% this year, or 4.5% next year, or 5% next year," whatever. Also, the heat map of, if you follow these optimal policies, what is the probability at a given year of your spending falling below a given level and your wealth falling below a given level? And then we talk about it with people.
Then there's this kind of iterative process where, between the answers people gave and these heat maps that they're looking at, we see how comfortable they look. Sometimes we get it really close on the first try. And sometimes people answer in a certain way. And then we look at the heat maps and they're like, "Oh, no, no. This shows a 30% chance of my spending falling below X five years from now. I can't tolerate that. That's absolutely intolerable to me."
That means that they're either more risk-averse than we originally thought or that their current spending is just too high relative to their wealth. And which is which will depend on the individual person and their individual situation. We just drill into it until we feel like we understand what's going on. But we really use the starting questionnaires and these heat maps are rising from the optimal policies iteratively to ultimately get to both a set of utility and bequesting risk aversions that feel comfortable for somebody, and a set of policies, both investing policies and spending policies that I feel comfortable for somebody.
I should say not just comfortable. It's really important to me to have policies for clients that are comfortable. Because if it's uncomfortable, you just know people won't follow it. But it's also important not to have policies where you only think about comfort, but ignore how sub-optimal this is. There can be a policy that's comfortable for somebody but it's just massively, massively sub-optimal too. And that's not good either.
One of the places where the utility framework comes in is helping people find policies which are jointly followable and comfortable and, I won't say strictly optimal, but in the vicinity of optimality. And one of the really nice things about the whole utility framework is that if you boil everything down to one dimension and you look at, let's say, expected utility or risk-adjusted return as a function of your one dimension of how much risk you're going to take, say. It's going to look more or less like a parabola.
In the vicinity of optimality, the slope is not going to be very great. Being really far away from the optimal is not good. But because the slope is just pretty shallow in the neighbourhood of optimality, it's not important to get bang on. It's important to be in the right ballpark.
Cameron Passmore: Why don't we see Merton-Samuelson spending built in a typical financial planning softwares?
James White: We don't know what everybody does. But I think the answer is similar to why you don't see the Merton share around a lot, which is that it hasn't been – not been written about in the mainstream very much yet. And what any investment advisor who's trying to appeal to a lot of people wants to do is take advantage of concepts that are already out there. That's probably a long-winded way of saying a much more glib answer, which is people haven't read our book yet.
I say that tongue-in-cheek but I think the generalized version of that is just that these concepts are only starting to move from academia to the mainstream. As you see it enter the mainstream more, it would really surprise us if people don't move in these directions.
Ben Felix: You mentioned a questionnaire that you use with clients to kind of work toward figuring out their utility function and their risk version and all that kind of stuff. How can a listener who's maybe managing their own money or maybe working with an advisor, how can they figure out their utility function, their risk aversion, and their time preference?
James White: There's a lot of calibrating questions you can ask yourself. And I would say there's really, formally, three domains for thinking about utility. There's utility of wealth, utility of spending, and utility of a queefing.
In some situations, utility of wealth and utility of spending are really combined. The Merton-Samuelson framework really combines them, where if you're following a proportional spending rule, utility of wealth and spending are really, really combined in there. Whereas let's say it's a context where you're not thinking about spending at all, like, you're running a fund or you're a gambler. You're trying to make bet-sizing decisions. You would think about utility of bankroll rather than utility of spending.
But there's these calibration questions you can answer in any of those cases. All of them have roughly the same form, which is, how would I feel about, for example, a 50-50 chance of losing 10% of my wealth versus making 10% of my wealth? Would I want to take that opportunity? Would I reject that? Or would I be indifferent to it?
By finding where you're indifferent, if you limit yourself to CRRA utility, then you can calibrate your utility by finding that indifference point. And there's a lot of ways you can ask that question. My personal favorite way of asking it, let's say with respect to wealth, is let's say there's an opportunity where there's a 50% chance of losing X% of wealth and a 50% chance that you'll become a multi-trillionaire. What is the highest X for which you would not take that bet?
Clearly, 50-50 chance, 5% versus trillionaire, I would take it. 50-50 chance of losing half of your wealth versus becoming a trillionaire, much harder question now. Your answer maps one-to-one onto your level of CRRA risk aversion. And I should add that we talk about this in greater detail in the book, but one thing we feel pretty good about is that, for most people, institutions and circumstances, CRRA risk aversion is flexible enough. It certainly doesn't capture all the nuances of utility in most circumstances. But in our experience, for most people in situations, it's flexible enough and it's not necessary to move beyond that.
The academic literature explores tons of different classes of utility functions and can get all kinds of bizarre results depending upon how bizarre your utility function looks. But in the real world with real people, we think CRRA utility really does a very good job. You'll see that variously called CRRA utility, isoelastic utility, and power utility. They're all really the same thing.
Maybe I'll also add that even though there are different contexts where you might want to think about utility of wealth, utility of spending, utility of bequesting. When working with clients, I usually start off with utility of spending. And the reason is I think, for most people at most levels of wealth, spending is really where the rubber meets the road.
People have a lot of experience, real personal experience dealing with what it's like having to cut their spending by 10%. But it's like having 10% more to spend. They really have lived experience and strong preferences about that. As you move away, the questions become somewhat more abstract. And so, I like to start with spending.
Now for some people, with extraordinarily wealthy people whose spending is like a tiny fraction of their potential spending, spending is less of a constraint. And so, you tend to focus on wealth more. But for most people, I feel like the thing that constrains their risk aversion more than anything is the volatility of spending they're willing to tolerate. And that people really give different answers sometimes when asked about spending versus wealth.
My experience is, when you dig into that, the spending answer is almost always the one reflective of true preferences. Sometimes you encounter people who, for example, express a desire to have very, very low volatility of spending. But then when you ask the same questions about wealth look like tolerant to very, very high levels of wealth, of wealth volatility.
If you dig into that and kind of walk people through the consequences of that, let's say the consequences of having a very volatile investment portfolio while following a very tight spending policy, I would say, in almost all circumstances, the result isn't that people say, "Oh, no. I was wrong. I could really tolerate a lot of spending volatility." That's almost never the case. People usually say, "Oh, I wasn't really thinking about the connection. I'm really not so tolerant to wealth volatility either."
Cameron Passmore: Here's a question for both of you. What advice do you have for young people as they start out their financial journeys?
Victor Haghani: I'm going to say I have three pieces of advice. But as I say them, I might come up with some more. The first one is that you're young. Your human capital is very large relative to your financial capital for most people and you should give that a lot of focus. You should really focus on what you want to do with your life in terms of work, and career, and all of that. Thinking about the risk-adjusted value of your human capital is a good way to think about it.
The different careers are going to appeal to you inherently in different ways. But also, you should take account of the riskiness of different kinds of careers and make some adjustments for that as well. I think a big focus on human capital would be number one.
Number two I think would be, make sure you're starting your financial education. Work some books into your routine. You don't want to spend all your time reading finance books. They can be pretty boring but make sure that you're getting a financial education by that time if you haven't had it already. Talk to people, think about it, and try to really get yourself educated.
And the third thing is start to develop some good habits. Even though the academics would say, "Well, you want smooth consumption. So, maybe you should borrow money when you're young and then save more as you get older." We would say like develop some good habits. Start doing some saving. Put specially tax-deferred saving and start thinking about investing.
We would say invest in low-cost, broadly diversified index funds. That's what we would say. But do your research. Do your financial education and hopefully, you'll get to that too. I think those would be the three bits of advice.
James White: Yeah. I think that was really well-stated. I mean, the only thing I would add is that people should really read our book too.
Ben Felix: It is a really good book. For the average person off the street, it's still pretty intense in terms of the level. But it's a whole lot better than trying to read one of Merton's papers. You guys both have experience in academic settings and you've seen some pretty wild stuff in practice. We talked about LTCM, Victor and James, it sounds like you've had a smaller but similar experience. What's the most important piece of wisdom in investing financial wisdom that you can leave our audience with?
Victor Haghani: I mean, I think if there's just one sort of golden rule of finance or investing, it would be what I said earlier on, which is that you can't expect higher returns without taking more risk. That return and risk are bound together. This is kind of related to the theme of the book also in terms – that we bind them together using this expected utility framework.
But the fact that you can't expect higher returns without taking more risk doesn't mean that you can't get more risk without getting more return. It's easy to get risk without getting returned. You can go to Vegas and you can get negative return for risk.
There's an important corollary there which you shouldn't expect higher returns for risks that you can eliminate through diversification. And I think that if you get this into your head, that you can't expect higher returns without taking more risks and that this is like enforced by the competitiveness and efficiency of markets. I mean, everybody's looking for more return without risk. It's the proverbial free lunch. And that's what makes it so difficult to find.
I think if people get a respect for the efficiency and competitiveness of markets and that you can get higher returns without more risk, that's going to be a great guide to your financial-ship through your life.
James White: I agree with all that. Maybe one thing I would add is I would advise people to spend as much time thinking about the how much to invest question as they spend on the what to invest in question. I think that goes for professional investors. Whether you're at a bank, or a hedge fund, or individual investors. For social reasons, for a variety of reasons, almost all the focus is on, what do I invest in?
And the reality is that you're going to make a lot of investment decisions over your life. Not all of them are going to be good. If you have a good way, a robust way of thinking about how to size those investments, how to take the right amount of risk, you'll be able to survive and better than survive through all of the bad decisions you make.
It's just not plausible to think that you're not going to make a lot of bad decisions over a sufficiently long period of time in terms of what to invest in. I think that's really the key that is really neglected in books and the literature and in people's own time-space.
Ben Felix: I love both those comments. And I think that they go together really nicely. Because as we talked about earlier, even somebody with a good framework might invest all their wealth in Tesla if they believe that there's a 76% expected return. But if they understand how risk is priced and how markets work, then they might revise downward their expectations.
Cameron Passmore: Our final question for both of you, how do you define success in your lives?
James White: The full credit answer for me at least, is still a work in progress, for sure, as it is for most people I think. But one aspect of it for me, I was exposed early to the famous Carl Sandberg quote, "Time is the coin of your life. It is the only coin you have, and only you can determine how to spend it. Be careful lest others try to spend it for you."
Certainly, at the end of every year, when I go back and look at the previous year and kind of think about the scorecard, this isn't the only thing on it, but "did I use my time wisely?” is a really big part of it. And I say that without any judgment about what use of time is worthwhile for any person. I think there's a lot of variation across individuals and even within individuals. What I find worthwhile spending my time on is very different now than it was 20 years ago.
But the wonky economic perspective is that time is maybe the ultimate scarce resource. I feel really strongly when I don't use it well that that was not successful than I do when it was.
Victor Haghani:I like that, James. That was great. Great to hear that. For me, I guess I feel like I just have had so much good luck in my life in different ways and good luck that has been delivered by different people. My parents were great. Well, my mom's still alive. My parents have been great. My family. And the people that have come along in my journey, and have helped me and given me chances, and educated me, and protected me and all of that.
I just feel so, so fortunate, not only for being born at a great time into the history of the earth or the history of humanity. But also, I feel super fortunate in terms of what people have done for me in my life. I think that I'll define a successful life as one where my deficit of what I've done for other people versus what I've been given is as small as possible. Maybe even positive. I think that's at the end, if there's one thing, I think that's what I would like to measure it by. And I'm still in quite a big deficit. I'll see how things go getting older.
Ben Felix: Wow. Yeah. Great answers from both of you guys. This has been a fantastic conversation. We really appreciate you coming on the podcast.
Victor Haghani: Thank you.
James White: Thank you, guys.
Cameron Passmore: Yeah. Great to meet you both. And thanks so much.
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Books From Today’s Episode:
The Missing Billionaires – https://www.amazon.com/Missing-Billionaires/dp/1119747910
Stumbling on Happiness — https://www.amazon.com/Stumbling-Happiness-Daniel-Gilbert/dp/1400077427
The Man Who Solved the Market – https://www.amazon.com/Man-Who-Solved-Market-Revolution/dp/B07P1NNTSD
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Victor Haghani on LinkedIn — https://www.linkedin.com/in/victorhaghani/
James White on LinkedIn — https://www.linkedin.com/in/james-white-b4310a47/
Elm Wealth — https://elmwealth.com/
When Genius Failed — https://www.amazon.com/When-Genius-Failed/dp/0375758259/
Where are all the Billionaires?: Victor Haghani at TEDxSPS – https://youtu.be/1yJWABvUXiU
‘What's Past is Not Prologue’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3034686
‘Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case’ – https://www.jstor.org/stable/1926560
‘Stock Prices, Earnings, and Expected Dividends’ – https://www.jstor.org/stable/2328190
‘No Place to Hide: Investing in a World With No Risk-Free Asset’ – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3903372
‘Sharpening Sharpe Ratios’ – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=325942
‘A Sharper Lens for Sizing Up Nickels and Steamrollers’ – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2874602
‘Do Options Belong in the Portfolios of Individual Investors?’ – https://elmwealth.com/do-options-belong-in-portfolios/