VALUE: After Hours (S08 E31): From Cartoon Apes to Reddit Mania: Why Crowds Lose Their Minds With Money – Alex Edmans

During their recent episode, Taylor, Carlisle, and Alex Edmans discussed:

• Can a World Cup Loss Move the Stock Market?
• Why Smart Investors Get Caught in Stock Market Bubbles
• The Curse of Knowledge in Investing
• How to Spot a Stock Market Bubble Before It Bursts
• AI Can Win and Investors Can Still Lose: Jake’s Brownian Bridge
• The Hidden Warning Sign in a CEO’s Private Jet

TRANSCRIPT

[Tobias] We’re live. I’m Tobias Carlisle. This is Value After Hours.

I’m joined as always by my co-host Jake Taylor. Our special guest today is Alex Edmans. He’s a professor of finance and the author of a brand new book called The Madness of Markets, Why Smart Investors Make Crazy Decisions and How to Exploit Them.

Welcome, Alex, how are you?

[Alex] Great, thanks. Thanks so much for having me on.

[Tobias] Can you give us a brief overview of the book?

[Alex] Yeah, so what I’m interested in is what drives the stock market. So what you learn in any finance class, and I did a PhD in finance, was it’s driven by fundamentals. So when you look at a stock price, you forecast future dividends and you discount them in a rational and dispassionate manner.

And certainly that happens to a large degree, but I wanted to highlight that it’s also affected by emotions and psychology. So perhaps when we are estimating dividends, we’re not just making a rational assessment about the company’s future prospects. We might be caught up in some hype.

So maybe now some people argue there could be AI hype. Maybe in the past there was tech hype or electric vehicle hype. And what I wanted to show is that even smart people may make these mistakes systematically.

And if so, that makes things interesting because for active investors like yourself, there are opportunities where the market is losing its mind.

[Tobias] You started at Morgan Stanley, were you in a trading position there? What were you doing at Morgan Stanley?

[Alex] I did two things. So I first started in investment banking. So that was doing mergers and acquisitions in London.

So that wasn’t really looking at the markets, although there was still irrational behaviour and a lot of emotion in banking. But when I got my first taste of markets in Morgan Stanley in New York City in the summer of 2004, I was doing fixed income sales and trading then. And that was the time of the European Championship of soccer in 2004.

And that was something I thought, well, it really shouldn’t affect Morgan Stanley in New York. Number one, Americans don’t really care about this talk to affair about the importance of baseball. And there’s three other American sports which are much more important than soccer.

And number two is the European Championship, which Team USA was not part of. But what was surprising to me is traders emotions were rising and falling with the games. And it wasn’t just people got happy or unhappy.

One, because his team got knocked out by Greece. He stormed out of the office. He didn’t return for a few days.

And this is a professional paid to trade bonds. And yet his emotions were heavily affected.

[Tobias] Victor Niederhofer, who was a trader and an author who’d recently passed away, wrote a book called The Education of a Speculator. And he liked to find these tiny little anomalies that existed in the markets. And one of them that he observed was that Monday mornings tended to be a little bit of a sell off, which he attributed to people recovering from their hangovers and overeating on the weekend.

So I think that there’s a reasonable argument that there’s a fair bit of non-rational, non-economic thinking going on in the markets. But you say that you can identify some of these anomalies, perhaps, that they’re exploitable?

[Alex] Yeah, absolutely. So actually, it’s interesting you’re mentioning the Monday effect, because that was one of the precursors to my first foray into behavioural finance. So the idea is, I want to show that the market is affected by emotions.

But that’s a bit tricky, because many factors that affect emotions may also affect fundamentals. So if I show that the market went down after a plane crash, one may say, well, that’s a negative sentiment. But one might also say there’s an economic effect on that.

After a plane crash, there might be less travel and less economic activity. So we can’t attribute it. So what I wanted to find was something that drives mood but doesn’t have an effect on fundamentals.

And the Monday effect was in that spirit, because really, there’s no reason why economic news should be worse on Monday than Friday. But even though there was this effect, people thought it might be a spurious correlation. There’s no real reason why Monday should be worse than Friday.

Yeah, some people might be upset, because they’re back to work. But some people did love their job and the trading. For all, they love the energy.

They love seeing their friends again. They love from their kids. And so it wasn’t clear to me that that was something negative.

And indeed, that was a result which went away over time. It was a statistical fluke for a few years in the data. So this is why I looked at sports.

And why do I look at soccer? Is this my English bias? I was doing my PhD in the US.

Why didn’t I look at baseball? But if the Boston Red Sox win and the New York Yankees lose, then some Americans are happy and some are unhappy. So it’s hard to see what’s going to happen to the US stock market.

But for an international, when England lose a soccer game, England will get upset. But there’s no effect really on profits or dividends or inflation. So if I find that the market goes down, then this might be due to mood.

 Can a World Cup Loss Move the Stock Market?

Now, clearly, if I found this in one game or 10 games, or even 100 games, I could have got lucky. So I looked at 1,150 games. What I found was a systematic pattern.

When a country gets eliminated from the World Cup, then the market goes down by, on average, half a percent, even when you control for the energy field or momentum, which is what the market did on the last day, and many other factors. Did that persist through the most recent World Cup? Yeah.

So interesting, when England was knocked out by Argentina, the market on the open on the Monday, even though there was positive economic news. And if you look at Yahoo Finance, which is a respectable source of news, they were attributing it, in part, due to the World Cup defeat. So actually, even Yahoo Finance, which you might think would only look at fundamentals, they were seeing that sentiment did matter.

I did not do a systematic analysis of this World Cup. But in 2014, which was after my paper was written, so I didn’t have that data in my original paper, I was on CNN. Richard Quest interviewed me right at the start.

And when I explained this to him, it was crazy. He thought there’s no way that this could be true. But he was open-minded, and I started to convince him over the course of the interview.

And he said to me, often, that was a great interview. I was really sceptical. Keep in touch with me and tell me what’s happening over the course of the World Cup.

And if things are going as you predict, then I’ll have you back on. And he had me on two more times. So in most cases, actually, things did pan out as they did.

With one big exception, which is when Brazil lost 7-1 to Germany. So even though this was 12 years ago, it may still be such a shocking result. Actually, the market went up.

Why? For a different reason. The president of Brazil, Dilma Rousseff, she had her identity linked to the team.

She’d built a lot of stadiums, which were earmarked for schools and hospitals. And so the view was that if Brazil got knocked out really badly, she might not last another presidency. And because she was seen as left-wing, then that was seen as good for the economy.

So again, soccer mattered in that case, but actually more for fundamental, rational reasons rather than sentiment.

[Tobias] Do you think that given the… I think that most market participants would be surprised to hear that fundamentals drive the market. Because I think if you polled most market participants, they would say that, particularly now, fundamentals don’t matter at all.

And it’s purely emotion and sentiment and perhaps flows and other technical matters that drive the market. Why do you think it is rational sort of fundamentals that tend to drive the market in the whole?

[Alex] Yeah, so in the corner of those who believe in efficient markets, and these are people much more knowledgeable than me, such as Eugene Farmer, who won the Nobel Prize a while ago. What they would argue is they would admit that emotions matter, but they would say that emotions cancel each other out. Maybe I am an Elon Musk fanboy, and I’m going to cancel Tesla.

And maybe you, Tobias, you just can’t stand his politics, and you’re going to sell his stock. But if we make mistakes in opposite directions, we will cancel each other out, and the Tesla stock price will be unaffected. Now, I think that’s wrong.

Why Smart Investors Get Caught in Stock Market Bubbles

Why? Because humans often act in the same way. And in particular, they may be herd mentality.

If you get excited about tech, I think I’m going to be missing out, so I will go into tech myself. And because we’re all driven by the same emotions, when we make mistakes, they’re not in opposite directions, they’re in the same direction. Then what the efficient markets people would argue is if indeed the market is overly inflated, and let’s say AI is in a bubble right now, there would still be smart hedge funds who realise this, and they will short, they’ll move in the opposite direction, because if there’s clear overpricing, somebody would correct it, so there could never be large amounts of efficiency.

But again, that’s not true, because in the world, you have the possibility that whatever sentiment has caused AI to be overvalued, that can only get worse before it gets better. Let’s give an example. If we go back to the tech bubble, Julian Robertson in 2000, the founder of Tiger Global Management, extremely respected investor, he saw tech was overpriced by any sensible valuation multiples, let’s say I think Cisco was trading at 190 times.

He did the sensible fundamental based thing of shorting, but the mispricing got even crazier, people got even excited into tech. And so he was bleeding losses when his rivals were making loads of money because they were still in tech. And of course, he pleaded with investors, he said, trust me, I’m in it for the fall, it will come right.

But people don’t believe him, they might have probably maybe actually lost it, he’s just too stuck in the dark ages, he doesn’t realise the revolution. And if you have flighty capital, that can withdraw from you. And so he was liquidated in March 2000.

Almost right on cue, the tech bubble burst afterwards, but he was just not around to enjoy the victory. So actually, even if I know the market is overvalued, the smart thing to do might be to pile onto the overvaluation, which Doros did, because if you think, well, the music might play a little bit more, then I’m actually want to be on the overvalued side of the trade. And maybe right now in terms of AI, there are, I don’t think it is as clearly overvalued as tech was back in 2000.

But some people believe, yes, it’s perhaps a bit pricey, but they don’t want to be right and early. It might be better to be wrong together with their fellow professionals who are also heavily invested in AI.

[Tobias] There’s been this story in the news over the last week or so that the situational awareness, Leo Ashenbrenner, who has this, he’s a young guy, he’s 24, he has a $45 billion hedge fund that has had, well, $10 billion hedge fund now, but he’s back. This is the point that I’m going to make is that there was talk that he was driving the market for a while because he’s levered four to one. He likes to use options to put his positions on, and that pushed up some of these AI related stocks.

And then when he was unwound, they saw a lot of weakness in those stocks. And there’s been a story for the last week that he’s had this $100 million bet in calls in some of these more racy AI stock names. And yesterday we saw the NASDAQ levitate 3% after we’d just seen interest rates increased on the Friday or the day before.

How does that fit in with your framework?

[Alex] Yeah, so there what you find is pretty large fluctuations based on some short-term news, which reverse. And my framework will highlight, well, often people actually overreact to information. So the soccer paper was an example of overreacting there.

It was overacting, which is clearly not newsworthy and clearly irrelevant for dividends, you might find. It’s overreaction to things that are genuinely relevant, but the market might make much more of it than it actually should. Let’s say we see Trump announcing tariffs, then people think, oh, this is absolutely terrible, not realise that actually maybe he’s going to reverse his position after the market disapproves of it.

Or you might look at a pandemic and think, oh, Zoom is going to do really well in a pandemic. It goes up by 700%. You forget that maybe rivals such as Teams or Google Meet might catch up, and maybe the world will reopen and people will realise that they enjoy human interaction.

So when we see some of these small fluctuations, I think the actual sensible thing to do is to ignore them, at least in the short term. Now, behind any bad or irrational behaviour, there’s always a grain of truth and sensibility behind it. And often why you see people overreacting is they think, you snooze, you lose, don’t just sit there, do something.

If there’s new information, oh, we lost Alex for a minute, figure or make some sounding what it’s going to do with interest rates. I might want to move to this because I think, okay, fundamentals have changed. But if the professional traders are analysing the same information, then maybe I don’t really have an edge.

So in the final chapter of the book, I give 20 tips on how the everyday investor or the professional investor can put the ideas of the book into practise. And one important one is know your edge. If you’re going to choose to do something, other than buying and holding the market portfolio, you have to have a competitive edge.

That means that you are going to be a professional to trade the markets every day. And it may well be that, yes, I see that the Fed has put his dovish statement out. But if everybody else knows that, then I don’t really have an edge.

We talked about the legendary Warren Buffett affair before the conversation. And one of his pieces of advice is actually don’t watch the market too closely. Now, we still want to watch the market and maybe you want to review our portfolio once every six months or 12 months if we’re a household investor.

But they’re always being on our phone to see the alerts that are across the 52-day week high and respond to that. I think it’s something which is healthy because I think we’re going to be trading and perhaps avoiding and missing out on one of those powerful things in the stock market, which is compounding if you’re in the market for any reasonable time period, but significantly less being out.

[Jake] Alex, one thing I remember reading about that oftentimes smart people can actually talk themselves into worse ideas sometimes because, you know, it checks the box of making them feel clever and they can come up with lots of good justifications for why they’re doing something because they’re so smart. How does that do it? What are your thoughts on that?

Is it actually like maybe if you’re smart, you should be even more careful sometimes?

 The Curse of Knowledge in Investing

[Alex] Absolutely. My thoughts are completely in agreement with you. And why can I be so unequivocal about this is this is behaviour that we see not just in the stock market, we see in all walks of life.

And generally, while my book is about investing in the laboratory, more about behavioural bias that we see in any business decision or in any life decision, we generally tend to be overconfident. So if you ask on average, anyone above average in terms of your driving ability, 90% of people will say they’re above average when clearly only 50% can be.

[Jake] But what if I really am above average?

[Alex] Somebody’s got to be. Yeah. What if I really am above average, Alex?

Well, 50% of people will be genuinely above average. But if 90% of people think that, then 40% are just deluding yourselves. So people typically will overestimate their own abilities.

And in particular, you might think, well, the smart people, they’re the ones who genuinely deserve to claim they’re above average because they are smart. But a little bit is a dangerous thing. You actually that your smarts may give you overconfidence because you might not realise that your smarts may not be in what is relevant for the task at hand.

So let’s give an example of that when it’s applied to investing. Let’s say I am a physician and I see this drug company is making beta blockers. I think, well, I know what beta blockers are.

They are a drug to address tension, high blood pressure. And I know that with changes in lifestyle, that high blood pressure is going to be even more of a problem going forward. Let me buy this beta blocker manufacturer.

But I have no idea on the production costs of the beta blockers, on the marketing spend, on the U.S. government’s likely movements in terms of pricing, on what competitors are doing, on the succession plan of the company if the CEO ends up retiring. And so I only see one small piece of the puzzle. I don’t see the entire jigsaw.

So this is something often known as the curse of knowledge. A little bit of knowledge is a dangerous thing. Actually, if you’re somebody who knows nothing about the markets, but to paraphrase Socrates, if you know that you know nothing, then you will just hold the market portfolio.

You’re going to be diversified. You’ll benefit from the compounding of your returns every year. Whereas if I think I have loads of knowledge and I can either time the market or pick individual stocks, not realising that I’m up against the professionals like you who do this for a living, then I’m probably getting my fingers burned.

[Tobias] You push back on the idea of the wisdom of crowds in the book. When do you know when the crowd has flipped from wise to crazy?

 How to Spot a Stock Market Bubble Before It Bursts

[Alex] So that’s a good starting point because my book is a counterpoint to that. But again, lets me stand in the corner of the other side. So the idea of the wisdom of the crowds is that when you look at, say, the market, you’re learning from the opinions of thousands or millions of people and you’re getting their collective knowledge.

So there was a book called The Wisdom of Crowds by James Turowicki, which was very influential. And it starts off with the example of people trying to guess the ox county fair. And the pundits who showed up were random people.

They were farmers. They had no expertise in getting the weight of an ox. And they would make huge mistakes.

But as we discussed earlier, if we make mistakes in opposite directions, they will cancel out. And what was found was the average guess was actually very close to the weight of the actual ox. I think this applies to financial markets because of emotions and sentiment.

Nobody gets emotionally attached to the weight of an ox. Nobody has opinions about that. People do get overly attached to narratives and stories such as vehicles will be the future or cryptocurrency is the democratisation of finance and we’re not enslaved to central banks anymore.

Or it will be that AI is going to be the future and it’s going to be transformative. And when we see this narrative, then we see lots of people acting in the same direction. Then your mission is, well, when do we know that the crowd has switched from wise to mad?

You might want to then start looking at fundamentals. And so that is the opposite. That’s the anchor to emotions.

And what are fundamentals? Well, this is what a business is truly worth, something price to earnings ratio, how much you’re paying for stock relative to the earnings that it generates. And so if we go to Cisco in 2000, as I mentioned, its price earnings ratio was 190.

Now, people might have thought, well, isn’t that justified because of the tech bubble? But Microsoft’s price earnings multiple was a third of that. And Intel’s was a quarter of that.

So here it was having a massive no speed valuation, which was towering above its tech rivals. And also to think about, well, what is engendered into that valuation? Cisco’s price earnings multiple was justified if it could grow five times, within the six times, sorry, within the next five years.

And then it did grow six times, but it took 25 years to get there. So often the market gets the direction right. Yes, the internet was the future, but the speed wrong.

They just overestimated the extent to where elderly people would want to do online banking, branch banking. One might say the same was the true in the EV bubble in the early 2020s, where people know that EVs will be the future, but maybe the adoption will be slow because we don’t yet have good battery storage or charging infrastructure. And so when we are trying to compare things to actual fundamentals, what are the profits that the companies make?

Or in the EV case, compare the value of the entire EV industry to the traditional car industry. And back in 2020, the EV industry was worth more than the traditional car industry, even though it was making a fraction of the revenue. There are benchmarks that we can look at to see whether the valuation is actually justified.

[Tobias] Many of these effects, the sort of sunny day effect or the Monday morning effect that we were discussing earlier, perhaps even the football defeats, how are they anything more than noise? Are they sort of large enough for an investor to exploit?

[Alex] Yeah, for the football result, what is interesting here is that it is in theory exploitable because it’s one sided. So what my co-authors and I found was that after a loss, the market goes down, but after a win, there is no corresponding bounce. And that could also be because of the psychological effects.

So if you win, you’re not too surprised because supporters are always optimistic. They always think my team is going to win. But if you lose because you have such lofty expectations, you’re bitterly disappointed.

You call for the manager to be fired. So because the effects are asymmetric, that’s why we found the stock market effects were also asymmetric. So that would give a trading strategy, at least in theory, if France are playing Germany, even though I don’t know them, I would short both countries’ national stock markets the day before.

And so the one that loses, the market will go down and the one that wins will be flat. However, we never took this seriously as a trading strategy because there would be a lot of illiquidity. In some countries which are maybe soccer crazy, they might have less liquid stock markets like Argentina and Brazil.

But more generally, the point of this, and I think this is also true for the weather paper, the Monday paper, wasn’t so much to find a trading strategy, but to show that the market overreacts and that sentiment affects the stock market. And once you realise that the market overreacts with a relatively clean experiment like the World Cup, then that opens the door to looking at other overreaction strategies like going the opposite direction of a bubble, like a contrarian strategy where you buy recent losers and sell losers. Other people have then developed trading strategies that exploit the same type of behaviour.

One of them is thanks to Dick Thaler, the Nobel Prize winner about a decade ago. And he said, well, there’s a very simple strategy. We could stand here, look at all stocks on the New York Stock Exchange over the last three years, the ones that did worst, the losers, the ones that did best, we call them the winners.

If we buy the losers and sell the winners and hold them for another three years, then over the next three years, we see a reversal. Recent losers become winners because the market has overreacted on the downside, and recent winners become losers because the market has overreacted on the upside. Now, this is not true in every single case, but on average, in the time period he studied, this was true.

While he looked at US stock markets in around 1945 to 1980, this has been replicated in other countries and in more recent time periods.

[Tobias] Behavioural finance has been around for a while. There’s that seminal paper in 1934, and then Kahneman and Thaler. What has the field learned in the decades since those books?

What’s it got wrong? What did they miss?

[Alex] Well, so what I see in my world, as in academics, the finance professors who teach the next generation of investors and practitioners, is a lot of sluggishness in terms of embracing behavioural finance. So there are many people who grew up in the efficient markets paradigm, who still believes that markets are efficient, and when they see some studies finding, oh, strategies or value or mentor might work, they may say, well, that was just lucky. Maybe that was specific to a particular time period.

When I talk to practitioners, and maybe this is a selected sample because I often talk to a lot of active managers, they say, no, in the real world, there are mispricings. I believe I can exploit them. I won’t be able to exploit them every time, but if I can exploit them 60% of the time, then I am still giving my clients a competitive edge.

And so I believe we’re in. Also, this belief isn’t just ideology. If you look at what happens in the data, that seems to be inconsistent with the idea of efficient markets.

What do I mean by this? So if markets were truly efficient, then firstly, there wouldn’t be these trading strategies in the first place. But second, once these trading strategies become public, then people should exploit them because they’ve been tried and tested and published in the top scientific journal.

What people have found is once a trading strategy becomes public, about a third of the return goes away, which makes sense for investors who are paying attention to the data and putting on the trades, but a surprising two-thirds still stays. Why might this be? It might be that some people just don’t read the scientific research, or it might be that people read the research, but it’s just so difficult to put it into practise.

It goes against our behavioural tendencies. When everyone else is buying into AI, it’s really difficult to be the lone sceptic just trying to call this out when your peers are invested in it. In the same way that one may say the research finding that smoking a lot or drinking alcohol is bad for your health, it is difficult sometimes to resist that.

And again, it’s our human biases which are made to practise. So even when the research tells us one thing, knowing it and putting it into practise are quite things. And so why what we see is these tendencies typically persisting.

[Tobias] We’re just coming up on the top of the hour. Let me just give a quick shout out to the guys. Lausanne, Switzerland.

Welcome, Breckridge, Colorado, Toronto, Tallahassee, Bendigo. Good morning, Snohomish, Bellevue. Snohomish, another one.

Jupiter, Florida, London, UK. There we go. Petit Tickfair, Gothenburg, Sweden, Toronto, Zagreb, Melbourne.

Good morning. Wiltshire, England, Temecula. JT, give us your veggies.

AI Can Win and Investors Can Still Lose: Jake’s Brownian Bridge

[Jake] All right. So shout out to my friend, Ben Benesh, who inspired today’s segment with a conversation we were having. Opened the scene that London, summer of 1827.

Robert Brown is 53 years old. He’s a Scottish botanist. And by this point, he’s already kind of a big deal.

He sailed to Australia and he came home with 3400 novel plant species, which is still one of the most productive outputs to come out of Australia. Sorry, Toby. Now, he’s at his microscope.

He’s looking at pollen from this little pink wildflower, and he puts the pollen in water. And inside the grains, tiny particles are dancing. They’re jittering.

They’re never stopping. And yet there’s no real pattern to it that he can find. His first thought is that kind of an obvious one.

He’s found life. He’s found something profound. So he does what any good scientist does.

He tries to kill it. He pulls pollen from herbarium specimens that have been dead for 100 years. And surely, you know, these things are dead.

And yet, strangely, they’re also still dancing under his microscope. He grinds up glass. It’s dancing.

Rock. It’s dancing. Somebody had given him a chip off of a stone off of the Great Sphinx.

He grinds that up, too, and looks at it. And the Sphinx is still dancing. So it’s not life.

It’s something in the water. And he has no idea what it is. He publishes it in 1828.

He can’t explain it. And he goes back to his botany. Let’s fast forward 78 years later.

And a patent clerk in Bern figures it out. This guy is named like something like Albert Einstein or something. He posits that the particles are getting hit by water molecules, trillions of tiny kicks a second from every direction.

No single kick really does anything. But together, they make it dance. Now, I know we have a very intelligent audience that listens here.

So they think I’m probably heading toward Brownian motion, which is where that came from, which was Brown, or Bachelier, or stock prices, or the random walk, efficient markets, all of that elegant math. But not today. We’re going to take Brown’s dust in a different direction.

So here’s the one little bit of math that you need for this. When something gets kicked at random, the kicks mostly cancel out, mostly, but not entirely. And so the distance it wanders doesn’t really grow with time directly.

It grows at the square root of time. So let’s say its annual uncertainty is 10%. After one year, that typical water will be 10%.

After four years, about 20%. And after nine years, about 30%. That’s that square root at work there.

So now here’s the tweak that I want to introduce and go in a different direction. Take a random walk, but you pin both ends of it. So you start at zero, and you force it to end at zero 10 years out.

And in between, it kind of goes wherever the kicks take it. Mathematicians call this a Brownian bridge. And here’s the whole point.

You can know exactly where you’re starting and where you’re ending, but it tells you almost nothing about how you got there. So Toby, you’ll recognise this. This very powerful force of mean reversion is a Brownian bridge.

You know where it’s going to most likely end. You don’t really know when. We’ve been standing on the Cape Brownian bridge now, staring into the icy waters below for, I don’t know, about a decade, right?

And the path has been, let’s just say, character building. Okay. So where might we apply this concept of the Brownian bridge to better understand the world today?

I would posit that the AI compute story that everyone’s trying to figure out might be an interesting analogy. There you have two exponential curves, demand for compute, and then supply, the chips, data centres, power, cooling, networking. The bull case, and I think that’s kind of the consensus case at this point, is both are going to grow around 30% a year for a decade.

And they will land in the same place. Everybody’s fine. And that is an endpoint forecast.

30% a year for a decade to get to like a 14X. But these aren’t just one curve that we’re talking about here. They’re two independent random walks.

So supply is the more committed of the two. Land, power contracts, grid hookups, chips ordered years out, debt needs to be raised, take or pay contracts signed. Once that concrete is poured, the capacity is coming.

And demand is much more adaptive. A model gets useful and, you know, demand for it jumps. A model gets more efficient and compute, you know, overall compute demand for the same work collapses.

I find it interesting actually right now that everyone’s pretty excited about Meta’s new personal AI assistant Muse. And yet, it’s not running on the state-of-the-art models. And I’d actually be pretty worried if I was an investor in OpenAI or Anthropic that there’s this seeing this happen with like a kind of interesting commercial use case that doesn’t require a state-of-the-art.

But I’m digressing a little bit there. Anyway, what matters is not where supply and demand end up necessarily. It’s any gaps that form between them year by year along the bridge that can have really serious consequences.

So let’s build this little Brownian AI bridge. We’ll grant the AI bulls everything. Supply and demand are perfectly matched today.

And by some miracle, guaranteed to be perfectly matched again in 2036, 10 years from now. We’ll pin both ends. Even with both ends pinned, a 10% annual wobble between supply and demand, which is not like a total crazy thing in a demanding assumption, I don’t think with how much everyone’s kind of guessing at what these are going to look like.

Well, that 10% wobble gives you an expected worst gap along the path of about 27%. I do think that 27% too much capacity for too long could have serious ramifications for all of this CapEx. Utilisation rates drop.

Pricing drops to be competitive, to just try to move some, get some revenue. Cash flow then drops. The lenders don’t care about the matching in 2036 necessarily.

They want their cash today often because it was an insurance company or some infrastructure fund that has obligations to behind that loan that they issued. So it could get a little crazy there. So at a 20% wiggle, which again, I don’t think is totally crazy, produces a 50% expected delta between supply and demand along the bridge.

So 50% overcapacity to me sounds like a death sentence potentially. And then now let’s unpin the ends, which is probably a little closer to reality. And obviously the variability can jump even further.

And we’ve run this experiment before. Alex was talking about it. Late 1990s, telecom industry laid something like half a trillion dollars of fibre, much of it on borrowed money.

The demand forecast, internet traffic’s going to explode, go to the moon, completely correct actually. But in 2002, well under 10% of that fibre had ever carried a signal. Global crossing, bankrupt by January of 2002, Worldcom, July 2002, the biggest bankruptcy in American history at the time.

The endpoint was right. The bridge killed everybody before they could get there. The fibre got used eventually, but by people who bought it out of bankruptcy for pennies.

So here’s the lesson. Being right about the endpoint is not the only factor. You can be right about 2036 and dead by 2029.

Leverage, liquidity, redemptions can kill you along the way. And those are what turn a mismatch into a drawdown, into a funeral. So let’s wrap this all up by going back to Brown, of the Brownian motion.

He wanted to believe that he found something magical about life, but he was a real scientist. So he ground up everything he could find and let the answer be whatever it was. And he found something really cool, but it wasn’t life.

That’s the job that we should all kind of take on of being scientists. When somebody hands you a deck with a beautiful 2036 projection on the last page, or a podcast full of 2036 proclamations about infinite demand, maybe that’s all true. And you don’t even actually need to argue with that endpoint.

You can grant it to them. But then grind up the years in between and see what’s utilisation in 2029 look like? Who holds the debt?

Can they wait when prices plunge from overcapacity for a couple years? Nobody ever went bankrupt at the endpoint. They go bankrupt on the bridge on the way to the promised land.

[Tobias] Good one, JT.

[Jake] Thanks.

[Tobias] Alex, one thing that I wanted to drill down on a little bit more. This has been an ongoing discussion on our podcast for a few years. We’ve been through the 2020 meme stock bubble, and then in 22, there was a pretty substantial sell-off.

23, yeah, there was somewhat of a sell-off, but perhaps not a true come to Jesus moment. But 23, 24, we’re back into the AI boom. The market’s ran up a great deal.

A lot of these companies trade on forward projections, so there’s not a lot of fundamentals there that an old school value investor, in any case, can make any sense of. So it seems to me that the real argument in the markets at the moment is not that emotion and sentiment drive the markets or that fundamentals or the technicals and flows and other things are the things that drive the markets. I think the difficulty that you would have persuading people is that they are in fact tethered to some underlying fundamental.

What do you think about that?

[Alex] Yeah, so the fundamentals here, in terms of AI, you might think are earnings. And so if you believe in that, then you would say, well, actually, the price earnings ratio of AI is not huge, because I think it’s around 25 to 30, depending what stocks you’re looking at, certainly not 190, as you had in the past. However, your caveat becomes an important one, because those earnings are forward projections, and they are not necessarily fundamentals.

So some of those earnings are high, because the hyperscale meta are spending huge amounts on AI that may not be sustainable. So there is concern by their own investors that that is excessive. And also, for some companies, the E earnings include some of the lofty valuation of AI companies with most of their own, I think, stakes in open AI and anthropic, and that’s why the earnings are boosted.

So there, you have it a bit self fulfilling, if indeed, the valuation of AI company has gone up, and then that justifies the current valuation, because it E the earnings, the denominator is high, then actually, what looks to be not fundamentally overvalued, could actually be overvalued, you realise that those earnings are somewhat fragile, and somewhat circular. So what is my personal perspective is around six to nine months ago, used to my position in AI and tech more generally, why partly, because I thought they’ve had a good valuation run, even though I may not be at the top. And even though I might look foolish in retrospect, because the hype continues to grow, I’m pretty happy with the gains that I’ve had over the last few years.

And number two, unusually short horizon for somebody of my age. Normally, you might think, Oh, shouldn’t I just keep my money in it for another 30 years, but because I have a mortgage being refinanced next year, and the rates are going to be much higher than when I took a mortgage out four years ago, then actually, I want to de-risk and take some money out. So for me, back to the question, I do think that part of what makes AI not seem to be a bubble is actually not as fundamental and not as solid as, and that’s why when I chose to, when I looked at it, I thought it was now time for me to take out my money, particularly given my investment objective.

[Tobias] I don’t know that folks would necessarily, the valuations for those sort of companies on a price earnings basis, I’m not sure. Maybe they’re fair, maybe they’re not. I think the thing that has turned the head of the market a little bit has been that they’ve traditionally been capex light.

They’ve traditionally generated a lot of free cashflow. And then they’ve gone into this period of time now where they’re reinvesting so heavily really in an unproven technology. We don’t see the demand there yet.

I’m not disputing that there might be the demand there in the future. It certainly seems to me that demand is growing very rapidly and that all of these investments are good ones. And it looks like it is a good thing to be doing, but when the free cashflow disappears, and these have historically perhaps been valued on free cashflow rather than on a price earnings basis.

And that’s the thing that makes it difficult for folks who previously they had looked at these as sort of free cashflow machines that you could take a multiple on, free cashflow disappears. Somewhat like when Meta started investing in the Metaverse, when it was Facebook started investing in the Metaverse, that seemed to spook investors at that point. They’ve been a little bit spooked this time around.

And then if that sort of spend ends up being illusory and it’s going to all of these beneficiaries of the CapEx, a lot of the semis and so on that seem to be having extraordinary runs and growing very, very rapidly. Given this uncertainty, perhaps you’d expect that the market would trade on a slightly lower multiple, but that’s not the case.

[Alex] No, and it may well be because we’re having the similar phenomenon to what we had in tech is that even if you think, oh, maybe the spending is unsustainable, I don’t want to be out early because if I’m out, I’m early, I’m wrong. If it’s got further to go, I’m going to be then significantly underperforming my peers. So this might be a way in which actually amateur investors can beat the professionals.

Now, that might be a thing to say, given my earlier caveat that you always have to ask what is your edge for individual stocks and individual investors very unlikely to have an edge compared to a professional if you’re a doctor and you’re analysing a pharmaceuticals. However, where you might have the edge is a longer runway and a longer investment season because you’re not investing other people’s money, which might be flighty and which might be withdrawn at any time. And so this might give me the ability to move out of what I might think could be overvalued AI because I’m not so worried about the bubble continuing to inflate and my own investors getting cold feet.

On the flip side, what I did in the start of 2009 is I increased my equity portfolio weight by 20 percentage points. Why? Because there was the financial crisis and really low valuations.

I did not know autumn had been hit and maybe things could go worse before they got better. But I wasn’t so concerned about that short term turbulence. I was comfortable with the fact that the long term trajectory was positive to your interjection early, which I very much agree with.

I was able to look more at the finishing line rather than the bridge to get there because I was investing my own money for my in, say, 40 to 50 years. That’s something that a professional manager of other people’s money was not able to look at. And so sometimes if you think that as an individual investor, you might have a longer runway than a professional that might allow you to be contrarian, even though the professionals might be too reticent to do that because they might be worried about underperforming their peers.

[Jake] Yeah, just avoiding doing something obviously stupid because everyone else is doing it and you have, you know, exactly.

[Alex] So as an individual, you’re not benchmarked to your peers in the way in which you will be having tracking error as a professional. And you don’t have the short term concerns about flighty capital if your trade is good in the long term, but suffers short term turbulence.

[Jake] Except for that annoying brother-in-law who was long Korean chip stocks or Korean memory.

[Alex] Well, but for every that annoying brother-in-law, you might have other brother-in-laws or second cousins or neighbours who also invested in single stocks and they got it wrong and they keep enquiring about it because they don’t want to admit it. And so what we see is a very skewed feed. If you believe the annoying brother-in-laws or the annoying person on Instagram, it makes people think it’s just so easy to pick the next Nvidia.

In reality, if you look at the dispassionate large scale data, sadly, it’s much harder than people think.

[Tobias] But folks with a pension or an IRA who are less trader-like, what’s the single most useful change in behaviour?

[Alex] I would say it’s to, and so this is one of the 20 pieces of advice I had at the end of the book. What I mean by this is be in the market. Because one of the biggest anomalies is the equitimian puzzle.

What is that? Over any reasonable period, stocks outperform cash or treasury bills. Now, some people will say, well, that’s not surprising.

Stocks are risky and therefore the higher return is just a risk premium. That’s just compensation for a bit like danger money. But people have run the numbers and even under the most aggressive assumptions about how risk-averse people are, they cannot justify the big difference between equity returns and the returns on cash or treasury bills.

And so why this is called the equity premium puzzle is the extra return that stocks give you over cash is just so large that it cannot be accounted for due to risk. So the idea of play the game means just be in the market. Resist the short-term turbulence and the temptation to get cold feet after a short market decline or to tinker with your portfolio too much.

Also realise that sometimes you might get withdrawals if indeed these are paying dividends or there’s a takeover. Make sure there’s a way of just automatically reinvesting that in the market. And if so, benefit from one of the most powerful forces in finance, which we’ll refer to earlier as the power of compounding.

So if you get a return of 10% per year for 10 years, that compounded is not 100%, it’s 50%. So just play the game, stick with the markets, even though there might be some temptation to tinker. And over any reasonable time period, that has earned some really good returns.

So yes, you might want to get rich quick by trying to predict the next big winner, but the most important thing is to get rich. And if you do so slowly, but gradually, but surely by being in the broader equity markets, that I think is a good way to get to the end goal that you want to achieve.

[Tobias] It’s been this tension in the academic finance, I guess, for 20, 30, 40 years between the efficient markets, purists and the behaviouralists. I’m guessing that you fall on the behaviouralist side, but can you perhaps discuss your thinking as you get there?

[Alex] Yeah, and it’s interesting that my view on the markets and my investment approach has changed quite a bit in the 20 years I’ve been studying this. So I used to think, okay, markets are extremely inefficient, and it’s possible to pick single stocks and was from my practitioner background, I was interacting a lot with those people. And I realised that there is some truth to the efficient markets camp, particularly in the US where markets are relatively efficient, there’s a lot of information around because of disclosure requirements.

There’s large funds out there, which are trying to eke out every edge. And so I invest far less in single stocks than I do nowadays. But what I will invest is managed funds, which anybody who believes in efficient markets would advise you against because you’re just paying higher fees and getting their outperformance.

However, often my actively managed funds will be investing what I believe to be an inefficiency based on the data. For example, one of my own papers, my most cited paper actually, finds that employee friendly companies, companies with strong corporate cultures, outperform and I looked at data over a 28 year period. And when I was in the US, my biggest holding was the Parnassus workplace fund, which invested on an investment criterion of corporate culture.

When Morningstar did a survey, this was the best performing fund in its universe over the past one and three and five and 10 years. Now my biggest holding in my personal portfolio is an emerging slash frontier markets fund. What they will do is look at pockets of inefficiencies, they’ll go where people don’t go, where there is not so much information, where they will do boots on the ground investing, they will go into a factory, factories actually not producing anything.

And this is not something you would notice from a Bloomberg terminal in New York. One of their great investment picks was noticing their war in Turkey in 2025. And this wiped out a third of the hazelnut crop and hazelnuts to Turkey is like coffee to Brazil.

But Turkish hazelnut sector is not exactly front page news on Bloomberg. And a lot of coverage on this was in Turkish language. They noticed it, they flew out to Turkey, and they found the biggest listed processor of hazelnuts sitting on just a vat of hazelnuts that they bought before the frost.

They put $20 million into this, and this went up by 40% within a few months. So this idea of boots on the ground investing, trying to find where are the pockets of inefficiency greatest, that is something that I do believe in, and it’s affected my investment approach. I’m more likely to invest in an active fund outside of the US.

I’m not sure I invest in any active funds in the US actually. I think I just hold index passive. Because I think yes, the markets are not perfectly efficient, but they’re not perfectly inefficient either.

There are markets which I think are more or less efficient. And while there are some great active managers still in the US, it might be difficult for someone like me to be able to pick them without doing a very meticulous due diligence of the investment process.

[Tobias] The subtitle of your book is about why smart investors make mistakes that can be exploited, which is one of the reasons that I reached out. Why do you fancy yourself a smart investor? Well, I’m interested in why smart investors are the ones that make the mistakes, and why those are the ones that can be exploited.

Why focus on the smart investors rather than that sort of a broader idea that many investors make mistakes?

[Alex] Well, I think it’s in two ways. First, many investors do make mistakes, including the smart ones. So one way of reading the title is it’s all investors, including the smart investors.

So it’s saying, oh, this is not for investing 101. This is a book for professional fund managers as well, which is why I’m flattered that you’re interested and reached out. But maybe I can go even stronger than that.

Maybe smart investors are particularly likely to make mistakes, rather than these are mistakes which everybody suffers, including smart people. While we go back to that illusion of knowledge, a little bit of knowledge is a dangerous thing, we might think, yeah, the average person who trades stocks underperform, but I am part of the ones who are truly above average in terms of my investing ability. Also, what smartness allows you to do is to interpret data in the way that you want.

What do we mean by this? So you might say, well, if people are overconfident about their ability to invest, then doesn’t car data shape them out of their delusion? So if you thought you were above average in driving, and then you’re involved in many car crashes, which were your fault, then you might reassess your assessment.

And unfortunately, there are not so many car crashes around, so you don’t get to see the data. But if we trade stocks all the time, and we can see the stock price moving around, isn’t there all of that data to show us actually you’re not that great an investor? Well, the problem is that we are often quite biassed in how we interpret the data.

So if you forgive me one more sporting analogy, if a team wins a game, the manager says, oh, that was my great tactics and substitution in my team talk. If a team loses, the manager will not admit they got the tactics wrong, they’ll blame the referee or the dark arts of the opposition. And this might be the same for investing.

If you trade and it does well, you say, well, that was my great pick. If it does badly, the smarter you are, the more able you are to engage in what’s known as motivated reasoning. You come up with any sort of explanation to explain away why you were right, but unlucky.

So this thing happened, which I was not able to predict, and no reasonable person could have done so, in the same way that the smart people in charge of Silicon Valley Bank were so smart that when the models predicted that the bank would go insolvent if interest rates rose, they came up with reasons to change the model and to argue why the model was wrong, whereas somebody with less knowledge of finance, they might have actually put more in the model because they thought that they wouldn’t have the expertise to overrule it.

So actually, in many cases, actually smart people might find reasons to dismiss the data. There’s another layer of this. If you work in an organisation and everybody else thinks you’re smart, you have the cult of the staff and manager, then maybe the people who work for you are unwilling to challenge you.

They may think the way to get ahead in the organisation is to find the data to back up the fund manager’s hunch because he or she is so smart, they might not challenge you and therefore your blind spots are not identified. So actually, rather than this being just a general psychological mistake that everybody falls for, there are reasons to think that smart investors might be even more susceptible to them rather than less.

[Tobias] In a similar vein, you cover CEO ego trips. How does the psychology of the person running the company interact with the psychology of the investors in the company?

[Alex] It makes a big difference. So the CEO is the most important person in the organisation, he or she is only one person, but because they set the tone of the organisation, they can have an outsized effect. And we’ve seen many examples of CEOs who might be crooked or running the company for reasons other than long term shareholder value.

So this Tyco CEO, through a multi-million dollar birthday bash for his wife and charged half of it to Tyco, how he was able to get away with this is a legitimate business expense, I don’t know. But what you’d love is you’d love to be able to find the CEOs who are crooked and short their companies. Now, the problem with these scandals, they came out after the fact, they were not actually able to be predicted without the value of hindsight.

But what one chapter, actually, my favourite chapter in the book, chapter seven, is about is you can find the equivalent of the poker tell, something small, which might give away what the CEO is truly like.

 The Hidden Warning Sign in a CEO’s Private Jet

And one example is the corporate jet. So corporate jets, you might think, well, they’re actually a good thing.

They get the CEO to a meeting where they’re able to push it at full throttle. But they could also be a sign of huge managerial excess. So the movie Barbarians at the Gate was very famous, and it highlighted Arjo and Obispo had 10 private planes, which ferried not just the CEO to meetings and events, but also a passenger called G Shepard.

That was the German Shepard, that was the dog of the CEO. So if this is happening, then this could be a case of massive corporate malfeasance. And what a study found was that companies where they allowed a personal usage of the jet, the stock price fell by about 4% per year, applied to the average value of $7.5 billion. That is $300 million of underperformance. Now you might think, well, do those numbers stack up? Because if you even rented a jet for $50,000, you’re forced to disclose that to the SEC.

So how can something that costs you $50,000 be worth $300 million? Well, it’s because it’s the tip of the iceberg. So the companies where the CEO was able to use the corporate jet, maybe the CEO was able to get away with a lot of other things.

Maybe the board is in the CEO’s pocket, which is why they allowed that. Maybe the CEO’s overpaid, maybe they’re making massive expansions and empire building acquisitions. And so this is a window into the culture of the company, and which is why I think it was such a great study.

And the Chapter 7 goes and looks at many other seemingly trivial, but actually data-reinforced measures of CEO power, which could be the size of your photo in the annual report compared to the rest of your top management team. Because that might suggest, I don’t need to listen to my colleagues, whether the CEO refers to I rather than we. And even something like the size of their signature when they’re signing the shelter letter.

Rupert Murdoch has notoriously a large signature, which one might say is consistent with this character. So what are the small tells that we can look at in a CEO’s character to tell us about what is the character of the person at the top?

[Tobias] If there’s one takeaway from the book that you’d like the reader to have, what would it be?

[Alex] It would be to know your edge. If you are going to do something other than invest in the market portfolio and buy and hold, you need to have a reason for why to do that. I might have particular knowledge of an individual stock.

I think that’s unlikely, although not impossible. It may be I understand the outperformance of employee satisfaction strategies, or I’m able to take a long-term perspective. But if you have no edge, there is absolutely no shame in admitting it.

The idea of just buying and holding the market is something which over any reasonable time period, significantly higher returns over cash or treasury bills. Just like if I realise that I’m not a professional in healthcare and I’m going to trust my doctor rather than doing my own internet research, then that’s a very sensible decision. And the idea of just holding the market portfolio because you know you don’t have an edge, that is something which can be very powerful.

[Tobias] Folks, the book is The Madness of Markets by Alex Edmans, why smart investors make crazy decisions and how to exploit them. Alex, if folks want to get in contact with you or follow along with what you’re doing, what’s the best way of doing that?

[Alex] Well, first, thank you very much if you’re interested in doing so. So my name is Alex Edmans, E-D-M-A-N-S. I’m most active on LinkedIn, so A, Edmans is my handle.

I also am on X for me, Twitter, but most of the time I’m on LinkedIn. My website is alexedmans.com, which has my academic research, but also my articles in the Wall Street Journal and Financial Times and Harvard Business Review. Something I’m really passionate about is translating complex academic research into simple language for a practitioner audience, but without dumbing it down and without removing the nuance and the intricate and interesting details.

[Tobias] JT, any final words? No, sir.

[Alex] Thanks, folks. We’ll see you.