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Speaker 1: The future may not be clear, but our commitment is

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Speaker 1: so when you sit with an advisor at Merrill Lynch,

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Speaker 1: we'll put your interests first. Visit mL dot com and

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Speaker 1: Lynch Pierce Veneran Smith Incorporated or Register Broker Dealer remember

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Speaker 1: s I PC. This is Masters in Business with very

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Speaker 1: Ridholtz on Boomberg Radio. This week on the show, I

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Speaker 1: have an extra special guest, Professor Andrew Lowe of m

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Speaker 1: I T. I first heard Professor Lowe speak at a

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Speaker 1: conference about a decade ago, and I was I was

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Speaker 1: quite fascinated by his willingness to I don't want to

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Speaker 1: say attack, but take on the efficient market hypothesis and

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Speaker 1: adapt it uh to what he sees as some obvious

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Speaker 1: modern changes in how people in technology are interacting with markets,

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Speaker 1: be they more efficient or less efficient. It's pretty clear

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Speaker 1: that the strong efficient market hypothesis, which essentially states markets

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Speaker 1: reflect the all information about stock prices in any given

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Speaker 1: instant's a little bit of an overstatement. You have to

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Speaker 1: work in the actual human side, the emotions and and

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Speaker 1: as well as the adaptive side, how markets are consisting

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Speaker 1: of organic participants and they themselves become organic and adapt

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Speaker 1: to changes fairly rapidly. UM. I think Professor Lowe's read

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Speaker 1: on the efficient efficient market hypothesis predated the Nobel Committee

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Speaker 1: giving Bob Shiller of Yell and Gene Fama of chicag

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Speaker 1: University of Chicago uh CO Nobel prizes, one for m

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Speaker 1: H and the other for behavioral economics the same year.

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Speaker 1: It's not a coincidence, and and he very much was

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Speaker 1: clued into that before the Nobel Committee was. Anyway, if

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Speaker 1: you are at all interested in how markets operate, how

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Speaker 1: they adapt, what investors do that's right and wrong, and

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Speaker 1: as well as the significance of hedge funds within the

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Speaker 1: universe of market participants, I think you'll find this to

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Speaker 1: be a fascinating conversation. So, with no further ado, my

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Speaker 1: conversation with Professor Andrew low of m I T. This

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Speaker 1: is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: My special guest today is Professor Andrew Lowe of the

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Speaker 1: m I T. Sloan School of Management. He has been

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Speaker 1: the director of m I T. S Laboratory for Financial Engineering,

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Speaker 1: since he comes to US with a PhD in economics

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Speaker 1: from Harvard. He briefly taught at Wharton before going to

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Speaker 1: m I T and is the author of numerous books

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Speaker 1: about finance, including A non random Walk Down Wall Street,

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Speaker 1: Analytic Perspective on hedge Funds, The Evolution of Technical Analysis,

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Speaker 1: most recently Adaptive Markets, Financial Evolution at the Speed of Thought.

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Speaker 1: Professor Andrew Low, Welcome to Bloomberg. Thanks for having me.

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Speaker 1: I'm thrilled you here. I've been a fan of your

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Speaker 1: work for a long time. Let's jump into your background

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Speaker 1: a little bit before we get to some of the

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Speaker 1: heavy financial engineering work. Ironically, you right that you weren't

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Speaker 1: an especially good math students as a kid, and yet

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Speaker 1: you end up in a field that is dominated by

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Speaker 1: heavy mathematics. How did that come about? Well, you know,

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Speaker 1: I didn't know this at the time, but when I

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Speaker 1: was going through elementary school and middle school, UM, I

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Speaker 1: had a learning issue, which was I guess the mathematical

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Speaker 1: equivalent of dyslexia. I think it's called DS calculia really,

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Speaker 1: and uh, I had a terrible time memorizing the multiplication

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Speaker 1: tables and were you transposing exactly? And uh, some of

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Speaker 1: my teachers told my mother that they thought I was

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Speaker 1: mentally retarded. That was the term of art in those

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Speaker 1: days and um, but it was a third grade teacher,

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Speaker 1: Mrs Barbara pick Laura that saw something in me and

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Speaker 1: encouraged me and appointed me to this position of class scientists,

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Speaker 1: where I was able to do experiments and demonstrate the

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Speaker 1: various kinds of knowledge that I had gotten over the

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Speaker 1: course of several periods during the school and that really

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Speaker 1: boosted my self confidence. I still struggle with math until

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Speaker 1: high school, where the new math came about, and which

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Speaker 1: is essentially when we talk about new math, we're talking

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Speaker 1: about what well replacing algebra, trigonometry, and geometry with things

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Speaker 1: like group theory, ring theory and abstract algebra. And it

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Speaker 1: was an incredibly important moment for me because I went

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Speaker 1: from a C student in math to an A student

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Speaker 1: pretty much overnight. And it was because I could handle

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Speaker 1: abstract concepts much better than being able to do multiplication,

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Speaker 1: and so it was a really wonderful thing. I know

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Speaker 1: that new math is viewed is a failure in New

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Speaker 1: York City back in the nineteen seventies, but for me

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Speaker 1: it was a godsend. So now let's let's jump forward

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Speaker 1: a little bit and and bring the idea of abstract

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Speaker 1: concepts to markets. What's more important to markets supply and

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Speaker 1: demands or fear and greed? Well, you know the answer

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Speaker 1: is both. And that's really the interesting thing that I

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Speaker 1: discovered about human nature. We have two aspects to our

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Speaker 1: cognitive functions. Were incredibly good at logical deliberation and being

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Speaker 1: able to balance supply and demand using various fancy mathematical

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Speaker 1: and statistical tools. But at the same time, every once

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Speaker 1: in a while, we freak out. And when we freak out,

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Speaker 1: emotion rules today, and fear and greed then take over.

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Speaker 1: And it's this jackal and hide aspect of human decision

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Speaker 1: making that I think we're missing in economics being able

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Speaker 1: to integrate those two. So behavioral economics has become a

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Speaker 1: huge field between Professor Schiller, Professor sailor Danny Kahneman. Go

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Speaker 1: down the list of all the folks um who have

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Speaker 1: who have worked in this area. What does this say

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Speaker 1: about the efficient market hypothesis? Is it wrong or is

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Speaker 1: it simply incomplete? Yeah, it's definitely not wrong. There's some

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Speaker 1: very important elements of efficient markets hypothesis that actually can

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Speaker 1: protect us from making some pretty bad investment mistakes. But

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Speaker 1: it is incomplete in the sense that it doesn't capture

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Speaker 1: the fear and greed aspect of it. And I think

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Speaker 1: it's trying to integrate the two that really got me

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Speaker 1: to start thinking down the road towards adaptive markets. So

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Speaker 1: let's talk about another duality. Rational rationality and irrationality coexist

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Speaker 1: even within the same person. How does that manifest itself

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Speaker 1: in in investment. Well, you know, it really comes from

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Speaker 1: the neuroscientific aspect of decision making. So neuroscientists like Antonio

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Speaker 1: Dimazio have come up with a really interesting notion of

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Speaker 1: what rationality means. And what he developed was this idea

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Speaker 1: that rationality actually requires a certain degree of emotion. In

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Speaker 1: other words, when you take a look at patients that

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Speaker 1: have had brain surgeries that have removed the emotional part

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Speaker 1: of the brain, they end up acting in a very

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Speaker 1: irrational way because they have no way of balancing the

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Speaker 1: various different demands on their time. You know, when we

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Speaker 1: think about showing up to work on time or meeting

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Speaker 1: a deadline, emotion actually plays an important role in those

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Speaker 1: kinds of behaviors. So with the proper balance of emotion

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Speaker 1: and logical deliberation, we end up seeming quite rational. But

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Speaker 1: when that balance goes askew, when we have too much

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Speaker 1: or too little emotion, we end up making mistakes. And

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Speaker 1: that's very much along the same lines of how investors

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Speaker 1: actually behave. So when we talk about fear and greed,

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Speaker 1: we're really talking about the moments when the irrational side

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Speaker 1: of the brain takes over and the logic steps back,

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Speaker 1: and no one says to themselves, g I'm gonna be

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Speaker 1: able to time my in and out perfectly. I'm going

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Speaker 1: to be able to jump back in despite having the

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Speaker 1: being in a terrible crash. We just tend to panic exactly.

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Speaker 1: And you know, panic is actually a very important evolutionary adaptation.

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Speaker 1: Fear and greed are a lot older than the ability

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Speaker 1: to solve differential equations, and so when we start becoming threatened,

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Speaker 1: we will react emotionally and and for physical threats that's

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Speaker 1: actually a great response, but for financial threats it doesn't

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Speaker 1: work out nearly as well. I'm Barry Hults. You're listening

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Speaker 1: to Masters in Business on Bloomberg Radio. My special guest

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Speaker 1: today is Professor Andrew Low of m I. T. Sloan School,

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Speaker 1: of management. Let's talk a little bit about economists, and

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Speaker 1: especially financial economists. I've long been a fan of the

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Speaker 1: phrase that economists suffer from physics envy, and there's a

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Speaker 1: quote of years that I have to share. Physicists can

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Speaker 1: explain of all observable physical phenomena using Newton's three laws

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Speaker 1: of motion. Economists probably have laws that explain three percent

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Speaker 1: of all economic behavior. Explain that, well, you know, economics

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Speaker 1: is about a much more complicated subject in physics, and

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Speaker 1: I think that's the first point that we have to

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Speaker 1: grapple with. I think it was the physicist Richard Feynman

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Speaker 1: who speaking at a Caltic graduation said, imagine how much

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Speaker 1: harder physics would be if electrons had feelings, And I

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Speaker 1: think that really captures it. We're dealing with people that

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Speaker 1: have feelings, and they react emotionally, sometimes as opposed to

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Speaker 1: logically and rationally, and so the laws of physics don't

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Speaker 1: really apply to human interactions in the same way that

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Speaker 1: they apply to particles in a gravitational field. And it's

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Speaker 1: very tempting for us to use the mathematics and the

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Speaker 1: mantle of physicists, but the fact is that we're dealing

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Speaker 1: with a very different object to say the very least.

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Speaker 1: So let's talk about the criticism of the profession. Do

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Speaker 1: you think, first, do you think economists get unfairly criticized

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Speaker 1: and if so, what four? I think they do get

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Speaker 1: unfairly criticized for certain things. Uh. It was once said

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Speaker 1: that economists predict five out of the last three recessions.

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Speaker 1: And uh, of course, you know, we don't have a

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Speaker 1: perfect track record or prediction, but neither do weather forecasters.

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Speaker 1: The nature of the task is much more challenging, and

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Speaker 1: so I think that people have to understand the difficulty

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Speaker 1: in making these kinds of economic predictions. We're getting better

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Speaker 1: all the time, but it's still not purely predictive science.

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Speaker 1: We need to actually take into account the art of

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Speaker 1: economic forecasts. So let me push back on that a

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Speaker 1: little bit. Yeah, that whether people are not perfect, but

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Speaker 1: they could usually tell you a day or so before

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Speaker 1: if it's going to rain, or if there's a possibility

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Speaker 1: of rain, bring your umbrella. It's always always couched in

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Speaker 1: probabilistic terms, which if you say there's a ten percent

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Speaker 1: chance of rain and it rains, hey, the one intent

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Speaker 1: chance came in. But the big criticism about economists, especially

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Speaker 1: in the past decade, has was how did you guys

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Speaker 1: miss the single biggest financial crisis since the Great Depression?

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Speaker 1: If you can't see that coming, how, how why should

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Speaker 1: people rely on you? And I know that's a loaded question,

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Speaker 1: but defend the entire profession for missing the financial crisis, Well,

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Speaker 1: you know, I'm not sure we did miss it. For example,

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Speaker 1: in two thousand and five, Bob Schiller, ragu Rajan, and myself,

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Speaker 1: all three of us wrote papers that described stress fractures

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Speaker 1: in the financial system. Bob Shiller talked a bit about

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Speaker 1: the real estate market and how that was going to

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Speaker 1: be another kind of irrational exuberance UH bubble that was

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Speaker 1: about to burst. In two thousand and five, ragu Rajan

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Speaker 1: talked about the banking system becoming over extended and developing

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Speaker 1: these kinds of UH risks of financial crisis. And some

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Speaker 1: of my students and I wrote about the fact that

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Speaker 1: the hedge fund industry was also heading for another debacle,

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Speaker 1: very much along the line NDS of what happened in

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Speaker 1: and so there was evidence in the data, and all

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Speaker 1: three of us wrote papers and gave talks about it.

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Speaker 1: But the fact is that unless you've got a very

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Speaker 1: strong notion from the entire financial profession that we're headed

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Speaker 1: to a crisis. It's very difficult to engage in policies

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Speaker 1: to try to do anything about it. And certainly the U. S.

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Speaker 1: Treasury and the FED were not in a position to

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Speaker 1: make very strong changes in policy because there was no

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Speaker 1: official word like the National Weather Service to tell you

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Speaker 1: that a hurricane was coming. We need to have something

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Speaker 1: like a national weather service for financial crisis. Isn't that

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Speaker 1: supposed to be part of the role of the Federal Reserve.

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Speaker 1: Shouldn't they be looking at the sustainability of the system

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Speaker 1: and looking for those hurricanes coming. Well, you know, that's

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Speaker 1: part of their responsibility. But their focus is on the

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Speaker 1: banking system, and as we know from the financial crisis,

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Speaker 1: it was the shadow banking system that actually grew very

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Speaker 1: very quickly, and that they don't actually have jurisdiction. But

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Speaker 1: the other thing is that the FED has a dual responsibility.

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Speaker 1: They're actually all is supposed to be encouraging economic growth

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Speaker 1: with their monetary policies, and so it's very difficult when

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Speaker 1: you've got this dual mission to be able to focus

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Speaker 1: single mindedly on preventing financial crises from occurring. See I

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Speaker 1: think that you yourself, um, Professor Schiller, and who was

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Speaker 1: which was Raja? And there were a handful of others

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Speaker 1: who had pointed out, um, the the huge um disquilibria

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Speaker 1: that was existing in the in the system um. Righthart

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Speaker 1: and Rogoff put out a paper in late oh seven

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Speaker 1: early oh eight saying, hey, here's what the prior five

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Speaker 1: financial crisis looked like and if the United States as one,

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Speaker 1: which they were strongly implying, this is what we should expect.

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Speaker 1: Turned out to be fairly prescient. But the run of

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Speaker 1: people of economists who were warning or at least talking

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Speaker 1: about it, UH are notable because they are the exceptions

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Speaker 1: and the by and large most of the profession kind

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Speaker 1: of missed it, Which raises the question how we learned

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Speaker 1: anything from the last crisis, and might the profession be

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Speaker 1: better suited to identify the next hurricane that comes along. Well,

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Speaker 1: there's no doubt that the economics profession has been indelibly

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Speaker 1: altered by the events of the past, chastened, absolutely humbled.

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Speaker 1: We've had many individuals who have stated publicly that they

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Speaker 1: felt economics, particularly macroeconomics, has let down the profession, and

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Speaker 1: I think part of it was a devotion to these

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Speaker 1: extraordinarily complex, rigorous mathematical formulations which were precise but precisely wrong.

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Speaker 1: And uh, someone once said that it's better to be

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Speaker 1: approximately right than precisely wrong. And I think we're sort

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Speaker 1: of taking that now to heart. Do do we? Are

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Speaker 1: we overly enamored with precision and data, specifically for things

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Speaker 1: that as you described, You know, the the old joke

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Speaker 1: is why why do economists um provide data to two

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Speaker 1: decimal points? And the answer is to shoot show they

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Speaker 1: have a sense of humor. Are we overly reliant on

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Speaker 1: the illusion of precision? And is that part of the

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Speaker 1: problem that we run into? Well, that's definitely a problem,

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Speaker 1: and I think that a number of economists have written

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Speaker 1: about it, and I believe that there is some kind

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Speaker 1: of a reaction or a backlash to the mathematization of economics.

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Speaker 1: There's no doubt that rigor has a role to play

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Speaker 1: in our field. But someone once said that along with

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Speaker 1: rigor usually goes mortis. So I think we have to

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Speaker 1: be very careful about how we use mathematics. I'm Barry Ridholts.

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Speaker 1: You're listening to Masters in Business on Bloomberg Radio. My

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Speaker 1: special guest today is Professor Andrew Lowe of the m

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Speaker 1: I T. Sloan School of Management, where he is the

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Speaker 1: director of the m I T Labs for Financial Engineering.

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Speaker 1: He has a PhD from Harvard and is the author

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Speaker 1: of numerous books, including most recently, Adaptive Markets Financial Evolution

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Speaker 1: at the Speed of Thought. Let's talk a little bit

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Speaker 1: about the new book, because I I it's really fascinating

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Speaker 1: and accessible. I don't want to call it a primer,

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Speaker 1: but really a next generation look at how markets have

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Speaker 1: changed over time and how investors should be interacting with them.

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Speaker 1: Let me let me pull a quote right to begin with.

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Speaker 1: The adaptive markets hypothesis is based on the insight than

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Speaker 1: investors and financial markets behave more like biology than physics,

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Speaker 1: comprising a population of living organisms competing to survive, not

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Speaker 1: a collection of inanimate objects subject to the immutable laws

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Speaker 1: of motion. How did you come about this biological organic

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Speaker 1: explanation as opposed to the more traditional mathematical physics explanation. Well,

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Speaker 1: you know I came about it. I guess really being dragged,

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Speaker 1: kicking and screaming through various different disciplines. So the book

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Speaker 1: is really a travelogue of my intellectual journey from a

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Speaker 1: I heard devotee of efficient markets and rational expectations into

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Speaker 1: the realm of first psychology and behavioral finance, and then

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Speaker 1: to neuroscience and how people really make decisions, and then

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Speaker 1: to the evolutionary biology of how our brain evolved, and

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Speaker 1: then ultimately to the ecology of all of the various

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Speaker 1: different dynamics of multiple competing species. So it wasn't that

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Speaker 1: I was looking for these other disciplines, but they ultimately

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Speaker 1: ended up becoming really important for understanding the very simple

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Speaker 1: idea that you've got to explain these facts using as

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Speaker 1: whatever tools you have at your disposal. We were speaking

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Speaker 1: um off air that I had seen you Um give

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Speaker 1: a lecture I think it was right after the financial crisis,

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Speaker 1: trying to explain some of the reasons why markets were

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Speaker 1: less efficient than they appear. How do you end up

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Speaker 1: with a crisis and you end up with these wild

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Speaker 1: highs and these crazy lows. If the markets are truly efficient,

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Speaker 1: it can't be. That can't be the case. And the

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Speaker 1: genesis of this process of the organic way of looking

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Speaker 1: at markets were evident in your lecture. How long have

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Speaker 1: you been playing with this idea? It has to be

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Speaker 1: at least a decade, if not longer. Well, it's actually longer.

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Speaker 1: It really started in when I started looking at the

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Speaker 1: data for the random walk hypothesis. That's a particular version

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Speaker 1: of efficient markets, which says that stock prices follow random walks.

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Speaker 1: You can't predict where you're gonna be tomorrow based upon

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Speaker 1: where you are today. At least that was the theory,

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Speaker 1: And when my co author Craig McKinley and I started

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Speaker 1: looking at the data, no matter which way we sliced it,

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Speaker 1: we couldn't get the random walk to work. In other words,

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Speaker 1: the data are not consistent with the random walk. There

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Speaker 1: are predictability, there's some persistence and some momentum and other factors.

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Speaker 1: In fact, you wrote a book A non random Walk

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Speaker 1: down Wall Street, and you also wrote a book on

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Speaker 1: the Heretics of Finance about technical analysts. How does that

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Speaker 1: play into the biological approach? Well, so it was really

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Speaker 1: looking at the technical analysis literature that got me to

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Speaker 1: start thinking a little bit more broadly. For those of

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Speaker 1: the listeners who don't know, technical analysis is really a

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Speaker 1: somewhat disreputable uh field of study that academics often pooh pooh,

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Speaker 1: but which I think actually has quite a lot of value,

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Speaker 1: and it's the idea that you can use geometric patterns

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Speaker 1: in price data to be able to make forecasts and

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Speaker 1: to try to understand how markets move. The technical analysts

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Speaker 1: from the nineties, forties and fifties really understood that markets

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Speaker 1: were driven not just by supplying demand, but also by

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Speaker 1: the nature of emotion, by fear and greed, and so

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Speaker 1: they would actually come up with patterns, trends, reversals, and

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Speaker 1: other kinds of tools that, for those days where they

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Speaker 1: didn't have computers and fancy technology, actually allowed them to

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Speaker 1: make pretty useful forecasts. And it was reading that literature

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Speaker 1: that got me to start thinking a little bit more

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Speaker 1: oddly that maybe the mathematical equations that we use in

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Speaker 1: modern finance is not the be all, in the end

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Speaker 1: all of how you think about financial markets. And to

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Speaker 1: that point, another quote of yours were more impulsive over

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Speaker 1: the short term and more logical over the long term,

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Speaker 1: very reminiscent of Danny Kahneman's thinking fast and slow. Tell

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Speaker 1: us what you mean by impulsive versus logical. Well, if

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Speaker 1: you take a look at how people make decisions, it

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Speaker 1: turns out that from the neurophysiological perspective, there are multiple

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Speaker 1: competing components of the brain that are at work. For

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Speaker 1: long term decisions where we have the luxury of thinking

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Speaker 1: carefully and deliberating on various different choices, for example, asset

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Speaker 1: allocation or retirement planning. We can make supremely rational, mathematically

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Speaker 1: precise decisions. But then crisis hits, events happen and we react,

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Speaker 1: and in those cases we're reacting emotionally, not necessarily logically.

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Speaker 1: And it's the emotional components of the brain, which are

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Speaker 1: far older than the ability to do mathematics, that kick in,

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Speaker 1: and the way they kick in they really overwhelm us.

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Speaker 1: We are overwhelmed by our emotional centers, and when that happens,

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Speaker 1: the decisions that we make are not particularly good when

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Speaker 1: it comes to for our financial health. I'm Barry rid Halts.

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Speaker 1: You're listening to Masters in Business on Bloomberg Radio. My

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Speaker 1: special guest today is Professor Andrew Lowe of the m I. T.

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Speaker 1: Sloan School of Management. Let's talk a little bit about

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Speaker 1: hedge funds, which you've written about extensively as well as

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Speaker 1: written a book, Hedge Funds and Analytic Perspective. Let's start

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Speaker 1: with a quote of yours. At the start, the general

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Speaker 1: partner brings all the experience and the limited partners bring

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Speaker 1: all the money. At the end, the general partner leaves

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Speaker 1: with all the money and the limited partner leaves with

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Speaker 1: all the experience. I find that to be a hilarious quote.

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Speaker 1: How do you really feel about hedge funds? Well, you know,

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Speaker 1: that was really meant to represent the kind of skepticism

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Speaker 1: that a lot of people have for hedge funds. But

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Speaker 1: I think that hedge funds play an incredibly important role

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Speaker 1: in the financial ecosystem. They're the tip of the spear

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Speaker 1: in terms of taking advantage of profit opportunities as they emerge,

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Speaker 1: but they're also the canary in the coal mine. They

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Speaker 1: get hit first when financial distress starts to develop. Why

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Speaker 1: is that? Why are they so fast to react when

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Speaker 1: an opportunity exists, but sometimes fast to embrace risks that

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Speaker 1: don't pay off in fact have a negative ramification. Well,

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Speaker 1: it's because of their unregulated nature. Hedge funds are allowed

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Speaker 1: to do anything and everything in order to make returns

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Speaker 1: for their investors, and the fact that they are unregulated

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Speaker 1: is actually really important. It's because it allows us to

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Speaker 1: see what all of the various different dynamics are in

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Speaker 1: financial markets unfettered by regulation of any sort. So they're

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Speaker 1: the laboratory. They're the experiment where these different things take place.

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Speaker 1: And while they're three trillion dollars, which sounds like a

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Speaker 1: lot of money in the grand scheme of global and

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Speaker 1: sestable assets, that really is a few percentage of total

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Speaker 1: um money that's out there at work. Well, it seems

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Speaker 1: like a small number compared to say, Vanguard. Vanguard just

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Speaker 1: passed their four trillion two right, So the entire hedge

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Speaker 1: fund industry is only three trillion, But that number is

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Speaker 1: misleading because hedge funds can use leverage, and they can

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Speaker 1: go short, and they can trade at much higher frequencies

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Speaker 1: than say mutual funds, So that three trillion goes a

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Speaker 1: long long way in terms of having market impact. And

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Speaker 1: one of the things that I'm always surprised when people

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Speaker 1: bring up the financial crisis and hedge funds, and somehow

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Speaker 1: I believe hedge funds were responsible. They really survived the crisis.

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Speaker 1: They might have taken a little bit of a beating.

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Speaker 1: I think the numbers were down, but they certainly weren't

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Speaker 1: the cause of the crisis. In any way, shape or

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Speaker 1: form or or Am I overstating it? No, No, you're

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Speaker 1: right that it's really hard to attribute cause to any

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Speaker 1: one player in the system. I think we all contributed

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Speaker 1: to the financial crisis in one form or another. It's

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Speaker 1: really the complex city of the system and the fact

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Speaker 1: that we have these non linearities that people really weren't

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Speaker 1: focusing on that ultimately caused this huge debacle. But really,

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Speaker 1: hedge funds were not a big player in all the

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Speaker 1: spaces that ultimately blew up. They weren't giant in securitization

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Speaker 1: of mortgages, they weren't underwriting uh, subprime credit. They really

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Speaker 1: were doing what they normally do, which is buying and

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Speaker 1: selling stocks, bonds, and even some derivatives exactly. But the

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Speaker 1: hedge fund industry showed some very important stress fractures that

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Speaker 1: had we been listening, had we really spent time watching

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Speaker 1: what was going on in the hedge fund world, we

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Speaker 1: would have seen all sorts of early warning signs, And

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Speaker 1: in fact, a number of us did write about them

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Speaker 1: back in two thousand and five and six. And I

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Speaker 1: think that's one of the reasons why the hedge fund

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Speaker 1: industry is such an important part of the financial ecosystem.

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Speaker 1: Has your thinking about hedge funds evolved over the years,

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Speaker 1: or or how has your perspectives? How might your perspectives

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Speaker 1: have changed? Well, you know, I'm now more convinced than

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Speaker 1: ever that hedge funds play an important role and that

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Speaker 1: we can learn a lot by monitoring the industry. But

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Speaker 1: I think that the hedge fund industry itself is undergoing

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Speaker 1: some pretty dramatic changes, both because hedge funds have become

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Speaker 1: more sophisticated technologically, but also because competition has actually winnowed

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Speaker 1: the field. A lot of hedge funds disappointed in their

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Speaker 1: returns over the last five to ten years, and so

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Speaker 1: the hedge fund industry has seen a lot of consolidation.

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Speaker 1: So the industry today is very different than what it

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Speaker 1: was a decade ago. Any theories as to why the

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Speaker 1: performance of hedge funds over the last decade has been

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Speaker 1: not only below with their traditional returns have been, they've

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Speaker 1: been below just a straight up sixty forty portfolio, Yeah,

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Speaker 1: you know, there are three factors that are going on

432
00:25:38,760 --> 00:25:41,480
Speaker 1: in the hedge fund industry that have really challenged its performance.

433
00:25:41,920 --> 00:25:44,919
Speaker 1: The first is that the risk free rate is actually

434
00:25:45,000 --> 00:25:48,080
Speaker 1: much lower now than before, and so hedge fund industries

435
00:25:48,240 --> 00:25:50,359
Speaker 1: relied to some degree on the risk free rate in

436
00:25:50,440 --> 00:25:53,960
Speaker 1: order to be able to add to its expected wouldn't

437
00:25:54,000 --> 00:25:56,879
Speaker 1: that make their borrowing costs that much cheaper and allow

438
00:25:56,920 --> 00:25:59,359
Speaker 1: their leverage to be more effective? Or am I oversimple

439
00:25:59,560 --> 00:26:01,680
Speaker 1: You would think so. But that's the second factor that's

440
00:26:01,720 --> 00:26:05,280
Speaker 1: actually hurting hedge fund returns. It's that leverage is way down.

441
00:26:05,400 --> 00:26:08,359
Speaker 1: Even though risk free rate is lower, the amount of

442
00:26:08,440 --> 00:26:11,440
Speaker 1: leverage that hedge funds are afforded is much lower now

443
00:26:11,480 --> 00:26:14,719
Speaker 1: because people are less risk seeking. And that we know

444
00:26:14,760 --> 00:26:18,400
Speaker 1: that policies like the FEDS changes in leverage restrictions among

445
00:26:18,440 --> 00:26:20,840
Speaker 1: banks have made it more difficult for hedge funds to

446
00:26:20,880 --> 00:26:22,679
Speaker 1: get the same kind of leverage that they did in

447
00:26:22,720 --> 00:26:25,520
Speaker 1: the early two thousands. And what's the third reason. And

448
00:26:25,560 --> 00:26:27,919
Speaker 1: the third reason is that if you look at the

449
00:26:28,040 --> 00:26:32,000
Speaker 1: volatility of markets, it's lower now than it's been in

450
00:26:32,080 --> 00:26:35,280
Speaker 1: quite a long time. And hedge funds really make money

451
00:26:35,400 --> 00:26:38,600
Speaker 1: on volatility. They actually look for high volatility to be

452
00:26:38,640 --> 00:26:41,320
Speaker 1: able to earn their returns. Well, we saw a lot

453
00:26:41,320 --> 00:26:44,160
Speaker 1: of volatility in the financial crisis, and a decent amount

454
00:26:44,200 --> 00:26:47,119
Speaker 1: of volatility in the first couple of years afterwards. They

455
00:26:47,119 --> 00:26:50,280
Speaker 1: didn't do too great that period as well. Well, you know,

456
00:26:50,280 --> 00:26:51,879
Speaker 1: there was actually a lot of diversity. There are some

457
00:26:51,920 --> 00:26:56,480
Speaker 1: hedge funds that did spectacularly well crisis, and but you're

458
00:26:56,520 --> 00:26:59,120
Speaker 1: right that overall hedge funds were challenged, and I think

459
00:26:59,119 --> 00:27:02,160
Speaker 1: it's because the relationships that they were trading on. Those

460
00:27:02,200 --> 00:27:05,639
Speaker 1: relationships changed when we had this huge shock called the

461
00:27:05,640 --> 00:27:08,720
Speaker 1: financial crisis, and over time they're gonna learn how to

462
00:27:08,760 --> 00:27:11,640
Speaker 1: adapt to that. But it's gonna take time and not surprisingly,

463
00:27:11,720 --> 00:27:14,359
Speaker 1: just like when we lost the dinosaurs, when that meteorite

464
00:27:14,440 --> 00:27:16,840
Speaker 1: hit the planet, kicked up a cloud of dust, killed

465
00:27:16,840 --> 00:27:19,720
Speaker 1: the trees, you know, sixty five million years ago, that

466
00:27:19,880 --> 00:27:23,480
Speaker 1: same kind of extinction event occurred in two thousand and eight,

467
00:27:23,480 --> 00:27:27,440
Speaker 1: two thousand and nine. So Michael Mobison of Colombia and

468
00:27:27,560 --> 00:27:31,960
Speaker 1: Credit Swiss talks about the paradox of of skill. Um

469
00:27:32,040 --> 00:27:35,720
Speaker 1: Jim Chenos of Kindicost Partners, who's been running a hedge

470
00:27:35,720 --> 00:27:38,160
Speaker 1: fund for about thirty years, said, in the early days,

471
00:27:38,200 --> 00:27:41,320
Speaker 1: there were a hundred hedge funds generating alpha, and now

472
00:27:41,359 --> 00:27:44,920
Speaker 1: there's eleven thousand, but it's the same hundred. How much

473
00:27:45,040 --> 00:27:50,399
Speaker 1: does that paradox of of skilled tremendous number of really sharp,

474
00:27:50,640 --> 00:27:54,760
Speaker 1: really smart guys, and it's mostly guys. Uh, not exclusively,

475
00:27:54,840 --> 00:27:59,520
Speaker 1: but but almost um primarily. How much does that wave

476
00:27:59,760 --> 00:28:04,840
Speaker 1: of new hedge funds reduce the amount of alpha that's

477
00:28:04,960 --> 00:28:07,560
Speaker 1: there to go around. Well, you know, there's no doubt

478
00:28:07,760 --> 00:28:10,960
Speaker 1: that you're going to have much more competition in any time.

479
00:28:11,000 --> 00:28:13,639
Speaker 1: You have an industry that does well for a period

480
00:28:13,680 --> 00:28:16,280
Speaker 1: of time because it draws people from all walks of

481
00:28:16,320 --> 00:28:19,640
Speaker 1: life to start applying their trade in the hedge fund industry.

482
00:28:20,160 --> 00:28:21,760
Speaker 1: And by the way, you're right that the hedge fund

483
00:28:21,760 --> 00:28:24,480
Speaker 1: industry seems to be dominated by men, but I want

484
00:28:24,520 --> 00:28:26,760
Speaker 1: to mention there is an organization called one Women in

485
00:28:26,800 --> 00:28:29,480
Speaker 1: Hedge Funds, So I think we are drawing women into

486
00:28:29,480 --> 00:28:32,160
Speaker 1: the field as well. Over time, you're going to see

487
00:28:32,160 --> 00:28:36,320
Speaker 1: that competition create these kinds of periods of consolidation. But

488
00:28:36,400 --> 00:28:39,080
Speaker 1: then when you have large events like the financial crisis,

489
00:28:39,160 --> 00:28:41,640
Speaker 1: that's also an opportunity for lots of new hedge funds

490
00:28:41,640 --> 00:28:44,880
Speaker 1: to spring up, like new species that come into existence

491
00:28:44,960 --> 00:28:48,600
Speaker 1: after an extinction event. So it's not surprising that, say,

492
00:28:48,720 --> 00:28:52,040
Speaker 1: the hedge high frequency trading funds popped up over the

493
00:28:52,080 --> 00:28:54,480
Speaker 1: course of the last ten years, whereas they didn't play

494
00:28:54,480 --> 00:28:57,320
Speaker 1: nearly as big a role the previous decade. And I

495
00:28:57,360 --> 00:29:01,200
Speaker 1: know I've seen studies about women as trade leaders generally

496
00:29:01,280 --> 00:29:05,800
Speaker 1: outperformed men. They're less emotional, they're less attached to bad decisions,

497
00:29:05,800 --> 00:29:09,600
Speaker 1: They're quicker to cut their losses. I'm surprised we haven't

498
00:29:09,600 --> 00:29:12,440
Speaker 1: seen more women hedge fund managers, given given some of

499
00:29:12,480 --> 00:29:15,160
Speaker 1: the academic data. I think that there's going to be

500
00:29:15,200 --> 00:29:17,720
Speaker 1: more women coming into the industry over time. And I

501
00:29:17,760 --> 00:29:20,360
Speaker 1: think I welcome that because you're right that women tend

502
00:29:20,400 --> 00:29:22,240
Speaker 1: to have a very different trading profile, and some of

503
00:29:22,240 --> 00:29:25,440
Speaker 1: our experiments we've seen that women do tend to perform better,

504
00:29:25,560 --> 00:29:28,000
Speaker 1: not just on an absolute basis, but on a risk

505
00:29:28,040 --> 00:29:30,280
Speaker 1: adjusted basis. They tend to be much more careful about

506
00:29:30,320 --> 00:29:33,280
Speaker 1: managing their risk. Let's let's talk about one of those

507
00:29:33,280 --> 00:29:36,360
Speaker 1: hedge funds that are in the few hundreds that generate alpha.

508
00:29:36,840 --> 00:29:40,520
Speaker 1: You you spoke with the David Shaw of d Shaw

509
00:29:41,200 --> 00:29:45,120
Speaker 1: and UM, which also spawns some guy named Jeff Bezos

510
00:29:45,240 --> 00:29:50,800
Speaker 1: and and Amazon UM. He said, anomalies that had previously

511
00:29:50,880 --> 00:29:55,400
Speaker 1: generated significant profits stopped making money, and you were forced

512
00:29:55,440 --> 00:29:59,719
Speaker 1: to discover other more complex effects that people had not

513
00:29:59,800 --> 00:30:03,960
Speaker 1: yet found. The market is never completely efficient, but it

514
00:30:04,080 --> 00:30:07,800
Speaker 1: certainly has a tendency to become more efficient over time.

515
00:30:08,200 --> 00:30:12,040
Speaker 1: Does that help to explain why we've seen hedge fund

516
00:30:12,080 --> 00:30:15,600
Speaker 1: performance sort of flatten out over the last decade. Absolutely.

517
00:30:15,720 --> 00:30:18,200
Speaker 1: I think what David was talking about is exactly this

518
00:30:18,400 --> 00:30:24,480
Speaker 1: process of adaptation, innovation, competition, and over time, the evolution

519
00:30:24,840 --> 00:30:28,640
Speaker 1: of financial markets with one trading strategy at a time.

520
00:30:28,680 --> 00:30:30,840
Speaker 1: And you know, he's a real pioneer in the in

521
00:30:30,880 --> 00:30:33,479
Speaker 1: the field and having developed some of the earliest trading

522
00:30:33,480 --> 00:30:38,000
Speaker 1: strategies for statistical arbitrage, and that's an area that's really

523
00:30:38,040 --> 00:30:41,240
Speaker 1: evolved quite a bit over the last decade. So our

524
00:30:41,280 --> 00:30:45,720
Speaker 1: markets now adapting too fast to all these changes in

525
00:30:45,760 --> 00:30:49,000
Speaker 1: all these new strategies. It used to be you would

526
00:30:49,040 --> 00:30:52,480
Speaker 1: come up with an idea before anybody else, you could

527
00:30:52,560 --> 00:30:54,400
Speaker 1: work on it for a while and it would be

528
00:30:54,440 --> 00:30:57,160
Speaker 1: profitable for a couple of years. It seems that these

529
00:30:57,240 --> 00:31:01,920
Speaker 1: ideas are either smaller and rower, or the markets adapting

530
00:31:02,000 --> 00:31:07,120
Speaker 1: that much faster and whatever edge exists goes away pretty quickly. Yeah.

531
00:31:07,160 --> 00:31:09,800
Speaker 1: You know, technology plays a much bigger role today than

532
00:31:09,840 --> 00:31:12,400
Speaker 1: it did ever before, and it's kind of a financial

533
00:31:12,440 --> 00:31:15,040
Speaker 1: arms race where if you've got a good idea you

534
00:31:15,080 --> 00:31:17,880
Speaker 1: can implement it now faster than you can even think

535
00:31:17,920 --> 00:31:21,160
Speaker 1: about it. Uh. It's what I call the confluence of

536
00:31:21,200 --> 00:31:24,880
Speaker 1: Moore's law meets Murphy's law. And I think that's the challenge.

537
00:31:24,880 --> 00:31:27,640
Speaker 1: It's that we now have technologies that are so powerful

538
00:31:27,680 --> 00:31:31,360
Speaker 1: it allows us to do things that we really never imagined,

539
00:31:31,440 --> 00:31:34,720
Speaker 1: and there are going to be unintended consequences. We have

540
00:31:34,840 --> 00:31:37,720
Speaker 1: been speaking with Professor Andrew low of m I T.

541
00:31:38,000 --> 00:31:41,680
Speaker 1: S Sloan School of Management. If you enjoy this conversation,

542
00:31:41,960 --> 00:31:45,080
Speaker 1: be sure and check out our podcast extras where we

543
00:31:45,240 --> 00:31:47,680
Speaker 1: keep the tape rolling and continue to talk about all

544
00:31:47,720 --> 00:31:52,280
Speaker 1: things financial engineering. You can find that on SoundCloud, iTunes

545
00:31:52,400 --> 00:31:55,800
Speaker 1: and Bloomberg dot com. You can find all the Professor

546
00:31:56,400 --> 00:31:59,280
Speaker 1: lows uh written work either at the m I T.

547
00:31:59,400 --> 00:32:05,240
Speaker 1: Website or at any bookstore Barnes, Noble or Amazon dot com.

548
00:32:05,360 --> 00:32:08,280
Speaker 1: Be sure and check out my daily column on Bloomberg

549
00:32:08,360 --> 00:32:12,080
Speaker 1: View dot com or follow me on Twitter at Rit Halts.

550
00:32:12,200 --> 00:32:15,520
Speaker 1: I'm Barry Rit Halts. You're listening to Masters in Business

551
00:32:15,520 --> 00:32:18,840
Speaker 1: on Bloomberg Radio. What could your future hold? More than

552
00:32:18,880 --> 00:32:20,840
Speaker 1: you think? Because at Merrill Lynch we work with you

553
00:32:20,880 --> 00:32:23,760
Speaker 1: to create a strategy built around your priorities. Visit mL

554
00:32:23,800 --> 00:32:26,160
Speaker 1: dot com and learn more about Merrill Lynch. An affiliated

555
00:32:26,160 --> 00:32:28,600
Speaker 1: Bank of America. Mary Lynch makes available pducts and services

556
00:32:28,600 --> 00:32:30,520
Speaker 1: offered by Merrill Lynch. Pierce, Feder and Smith Incorporated, a

557
00:32:30,560 --> 00:32:33,600
Speaker 1: registered broker dealer. Remember s I PC. Welcome to the podcast.

558
00:32:33,640 --> 00:32:35,880
Speaker 1: Thank you Professor Lowe for doing this and being so

559
00:32:36,000 --> 00:32:40,400
Speaker 1: generous with your time. I had I had mentioned um earlier,

560
00:32:40,440 --> 00:32:42,920
Speaker 1: I had seen you speak it's got to be a

561
00:32:42,960 --> 00:32:46,480
Speaker 1: decade ago. It was right after the financial crisis, and

562
00:32:47,760 --> 00:32:51,880
Speaker 1: I had discussed with you how how the high efficient

563
00:32:51,880 --> 00:32:55,560
Speaker 1: market hypothesis could really exist in its strong form When

564
00:32:55,560 --> 00:32:57,760
Speaker 1: we see how can I how can I market be

565
00:32:57,760 --> 00:33:00,240
Speaker 1: worth X on day one and Y on day too,

566
00:33:00,240 --> 00:33:03,120
Speaker 1: it doesn't really seem to make a whole lot of sense.

567
00:33:03,600 --> 00:33:10,840
Speaker 1: And I thought you're marriage of of the biological to

568
00:33:11,040 --> 00:33:13,880
Speaker 1: to the markets is really fascinating, and because it brings

569
00:33:13,880 --> 00:33:17,160
Speaker 1: in not only behavioral economics, but it brings in some

570
00:33:17,320 --> 00:33:20,360
Speaker 1: of the neuroscience that's out there, that that's pretty fascinating.

571
00:33:20,920 --> 00:33:25,080
Speaker 1: You do some interesting experiments at M I t looking

572
00:33:25,080 --> 00:33:28,440
Speaker 1: at this sort of stuff. Um. The one that stood out,

573
00:33:28,480 --> 00:33:31,840
Speaker 1: I think it was this book the study where you

574
00:33:31,920 --> 00:33:35,800
Speaker 1: took twenty eight students and had them go through a

575
00:33:35,920 --> 00:33:40,840
Speaker 1: simulated market, and you ended up with fairly efficient um

576
00:33:40,960 --> 00:33:45,920
Speaker 1: decision making with just participants. Am I? Am I misstating that?

577
00:33:46,120 --> 00:33:49,440
Speaker 1: Or no, that's right, So so explain explain that lab

578
00:33:49,480 --> 00:33:52,480
Speaker 1: a little bit and and what you're looking to accomplish

579
00:33:52,560 --> 00:33:55,720
Speaker 1: with it and what that experiment showed. Sure, well, you know,

580
00:33:55,720 --> 00:33:59,959
Speaker 1: it was an interesting collaboration between myself and a neuroscience

581
00:34:00,000 --> 00:34:04,520
Speaker 1: as Tommy Poggio, and a marketing expert Elita Han, and

582
00:34:04,600 --> 00:34:07,760
Speaker 1: a couple of our students Nicolas Chan and Adler Kim.

583
00:34:07,800 --> 00:34:09,839
Speaker 1: What we were trying to do there was to understand

584
00:34:10,160 --> 00:34:15,319
Speaker 1: how the standard consumer marketing surveys could be replaced with

585
00:34:15,600 --> 00:34:19,520
Speaker 1: a simple market simulation. And we did the experiment where

586
00:34:19,520 --> 00:34:22,919
Speaker 1: we compared a situation where you were trying to get

587
00:34:22,960 --> 00:34:27,600
Speaker 1: consumers to express their preferences about different kinds of bicycle pumps.

588
00:34:27,680 --> 00:34:32,160
Speaker 1: And so typically a consumer survey would involve a long

589
00:34:32,280 --> 00:34:35,000
Speaker 1: series of studies where you ask a bunch of customers

590
00:34:35,239 --> 00:34:37,839
Speaker 1: features about particular bicycle pump that they may or may

591
00:34:37,840 --> 00:34:41,520
Speaker 1: not like, and these surveys generally take many hundreds of

592
00:34:41,520 --> 00:34:44,680
Speaker 1: thousands of dollars and weeks and weeks to conduct. We

593
00:34:44,760 --> 00:34:49,239
Speaker 1: decided to run a simulation where we created synthetic securities

594
00:34:49,280 --> 00:34:52,840
Speaker 1: representing each of these different bicycle pumps, and we allowed

595
00:34:52,840 --> 00:34:56,360
Speaker 1: students to trade them over the course of thirty minute

596
00:34:56,360 --> 00:34:59,040
Speaker 1: interval in a kind of a mock trading session in

597
00:34:59,080 --> 00:35:01,759
Speaker 1: our trading lab. And what we found at the end

598
00:35:01,760 --> 00:35:05,640
Speaker 1: of that thirty minutes is that the relative prices of

599
00:35:05,680 --> 00:35:11,400
Speaker 1: these synthetic securities actually corresponded precisely to the marketing surveys

600
00:35:11,440 --> 00:35:14,040
Speaker 1: that took weeks and weeks to conduct. In other words,

601
00:35:14,080 --> 00:35:17,280
Speaker 1: by using the market, you could actually collect the wisdom

602
00:35:17,320 --> 00:35:20,600
Speaker 1: of crowds much more quickly than if you've just done

603
00:35:20,600 --> 00:35:24,600
Speaker 1: the surveys individual by individual. Even a simulated market with

604
00:35:24,680 --> 00:35:28,080
Speaker 1: a small number of participants exactly so instead of having

605
00:35:28,080 --> 00:35:31,440
Speaker 1: to use hundreds or thousands of consumers, just a small

606
00:35:31,520 --> 00:35:34,080
Speaker 1: number over a short period of time can actually give

607
00:35:34,120 --> 00:35:36,640
Speaker 1: you much the same results. How do you set that

608
00:35:36,719 --> 00:35:40,360
Speaker 1: up where you're incentivizing the participants to actually put the

609
00:35:40,400 --> 00:35:43,560
Speaker 1: time and energy into it to to do it so

610
00:35:43,600 --> 00:35:46,560
Speaker 1: it's functional and efficient. Well, that's the beauty of markets.

611
00:35:46,640 --> 00:35:49,560
Speaker 1: People are already incentivized to try to beat the market,

612
00:35:49,600 --> 00:35:52,120
Speaker 1: that to try to come up with the winning picks.

613
00:35:52,160 --> 00:35:55,719
Speaker 1: And it's that process, that kind of competitive spirit that

614
00:35:55,760 --> 00:35:58,560
Speaker 1: financial markets bring out in people that allow us to

615
00:35:58,680 --> 00:36:02,839
Speaker 1: extract information in a much more efficient manner. So let me, UM,

616
00:36:02,920 --> 00:36:07,960
Speaker 1: I'm gonna pull something from UM one of the efficient

617
00:36:08,040 --> 00:36:11,560
Speaker 1: market issues that had come up that has always it

618
00:36:11,640 --> 00:36:14,440
Speaker 1: has always annoyed me, and it had to do with

619
00:36:14,480 --> 00:36:17,960
Speaker 1: the This is why I never print on two page,

620
00:36:18,040 --> 00:36:21,279
Speaker 1: two sided. UM. It always had had to do with

621
00:36:21,320 --> 00:36:26,239
Speaker 1: the Challenger explosion and the reaction. And I always thought, Um,

622
00:36:26,280 --> 00:36:28,359
Speaker 1: the wisdom of crowds got it wrong, and a few

623
00:36:28,400 --> 00:36:30,319
Speaker 1: other things got it wrong. But I'm curious as to

624
00:36:30,400 --> 00:36:34,760
Speaker 1: your perspective. Here, here's something we pulled from the book.

625
00:36:35,280 --> 00:36:40,040
Speaker 1: The day the Challenger exploded provided evidence that prices rapidly

626
00:36:40,080 --> 00:36:45,400
Speaker 1: reflect all available information. So I'm gonna stop there. Morton

627
00:36:45,560 --> 00:36:49,279
Speaker 1: Theicole was halted for training thirteen minutes after the Challenger exploded,

628
00:36:49,600 --> 00:36:52,839
Speaker 1: even though it was one of four potential culprits had

629
00:36:52,920 --> 00:36:56,640
Speaker 1: closed the day down twelve no evidence of insider trading.

630
00:36:57,000 --> 00:36:58,640
Speaker 1: The other three didn't sell off. And I want to

631
00:36:58,680 --> 00:37:03,440
Speaker 1: say the other three were wing Um. It might have

632
00:37:03,480 --> 00:37:05,800
Speaker 1: been Northbrook, Brumen. I don't know if they were separate

633
00:37:05,800 --> 00:37:09,840
Speaker 1: companies back then and one other. Um what took the

634
00:37:09,880 --> 00:37:13,160
Speaker 1: smartest minds five months to figure out, the stock market

635
00:37:13,200 --> 00:37:17,320
Speaker 1: figured out in hours. I've always hated that argument because

636
00:37:18,080 --> 00:37:24,000
Speaker 1: Morton theicle essentially had a soul. Their business was providing

637
00:37:24,200 --> 00:37:27,239
Speaker 1: the sort of stuff too the government, these sort of

638
00:37:27,280 --> 00:37:32,080
Speaker 1: contracts for aerospace and aviation. All the other companies had massive,

639
00:37:32,600 --> 00:37:36,680
Speaker 1: unrelated businesses to the Challenger. So it wasn't so much

640
00:37:36,760 --> 00:37:40,480
Speaker 1: that the market figured out who made the OH ring

641
00:37:41,120 --> 00:37:43,040
Speaker 1: and we knew and the market figured out the O

642
00:37:43,200 --> 00:37:46,359
Speaker 1: ring was. It was essentially a bet. Hey, if it

643
00:37:46,400 --> 00:37:50,600
Speaker 1: was Grumman or north Rope or Boeing, the business has

644
00:37:50,640 --> 00:37:52,120
Speaker 1: nothing to do with this, it's not gonna have a

645
00:37:52,120 --> 00:37:54,880
Speaker 1: big impact. But if it's Morton theical, hey, this is

646
00:37:54,920 --> 00:38:00,200
Speaker 1: really potentially problematic. It wasn't. It's always presented as Oh,

647
00:38:00,239 --> 00:38:02,719
Speaker 1: the market figured out that Morton the cole knew that

648
00:38:02,920 --> 00:38:08,080
Speaker 1: they were responsible for the Challenger disaster. In reality, their

649
00:38:08,080 --> 00:38:10,319
Speaker 1: business was so tied to it that it mattered more

650
00:38:10,360 --> 00:38:14,239
Speaker 1: to them than everybody else. That does that criticism hold

651
00:38:14,239 --> 00:38:16,880
Speaker 1: water well? I think it does hold water to some degree,

652
00:38:17,120 --> 00:38:19,640
Speaker 1: but it's very difficult for us to separate out the

653
00:38:19,680 --> 00:38:23,719
Speaker 1: two effects, because you know, Morton Thiacle was responsible for

654
00:38:23,800 --> 00:38:28,400
Speaker 1: creating the booster rocket that ultimately exploded, whereas Rockwell International,

655
00:38:28,480 --> 00:38:31,360
Speaker 1: Martin Marietta, and Lockheed were involved in other parts of

656
00:38:31,360 --> 00:38:33,600
Speaker 1: the Space Shuttle mission. And you're quite right that they're

657
00:38:33,600 --> 00:38:36,520
Speaker 1: bigger companies that were involved in different aspects. But the

658
00:38:36,560 --> 00:38:40,239
Speaker 1: fact that within minutes of this event, Martin thy Call

659
00:38:40,360 --> 00:38:43,359
Speaker 1: was singled out does suggest that, for whatever reason, there

660
00:38:43,400 --> 00:38:45,800
Speaker 1: was wisdom in the crowds in saying that that company

661
00:38:45,880 --> 00:38:47,879
Speaker 1: was going to be more effective. But you're absolutely right

662
00:38:48,280 --> 00:38:50,400
Speaker 1: that we had no idea about the o ring and

663
00:38:50,440 --> 00:38:53,000
Speaker 1: that really required the five and a half month investigation

664
00:38:53,080 --> 00:38:56,359
Speaker 1: that the Roger's Commission conducted. Yeah, I've always looked at

665
00:38:56,400 --> 00:38:59,040
Speaker 1: as as hey, if any of these other companies are

666
00:38:59,040 --> 00:39:02,000
Speaker 1: found responsible, this is a small part of their business.

667
00:39:02,080 --> 00:39:05,719
Speaker 1: But for Morton Thoicle, this this is really problematic, and

668
00:39:05,760 --> 00:39:09,920
Speaker 1: it's always always seems to be um misdescribed, or at

669
00:39:10,000 --> 00:39:12,439
Speaker 1: least that's that's how I've looked at it. We talked

670
00:39:12,440 --> 00:39:15,640
Speaker 1: about hedge funds before. What we didn't get to two

671
00:39:15,719 --> 00:39:19,920
Speaker 1: questions that I thought were really important. One was hedge

672
00:39:19,920 --> 00:39:23,600
Speaker 1: funds and an e T F rapper. Good idea, terrible idea.

673
00:39:23,680 --> 00:39:25,799
Speaker 1: What do we think about this? Well, I think it's

674
00:39:25,800 --> 00:39:29,560
Speaker 1: a mixed idea. And the reason is that for many

675
00:39:29,640 --> 00:39:34,160
Speaker 1: kinds of hedge fund strategies, retail investors should and want

676
00:39:34,200 --> 00:39:37,000
Speaker 1: to get access to them. But the problem is that

677
00:39:37,120 --> 00:39:40,919
Speaker 1: certain kinds of hedge funds strategies carry with them very

678
00:39:40,920 --> 00:39:44,120
Speaker 1: subtle risks that retail investors are not in a position

679
00:39:44,200 --> 00:39:46,920
Speaker 1: to be able to evaluate. So a good example is

680
00:39:47,080 --> 00:39:52,560
Speaker 1: tail risk, for example, catastrophe reinsurance. UH, that kind of

681
00:39:52,680 --> 00:39:55,800
Speaker 1: risk is very subtle in the sense that it generally

682
00:39:55,960 --> 00:40:00,560
Speaker 1: doesn't happen the time, and so most to the time

683
00:40:00,600 --> 00:40:03,759
Speaker 1: you're earning pretty decent returns at relatively the low risk.

684
00:40:03,840 --> 00:40:06,400
Speaker 1: It sounds like a great deal. But every once in

685
00:40:06,400 --> 00:40:08,359
Speaker 1: a while, in the parlance of Wall Street, you get

686
00:40:08,360 --> 00:40:11,560
Speaker 1: your face ripped off. And that's the thing that retail

687
00:40:11,600 --> 00:40:15,960
Speaker 1: investors don't fully appreciate, don't understand, and aren't really prepared for.

688
00:40:16,480 --> 00:40:18,840
Speaker 1: So the hedge fund strategies that have those kinds of

689
00:40:18,920 --> 00:40:21,520
Speaker 1: risks are going to be very difficult for investors to

690
00:40:21,560 --> 00:40:24,160
Speaker 1: tolerate in an E t F format, and that that's

691
00:40:24,160 --> 00:40:26,480
Speaker 1: the concern that I have about these kinds of strategies.

692
00:40:27,400 --> 00:40:32,000
Speaker 1: The criticism that i've I've seen about the E t

693
00:40:32,200 --> 00:40:35,520
Speaker 1: F hedge funds, some people have described them as muppets.

694
00:40:35,760 --> 00:40:40,600
Speaker 1: Famous quote from one of the earlier Goldman sachs Um

695
00:40:40,840 --> 00:40:45,200
Speaker 1: litigations that was pulled out of context, but salespeople describing

696
00:40:45,239 --> 00:40:48,759
Speaker 1: their clients as muppets. But what it means in this

697
00:40:48,880 --> 00:40:52,080
Speaker 1: context is, Hey, the really good hedge funds are filled up.

698
00:40:52,160 --> 00:40:56,640
Speaker 1: They have institutions that can swing billions of dollars around.

699
00:40:57,040 --> 00:40:58,880
Speaker 1: By the time you get to the hedge funds that

700
00:40:58,920 --> 00:41:02,040
Speaker 1: are put into an e t F rapper, lesser managers,

701
00:41:02,160 --> 00:41:06,080
Speaker 1: lesser track records, not as strong a model, or at

702
00:41:06,120 --> 00:41:09,359
Speaker 1: least a perspective model looking forward, So you're left with

703
00:41:10,239 --> 00:41:12,640
Speaker 1: second tier hedge funds in an E t F rapper

704
00:41:12,719 --> 00:41:16,680
Speaker 1: for them uppets, fair criticism or again overstating it. I

705
00:41:16,719 --> 00:41:18,399
Speaker 1: think that's a bit of an overstatement, and I think

706
00:41:18,400 --> 00:41:22,400
Speaker 1: it misses the point that there's actually a spectrum of

707
00:41:22,719 --> 00:41:27,359
Speaker 1: risk reward opportunities that investors are really looking for. At

708
00:41:27,400 --> 00:41:29,960
Speaker 1: the one extreme of that spectrum are investors that are

709
00:41:29,960 --> 00:41:33,719
Speaker 1: looking for absolute return, high octane investments. They don't care

710
00:41:33,760 --> 00:41:36,000
Speaker 1: if you lose in a year as long as there's

711
00:41:36,040 --> 00:41:39,000
Speaker 1: a chance of making in a year. And that's perfectly

712
00:41:39,000 --> 00:41:43,360
Speaker 1: fine for that group of investors. But for typical retail

713
00:41:43,400 --> 00:41:45,480
Speaker 1: investors that are saving money for their four oh one

714
00:41:45,560 --> 00:41:47,600
Speaker 1: K plan, they don't want that to turn into a

715
00:41:47,640 --> 00:41:50,319
Speaker 1: tool one K plan. They want to make sure that

716
00:41:50,640 --> 00:41:54,120
Speaker 1: the downside is not going to be completely devastating, and

717
00:41:54,120 --> 00:41:56,600
Speaker 1: so you've got to put in various kinds of protections

718
00:41:57,040 --> 00:42:01,000
Speaker 1: and risk management tools that will definitely reduce the upside,

719
00:42:01,160 --> 00:42:03,520
Speaker 1: but it will also reduce the downside. There's no free lunch,

720
00:42:03,680 --> 00:42:05,920
Speaker 1: as the old adage goes, and so you've got to

721
00:42:05,960 --> 00:42:07,759
Speaker 1: take the good with the bad. And I think there's

722
00:42:07,760 --> 00:42:09,960
Speaker 1: a role for those kinds of vehicles, and that's really

723
00:42:09,960 --> 00:42:13,520
Speaker 1: what the hedge fund industry is transforming into when they

724
00:42:13,640 --> 00:42:16,840
Speaker 1: develop mutual fund products. It reminds me of yet another

725
00:42:16,880 --> 00:42:20,760
Speaker 1: quote of yours. Traditional market cap weighted static index funds

726
00:42:21,200 --> 00:42:24,680
Speaker 1: still work very well for the average investor, but some

727
00:42:24,760 --> 00:42:28,280
Speaker 1: investors continue to look for an edge, hoping to find

728
00:42:28,360 --> 00:42:32,080
Speaker 1: alpha in an ocean of beta. That raises two questions.

729
00:42:32,200 --> 00:42:35,080
Speaker 1: One is is it really just a drop of alpha

730
00:42:35,160 --> 00:42:39,400
Speaker 1: in an ocean of data? How much alpha exists for

731
00:42:39,560 --> 00:42:43,360
Speaker 1: the hedge fund industry to to go after. Well, you know,

732
00:42:43,600 --> 00:42:46,640
Speaker 1: the way I think about alpha is that really represents

733
00:42:46,640 --> 00:42:52,319
Speaker 1: sort of the the creative opportunities that active competitive investors

734
00:42:52,400 --> 00:42:55,000
Speaker 1: are trying to come up with. And so by definition,

735
00:42:55,400 --> 00:42:57,840
Speaker 1: all of these kinds of creative opportunities are going to

736
00:42:57,880 --> 00:43:01,040
Speaker 1: be limited. The more compare editive a market is, the

737
00:43:01,080 --> 00:43:04,080
Speaker 1: more difficult it is to be coming up with genuine alpha.

738
00:43:04,360 --> 00:43:07,200
Speaker 1: You know, the famed investor Marty Lebowitz wrote an article

739
00:43:07,239 --> 00:43:13,360
Speaker 1: once UH that called uh, these particular objects alpha hunters

740
00:43:13,520 --> 00:43:17,120
Speaker 1: versus beta grazers, And I think that really captures the spirit,

741
00:43:17,160 --> 00:43:20,719
Speaker 1: that's the dynamic between alpha and beta, alpha hunters and

742
00:43:20,920 --> 00:43:24,719
Speaker 1: beta grazers. I like the way, I like the way

743
00:43:24,800 --> 00:43:28,320
Speaker 1: that UM sounds. Let's let's talk a little bit about

744
00:43:28,440 --> 00:43:34,560
Speaker 1: something that is UM somewhat related. So, over time, we've

745
00:43:34,640 --> 00:43:38,799
Speaker 1: seen the drag that high fees put on returns. We

746
00:43:38,920 --> 00:43:42,000
Speaker 1: talked earlier about Vanguard at four point to trillion. They've

747
00:43:42,040 --> 00:43:45,520
Speaker 1: been notorious for driving fees down. It's even called the

748
00:43:45,600 --> 00:43:49,800
Speaker 1: Vanguard effect. What are we given what we know about

749
00:43:49,880 --> 00:43:53,319
Speaker 1: the drag of high fees and the effect of compounding

750
00:43:53,360 --> 00:43:57,400
Speaker 1: over time, are we likely to see a substantial change

751
00:43:57,440 --> 00:44:01,439
Speaker 1: in hedge fund fees anytime soon. Well, there's no doubt

752
00:44:01,520 --> 00:44:03,640
Speaker 1: that it's going to be pressure on hedge fund fees

753
00:44:03,680 --> 00:44:07,520
Speaker 1: because of all of the very different lower cost vehicles. Uh.

754
00:44:07,560 --> 00:44:12,640
Speaker 1: In fact, you know Jack Bogel's principle, the cost matter hypothesis,

755
00:44:12,840 --> 00:44:16,640
Speaker 1: I think really summarizes at all. Uh. Well, I think

756
00:44:16,640 --> 00:44:18,799
Speaker 1: we have to be careful about that trend. But at

757
00:44:18,800 --> 00:44:22,879
Speaker 1: the same time, investors are also looking for new investment opportunities,

758
00:44:22,880 --> 00:44:24,640
Speaker 1: and to the degree that they can come up with

759
00:44:25,080 --> 00:44:28,480
Speaker 1: great returns beyond fees, they're going to actually be able

760
00:44:28,520 --> 00:44:31,719
Speaker 1: to command whatever it is that the market will bear. So,

761
00:44:31,880 --> 00:44:35,880
Speaker 1: speaking about Jack Bogel, the rise of indexing has really

762
00:44:35,960 --> 00:44:39,520
Speaker 1: taken off again since the uh this is a story

763
00:44:39,600 --> 00:44:44,240
Speaker 1: that's fifty years in the making. One was Bogel's famous paper.

764
00:44:44,320 --> 00:44:46,880
Speaker 1: He launched Vanguard, and I think seventy two or seventy

765
00:44:46,920 --> 00:44:49,920
Speaker 1: four something like that didn't do too well for the

766
00:44:49,960 --> 00:44:54,719
Speaker 1: first five years. Really, since the financial crisis, they've exploded.

767
00:44:54,760 --> 00:44:57,640
Speaker 1: I want to say they were about a trillion dollars

768
00:44:57,680 --> 00:45:01,239
Speaker 1: before the financial crisis. Now they're over fourt brilliant. That

769
00:45:01,320 --> 00:45:05,719
Speaker 1: raises an interesting question, does the rise of indexing distort

770
00:45:05,800 --> 00:45:10,640
Speaker 1: markets and at what point does indexing become too big?

771
00:45:11,239 --> 00:45:14,400
Speaker 1: Estimates are all over the place. It's five percent, it's

772
00:45:14,440 --> 00:45:19,640
Speaker 1: fift it's of global investable assets um. Even Vanguard is

773
00:45:19,680 --> 00:45:22,440
Speaker 1: a third active management there over a trillion dollars an

774
00:45:22,480 --> 00:45:27,560
Speaker 1: active low cost funds. How big is too big for indexing?

775
00:45:28,000 --> 00:45:30,400
Speaker 1: That's a great question, and it's funny because that's a

776
00:45:30,480 --> 00:45:32,960
Speaker 1: question that I would have expected people would have asked

777
00:45:33,000 --> 00:45:35,919
Speaker 1: long time ago, just the same way that people ask

778
00:45:36,239 --> 00:45:39,680
Speaker 1: how much capacity does a hedgeman have. It's only recently

779
00:45:39,680 --> 00:45:43,520
Speaker 1: that people who started asking the question about index funds. So,

780
00:45:43,800 --> 00:45:47,520
Speaker 1: first of all, index funds are an unqualified success. It's

781
00:45:47,600 --> 00:45:50,480
Speaker 1: clearly that they really benefit investors in the long run

782
00:45:50,520 --> 00:45:54,680
Speaker 1: by reducing costs and giving them diversification. However, there is

783
00:45:54,719 --> 00:45:57,160
Speaker 1: one aspect that we have to think about, and that's

784
00:45:57,200 --> 00:45:59,960
Speaker 1: something that the adaptive markets hypothesis points to, which is

785
00:46:00,080 --> 00:46:04,680
Speaker 1: that when everybody starts investing in the same vehicle, that

786
00:46:04,760 --> 00:46:09,719
Speaker 1: means that there's gonna be a hardwired correlation that we

787
00:46:09,880 --> 00:46:14,359
Speaker 1: create among various different investors experiences. Because now, if we've

788
00:46:14,400 --> 00:46:16,960
Speaker 1: got lots of people investing in the same index fund,

789
00:46:17,400 --> 00:46:21,160
Speaker 1: if and when that index fund declines we're all going

790
00:46:21,239 --> 00:46:24,239
Speaker 1: to be facing those declines at the same time, and

791
00:46:24,320 --> 00:46:27,960
Speaker 1: if those declines are severe enough to trigger our emotional reaction,

792
00:46:28,120 --> 00:46:30,600
Speaker 1: we're all going to be freaking out at the same time.

793
00:46:31,120 --> 00:46:35,600
Speaker 1: So inadvertently, these kinds of index fund holdings could actually

794
00:46:35,640 --> 00:46:39,160
Speaker 1: create more systematic risk in the financial system. It's not

795
00:46:39,200 --> 00:46:42,120
Speaker 1: to say that they don't add value. Absolutely they do,

796
00:46:42,280 --> 00:46:45,680
Speaker 1: and they're an incredible part, important part of the financial ecosystem.

797
00:46:46,040 --> 00:46:49,120
Speaker 1: But because they're so big, they can actually create these

798
00:46:49,200 --> 00:46:52,560
Speaker 1: kinds of ripple effects that were only now seeing. So

799
00:46:52,680 --> 00:46:56,080
Speaker 1: let me tell this back to your earlier UM study

800
00:46:56,120 --> 00:46:59,239
Speaker 1: where you had the limited number of students replacing the

801
00:46:59,480 --> 00:47:05,000
Speaker 1: giant UM public survey. How many people does it take

802
00:47:05,160 --> 00:47:10,319
Speaker 1: to make markets efficient enough? For asked differently, what percentage

803
00:47:10,440 --> 00:47:15,040
Speaker 1: of investors have to be active traders in order for

804
00:47:15,239 --> 00:47:18,880
Speaker 1: price discovery to work its magic. Well, it looks like

805
00:47:19,080 --> 00:47:22,640
Speaker 1: from the experiments that we conducted, if you've got very

806
00:47:22,680 --> 00:47:27,560
Speaker 1: well funded traders, only a few percentage points of markets

807
00:47:27,920 --> 00:47:30,880
Speaker 1: need to be informed trading in order to make them

808
00:47:30,960 --> 00:47:35,360
Speaker 1: very efficially. So we could have making up numbers of

809
00:47:35,440 --> 00:47:39,080
Speaker 1: the investing public indexing as long as ten percent as

810
00:47:39,120 --> 00:47:42,160
Speaker 1: active management. You'll still get price discovery, you'll still get

811
00:47:42,200 --> 00:47:47,839
Speaker 1: markets working efficiently, will still actually be equivalent to as

812
00:47:47,880 --> 00:47:51,520
Speaker 1: if everybody was was actively trading. Well, in fact, I

813
00:47:51,520 --> 00:47:54,320
Speaker 1: think that it might work even better if everybody weren't

814
00:47:54,360 --> 00:47:58,960
Speaker 1: actively trading, because when you have everybody competing to make

815
00:47:59,040 --> 00:48:02,160
Speaker 1: a slight margin, than any small bump in the road

816
00:48:02,280 --> 00:48:05,680
Speaker 1: can quickly escalate into a financial crisis. You want to

817
00:48:05,760 --> 00:48:09,719
Speaker 1: have a majority of the market participants focusing on passive,

818
00:48:10,000 --> 00:48:13,319
Speaker 1: long term investments in order to maintain market stability. So

819
00:48:13,400 --> 00:48:18,200
Speaker 1: indexing lowers volatility in the long haul, It certainly can

820
00:48:18,360 --> 00:48:21,920
Speaker 1: as the potential right that that that's quite fascinating. One

821
00:48:21,920 --> 00:48:25,600
Speaker 1: of the complaints I've heard from the active community about

822
00:48:25,719 --> 00:48:29,359
Speaker 1: indexing is, well, there goes price discovery, the markets will

823
00:48:29,360 --> 00:48:32,920
Speaker 1: no longer be efficient, and how can you identify the

824
00:48:32,960 --> 00:48:34,960
Speaker 1: true value of a company when you have these big

825
00:48:35,000 --> 00:48:38,880
Speaker 1: indexers just buying everything. You're saying, that's not necessarily a

826
00:48:38,880 --> 00:48:41,680
Speaker 1: fair criticism, not at all, because I think that if

827
00:48:41,719 --> 00:48:44,399
Speaker 1: you take a look at the size of hedge funds

828
00:48:44,480 --> 00:48:47,400
Speaker 1: and the ability for them to trade and take advantage

829
00:48:47,400 --> 00:48:50,440
Speaker 1: of market opportunities, despite the fact that they don't have

830
00:48:50,560 --> 00:48:53,680
Speaker 1: nearly the size of assets as the passive index funds,

831
00:48:53,719 --> 00:48:56,640
Speaker 1: they can move markets much more rapidly and in greater

832
00:48:56,719 --> 00:49:00,359
Speaker 1: depth on any given occasion. Alright, So so it gives

833
00:49:00,480 --> 00:49:03,879
Speaker 1: rise to another quote of yours, which I really want

834
00:49:03,920 --> 00:49:06,759
Speaker 1: you to explain, because, um, I kept coming back to

835
00:49:06,840 --> 00:49:09,280
Speaker 1: it within the book. I was having a hard time,

836
00:49:10,000 --> 00:49:13,759
Speaker 1: UM grasping part of it. It takes a theory to

837
00:49:13,840 --> 00:49:17,520
Speaker 1: beat a theory. Explain what you mean by that. In academia,

838
00:49:17,719 --> 00:49:20,800
Speaker 1: it's not good enough to just throw stones at an idea.

839
00:49:21,320 --> 00:49:22,640
Speaker 1: You've got to come up with a better one. You

840
00:49:22,680 --> 00:49:26,160
Speaker 1: have to replace the bad idea with a good idea exactly.

841
00:49:26,400 --> 00:49:28,160
Speaker 1: Or the way I would put it is that there

842
00:49:28,200 --> 00:49:33,080
Speaker 1: really aren't any bad ideas. We really have approximations to reality,

843
00:49:33,239 --> 00:49:36,399
Speaker 1: and we try to improve on those approximations one after

844
00:49:36,440 --> 00:49:39,439
Speaker 1: the other. So version one point oh is a starting point,

845
00:49:39,440 --> 00:49:41,040
Speaker 1: but then you've got to get to one point one

846
00:49:41,080 --> 00:49:43,200
Speaker 1: and then eventually two point oh. So we're trying to

847
00:49:43,200 --> 00:49:46,480
Speaker 1: come up with theories that can actually beat existing theories

848
00:49:46,480 --> 00:49:49,960
Speaker 1: in order to move of these ideas forward. So I'm

849
00:49:50,000 --> 00:49:52,719
Speaker 1: I'm gonna mangle a quote and I don't remember if

850
00:49:52,719 --> 00:49:55,640
Speaker 1: it was physics or economics that I think it was

851
00:49:55,680 --> 00:49:59,799
Speaker 1: physics originally, Um, and the quote and it'll come to

852
00:49:59,840 --> 00:50:03,440
Speaker 1: me later who said it, physics advances one funeral at

853
00:50:03,440 --> 00:50:06,680
Speaker 1: a time, meaning you have these theories that let's call

854
00:50:06,760 --> 00:50:09,359
Speaker 1: him the one point oh theories, and there's a whole

855
00:50:09,400 --> 00:50:12,839
Speaker 1: generation of grad students and subsequent scientists trained on it,

856
00:50:13,120 --> 00:50:16,319
Speaker 1: and it takes a long time before these theories are

857
00:50:16,360 --> 00:50:20,160
Speaker 1: finally put to rest and the newer, faster, smarter, better

858
00:50:20,200 --> 00:50:24,560
Speaker 1: theories replace them. How much inertia is there in academia

859
00:50:24,560 --> 00:50:27,319
Speaker 1: and how much inertia is in the world of financial

860
00:50:27,440 --> 00:50:30,839
Speaker 1: modeling and theorism. I think it was Max Planck who

861
00:50:30,880 --> 00:50:34,440
Speaker 1: originally said that, and then Paul Samuelson paraphrase that to

862
00:50:34,520 --> 00:50:37,640
Speaker 1: said they to say that science progresses funeral by funeral,

863
00:50:38,200 --> 00:50:40,880
Speaker 1: and uh, you know, that's a particularly morbid kind of

864
00:50:41,239 --> 00:50:43,919
Speaker 1: an average. Um. But I think there's a truth. There's

865
00:50:43,920 --> 00:50:46,040
Speaker 1: a certain element of truth to it, and it has

866
00:50:46,080 --> 00:50:48,880
Speaker 1: to do with the fact that, you know, academics become

867
00:50:49,000 --> 00:50:52,520
Speaker 1: very attached to their ideas, and so at some point,

868
00:50:52,560 --> 00:50:55,560
Speaker 1: in order to challenge an existing theory, you really need

869
00:50:55,600 --> 00:51:01,239
Speaker 1: to develop a competing alternative that really provides some compelling actions. UM.

870
00:51:01,280 --> 00:51:05,400
Speaker 1: Cliff Astness tells the story. When he was uh doing

871
00:51:05,480 --> 00:51:10,000
Speaker 1: his doctoral work, his his financial advice, his academic advisor

872
00:51:10,520 --> 00:51:13,439
Speaker 1: was Eugene Fama, and he had the bright idea of

873
00:51:14,040 --> 00:51:18,360
Speaker 1: writing his PhD thesis on why momentum actually worked and

874
00:51:18,400 --> 00:51:21,960
Speaker 1: the markets weren't really all that efficient. And to Fama's credit,

875
00:51:22,160 --> 00:51:25,920
Speaker 1: he approved the idea and ultimately that was asthnes is

876
00:51:26,040 --> 00:51:30,200
Speaker 1: of a q rs um thesis. So there are some

877
00:51:30,239 --> 00:51:31,960
Speaker 1: I don't want to paint with tubro to brush. There

878
00:51:31,960 --> 00:51:37,319
Speaker 1: are some academics who clearly display and intellectual flexibility. I

879
00:51:37,360 --> 00:51:40,680
Speaker 1: give huge credit to Fama for saying, sure, poke hole

880
00:51:40,719 --> 00:51:43,040
Speaker 1: in the thesis and if it works, well, we'll start

881
00:51:43,080 --> 00:51:45,880
Speaker 1: calling you doctor. I think that's a but, but that

882
00:51:46,040 --> 00:51:50,680
Speaker 1: isn't necessarily true. Um. Everywhere you you do end up

883
00:51:50,760 --> 00:51:53,719
Speaker 1: with certain theories that have I mean, look how long

884
00:51:53,800 --> 00:51:57,160
Speaker 1: it took for behavioral economics to really catch on, And

885
00:51:57,560 --> 00:52:02,200
Speaker 1: it was so clear that Homo economists was a fabricated

886
00:52:02,640 --> 00:52:07,799
Speaker 1: um description of human behavior. But it took you know,

887
00:52:08,000 --> 00:52:13,520
Speaker 1: almost two generations before these ideas. So so why what

888
00:52:13,719 --> 00:52:17,000
Speaker 1: is it about human nature that we marry even bad

889
00:52:17,080 --> 00:52:19,960
Speaker 1: ideas and we're so slow to change? And again, how

890
00:52:20,000 --> 00:52:23,120
Speaker 1: does that manifest itself in markets. Well, you know, now

891
00:52:23,120 --> 00:52:26,200
Speaker 1: we're veering into a topic on the sociology of science,

892
00:52:26,200 --> 00:52:28,960
Speaker 1: and I'm not sure I'm an expert hunt, but there

893
00:52:29,040 --> 00:52:31,560
Speaker 1: is a definite cultural element to our field. We do.

894
00:52:31,680 --> 00:52:34,120
Speaker 1: We get attached to certain ideas and theories and we

895
00:52:34,160 --> 00:52:37,800
Speaker 1: start thinking along these lines. You know, I call that narrative.

896
00:52:37,840 --> 00:52:40,319
Speaker 1: You know, we all have our own narrative of what's

897
00:52:40,360 --> 00:52:43,399
Speaker 1: going on. And the fact is that unless we take

898
00:52:43,400 --> 00:52:46,120
Speaker 1: our narrative and try to match them to the data,

899
00:52:46,560 --> 00:52:50,960
Speaker 1: will always be caught up in our own hypotheses and theories.

900
00:52:51,320 --> 00:52:54,200
Speaker 1: But when you start confronting these theories with data and

901
00:52:54,239 --> 00:52:56,440
Speaker 1: you see that they don't fit, then at some point

902
00:52:56,760 --> 00:52:59,080
Speaker 1: you're gonna actually have to develop better theories. And that's

903
00:52:59,080 --> 00:53:02,799
Speaker 1: really what I experienced over time. I have to bring

904
00:53:02,880 --> 00:53:05,799
Speaker 1: up an anecdote, which is what led me to these

905
00:53:05,880 --> 00:53:10,359
Speaker 1: previous questions. The first time you gave a presentation at

906
00:53:10,400 --> 00:53:14,280
Speaker 1: the National Bureau of Economic Research, you got called out

907
00:53:14,960 --> 00:53:17,760
Speaker 1: with the accusation, this work is wrong when the numbers

908
00:53:17,800 --> 00:53:21,160
Speaker 1: don't add up to something off Here, it turns out

909
00:53:21,400 --> 00:53:26,399
Speaker 1: you were right. Describe that anecdote and explain how this,

910
00:53:26,920 --> 00:53:29,760
Speaker 1: how this came about. Yeah, that was a very memorable

911
00:53:29,800 --> 00:53:35,680
Speaker 1: event November. It was the first presentation that I'd given

912
00:53:35,840 --> 00:53:38,640
Speaker 1: in an academic forum among my peers. I was an

913
00:53:38,680 --> 00:53:41,759
Speaker 1: assistant professor, just graduated a couple of years ago from

914
00:53:41,800 --> 00:53:44,600
Speaker 1: graduate school. And Craig McKinley, a colleague of mine at

915
00:53:44,600 --> 00:53:47,200
Speaker 1: the Wharton School, and I we'd written a paper rejecting

916
00:53:47,200 --> 00:53:51,640
Speaker 1: the random walk hypothesis, and it's basically saying markets are

917
00:53:51,680 --> 00:53:55,000
Speaker 1: not quite random. There is there's I'm doing this from memory,

918
00:53:55,000 --> 00:53:59,359
Speaker 1: but it's persistence and momentum and other factors that said, hey,

919
00:53:59,400 --> 00:54:02,920
Speaker 1: if I know this information, I have a better than

920
00:54:03,040 --> 00:54:06,520
Speaker 1: coin flip chance of predicting that. Exactly. We were looking

921
00:54:06,560 --> 00:54:09,759
Speaker 1: at weekly stock returns and we found short term momentum

922
00:54:09,840 --> 00:54:12,920
Speaker 1: in the data, and no matter which way we sliced it,

923
00:54:13,000 --> 00:54:16,120
Speaker 1: we couldn't get rid of this kind of anomaly. And

924
00:54:16,160 --> 00:54:19,000
Speaker 1: so we presented the results as we found them, and

925
00:54:19,239 --> 00:54:23,720
Speaker 1: our discussant, who is a very distinguished academic economist, reviewed

926
00:54:23,719 --> 00:54:26,640
Speaker 1: our results and said, the theory is very interesting, but

927
00:54:26,719 --> 00:54:29,440
Speaker 1: the numbers have to be wrong because this would imply

928
00:54:29,680 --> 00:54:32,799
Speaker 1: way too many profits for our Wall Street traders, And

929
00:54:33,200 --> 00:54:35,520
Speaker 1: so we were really taken aback by that is the

930
00:54:35,520 --> 00:54:37,360
Speaker 1: first time that we sort of got hit with the

931
00:54:37,440 --> 00:54:41,839
Speaker 1: route awakening that you could actually get publicly shamed for

932
00:54:42,040 --> 00:54:45,040
Speaker 1: your research. What was it? This was in a public forum. Ye,

933
00:54:45,440 --> 00:54:49,920
Speaker 1: and we'll we'll leave the accuser's name out, but essentially, no, no,

934
00:54:50,040 --> 00:54:52,240
Speaker 1: you have to be wrong. Did did they at least

935
00:54:52,239 --> 00:54:55,440
Speaker 1: give a basis for saying why you're wrong other than

936
00:54:56,000 --> 00:54:59,719
Speaker 1: hold the data aside, we just don't like the theory. Well? No,

937
00:55:00,239 --> 00:55:02,760
Speaker 1: The only basis was that if this were really correct,

938
00:55:02,800 --> 00:55:07,040
Speaker 1: then this would imply untold profits for traders. But aren't

939
00:55:07,040 --> 00:55:10,839
Speaker 1: there untold profits? Well, it turned out, unbeknownst to us

940
00:55:10,880 --> 00:55:14,839
Speaker 1: and this discussant, this was exactly the time when statistical

941
00:55:14,960 --> 00:55:18,480
Speaker 1: arbitrage came into its own and when David Shaw was

942
00:55:18,640 --> 00:55:22,320
Speaker 1: engaged in what would then become a multibillion dollar hedge

943
00:55:22,320 --> 00:55:25,600
Speaker 1: fund and many many billions of dollars of profits for investors.

944
00:55:25,840 --> 00:55:28,680
Speaker 1: Did you ever get a mia culpa from the person

945
00:55:28,800 --> 00:55:31,600
Speaker 1: who made the ruling accusation? We did a few months

946
00:55:31,680 --> 00:55:35,120
Speaker 1: later he wrote back saying that he had apparently checked

947
00:55:35,120 --> 00:55:38,839
Speaker 1: our results and in fact agreed that the data are

948
00:55:39,160 --> 00:55:43,880
Speaker 1: definitely inconsistent with the findings and how interesting, So we

949
00:55:43,920 --> 00:55:46,040
Speaker 1: did come to uh two terms and I think a

950
00:55:46,160 --> 00:55:49,239
Speaker 1: number of academics went back to their home institutions and

951
00:55:49,280 --> 00:55:53,040
Speaker 1: replicated our results. How So, in a way, the act

952
00:55:53,239 --> 00:55:57,200
Speaker 1: public accusation helped validate the research, which is a good

953
00:55:57,200 --> 00:56:00,160
Speaker 1: thing it did, and in fact, that illustrates the kind

954
00:56:00,160 --> 00:56:03,719
Speaker 1: of adaptive nature of academics. It's very competitive. You come

955
00:56:03,800 --> 00:56:07,160
Speaker 1: up with innovations, and if you survive over time, then

956
00:56:07,200 --> 00:56:10,240
Speaker 1: your theory ultimately takes over. And that was a pretty

957
00:56:10,560 --> 00:56:15,520
Speaker 1: um pivotal theory. Really early in the process of saying

958
00:56:15,640 --> 00:56:19,799
Speaker 1: the strong E. Mh thesis isn't the best one, you

959
00:56:19,800 --> 00:56:22,839
Speaker 1: really want to look at the weaker meaning. Markets are

960
00:56:22,920 --> 00:56:27,280
Speaker 1: mostly sort of efficient, but at times there are inefficiencies

961
00:56:27,320 --> 00:56:29,719
Speaker 1: and sometimes they can last a long time. We still

962
00:56:29,760 --> 00:56:32,839
Speaker 1: have the Fama French five factor model, which would start

963
00:56:32,880 --> 00:56:37,960
Speaker 1: as three factors but essentially says, well, markets aren't perfectly efficient.

964
00:56:38,080 --> 00:56:41,319
Speaker 1: And that seems to be the takeaway. UM. I love

965
00:56:41,360 --> 00:56:45,080
Speaker 1: that story. I think it's fascinating that someone could actually

966
00:56:45,160 --> 00:56:49,680
Speaker 1: call you out and turn turn out to be um wrong.

967
00:56:49,719 --> 00:56:53,000
Speaker 1: I'm going through the questions that we missed. There's one

968
00:56:53,080 --> 00:56:55,480
Speaker 1: or two I want to get to. Here's one that

969
00:56:55,560 --> 00:57:00,839
Speaker 1: I find really interesting. While money again, another quote from UH,

970
00:57:00,920 --> 00:57:05,280
Speaker 1: the adaptive market. Adaptive markets, financial evolution at the speed

971
00:57:05,320 --> 00:57:09,520
Speaker 1: of thought. While money is historically ancient, it's a novelty

972
00:57:09,719 --> 00:57:13,040
Speaker 1: in comparison to the length of time the human species

973
00:57:13,080 --> 00:57:16,720
Speaker 1: has been on the planet. We're using our old brains

974
00:57:16,840 --> 00:57:21,840
Speaker 1: to respond to new ideas discuss well, you know, Homo

975
00:57:21,920 --> 00:57:25,480
Speaker 1: sapiens has been in the current form for about a

976
00:57:25,520 --> 00:57:29,240
Speaker 1: hundred thousand years, and what that means is that the

977
00:57:29,320 --> 00:57:34,040
Speaker 1: adaptations that are with us today we're really the kind

978
00:57:34,080 --> 00:57:37,280
Speaker 1: of features that were most useful for the Neolithic Ice

979
00:57:37,360 --> 00:57:40,120
Speaker 1: Age and uh, if you take a look at what

980
00:57:40,160 --> 00:57:43,360
Speaker 1: we're dealing with though in modern society, things like financial

981
00:57:43,400 --> 00:57:48,520
Speaker 1: markets are much much newer innovation, and so our decision

982
00:57:48,560 --> 00:57:52,640
Speaker 1: making capabilities are not ideally adapted to that environment. And

983
00:57:52,680 --> 00:57:55,520
Speaker 1: so it's not surprising that what helps us on the

984
00:57:55,560 --> 00:57:58,800
Speaker 1: planes of the African savannah don't necessarily help us on

985
00:57:58,840 --> 00:58:01,040
Speaker 1: the floor of the New York Stock Exchange. We have

986
00:58:01,080 --> 00:58:04,680
Speaker 1: to develop new capabilities that aren't quite there yet, and

987
00:58:04,720 --> 00:58:07,880
Speaker 1: so periodically we're going to be left with some very

988
00:58:07,920 --> 00:58:11,280
Speaker 1: poor reactions to financial market threats. One of the bigger

989
00:58:11,320 --> 00:58:13,919
Speaker 1: books of the past couple of years has been Sapiens,

990
00:58:14,520 --> 00:58:17,280
Speaker 1: but the book I always mentioned whenever that comes up.

991
00:58:17,960 --> 00:58:22,920
Speaker 1: Um is a look at evolutionary biology called last ape standing,

992
00:58:23,560 --> 00:58:27,080
Speaker 1: and there's some at least according to the fossil record,

993
00:58:27,520 --> 00:58:31,960
Speaker 1: there are twenty eight different species of hominid that existed

994
00:58:32,400 --> 00:58:35,680
Speaker 1: or coexisted with humans over the past few hundred thousand

995
00:58:35,800 --> 00:58:40,840
Speaker 1: years or past a few million years for the immediate ancestors.

996
00:58:40,920 --> 00:58:43,440
Speaker 1: And it's really a little bit of a little bit

997
00:58:43,440 --> 00:58:46,160
Speaker 1: of luck involved that we are the last ape standing.

998
00:58:46,600 --> 00:58:49,480
Speaker 1: Apparently there were a number of places where we were

999
00:58:49,520 --> 00:58:53,280
Speaker 1: not all that far from being wiped out and got

1000
00:58:53,320 --> 00:58:56,120
Speaker 1: a little bit lucky versus Chro magnum and a couple

1001
00:58:56,120 --> 00:59:00,000
Speaker 1: of other um, a couple of other near human species

1002
00:59:00,120 --> 00:59:07,000
Speaker 1: is which raises the question how poorly adapted are we

1003
00:59:07,280 --> 00:59:12,560
Speaker 1: for making the sort of capital market decisions that you describe. Well,

1004
00:59:12,600 --> 00:59:15,720
Speaker 1: that's exactly right, you know. Ian Tattersall at the American

1005
00:59:15,800 --> 00:59:19,440
Speaker 1: Museum and Natural History has some wonderful writings about how

1006
00:59:19,440 --> 00:59:22,720
Speaker 1: Homo sapiens came to be and how we competed with

1007
00:59:22,840 --> 00:59:27,520
Speaker 1: Neanderthals and other early hominids, and at some point we

1008
00:59:27,560 --> 00:59:30,960
Speaker 1: succeeded beyond all expectation. And the theory is because we

1009
00:59:31,120 --> 00:59:34,920
Speaker 1: developed the ability for abstract thought, and that allowed us

1010
00:59:34,920 --> 00:59:38,480
Speaker 1: to cooperate. We developed language and engaged in all sorts

1011
00:59:38,520 --> 00:59:43,480
Speaker 1: of activities and toolmaking that allowed us to dominate our civilization.

1012
00:59:44,000 --> 00:59:46,680
Speaker 1: The problem is that we haven't yet developed all of

1013
00:59:46,680 --> 00:59:49,960
Speaker 1: the necessary tools to dominate the financial landscape that we

1014
00:59:50,000 --> 00:59:52,360
Speaker 1: live in today. It's no longer the case that we

1015
00:59:52,400 --> 00:59:55,320
Speaker 1: have to live by our wits and survive with physical threats.

1016
00:59:55,320 --> 00:59:58,760
Speaker 1: We have to actually think about surviving financial threats. And

1017
00:59:58,840 --> 01:00:01,200
Speaker 1: so we're still a work in progress, and we have

1018
01:00:01,280 --> 01:00:04,520
Speaker 1: to worry about how the various different evolutionary mechanisms will

1019
01:00:04,600 --> 01:00:08,680
Speaker 1: interact with modern life. The what made me think of

1020
01:00:08,760 --> 01:00:13,520
Speaker 1: last State Standing while speaking with you is that author's key.

1021
01:00:13,720 --> 01:00:16,600
Speaker 1: It wasn't just tools, because lots of other species had tools.

1022
01:00:17,200 --> 01:00:20,600
Speaker 1: We did have language, but more than anything else we had,

1023
01:00:20,800 --> 01:00:24,760
Speaker 1: we were more adaptable than every other species, and we

1024
01:00:24,800 --> 01:00:29,000
Speaker 1: could survive in a range of um environments, or range

1025
01:00:29,000 --> 01:00:33,240
Speaker 1: of geographies, or range of weather conditions. Uh not everybody

1026
01:00:33,280 --> 01:00:37,400
Speaker 1: had that ability. Especially if you're bigger and stronger, well

1027
01:00:37,440 --> 01:00:40,160
Speaker 1: then you need a lot of resources. And if you're

1028
01:00:40,960 --> 01:00:45,720
Speaker 1: uh like humans, a little somewhat frail or somewhat smaller,

1029
01:00:45,880 --> 01:00:50,080
Speaker 1: well you don't need the same range of of calories

1030
01:00:50,120 --> 01:00:53,520
Speaker 1: to to survive, and so when the weather changed, it

1031
01:00:53,560 --> 01:00:56,040
Speaker 1: was real problem. Well that's what I call the revenge

1032
01:00:56,040 --> 01:00:59,520
Speaker 1: of the nerds, to say the least. All Right, so

1033
01:01:00,040 --> 01:01:01,960
Speaker 1: I know I only have you for a finite amount

1034
01:01:01,960 --> 01:01:05,440
Speaker 1: of time. Let's let's jump into some of our favorite

1035
01:01:05,520 --> 01:01:09,800
Speaker 1: questions that we ask all of our guests. Um, so

1036
01:01:10,000 --> 01:01:12,640
Speaker 1: have you you pretty much have always been in academia?

1037
01:01:12,640 --> 01:01:15,080
Speaker 1: Is that fair statemon? So you come out of Harvard

1038
01:01:15,080 --> 01:01:18,200
Speaker 1: with PhD, you go straight to Wharton, Isn't that right?

1039
01:01:18,240 --> 01:01:20,240
Speaker 1: And then from Wharton you end up at M I T.

1040
01:01:20,360 --> 01:01:22,440
Speaker 1: A Was there a way station? That was it? And

1041
01:01:22,480 --> 01:01:25,200
Speaker 1: you've been in M I T now for years? Well,

1042
01:01:25,200 --> 01:01:27,960
Speaker 1: I was gonna say almost thirty years. That's that's a

1043
01:01:28,040 --> 01:01:31,640
Speaker 1: great run. Um, tell us about some of your early mentors,

1044
01:01:31,640 --> 01:01:35,680
Speaker 1: who were the people who inspired you and shepherded your

1045
01:01:35,720 --> 01:01:38,760
Speaker 1: career along when you first began. You know, I was

1046
01:01:38,880 --> 01:01:43,680
Speaker 1: very lucky in having a whole series of extraordinary teachers. Know,

1047
01:01:43,800 --> 01:01:46,040
Speaker 1: I grew up in New York City and benefited from

1048
01:01:46,040 --> 01:01:49,440
Speaker 1: the New York City public school system, the best education

1049
01:01:49,480 --> 01:01:51,880
Speaker 1: that money didn't have to buy. Um had a great

1050
01:01:51,920 --> 01:01:55,000
Speaker 1: third grade teacher Mrs Barbara Pico Laura who really believed

1051
01:01:55,000 --> 01:01:58,000
Speaker 1: in me and gave me the runway to develop intellectually.

1052
01:01:58,360 --> 01:02:00,000
Speaker 1: Then in high school and went to the Bronx High

1053
01:02:00,040 --> 01:02:03,480
Speaker 1: School of Science, the best education that I've gotten even

1054
01:02:03,520 --> 01:02:05,640
Speaker 1: to date. I'm just amazed by the quality of the

1055
01:02:05,680 --> 01:02:10,240
Speaker 1: faculty there and Mrs Henriette amazing. Mr Milton Copleman fantastic teacher,

1056
01:02:10,280 --> 01:02:13,800
Speaker 1: is very supportive and really gave me the thirst of

1057
01:02:13,880 --> 01:02:17,080
Speaker 1: knowledge that I still benefit from today. And then in

1058
01:02:17,160 --> 01:02:20,600
Speaker 1: college I had very fortunate to be able to have

1059
01:02:20,760 --> 01:02:23,280
Speaker 1: us all Leve Moore from my econ one on one class,

1060
01:02:23,320 --> 01:02:27,520
Speaker 1: and my advisor Sharon Aster was incredibly inspirational. And then

1061
01:02:27,520 --> 01:02:31,320
Speaker 1: in grad school, um Andy Abel my thesis advisor, Jerry

1062
01:02:31,320 --> 01:02:34,520
Speaker 1: Housman another thesis advisor, and of course Bob Merton, the

1063
01:02:34,560 --> 01:02:37,640
Speaker 1: inspirational finance professor that really got me to start thinking

1064
01:02:37,680 --> 01:02:40,960
Speaker 1: about a career in finance. So all of these individuals

1065
01:02:41,240 --> 01:02:44,960
Speaker 1: are just extraordinarily important in giving me the boost that

1066
01:02:45,040 --> 01:02:47,160
Speaker 1: got me to where I am today. Let's let's talk

1067
01:02:47,200 --> 01:02:52,600
Speaker 1: about investing in general. Who what investors and or authors

1068
01:02:53,200 --> 01:02:57,760
Speaker 1: influence the way you approach the world of investment. Oh,

1069
01:02:57,880 --> 01:03:01,400
Speaker 1: there are whole host of them. Obviously, Jim Simons at

1070
01:03:01,400 --> 01:03:05,800
Speaker 1: Renaissance Technologies, David Shaw at d Shaw. These are the

1071
01:03:05,800 --> 01:03:09,440
Speaker 1: first quants that really demonstrated that using mathematical models can

1072
01:03:09,480 --> 01:03:12,640
Speaker 1: actually add value. But then there's Warren Buffett and George

1073
01:03:12,640 --> 01:03:15,320
Speaker 1: Soros who have made their money in very different ways

1074
01:03:15,400 --> 01:03:19,640
Speaker 1: using qualitative aspects of the business world. And it demonstrates

1075
01:03:19,680 --> 01:03:21,840
Speaker 1: that there's more than one way to skin a cat,

1076
01:03:22,080 --> 01:03:25,640
Speaker 1: and it really gave me some some fascinating ideas about

1077
01:03:25,680 --> 01:03:29,240
Speaker 1: how to integrate the two worlds. Let's let's talk about books,

1078
01:03:29,480 --> 01:03:33,720
Speaker 1: be they fiction or nonfiction, investing related or not. This

1079
01:03:34,160 --> 01:03:38,000
Speaker 1: is the single biggest question we get from from listeners.

1080
01:03:38,400 --> 01:03:40,640
Speaker 1: Tell us about some of your favorite books. What, what

1081
01:03:40,680 --> 01:03:44,400
Speaker 1: do you enjoy, what influenced you? What are you reading currently?

1082
01:03:45,000 --> 01:03:47,120
Speaker 1: So you know, I'm a big science fiction fan, and

1083
01:03:47,320 --> 01:03:48,920
Speaker 1: in a way, I think that's what really got me

1084
01:03:48,960 --> 01:03:51,720
Speaker 1: to start thinking along the lines of economics and finance.

1085
01:03:51,760 --> 01:03:55,040
Speaker 1: It was Isaac Asthmas Foundation Trilogy in high school. You

1086
01:03:55,120 --> 01:04:00,240
Speaker 1: and Paul Krugman, I'm in great company. There um very

1087
01:04:00,240 --> 01:04:05,280
Speaker 1: interesting idea of psycho history, the fictitious branch of mathematics

1088
01:04:05,280 --> 01:04:07,880
Speaker 1: that allow you to predict human behavior using the law

1089
01:04:07,920 --> 01:04:10,600
Speaker 1: of large numbers in the central limit theorem. But I

1090
01:04:10,640 --> 01:04:13,520
Speaker 1: loved Arthur C. Clark and more recently or Sin Scott

1091
01:04:13,560 --> 01:04:17,120
Speaker 1: Card and Endo games Enders Game. Yeah, the whole Enders series.

1092
01:04:17,120 --> 01:04:20,160
Speaker 1: Speaker for the dad, just fascinating ideas. It really allowed

1093
01:04:20,200 --> 01:04:24,320
Speaker 1: you to to let your imagination run wild. How about, um,

1094
01:04:24,400 --> 01:04:27,280
Speaker 1: something finance related. Tell us some books that you've enjoyed

1095
01:04:27,320 --> 01:04:29,600
Speaker 1: in that space. Well, you know, the first book that

1096
01:04:29,680 --> 01:04:32,080
Speaker 1: really got me thinking along the lines of finance and

1097
01:04:32,120 --> 01:04:36,280
Speaker 1: economics was Hal Browner's Worldly Philosophers. I loved that book.

1098
01:04:36,280 --> 01:04:38,760
Speaker 1: And then after that, of course Burton mal Kills random

1099
01:04:38,800 --> 01:04:41,400
Speaker 1: walked down Wall Street. I mean he writes so clearly

1100
01:04:41,800 --> 01:04:44,720
Speaker 1: and makes finance come alive that that just got me

1101
01:04:44,760 --> 01:04:47,360
Speaker 1: really excited about the stock market and thinking about all

1102
01:04:47,360 --> 01:04:50,080
Speaker 1: of these financial issues. Any of the books you wanna

1103
01:04:50,360 --> 01:04:53,280
Speaker 1: mention reference? I know you have a giant library of

1104
01:04:53,320 --> 01:04:56,200
Speaker 1: stuff that it would take readers a lifetime to plow.

1105
01:04:56,400 --> 01:04:59,720
Speaker 1: I do I have. That's my guilty pleasure. I love books. Um. Well,

1106
01:04:59,760 --> 01:05:03,440
Speaker 1: you know E. L. Wilson, the famous evolutionary biologist, has

1107
01:05:03,440 --> 01:05:06,200
Speaker 1: been a long time hero of mine. Uh, not just

1108
01:05:06,280 --> 01:05:09,320
Speaker 1: because of his theories and his impact and sociobiology, but

1109
01:05:09,680 --> 01:05:13,080
Speaker 1: because he writes like an angel, it's just extraordinary. Reading

1110
01:05:13,200 --> 01:05:15,840
Speaker 1: his work is just such a pleasure. Give us one

1111
01:05:15,880 --> 01:05:20,040
Speaker 1: book of Wilson's, his current book, Half Earth, that describes

1112
01:05:20,240 --> 01:05:24,320
Speaker 1: a new way of thinking about conservation and environmental impact.

1113
01:05:24,400 --> 01:05:27,200
Speaker 1: It's really fascinating. It's a very important book that I'm

1114
01:05:27,240 --> 01:05:29,960
Speaker 1: hoping more and more people will read. All Right, anything

1115
01:05:29,960 --> 01:05:32,680
Speaker 1: else before we leave books? That that's a good starting run.

1116
01:05:33,400 --> 01:05:35,640
Speaker 1: I think that's uh. I think that's it. We'll we'll

1117
01:05:35,720 --> 01:05:37,360
Speaker 1: leave it with that. That that's a that's a good

1118
01:05:37,440 --> 01:05:41,280
Speaker 1: run of books. Um, So, since you started looking at finance,

1119
01:05:41,320 --> 01:05:44,440
Speaker 1: what do you think the most significant changes in the

1120
01:05:44,520 --> 01:05:48,439
Speaker 1: industry has been and and are these for the better

1121
01:05:48,520 --> 01:05:51,040
Speaker 1: of for the worst. I think one of the most

1122
01:05:51,040 --> 01:05:54,840
Speaker 1: significant changes is the much bigger role that technology has

1123
01:05:54,880 --> 01:05:58,560
Speaker 1: played in our industry. It's really transformed the financial system,

1124
01:05:58,600 --> 01:06:01,200
Speaker 1: and I think it's both bad and good. I think

1125
01:06:01,200 --> 01:06:04,120
Speaker 1: that obviously technology has allowed us to engage in all

1126
01:06:04,160 --> 01:06:08,400
Speaker 1: sorts of financial transactions and services that we really wouldn't

1127
01:06:08,400 --> 01:06:11,360
Speaker 1: have been able to undertake. But at the same time,

1128
01:06:11,720 --> 01:06:14,280
Speaker 1: I think it's also created some vulnerabilities that we don't

1129
01:06:14,280 --> 01:06:16,920
Speaker 1: fully understand the financial system is a lot more complex

1130
01:06:16,960 --> 01:06:19,280
Speaker 1: now than it ever was, And I'm not sure that

1131
01:06:19,320 --> 01:06:22,040
Speaker 1: we really think about the system as a system. You know,

1132
01:06:22,080 --> 01:06:25,480
Speaker 1: we have regulators that focus on mutual funds and futurest

1133
01:06:25,520 --> 01:06:28,720
Speaker 1: markets and banks, but we don't have any regulator focused

1134
01:06:28,760 --> 01:06:31,360
Speaker 1: on the stability of the financial system as a whole.

1135
01:06:31,760 --> 01:06:34,440
Speaker 1: And I think that's really an accident waiting to happen.

1136
01:06:35,240 --> 01:06:40,400
Speaker 1: Wasn't Dodd Frank supposed to introduce all of these systemic

1137
01:06:40,800 --> 01:06:48,320
Speaker 1: survivability issues companies that are systemically important financial um? What

1138
01:06:48,480 --> 01:06:53,040
Speaker 1: is it? Cities? And uh, the idea that a bank

1139
01:06:53,160 --> 01:06:59,120
Speaker 1: needs a living will um and then discussions of now

1140
01:06:59,160 --> 01:07:03,440
Speaker 1: that's fascinating the new crew in d C. There seems

1141
01:07:03,480 --> 01:07:09,120
Speaker 1: to be a new impetus to bring Backglass Stiegel separate

1142
01:07:09,760 --> 01:07:14,880
Speaker 1: depository institutions and with checking accounts and and mortgages from

1143
01:07:15,120 --> 01:07:17,920
Speaker 1: financial institutions that are going to engage in trading and

1144
01:07:18,240 --> 01:07:21,600
Speaker 1: syndication underwriting. And if you do that goes the argument

1145
01:07:21,640 --> 01:07:23,480
Speaker 1: you could get rid of most of these regulations. You

1146
01:07:23,560 --> 01:07:27,040
Speaker 1: just need some minimum capital rules. And if one of

1147
01:07:27,080 --> 01:07:29,920
Speaker 1: those companies blows up, so what you're not affecting the

1148
01:07:29,920 --> 01:07:32,400
Speaker 1: rest of the financial system. What what are your thoughts

1149
01:07:32,400 --> 01:07:35,920
Speaker 1: on that well. Financial regulation is also an adaptive process,

1150
01:07:35,960 --> 01:07:37,760
Speaker 1: and that was one of the things that I really

1151
01:07:37,880 --> 01:07:41,000
Speaker 1: learned from watching the process of Dodd Frank. You know,

1152
01:07:41,080 --> 01:07:43,400
Speaker 1: DoD Frank isn't perfect, but it actually has done some

1153
01:07:43,560 --> 01:07:46,200
Speaker 1: very important things in changing the way we think about

1154
01:07:46,240 --> 01:07:49,880
Speaker 1: financial regulation, for example, creating the Office of Financial Research

1155
01:07:49,960 --> 01:07:52,840
Speaker 1: to collect data and to monitor the stability of the

1156
01:07:52,840 --> 01:07:56,600
Speaker 1: financial system. So I think that we've gotten a long

1157
01:07:56,640 --> 01:07:59,920
Speaker 1: ways away from the old days of the wild wild West.

1158
01:08:00,320 --> 01:08:02,280
Speaker 1: But at the same time, I don't think that we're

1159
01:08:02,280 --> 01:08:05,760
Speaker 1: focusing on financial regulation from a systemic perspective. You know,

1160
01:08:05,800 --> 01:08:08,760
Speaker 1: we do have the Financial Stability Oversight Council, which is

1161
01:08:08,800 --> 01:08:13,320
Speaker 1: this college of Financial Regulators and the US Treasury Secretary

1162
01:08:13,400 --> 01:08:16,760
Speaker 1: as the head, but that College isn't really a single

1163
01:08:16,880 --> 01:08:22,080
Speaker 1: regulatory body focus specifically on financial stability. It doesn't necessarily

1164
01:08:22,080 --> 01:08:26,000
Speaker 1: have regulatory authority that cuts across all the different jurisdictions

1165
01:08:26,000 --> 01:08:28,439
Speaker 1: of financial regulations. So I think we have to start

1166
01:08:28,560 --> 01:08:32,400
Speaker 1: thinking more adaptively about financial regulation. We have to think

1167
01:08:32,439 --> 01:08:35,679
Speaker 1: about the system not going from one extreme to the other,

1168
01:08:35,800 --> 01:08:40,120
Speaker 1: but rather changing in terms of its regulatory approach as

1169
01:08:40,240 --> 01:08:43,760
Speaker 1: markets heat up and as they cool down, so that

1170
01:08:43,960 --> 01:08:47,760
Speaker 1: that's pretty straightforward. Um, look at that, let where do

1171
01:08:47,800 --> 01:08:51,320
Speaker 1: you see as the next major shifts? And I know

1172
01:08:51,400 --> 01:08:57,040
Speaker 1: I famously say no forecasts. However, you're in a unique

1173
01:08:57,040 --> 01:09:00,960
Speaker 1: situation where you're looking at trends that are changing and

1174
01:09:01,000 --> 01:09:04,120
Speaker 1: you're seeing where those canaries in the coal mines are

1175
01:09:04,160 --> 01:09:07,840
Speaker 1: starting to either tweet or not. What is the next

1176
01:09:07,880 --> 01:09:11,000
Speaker 1: thing that the financial industry is going to have to

1177
01:09:11,080 --> 01:09:16,120
Speaker 1: adapt to? I think the process of convergence between hedge

1178
01:09:16,120 --> 01:09:19,000
Speaker 1: funds and mutual funds is one trend that we have

1179
01:09:19,080 --> 01:09:23,000
Speaker 1: to watch. Because of a combination of competition and innovation

1180
01:09:23,240 --> 01:09:27,040
Speaker 1: and the demand from investors looking for more active strategies

1181
01:09:27,080 --> 01:09:31,320
Speaker 1: and higher yield, we're going to see a greater retailization

1182
01:09:31,560 --> 01:09:34,880
Speaker 1: of hedge fund strategies. That's both an opportunity as well

1183
01:09:34,920 --> 01:09:39,080
Speaker 1: as a potential source of financial instability. Second is the

1184
01:09:39,200 --> 01:09:42,080
Speaker 1: role of financial technology, or fintech as we call it.

1185
01:09:42,640 --> 01:09:46,320
Speaker 1: The fact that investors are now engaging in robo advising

1186
01:09:46,360 --> 01:09:49,559
Speaker 1: services means that they're going to be subject to again

1187
01:09:49,680 --> 01:09:53,560
Speaker 1: greater algorithmic shifts as we see more and more sophisticated

1188
01:09:53,640 --> 01:09:56,400
Speaker 1: robo advisors. Just like we have driverless cars, at some

1189
01:09:56,439 --> 01:10:00,800
Speaker 1: point we may have driverless portfolios. And that's again both

1190
01:10:00,960 --> 01:10:02,800
Speaker 1: a good thing and a bad thing because it can

1191
01:10:02,840 --> 01:10:08,200
Speaker 1: create unintended consequences. This is another reader question. Tell us

1192
01:10:08,200 --> 01:10:10,920
Speaker 1: about a time you tried something and failed. What did

1193
01:10:10,920 --> 01:10:16,960
Speaker 1: you learn from the experience. How should investors deal with

1194
01:10:16,960 --> 01:10:21,040
Speaker 1: with failure, and how should they adapt to to that experience. Well,

1195
01:10:21,160 --> 01:10:24,240
Speaker 1: one form of failure was when Craig McKinley and I

1196
01:10:24,360 --> 01:10:27,200
Speaker 1: failed to realize how much of a sacred cow we

1197
01:10:27,200 --> 01:10:29,400
Speaker 1: were attacking when we started presenting our work on the

1198
01:10:29,479 --> 01:10:33,240
Speaker 1: random walk hypothesis. And I think that's a broader theme,

1199
01:10:33,360 --> 01:10:36,519
Speaker 1: which is that one has to be careful about the

1200
01:10:36,560 --> 01:10:40,400
Speaker 1: fact that other people have very strong narratives, and whether

1201
01:10:40,439 --> 01:10:44,479
Speaker 1: the narrative is passive investments or only active investments. We

1202
01:10:44,520 --> 01:10:46,559
Speaker 1: have to understand where investors are coming from. We have

1203
01:10:46,600 --> 01:10:49,240
Speaker 1: to understand the lens through which they're looking at the

1204
01:10:49,280 --> 01:10:52,360
Speaker 1: financial landscape, and we have to try to be realistic

1205
01:10:52,400 --> 01:10:56,360
Speaker 1: and develop products and services that take into account those lenses,

1206
01:10:56,479 --> 01:11:00,360
Speaker 1: as opposed to trying to force investors into particular ways

1207
01:11:00,360 --> 01:11:03,639
Speaker 1: of thinking that they're simply not equipped to do. I'm

1208
01:11:03,720 --> 01:11:07,160
Speaker 1: I'm about to google. I'm trying to remember who said

1209
01:11:07,240 --> 01:11:11,800
Speaker 1: sacred cows make the best hamburgers, and I'm trying to

1210
01:11:12,360 --> 01:11:15,960
Speaker 1: it comes to my head. But um, it was a book,

1211
01:11:16,120 --> 01:11:20,679
Speaker 1: there you go, and it was a book by Robert Kriegel.

1212
01:11:20,800 --> 01:11:22,920
Speaker 1: All Right, I knew that I knew the phrase was

1213
01:11:22,920 --> 01:11:26,160
Speaker 1: out there, but I didn't know where where it came from. Um,

1214
01:11:26,200 --> 01:11:28,759
Speaker 1: what do you do to relax outside of the office? Again?

1215
01:11:28,800 --> 01:11:31,560
Speaker 1: Another reader question, listener question, what do you do to

1216
01:11:31,600 --> 01:11:37,719
Speaker 1: say mentally and or physically fit well? For? For mental fitness,

1217
01:11:37,920 --> 01:11:41,680
Speaker 1: obviously doing research and being challenged by my students, I

1218
01:11:41,720 --> 01:11:44,599
Speaker 1: work with a lot of undergraduate and graduate students and

1219
01:11:44,640 --> 01:11:46,880
Speaker 1: at M I t these students are extraordinary, so that

1220
01:11:46,960 --> 01:11:50,000
Speaker 1: keeps me mentally fit. I would imagine for physical fitness,

1221
01:11:50,160 --> 01:11:52,960
Speaker 1: I'm an avid squash fan. I'm not a very good

1222
01:11:53,000 --> 01:11:55,000
Speaker 1: squash player, but I'm what I What I lack in

1223
01:11:55,280 --> 01:11:58,559
Speaker 1: skill I make up for an enthusiasm. And um, what

1224
01:11:58,600 --> 01:12:00,960
Speaker 1: about relaxation? What do you do outside of the office

1225
01:12:01,000 --> 01:12:04,759
Speaker 1: to relax well? At this point, my kids are still

1226
01:12:05,040 --> 01:12:08,080
Speaker 1: my number one focus. My younger son is in high school,

1227
01:12:08,320 --> 01:12:10,960
Speaker 1: my oldest son is just graduated from college, so spending

1228
01:12:11,000 --> 01:12:14,240
Speaker 1: time with them has been my best source of relaxation.

1229
01:12:14,760 --> 01:12:17,800
Speaker 1: I can imagine um. You work with a lot of

1230
01:12:17,800 --> 01:12:21,280
Speaker 1: of college students and and millennials. If one of them

1231
01:12:21,320 --> 01:12:24,519
Speaker 1: comes to you and says Professor Loh, I'm thinking about

1232
01:12:24,520 --> 01:12:27,320
Speaker 1: a career in finance, what sort of advice would you

1233
01:12:27,320 --> 01:12:29,720
Speaker 1: give them? I would say that finance is one of

1234
01:12:29,760 --> 01:12:32,639
Speaker 1: the most exciting fields to go into, but to keep

1235
01:12:32,640 --> 01:12:35,439
Speaker 1: in mind that finance is really a means to an end,

1236
01:12:35,560 --> 01:12:38,519
Speaker 1: not an end unto itself, and I think very often

1237
01:12:38,600 --> 01:12:40,680
Speaker 1: we lose sight of that. Even I lose sight of

1238
01:12:40,680 --> 01:12:43,080
Speaker 1: that because of the research that I do. But over

1239
01:12:43,120 --> 01:12:45,320
Speaker 1: the course of the last few years, I've begun to see,

1240
01:12:45,560 --> 01:12:48,320
Speaker 1: number one, how finance can be perverted in ways that

1241
01:12:48,360 --> 01:12:50,720
Speaker 1: it was never intended. But at the same time, I

1242
01:12:50,760 --> 01:12:53,400
Speaker 1: also see that finance can be used to achieve some

1243
01:12:53,479 --> 01:12:57,599
Speaker 1: of the greatest challenges that are facing mankind, including things

1244
01:12:57,640 --> 01:13:02,240
Speaker 1: like dealing with cancer, Alzheimer's, energy, all sorts of societal

1245
01:13:02,320 --> 01:13:06,240
Speaker 1: challenges that require large amounts of financing. So I think

1246
01:13:06,240 --> 01:13:08,160
Speaker 1: that this is a great feel to be in and

1247
01:13:08,200 --> 01:13:12,320
Speaker 1: it's an important one to focus on. And our final question,

1248
01:13:12,760 --> 01:13:17,160
Speaker 1: what is it that you know about financial engineering today

1249
01:13:17,200 --> 01:13:19,640
Speaker 1: that you wish you knew thirty years ago when you

1250
01:13:19,680 --> 01:13:22,880
Speaker 1: were first arriving in M I T. I wish I

1251
01:13:23,000 --> 01:13:27,800
Speaker 1: understood just how important human emotion is in financial decisions.

1252
01:13:27,840 --> 01:13:31,639
Speaker 1: I didn't really appreciate enough that logic was not enough

1253
01:13:31,800 --> 01:13:35,880
Speaker 1: in determining how people actually behave that. That is a

1254
01:13:36,320 --> 01:13:39,880
Speaker 1: answer that I've heard from a number of of guests,

1255
01:13:39,920 --> 01:13:43,960
Speaker 1: the behavioral side. It was overlooked way back when, and

1256
01:13:44,040 --> 01:13:47,680
Speaker 1: now it's really risen risen to the four. Professor low

1257
01:13:47,760 --> 01:13:50,920
Speaker 1: thank you for being so generous with your time. We

1258
01:13:51,000 --> 01:13:54,720
Speaker 1: have been speaking with Professor Andrew Lowe of M I T,

1259
01:13:55,320 --> 01:14:00,800
Speaker 1: author most recently of Adaptive Markets, Financial Evolution at the

1260
01:14:00,840 --> 01:14:04,320
Speaker 1: Speed of Thought. Book is getting tremendous reviews. We read

1261
01:14:04,360 --> 01:14:08,240
Speaker 1: it in our office and everybody seemed to really enjoy it.

1262
01:14:08,479 --> 01:14:12,280
Speaker 1: Speaking of enjoyment, if you enjoy this conversation, be sure

1263
01:14:12,280 --> 01:14:14,920
Speaker 1: and look up an inch or down an inch on

1264
01:14:15,000 --> 01:14:18,280
Speaker 1: Apple iTunes and you could see the other Let's call

1265
01:14:18,320 --> 01:14:21,840
Speaker 1: it hundred and forty or so such conversations that we've

1266
01:14:21,840 --> 01:14:25,840
Speaker 1: had over the past almost three years. I would be

1267
01:14:25,920 --> 01:14:30,360
Speaker 1: remiss if I did not thank my producer, Taylor Riggs,

1268
01:14:30,479 --> 01:14:34,520
Speaker 1: my head of research, Michael Batnick, and my recording engineer

1269
01:14:35,000 --> 01:14:39,280
Speaker 1: Medina Parwana. I'm Barry Ridholts. You're listening to Masters in

1270
01:14:39,360 --> 01:14:43,120
Speaker 1: Business on Bloomberg Radio. Our world is always moving, so

1271
01:14:43,160 --> 01:14:46,120
Speaker 1: with Mery Lynch you can get access to financial guidance online,

1272
01:14:46,240 --> 01:14:48,760
Speaker 1: in person or through the app. Visit mL dot com

1273
01:14:48,800 --> 01:14:51,280
Speaker 1: and learn more about Mery Lynch. An affiliated Bank of America,

1274
01:14:51,439 --> 01:14:53,679
Speaker 1: Mary Lynch makes available products and services offered by Merrill

1275
01:14:53,720 --> 01:14:56,120
Speaker 1: Lynch Pierce Federan Smith Incorporated or Registered Broker Dealer remember

1276
01:14:56,200 --> 01:14:56,559
Speaker 1: s I PC