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

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: I'm gonna bet you haven't heard of this money manager,

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Speaker 1: but you should. His name is Dr Raife Jovinazzo. He

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Speaker 1: runs the very interesting behavioral small cap equity strategy at

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Speaker 1: Fuller and Thaylor. For the past five years, the fund

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Speaker 1: has compounded and it has beaten of all its peers,

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Speaker 1: so they are doing something right there. Uh. He has

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Speaker 1: a fascinating background, and you'll hear about both his academic

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Speaker 1: background at Princeton and the University of Chicago working for

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Speaker 1: two of the giants in the world of behavioral economics,

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Speaker 1: Danny Kahneman and Richard Thaylor. How's that for a pair

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Speaker 1: of advisors. Uh. And he spent his entire your career

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Speaker 1: working in that space, trying to figure out how to

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Speaker 1: apply the knowledge and the wisdom that those gentlemen and

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Speaker 1: others have created in the field of of behavioral finance

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Speaker 1: to the world of investing. I think you will find

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Speaker 1: this to be an absolutely fascinating conversation. I know I

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Speaker 1: enjoyed it a great deal. So with no further ado,

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Speaker 1: my conversation with Raife Jovinazzo. I'm Barry Ridhults. 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 Dr Raith Jovinazzo. He is responsible for managing

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Speaker 1: the Fuller and Failor behavioral small cap equity strategy at

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Speaker 1: the firm Fuller and Failor. He has a fascinating background. Previously,

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Speaker 1: he was researcher and co portfolio manager with black Rocks

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Speaker 1: Scientific Active Equity Group. He has a BA in sociology

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Speaker 1: from Princeton and an m b a PhD from the

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Speaker 1: Booth School of Business at University of Chicago. But his

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Speaker 1: advisors were Danny Khanoman at Princeton and Richard Taylor at Chicago.

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Speaker 1: So I expect this to be a fascinating conversation. Dr

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Speaker 1: Raef Gina Venazzo, welcome to Bloomberg. Thank you very much.

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Speaker 1: So let's start with your advisors. That's pretty elite group

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Speaker 1: of people to work within your career. Kneman is your

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Speaker 1: undergraduate advisor and Failor as your m b A and

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Speaker 1: PhD advisor. That's some serious intellectual firepower. They are smart folks.

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Speaker 1: I was lucky, you know, I took a class by

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Speaker 1: Knoman on decision making. I loved it, asked him to

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Speaker 1: be my thesis advisor. The funny thing is, at that

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Speaker 1: point in time, I didn't understand that he was a

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Speaker 1: giant in his field. I knew he was very smart. Obviously,

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Speaker 1: in the course of doing my research, I realized I'm

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Speaker 1: talking to the number one expert in this subject matter. Uh.

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Speaker 1: And then he actually connected me ultimately with Taylor. He said,

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Speaker 1: I called him up about five years after I've been

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Speaker 1: working as a strategy consultant and said, I want to

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Speaker 1: go back and I want to combine study of business

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Speaker 1: with the study of psychology. And he said, well, you

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Speaker 1: need to go study under my best friend, Dick Taylor

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Speaker 1: the University of Chicago. How crazy is that? Well so, so,

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Speaker 1: during the time these folks were advising you, you had

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Speaker 1: an appreciation of how unbelievably fortunate you are. I did,

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Speaker 1: you know. And that's a nice thing, you know, it's

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Speaker 1: a great thing. Let me talk up my alma made

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Speaker 1: to the University of Chicago. There's so many noble prize

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Speaker 1: winners there, you know, I mean, ironically, I don't know

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Speaker 1: if you knew this fact. On my dissertation committee were

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Speaker 1: both Taylor and Fama, which is the only time that's

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Speaker 1: ever happened. And you know that, to my knowledge, that

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Speaker 1: those two have been on the same and they're golfing buddies. Yeah,

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Speaker 1: they get along much much better than people think they

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Speaker 1: do well. Philosophically, they're completely the opposite sides of the universe.

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Speaker 1: And yet I would disagree actually, and that they both

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Speaker 1: strongly believe in paying attention to evidence. Let's look at

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Speaker 1: the data, and that is it's actually a big unifying

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Speaker 1: approach as opposed to we'll just look, we'll have a

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Speaker 1: theory about what should work, if we don't actually care

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Speaker 1: if it actually happens in the real world. So they

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Speaker 1: start with the data, but they end up in very

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Speaker 1: different places. They have different interpretations of the data. That's

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Speaker 1: certainly true to say the least. So the other question

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Speaker 1: that comes to mind is how do you go from sociology,

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Speaker 1: which is definitely not a moneyed um economic slash business

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Speaker 1: oriented coursework to write in the mix of finance and

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Speaker 1: asset management. What made you decide sociology leads to NBA.

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Speaker 1: So it happens that at Princeton the most flexible major

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Speaker 1: was sociology and I actually only took five sociology classes

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Speaker 1: as a quote sociology major. Uh. To be honest, I'm

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Speaker 1: was never were very well trained in the discipline of sociology,

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Speaker 1: but it allowed me to take all sorts of social

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Speaker 1: science classes. And I think at the time I was

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Speaker 1: really searching for this understanding that it's more than just economics.

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Speaker 1: There is something that matters about how people think, how

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Speaker 1: people behave, what are norms? And so I think it

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Speaker 1: was a very natural transition to where I ultimately ended

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Speaker 1: up even at Princeton, to really being focused on this

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Speaker 1: decision making, which is an intersection of psychology and economics.

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Speaker 1: So so you come out of Princeton, you spend five

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Speaker 1: years doing some consulting work. Uh, you end up going

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Speaker 1: to Chicago. What was that four years NBA PhD? I

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Speaker 1: wish it was only four. So the NBA is two years,

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Speaker 1: and then it's another three or four years on top

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Speaker 1: of that. You know what's ironic is I told my

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Speaker 1: wife when I joined the PhD program, Oh, I think

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Speaker 1: I'll be able to do it in three years. It

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Speaker 1: turns out nobody does it in three years, and almost

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Speaker 1: nobody does it in four years. And so I was

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Speaker 1: the norm. I did it in five years. It's kind

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Speaker 1: of funny that meets the standard planning biases that I

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Speaker 1: was about to bring that up exactly. The planning talks

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Speaker 1: about that all the time. When they when they started

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Speaker 1: working on an economics textbook, he said, well, you know,

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Speaker 1: we're smarter than most of those other guys. This will

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Speaker 1: only take us. And he said we were off by

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Speaker 1: a factor of ten x, which is exactly I did

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Speaker 1: the same thing. You know, what he says is people

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Speaker 1: use an internal model the forecast. They think about their

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Speaker 1: own personal situation, and they downweight the external model of forecasting,

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Speaker 1: which would be what happened to everybody else. So I

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Speaker 1: felt like, look, I've been here two years, I've taken

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Speaker 1: some PhD classes. I know what I'm doing. I have

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Speaker 1: a focused area. I should be able to do it quickly. Yeah,

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Speaker 1: I mean, listen, how hard is a trade war? It's not.

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Speaker 1: It's not that difficult. That is the classic planning fallacy

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Speaker 1: right there. It is. So, so you get your PhD

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Speaker 1: and your m b A. And what's your first job

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Speaker 1: out of out of B school slash PhD school. Yeah,

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Speaker 1: so the PhD program is trying to train researchers fundamentally UH,

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Speaker 1: and at the time I actually thought I wanted to,

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Speaker 1: if possible, go into an academic position, but it was

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Speaker 1: fascinated by predicting stock returns. I did always wonder a

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Speaker 1: little bit, if you really want to predict stock returns,

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Speaker 1: should you be just studying it? Why not actually try

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Speaker 1: to put it in practice? But primarily I interviewed for

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Speaker 1: academic positions, but as well as a few UH positions

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Speaker 1: in industry as well. I was fortunately I did get

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Speaker 1: a number of job offers. My wife is a Harvard

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Speaker 1: Business School graduate. She's a successful executive, and it was

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Speaker 1: very hard to to match some of the locations. I

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Speaker 1: had an offer at a Dartmouth actually with Ken French,

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Speaker 1: which was great, but there's no there's that as a

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Speaker 1: tiny town. There's no real positions for no positions for

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Speaker 1: my wife. And I also realized, you know again, I'd

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Speaker 1: like to be closer to actually amending this, so I

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Speaker 1: actually had a conversation with Dick. I had a few

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Speaker 1: offers in industry. One of them was at Barkley's Global

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Speaker 1: Investors in the Scientific Act of Equity that eventually became

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Speaker 1: Black Rock, which eventually became Black Rock and I've also

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Speaker 1: had an offer, fortunately from Fuller and Failure and Dick said, look,

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Speaker 1: if what you want to do is learn all about

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Speaker 1: the investment process and be involved in everything with that,

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Speaker 1: join our firm. We're a small firm. You will do everything.

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Speaker 1: If what you want to do is just focus on research, well,

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Speaker 1: if you join a twenty PhD uh staffed research group

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Speaker 1: at Barkley's Global Investors, you can focus on research and

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Speaker 1: since at the time I had been struggling with academia

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Speaker 1: or industry, that seemed like a happy medium. But by

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Speaker 1: the end of my time there, I had already moved

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Speaker 1: over into more of a portfolio management role and realized

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Speaker 1: that's what I wanted to do. So it was a

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Speaker 1: great opportunity to come to Fuller and Taylor five years later.

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Speaker 1: That's fascinating. Let's let's talk a little bit about behavioral finance.

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Speaker 1: So you you leave Princeton under Danny Kahneman's tutelage and

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Speaker 1: end up with Richard Thaylor, widely known at the University

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Speaker 1: of Chicago, widely known as the father of behavioral finance.

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Speaker 1: What was it like learning at the knee of the master.

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Speaker 1: It's great, you know, one thing that impresses me about

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Speaker 1: both of them is their willingness to actually be challenged

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Speaker 1: and change their minds. I think they both like me

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Speaker 1: because I was willing to disagree with them, and that's

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Speaker 1: good because if they didn't like that, then things could

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Speaker 1: have been a little bit rocky. I saw you do

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Speaker 1: an interview of Dr Khaman not too long ago, um,

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Speaker 1: and you talk to him about the role of money

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Speaker 1: buying happiness, how much it can and his perspective on

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Speaker 1: that has swung around. Tell us a little bit about

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Speaker 1: that conversation, because I thought you're back and forth with

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Speaker 1: charming well well in college. He at that point time,

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Speaker 1: back in the late nineties, he was doing a lot

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Speaker 1: of research about experienced happiness as opposed to predicted happiness,

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Speaker 1: and part of that study was an analysis of how

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Speaker 1: happy does money really make us. We all think it

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Speaker 1: makes us tremendously happy, doesn't make us as happy as

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Speaker 1: we think, And one of his conclusions at the time

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Speaker 1: was that there is a kink in the amount of

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Speaker 1: happiness we experience from money happening defined that kink sorry,

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Speaker 1: meaning that the amount of happiness goes up up up

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Speaker 1: with money up until at the time he said about

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Speaker 1: twenty thousand dollars of income, which then thirty years ago

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Speaker 1: meant economic security. You're paying for healthcare, and we even

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Speaker 1: even then, you know twenty you know, in a lot

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Speaker 1: of mid nineties, twenty thousand is not a whole lot um.

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Speaker 1: And he set up till twenty thousand, it increases, your

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Speaker 1: happiness increases with money, but after that it's flat. And

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Speaker 1: I was joking with him, actually talked to him about

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Speaker 1: five years after college working in New York making fortunately

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Speaker 1: more than twenty thousand dollars, but not a whole lot more.

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Speaker 1: And I recalled when I got my first raise, made

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Speaker 1: me a lot happier. That was quite useful. I said,

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Speaker 1: I gotta tell you, this does not match my experience

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Speaker 1: of the idea that money beyond twenty dollars doesn't do

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Speaker 1: anything more for my happiness. And he at the time said, oh, rafe,

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Speaker 1: we got it wrong. Actually, it's not kinked. It's log linear,

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Speaker 1: meaning it gets more and more gradual. How much happiness

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Speaker 1: you get from money, it starts the plateau. The rule

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Speaker 1: of diminishing returns kicks in. It's but to be precise,

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Speaker 1: a plateau is literally a flat point whereas diminishing returns

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Speaker 1: is the slope is going down. So his point was

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Speaker 1: just it still goes up, but much more slowly goes up,

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Speaker 1: much more slowly, exactly. And so I was just struck

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Speaker 1: by this is a giant in his field. What giant

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Speaker 1: in their field? Have you ever heard say, oh I

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Speaker 1: made a mistake. Oh I'm changing my mind. Although then

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Speaker 1: in our interview which you witnessed, he said, Rafe, I've

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Speaker 1: now gone back to thinking that it is in fact

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Speaker 1: kinked and there's a permanent plateau where it doesn't get higher.

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Speaker 1: I wonder if that's just a function of longevity and

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Speaker 1: seeing the world from your seventies and eighties as opposed

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Speaker 1: to your thirties and forties, that sort of smile of happiness,

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Speaker 1: you're you're really happy as a young in and then

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Speaker 1: it sort of drops and then peaks again in your

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Speaker 1: later years. I wonder if that that's relevant, could be.

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Speaker 1: But knowing Dr conom and I bet it's based on

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Speaker 1: some very hard data which I just haven't seen now.

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Speaker 1: The other data on this one point, which I find fascinating.

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Speaker 1: It turns out that once you control for life events

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Speaker 1: such as divorce, the numbers skew much higher. And if

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Speaker 1: there's a divorce in there, no matter how much money

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Speaker 1: you have, it ends up being miserable or so some

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Speaker 1: economists have have referenced, you know, uh, Dick Taylor has

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Speaker 1: a phrase which is your happiness is the minimum of

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Speaker 1: yours and your spouses happiness. So that makes Amie accordingly

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Speaker 1: that that makes that makes perfect sense. So you're at

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Speaker 1: the University of Chicago with Sailor. Did you ever participate

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Speaker 1: in any of his studies as a subject. I don't

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Speaker 1: think I did. Actually, you know, the standard psychological experiments

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Speaker 1: usually use a little bit of misdirection, and if they

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Speaker 1: say it's about one thing, but it's really about something

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Speaker 1: slightly different, it makes it a little bit tricky. If

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Speaker 1: you have people who know too much of the psychology

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Speaker 1: to be able to see through what are you really

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Speaker 1: asking about? You would be the ringer at the table

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Speaker 1: that was wo That would throw it off. And um,

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Speaker 1: let's talk a little bit about blind spot bias, the

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Speaker 1: people's own failure to recognize their own biases, even if

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Speaker 1: they're knowledgeable about biases and other people. That was also

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Speaker 1: discussed with Professor Khaneman. Part of it is our biases

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Speaker 1: are hardwired. They really come deep innately from how we

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Speaker 1: think about the world. You know, It's funny as the

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Speaker 1: conversation I recently had with Danny that you witnessed, he

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Speaker 1: pointed out, I was making a joke at the research

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Speaker 1: that he started uses this complicated word heuristics that people

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Speaker 1: use a heuristic which is really just a rule of thumb,

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Speaker 1: and I tease them about why don't you call it

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Speaker 1: a rule of thumb, which is an English phrase that

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Speaker 1: people understand. He he reacted very strongly to that. He

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Speaker 1: also reacted brilliantly to point out that a rule of

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Speaker 1: thumb is a deliberately chosen approximation, but in fact our

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Speaker 1: biases come from unconscious rules of thumb. In fact, he said,

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Speaker 1: rules of thumb is therefore the wrong word because it's

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Speaker 1: it's an unconscious process that makes us quickly jumped to

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Speaker 1: conclusions that are in fact incorrect. And it's precisely because

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Speaker 1: it is these unconscious processes that make us do this,

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Speaker 1: that make it so hard to overcome our biases, even

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Speaker 1: if we're aware that we should be looking out for them.

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Speaker 1: Let's jump right into that and describe what it's like

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Speaker 1: to build a firm based on known behavioral biases. How

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Speaker 1: is the firm structured and how does it take advantage

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Speaker 1: of all of these eras that investors make. Yeah, everything

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Speaker 1: we do is based on the study of investor mistakes,

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Speaker 1: and we try to invest on the opposite side of

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Speaker 1: where we think other investors are making those mistakes. Broadly,

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Speaker 1: psychologists have documented dozens of mistakes, but you can group

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Speaker 1: them in two categories. Sometimes people overreact, sometimes people underreact.

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Speaker 1: The trick, of course, is knowing when they're likely to

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Speaker 1: do one versus the other, and then we try to

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Speaker 1: buy when people have overreacted to bad news, to panicking,

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Speaker 1: or when they're underreacting to good news, and we think

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Speaker 1: that prices are going to go up even further and

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Speaker 1: faster than they realize. So if if you were managing

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Speaker 1: a portfolio at the current portfolio you're running at a

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Speaker 1: different firm, I would say, oh, it looks like Rape

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Speaker 1: is managing those small cap value tilted FAMA French factor portfolio.

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Speaker 1: But that's not exactly what you're doing, is it. No? Oh,

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Speaker 1: so I would say that the overreaction to bad news

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Speaker 1: is naturally going to find stocks that have bad news.

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Speaker 1: They're gonna be guess what we're likely value strategies value companies,

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Speaker 1: And in fact we run a strategy that is a

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Speaker 1: pure overreaction strategy, and that does look like small value.

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Speaker 1: Of course, uh by by construction, the strategy that I

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Speaker 1: run is actually a small cap core because it includes

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Speaker 1: this examination of do we think people are under reacting

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Speaker 1: to good news? And therefore it sort of removes the value.

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Speaker 1: Tilton ends up being core portfolio because of that reason,

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Speaker 1: and I just have to reference that. At the failure

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Speaker 1: economent event, I was sitting at the overreaction table and

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Speaker 1: all the tables had cognitive biases. Instead of table one,

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Speaker 1: table two, table three, it was table confirmation bias, table um, overreaction,

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Speaker 1: et cetera. I found that terribly amusing. So so let's

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Speaker 1: talk a little bit about small cap um. The argument

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Speaker 1: could easily be made that small caps outperform over time

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Speaker 1: because you're taking more risk, you have liquidity risks, they're

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Speaker 1: much less followed by analysts, they're much less known, and

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Speaker 1: and therefore that out performances on a risk adjusted basis

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Speaker 1: goes away or or does it? What? What are you?

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Speaker 1: What are your thoughts on the so called UH small

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Speaker 1: cap premium? So I think the reality is that some

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Speaker 1: of the small cap premium is truly risk and most

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Speaker 1: of the small cap premium is not risk. But it's both.

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Speaker 1: On a side note, you know, academics often write papers

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Speaker 1: that are either everything's a mistake or everything is rational,

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Speaker 1: but if you talk to them, almost all of them

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Speaker 1: agree it's both. So specifically, let me talk about the

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Speaker 1: small cap premium. You mentioned liquidity risk. I think that

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Speaker 1: there is some risk of owning things that have less liquidity,

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Speaker 1: that if you need to sell them, they're going to

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Speaker 1: perform worse. Although interesting note, in general, stocks that have

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Speaker 1: lower liquidity actually have a lower beta, they have lower

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Speaker 1: exposure to the market. It appears that when the market panics,

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Speaker 1: people tend to sell their most liquid names. Not exactly

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Speaker 1: so the intuition of oh, when people are desperate, the

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Speaker 1: least liquid will be terrible. It doesn't do bad on

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Speaker 1: a price scent. In terms of prices. It is of

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Speaker 1: course a challenge if you really do need to sell it, though,

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Speaker 1: makes a lot of sense. So if it's not all risk,

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Speaker 1: where does the rest of the return come from? In

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Speaker 1: small caps? So you mentioned the other issues that there's

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Speaker 1: less followed not a lot of analysts not well known

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Speaker 1: and not well known. So to me, none of those

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Speaker 1: actually are truly risk. They're sort of a lack of comfort,

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Speaker 1: and I think that's where a lot of the return

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Speaker 1: can come from, in that people don't know about them,

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Speaker 1: and so they're willing, they're unwilling perhaps to to take

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Speaker 1: a gamble on them and invest as much in them.

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Speaker 1: But that really truly isn't risk in the sense of

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Speaker 1: risk to your money. It's just perhaps risk to your psyche.

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Speaker 1: And I would call that kind of risk to your

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Speaker 1: psyche something that is really more behavioral. And I should

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Speaker 1: add there's there's one other element that I do think

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Speaker 1: really matters, and this is a little bit technical, but

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Speaker 1: if you think about it, once you assume that there

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Speaker 1: are errors in the prices of companies, Naturally, if a

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Speaker 1: company is overvalued, it will be a little bit bigger,

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Speaker 1: if a vent company is undervalued, it will be a

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Speaker 1: little bit smaller. So as soon as you think that

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Speaker 1: there are any errors, sorting on size is not going

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Speaker 1: to be a very direct way of finding those errors,

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Speaker 1: but it will be correlated with the errors. And so

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Speaker 1: I do think that matters as well, that just beaten

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Speaker 1: up stocks are more likely to be small, just because

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Speaker 1: any any mistake people made would automatically make them a

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Speaker 1: little bit smaller than they really should have been. The

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Speaker 1: description of of the psychological risk of not being familiar

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Speaker 1: with the names reminds me a little bit of the

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Speaker 1: home country bias that people tend to be wildly overweighted

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Speaker 1: around the world. Wherever you live, you own the companies,

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Speaker 1: you're familiar with, the CEOs, you've seen on local television,

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Speaker 1: and it creates a very distorted portfolio. You're quite right,

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Speaker 1: And of course, the United States is such a large

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Speaker 1: economy and it's so well diversified, having a home bias

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Speaker 1: in the United States is probably fine. Furthermore, a lot

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Speaker 1: of the largest companies in the United States nowadays they

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Speaker 1: have international operations. You actually are getting exposure. But what's

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Speaker 1: fascinating to me is always you take a small company

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Speaker 1: in Norway, and Norwegian investors are more likely to invest

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Speaker 1: a predominant amount of their investments in Norwegian stocks. That

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Speaker 1: really doesn't make sense overall, other than the fact that

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Speaker 1: they're so familiar with those It does give them, It

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Speaker 1: does give them psychological comfort, and you know there might

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Speaker 1: be a value to that. But if from purely rational.

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Speaker 1: All I care about is my money and my return standpoint.

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Speaker 1: We're taking a lot of risk with the homebias. It

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Speaker 1: seems that things that investors do that give them comfort

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Speaker 1: don't seem to work to their advantage in the markets.

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Speaker 1: Is that a fair statement. I think that's a very

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Speaker 1: fair broad statement. It matters a little bit exactly how

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Speaker 1: you implement it, of course. But but the idea is,

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Speaker 1: if people are going to pile into something because it

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Speaker 1: makes them feel good, it should cause the returns on

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Speaker 1: that particular feel good strategy to be a little bit lower.

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Speaker 1: So here's the key question I have about the portfolio

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Speaker 1: you manage. When stocks get thrown out by a large

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Speaker 1: group of investors and they become a little smaller and

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Speaker 1: the price becomes a little lower, how can you identify

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Speaker 1: when it is either a behavioral issue of people overreacting

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Speaker 1: or genuinely decaying fundamentals. So a lot of the Reeds

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Speaker 1: that own malls and and other things they've gotten shell act.

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Speaker 1: Is this a permanent change in society or people over reacting?

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Speaker 1: Maybe that's not the best example. We could use steam

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Speaker 1: engines and leather belt companies back in the early days

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Speaker 1: of the DAO. But the key question is how can

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Speaker 1: you tell the difference between when something is fundamentally deteriorating

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Speaker 1: on a long term, secular basis versus investors overreaction. Yeah,

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Speaker 1: that of course is the key question. And you know,

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Speaker 1: naturally I could talk for hours and hours about the

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Speaker 1: details of how we implement stuff and just touch the

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Speaker 1: surface of how we approach it. But here's the broad outline.

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Speaker 1: Our approach is to try to look at queues where

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Speaker 1: we think and other investors are likely to make mistakes here.

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Speaker 1: Some of the cues we use, for example, in the

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Speaker 1: retail space, because we see a lot of overreaction to

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Speaker 1: problems in retail right now, are to say an individual

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Speaker 1: company basis, what are the insiders doing, What is the

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Speaker 1: manage meant doing? Are they buying back shares of their company?

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Speaker 1: Are they selling shares? That's an entry point to say

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Speaker 1: is there a real problem here? Then we're gonna look

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Speaker 1: at how the investors reacted to the news. In this case,

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Speaker 1: it's pretty easy to understand there's a lot of negative

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Speaker 1: news out there about retail. But then we do also

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Speaker 1: have to do the hard work of digging in and saying, Okay,

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Speaker 1: what is the real opportunities of this individual firm. Part

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Speaker 1: of our approach is to try to not make an

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Speaker 1: overall description, overall analysis of hair are people overreacting to,

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Speaker 1: you know, mall reads, but instead really look at individual

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Speaker 1: companies and say, how about this particular read. And when

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Speaker 1: you dig into individual companies, of course they have individual

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Speaker 1: nuances that can make a real difference. Let's talk a

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Speaker 1: little bit about the success your funds has had over

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Speaker 1: the past a half decade prior five years, you've compounded

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Speaker 1: at seventeen percent a year, beating of other small cap funds.

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Speaker 1: So what do you owe this success too, and what

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Speaker 1: are your expectations about this fun going forward. The success

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Speaker 1: is due to our process and that's the key to

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Speaker 1: how we invest. We are trying to spot where other

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Speaker 1: investors might be biased. Make no doubt, I'm biased, just

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Speaker 1: like every other portfolio manager at Fuller and Taylor, just

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Speaker 1: like you, just like everyone. And it's our process that

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Speaker 1: lets us overcome those biases and find opportunities. And we

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Speaker 1: think that's going to continue working. Obviously, no guarantees. My

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Speaker 1: compliance person would get very mad if I made any

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Speaker 1: kind of guarantees. But what we continue to have very

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Speaker 1: comfortable with the process. Yeah, we think it makes sense

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Speaker 1: when investing to try to figure out where are other

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Speaker 1: people making a mistake. So, so here's the key arbitrage

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Speaker 1: a way, the alpha question. You guys have carved out

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Speaker 1: a niche in behavior investing, and obviously when you have

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Speaker 1: people like Richard Taylor and Danny Kahneman involved, that gives

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Speaker 1: you a big edge. But what about other firms recognizing

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Speaker 1: this and coming into the same space and saying, hey,

429
00:25:06,760 --> 00:25:10,080
Speaker 1: we want to catch some of that behavioral alpha as well.

430
00:25:10,880 --> 00:25:13,399
Speaker 1: Do you see any are there any competitors and do

431
00:25:13,440 --> 00:25:16,600
Speaker 1: you see any competitors coming in eventually? Yeah, we see

432
00:25:16,600 --> 00:25:19,320
Speaker 1: a lot of folks talk about behavioral finance. It's a

433
00:25:19,320 --> 00:25:22,240
Speaker 1: fun topic. People like to use it to justify what

434
00:25:22,280 --> 00:25:26,600
Speaker 1: they're doing. But honestly, we have never seen really anybody

435
00:25:26,640 --> 00:25:29,600
Speaker 1: who's doing exactly what we do. And there's a few

436
00:25:29,600 --> 00:25:34,000
Speaker 1: reasons for that. I think one is doing what we

437
00:25:34,080 --> 00:25:38,320
Speaker 1: do requires not doing a lot of the traditional things

438
00:25:38,359 --> 00:25:41,760
Speaker 1: that cause bias that makes people uncomfortable. We talked about

439
00:25:41,760 --> 00:25:44,480
Speaker 1: how people like to do things that leave them comfortable.

440
00:25:44,800 --> 00:25:48,360
Speaker 1: Omitting some of the steps that people typically do will

441
00:25:48,359 --> 00:25:51,560
Speaker 1: make people uncomfortable, and really focusing are the way we

442
00:25:51,640 --> 00:25:54,320
Speaker 1: do just on the mistakes often can make other folks

443
00:25:54,400 --> 00:25:56,359
Speaker 1: uncomfortable and so they don't do it for that reason.

444
00:25:56,760 --> 00:25:58,879
Speaker 1: And as you know, as we talked about in the beginning,

445
00:25:59,200 --> 00:26:01,880
Speaker 1: these biases are hardwired. So it's not that the investor

446
00:26:01,920 --> 00:26:05,280
Speaker 1: mistakes are going to go away, but you really need

447
00:26:05,320 --> 00:26:08,440
Speaker 1: to have a process that's laser focused on finding them

448
00:26:08,640 --> 00:26:11,640
Speaker 1: to work in the long term. So we we discussed earlier.

449
00:26:11,960 --> 00:26:17,520
Speaker 1: Um Fama and Failure play golf together their buddies. Um,

450
00:26:17,560 --> 00:26:22,040
Speaker 1: there's clearly inefficiencies in the market over the short term.

451
00:26:22,119 --> 00:26:27,080
Speaker 1: Our markets mostly kind of eventually sort of efficient. Like,

452
00:26:27,200 --> 00:26:32,119
Speaker 1: how do you think about markets over longer periods of time? Yeah,

453
00:26:32,160 --> 00:26:35,400
Speaker 1: you know, Fisher Black had a rule that he said

454
00:26:35,440 --> 00:26:38,480
Speaker 1: markets are efficient probably do a factor of two that

455
00:26:38,600 --> 00:26:41,800
Speaker 1: prices are within plus or minus two times the correct price.

456
00:26:42,480 --> 00:26:45,159
Speaker 1: Now you tell me, it's actually pretty reasonable in the

457
00:26:45,200 --> 00:26:48,840
Speaker 1: scale of things to say that's reasonably efficient. Okay, they're

458
00:26:48,880 --> 00:26:51,160
Speaker 1: not ten times what they should be. On the other hand,

459
00:26:51,200 --> 00:26:54,680
Speaker 1: that leads a lot of room for is a giant swing. Yeah,

460
00:26:54,760 --> 00:26:57,080
Speaker 1: that's a big swing. So I think it depends on

461
00:26:57,119 --> 00:27:01,040
Speaker 1: how you think about in defining what would efficiency be

462
00:27:01,359 --> 00:27:04,399
Speaker 1: the idea that all prices are perfect. I think it

463
00:27:04,480 --> 00:27:06,720
Speaker 1: is about as ridiculous as thinking all people are perfect,

464
00:27:06,920 --> 00:27:10,160
Speaker 1: But the idea that they're pretty good it's about as

465
00:27:10,160 --> 00:27:12,280
Speaker 1: reasonable as thinking that most people are pretty good. But

466
00:27:13,400 --> 00:27:16,760
Speaker 1: there's a lot of rooms still to find improvements that

467
00:27:16,760 --> 00:27:18,919
Speaker 1: that we look out for. So if I had to

468
00:27:19,000 --> 00:27:23,440
Speaker 1: define the era following the financial crisis, I think most

469
00:27:23,480 --> 00:27:26,600
Speaker 1: people would look at it as the era of indexing

470
00:27:26,680 --> 00:27:29,639
Speaker 1: and simultaneously the era of e t f s. The

471
00:27:29,760 --> 00:27:32,040
Speaker 1: question that comes up over and over again is what

472
00:27:32,119 --> 00:27:36,400
Speaker 1: does indexing due to price discovery? How does that affect

473
00:27:36,520 --> 00:27:40,880
Speaker 1: the ability to identify miss pricing. Yeah, so I think

474
00:27:40,920 --> 00:27:43,840
Speaker 1: that e t f s and indexing have a variety

475
00:27:44,119 --> 00:27:48,399
Speaker 1: of impacts. One impact is that there's a bunch of

476
00:27:48,400 --> 00:27:52,040
Speaker 1: people who used to be analyzing individual securities that aren't anymore.

477
00:27:52,640 --> 00:27:55,639
Speaker 1: That's gonna make individual prices, as you can imagine, a

478
00:27:55,680 --> 00:27:58,440
Speaker 1: little bit less efficient. On some level. That makes our

479
00:27:58,520 --> 00:28:01,000
Speaker 1: job as an active man, you're a little bit easier

480
00:28:01,040 --> 00:28:03,359
Speaker 1: because you've taken some of the people who used to

481
00:28:03,400 --> 00:28:06,480
Speaker 1: be analyzing things out of the out of the game.

482
00:28:06,880 --> 00:28:10,640
Speaker 1: At the same time, I think there are increasingly more

483
00:28:10,760 --> 00:28:14,840
Speaker 1: swings that happen because people are moving into a utility

484
00:28:14,920 --> 00:28:16,920
Speaker 1: z et F, and now they're moving into a financials

485
00:28:16,920 --> 00:28:19,080
Speaker 1: et F, and now they're moving into a smart bay

486
00:28:19,080 --> 00:28:20,600
Speaker 1: to e t F. And there are a lot of

487
00:28:20,640 --> 00:28:23,560
Speaker 1: movements that are happening more at the macro level in

488
00:28:23,720 --> 00:28:28,760
Speaker 1: terms of individual E t F decision making. And you know,

489
00:28:29,440 --> 00:28:31,520
Speaker 1: the funny thing is, I mean, I've heard one phrase

490
00:28:31,680 --> 00:28:35,119
Speaker 1: is a sort of an extreme phrase, that that indexing

491
00:28:35,200 --> 00:28:39,080
Speaker 1: is communist in the sense of you've trusted everybody else

492
00:28:39,120 --> 00:28:40,880
Speaker 1: to set the price. You really do have to have

493
00:28:40,960 --> 00:28:43,920
Speaker 1: confidence that the market is reasonably efficient, that the price

494
00:28:43,960 --> 00:28:46,120
Speaker 1: that you're buying things is right, And that's not a

495
00:28:46,120 --> 00:28:48,920
Speaker 1: bad approximation. But I think I do think you can

496
00:28:48,920 --> 00:28:50,800
Speaker 1: do a little bit better. I'm I'm biased, of course,

497
00:28:50,800 --> 00:28:53,320
Speaker 1: because I'm an active manager. I'm pulling this from memory.

498
00:28:53,360 --> 00:28:57,880
Speaker 1: I believe it was Alliance Bernstein called indexing Marxist, but

499
00:28:58,000 --> 00:29:01,520
Speaker 1: I'm not positive that was my search piece. Um, but

500
00:29:01,520 --> 00:29:04,880
Speaker 1: but the same the same issue applies. So here's the

501
00:29:04,960 --> 00:29:09,640
Speaker 1: counter argument about indexing, and it's a behavioral argument. Uh,

502
00:29:09,800 --> 00:29:13,240
Speaker 1: humans are irrational. We're not especially good at stock picking,

503
00:29:13,280 --> 00:29:17,480
Speaker 1: we're not especially disciplined at staying on even if we

504
00:29:17,560 --> 00:29:20,240
Speaker 1: come up with a quantitative method, we have a tendency

505
00:29:20,280 --> 00:29:25,040
Speaker 1: to get caught by narratives and stories. You know, if

506
00:29:25,080 --> 00:29:27,520
Speaker 1: I just index and forget about it for forty years,

507
00:29:27,520 --> 00:29:30,720
Speaker 1: I'm better off than getting in my own way. What's

508
00:29:30,720 --> 00:29:33,680
Speaker 1: the counter to that? From a behavioral perspective, I think

509
00:29:33,720 --> 00:29:36,880
Speaker 1: you're right that people get in their own way all

510
00:29:36,880 --> 00:29:40,880
Speaker 1: the time. You know, Dick Taylor's got a phrase. Instead

511
00:29:40,880 --> 00:29:44,719
Speaker 1: of watching CNBC, you should be watching ESPN. The idea

512
00:29:44,800 --> 00:29:48,280
Speaker 1: being that tracking how you're doing every day is going

513
00:29:48,360 --> 00:29:53,200
Speaker 1: to cause tremendous unhappiness and it's gonna lead to more biases. Actually,

514
00:29:53,200 --> 00:29:55,600
Speaker 1: we worked with separate note. We worked with one of

515
00:29:55,600 --> 00:30:00,560
Speaker 1: our academic advisors, Professor Joey Engelberg, who's that U see

516
00:30:00,600 --> 00:30:05,480
Speaker 1: you c s D. And he's done research that when

517
00:30:05,600 --> 00:30:09,400
Speaker 1: the market goes down there's more admittances for heart attacks

518
00:30:09,560 --> 00:30:12,440
Speaker 1: to hospitals around the country. Not surprised it really, it

519
00:30:12,480 --> 00:30:14,960
Speaker 1: really makes people unhappy. So the set it and forget

520
00:30:15,000 --> 00:30:18,480
Speaker 1: it I think actually is right. At the individual investor level,

521
00:30:18,680 --> 00:30:20,760
Speaker 1: people should set it and forget it. I think there's

522
00:30:20,760 --> 00:30:24,240
Speaker 1: a role for picking the right active manager as part

523
00:30:24,240 --> 00:30:26,240
Speaker 1: of your set it and forget it strategy, But it's

524
00:30:26,240 --> 00:30:29,840
Speaker 1: certainly true that trying to make those individual timing decisions,

525
00:30:30,040 --> 00:30:32,960
Speaker 1: you're going to be very subject to biases. The The

526
00:30:32,960 --> 00:30:36,200
Speaker 1: other um favorite example, and I'm trying to remember who

527
00:30:36,320 --> 00:30:39,320
Speaker 1: brought this up recently, is imagine if you've got your

528
00:30:39,320 --> 00:30:43,000
Speaker 1: home price quoted every single day, and it would make

529
00:30:43,040 --> 00:30:45,360
Speaker 1: people crazy. You wouldn't you wouldn't be able to deal

530
00:30:45,400 --> 00:30:48,080
Speaker 1: with it. You get a price when either you put

531
00:30:48,160 --> 00:30:50,160
Speaker 1: up your house for sale or one of your neighbor's

532
00:30:50,240 --> 00:30:54,440
Speaker 1: house cells, and even then we redid our kitchen, they didn't, etcetera.

533
00:30:54,880 --> 00:30:58,320
Speaker 1: So what what does the daily price action tell us

534
00:30:58,880 --> 00:31:04,440
Speaker 1: about investor psychology? Yeah, you know, I'm sure you're aware

535
00:31:04,600 --> 00:31:08,040
Speaker 1: that no matter what happens in the market, people are

536
00:31:08,120 --> 00:31:11,200
Speaker 1: always on the news spinning here's why it did what

537
00:31:11,240 --> 00:31:14,160
Speaker 1: it did. But the reality is, I gotta tell you,

538
00:31:14,200 --> 00:31:17,280
Speaker 1: as as an academic, we see a lot of movements

539
00:31:17,280 --> 00:31:19,520
Speaker 1: that we don't really know why it moved. Here's here's

540
00:31:19,520 --> 00:31:22,560
Speaker 1: a different way, more technical way of phrasing it. Uh.

541
00:31:22,600 --> 00:31:25,640
Speaker 1: The academic perspective, more broadly is that most of the

542
00:31:25,680 --> 00:31:29,080
Speaker 1: price movements that happened in the market are changes in

543
00:31:29,240 --> 00:31:33,640
Speaker 1: the required return, not changes in cash flows. What that

544
00:31:33,720 --> 00:31:36,520
Speaker 1: means is it's not that people have a new, updated

545
00:31:36,640 --> 00:31:41,120
Speaker 1: vision generally about how well the core business is going

546
00:31:41,160 --> 00:31:45,600
Speaker 1: to perform. Rather, they've changed their mind and said, oh,

547
00:31:45,640 --> 00:31:47,880
Speaker 1: I'll accept a little bit lower return, and therefore the

548
00:31:47,920 --> 00:31:50,400
Speaker 1: price went up, or oh, I'll accept a little bit

549
00:31:50,560 --> 00:31:52,920
Speaker 1: higher return, so that I demand a little bit higher

550
00:31:52,960 --> 00:31:55,080
Speaker 1: returns to the price went down. But for you and me,

551
00:31:55,160 --> 00:31:57,560
Speaker 1: that's equal to the same thing as there's just noise.

552
00:31:57,600 --> 00:32:00,200
Speaker 1: It just moved for kind of no reason, and as

553
00:32:00,200 --> 00:32:03,320
Speaker 1: long as you brought up noise, I understand. Professor Khneman

554
00:32:03,400 --> 00:32:06,720
Speaker 1: is working on a new book on noise. I'm absolutely

555
00:32:06,720 --> 00:32:10,640
Speaker 1: fascinated by that. Why is it that we're so compelled

556
00:32:10,760 --> 00:32:14,720
Speaker 1: to take what appears to be to the educated eye

557
00:32:15,040 --> 00:32:22,040
Speaker 1: random noise and impose a narrative storyline on that drunkards walk?

558
00:32:22,440 --> 00:32:27,920
Speaker 1: That ability to very quickly find patterns is crucial to learning.

559
00:32:28,080 --> 00:32:32,160
Speaker 1: Think about how quickly toddlers learn new words. Think about

560
00:32:32,200 --> 00:32:36,480
Speaker 1: how quickly kids figure out what the rules of social

561
00:32:36,520 --> 00:32:39,240
Speaker 1: interaction are. And they do it all by just watching

562
00:32:39,240 --> 00:32:42,360
Speaker 1: a few times. Sometimes I'm sure you've seen toddlers might

563
00:32:42,400 --> 00:32:44,160
Speaker 1: hear a word once and then they start being able

564
00:32:44,200 --> 00:32:47,880
Speaker 1: to use it. Why because yeah, exactly, of course they

565
00:32:47,880 --> 00:32:52,120
Speaker 1: figure that out. And why because we're experts at quickly

566
00:32:52,160 --> 00:32:56,920
Speaker 1: identifying patterns, and that's of course useful from an evolutionary standpoint,

567
00:32:57,640 --> 00:32:59,920
Speaker 1: but then it trips us up on things that truly

568
00:33:00,040 --> 00:33:02,680
Speaker 1: are random. We start to see patterns that don't exist,

569
00:33:03,120 --> 00:33:07,880
Speaker 1: and we interpret that in ways that then can cause bias.

570
00:33:07,920 --> 00:33:10,360
Speaker 1: So a big part of the way you manage the

571
00:33:10,440 --> 00:33:15,400
Speaker 1: portfolio is capitalizing on overreactions. What do we see more

572
00:33:15,440 --> 00:33:19,720
Speaker 1: of overreactions to the upside based on green and enthusiasm,

573
00:33:20,080 --> 00:33:24,880
Speaker 1: or overreactions to the downside based on risk aversion and panic.

574
00:33:25,840 --> 00:33:28,160
Speaker 1: You see both. I think you see a little bit

575
00:33:28,200 --> 00:33:32,160
Speaker 1: more overreactions to the down side. Now I say that,

576
00:33:32,200 --> 00:33:35,400
Speaker 1: but I may be biased because we're focused on overreactions

577
00:33:35,400 --> 00:33:37,800
Speaker 1: to the downside. That's when we buy. If there's a

578
00:33:37,840 --> 00:33:41,000
Speaker 1: company that's overhyped, we're not going to buy it, and

579
00:33:41,320 --> 00:33:43,960
Speaker 1: we do a little bit of long short investing, but

580
00:33:44,040 --> 00:33:46,040
Speaker 1: for the most part, we're not going to pay attention

581
00:33:46,080 --> 00:33:48,960
Speaker 1: to those super hype stocks. And so maybe that's why

582
00:33:48,960 --> 00:33:51,000
Speaker 1: I'm giving a biased answer that it seems people more

583
00:33:51,040 --> 00:33:53,800
Speaker 1: likely overreact to the downside because that's what we focus on.

584
00:33:53,920 --> 00:33:55,280
Speaker 1: Can you stick around a little bit. I have a

585
00:33:55,280 --> 00:33:58,320
Speaker 1: few more questions to ask you. We have been chatting

586
00:33:58,760 --> 00:34:02,560
Speaker 1: with Dr rap Jovinazzo of Fuller and Thailor, where he

587
00:34:02,600 --> 00:34:07,440
Speaker 1: manages the behavioral small cap equity portfolio. If you enjoy

588
00:34:07,560 --> 00:34:10,400
Speaker 1: this conversation, be sure and check out our podcast extras,

589
00:34:10,400 --> 00:34:13,279
Speaker 1: where we keep the tape rolling continue to discussing all

590
00:34:13,320 --> 00:34:18,120
Speaker 1: things behavioral. You can find that at iTunes, overcast, Bloomberg

591
00:34:18,160 --> 00:34:22,520
Speaker 1: dot com, wherever funer podcasts are sold. We love your comments,

592
00:34:22,520 --> 00:34:26,400
Speaker 1: feedback and suggestions right to us at m IB podcast

593
00:34:26,440 --> 00:34:29,280
Speaker 1: at Bloomberg dot net. You can check out my daily

594
00:34:29,320 --> 00:34:32,200
Speaker 1: column on Bloomberg View dot com. Follow me on Twitter

595
00:34:32,760 --> 00:34:36,040
Speaker 1: at rid Halts. I'm Barry rid Halts. You're listening to

596
00:34:36,160 --> 00:34:53,680
Speaker 1: Masters in Business on Bloomberg Radio. Welcome to the podcast, Ray,

597
00:34:53,719 --> 00:34:55,360
Speaker 1: Thank you so much for doing this. This is really

598
00:34:55,640 --> 00:34:58,920
Speaker 1: a fascinating topic that I am, you know, have been

599
00:34:59,000 --> 00:35:04,440
Speaker 1: enamored for or decades about and offline, we were talking

600
00:35:04,480 --> 00:35:11,640
Speaker 1: about the problem with our cognitive issues is we evolved

601
00:35:11,680 --> 00:35:15,640
Speaker 1: for very different purpose. It's it's I give a presentation

602
00:35:15,680 --> 00:35:20,440
Speaker 1: where I use via Gra as an example. It's off label. Originally,

603
00:35:20,560 --> 00:35:26,080
Speaker 1: Viagra was a hypertension and heart disease um products, And

604
00:35:26,640 --> 00:35:28,640
Speaker 1: as they do when they do these studies, they ask

605
00:35:28,800 --> 00:35:32,719
Speaker 1: the the subjects any side effects. Yeah, my wife has

606
00:35:32,760 --> 00:35:36,440
Speaker 1: locked herself in the bathroom and she won't come out. Um. Oh,

607
00:35:36,480 --> 00:35:40,560
Speaker 1: maybe we have a different use for this product. So um.

608
00:35:40,600 --> 00:35:43,279
Speaker 1: The question that comes up time and again with all

609
00:35:43,320 --> 00:35:48,200
Speaker 1: these biases and all these cognitive issues was the human

610
00:35:48,280 --> 00:35:52,120
Speaker 1: mind or has the human mind evolved for very different

611
00:35:52,160 --> 00:35:56,279
Speaker 1: purposes than we we are currently using them? Yeah? We

612
00:35:56,280 --> 00:36:00,719
Speaker 1: were evolved in an environment where there were is not

613
00:36:00,880 --> 00:36:05,600
Speaker 1: a news media that were not graphs and charts that

614
00:36:05,640 --> 00:36:08,680
Speaker 1: we had to interpret, where there was no Excel, no

615
00:36:08,800 --> 00:36:12,600
Speaker 1: financial statements. We were designed for very rapid decisions of

616
00:36:12,640 --> 00:36:14,879
Speaker 1: you here rustling in the bushes, You better get out

617
00:36:14,880 --> 00:36:16,840
Speaker 1: of the way quickly because it might be a linn Not.

618
00:36:17,520 --> 00:36:20,279
Speaker 1: How do I plan a twenty year investment in a

619
00:36:20,320 --> 00:36:23,920
Speaker 1: power plant? That's not what we're designed for. And so

620
00:36:24,160 --> 00:36:30,560
Speaker 1: we end up applying our cognitive skill set to a

621
00:36:30,600 --> 00:36:35,319
Speaker 1: bunch of issues that we really are not built for. Yeah,

622
00:36:35,440 --> 00:36:37,319
Speaker 1: let me let me give you an example which I

623
00:36:37,320 --> 00:36:41,040
Speaker 1: think will be illustrative. Maybe I'm biased because it was

624
00:36:41,120 --> 00:36:44,839
Speaker 1: the research I did with Dr Khneman at Princeton. I

625
00:36:44,880 --> 00:36:47,359
Speaker 1: began trying to do research on how do we come

626
00:36:47,400 --> 00:36:50,600
Speaker 1: up with the shortlist from which we make a decision,

627
00:36:50,880 --> 00:36:54,600
Speaker 1: because the research on decision making is all about choices,

628
00:36:55,160 --> 00:36:58,360
Speaker 1: and in the process of trying to answer this question

629
00:36:58,400 --> 00:37:01,040
Speaker 1: of how do we come up with the shortlist, realize

630
00:37:01,080 --> 00:37:03,680
Speaker 1: the answer is we don't come up with a short list.

631
00:37:03,880 --> 00:37:06,799
Speaker 1: In fact, we're sort of hardwired to instantly jump to

632
00:37:06,880 --> 00:37:10,040
Speaker 1: the answer that what we're doing is something which in

633
00:37:10,080 --> 00:37:13,960
Speaker 1: cognitive psychology is often called problem solving as opposed to choosing.

634
00:37:14,120 --> 00:37:16,880
Speaker 1: But it's precisely because we want to instantly get to

635
00:37:16,920 --> 00:37:20,800
Speaker 1: the final answer that we're very lousy at making choices

636
00:37:20,800 --> 00:37:22,440
Speaker 1: when we have a range of choices that we need

637
00:37:22,480 --> 00:37:25,000
Speaker 1: to think carefully about. You know, it's funny funny you

638
00:37:25,040 --> 00:37:29,560
Speaker 1: say that. I um years ago on a trading desk,

639
00:37:29,640 --> 00:37:32,640
Speaker 1: someone would bring an idea to me and very often

640
00:37:33,360 --> 00:37:37,120
Speaker 1: that piece of junk. And I eventually learned that that

641
00:37:37,200 --> 00:37:42,400
Speaker 1: reaction is, hey, maybe there's an overreaction on everybody's perspective,

642
00:37:42,440 --> 00:37:45,800
Speaker 1: because when someone brings you I was a fan of Apple.

643
00:37:45,880 --> 00:37:50,000
Speaker 1: When the iPod not iPhone iPod first came out, every

644
00:37:50,040 --> 00:37:53,560
Speaker 1: person I showed that stock to same reaction a piece

645
00:37:53,600 --> 00:37:57,080
Speaker 1: of junk company going out of business, the stock a

646
00:37:57,160 --> 00:37:59,480
Speaker 1: six for one split ago on a two for one

647
00:37:59,520 --> 00:38:01,560
Speaker 1: split of for that I want to say it was

648
00:38:01,640 --> 00:38:07,160
Speaker 1: fifteen with thirteen in cash, and that reaction of junk.

649
00:38:07,840 --> 00:38:12,480
Speaker 1: That's that immediate response. As opposed to Professor Koneman, we

650
00:38:12,600 --> 00:38:15,520
Speaker 1: call its system to where you're actually thinking through. You know,

651
00:38:15,600 --> 00:38:19,600
Speaker 1: here's a funny fact related to that mediate emotional reaction.

652
00:38:20,360 --> 00:38:25,399
Speaker 1: Our ability to know whether we like someone operates far

653
00:38:25,560 --> 00:38:29,040
Speaker 1: faster than our ability to remember any fact about them.

654
00:38:29,280 --> 00:38:31,799
Speaker 1: We know whether we like that person before we can

655
00:38:31,840 --> 00:38:35,080
Speaker 1: remember their name, because that is what, if you think

656
00:38:35,080 --> 00:38:37,080
Speaker 1: about it, we're built to be able to make very

657
00:38:37,239 --> 00:38:39,600
Speaker 1: rapid is that person a friend? Or do I need

658
00:38:39,640 --> 00:38:42,319
Speaker 1: to run away decisions? But it can really trip us

659
00:38:42,360 --> 00:38:45,799
Speaker 1: up when analyzing something abstract like a stock that we've

660
00:38:45,840 --> 00:38:49,359
Speaker 1: got decades to invest in or not invest in. That's

661
00:38:49,360 --> 00:38:50,960
Speaker 1: not a good system to be able to make us

662
00:38:51,280 --> 00:38:55,320
Speaker 1: a decision in milliseconds, even though from a evolutionary standpoint

663
00:38:55,400 --> 00:38:58,879
Speaker 1: was quite useful back when we were cavemen cave women.

664
00:38:59,000 --> 00:39:03,239
Speaker 1: That that same here's an instantaneous reaction which has a

665
00:39:03,280 --> 00:39:06,399
Speaker 1: purpose in in keeping you alive. Ps. The people who

666
00:39:06,440 --> 00:39:10,719
Speaker 1: did not have that, that's that particular hard wire, well

667
00:39:10,760 --> 00:39:13,200
Speaker 1: none of their progeny around think they got eaten by

668
00:39:13,200 --> 00:39:15,480
Speaker 1: the right. They were literally people's lunch, all right, So

669
00:39:15,560 --> 00:39:18,680
Speaker 1: I only have you for a limited amount of time. Uh,

670
00:39:18,760 --> 00:39:21,440
Speaker 1: let's jump to some of our favorite questions that we

671
00:39:21,480 --> 00:39:25,000
Speaker 1: ask all of our guests. What's the most important thing

672
00:39:25,080 --> 00:39:29,319
Speaker 1: people don't know about you or your background? I think

673
00:39:29,440 --> 00:39:34,200
Speaker 1: the important thing to me that maybe people don't know

674
00:39:34,320 --> 00:39:37,279
Speaker 1: given the focus on behavioral finance that we have, is

675
00:39:37,360 --> 00:39:41,120
Speaker 1: my father is a retired professor of accounting, so I'm

676
00:39:41,160 --> 00:39:44,400
Speaker 1: actually one of those geeks who likes analyzing financial statements

677
00:39:44,400 --> 00:39:47,920
Speaker 1: as well. It's not pure psychology. Sometimes people think it's

678
00:39:47,960 --> 00:39:51,040
Speaker 1: all about psychology, but what we do is a combination

679
00:39:51,080 --> 00:39:53,960
Speaker 1: of finance and psychology. And honestly, it's the finance that's

680
00:39:53,960 --> 00:39:58,239
Speaker 1: more important than the psychology. But the psychological combination and

681
00:39:58,280 --> 00:40:01,040
Speaker 1: addition sort of changes the flavor of everything, gives it

682
00:40:01,080 --> 00:40:04,600
Speaker 1: a different dimension. Dimension around now is when I ask

683
00:40:04,760 --> 00:40:08,560
Speaker 1: people who they're early mentors were. But we know at

684
00:40:08,640 --> 00:40:12,360
Speaker 1: least two of your mentors, Danny Khneman and Richard Faylor.

685
00:40:12,960 --> 00:40:16,080
Speaker 1: Any other mentors you want to share. That's those are

686
00:40:16,120 --> 00:40:18,359
Speaker 1: two pretty good names to drop. They're pretty good. They're

687
00:40:18,360 --> 00:40:21,279
Speaker 1: pretty good. My father was a mentor. Of course, I

688
00:40:21,320 --> 00:40:24,239
Speaker 1: did a lot of consulting work work for him in

689
00:40:24,320 --> 00:40:27,800
Speaker 1: his consulting business when I was in high school and college,

690
00:40:27,800 --> 00:40:30,279
Speaker 1: and that was some useful training as well. So let's

691
00:40:30,320 --> 00:40:34,359
Speaker 1: talk about investors who influenced your approach to put in

692
00:40:34,400 --> 00:40:40,240
Speaker 1: capital at risk. Yeah, I've been obviously tremendously influenced by

693
00:40:40,360 --> 00:40:43,759
Speaker 1: all of the research and behavioral finance. There are patterns

694
00:40:44,000 --> 00:40:47,640
Speaker 1: to people's mistakes and therefore their patterns for investing that

695
00:40:47,680 --> 00:40:50,640
Speaker 1: make a difference. Uh. And if you follow those patterns,

696
00:40:50,680 --> 00:40:54,200
Speaker 1: you can earn higher returns. And uh, let's talk about books.

697
00:40:54,239 --> 00:40:56,759
Speaker 1: This is everybody's favorite question. What are some of your

698
00:40:56,760 --> 00:41:00,759
Speaker 1: favorite books, be they fiction, nonfiction, investing, add or not.

699
00:41:01,960 --> 00:41:05,000
Speaker 1: That's a hard one. I've got a lot of favorite books. Um,

700
00:41:05,040 --> 00:41:06,719
Speaker 1: I do think you know. I have to give a

701
00:41:06,719 --> 00:41:11,239
Speaker 1: plug for Thinking Fast and Slow by Daniel Conoman. It's

702
00:41:11,239 --> 00:41:15,759
Speaker 1: incredibly comprehensive and that can also read is a little

703
00:41:15,760 --> 00:41:18,440
Speaker 1: bit slow and dense, but you really learn a tremendous

704
00:41:18,480 --> 00:41:21,600
Speaker 1: amount from reading it. I learned a lot of that

705
00:41:21,880 --> 00:41:23,480
Speaker 1: at the Feet of the Master. But I think for

706
00:41:23,520 --> 00:41:26,360
Speaker 1: people who haven't had that lucky experience, it's it's a

707
00:41:26,400 --> 00:41:29,640
Speaker 1: great book for learning. It's wonderful, and if you read

708
00:41:29,680 --> 00:41:32,200
Speaker 1: the whole thing once sitting, it's dense. The first half

709
00:41:32,239 --> 00:41:35,600
Speaker 1: of it really flies. Yeah, you know other other books

710
00:41:35,640 --> 00:41:38,040
Speaker 1: that I have enjoyed. I'm a big science fiction and

711
00:41:38,080 --> 00:41:41,120
Speaker 1: fantasy fan. Um. I don't mean to tell us some

712
00:41:41,200 --> 00:41:43,600
Speaker 1: of your favorite you know. I've got a bunch of

713
00:41:43,800 --> 00:41:47,640
Speaker 1: The King Killer Chronicles are one. I'm really hoping Patrick

714
00:41:47,719 --> 00:41:49,799
Speaker 1: Rothlist will hurry up and write that third book. I

715
00:41:49,800 --> 00:41:52,920
Speaker 1: don't know if you've read that. Yeah, I loved the

716
00:41:52,960 --> 00:41:56,439
Speaker 1: books the Game of Thrones. Obviously that's quite popular now.

717
00:41:56,960 --> 00:41:59,279
Speaker 1: I wish George R. R. Martin would go ahead and

718
00:41:59,320 --> 00:42:03,080
Speaker 1: finish those book as well. Uh, those are something, you know.

719
00:42:03,120 --> 00:42:05,880
Speaker 1: I like a lot of those, uh classics in the

720
00:42:06,160 --> 00:42:08,720
Speaker 1: in that in that realm, if they're kind of fun.

721
00:42:09,000 --> 00:42:12,480
Speaker 1: Any other science fiction authors float your boat? You know?

722
00:42:12,719 --> 00:42:15,120
Speaker 1: I really like Werner Vinge. I don't know if you're

723
00:42:15,120 --> 00:42:18,319
Speaker 1: familiar with him, the science fiction author. He's got a

724
00:42:18,360 --> 00:42:22,560
Speaker 1: neat book called Rainbows End, which is trying to imagine

725
00:42:22,640 --> 00:42:25,359
Speaker 1: sort of a near future where there's a tremendous use

726
00:42:25,400 --> 00:42:28,560
Speaker 1: of artificial intelligence, and what does the economy look like?

727
00:42:28,760 --> 00:42:30,719
Speaker 1: Which I think is pretty smart. It's got some other

728
00:42:30,760 --> 00:42:34,800
Speaker 1: good books fire on the Deep that deals with artificial intelligence.

729
00:42:34,840 --> 00:42:37,279
Speaker 1: I think it's kind of neat, quite interesting. That's a

730
00:42:37,320 --> 00:42:40,720
Speaker 1: good list. Tell us what you're most excited about today,

731
00:42:40,920 --> 00:42:44,480
Speaker 1: like these days? What what really do you find fascinating?

732
00:42:44,719 --> 00:42:48,959
Speaker 1: You know, I never stopped being fascinated by investing and

733
00:42:49,080 --> 00:42:53,439
Speaker 1: by behavioral finance. I know it seems odd, but that's

734
00:42:53,480 --> 00:42:55,560
Speaker 1: part of what makes it so easy to do what

735
00:42:55,600 --> 00:42:59,880
Speaker 1: I'm doing, is that there's always new wrinkles in the

736
00:43:00,080 --> 00:43:03,160
Speaker 1: kinds of mistakes that people make. I also like to

737
00:43:03,160 --> 00:43:06,359
Speaker 1: apply it to myself and say, what which of these

738
00:43:06,360 --> 00:43:08,480
Speaker 1: mistakes am I making? And how can I try to

739
00:43:08,480 --> 00:43:13,239
Speaker 1: correct that? So it's a perpetual passion. It's endlessly fascinating,

740
00:43:13,239 --> 00:43:16,080
Speaker 1: there's no there's no doubt about that. Given that you

741
00:43:16,160 --> 00:43:18,680
Speaker 1: work in portfolio management, what do you see as the

742
00:43:18,760 --> 00:43:22,439
Speaker 1: next major shifts that are going to take place? Yeah? Well,

743
00:43:22,480 --> 00:43:28,000
Speaker 1: one shift clearly is increasing use of passive investments, and

744
00:43:28,040 --> 00:43:30,400
Speaker 1: how does that fit with active you know, I think

745
00:43:30,719 --> 00:43:36,160
Speaker 1: it's no surprise there's some reasonable arguments in more efficient spaces,

746
00:43:36,200 --> 00:43:39,239
Speaker 1: maybe in large cap to to consider passive. We think

747
00:43:39,280 --> 00:43:43,000
Speaker 1: in inefficient spaces like small cap, really doesn't make sense

748
00:43:43,000 --> 00:43:46,360
Speaker 1: to be passive. But but that continual trend is clearly

749
00:43:46,400 --> 00:43:49,320
Speaker 1: going to be happening. I think a second trend that's

750
00:43:49,520 --> 00:43:52,920
Speaker 1: going on is how do we incorporate all of the

751
00:43:52,960 --> 00:43:56,400
Speaker 1: information and what's the role of artificial intelligence in trying

752
00:43:56,400 --> 00:43:59,840
Speaker 1: to use be used for predicting returns. On that note,

753
00:44:00,120 --> 00:44:01,799
Speaker 1: I have a little bit of a different perspective than

754
00:44:01,800 --> 00:44:04,520
Speaker 1: a lot of people people. I think artificial intelligence is

755
00:44:04,520 --> 00:44:08,640
Speaker 1: obviously incredibly powerful, but it needs a tremendous amount of feedback.

756
00:44:09,000 --> 00:44:10,760
Speaker 1: If you think about it, if you want to build

757
00:44:10,960 --> 00:44:13,840
Speaker 1: an AI system to do minute to minute or second

758
00:44:13,880 --> 00:44:17,239
Speaker 1: to second trading, there's a tremendous amount of feedback that's

759
00:44:17,239 --> 00:44:19,520
Speaker 1: going to be very powerful. If you're trying to predict

760
00:44:19,800 --> 00:44:22,720
Speaker 1: five year returns, well over a twenty five year period,

761
00:44:22,760 --> 00:44:25,080
Speaker 1: you only have five data points. There is no way

762
00:44:25,360 --> 00:44:27,879
Speaker 1: to build an artificial intelligence just feed it the data

763
00:44:27,920 --> 00:44:30,160
Speaker 1: and let it figure things out. That's not gonna work.

764
00:44:30,360 --> 00:44:31,959
Speaker 1: But I'm sure that a lot a lot of people

765
00:44:32,000 --> 00:44:34,960
Speaker 1: are gonna try. So tell us about a time you

766
00:44:35,120 --> 00:44:38,320
Speaker 1: failed and what you learned from the experience. Well, I

767
00:44:38,400 --> 00:44:42,960
Speaker 1: have what I think is a rather amusing investment failure story.

768
00:44:43,160 --> 00:44:49,440
Speaker 1: Um So, during the original Internet bubble around late late nineties,

769
00:44:49,920 --> 00:44:52,000
Speaker 1: you know, there was somebody put out a report that

770
00:44:52,080 --> 00:44:54,960
Speaker 1: said Amazon dot Com is going to double in the

771
00:44:55,000 --> 00:44:57,520
Speaker 1: next year. And I just said, this is ridiculous. No,

772
00:44:57,719 --> 00:45:00,320
Speaker 1: you can't predict that a stock is going to double. This,

773
00:45:00,320 --> 00:45:03,240
Speaker 1: this is crazy. Henry Blodget. That was a very famous

774
00:45:03,280 --> 00:45:05,799
Speaker 1: piece he put out at Oppenheimer, I want to say,

775
00:45:07,360 --> 00:45:10,319
Speaker 1: and then eventually moved over to Merrill Lynch and things

776
00:45:10,360 --> 00:45:14,279
Speaker 1: won't arrive from them exactly. So I decided, you know

777
00:45:14,360 --> 00:45:17,319
Speaker 1: what I'm gonna I'm gonna short this in my personal portfolio.

778
00:45:17,680 --> 00:45:21,040
Speaker 1: At the time, I had about twenty five thousand dollars

779
00:45:21,040 --> 00:45:23,080
Speaker 1: in savings, and I decided I was going to put

780
00:45:23,080 --> 00:45:26,200
Speaker 1: on a five thousand dollar position on the theory of well,

781
00:45:26,239 --> 00:45:28,239
Speaker 1: if this actually does double in a year and I

782
00:45:28,280 --> 00:45:31,239
Speaker 1: lose five thousand dollars, I can handle that in the

783
00:45:31,360 --> 00:45:33,319
Speaker 1: in the short term, and in the long term, I

784
00:45:33,360 --> 00:45:35,120
Speaker 1: hope that I'm going to have a lot more savings.

785
00:45:35,160 --> 00:45:37,960
Speaker 1: And you know, this won't this won't break my lifetime earnings.

786
00:45:38,000 --> 00:45:41,680
Speaker 1: I'm not gonna be bankrupt forever. Well, first of all,

787
00:45:41,719 --> 00:45:45,919
Speaker 1: I couldn't borrow the shares for three weeks because lots

788
00:45:45,920 --> 00:45:47,799
Speaker 1: of people were trying to short the Internet stocks at

789
00:45:47,840 --> 00:45:50,279
Speaker 1: that point in time, during which time the price went up.

790
00:45:51,680 --> 00:45:55,280
Speaker 1: Brilliant me said, oh this is great, even better, even

791
00:45:55,320 --> 00:45:58,960
Speaker 1: better time to short to short this stock. Finally I

792
00:45:59,000 --> 00:46:02,440
Speaker 1: put on the position, and by the way, instead of

793
00:46:02,600 --> 00:46:05,720
Speaker 1: doubling in one year, the price then went to double

794
00:46:05,800 --> 00:46:09,000
Speaker 1: at that original call in another two weeks. It was

795
00:46:09,239 --> 00:46:10,880
Speaker 1: like a month and a half or two months. It

796
00:46:10,960 --> 00:46:14,040
Speaker 1: was crazy fast. At which point in time, now I

797
00:46:14,080 --> 00:46:18,280
Speaker 1: have a position which is closer to ten thousand dollars short, uh,

798
00:46:18,320 --> 00:46:20,799
Speaker 1: in which case, now if this doubles again and I

799
00:46:20,840 --> 00:46:25,120
Speaker 1: lose ten thousand dollars, now I'm starting to have some problems. Unsurprisingly,

800
00:46:25,120 --> 00:46:27,640
Speaker 1: I was working as a strategy consultant at that time,

801
00:46:27,680 --> 00:46:30,440
Speaker 1: but I was spending a lot of time watching the

802
00:46:30,560 --> 00:46:34,279
Speaker 1: daily that minute to minute price movements of Amazon dot Com.

803
00:46:34,560 --> 00:46:37,920
Speaker 1: While I got to feel emotionally what it feels like

804
00:46:38,200 --> 00:46:41,640
Speaker 1: to be losing money and worried about things. UH. At

805
00:46:41,719 --> 00:46:44,680
Speaker 1: one point in time, I decided, as it keep kept

806
00:46:44,760 --> 00:46:46,960
Speaker 1: going the wrong, wake up, going up, up, up, g

807
00:46:47,160 --> 00:46:50,920
Speaker 1: I need to reduce this position. I did something complicated

808
00:46:50,960 --> 00:46:53,320
Speaker 1: just because it had taken three weeks to borrow the shares,

809
00:46:53,360 --> 00:46:55,600
Speaker 1: and that I shorted against the box, so I actually

810
00:46:55,760 --> 00:46:59,560
Speaker 1: bought seven I bought seventy five shares while still being

811
00:46:59,560 --> 00:47:02,480
Speaker 1: short hundred and fifty shares. This was the massive amounts

812
00:47:02,480 --> 00:47:04,719
Speaker 1: of money. I was covered half of the covered half

813
00:47:04,719 --> 00:47:08,120
Speaker 1: of the position, and I think this is really an

814
00:47:08,200 --> 00:47:13,360
Speaker 1: impressive accomplishment. Frankly, because I was doing this sort of

815
00:47:13,400 --> 00:47:18,880
Speaker 1: panicked decision during the day, I managed to close this position.

816
00:47:18,960 --> 00:47:22,279
Speaker 1: I e by the seventy five shares at literally the

817
00:47:22,400 --> 00:47:26,000
Speaker 1: high point of the day, and then as during the day,

818
00:47:26,040 --> 00:47:28,400
Speaker 1: it then started to drop for the first time, and

819
00:47:28,440 --> 00:47:31,680
Speaker 1: I thought, what am I doing? This is crazy. I'm

820
00:47:31,719 --> 00:47:33,920
Speaker 1: not supposed to be. This was my whole thesis and

821
00:47:33,960 --> 00:47:36,680
Speaker 1: now it's not working. So I undid a portion of

822
00:47:36,719 --> 00:47:41,080
Speaker 1: that uh at and I managed to both buy at

823
00:47:41,120 --> 00:47:44,200
Speaker 1: the high of the day and sell at the low

824
00:47:44,239 --> 00:47:46,279
Speaker 1: of the day in the same day, which I think

825
00:47:46,360 --> 00:47:49,160
Speaker 1: is a rare accomplishment. And I learned an important little fact.

826
00:47:49,280 --> 00:47:52,840
Speaker 1: By the way, the published prices are only four round

827
00:47:52,880 --> 00:47:56,120
Speaker 1: lots of hundred shares. And because I was trading at

828
00:47:56,200 --> 00:47:59,720
Speaker 1: seventy five shares they're not quoted prices, and in fact

829
00:48:00,120 --> 00:48:03,200
Speaker 1: managed to sell below the low of the days for

830
00:48:03,280 --> 00:48:07,560
Speaker 1: my seventy five shares. You know, eventually I held onto

831
00:48:07,560 --> 00:48:10,000
Speaker 1: the position for years, and eventually the position kind of

832
00:48:10,120 --> 00:48:13,359
Speaker 1: came positive and consistent with every behavioral bias. Once I

833
00:48:13,400 --> 00:48:16,200
Speaker 1: was able to declare victory of okay, I've no longer

834
00:48:16,280 --> 00:48:19,359
Speaker 1: lost money, then I closed out the position. But longer term,

835
00:48:19,360 --> 00:48:21,880
Speaker 1: look how well Amazon has done. I would the better

836
00:48:21,880 --> 00:48:24,600
Speaker 1: trade was close out the position and then go long

837
00:48:25,160 --> 00:48:29,759
Speaker 1: exactly if you term, If you look longer term, they've

838
00:48:29,760 --> 00:48:31,600
Speaker 1: been one of the successful ones. There was a lot

839
00:48:31,680 --> 00:48:37,400
Speaker 1: of insanity during during the financial crisis, but I, in

840
00:48:37,480 --> 00:48:41,840
Speaker 1: my naivete at that time twenty plus years ago, picked

841
00:48:42,000 --> 00:48:44,440
Speaker 1: one of the wrong things to bet against. So that

842
00:48:44,520 --> 00:48:48,799
Speaker 1: bet was fundamentally wrong. But but I'll tell you I

843
00:48:48,880 --> 00:48:50,680
Speaker 1: felt at the time I did have the wisdom to

844
00:48:50,760 --> 00:48:54,200
Speaker 1: know I'm gonna learn something here. I've learned a lot.

845
00:48:54,360 --> 00:48:57,000
Speaker 1: That is one of the formative investment experiences for me

846
00:48:57,080 --> 00:49:01,280
Speaker 1: about knowing about proper position sizing, knowing when to take risks.

847
00:49:01,480 --> 00:49:03,239
Speaker 1: And one of the lessons that I have too is

848
00:49:03,600 --> 00:49:05,799
Speaker 1: when you see something that looks crazy. I'll tell you,

849
00:49:05,840 --> 00:49:09,319
Speaker 1: I think bitcoin looks crazy. You're very, very wary of

850
00:49:09,400 --> 00:49:12,080
Speaker 1: shorting it. I think the Canes quote was the market

851
00:49:12,120 --> 00:49:15,440
Speaker 1: can remain irrational a lot longer than you can remain solvent,

852
00:49:15,520 --> 00:49:19,120
Speaker 1: to say the to say the very least. Um, what

853
00:49:19,160 --> 00:49:21,000
Speaker 1: do you do for fun? What do you do outside

854
00:49:21,000 --> 00:49:24,399
Speaker 1: of the office, either to relax, or to stay physically fit,

855
00:49:24,920 --> 00:49:27,880
Speaker 1: or or just for left Yeah. The two of the

856
00:49:27,920 --> 00:49:30,800
Speaker 1: hobbies of mine is one I coach my kids in sports,

857
00:49:31,560 --> 00:49:35,120
Speaker 1: which is one nice thing actually about being on the

858
00:49:35,160 --> 00:49:39,240
Speaker 1: West Coast is because and being on market hours actually

859
00:49:39,239 --> 00:49:42,360
Speaker 1: get out of work, start at some insane hour in

860
00:49:42,360 --> 00:49:43,960
Speaker 1: the morning, but get out of work in time to

861
00:49:44,000 --> 00:49:47,080
Speaker 1: actually go coach a baseball practice or soccer practice. Markets

862
00:49:47,080 --> 00:49:48,960
Speaker 1: close at one o'clock. You can be home by the

863
00:49:49,000 --> 00:49:51,080
Speaker 1: time they're back from so I'm not leaving at one o'clock,

864
00:49:51,160 --> 00:49:53,120
Speaker 1: but but I can. I can make a five thirty

865
00:49:53,160 --> 00:49:56,200
Speaker 1: baseball practice, so that's good. Uh. Second thing that I

866
00:49:56,239 --> 00:49:58,840
Speaker 1: do is I love board games. I mean, I'm a

867
00:49:58,880 --> 00:50:02,040
Speaker 1: super geek, so UH play a lot of board games.

868
00:50:02,120 --> 00:50:05,120
Speaker 1: Actually make tried making some board games for my friends.

869
00:50:05,160 --> 00:50:08,760
Speaker 1: I mean, I'm I'm I'm ultra geek. What what board

870
00:50:08,800 --> 00:50:11,360
Speaker 1: games do you like? Uh, you know, I like a

871
00:50:11,400 --> 00:50:14,960
Speaker 1: lot of the board games that have been quite popular. Um.

872
00:50:15,000 --> 00:50:16,960
Speaker 1: I like a game, you know, Ticket to Ride is

873
00:50:17,000 --> 00:50:20,799
Speaker 1: a simple game, but it's pretty good. Another game that

874
00:50:20,880 --> 00:50:24,120
Speaker 1: we have played a bunch this is is something called

875
00:50:24,280 --> 00:50:26,839
Speaker 1: Scithe that's a little bit of a war game. It's

876
00:50:26,880 --> 00:50:30,760
Speaker 1: a little bit stranger. Um. Just to give you an idea,

877
00:50:30,800 --> 00:50:34,080
Speaker 1: I owned about two hundred board games. So asking me

878
00:50:34,080 --> 00:50:36,640
Speaker 1: to pick my favorite, that's that's kind of tough. See,

879
00:50:36,680 --> 00:50:39,880
Speaker 1: that's the most important thing. People don't know about your background. Uh.

880
00:50:39,920 --> 00:50:43,400
Speaker 1: And I assume you've seen the game Night the movie

881
00:50:44,040 --> 00:50:46,719
Speaker 1: you know, it's funny, it's my wife and exactly. So

882
00:50:46,800 --> 00:50:49,759
Speaker 1: my wife and I try to go to that on

883
00:50:49,800 --> 00:50:52,239
Speaker 1: a date a couple of weeks ago after it's been out,

884
00:50:52,280 --> 00:50:55,239
Speaker 1: I think a month, and every movie theater we went

885
00:50:55,280 --> 00:50:57,680
Speaker 1: to had been sold out, so we up watching a

886
00:50:57,719 --> 00:50:59,839
Speaker 1: different movie. So it's still on our list. The weird

887
00:51:00,000 --> 00:51:02,560
Speaker 1: thing is these movies come and go so quickly these days,

888
00:51:03,040 --> 00:51:05,000
Speaker 1: in theaters for two weeks and then and then they're going.

889
00:51:05,080 --> 00:51:06,839
Speaker 1: But the good news is now we can watch any

890
00:51:06,880 --> 00:51:09,799
Speaker 1: of them on demand. Right. You might want to think

891
00:51:09,840 --> 00:51:12,839
Speaker 1: about shorting Netflix, which continues to go higher. That that

892
00:51:12,840 --> 00:51:15,279
Speaker 1: would be the other side of ill be honest, I

893
00:51:15,360 --> 00:51:18,520
Speaker 1: don't understand Netflix. They're hemorrhage in cash. They just did

894
00:51:18,520 --> 00:51:22,720
Speaker 1: a billion and a half dollar financing to acquire more content. Yeah, exactly,

895
00:51:22,760 --> 00:51:25,200
Speaker 1: I don't get them, but I have learned. Yeah, I'm

896
00:51:25,200 --> 00:51:28,480
Speaker 1: not going to short that right. Yeah that sometimes you

897
00:51:28,560 --> 00:51:31,200
Speaker 1: don't want to get in front of the runaway locomotive.

898
00:51:31,480 --> 00:51:33,920
Speaker 1: Let let somebody else be the hero. And and our

899
00:51:34,040 --> 00:51:37,200
Speaker 1: last two questions, what sort of career advice would you

900
00:51:37,239 --> 00:51:40,200
Speaker 1: give a millennial or recent college graduate who came to

901
00:51:40,239 --> 00:51:43,640
Speaker 1: you and said, I'm thinking about a career in asset management.

902
00:51:44,520 --> 00:51:47,799
Speaker 1: So let me give you some general career advice that

903
00:51:47,840 --> 00:51:50,040
Speaker 1: I think is very important for young people, and then

904
00:51:50,080 --> 00:51:53,640
Speaker 1: I'll give some specific advice for asset management. My general

905
00:51:53,680 --> 00:51:57,359
Speaker 1: advice to people is, you hear these phrases do what

906
00:51:57,400 --> 00:52:01,040
Speaker 1: you love, and I think the correct phrasing is do

907
00:52:01,600 --> 00:52:06,719
Speaker 1: something you love, the distinction being don't ask yourself what's

908
00:52:06,760 --> 00:52:09,320
Speaker 1: the thing I love the most of all in life,

909
00:52:09,640 --> 00:52:12,879
Speaker 1: because it's gonna be probably something that has no commercial value.

910
00:52:13,040 --> 00:52:15,360
Speaker 1: I love playing video games the most in life, or

911
00:52:15,400 --> 00:52:18,520
Speaker 1: for me, it's seriously considered. Because I love board games,

912
00:52:18,640 --> 00:52:21,239
Speaker 1: maybe I should become a board game designer. And one

913
00:52:21,280 --> 00:52:24,040
Speaker 1: of the best decisions I ever made was, I'll have

914
00:52:24,120 --> 00:52:26,600
Speaker 1: that be a hobby. I can spend some time doing

915
00:52:26,640 --> 00:52:29,680
Speaker 1: this for fun, but I should actually pick a job

916
00:52:29,880 --> 00:52:32,640
Speaker 1: that I also love, but not try to figure out

917
00:52:32,719 --> 00:52:34,960
Speaker 1: what's the thing that's absolute most fun. So that's that's

918
00:52:35,000 --> 00:52:38,160
Speaker 1: my advice. To pick something you love and enjoy, but

919
00:52:38,239 --> 00:52:40,640
Speaker 1: don't try to pick the thing you love the most, uh,

920
00:52:41,080 --> 00:52:43,160
Speaker 1: unless you're lucky enough to have it be something that's

921
00:52:43,200 --> 00:52:47,359
Speaker 1: highly lucrative. The second advice is specific to asset management,

922
00:52:47,520 --> 00:52:51,160
Speaker 1: is I just think there's a tremendous power and maybe

923
00:52:51,239 --> 00:52:55,680
Speaker 1: this is my bias from having come from uh some

924
00:52:55,920 --> 00:53:01,560
Speaker 1: affirm that specializes in behavioral finance, there's tremendous power to understanding.

925
00:53:01,560 --> 00:53:04,279
Speaker 1: Are you in a realm where you need to sort

926
00:53:04,280 --> 00:53:07,439
Speaker 1: of figure out where other people are making mistakes, which

927
00:53:07,480 --> 00:53:10,280
Speaker 1: I would say is true in markets like the stock market,

928
00:53:10,600 --> 00:53:12,560
Speaker 1: or are you in a realm where what matters is

929
00:53:12,800 --> 00:53:15,319
Speaker 1: are you making the mistake, in which case you've got

930
00:53:15,320 --> 00:53:17,920
Speaker 1: to do the financial analysis. Well, So the distinction I

931
00:53:17,920 --> 00:53:20,160
Speaker 1: would sort of make is I've never heard of behavioral

932
00:53:20,160 --> 00:53:23,279
Speaker 1: private equity. If you're the only person bidding, if you're

933
00:53:23,320 --> 00:53:25,680
Speaker 1: the only person bidding on a company, you just got

934
00:53:25,680 --> 00:53:28,200
Speaker 1: to do the analysis right. You want to focus on

935
00:53:28,400 --> 00:53:32,440
Speaker 1: financial knowledge. If you're competing with lots and lots of

936
00:53:32,440 --> 00:53:35,160
Speaker 1: other folks, then I think being an expert on what

937
00:53:35,239 --> 00:53:37,719
Speaker 1: are the mistakes other people are gonna make that's going

938
00:53:37,760 --> 00:53:39,920
Speaker 1: to be more important. So it's good to go which

939
00:53:39,960 --> 00:53:42,400
Speaker 1: area you're trying to focus on and get an expertise

940
00:53:42,400 --> 00:53:45,719
Speaker 1: that matches that area. And a final question, tell us

941
00:53:45,760 --> 00:53:48,800
Speaker 1: something you know about investing today that you wish you

942
00:53:48,880 --> 00:53:52,640
Speaker 1: knew twenty years ago. Well, I already told you that

943
00:53:52,719 --> 00:53:58,719
Speaker 1: story I wish I knew, don't know. It's it's be

944
00:53:58,840 --> 00:54:03,680
Speaker 1: aware of betting against extreme hype because it can go

945
00:54:04,000 --> 00:54:06,400
Speaker 1: It can go the wrong way much longer than you

946
00:54:06,400 --> 00:54:11,799
Speaker 1: would expect. We have been speaking with Dr Raife Jovinazzo

947
00:54:12,120 --> 00:54:15,640
Speaker 1: of Fuller and Thailor. If you enjoy this conversation, be

948
00:54:15,719 --> 00:54:17,440
Speaker 1: sure and look up an Inch or down an Inch

949
00:54:17,480 --> 00:54:20,400
Speaker 1: on Apple iTunes when you can see any of the

950
00:54:20,440 --> 00:54:24,960
Speaker 1: other two hundred such conversations we've had previously. We love

951
00:54:25,040 --> 00:54:29,279
Speaker 1: your comments, feedback and suggestions right to us at m

952
00:54:29,320 --> 00:54:33,440
Speaker 1: IB podcast at Bloomberg dot Net. I would be remiss

953
00:54:33,480 --> 00:54:35,800
Speaker 1: if I did not thank our crack staff who helps

954
00:54:35,840 --> 00:54:40,400
Speaker 1: put together these conversations each week. Taylor Riggs is our producer,

955
00:54:40,440 --> 00:54:45,240
Speaker 1: Booker m Medina Parwanner is our audio engineer. Slash producer

956
00:54:45,719 --> 00:54:49,560
Speaker 1: Michael Batnick is our head of research. I'm Barry Ritolts.

957
00:54:49,800 --> 00:54:53,160
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio

958
00:55:00,400 --> 00:55:00,640
Speaker 1: ex