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Speaker 1: This week on the podcast, we have a special repeat guest,

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Speaker 1: and he is one of my favorite people in finance.

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Speaker 1: His name is Michael Mobisan and he is the chief

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Speaker 1: strategist at Credit Swiss First Boston. UH. He used to

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Speaker 1: work with Bill Miller over at leg Mason, who actually

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Speaker 1: very recently just bought himself private. I really love the

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Speaker 1: work of Mobisan. He I think he is a unknown

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Speaker 1: or relatively unknown rock star. I find the way he

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Speaker 1: looks at the world, the framework uh he creates two

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Speaker 1: understand finance, understand how investors behave UH to be just

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Speaker 1: absolutely fascinating and tremendously, tremendously insightful. UH. He is one

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Speaker 1: of the few guests that we have invited back for

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Speaker 1: a second time, and truth be told, our first interview,

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Speaker 1: I was pretty uh. Met is probably the the nicest

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Speaker 1: thing I could say about my own performance. He's always

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Speaker 1: awesome and and I think you'll really enjoy this. So,

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Speaker 1: with no further ado, here is my conversation with Michael Mobison.

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

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Speaker 1: My special guest this week is Michael Mobisan. He is

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Speaker 1: the head of Global Financial Strategies at Credit Swiss, he

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Speaker 1: has quite the curriculum VITA. Previously, he was chief investment

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Speaker 1: strategist at leg Mason Capital Management, working with famed investor

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Speaker 1: Bill Miller. He's been a professor at the Columbia School

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Speaker 1: of Business since, where he's won numerous awards for teaching excellence,

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Speaker 1: as well as being named to the Institutional Investors All

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Speaker 1: Star Research Team. He is the author of a number

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Speaker 1: of books, most recently The Success Equation, Untangling Skill and

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Speaker 1: Luck in Business, Sports and Investing, and he is also

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Speaker 1: the author of some of the most interesting white papers

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Speaker 1: in all of finance. Michael Mobiso, welcome back to Bloomberg.

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Speaker 1: Thanks very awesome to do with you. So you and

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Speaker 1: I know each other for a while, and I've been

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Speaker 1: a fan of the sort of work you've done on

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Speaker 1: a number of things. Let's let's start out with a

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Speaker 1: quote of yours and see where you want to go

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Speaker 1: with this. You said, perhaps the single greatest error in

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Speaker 1: the investment business is a failure to distinguish between the

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Speaker 1: knowledge of a company's fundamentals and the expectations implied by

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Speaker 1: market price. What exactly do you mean by that? Yeah, So,

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Speaker 1: in order to make money in markets you have to

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Speaker 1: have a belief that's different than what the market believes.

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Speaker 1: And the general tendency we all have is if things

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Speaker 1: are going well, we want to buy, and we know that,

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Speaker 1: of course from aggregate investor behavior, and when things are

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Speaker 1: going poorly, we want to sell. But the way to

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Speaker 1: make money is to have a view that's different than

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Speaker 1: the market. And the metaphor that works the most vividly

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Speaker 1: is the racetrack. Right. If you're a handicapper and you

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Speaker 1: want to make money, there are two things that are important.

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Speaker 1: One is how fast the horse is gonna run, we'll

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Speaker 1: call that the fundamentals. And the second are the odds

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Speaker 1: and the toweboard. And the way you make money is

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Speaker 1: not picking the winner. The way you make money is

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Speaker 1: picking miss price odds. And so that idea really carries

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Speaker 1: over to the world investing. So I think it's most

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Speaker 1: of us investors we sort of blur those two things.

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Speaker 1: And I'll mention it kind of a funny story. I

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Speaker 1: was in a big money management organization. They said, is

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Speaker 1: there anything we could do that would be radical you

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Speaker 1: in terms of how we approached the job. I said,

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Speaker 1: here's an idea for you have half your analysts only

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Speaker 1: work on fundamental stuff, right, one guy just works on

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Speaker 1: Apple's future, you know, sales and profits and so forth,

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Speaker 1: and then have the other guy just work on the

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Speaker 1: expectations what does a hundred dollar stock price mean for performance?

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Speaker 1: And then bring the two people together at the very

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Speaker 1: last minute and see what happens, right, Because the problem

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Speaker 1: is we tend to meld those two And I would

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Speaker 1: say the great investors are those that really do think

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Speaker 1: about those things separately. And I think it's a it's

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Speaker 1: a real challenge for the rest of us. Again, things

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Speaker 1: are going well, you want to buy it, things are

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Speaker 1: going bad that you want to sell it, and you

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Speaker 1: don't make those distinctions the way you should. What was

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Speaker 1: the reaction to that proposal? And they kind of they

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Speaker 1: kind of said, oh, that's kind of a cool idea. Yeah,

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Speaker 1: see you later, So go very far. The interesting thing

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Speaker 1: is when I when I speak with the quants, when

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Speaker 1: I speak with investors with a mathematical basis, they tend

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Speaker 1: to do pretty much what exactly what you're saying is

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Speaker 1: they have an understanding of what the fundamental metrics are,

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Speaker 1: and then their quantitative overlay is here's what we've seen

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Speaker 1: in price relative to value, relative to book, relative to

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Speaker 1: all these metrics. Hey, these this little subgroup looks wildly

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Speaker 1: mispriced exactly, so I think the quantz I mean, this

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Speaker 1: is another I'm sure we'll talk more about this, but

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Speaker 1: I think the quants obviously have some of this captured

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Speaker 1: in the way they approach the world, which makes enormous

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Speaker 1: amounts of sense. The questions, are there things that fundamental

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Speaker 1: analyst can do that quants can't do? Are there thing

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Speaker 1: nuances they can pick up that might be? And that's

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Speaker 1: an interesting sub separate conversation, but right, the virtue of quants,

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Speaker 1: or even even basic rules for buying and selling are

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Speaker 1: precisely you take the emotion out and that you're making

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Speaker 1: those two the quantity of the fundamentals and expectations too separate.

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Speaker 1: So anyone who's systematic, anyone who involved who is using

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Speaker 1: some form of screening in order to help determine anybody

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Speaker 1: who's using it's almost a cliche these days, but a

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Speaker 1: factor or a dimension small cap value, momentum, quality, etcetera.

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Speaker 1: They're using the math in order to help identify that

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Speaker 1: that gulf on thing. That's right, I mean, the one

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Speaker 1: thing this is a little bit off Barry, but I

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Speaker 1: think this is interesting. So one of the things that

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Speaker 1: I think a lot about is this idea of freestyle chess.

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Speaker 1: I don't know if we've talked about this before, have

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Speaker 1: you may know this? So you know, cas Brow loses

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Speaker 1: Deep Blue, and so machine beats man. Fine, But surely

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Speaker 1: thereafter what what emerged with something called freestyle chess? And

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Speaker 1: I means you and I are playing a match, but

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Speaker 1: we can avail ourselves or whatever aids we want. So

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Speaker 1: we and on computer programs you can call your lifeline

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Speaker 1: to your grand master, buddy or whatever it is. And

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Speaker 1: it turns out these freestyle teams are better than the

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Speaker 1: programs by themselves or of course any man or person. Right,

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Speaker 1: So man plus machine beats man or machine, which is

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Speaker 1: really interesting. And there's there's you know, Kevin Kelly, who's

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Speaker 1: you know, just wrote a new book called The Inevitable.

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Speaker 1: He's got a little section on this in the book.

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Speaker 1: So this, to me is a very intriguing model to say.

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Speaker 1: Are there cases where most of the time you default

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Speaker 1: to the quantitative approach or to the program, But for

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Speaker 1: every now and then, if you have a good feel

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Speaker 1: for the game, you can step in and do something

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Speaker 1: that's a little bit different that adds value. Now that's

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Speaker 1: open open question whether that's true and investing, But to me,

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Speaker 1: that's a really intriguing model for thinking about what where

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Speaker 1: sort of humans and quantitative techniques may merge in the future. Well,

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Speaker 1: we saw in late two thousand and seven and again

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Speaker 1: a couple of years ago where all the quants kind

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Speaker 1: of walking in lotstep and following their programs, and some

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Speaker 1: see change took place, whether it was the financial crisis

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Speaker 1: or something more recent, and even the math still got shelax.

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Speaker 1: Sometimes the ground rules changed sufficiently that hey, whatever your

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Speaker 1: methodology is is gonna at least temporarily be wildly out

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Speaker 1: of favor. Which leads me to an interesting question. Um,

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Speaker 1: so much of this stuff is not particularly intuitive, and

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Speaker 1: think twice you use the phrase counter intuition. What is counterintuition? Well,

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Speaker 1: I'm not even sure that's a word. My editor said

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Speaker 1: that would be a good subtitle, but no, connor intuition.

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Speaker 1: The big idea think twice is when we're faced with

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Speaker 1: certain types of situations, our minds are naturally gonna want

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Speaker 1: to think about the problem one way when there's a

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Speaker 1: better way to think about it. Right, and so counterintuition

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Speaker 1: is saying, hey, can I check myself and say I

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Speaker 1: should think about it more effectively. Let me give an

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Speaker 1: example to very famous one comes from Danny Kahneman, who

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Speaker 1: obviously you know great psychologists, won the Nobel Price. He

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Speaker 1: was our guest last week. Yeah, how is that? I

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Speaker 1: gotta listen to it. He's awesome. Right, there is a

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Speaker 1: fascinating story I'll share with you about about him in

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Speaker 1: uh in the podcast portion, the idea is the inside.

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Speaker 1: It calls the inside versity outside of you. So, Barry,

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Speaker 1: if I give you a problem, it could be anything.

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Speaker 1: It could be how long will take your remodel your kitchen,

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Speaker 1: how much it will cost, or how the asset class do.

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Speaker 1: The classic way to think about it, See if this

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Speaker 1: resonates is you gather tons of information, you combine it

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Speaker 1: with your inputs and your project into the future, right

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Speaker 1: and left our own devices. That's how we solve problems.

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Speaker 1: The outside view is by by contrast, is thinking about

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Speaker 1: your problems in an instance of a larger reference class,

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Speaker 1: basically asking what happened when other people are in this

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Speaker 1: situation before. And it's a very unnatural way to think

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Speaker 1: a because you have to leave aside this cherish information

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Speaker 1: and be you have to find and avail yourself of

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Speaker 1: this reference class. Take yourself out of yourself and look

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Speaker 1: at it exactively. And let me give you an example.

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Speaker 1: It's a trivial on Let's say you are remodeling your kitchen. Right,

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Speaker 1: so you sit down with the contractor, you pick out

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Speaker 1: all stuff, and you're all set to go where you've

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Speaker 1: gotta budget. And so the work starts and your neighbor

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Speaker 1: strolls over, say, Barry, what's going on. You go, Yeah,

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Speaker 1: we're remodeling your kitchen. It's gonna cost us X and

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Speaker 1: it's gonna will be done by this day. What does

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Speaker 1: your neighbor say to you, knowing nothing about the details? Right,

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Speaker 1: something like double the money, double the time, or something along. Right,

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Speaker 1: you have all the inside view, right, you have all

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Speaker 1: the ins he's just doesn't know about, but he's got

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Speaker 1: the outside of you. So the point is that psychologists

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Speaker 1: have demonstrated that introducing the outside of you almost always,

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Speaker 1: almost invariably improves the quality of decisions. That's a classic example.

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Speaker 1: Your mind naturally goes one way. Counter intuition would tell you,

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Speaker 1: let's avail ourselves with this other information and we're gonna

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Speaker 1: make more accurate forecasts. And the story Danny Kneman tells

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Speaker 1: is when him and a group of psychologists we're working

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Speaker 1: on a textbook, and these are folks who specialize in

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Speaker 1: cognitive eras and foibles of of the mind. Uh, they

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Speaker 1: ended up taking far beyond what their their consensus was,

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Speaker 1: and it was it was right, uh, right in the

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Speaker 1: sweet spott of what you're talking about. I'm Barry Ritolts.

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

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Speaker 1: special guest today is Michael Mobisan. He is head of

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Speaker 1: Global Financial Strategies of Credit Swiss and author of numerous books,

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Speaker 1: most recently The Success Equation Untangling Skill and Luck in Business,

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Speaker 1: Sports and Investing. I love that title. It's so it's

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Speaker 1: so fascinating. Let's let's pull a um a phrase out

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Speaker 1: of that book. And and have you discussed that. I

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Speaker 1: really enjoyed quote. Our love of stories and our need

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Speaker 1: to connect cause and effect leads to all sorts of problems.

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Speaker 1: The blend of those two ingredients leads us to believe

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Speaker 1: that the past was inevitable and to underestimate what else

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Speaker 1: might have happened. So obviously that applies to investing, but

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Speaker 1: it also applies to sports and business to talk to them. Absolutely,

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Speaker 1: it's a It is fascinating and bear you know when

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Speaker 1: you when when an author puts down a book, you

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Speaker 1: you've been through the same process. There may be a

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Speaker 1: couple of ideas that keep banging around in your head

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Speaker 1: and for me, this is one of those ideas. Um.

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Speaker 1: The work here was done by professor named Michael Gazzaniga,

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Speaker 1: who's a neuroscientist and and he did famous work on

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Speaker 1: so called split brain patients. So these are people who

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Speaker 1: deabilitating epilepsy, they failed all their treatments and as the

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Speaker 1: last of jeffort, they sever the corpus close from the

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Speaker 1: bundle of nerves between the two hemispheres of the brain

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Speaker 1: left and the right hemisphere. And what allows the researchers

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Speaker 1: to do is to figure out what what's going on

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Speaker 1: each part of your brain. And to make a long

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Speaker 1: story short, what they found and absolutely fascinating researches. And

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Speaker 1: in your left hemisphere, which is where your language resides,

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Speaker 1: there's a module they now call the interpreter, and the

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Speaker 1: job of the interpreter is to close cause and effect loops.

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Speaker 1: So I throw an effect at you, and this little

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Speaker 1: module in your brain is gonna is gonna try to

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Speaker 1: figure out how to close the close the loop. Right,

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Speaker 1: So you know, we know the future is buzzing with possibility.

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Speaker 1: Everybody gets that. But once an event occurs, all of

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Speaker 1: us effortlessly and naturally create a narrative to explain that outcome, right,

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Speaker 1: your interpreter. And when that happens, two things kick in.

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Speaker 1: The first is hindsight biases. We start to believe that

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Speaker 1: we knew what was going to happen with a greater

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Speaker 1: probability than we actually did. And by the way, I

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Speaker 1: cannot even count how the people have called me or

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Speaker 1: emailed me to say I knew Brexit was gonna happen.

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Speaker 1: So I said him, immediately, where did you write that down?

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Speaker 1: Where's your pile of money from your wealth and your

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Speaker 1: built right? I hear this about the financial crisis, So

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Speaker 1: anybody could have told this was well, why were you long?

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Speaker 1: And I'm actually exposed to banks, home builders and mortgage

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Speaker 1: lenders right into the collapse if you knew it was

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Speaker 1: coming precisely. And the second thing that happens, which is related,

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Speaker 1: is this idea called creeping determines that you start to

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Speaker 1: believe that what happened was the only thing that could

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Speaker 1: have happened, right, So this is a little module in

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Speaker 1: your brain. Now, the tie back to the skill and

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Speaker 1: luck stuff is that the interpreter really knows nothing about luck.

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Speaker 1: So if something good happens, your mind is going to

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Speaker 1: assume that something good was behind it. And something bad happens, again,

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Speaker 1: your mind's gonna make So this is a fascinating you know,

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Speaker 1: we're in a world that's got a lot of randomness,

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Speaker 1: a lot of luck, and we've got a little module

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Speaker 1: in our brain that really struggles to understand what happened.

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Speaker 1: So so to me the idea and you know, I know,

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Speaker 1: you know, one of your guests is Phil Tetlock, who

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Speaker 1: was horrific, and you know, Phil talks a lot about

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Speaker 1: this idea of counter factuals is being being open obviously

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Speaker 1: the future different futures, but also recognizing that the past

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Speaker 1: we lived through was only one of many possible pasts.

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Speaker 1: That's that's a very unnatural thing to think about, but

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Speaker 1: it's also a very fertile thing to think about in

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Speaker 1: terms of really understanding the complexity of the world. Warn

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Speaker 1: Buffett's partner Charlie Monger is famous for saying, invert, always invert.

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Speaker 1: When you're looking at a situation, Assume one of the

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Speaker 1: main variables is either zero or a hundred and eighty

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Speaker 1: degrees from what it is, and then see how things

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Speaker 1: play out. And suddenly you end up with a very

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Speaker 1: different set of possible outcomes. Uh. And and the past

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Speaker 1: may not have been as as inevitable as as some

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Speaker 1: people have suggested. There there are a number of things

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Speaker 1: related to this that that are really fascinating. But but

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Speaker 1: let's stick with the luck issue and let's try an inversion. So,

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Speaker 1: so one of your comments was luck is a residual.

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Speaker 1: It's what's left over after you've subtracted skill from the outcome.

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Speaker 1: So let's invert that. Uh. If after a decade, I

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Speaker 1: think it's safe to say that good luck and bad

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Speaker 1: luck should probably cancel out. If you're a business person,

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Speaker 1: or an athlete, or a or an investor, maybe not

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Speaker 1: a dent. There'll be some outliers, but for a lot

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Speaker 1: of them. So can we say that after luck cancels out,

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Speaker 1: or you're left with this skill, I think that's a

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Speaker 1: fair statement. Um. Now, I would say, when you think

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Speaker 1: about luck. There are two different sort of categories where

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Speaker 1: with luck is more independent a little bit like you described,

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Speaker 1: and that would be athletes or or even money management

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Speaker 1: probably realistic. But there are other other elements about luck

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Speaker 1: that has a lot of a path dependence. So what

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Speaker 1: happened before effects what happens next and so forth. And

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Speaker 1: when you think about the success of music sales or

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Speaker 1: book sales or any sort of social products, those social

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Speaker 1: effects are actually, uh, make it a little bit of

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Speaker 1: a different dynamic. So yeah, yeah, the answer is yes.

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Speaker 1: Over the longer the sample size, the greater the sample size,

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Speaker 1: the more we can we could get a signal from

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Speaker 1: skill and less important luck is. So yeah, there's no

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Speaker 1: question that's the case. So index thing is as big

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Speaker 1: today as it's ever been. I think Vanguard is now

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Speaker 1: coming up on four trillion dollars, black Rock is they're already.

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Speaker 1: Is it safe to say that a lot of the

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Speaker 1: public has looked at this process and said, you know,

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Speaker 1: I'm kind of done with stock pickers. I'm just gonna

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Speaker 1: index and not have to worry about it anymore. Is

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Speaker 1: that a driving factor? And I think there's no question

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Speaker 1: about that. And you know, for most people that's an

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Speaker 1: absolutely correct prescription. Now, you know, that's an argument you

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Speaker 1: can you can't take to the extreme. In other words,

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Speaker 1: not every single person can index. There has to be

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Speaker 1: some act of management in some way, shape or form

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Speaker 1: to basically and by the way, they're creating a positive externality,

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Speaker 1: which is basically, efficient markets were good prices that the

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Speaker 1: indexers can take advantage of. But for most people that's

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Speaker 1: really relevant. And you know, I was just looking at

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Speaker 1: the numbers the other day. I mean, I know that

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Speaker 1: Jack Bogel started the you know, his index fund in

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Speaker 1: the seventies, but really for all intensive purposes, it really

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Speaker 1: wasn't even going until sort of late eighties, early nineties, right,

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Speaker 1: So this is a fairly new phenomenon we're seeing. And

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Speaker 1: and the bulk of the assets at a place like

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Speaker 1: Vanguard UH really have have come post financial crisis exactly,

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Speaker 1: So so you know, it's interesting. And the other thing

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Speaker 1: to think about when you think about that dimension is that,

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Speaker 1: you know, active investing the zero sum game in the

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Speaker 1: sense that UH, for any particular year, the winner's positive

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Speaker 1: alpha have to be offset by losers negative alpha, right,

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Speaker 1: just mathematically that has to be the case. So it's

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Speaker 1: interesting if you if you say, sort of an analogy

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Speaker 1: is sort of the poker table, right, So we have

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Speaker 1: guys sitting around the poker table playing one night of poker.

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Speaker 1: You know, if the people who are the less capable

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Speaker 1: are walking out and indexing right, in effect, who's left

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Speaker 1: sitting at the table the most skilled the most skilled players.

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Speaker 1: So let's talk a little bit about the paradox of skill,

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Speaker 1: and this sets right up the paradox of skills. So

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Speaker 1: the paradox of skill, which is not my idea but

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Speaker 1: I gave that name, says it at when activities have

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Speaker 1: both skill and luck, which is most stuff in life.

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Speaker 1: As skill increases, luck becomes more important, which doesn't seem

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Speaker 1: to make sense, but they counterintuitive way to think about

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Speaker 1: this is luck has a skill has two dimensions. One

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Speaker 1: is absolute skill, but the second dimension is relative skill.

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Speaker 1: And that's really what it's about when you're thinking about

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Speaker 1: outperforming others. And what we've also seen is a collapse

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Speaker 1: of relative skill. So say the fancier way to say,

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Speaker 1: it's a standard deviation of skills gone down. So one

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Speaker 1: examples in batting average in baseball or the the average

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Speaker 1: batting average doesn't change all that much because it's picture

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Speaker 1: hitter interaction, but the standard deviations collapse, and as a consequence,

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Speaker 1: there aren't these sort of extreme hitters. No more, No more.

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Speaker 1: Ted Williams, I'm Barry Ridults. You're listening to Masters in

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Speaker 1: Business on Bloomberg Radio. My special guest today is Michael Mobisan.

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Speaker 1: He is the head of Global financial Strategies at Credit Swiss,

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Speaker 1: previously chief investment strategist at leg Mason, working with Bill

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Speaker 1: Miller Uh Professor of Finance at Columbia Business School, and

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Speaker 1: author of numerous books. Let's talk a little bit about

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Speaker 1: how the great investors think and again another quote of yours,

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Speaker 1: I'm gonna pull what is being wrong? Teach us about

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Speaker 1: being right? It's a fascinating question. And by the way,

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Speaker 1: there's a great book by Katherine Schulz basically Being Wrong.

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Speaker 1: Of that title. Here's the interesting thing, Barry. We wake

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Speaker 1: up every day and think that what we believe is right.

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Speaker 1: What yours in your head is what you think is right.

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Speaker 1: So then you wander into the world and you're confronted

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Speaker 1: with the reality that all the stuff you believe may

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Speaker 1: not be right. And the real big question is how

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Speaker 1: do you deal with that? Right? And so there are

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Speaker 1: some mult you and I disagree on something. There might

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Speaker 1: be some ways I say, either Barry doesn't understand the facts,

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Speaker 1: and if I just tell him the facts, he's going

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Speaker 1: to come around to my point of view. It could

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Speaker 1: be Berry's not quite smart enough to understand my nuanced

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Speaker 1: point of view, or could be Berry's turned his back.

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Speaker 1: You know, he doesn't get it anymore, and you know

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Speaker 1: he's being, in a sense almost evil about this thing.

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Speaker 1: So I think this idea of of recognizing, especially the

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Speaker 1: world of investing right where where so important, so important

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Speaker 1: because what happens is I mean, what is what is

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Speaker 1: rational being? What does it mean to be rational? And

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Speaker 1: the answer is that your beliefs map accurately to the world.

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Speaker 1: That's a real challenge if the world's constantly changing, because

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Speaker 1: that requires you to change your own views. And most

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Speaker 1: of us, truth be told, are fairly cognitively lazy, would

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Speaker 1: rather just sort of keep doing what we're doing. So

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Speaker 1: that to me, when we say what are the characteristics,

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Speaker 1: that's a big one. And being wrong teach you about

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Speaker 1: being right is to say I need, yeah, I need.

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Speaker 1: My map is off and I need to adjust it.

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Speaker 1: The other thing I'll say, you know, you mentioned Charlie

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Speaker 1: Munger a few moments ago. You know, Charlie Munger has

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Speaker 1: got this great coro says, we got we did well

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Speaker 1: Berkshire Health Hathway less by being brilliant and more by

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Speaker 1: not screwing up a lot. And there's a little of

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Speaker 1: that effect as well. So just by not making as

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Speaker 1: many mistakes, yeah, you you, you will do better over time.

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Speaker 1: So I think that's another aspect of the not being

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Speaker 1: wrong dimension. You know, I'm fascinated now that we're in

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Speaker 1: the midst of the political silly season, the idea of

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Speaker 1: cognitive dissonance, that when a person has a wrong map

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Speaker 1: and you can front them with in controvertable facts, Hey,

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Speaker 1: crime is at decade lows, the number of police killed

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Speaker 1: are at record lows relative to any time over the past,

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Speaker 1: you know, thirty forty years. How the brain manages to

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Speaker 1: either ignore the data or somehow deal with it because

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Speaker 1: otherwise their whole worldview, uh falls apart. What does that

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Speaker 1: How how does that manifest itself with investors? Yeah? I

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Speaker 1: think that I think that people prefer not to change

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Speaker 1: their view, right, and uh so they don't even want

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Speaker 1: to entertain the evidence. But you know that if you

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Speaker 1: if you said you know of conomin traversity's biases, you know,

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Speaker 1: the one that probably shows up, well, maybe not the most,

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Speaker 1: but certainly the top three would be confirmation bias, right,

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Speaker 1: which is, once you've made a decision, you may have

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Speaker 1: agonized over, but once you made a decision, you seek

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Speaker 1: information that confirms your point of view. And you just

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Speaker 1: avowed dismiss or discount, as you said, disconfirming evidence. And

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Speaker 1: it's a very natural thing for all of us to do.

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Speaker 1: And again, I think that characteristics of great investors are

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Speaker 1: those who remain actively open minded, who remain open to

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Speaker 1: the evidence, and who are willing and able to change

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Speaker 1: their minds when the evidence suggests that they should. By

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Speaker 1: the way in life, certainly with politicians, consistency is valued

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Speaker 1: is a good thing, and if you're changing your view,

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Speaker 1: you're called a flip flopper. Right in investing, if you're

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Speaker 1: a flip flopper, you're if you're doing the right thing,

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Speaker 1: that's what you need to do. It's not even a

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Speaker 1: question of whether it's a good thing matter. So so

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Speaker 1: that's another you know, that's that whole confirmation bias and

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Speaker 1: this idea of consistency and the fact is once you

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Speaker 1: most people have come up with a point of view,

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00:21:34,840 --> 00:21:36,840
Speaker 1: they'd much rather just stick with what they're doing than

428
00:21:36,920 --> 00:21:39,680
Speaker 1: to change their views, even if it's money losing. Even

429
00:21:39,720 --> 00:21:42,600
Speaker 1: if it's money losing, that that that's quite uh, that's

430
00:21:42,680 --> 00:21:45,959
Speaker 1: quite a stuff of thing. So that that leads to

431
00:21:46,200 --> 00:21:49,199
Speaker 1: a related question. So what are some of the bigger

432
00:21:49,280 --> 00:21:54,280
Speaker 1: misconceptions about how investors should approach the market. What should

433
00:21:54,320 --> 00:21:57,360
Speaker 1: they be doing that they're not, and what shouldn't they

434
00:21:57,359 --> 00:22:00,760
Speaker 1: be doing that they are. You know, if saying I say, Baron,

435
00:22:00,800 --> 00:22:02,960
Speaker 1: we talked a bit about this. For for most people,

436
00:22:03,280 --> 00:22:07,120
Speaker 1: the answer is that they should probably using index funds

437
00:22:07,200 --> 00:22:11,600
Speaker 1: or some sort of low cost approaches, having appropriately diversified portfolios,

438
00:22:11,720 --> 00:22:13,199
Speaker 1: you know, doing all the things you're supposed to do

439
00:22:13,240 --> 00:22:15,880
Speaker 1: rebalancing and tax efficiency and so forth. Right, So for

440
00:22:15,880 --> 00:22:18,159
Speaker 1: for for a vast prenority people, that's probably the right

441
00:22:18,160 --> 00:22:20,560
Speaker 1: way to go. If you're going to be an active manager,

442
00:22:20,800 --> 00:22:22,879
Speaker 1: I would say the key thing is to think about

443
00:22:22,960 --> 00:22:25,040
Speaker 1: what is your source of edge, What do you truly

444
00:22:25,080 --> 00:22:27,919
Speaker 1: believe that you can do that's different than others, right,

445
00:22:28,119 --> 00:22:30,720
Speaker 1: and and then organize your investment firm in order to

446
00:22:30,760 --> 00:22:34,120
Speaker 1: do that. And then finally think a lot about portfolio construction,

447
00:22:34,160 --> 00:22:36,320
Speaker 1: how you put together your bets in a way that's effective.

448
00:22:36,880 --> 00:22:39,720
Speaker 1: Um so those it's it's really about having a really

449
00:22:39,720 --> 00:22:43,640
Speaker 1: good process. It's also, as we were speaking a moment ago,

450
00:22:43,680 --> 00:22:47,639
Speaker 1: it's about understanding and managing or mitigating the behavioral biases

451
00:22:47,720 --> 00:22:50,399
Speaker 1: that are gonna inevitably show up. You know, way will

452
00:22:50,400 --> 00:22:51,960
Speaker 1: avoid that, I know there is, but you try to

453
00:22:52,000 --> 00:22:54,840
Speaker 1: weave this into your process. Maybe checkpoints will call them

454
00:22:54,920 --> 00:22:56,920
Speaker 1: or stop points where you say, hey, am I thinking

455
00:22:56,960 --> 00:22:59,560
Speaker 1: about this properly? And the final thing I'll say, which

456
00:22:59,600 --> 00:23:01,760
Speaker 1: is a big issue, is that are you in an

457
00:23:01,840 --> 00:23:05,000
Speaker 1: investment organization or any kind of organization that really is

458
00:23:05,080 --> 00:23:07,399
Speaker 1: helping you versus hindering you? And I think that this

459
00:23:07,440 --> 00:23:09,439
Speaker 1: is something again Charlie Ellis has talked a lot about

460
00:23:09,880 --> 00:23:13,120
Speaker 1: is this business versus profession? Right? When when people start

461
00:23:13,200 --> 00:23:16,679
Speaker 1: getting driven by raising assets under management or selling the

462
00:23:16,720 --> 00:23:20,479
Speaker 1: hot product versus delivering access returns, that changes the nature

463
00:23:20,760 --> 00:23:24,520
Speaker 1: of the basic proposition. I'm Barry Riddlts. You're listening to

464
00:23:24,680 --> 00:23:28,719
Speaker 1: Masters in Business on Bloomberg Radio. My special guest today

465
00:23:28,920 --> 00:23:33,000
Speaker 1: is Michael Mobisaw. He is of Credit Swiss and author

466
00:23:33,160 --> 00:23:38,960
Speaker 1: of numerous books on investing, uh, human behavior psychology. Let's

467
00:23:39,040 --> 00:23:42,399
Speaker 1: let's talk a little bit about emotions and decision making.

468
00:23:43,040 --> 00:23:46,760
Speaker 1: And since since you mentioned Danny Kahneman, let's let's start

469
00:23:46,840 --> 00:23:51,480
Speaker 1: with with one of the big subjects and again quote

470
00:23:51,640 --> 00:23:56,439
Speaker 1: from you. We all operate with certain heuristics, rules of thumb,

471
00:23:57,000 --> 00:24:03,359
Speaker 1: and predictable biases that emanate from those heuristics. Discuss Yeah,

472
00:24:03,520 --> 00:24:07,080
Speaker 1: so you know Conomen and traverse ky Uh. Well, Conomen

473
00:24:08,119 --> 00:24:11,760
Speaker 1: made these two incredible contributions to our understanding. One is

474
00:24:11,800 --> 00:24:13,919
Speaker 1: the heuristics and biases, which we'll talk about in just

475
00:24:13,960 --> 00:24:16,400
Speaker 1: a second, and the other is prospect theory. So how

476
00:24:16,600 --> 00:24:20,680
Speaker 1: human behaviors depart systematically from what economic theories would mean,

477
00:24:20,880 --> 00:24:26,639
Speaker 1: meaning people on the perfect profits seeking rational machines that

478
00:24:26,720 --> 00:24:31,159
Speaker 1: economists describe them as precisely, and the fact that they

479
00:24:31,240 --> 00:24:34,560
Speaker 1: depart in ways that are fairly systematic and predictable. So

480
00:24:34,600 --> 00:24:37,720
Speaker 1: the heuristics and biases literature is really interesting because it

481
00:24:37,840 --> 00:24:40,040
Speaker 1: says that, as you point out the word as rules

482
00:24:40,040 --> 00:24:41,960
Speaker 1: of thumbs, so we all we operate with lots of

483
00:24:42,000 --> 00:24:44,800
Speaker 1: rules of thumb for life. And what's good about rules

484
00:24:44,800 --> 00:24:46,600
Speaker 1: of thumb? The answer is they save you a ton

485
00:24:46,640 --> 00:24:50,280
Speaker 1: of time. And they're often right, keep you alive on

486
00:24:50,320 --> 00:24:54,080
Speaker 1: the savannah Savanna, right. So that's all good, But the

487
00:24:54,119 --> 00:24:57,160
Speaker 1: problem is that these heuristics often come with blind spots

488
00:24:57,240 --> 00:25:00,640
Speaker 1: or these biases that can lead to suboptimal decisions. Let's

489
00:25:00,680 --> 00:25:03,399
Speaker 1: talk about a couple of the big daddies of these biases.

490
00:25:03,840 --> 00:25:07,320
Speaker 1: The first one is over confidence, and by the way,

491
00:25:07,359 --> 00:25:10,000
Speaker 1: that tends to be more expressed than males and females.

492
00:25:10,119 --> 00:25:11,960
Speaker 1: So just there's that's one where there's that tend to

493
00:25:11,960 --> 00:25:14,080
Speaker 1: be a difference in gender. There's a lot of data

494
00:25:14,119 --> 00:25:17,959
Speaker 1: that shows that female fund managers tend to app perform

495
00:25:18,280 --> 00:25:21,800
Speaker 1: male fund managers because they are not afflicted with the

496
00:25:21,880 --> 00:25:25,720
Speaker 1: same sort of testosterone poisoning that leads to over over

497
00:25:25,800 --> 00:25:29,200
Speaker 1: confidence exactly. So how does over confidence manifest? And you've

498
00:25:29,200 --> 00:25:31,439
Speaker 1: already given a little clue on that, right, there are

499
00:25:31,440 --> 00:25:34,639
Speaker 1: two things. One is we we tend to project in

500
00:25:35,359 --> 00:25:38,240
Speaker 1: ranges that are too narrow. We're we we think we

501
00:25:38,359 --> 00:25:41,720
Speaker 1: understand the future much better than we actually do. Right,

502
00:25:41,880 --> 00:25:45,000
Speaker 1: So that's one that's one aspect of it. The second

503
00:25:45,119 --> 00:25:47,480
Speaker 1: is where you had the male female study, you decided

504
00:25:47,800 --> 00:25:49,840
Speaker 1: is it men tend to trade a lot more, right,

505
00:25:49,880 --> 00:25:51,560
Speaker 1: So they think they know what's going on, so they're

506
00:25:51,600 --> 00:25:53,840
Speaker 1: much more active, and that just choose up performance in

507
00:25:53,840 --> 00:25:56,639
Speaker 1: the form of costs. So over confidence. How do you

508
00:25:56,760 --> 00:25:59,960
Speaker 1: mitigate that? The answer would be using things like base rates,

509
00:26:00,000 --> 00:26:01,879
Speaker 1: which we talked about before, just saying what does the

510
00:26:01,920 --> 00:26:04,919
Speaker 1: distribution of outcomes where does it look like historically? And

511
00:26:04,960 --> 00:26:07,320
Speaker 1: if I overlay that on what I'm thinking about today,

512
00:26:07,680 --> 00:26:10,399
Speaker 1: is a reason for me to stretch my expectations. So

513
00:26:10,480 --> 00:26:12,800
Speaker 1: that's over confidence. And that's a really a big one.

514
00:26:12,920 --> 00:26:15,480
Speaker 1: And even for money managers don't trade too much. Those

515
00:26:15,480 --> 00:26:19,200
Speaker 1: are that's a that's the second one. UH framing is

516
00:26:19,240 --> 00:26:21,960
Speaker 1: another really big one. And so the story here buries.

517
00:26:22,000 --> 00:26:25,600
Speaker 1: If I present a story to you with mathematically one

518
00:26:25,600 --> 00:26:28,959
Speaker 1: way mathematically, and then I present the same mathematical puzzle

519
00:26:29,000 --> 00:26:32,119
Speaker 1: to you a different way, I can systematically get you

520
00:26:32,200 --> 00:26:34,600
Speaker 1: to select one or the other right, And that just

521
00:26:34,640 --> 00:26:36,720
Speaker 1: doesn't make any sense because people are actually just doing

522
00:26:36,760 --> 00:26:38,639
Speaker 1: the math. They won't do it that way. So so

523
00:26:38,720 --> 00:26:40,720
Speaker 1: let's let's take a look at an example of that.

524
00:26:40,840 --> 00:26:46,600
Speaker 1: The one that I recall is the hypothetical UH illness,

525
00:26:46,920 --> 00:26:50,720
Speaker 1: where if a doctor says bad news, you have fatal disease.

526
00:26:50,800 --> 00:26:53,920
Speaker 1: Good news is a surgery. And here's the variable if

527
00:26:53,960 --> 00:26:58,159
Speaker 1: the if this doctor says six chance of survival, if

528
00:26:58,200 --> 00:27:00,960
Speaker 1: you have the surgery, a huge number of people do it,

529
00:27:01,480 --> 00:27:06,560
Speaker 1: and the control group gets the bad news fatal disease,

530
00:27:06,640 --> 00:27:09,720
Speaker 1: good news. Hey there's a surgery. One of three people

531
00:27:09,760 --> 00:27:12,800
Speaker 1: don't survive the surgery, but you know it's the only

532
00:27:12,840 --> 00:27:17,560
Speaker 1: choice we have, and that generates a totally different acceptance.

533
00:27:17,600 --> 00:27:21,200
Speaker 1: Right precisely, so we're not we're not doing the math right,

534
00:27:21,320 --> 00:27:24,800
Speaker 1: We're reacting with our affect or emotion because two out

535
00:27:24,840 --> 00:27:27,119
Speaker 1: of three and one out of three or essentially the

536
00:27:27,200 --> 00:27:29,560
Speaker 1: same numbers. If if it's one side as two out

537
00:27:29,560 --> 00:27:31,640
Speaker 1: of three and the alternative is one out of three,

538
00:27:31,720 --> 00:27:34,600
Speaker 1: it's the same transaction. So now you think about the world,

539
00:27:34,640 --> 00:27:37,280
Speaker 1: to go out in the world and say how often

540
00:27:37,320 --> 00:27:40,720
Speaker 1: are these framing effects influencing my own decisions? And you

541
00:27:40,720 --> 00:27:42,800
Speaker 1: would have to guess that it's a pretty substantial amount

542
00:27:42,840 --> 00:27:46,000
Speaker 1: of the time. So can we all systematically or be

543
00:27:46,040 --> 00:27:50,119
Speaker 1: good at translating these different frames into more mathematical or

544
00:27:50,160 --> 00:27:53,639
Speaker 1: more appropriate framework so allow us to make the right decisions.

545
00:27:53,680 --> 00:27:55,520
Speaker 1: And it goes back to our discussion a few moments

546
00:27:55,560 --> 00:27:58,760
Speaker 1: ago also about storytelling. Right if there's a little causality.

547
00:27:58,800 --> 00:27:59,920
Speaker 1: And by the way, I mean, I was talking to

548
00:28:00,000 --> 00:28:02,000
Speaker 1: Position about the same thing. He said. He said, you know,

549
00:28:02,000 --> 00:28:04,200
Speaker 1: if I have a treatment that's fifty fifty in my office,

550
00:28:04,200 --> 00:28:06,320
Speaker 1: some guy comes in with a f goes if I

551
00:28:06,359 --> 00:28:07,879
Speaker 1: want the guy to use this, here's what I say,

552
00:28:07,920 --> 00:28:10,640
Speaker 1: say Barry, it's do you understand that? And the guy,

553
00:28:10,680 --> 00:28:14,280
Speaker 1: not the patient, nods his head say great. Last guy

554
00:28:14,320 --> 00:28:18,480
Speaker 1: who's doing the treatment is doing great, and the guys

555
00:28:18,560 --> 00:28:21,399
Speaker 1: like so the end of one, right, sample size of

556
00:28:21,480 --> 00:28:24,440
Speaker 1: one people will go for right. And if he says not, Barry,

557
00:28:24,480 --> 00:28:27,280
Speaker 1: the last guy who's doing it didn't work so well, right,

558
00:28:27,680 --> 00:28:31,320
Speaker 1: then you know basically they all so it's it's this tagline, right,

559
00:28:31,320 --> 00:28:34,920
Speaker 1: which is even though the numbers are the same, Hey,

560
00:28:34,920 --> 00:28:40,160
Speaker 1: it's a coin to us that that that one example, Um,

561
00:28:40,240 --> 00:28:42,400
Speaker 1: you know that that kind of goes back to back

562
00:28:42,440 --> 00:28:45,480
Speaker 1: to Danny Kahneman, the whole issue of anchoring. If we

563
00:28:45,520 --> 00:28:48,960
Speaker 1: toss out a number high enough when you're starting the negotiation,

564
00:28:49,040 --> 00:28:52,320
Speaker 1: everybody is magically drawn because you're trying to make sense

565
00:28:52,360 --> 00:28:54,720
Speaker 1: of that number and your brain lops in on it. Yeah,

566
00:28:54,760 --> 00:28:56,400
Speaker 1: and anchor is another one I was going to mention

567
00:28:56,400 --> 00:28:58,120
Speaker 1: and I do this with my students at Columbia Business

568
00:28:58,160 --> 00:29:00,000
Speaker 1: School and it's awesome. Right. So I say to him,

569
00:29:00,280 --> 00:29:02,080
Speaker 1: very first day class, I say, right down the last

570
00:29:02,200 --> 00:29:04,480
Speaker 1: four digits of your phone number, right, so every no problem,

571
00:29:04,520 --> 00:29:06,480
Speaker 1: kids do that. And I say, second question, I got

572
00:29:06,480 --> 00:29:09,000
Speaker 1: this from Danny directly. Uh, the number of doctors in

573
00:29:09,040 --> 00:29:12,040
Speaker 1: Manhattan burro of Manhattan higher or lower than that number? Right?

574
00:29:12,080 --> 00:29:14,280
Speaker 1: They usually they know it's gonna be a little bit higher.

575
00:29:14,320 --> 00:29:16,200
Speaker 1: And I say, write down your estimate of the number

576
00:29:16,200 --> 00:29:19,760
Speaker 1: of doctors. And just as predictable as night follows day,

577
00:29:19,840 --> 00:29:22,120
Speaker 1: the people who have high phone number last, which is high,

578
00:29:22,240 --> 00:29:24,680
Speaker 1: guess many more doctors, and the people will low digits

579
00:29:24,680 --> 00:29:26,600
Speaker 1: on their phone number. Right. And so a couple of

580
00:29:26,640 --> 00:29:28,560
Speaker 1: things should be obvious about that number. One is they

581
00:29:28,760 --> 00:29:31,360
Speaker 1: the students know there's no relationship between their phone number

582
00:29:31,440 --> 00:29:34,320
Speaker 1: and the number of doctors. And had I asked those

583
00:29:34,320 --> 00:29:37,680
Speaker 1: same questions in reverse order, tell me the number estimate

584
00:29:37,720 --> 00:29:39,360
Speaker 1: of doctors and then tell me your phone number, you

585
00:29:39,440 --> 00:29:41,480
Speaker 1: get totally different answers. So you know that this is

586
00:29:41,520 --> 00:29:43,760
Speaker 1: an effect. And I think the point that that Danny

587
00:29:43,840 --> 00:29:46,640
Speaker 1: Conmon makes is that this is fairly pernicious in the

588
00:29:46,680 --> 00:29:49,720
Speaker 1: sense that you can do an hour lecture on anchoring

589
00:29:50,760 --> 00:29:53,680
Speaker 1: and do another experiment on it and it still works. Right.

590
00:29:53,720 --> 00:29:55,560
Speaker 1: So in other words, it's like your mind. It's a

591
00:29:55,600 --> 00:29:57,160
Speaker 1: really hard one for your mind to get around. And

592
00:29:57,160 --> 00:29:59,160
Speaker 1: you mentioned you know, M and A, and this shows up.

593
00:29:59,200 --> 00:30:01,640
Speaker 1: Fifty two weeks is high levels for the markets, all

594
00:30:01,680 --> 00:30:05,239
Speaker 1: these you know, round numbers for the down all these

595
00:30:05,240 --> 00:30:07,800
Speaker 1: sorts of things show up. You know, what does what

596
00:30:07,800 --> 00:30:10,840
Speaker 1: does it mean? The answer is basically nothing, but it

597
00:30:11,400 --> 00:30:14,240
Speaker 1: has these sort of uh, these anchoring effects. So yeah,

598
00:30:14,280 --> 00:30:16,440
Speaker 1: that's another one. So so there's a very rich literature

599
00:30:16,480 --> 00:30:18,800
Speaker 1: on this. And again I say to people, really important

600
00:30:18,880 --> 00:30:21,960
Speaker 1: understand it. Uh. And then as important is to try

601
00:30:21,960 --> 00:30:24,680
Speaker 1: to think about are there ways again can I offset

602
00:30:24,720 --> 00:30:30,320
Speaker 1: these forces? What is the antidote two overconfidence to framing,

603
00:30:30,440 --> 00:30:32,280
Speaker 1: to anchoring, and how do I try to try to

604
00:30:32,280 --> 00:30:33,719
Speaker 1: bring those to bear when I need to to make

605
00:30:33,720 --> 00:30:37,360
Speaker 1: good decisions. So let's talk about one of my pet peeves,

606
00:30:37,360 --> 00:30:42,320
Speaker 1: which which you have discussed many times, which is I

607
00:30:42,400 --> 00:30:45,600
Speaker 1: get miffed every time someone is on TV and blaming

608
00:30:45,680 --> 00:30:49,880
Speaker 1: uncertainty for the economy, the stock market for whatever. Um.

609
00:30:50,040 --> 00:30:53,160
Speaker 1: I always think that's a dodge, And I really like

610
00:30:53,320 --> 00:30:57,320
Speaker 1: the way you described it, uh, talking about whether or

611
00:30:57,360 --> 00:31:02,640
Speaker 1: not the underlying distribution of outcome is undefined, UH, and

612
00:31:02,720 --> 00:31:06,600
Speaker 1: what the risk distribution looks like. In other words, when

613
00:31:06,640 --> 00:31:08,880
Speaker 1: you spin a Roulette wheel or roll a pair of dice,

614
00:31:09,640 --> 00:31:11,959
Speaker 1: you may not know what numbers are going to come up,

615
00:31:12,200 --> 00:31:16,120
Speaker 1: but the set of possible outcomes is well known in advance.

616
00:31:17,080 --> 00:31:20,080
Speaker 1: What is it about uncertainty that leads people to so

617
00:31:20,200 --> 00:31:23,360
Speaker 1: totally misunderstand that? Yeah, I don't know, and I think

618
00:31:23,360 --> 00:31:25,280
Speaker 1: that you know this is by the way, this concept

619
00:31:25,320 --> 00:31:28,080
Speaker 1: that you've articulated came from Frank Knight. So it's been

620
00:31:28,120 --> 00:31:31,080
Speaker 1: an idea that's been around for mathematicians. Yeah, I think

621
00:31:31,080 --> 00:31:33,880
Speaker 1: it was an economist years ago. So it's been around

622
00:31:33,880 --> 00:31:35,320
Speaker 1: for a long time. And you know, I think it's

623
00:31:35,360 --> 00:31:37,360
Speaker 1: a debate of you know, sort of an ongoing debate

624
00:31:37,400 --> 00:31:41,000
Speaker 1: among economists and philosophers and so forth. But no, exactly

625
00:31:41,040 --> 00:31:42,720
Speaker 1: what you said, So, risk is this idea that you

626
00:31:42,720 --> 00:31:45,040
Speaker 1: don't know the outcome, but you know all the possible

627
00:31:45,360 --> 00:31:47,400
Speaker 1: out as you said, to rule that wheel or the

628
00:31:47,440 --> 00:31:49,640
Speaker 1: turn of car or what have you. And uncertainty is

629
00:31:49,680 --> 00:31:51,840
Speaker 1: where we don't know the outcome, but we don't really

630
00:31:51,840 --> 00:31:54,400
Speaker 1: know what the underlying distribution. So that's in character a

631
00:31:54,560 --> 00:31:57,560
Speaker 1: very very different thing. War invasion of Iraq is a

632
00:31:57,600 --> 00:31:59,640
Speaker 1: good exactly, But there's some interesting I mean, even some

633
00:31:59,680 --> 00:32:01,479
Speaker 1: of these things you know not seemte lab is very

634
00:32:01,520 --> 00:32:04,360
Speaker 1: well known for talking about things like black swans, but

635
00:32:04,600 --> 00:32:07,640
Speaker 1: you know, in reality, most things in life are really

636
00:32:07,640 --> 00:32:09,440
Speaker 1: not in the black swan realm. They're more in the

637
00:32:09,480 --> 00:32:12,120
Speaker 1: gray swan realm. So for example, earthquakes, you know they

638
00:32:12,160 --> 00:32:15,280
Speaker 1: follow power law, you can't really predict them, but we

639
00:32:15,320 --> 00:32:17,880
Speaker 1: know what the distribution looks like, right, so you know, okay,

640
00:32:17,880 --> 00:32:19,280
Speaker 1: So that so you have a sense of what's going on.

641
00:32:19,680 --> 00:32:21,760
Speaker 1: So the question is whether these things are Are they

642
00:32:21,840 --> 00:32:24,200
Speaker 1: practical in your day to day life. I think the

643
00:32:24,200 --> 00:32:27,000
Speaker 1: answer to that is yes. But the second thing, and

644
00:32:27,040 --> 00:32:29,080
Speaker 1: also I think this is an issue that we're we

645
00:32:29,240 --> 00:32:33,040
Speaker 1: were in agreement on, is if you ask the question

646
00:32:33,160 --> 00:32:39,640
Speaker 1: our markets risky or our markets uncertain? Now, if they're risky,

647
00:32:39,720 --> 00:32:42,600
Speaker 1: we can try out a bunch of mathematics to model them.

648
00:32:42,640 --> 00:32:45,680
Speaker 1: If they're not, or they have components of uncertainty, then

649
00:32:45,720 --> 00:32:47,360
Speaker 1: we're not using the right tools. And I think I

650
00:32:47,360 --> 00:32:49,840
Speaker 1: would just basically say most of the standard finance is

651
00:32:49,880 --> 00:32:55,040
Speaker 1: based on normal distributions. Whenever you utter phrases like alpha, beta, uh,

652
00:32:55,280 --> 00:33:00,320
Speaker 1: standard deviation. Right, you're using the language of risk to

653
00:33:00,480 --> 00:33:04,440
Speaker 1: describe a system which may have elements and maybe big

654
00:33:04,480 --> 00:33:07,360
Speaker 1: elements of uncertainty. So that to me is another really

655
00:33:07,360 --> 00:33:12,000
Speaker 1: interesting disconnect um. And look, it may work of the time,

656
00:33:12,040 --> 00:33:15,440
Speaker 1: but that one percent may be really challenging. So I

657
00:33:15,440 --> 00:33:17,680
Speaker 1: think you're writing like you said, I mean, uncertainty is

658
00:33:17,720 --> 00:33:19,800
Speaker 1: a phrase. It's a catch all, right, So it basically

659
00:33:19,840 --> 00:33:21,320
Speaker 1: means I don't know what's going on, you throw it

660
00:33:21,320 --> 00:33:24,720
Speaker 1: into uncertainty. But but I think that I found that distinction,

661
00:33:24,760 --> 00:33:27,200
Speaker 1: at least personally, to be a useful one to make

662
00:33:27,440 --> 00:33:29,880
Speaker 1: as as I try to navigate the world. We've been

663
00:33:29,920 --> 00:33:34,000
Speaker 1: speaking with Michael Mobison, head of Global Strategies at Credit Swiss,

664
00:33:34,520 --> 00:33:38,560
Speaker 1: author of such books as The Success Equation, Think Twice

665
00:33:38,880 --> 00:33:41,600
Speaker 1: and more than you know. If people want to find

666
00:33:41,760 --> 00:33:44,240
Speaker 1: your writings, where's the best place for them to to

667
00:33:44,360 --> 00:33:49,240
Speaker 1: either get your white papers or any of your other writings. Um, well,

668
00:33:49,280 --> 00:33:51,680
Speaker 1: I have a website which is Michael Mobison dot com.

669
00:33:51,880 --> 00:33:53,560
Speaker 1: So you'll probably look up the spelling, but if you

670
00:33:53,600 --> 00:33:55,680
Speaker 1: google it you'll find it probably one way or another.

671
00:33:56,240 --> 00:33:58,760
Speaker 1: And um, the other thing is probably Twitter is another

672
00:33:58,880 --> 00:34:01,680
Speaker 1: you know at MG Mobison, and so a bunch of

673
00:34:01,680 --> 00:34:03,719
Speaker 1: the stuff tends to find its way onto Twitter one

674
00:34:03,720 --> 00:34:06,080
Speaker 1: way or another. So those are probably the two best horses.

675
00:34:06,520 --> 00:34:09,280
Speaker 1: If you enjoy this conversation, be sure and stick around

676
00:34:09,280 --> 00:34:11,840
Speaker 1: and check out the podcast extras, where we keep the

677
00:34:11,880 --> 00:34:16,320
Speaker 1: tape rolling and continue chatting about all things UH, decision

678
00:34:16,360 --> 00:34:20,480
Speaker 1: making and cognitive. Be sure and follow my daily column

679
00:34:20,480 --> 00:34:24,399
Speaker 1: on Bloomberg dot com or follow me on Twitter at

680
00:34:24,520 --> 00:34:28,160
Speaker 1: rit Halts. I'm Barry Rit Halts. You've been listening to

681
00:34:28,320 --> 00:34:33,560
Speaker 1: Masters in Business on Bloomberg Radio. Welcome to the podcast portion. Uh, Mike,

682
00:34:33,640 --> 00:34:35,319
Speaker 1: thanks so much for doing this. You know you're one

683
00:34:35,360 --> 00:34:39,400
Speaker 1: of our few repeat guests. And I listened to the first,

684
00:34:39,719 --> 00:34:43,160
Speaker 1: uh first podcast you and I did. You were literally

685
00:34:43,200 --> 00:34:45,960
Speaker 1: one of the first dozen or so podcasts, and I

686
00:34:46,040 --> 00:34:48,600
Speaker 1: have to tell you, I was awful, but you were great.

687
00:34:49,520 --> 00:34:52,719
Speaker 1: So UM, I would like to think that in the

688
00:34:52,920 --> 00:34:56,439
Speaker 1: ensuing one hundred podcasts in the ensuing two years, I've

689
00:34:56,480 --> 00:35:01,320
Speaker 1: developed some skill at shutting up, listening in and asking questions.

690
00:35:01,360 --> 00:35:06,240
Speaker 1: Although that's arguable this there's a lot of questions that

691
00:35:06,239 --> 00:35:09,799
Speaker 1: that we missed. I wanna jump back to some of them,

692
00:35:09,840 --> 00:35:12,239
Speaker 1: but before I do, I have to come back to

693
00:35:12,400 --> 00:35:16,640
Speaker 1: the uncertainty name. So I've been using your definition, which

694
00:35:16,680 --> 00:35:21,960
Speaker 1: is based on the earlier mathematical definition. But the footnote

695
00:35:22,000 --> 00:35:24,239
Speaker 1: to that that I wanted to share with you is

696
00:35:25,200 --> 00:35:28,240
Speaker 1: the reason. My belief as to why the reason people

697
00:35:28,320 --> 00:35:31,879
Speaker 1: fall back on it is we talk about how our

698
00:35:32,080 --> 00:35:37,799
Speaker 1: expectations map or don't to reality. My belief is that

699
00:35:37,960 --> 00:35:43,600
Speaker 1: most people's sixty degree in time and space worldview doesn't

700
00:35:43,640 --> 00:35:47,600
Speaker 1: map remotely close. There are just little areas where almost

701
00:35:47,640 --> 00:35:51,520
Speaker 1: accidentally it's right, and we lie to ourselves so successfully

702
00:35:51,640 --> 00:35:54,880
Speaker 1: that we understand otherwise how do you even cross the street.

703
00:35:55,280 --> 00:35:59,080
Speaker 1: So there's this bit of subconscious self deception that allows

704
00:35:59,160 --> 00:36:04,600
Speaker 1: us to operate under normal circumstances. And there are moments

705
00:36:04,680 --> 00:36:08,520
Speaker 1: of terror when it's revealed to us by the events

706
00:36:09,040 --> 00:36:13,920
Speaker 1: that we really are completely um poorly mapped, let's call

707
00:36:13,960 --> 00:36:17,680
Speaker 1: it that. And that's when people tried out the uncertainty defense.

708
00:36:18,160 --> 00:36:20,640
Speaker 1: And you see it during financial crisis, you see it

709
00:36:20,719 --> 00:36:24,640
Speaker 1: during geopolitics. It's a way of saying, all right, I

710
00:36:24,680 --> 00:36:27,520
Speaker 1: will admit temporarily that I have no idea what's going on,

711
00:36:27,960 --> 00:36:30,360
Speaker 1: and soon I'll be able to go back to deluding

712
00:36:30,400 --> 00:36:32,839
Speaker 1: myself that I have a good handle. And we see

713
00:36:32,840 --> 00:36:37,040
Speaker 1: it anytime people talk about the future, the the confidence

714
00:36:37,120 --> 00:36:39,799
Speaker 1: or lack thereof as to here's what the next six

715
00:36:39,840 --> 00:36:43,040
Speaker 1: months or a year it was gonna look like. Phil

716
00:36:43,080 --> 00:36:47,040
Speaker 1: Tetlock does such a great job on on taking that apart. Absolutely.

717
00:36:47,160 --> 00:36:49,239
Speaker 1: You know, there's a there's a great book by Dan

718
00:36:49,280 --> 00:36:53,680
Speaker 1: Gilbert and Stumbling on Happiness where he makes the point

719
00:36:53,760 --> 00:37:00,120
Speaker 1: that mentally healthy people are mildly cognitively delusional. So you

720
00:37:00,200 --> 00:37:04,320
Speaker 1: are mentally healthy, you have a little bubble around yourself

721
00:37:04,360 --> 00:37:06,560
Speaker 1: thinking that you, you know, know a little bit more

722
00:37:06,600 --> 00:37:08,680
Speaker 1: than you do, your a little better looking than you

723
00:37:08,719 --> 00:37:11,400
Speaker 1: actually are, and so forth, hence all the other confidence

724
00:37:11,440 --> 00:37:14,080
Speaker 1: and yeah, exactly, and and in fact, people who are

725
00:37:14,200 --> 00:37:16,880
Speaker 1: mildly clinically depressed actually have a much more accurate view

726
00:37:16,920 --> 00:37:19,760
Speaker 1: of the world than the rest of us. Now, that's

727
00:37:19,880 --> 00:37:23,319
Speaker 1: very funny, and that that explains everything that's going on

728
00:37:23,360 --> 00:37:27,919
Speaker 1: in Bridgewater these days. With their radical transparency, I get

729
00:37:27,920 --> 00:37:36,000
Speaker 1: the sense that they probably are mildly clinically depressed, but

730
00:37:36,280 --> 00:37:38,120
Speaker 1: have a more accurate view of the world. Be couse,

731
00:37:39,040 --> 00:37:43,600
Speaker 1: these guys call each other out for everything, it's quite amazing. Right.

732
00:37:43,640 --> 00:37:47,560
Speaker 1: So so the what's good about this mild delusion often

733
00:37:47,560 --> 00:37:49,440
Speaker 1: as it gets people out of bed in the morning. Right.

734
00:37:49,480 --> 00:37:50,719
Speaker 1: So in other words, you if you think you're a

735
00:37:50,719 --> 00:37:52,799
Speaker 1: little better than you are and so forth, you sort

736
00:37:52,800 --> 00:37:55,000
Speaker 1: of you go at it again, and if you really

737
00:37:55,000 --> 00:37:56,799
Speaker 1: had a more accurate view of the world, you might

738
00:37:56,840 --> 00:37:58,719
Speaker 1: just stay in bed. But it is, it is this

739
00:37:58,840 --> 00:38:01,640
Speaker 1: intriging thing. So so the key is for that that

740
00:38:01,800 --> 00:38:05,680
Speaker 1: mentally healthy condition not to be debilitating your decision making

741
00:38:05,719 --> 00:38:08,440
Speaker 1: it to your points. So you in the world of investing,

742
00:38:08,680 --> 00:38:10,400
Speaker 1: you do have to have an accurate map. You have

743
00:38:10,480 --> 00:38:12,920
Speaker 1: to have you have to have a good belief updating system,

744
00:38:13,280 --> 00:38:15,239
Speaker 1: you have to be actively open minded, you have to

745
00:38:15,239 --> 00:38:18,399
Speaker 1: consider different points of view, and all those things take

746
00:38:18,440 --> 00:38:22,800
Speaker 1: a lot of cognitive energy that most of us, truth

747
00:38:22,920 --> 00:38:25,560
Speaker 1: be told, would rather not expend or we'd rather do

748
00:38:25,640 --> 00:38:28,680
Speaker 1: something else. Right. So so that's that's another thing. Who's

749
00:38:28,920 --> 00:38:32,160
Speaker 1: what distinguishes great decision makers, not just investors, but in

750
00:38:32,480 --> 00:38:35,279
Speaker 1: sports team managers or in business from the rest of us.

751
00:38:35,600 --> 00:38:37,480
Speaker 1: And the answer is they tend to be those folks

752
00:38:37,480 --> 00:38:41,000
Speaker 1: that are more malleable cognitively than the rest of us.

753
00:38:41,920 --> 00:38:45,560
Speaker 1: In super foecasters. Tet Luck goes over the group of

754
00:38:45,600 --> 00:38:50,319
Speaker 1: people who approached decision making almost with the checklist of

755
00:38:50,400 --> 00:38:52,680
Speaker 1: what do we know to be true, what's relevant, what

756
00:38:52,800 --> 00:38:55,960
Speaker 1: can we rely on? Where a blind spots before they

757
00:38:55,960 --> 00:38:59,719
Speaker 1: even start the process of making So any of their

758
00:38:59,760 --> 00:39:04,279
Speaker 1: so cold forecasts really are just more educated guesses than

759
00:39:04,320 --> 00:39:07,799
Speaker 1: the rest of us lazy civilians. Engaging. Yeah, and the

760
00:39:07,800 --> 00:39:10,520
Speaker 1: other thing I liked about super forecasters is that you

761
00:39:10,560 --> 00:39:13,320
Speaker 1: know they're not the folks are making those good forecasts

762
00:39:13,360 --> 00:39:15,560
Speaker 1: are not geniuses. I mean, they're they're brighter than average,

763
00:39:15,600 --> 00:39:17,839
Speaker 1: but they're not geniuses, right, And like you said, it's

764
00:39:17,920 --> 00:39:23,280
Speaker 1: much more about their systematic approach to making forecasts. And

765
00:39:23,480 --> 00:39:25,480
Speaker 1: I think what's encouraging for the rest of us is

766
00:39:25,520 --> 00:39:28,520
Speaker 1: that many of those behaviors can be emulated, many of

767
00:39:28,520 --> 00:39:31,800
Speaker 1: those behaviors can be copied. Right, So so there's some hope,

768
00:39:31,800 --> 00:39:33,840
Speaker 1: at least, even if I can't change anybody's I Q,

769
00:39:34,160 --> 00:39:37,399
Speaker 1: that maybe we can contribute to their quality their decision making.

770
00:39:37,520 --> 00:39:40,479
Speaker 1: So and better decision making would certainly go a long way.

771
00:39:40,760 --> 00:39:44,120
Speaker 1: You know, I don't recall if we mentioned this last time,

772
00:39:44,120 --> 00:39:46,399
Speaker 1: but I wanted to bring it up. Did we talk

773
00:39:46,440 --> 00:39:51,000
Speaker 1: about Wall Street trading desks and why they're populated with

774
00:39:51,600 --> 00:39:54,360
Speaker 1: college athletes that has that? Has that ever come up

775
00:39:54,400 --> 00:39:56,919
Speaker 1: with us? No? I don't think so, have you? Well,

776
00:39:56,960 --> 00:40:00,360
Speaker 1: so in my experience, I've noticed half of the desks

777
00:40:00,360 --> 00:40:04,279
Speaker 1: on the street people played ball in college. Have you

778
00:40:04,560 --> 00:40:09,400
Speaker 1: witnessed something similar? Yeah, you're young. Yes, so it seems

779
00:40:09,400 --> 00:40:13,879
Speaker 1: like a lot of more lacrosse players. Yes, lacrosse. Um, well,

780
00:40:14,000 --> 00:40:20,040
Speaker 1: usually because uh, there's only so many football, baseball, basketball players,

781
00:40:20,080 --> 00:40:22,520
Speaker 1: and and but you see a lot of that, you

782
00:40:22,560 --> 00:40:26,120
Speaker 1: see track. So my pet thesis on that is something

783
00:40:26,160 --> 00:40:31,080
Speaker 1: that is your paradox of skill, which is, if you're

784
00:40:31,080 --> 00:40:35,319
Speaker 1: playing an n c A sport, pick any division in

785
00:40:35,400 --> 00:40:39,200
Speaker 1: Division one, A, Division two, any any any team can

786
00:40:39,239 --> 00:40:41,520
Speaker 1: beat any other team on any other game the weekend.

787
00:40:41,960 --> 00:40:44,160
Speaker 1: It's not you know, you don't end up or you

788
00:40:44,239 --> 00:40:47,800
Speaker 1: very rarely end up with a dominant team that goes

789
00:40:48,000 --> 00:40:50,640
Speaker 1: sixteen and oh for the uh, you win some, you

790
00:40:50,680 --> 00:40:55,160
Speaker 1: lose some. And because of the skill level is fairly

791
00:40:55,840 --> 00:40:59,600
Speaker 1: that tight range like batting averages, a bad call, a

792
00:40:59,680 --> 00:41:03,680
Speaker 1: lucky bounce, an injury, and any team can beat any

793
00:41:03,680 --> 00:41:07,560
Speaker 1: other team in any given game. So after you worked

794
00:41:07,560 --> 00:41:09,440
Speaker 1: your butt off all week and then you go and

795
00:41:09,480 --> 00:41:13,840
Speaker 1: then some bead you know, bounce loses the game for you.

796
00:41:13,840 --> 00:41:16,440
Speaker 1: You have to get up Monday morning and start your

797
00:41:16,520 --> 00:41:22,440
Speaker 1: routine all over again. Which describes sports as as accurately

798
00:41:22,440 --> 00:41:25,399
Speaker 1: as it does working on a trading desk. I mean

799
00:41:25,440 --> 00:41:27,440
Speaker 1: I buy all that. I mean I think that a

800
00:41:27,480 --> 00:41:30,600
Speaker 1: lot of this athletes on trading desks. Is also the

801
00:41:30,640 --> 00:41:34,720
Speaker 1: fact that, uh, they're often especially around here East Coast

802
00:41:34,880 --> 00:41:38,360
Speaker 1: schools for the most part. Um also a lot of

803
00:41:38,400 --> 00:41:41,640
Speaker 1: the lums. So there's a whole little pipeline, you know,

804
00:41:41,719 --> 00:41:43,880
Speaker 1: in terms of what I could see the athletes you know,

805
00:41:43,960 --> 00:41:46,600
Speaker 1: typically right, they work together, they were they're used to

806
00:41:46,640 --> 00:41:51,600
Speaker 1: hard discipline, team or stuff. Is true. So um, yeah,

807
00:41:51,640 --> 00:41:54,240
Speaker 1: so I think that's all. I buy all that. So

808
00:41:54,239 --> 00:41:58,080
Speaker 1: so let's take before we get down to our favorite questions.

809
00:41:58,200 --> 00:42:01,520
Speaker 1: Let's let's let's go through some of the questions that

810
00:42:01,520 --> 00:42:06,080
Speaker 1: that we missed. Um, we've we've talked. Let's let's talk

811
00:42:06,120 --> 00:42:09,799
Speaker 1: a little bit about probabilities and some interesting other factors.

812
00:42:10,560 --> 00:42:15,319
Speaker 1: Another quote of your success in a probabilistic field requires

813
00:42:15,320 --> 00:42:21,160
Speaker 1: waiting probabilities and outcomes. That is an expected value mindset.

814
00:42:22,239 --> 00:42:25,200
Speaker 1: Why is that? Let's describe that a little bit. You know,

815
00:42:25,280 --> 00:42:31,440
Speaker 1: it's a well because again, the future has many possible outcomes,

816
00:42:31,480 --> 00:42:33,600
Speaker 1: and what you're really trying to do is figure out

817
00:42:33,640 --> 00:42:36,200
Speaker 1: situations where you have some sort of advantage or some

818
00:42:36,280 --> 00:42:39,080
Speaker 1: sort of an edge, where if you were able to

819
00:42:39,120 --> 00:42:41,399
Speaker 1: play the world a lot of different times, you would

820
00:42:41,400 --> 00:42:43,600
Speaker 1: win a lot more times than you would lose. And

821
00:42:43,600 --> 00:42:45,399
Speaker 1: and of course there are scenarios where you would lose,

822
00:42:45,400 --> 00:42:47,279
Speaker 1: but you would win many more times than you would lose.

823
00:42:48,000 --> 00:42:51,759
Speaker 1: And the question then becomes, are there ways that we

824
00:42:51,800 --> 00:42:56,239
Speaker 1: can thoughtfully assign probabilities and outcomes? But I think as

825
00:42:56,239 --> 00:42:59,320
Speaker 1: a structured way of approaching any almost any kind of problem,

826
00:42:59,400 --> 00:43:02,279
Speaker 1: it works. Right. So you might say even a more

827
00:43:02,360 --> 00:43:04,600
Speaker 1: mundane thing like yeah, you're the general manager of the

828
00:43:04,640 --> 00:43:07,080
Speaker 1: Yankees now or the Mets or whatever it is, and

829
00:43:07,120 --> 00:43:10,600
Speaker 1: you have to find athletes, uh to play your team.

830
00:43:10,600 --> 00:43:12,479
Speaker 1: You have to draft somebody. You know, you don't really

831
00:43:12,480 --> 00:43:14,600
Speaker 1: know what that guy is about. You don't really have

832
00:43:14,680 --> 00:43:18,680
Speaker 1: a completely known entity. It's a probabilistic view. But if

833
00:43:18,719 --> 00:43:21,279
Speaker 1: you say, guys like this of this age and this

834
00:43:21,400 --> 00:43:24,759
Speaker 1: ability have played certain way x percent have done well, right,

835
00:43:24,760 --> 00:43:26,920
Speaker 1: so that's a probability and some sort of an outcome,

836
00:43:27,440 --> 00:43:29,040
Speaker 1: and again they're gonna be some variance from that, but

837
00:43:29,080 --> 00:43:30,560
Speaker 1: that's probably the right way to think about it. And

838
00:43:30,560 --> 00:43:32,360
Speaker 1: then that allows you to understand how much you should

839
00:43:32,360 --> 00:43:34,279
Speaker 1: probably wanting to pay for the guy right in terms

840
00:43:34,280 --> 00:43:37,120
Speaker 1: of contract and so forth. I think that idea spills

841
00:43:37,160 --> 00:43:40,239
Speaker 1: over to most things we look at, So it's just

842
00:43:40,440 --> 00:43:42,840
Speaker 1: it is just getting into this constant discipline understanding. I

843
00:43:43,000 --> 00:43:46,040
Speaker 1: know the range of outcomes, I know the probabilities or

844
00:43:46,200 --> 00:43:48,640
Speaker 1: some sense of those things, and I'm only gonna bet,

845
00:43:48,640 --> 00:43:52,319
Speaker 1: by the way, when the odds are really good and others.

846
00:43:52,360 --> 00:43:53,759
Speaker 1: I can make a lot of money if I'm right,

847
00:43:53,800 --> 00:43:55,759
Speaker 1: and I don't lose so much fun wrong. That's what

848
00:43:55,880 --> 00:43:59,560
Speaker 1: made Money Bull so fascinating is the underlying expectations for

849
00:43:59,680 --> 00:44:05,480
Speaker 1: the ability was not based on provable, quantified data, but

850
00:44:06,120 --> 00:44:09,400
Speaker 1: all those heuristics and rule of thumbs that had basically

851
00:44:09,440 --> 00:44:13,000
Speaker 1: dominated baseball for a century. And I found that book

852
00:44:13,360 --> 00:44:16,120
Speaker 1: as well as the movie to be so fascinating because

853
00:44:16,120 --> 00:44:19,279
Speaker 1: suddenly you have an industry that wakes up and realizes, oh,

854
00:44:19,320 --> 00:44:22,080
Speaker 1: we've been doing this wrong for a century. Yeah, and Barry,

855
00:44:22,120 --> 00:44:25,600
Speaker 1: I think that this remains remarkably, remains fairly pervasive. I

856
00:44:25,640 --> 00:44:27,960
Speaker 1: think it's less true and Major League Baseball because everyone's

857
00:44:28,000 --> 00:44:30,040
Speaker 1: read the book and has their analytical staffs. But you

858
00:44:30,040 --> 00:44:32,080
Speaker 1: look at other professional sports, by the way, you know,

859
00:44:32,120 --> 00:44:35,160
Speaker 1: things like the NFL, where they're big dollars at stake,

860
00:44:35,360 --> 00:44:37,200
Speaker 1: they're still doing some things that don't make a ton

861
00:44:37,239 --> 00:44:39,960
Speaker 1: of sense. The NHL right where it's a much more

862
00:44:39,960 --> 00:44:42,880
Speaker 1: difficult analytical task, a lot of guys making decisions that

863
00:44:42,920 --> 00:44:45,360
Speaker 1: don't make a lot of sense. So continues to be

864
00:44:45,440 --> 00:44:48,200
Speaker 1: the case out there generally speaking. And a lot of it,

865
00:44:48,239 --> 00:44:51,200
Speaker 1: by the way, is that people grew up with a sport,

866
00:44:51,640 --> 00:44:54,680
Speaker 1: so they rely on their own experience, They rely on

867
00:44:54,719 --> 00:44:57,040
Speaker 1: their own view of the world. It goes back before

868
00:44:57,080 --> 00:44:59,839
Speaker 1: their frame or reference right, and they can't expand their

869
00:45:00,040 --> 00:45:03,120
Speaker 1: you to understand different alternative points of view, and that

870
00:45:03,160 --> 00:45:06,880
Speaker 1: can be really problematic. I am not a huge sports

871
00:45:07,040 --> 00:45:11,520
Speaker 1: book fan. I find them to be predictable and tedious.

872
00:45:11,560 --> 00:45:14,600
Speaker 1: But one that I always recommend to people is the

873
00:45:14,680 --> 00:45:18,320
Speaker 1: former UH coach of the Giants, Tom Conklin, did a

874
00:45:18,360 --> 00:45:21,680
Speaker 1: book called Earned the Right to Win, and one of

875
00:45:21,760 --> 00:45:25,440
Speaker 1: his linebackers tells the story, and this guy goes on

876
00:45:25,480 --> 00:45:27,200
Speaker 1: to have an all star career and he worked with

877
00:45:27,239 --> 00:45:29,480
Speaker 1: Conklin for a few years, and a lot of the

878
00:45:29,560 --> 00:45:35,080
Speaker 1: athletes chafed at Conklin's version of moneyball, So he would

879
00:45:35,320 --> 00:45:38,120
Speaker 1: run a whole bunch of stats. What does this team

880
00:45:38,200 --> 00:45:40,760
Speaker 1: do when it's third and one? What does this team

881
00:45:40,800 --> 00:45:45,000
Speaker 1: do on on you know, first deep in their own territory.

882
00:45:45,000 --> 00:45:47,239
Speaker 1: What they came up with. You're not gonna come up

883
00:45:47,280 --> 00:45:50,439
Speaker 1: with every parameter people can possibly remember that, but they

884
00:45:50,520 --> 00:45:53,719
Speaker 1: came up with enough of them. And this this linebacker

885
00:45:54,040 --> 00:45:57,200
Speaker 1: is whose name escapes me at the moment, chafed and

886
00:45:57,280 --> 00:46:01,000
Speaker 1: chafed and chafed about it, eventually adapts to the system

887
00:46:01,080 --> 00:46:05,760
Speaker 1: and sort of subconsciously adapts to it. Years later, he's

888
00:46:05,840 --> 00:46:09,279
Speaker 1: traded and he's playing for a different team and it's

889
00:46:09,360 --> 00:46:11,400
Speaker 1: third and one, and he says, all right, what do

890
00:46:11,520 --> 00:46:14,160
Speaker 1: these guys do on third and one? And he goes,

891
00:46:14,160 --> 00:46:16,759
Speaker 1: oh my god, I don't know. Conklin was right all

892
00:46:16,760 --> 00:46:19,760
Speaker 1: these year year and actually reached out to him and said, hey,

893
00:46:20,000 --> 00:46:22,640
Speaker 1: you're right. Sorry, I gave you grief about it. It's

894
00:46:22,680 --> 00:46:25,320
Speaker 1: it's one of those things. But stop and think about

895
00:46:25,360 --> 00:46:29,400
Speaker 1: how many sports haven't adapted that, and how much money

896
00:46:29,440 --> 00:46:33,600
Speaker 1: is involved. It's really quite fascinating. So speaking of not

897
00:46:33,760 --> 00:46:38,440
Speaker 1: adapting and and how much money is involved. UM, I

898
00:46:38,480 --> 00:46:43,480
Speaker 1: love this data point which my head of research, Mike Batnick, uncovered,

899
00:46:43,480 --> 00:46:47,480
Speaker 1: and it's it's fascinating. Since two thousand and five, actually,

900
00:46:47,480 --> 00:46:50,120
Speaker 1: from the period from two thousand five to two thousand thirteen,

901
00:46:50,760 --> 00:46:55,560
Speaker 1: approximately fifty thousand new global funds were launched. That's not

902
00:46:55,640 --> 00:46:59,520
Speaker 1: total funds. That's the number of new mutual funds, ETFs,

903
00:47:00,000 --> 00:47:04,480
Speaker 1: hedge funds, etcetera. Were launched. Fifty seven thousand. It's ten

904
00:47:04,560 --> 00:47:08,759
Speaker 1: times in a number of stocks in the US. What

905
00:47:08,880 --> 00:47:13,840
Speaker 1: does this tell us about our abilities to distinguish between

906
00:47:13,880 --> 00:47:17,040
Speaker 1: skill and luck. I don't know if it's about skill

907
00:47:17,080 --> 00:47:20,080
Speaker 1: and luck as much as it is about um the

908
00:47:20,120 --> 00:47:23,480
Speaker 1: idea of marketing and raising assets. And uh, you know

909
00:47:23,719 --> 00:47:27,760
Speaker 1: before we talked a few moments before about the really

910
00:47:27,880 --> 00:47:31,719
Speaker 1: powerful trend and it's accelerated since the financial crisis of

911
00:47:31,800 --> 00:47:35,000
Speaker 1: a move into passive investing. And some of that's been

912
00:47:35,040 --> 00:47:36,680
Speaker 1: index funds, but a lot of it's been E t

913
00:47:36,960 --> 00:47:40,120
Speaker 1: f s as well. And uh, but not but there

914
00:47:40,200 --> 00:47:43,399
Speaker 1: isn't t s, but there are others, so they're fun.

915
00:47:43,480 --> 00:47:44,840
Speaker 1: So so I think there are a couple of things

916
00:47:44,880 --> 00:47:47,040
Speaker 1: that you know, this is what Charlie Ellis talked about

917
00:47:47,080 --> 00:47:49,759
Speaker 1: the difference between the profession and the business, and the

918
00:47:49,840 --> 00:47:52,799
Speaker 1: professions about delivering and and Jack Bogo and others, the

919
00:47:52,800 --> 00:47:56,160
Speaker 1: professions about delivering access returns. The business is about selling

920
00:47:56,239 --> 00:47:59,680
Speaker 1: people what is in demand today. And going back to

921
00:47:59,680 --> 00:48:02,000
Speaker 1: even five, what happened is we had a huge run

922
00:48:02,080 --> 00:48:05,600
Speaker 1: up and energy. Guess what happened. Zillions of energy funds

923
00:48:05,640 --> 00:48:08,560
Speaker 1: got launched. We had gold that was hot for a while.

924
00:48:08,680 --> 00:48:12,759
Speaker 1: What happened, zillions of gold funds got introduced. You know.

925
00:48:13,000 --> 00:48:15,359
Speaker 1: Then then the markets go down. People go, I need

926
00:48:15,400 --> 00:48:18,160
Speaker 1: given in, I need yields, and now you have yield funds. Right,

927
00:48:18,200 --> 00:48:21,840
Speaker 1: So whatever worked in the last two or three years

928
00:48:22,440 --> 00:48:25,840
Speaker 1: is what people want to do now, and the marketers

929
00:48:25,840 --> 00:48:28,960
Speaker 1: are more than happy to accommodate that. And that's the business,

930
00:48:29,040 --> 00:48:32,759
Speaker 1: the business of finance, right and so that you know,

931
00:48:32,800 --> 00:48:35,279
Speaker 1: So I mean these on on the you know, I

932
00:48:35,280 --> 00:48:37,000
Speaker 1: don't even know what to say about a statistic like

933
00:48:37,000 --> 00:48:39,719
Speaker 1: this is it seems mind blowing, and you know, not

934
00:48:39,719 --> 00:48:41,200
Speaker 1: not a lot of these guys can have a lot

935
00:48:41,239 --> 00:48:46,680
Speaker 1: of assets and so forth. But that is marketers trying

936
00:48:46,800 --> 00:48:51,960
Speaker 1: to take advantage of recent asset class performance with an

937
00:48:52,000 --> 00:48:54,960
Speaker 1: overlay probably of this move to passive and and to

938
00:48:55,080 --> 00:48:57,359
Speaker 1: me so so and right and for investors, I think

939
00:48:57,400 --> 00:49:00,000
Speaker 1: for our listeners, the main thing we want to emphasize

940
00:49:00,120 --> 00:49:03,480
Speaker 1: is that it's just be very careful about. Uh, you

941
00:49:03,520 --> 00:49:05,799
Speaker 1: know what has done well in last two years is

942
00:49:05,880 --> 00:49:08,239
Speaker 1: you know again what you're gonna want. You're gonna say, hey, gee,

943
00:49:08,360 --> 00:49:10,720
Speaker 1: these guys have made a lot of be careful about

944
00:49:10,719 --> 00:49:13,719
Speaker 1: that idea because if it's done well or it's gone

945
00:49:13,760 --> 00:49:16,480
Speaker 1: out for any particular set of circumstances, very unlikely those

946
00:49:16,480 --> 00:49:19,239
Speaker 1: circumstances will repeat in the next twenty four, three, six,

947
00:49:19,280 --> 00:49:22,040
Speaker 1: five years or whatever it is going forward. Mean reversion

948
00:49:22,320 --> 00:49:25,160
Speaker 1: is a alive and well yep, to say the least.

949
00:49:25,600 --> 00:49:28,600
Speaker 1: Um So another quote of yours I really like, along

950
00:49:28,600 --> 00:49:31,920
Speaker 1: with the suggestions you have for dealing with this, is

951
00:49:32,200 --> 00:49:35,040
Speaker 1: different levels of skill and of good and bad luck

952
00:49:35,120 --> 00:49:38,719
Speaker 1: are the realities that shape our lives. Yet we aren't

953
00:49:38,840 --> 00:49:43,800
Speaker 1: very good at distinguishing between the two. So that leads to, uh,

954
00:49:43,880 --> 00:49:48,319
Speaker 1: the immediate question, how can we improve at at separating

955
00:49:48,440 --> 00:49:52,880
Speaker 1: and identifying skill from luck, not only amongst ourselves, but

956
00:49:53,120 --> 00:49:56,760
Speaker 1: at the people we hire, whether it's an investment manager

957
00:49:57,040 --> 00:50:00,400
Speaker 1: or someone in a firm, a business, how can we

958
00:50:00,560 --> 00:50:06,000
Speaker 1: separate skill from luck as a as a personal uh

959
00:50:06,000 --> 00:50:10,120
Speaker 1: approach to the world of finance. So it's an amazing question. Uh.

960
00:50:10,280 --> 00:50:12,640
Speaker 1: The first thing I would start with is sort of

961
00:50:12,680 --> 00:50:15,080
Speaker 1: the centerpiece of the book. The success equation is what

962
00:50:15,120 --> 00:50:17,480
Speaker 1: we call the luck skill continuum. So you might imagine

963
00:50:17,520 --> 00:50:20,480
Speaker 1: sets of activities that are all luck, no skill, right,

964
00:50:20,719 --> 00:50:24,000
Speaker 1: roulet wheels, lotteries, those types of things, and you might

965
00:50:24,000 --> 00:50:26,440
Speaker 1: have a system for the lottery. I have these numbers.

966
00:50:27,280 --> 00:50:31,319
Speaker 1: We'll talk about that later. And then on the other extreme, UH,

967
00:50:31,520 --> 00:50:34,160
Speaker 1: all skill, no luck, right, So you know, you think

968
00:50:34,160 --> 00:50:37,799
Speaker 1: about Olympic sprinters, the best guy is gonna win. Luck

969
00:50:37,880 --> 00:50:39,600
Speaker 1: may play a tiny role, but for the most part,

970
00:50:39,640 --> 00:50:42,520
Speaker 1: it's mostly skilled. Chess is probably over there. And then

971
00:50:42,520 --> 00:50:44,560
Speaker 1: you can think about, you know, how you might even

972
00:50:44,640 --> 00:50:49,560
Speaker 1: qualitatively place activities on that continuum and why you would

973
00:50:49,560 --> 00:50:52,480
Speaker 1: play some And I think just that mental exercise in

974
00:50:52,520 --> 00:50:55,040
Speaker 1: and of itself gives you a lot of insight. And

975
00:50:55,080 --> 00:50:58,080
Speaker 1: by the way, tying into the comment on mean reversion,

976
00:50:58,280 --> 00:51:00,719
Speaker 1: if you're on the luck side of the continuum, And

977
00:51:00,800 --> 00:51:03,080
Speaker 1: by the way, even like performance of the markets are

978
00:51:03,360 --> 00:51:07,440
Speaker 1: less from year to year, is basically random. It tells

979
00:51:07,440 --> 00:51:09,600
Speaker 1: you there's complete reversion to the mean. In other words,

980
00:51:09,800 --> 00:51:11,759
Speaker 1: outcomes that are far from marverage will be followed by

981
00:51:11,760 --> 00:51:14,640
Speaker 1: an outcome it's expected to be very close to the average.

982
00:51:15,360 --> 00:51:17,640
Speaker 1: And and and when there's all skill, no luck, there's

983
00:51:17,680 --> 00:51:19,560
Speaker 1: basically no reversion to mean at all. Right, we sprint

984
00:51:19,560 --> 00:51:23,640
Speaker 1: against usine bolt, he's gonna win every time we run, right, Um,

985
00:51:23,680 --> 00:51:26,759
Speaker 1: so so that's another that's another really interesting. So so

986
00:51:26,920 --> 00:51:28,680
Speaker 1: then you say, well, how do we identify skill? A

987
00:51:28,719 --> 00:51:30,480
Speaker 1: couple of things come to mind on this. The first

988
00:51:30,560 --> 00:51:33,120
Speaker 1: really fat. There's a fascinating strand of research by a

989
00:51:33,120 --> 00:51:38,000
Speaker 1: guy named Boris Groisberg at Harvard Business School, and he

990
00:51:38,040 --> 00:51:42,000
Speaker 1: wrote a book called Chasing Stars, and the idea is

991
00:51:42,080 --> 00:51:45,200
Speaker 1: that when stars from one organization go to the other

992
00:51:45,440 --> 00:51:50,799
Speaker 1: to another, their performance, almost without failure, degrades. Now, there

993
00:51:50,800 --> 00:51:52,360
Speaker 1: could be a couple of reasons for that. The first

994
00:51:52,440 --> 00:51:54,680
Speaker 1: is just classic mean reversion. So someone's been the star,

995
00:51:54,760 --> 00:51:57,439
Speaker 1: they've been lucky, and so that doesn't carry over. So okay,

996
00:51:57,480 --> 00:51:59,600
Speaker 1: we'll check that one off. But the second was that

997
00:51:59,680 --> 00:52:03,600
Speaker 1: people massively underestimate the role of their organization in their

998
00:52:03,640 --> 00:52:05,719
Speaker 1: own success, so they think that they're the one that's

999
00:52:05,760 --> 00:52:08,319
Speaker 1: carrying the weight, but in fact it's everything that's going

1000
00:52:08,360 --> 00:52:10,480
Speaker 1: on around them. So that's the first thing, is just

1001
00:52:10,520 --> 00:52:13,520
Speaker 1: to be mindful of Hiring stars seems to be the

1002
00:52:13,600 --> 00:52:17,040
Speaker 1: path to quick success, but the studies on this, especially

1003
00:52:17,080 --> 00:52:19,160
Speaker 1: in the world of finance, demonstrate that that's actually not

1004
00:52:19,200 --> 00:52:21,239
Speaker 1: such a good thing. So the last thing I would

1005
00:52:21,280 --> 00:52:23,279
Speaker 1: say is when I if I were trying to identify skill,

1006
00:52:23,360 --> 00:52:24,719
Speaker 1: what I would A lot of things I would I

1007
00:52:24,760 --> 00:52:27,839
Speaker 1: would really want to do things that get at people's behaviors,

1008
00:52:27,880 --> 00:52:30,760
Speaker 1: because in interviews you tend to skim along the surface

1009
00:52:30,800 --> 00:52:33,239
Speaker 1: and see if you like the person. What you really

1010
00:52:33,239 --> 00:52:36,440
Speaker 1: want to do is press into their actual behaviors how

1011
00:52:36,480 --> 00:52:39,320
Speaker 1: they actually make decisions. So if I were interviewing a

1012
00:52:39,400 --> 00:52:42,520
Speaker 1: portfolio manager or an analyst, I would truly want to say, like,

1013
00:52:42,800 --> 00:52:45,560
Speaker 1: how do you value businesses? How do you think about strategy?

1014
00:52:45,600 --> 00:52:48,360
Speaker 1: Not just high level, get into the nuts and bolts

1015
00:52:48,400 --> 00:52:50,840
Speaker 1: of how they do that, to see what their actual

1016
00:52:50,920 --> 00:52:54,439
Speaker 1: processes are, what they're described their actual behaviors, and that's

1017
00:52:54,440 --> 00:52:56,680
Speaker 1: the best indication as to whether they're going to continue

1018
00:52:56,719 --> 00:52:59,200
Speaker 1: to do that. So so that's a couple of ideas. First,

1019
00:52:59,320 --> 00:53:03,320
Speaker 1: just the raw overarching messages is hard, right, And second

1020
00:53:03,360 --> 00:53:04,799
Speaker 1: is if you're gonna, if you do have someone you're

1021
00:53:04,800 --> 00:53:06,920
Speaker 1: trying to talk to, you is just to do as

1022
00:53:06,920 --> 00:53:10,080
Speaker 1: good a job as possible figuring out their actual behaviors,

1023
00:53:10,760 --> 00:53:14,359
Speaker 1: not just gimming along the surface of superficial questions. And

1024
00:53:14,400 --> 00:53:17,560
Speaker 1: you said something else that I thought it was similarly fascinating,

1025
00:53:17,640 --> 00:53:20,920
Speaker 1: which was, when you're trying to determine if something is

1026
00:53:21,160 --> 00:53:24,680
Speaker 1: the results of skill or luck, ask yourself the simple question,

1027
00:53:25,280 --> 00:53:28,360
Speaker 1: can you lose on purpose? And I found that to

1028
00:53:28,400 --> 00:53:30,719
Speaker 1: be quite fascinating. I love that, And you know, I

1029
00:53:30,960 --> 00:53:32,600
Speaker 1: don't want I don't want to take credit for that either.

1030
00:53:32,680 --> 00:53:35,640
Speaker 1: That came from the poker the poker community, but it's

1031
00:53:35,640 --> 00:53:37,840
Speaker 1: an interesting question. I I pose it to my students.

1032
00:53:37,840 --> 00:53:40,840
Speaker 1: On January one of every year, you say, give me

1033
00:53:40,920 --> 00:53:43,960
Speaker 1: twenty five stocks you're convinced will beat the SMP five.

1034
00:53:44,440 --> 00:53:46,200
Speaker 1: Let's get the list right, and we're gonna freeze it

1035
00:53:46,200 --> 00:53:49,440
Speaker 1: for the full year. Now, let's get January first. Give

1036
00:53:49,480 --> 00:53:53,120
Speaker 1: me the twenty five stocks you're convinced will underperform the market.

1037
00:53:53,680 --> 00:53:56,160
Speaker 1: You're sure you would short them with your own money,

1038
00:53:56,280 --> 00:53:58,200
Speaker 1: and let's tale up the results of in the year

1039
00:53:58,239 --> 00:53:59,759
Speaker 1: and by the way. If you can do the latter,

1040
00:53:59,840 --> 00:54:03,400
Speaker 1: you can do the former, right now, So as you know,

1041
00:54:03,520 --> 00:54:05,680
Speaker 1: I mean, your sense on this would be, it's really

1042
00:54:05,680 --> 00:54:08,080
Speaker 1: hard to beat the market. It's also really hard to

1043
00:54:08,160 --> 00:54:10,719
Speaker 1: do worse than the market on purpose, given the same constraints, right,

1044
00:54:10,719 --> 00:54:12,399
Speaker 1: given the same number stocks and so on and and so forth,

1045
00:54:12,440 --> 00:54:14,560
Speaker 1: you can you can do worse by treating. But that's

1046
00:54:14,560 --> 00:54:16,759
Speaker 1: a fascinating concept, right, And that just tells you that

1047
00:54:16,880 --> 00:54:20,839
Speaker 1: investing again not because of a lack of skill, rather

1048
00:54:20,920 --> 00:54:23,520
Speaker 1: because of a surfeit of skill, which means that everything

1049
00:54:23,600 --> 00:54:26,040
Speaker 1: is priced in. It's really hard to beat the market.

1050
00:54:26,200 --> 00:54:27,920
Speaker 1: You know. At the end of the second quarter and

1051
00:54:28,080 --> 00:54:29,600
Speaker 1: the first half of the year, there was a I

1052
00:54:29,600 --> 00:54:31,960
Speaker 1: want to say, wool Street Journal article. I don't know

1053
00:54:32,000 --> 00:54:35,480
Speaker 1: if this is consistent over time, but they had noted

1054
00:54:35,520 --> 00:54:40,440
Speaker 1: that the first half of the lowest ranked stocks amongst

1055
00:54:40,480 --> 00:54:46,080
Speaker 1: the analyst community had significantly outperformed the top rank stocks

1056
00:54:46,120 --> 00:54:48,919
Speaker 1: and the best stocks. I wonder if that's something that's

1057
00:54:48,920 --> 00:54:51,319
Speaker 1: consistent over time. Yeah, I mean, and I would say

1058
00:54:51,360 --> 00:54:54,040
Speaker 1: that seems that feels very consistent what we know to

1059
00:54:54,080 --> 00:54:56,400
Speaker 1: be the value factor. Right, So we talked about different

1060
00:54:56,400 --> 00:54:58,440
Speaker 1: factors that contribute to returns, and we know that over

1061
00:54:58,480 --> 00:55:00,680
Speaker 1: a long period of time, value factors works are basically

1062
00:55:00,760 --> 00:55:02,839
Speaker 1: cheap stocks we could say price to book or whatever

1063
00:55:02,880 --> 00:55:05,600
Speaker 1: it is, and those tend to be unloved. I mean,

1064
00:55:05,640 --> 00:55:08,400
Speaker 1: so that there's probably some relationship between what the analysts

1065
00:55:08,400 --> 00:55:10,560
Speaker 1: don't like and what's cheap, and those tend out perform.

1066
00:55:10,560 --> 00:55:12,160
Speaker 1: And when we say out perform, we don't mean just

1067
00:55:12,600 --> 00:55:15,640
Speaker 1: you know, doing better than the market. It's adjusted for risk. Right,

1068
00:55:15,680 --> 00:55:19,520
Speaker 1: So these are yeah, So that's a really interesting um observation.

1069
00:55:19,560 --> 00:55:22,360
Speaker 1: And again it goes back to our discussion about fundamentals

1070
00:55:22,400 --> 00:55:25,040
Speaker 1: and expectations. Right. People want to buy what's doing well,

1071
00:55:25,080 --> 00:55:26,880
Speaker 1: and they want to sell what's doing poorly, and they

1072
00:55:26,880 --> 00:55:29,800
Speaker 1: don't distinguish between what's priced in and what's likely to unfold.

1073
00:55:29,880 --> 00:55:34,240
Speaker 1: And you had also written about the morning star five

1074
00:55:34,239 --> 00:55:37,839
Speaker 1: star versus one star ratings and that funds that are

1075
00:55:37,880 --> 00:55:41,520
Speaker 1: five star come back next year they're not five star anymore.

1076
00:55:41,600 --> 00:55:44,560
Speaker 1: And the reverse funds that are at the bottom of

1077
00:55:44,560 --> 00:55:48,080
Speaker 1: the scale, the one star funds the following year, they're

1078
00:55:48,080 --> 00:55:50,880
Speaker 1: not ones. So what does that tell us about mean reversion?

1079
00:55:50,960 --> 00:55:54,239
Speaker 1: Is this just something that there's no escaping and and

1080
00:55:54,280 --> 00:55:56,880
Speaker 1: that's it. In the investment world. Right, So the degree

1081
00:55:56,880 --> 00:56:00,839
Speaker 1: to which UH investing is a luck laden activity. And

1082
00:56:00,880 --> 00:56:03,640
Speaker 1: again over short periods of time, certainly a year. Uh,

1083
00:56:03,960 --> 00:56:07,239
Speaker 1: it's got huge doses of luck. Um As I said,

1084
00:56:07,239 --> 00:56:09,080
Speaker 1: it's on the luck side of the continuum. That's rapid

1085
00:56:09,120 --> 00:56:12,040
Speaker 1: reversion to the mean. Right. So the best estimate of

1086
00:56:12,040 --> 00:56:15,120
Speaker 1: the expected value some measure much closer to the average

1087
00:56:15,120 --> 00:56:17,520
Speaker 1: of the population. So yeah, and by the way, I

1088
00:56:17,520 --> 00:56:19,680
Speaker 1: mean those morning stars, you know, these morning start their

1089
00:56:19,719 --> 00:56:22,319
Speaker 1: forced curve. So the vast majority of company funds or

1090
00:56:22,560 --> 00:56:24,400
Speaker 1: three star and then the two and four less and

1091
00:56:24,400 --> 00:56:26,799
Speaker 1: than one five or the extremes. So right, the best

1092
00:56:26,920 --> 00:56:28,960
Speaker 1: estimate for a five star once are fund the subsequent

1093
00:56:29,000 --> 00:56:31,440
Speaker 1: years basically three And that's roughly what we see my

1094
00:56:32,120 --> 00:56:35,640
Speaker 1: uh A little digression. There was a study that I

1095
00:56:35,680 --> 00:56:39,120
Speaker 1: give Morning Star credit for releasing it. Someone said, let's

1096
00:56:39,120 --> 00:56:42,240
Speaker 1: forget about the star rating and only look at one factor,

1097
00:56:42,640 --> 00:56:44,680
Speaker 1: and they went through all the price the book either

1098
00:56:44,800 --> 00:56:46,759
Speaker 1: if we can only look at one factor, what would

1099
00:56:46,800 --> 00:56:50,480
Speaker 1: it be? And more Star themselves discovered we look at

1100
00:56:50,520 --> 00:56:53,239
Speaker 1: the cost factor of owning that funds. If you know

1101
00:56:53,320 --> 00:56:56,520
Speaker 1: nothing else, but by the cheapest funds you're ahead of

1102
00:56:56,520 --> 00:56:59,640
Speaker 1: of the five star rating, they kind of eliminated their

1103
00:56:59,640 --> 00:57:03,880
Speaker 1: own for existence. I think they wish that that memory

1104
00:57:03,880 --> 00:57:05,680
Speaker 1: of that would go away, but I seem to bring

1105
00:57:05,680 --> 00:57:08,799
Speaker 1: it up every six months or so. It's quite astonishing,

1106
00:57:08,840 --> 00:57:10,839
Speaker 1: isn't that it is? And that's consistent with everyone we've

1107
00:57:10,840 --> 00:57:12,960
Speaker 1: been talking about, and you know that's That's the other

1108
00:57:13,000 --> 00:57:15,839
Speaker 1: thing is it's interesting when you think about fees, and look,

1109
00:57:15,840 --> 00:57:18,320
Speaker 1: I don't think anybody begrudges paying fees, but you know,

1110
00:57:18,360 --> 00:57:20,600
Speaker 1: when you're paying a fee of let's say, for an

1111
00:57:20,600 --> 00:57:23,360
Speaker 1: average metual fund on Hunter twenty five basis points in

1112
00:57:23,400 --> 00:57:25,800
Speaker 1: a world where the markets up, you know, ten fifteen

1113
00:57:25,840 --> 00:57:29,000
Speaker 1: percent a year as we saw nobody, it rolls right

1114
00:57:29,040 --> 00:57:32,120
Speaker 1: off your back. But now when you have effectively zero

1115
00:57:32,440 --> 00:57:34,840
Speaker 1: real interest rates in the States, you have you know,

1116
00:57:35,400 --> 00:57:38,560
Speaker 1: negative overseas, never the negative overseas, and it's hard to see,

1117
00:57:38,920 --> 00:57:42,160
Speaker 1: you know, X returns for the equity markets vastly higher

1118
00:57:42,160 --> 00:57:45,320
Speaker 1: and certainly in the double digits. Uh, those those numbers

1119
00:57:45,400 --> 00:57:47,760
Speaker 1: feel they sting a lot more. And then you look

1120
00:57:47,800 --> 00:57:51,479
Speaker 1: around and there are certain vanguard funds that are six

1121
00:57:51,560 --> 00:57:55,800
Speaker 1: basis points on the Admiral Institutional Clash d f A

1122
00:57:55,800 --> 00:57:59,960
Speaker 1: also really low fees, it becomes more and more challenging

1123
00:58:00,120 --> 00:58:04,000
Speaker 1: to justify. Maybe that's why there's fifty seven thousand new funds.

1124
00:58:04,000 --> 00:58:07,280
Speaker 1: Something will will get hot and stick, and maybe it's

1125
00:58:07,320 --> 00:58:09,440
Speaker 1: just part of the culling process. Hey, throw it all

1126
00:58:09,480 --> 00:58:12,120
Speaker 1: against the wall, see what what survives, and we can

1127
00:58:12,160 --> 00:58:14,040
Speaker 1: get rid of the rest. And I don't think that's

1128
00:58:14,080 --> 00:58:16,880
Speaker 1: I mean, I think the fifty seven thousand numbers obviously

1129
00:58:16,880 --> 00:58:19,479
Speaker 1: a huge number, and that's probably what is somewhat new.

1130
00:58:19,520 --> 00:58:22,800
Speaker 1: But this this idea of rolling out products of what's

1131
00:58:22,800 --> 00:58:25,200
Speaker 1: hot is it's certainly not a not a new thing. Right.

1132
00:58:26,040 --> 00:58:28,240
Speaker 1: So before I get to my favorite questions, I have

1133
00:58:28,360 --> 00:58:32,360
Speaker 1: to ask you one more question that really sums up

1134
00:58:32,400 --> 00:58:35,280
Speaker 1: a lot of of what we've been talking about and

1135
00:58:35,360 --> 00:58:40,000
Speaker 1: applying it to um the world of investing. So we

1136
00:58:40,080 --> 00:58:43,440
Speaker 1: all know who the great managers were, and and we

1137
00:58:43,480 --> 00:58:47,440
Speaker 1: always seem to discover these folks after their best years.

1138
00:58:48,400 --> 00:58:52,880
Speaker 1: Is it possible to identify skilled managers in advance? Is

1139
00:58:52,920 --> 00:58:58,040
Speaker 1: that something that's realistic for the average pension fund, the

1140
00:58:58,080 --> 00:59:01,840
Speaker 1: average institutional investor, or or are we just always chasing

1141
00:59:01,880 --> 00:59:05,280
Speaker 1: our tel It's a great question. I think it's really

1142
00:59:05,280 --> 00:59:07,720
Speaker 1: difficult to do, but there may be some things you

1143
00:59:07,760 --> 00:59:10,240
Speaker 1: can do to skew the odds a bit in your favor. So,

1144
00:59:10,280 --> 00:59:12,840
Speaker 1: if I wanted to be optimistic or give that optimistic

1145
00:59:12,880 --> 00:59:15,360
Speaker 1: side of the story, a couple of things I would say.

1146
00:59:15,400 --> 00:59:18,280
Speaker 1: The first is going back to a discussion we had

1147
00:59:18,320 --> 00:59:21,080
Speaker 1: a few moments ago, which is does that investor have

1148
00:59:21,440 --> 00:59:26,800
Speaker 1: a thoughtful process, analytical process and portfolio construction related? Are

1149
00:59:26,840 --> 00:59:30,160
Speaker 1: they mindful of the behavioral issues? And do they have

1150
00:59:30,200 --> 00:59:33,280
Speaker 1: an organization that tends to be the proper type of organization?

1151
00:59:33,720 --> 00:59:35,000
Speaker 1: But there are a couple of the things that will

1152
00:59:35,040 --> 00:59:37,560
Speaker 1: also skew your odds in your favor. Um. The first,

1153
00:59:37,600 --> 00:59:40,600
Speaker 1: interestingly is the age of the money manager, and UH

1154
00:59:40,720 --> 00:59:43,720
Speaker 1: research suggests that the optimal age for a money manager

1155
00:59:43,880 --> 00:59:48,280
Speaker 1: is in his or her early forties. Really so UM,

1156
00:59:48,320 --> 00:59:52,560
Speaker 1: so that's one thing. Another would be, UH, if they've gone,

1157
00:59:52,560 --> 00:59:56,240
Speaker 1: if they're bright people and silly, as it sounds, people

1158
00:59:56,240 --> 00:59:59,000
Speaker 1: that go to better schools or better SAT scores on average,

1159
00:59:59,080 --> 01:00:02,120
Speaker 1: or better investors. Uh. You'd like to see the size

1160
01:00:02,120 --> 01:00:04,480
Speaker 1: of the fund being not too big. So we know

1161
01:00:04,560 --> 01:00:06,640
Speaker 1: that size tends to be challenging, so you want to

1162
01:00:06,640 --> 01:00:08,480
Speaker 1: be big enough to have some critical masses and the

1163
01:00:08,480 --> 01:00:11,200
Speaker 1: resources you need, but not so so large that you're

1164
01:00:11,200 --> 01:00:13,960
Speaker 1: moving tons of money around, which makes it difficult. And

1165
01:00:14,000 --> 01:00:17,480
Speaker 1: the last one, which I think is still controversial, but um,

1166
01:00:17,520 --> 01:00:19,120
Speaker 1: but I think it's probably a heristic for something it

1167
01:00:19,200 --> 01:00:21,240
Speaker 1: is useful, and that's high active share, which is they're

1168
01:00:21,240 --> 01:00:23,680
Speaker 1: doing something quite different, right, So you're paying if you're

1169
01:00:23,680 --> 01:00:25,400
Speaker 1: paying someone a fee, you want them to be doing

1170
01:00:25,440 --> 01:00:29,000
Speaker 1: something quite different than say the SP five index. And

1171
01:00:29,080 --> 01:00:32,040
Speaker 1: so if you have a couple of those things working

1172
01:00:32,080 --> 01:00:35,320
Speaker 1: for you, the age, they're bright people, the size is appropriate,

1173
01:00:35,400 --> 01:00:37,760
Speaker 1: that they're doing something different, and the process seems to

1174
01:00:37,760 --> 01:00:41,960
Speaker 1: be sensible. Those probably shade the odds in your favor

1175
01:00:42,040 --> 01:00:46,240
Speaker 1: to some degree. But as you point out, I think correctly, Um,

1176
01:00:46,320 --> 01:00:50,520
Speaker 1: it's difficult to to find an anticipate performance excess returns

1177
01:00:50,520 --> 01:00:52,760
Speaker 1: in the future, for sure. Al Right, So in the

1178
01:00:52,840 --> 01:00:55,040
Speaker 1: last ten minutes or so that we have, let's let's

1179
01:00:55,040 --> 01:00:59,120
Speaker 1: go over um my standard questions. These were not in

1180
01:00:59,200 --> 01:01:03,480
Speaker 1: existence when we first did this two years ago. So, um,

1181
01:01:03,520 --> 01:01:07,080
Speaker 1: some of these are kind of kind of interesting. Um,

1182
01:01:07,080 --> 01:01:10,560
Speaker 1: how did you find your way into the financial services industry?

1183
01:01:10,600 --> 01:01:13,640
Speaker 1: You you come out of school with the b A

1184
01:01:14,000 --> 01:01:17,120
Speaker 1: and what made you attack in that direction? Well, can

1185
01:01:17,120 --> 01:01:19,680
Speaker 1: I tell you a very quick funny story about this.

1186
01:01:19,760 --> 01:01:21,720
Speaker 1: So I was. I went to Georgetown. I was a

1187
01:01:21,720 --> 01:01:23,840
Speaker 1: government major, had no idea what I want to do.

1188
01:01:24,240 --> 01:01:27,200
Speaker 1: No I knew I needed a job, and uh one

1189
01:01:27,240 --> 01:01:29,680
Speaker 1: of the firms interviewed on campus with Drexel Burnham Lombert,

1190
01:01:29,720 --> 01:01:31,840
Speaker 1: which you may recall, was it quite a firm, quite

1191
01:01:31,840 --> 01:01:33,840
Speaker 1: a hot firm back in the mid nine eighties. So

1192
01:01:33,880 --> 01:01:36,040
Speaker 1: I did well enough of my New York Washington interviews

1193
01:01:36,040 --> 01:01:37,560
Speaker 1: that invited me to New York. Isn't a big deal.

1194
01:01:37,640 --> 01:01:40,320
Speaker 1: Get my best suit, my best tie on, and we

1195
01:01:40,360 --> 01:01:42,680
Speaker 1: sit around. All the candidates sit around the table, you know,

1196
01:01:42,880 --> 01:01:44,280
Speaker 1: before the big day of interviews, and they say, hey,

1197
01:01:44,280 --> 01:01:46,240
Speaker 1: you're gonna have six interviews with different people on our program,

1198
01:01:46,280 --> 01:01:48,120
Speaker 1: and you get ten minutes with a head guy. Right,

1199
01:01:48,120 --> 01:01:49,600
Speaker 1: So obviously you want to be good all day. But

1200
01:01:50,000 --> 01:01:52,480
Speaker 1: that's a game for your for the day. So I

1201
01:01:52,520 --> 01:01:55,120
Speaker 1: go through the interviews. It's fine. I meet the big

1202
01:01:55,160 --> 01:01:57,280
Speaker 1: guy and you know, he's a great guy, really warm,

1203
01:01:57,360 --> 01:01:59,120
Speaker 1: and I sit down and I see peeking out from

1204
01:01:59,200 --> 01:02:02,000
Speaker 1: underneath his death Washington Redskins, trash can. I went to

1205
01:02:02,080 --> 01:02:03,840
Speaker 1: went to Georgia. The riskins were good back then I'd

1206
01:02:03,840 --> 01:02:05,520
Speaker 1: gone to a couple of games. So I say to him,

1207
01:02:05,560 --> 01:02:08,520
Speaker 1: kind of off handily, hey that's a great trash can, literally,

1208
01:02:08,920 --> 01:02:11,360
Speaker 1: and this triggers this guy's emotional seats. So he goes

1209
01:02:11,480 --> 01:02:14,840
Speaker 1: on about, you know, the virtues of athletics, and you know,

1210
01:02:15,000 --> 01:02:17,480
Speaker 1: metaphor for life, how much you love living in Washington,

1211
01:02:18,080 --> 01:02:20,680
Speaker 1: and my my ten minute interview becomes fifteen minutes and

1212
01:02:20,720 --> 01:02:23,360
Speaker 1: me mostly nodding up and down in agreement with everything

1213
01:02:23,360 --> 01:02:25,280
Speaker 1: he said. So I go back to school a couple

1214
01:02:25,280 --> 01:02:27,840
Speaker 1: of weeks later, get the letter offered the jobs is awesome,

1215
01:02:28,120 --> 01:02:31,080
Speaker 1: start the program, and about three months into it, one

1216
01:02:31,080 --> 01:02:32,640
Speaker 1: of the guys pulls me aside says, hey, kid, you're

1217
01:02:32,680 --> 01:02:35,200
Speaker 1: doing fine, so you know it's okay, but I have

1218
01:02:35,280 --> 01:02:37,360
Speaker 1: to tell you that the six people who you interviewed with,

1219
01:02:37,400 --> 01:02:39,960
Speaker 1: who are the core of the interview process, voted against

1220
01:02:40,040 --> 01:02:43,200
Speaker 1: hiring you. He goes but the head guy came down

1221
01:02:43,240 --> 01:02:46,520
Speaker 1: and reviewed our sheets and recommendations and said, Override, you

1222
01:02:46,520 --> 01:02:48,840
Speaker 1: have to hire this kid. He's great. Right, So, as

1223
01:02:48,880 --> 01:02:50,960
Speaker 1: I like to say, my my career was launched by

1224
01:02:50,960 --> 01:02:53,520
Speaker 1: a trash can, right, which is quite literally the case.

1225
01:02:53,560 --> 01:02:56,920
Speaker 1: And and and thankfully these more formal processes weren't in

1226
01:02:56,960 --> 01:02:58,560
Speaker 1: place at the time because I wouldn't have been hired.

1227
01:02:58,600 --> 01:03:00,560
Speaker 1: So so that was it. And and that was a

1228
01:03:00,600 --> 01:03:03,240
Speaker 1: really interesting experience because it was a year and a

1229
01:03:03,280 --> 01:03:06,040
Speaker 1: half long training program that led to be a financial advisor.

1230
01:03:06,560 --> 01:03:08,440
Speaker 1: So we did a lot of classroom stuff for a

1231
01:03:08,480 --> 01:03:11,520
Speaker 1: liberal arts guy, tremendous, right, so basic accounting and finance

1232
01:03:11,560 --> 01:03:14,240
Speaker 1: and so forth, and then we rotated through about twenty

1233
01:03:14,280 --> 01:03:19,360
Speaker 1: different departments at Drexel Burnham, everywhere from operations to investment

1234
01:03:19,360 --> 01:03:22,320
Speaker 1: banking to research, all the trading desks. So if you're

1235
01:03:22,360 --> 01:03:24,680
Speaker 1: a person that didn't know what your future, what you

1236
01:03:24,680 --> 01:03:28,000
Speaker 1: were about, you're gonna find yourself right through that program.

1237
01:03:28,040 --> 01:03:29,720
Speaker 1: And then I went on to be a financial advisor

1238
01:03:29,880 --> 01:03:33,120
Speaker 1: at drug we call them brokers back then, uh at Drexel,

1239
01:03:33,200 --> 01:03:38,040
Speaker 1: and was an abject failure at that job. Abject sales

1240
01:03:38,160 --> 01:03:40,760
Speaker 1: job as a sales job, and it was it was

1241
01:03:41,320 --> 01:03:44,400
Speaker 1: started in early so that was on the heels of

1242
01:03:44,440 --> 01:03:47,120
Speaker 1: the crash of eighty seven and Drexel at that point

1243
01:03:47,200 --> 01:03:49,080
Speaker 1: was in a little bit of hot water, so those

1244
01:03:49,080 --> 01:03:51,440
Speaker 1: were sort of mitigating factors. But basically I was not

1245
01:03:51,840 --> 01:03:54,560
Speaker 1: good at this at all. So fortunately I was able

1246
01:03:54,600 --> 01:03:56,080
Speaker 1: to figure out a little bit of what I wanted

1247
01:03:56,120 --> 01:04:00,600
Speaker 1: to do. But that was my my store, my exactly,

1248
01:04:00,640 --> 01:04:01,920
Speaker 1: but it was it was in some ways it was

1249
01:04:01,960 --> 01:04:04,200
Speaker 1: a great I mean, it was a great experience for sure,

1250
01:04:04,720 --> 01:04:07,760
Speaker 1: but knowing what you're not good at was a wake

1251
01:04:07,840 --> 01:04:09,440
Speaker 1: up call to saying, like I should go off and

1252
01:04:09,480 --> 01:04:12,280
Speaker 1: do something different. So so that raises the next question,

1253
01:04:12,320 --> 01:04:15,520
Speaker 1: so who are your mentors? So in my training program,

1254
01:04:15,560 --> 01:04:18,600
Speaker 1: actually a guy in my group gave me a copy

1255
01:04:18,800 --> 01:04:21,200
Speaker 1: of a book called Creating Sheer Older Value by a

1256
01:04:21,240 --> 01:04:25,560
Speaker 1: professor at Northwestern named L. Rappaport. And I was, you know,

1257
01:04:25,600 --> 01:04:27,400
Speaker 1: a liberal arts guy, and these guys are talking all

1258
01:04:27,400 --> 01:04:29,720
Speaker 1: this finance jargon. It was way over my head and

1259
01:04:29,760 --> 01:04:32,760
Speaker 1: actually candidly didn't make a lot of sense. That book

1260
01:04:32,800 --> 01:04:34,680
Speaker 1: was the first book that really made sense to me.

1261
01:04:34,760 --> 01:04:37,120
Speaker 1: And and and rapp reports said three things that to

1262
01:04:37,200 --> 01:04:39,360
Speaker 1: this day remained at my core. And I would say,

1263
01:04:39,360 --> 01:04:42,440
Speaker 1: he's not only a mentor, he's a dear friend and

1264
01:04:42,480 --> 01:04:45,040
Speaker 1: a co author and so forth. The first was that

1265
01:04:45,120 --> 01:04:48,320
Speaker 1: it's not about accounting numbers, is about economic value, which

1266
01:04:48,520 --> 01:04:51,080
Speaker 1: is really important. And we forget that lesson but it

1267
01:04:51,120 --> 01:04:53,520
Speaker 1: comes it rears its head from time to time. Second

1268
01:04:53,600 --> 01:04:59,960
Speaker 1: is that valuation requires understanding both finance and competitive strategy intimately,

1269
01:05:00,120 --> 01:05:03,840
Speaker 1: so they're not Valuation and strategy are not two separate activities,

1270
01:05:03,960 --> 01:05:05,520
Speaker 1: which is how we teach them in business school, by

1271
01:05:05,560 --> 01:05:07,520
Speaker 1: the way, but really should be joined at the hip.

1272
01:05:08,040 --> 01:05:11,400
Speaker 1: And the third was chapter seven called stock Market Signals

1273
01:05:11,440 --> 01:05:13,880
Speaker 1: for Managers, and it was the argument that stock prices

1274
01:05:13,920 --> 01:05:17,720
Speaker 1: reflect expectations and that a manager just investing in a

1275
01:05:17,760 --> 01:05:19,600
Speaker 1: way that earns the cost of cabal isn't going to

1276
01:05:19,680 --> 01:05:22,720
Speaker 1: get you excess returns. It's beating with the market believes,

1277
01:05:23,520 --> 01:05:25,600
Speaker 1: and that for me, it was a huge revelation, right,

1278
01:05:25,720 --> 01:05:28,600
Speaker 1: and he was His target was corporate executives, but the

1279
01:05:28,720 --> 01:05:32,600
Speaker 1: relevance for investors was obvious. So I started emulating a

1280
01:05:32,640 --> 01:05:35,880
Speaker 1: lot of the Rappaport techniques, which are basically standard finance techniques,

1281
01:05:36,040 --> 01:05:37,920
Speaker 1: and a lot of my research, including deep dies on

1282
01:05:37,960 --> 01:05:40,960
Speaker 1: competitive strategy and a lot of valuation work. And again

1283
01:05:41,000 --> 01:05:42,560
Speaker 1: that's how I got to I got to meet him

1284
01:05:42,600 --> 01:05:45,600
Speaker 1: in the early nines, about twenty five years ago, and

1285
01:05:45,600 --> 01:05:48,160
Speaker 1: from then we we sort of cultivated. So he's been

1286
01:05:48,200 --> 01:05:51,160
Speaker 1: he has been tremendous. The other guy for me has

1287
01:05:51,160 --> 01:05:53,120
Speaker 1: also been Bill Miller. And you mentioned I worked with

1288
01:05:53,120 --> 01:05:55,080
Speaker 1: Bill for nine years, but even going back to the

1289
01:05:55,080 --> 01:05:57,480
Speaker 1: early one of the early and this is before he

1290
01:05:57,520 --> 01:05:59,880
Speaker 1: was the famous money manager right the S and p F.

1291
01:06:01,440 --> 01:06:04,400
Speaker 1: This is before, this is before it was just a streak,

1292
01:06:04,600 --> 01:06:07,600
Speaker 1: highly unlikely random. It won't it won't get I don't

1293
01:06:07,600 --> 01:06:10,320
Speaker 1: think that one will be broken. But but he's also

1294
01:06:10,360 --> 01:06:13,480
Speaker 1: a guy who's uh, you know, very valuation focused, very

1295
01:06:13,480 --> 01:06:17,360
Speaker 1: widely read guy, very multi disciplinary guy. Um and you know,

1296
01:06:17,520 --> 01:06:20,200
Speaker 1: a wonderful guy to just talk to and learn from.

1297
01:06:20,240 --> 01:06:22,600
Speaker 1: So those are a couple of guys that certainly stand out,

1298
01:06:22,840 --> 01:06:25,720
Speaker 1: um in terms of in terms of mentor but Rapp

1299
01:06:25,840 --> 01:06:28,040
Speaker 1: in terms of actually understand the business and thinking about

1300
01:06:28,520 --> 01:06:31,400
Speaker 1: Al Rapp Reports stands above all for me as it's

1301
01:06:31,440 --> 01:06:36,200
Speaker 1: just a hugely, deeply influential person. So let's talk about books.

1302
01:06:36,240 --> 01:06:39,680
Speaker 1: We we've mentioned a number of different books. Uh, what

1303
01:06:39,720 --> 01:06:42,320
Speaker 1: are some of your favorite books? Be them, be they

1304
01:06:42,480 --> 01:06:49,440
Speaker 1: investing tons or otherwise just beside yours? Okay, okay, it

1305
01:06:49,520 --> 01:06:53,440
Speaker 1: goes without saying right, So UM, look, I mentioned already

1306
01:06:53,440 --> 01:06:55,880
Speaker 1: Al rapp Reports book Creating Shoulder Value, UM, which was

1307
01:06:55,920 --> 01:06:59,280
Speaker 1: written original version of it. A couple other books that

1308
01:06:59,360 --> 01:07:04,720
Speaker 1: for me been tremendously valuable. Um, Mitch Waldrop's book on Complexity.

1309
01:07:04,800 --> 01:07:06,680
Speaker 1: So this is the history of the of the Santa

1310
01:07:06,680 --> 01:07:09,520
Speaker 1: Fe Institute. So that introduced a whole sleuve ideas that

1311
01:07:09,560 --> 01:07:14,920
Speaker 1: were wildly influential for me. Love Robert Schildini's book Influence

1312
01:07:14,920 --> 01:07:17,200
Speaker 1: of Psychology or Persuasion. That should be a must read,

1313
01:07:17,320 --> 01:07:21,880
Speaker 1: especially for young people. Peter Bernstein just was whatever and

1314
01:07:21,960 --> 01:07:23,959
Speaker 1: so whatever he writes, but the two I would mention

1315
01:07:24,000 --> 01:07:26,280
Speaker 1: to be against the God. I just read that. You

1316
01:07:26,320 --> 01:07:29,560
Speaker 1: know it can't get enough right and you should reread that.

1317
01:07:29,720 --> 01:07:33,680
Speaker 1: And Capital Ideas was also terrific Um and then a

1318
01:07:33,720 --> 01:07:36,280
Speaker 1: couple more. Another ones a little bit off the radar

1319
01:07:36,360 --> 01:07:38,800
Speaker 1: for the investing world is John Gaddis's book The Landscape

1320
01:07:38,800 --> 01:07:42,360
Speaker 1: of History Really, and Gaddis is a professor of history Yale,

1321
01:07:42,560 --> 01:07:44,800
Speaker 1: And Um, this is a night it's actually a series

1322
01:07:44,840 --> 01:07:46,960
Speaker 1: of lectures that are written in a book. And it's

1323
01:07:47,000 --> 01:07:49,440
Speaker 1: the craft. It's about asking the questions about what is

1324
01:07:49,480 --> 01:07:52,320
Speaker 1: the craft of history? And I think what's so interesting

1325
01:07:52,360 --> 01:07:54,840
Speaker 1: to me is that many of those ideas are ideas

1326
01:07:54,840 --> 01:07:57,560
Speaker 1: that spill right over to the world of investing as

1327
01:07:57,600 --> 01:08:00,200
Speaker 1: an as an as an analyst or portfolio man your

1328
01:08:00,760 --> 01:08:03,720
Speaker 1: how do you craft stories, how do you understand causality?

1329
01:08:03,960 --> 01:08:08,480
Speaker 1: How do you grapple with complexity? So gaddis wonderful and

1330
01:08:08,480 --> 01:08:11,920
Speaker 1: it's it's really interestingly written book. So, um, you know.

1331
01:08:11,920 --> 01:08:13,920
Speaker 1: And then the other one I just mentioned is is E. O.

1332
01:08:14,000 --> 01:08:16,519
Speaker 1: Wilson's book Consilience. We may have talked about that before,

1333
01:08:16,520 --> 01:08:20,800
Speaker 1: but we have not ed. Wilson is Uh. He's a criminologists,

1334
01:08:20,960 --> 01:08:24,719
Speaker 1: a professor. As a professor at Harvard, he is the

1335
01:08:24,720 --> 01:08:29,960
Speaker 1: world's leading expert in ants. Okay, so I'm thinking of

1336
01:08:29,960 --> 01:08:32,960
Speaker 1: a difference. So yeah, it could be. And so and

1337
01:08:33,560 --> 01:08:36,840
Speaker 1: he wrote a book of years ago cults consilience, and

1338
01:08:36,920 --> 01:08:40,519
Speaker 1: consilience is one of these old words which means the

1339
01:08:40,680 --> 01:08:44,320
Speaker 1: unification of knowledge. And the argument that Wilson made in

1340
01:08:44,360 --> 01:08:48,479
Speaker 1: this book was, Hey, we've made enormous strides, uh in

1341
01:08:49,160 --> 01:08:53,760
Speaker 1: the last few centuries by being reductionist and disciplinary. So

1342
01:08:53,760 --> 01:08:55,960
Speaker 1: in other words, the biologists hang out with the biologist

1343
01:08:56,000 --> 01:08:59,160
Speaker 1: and with business. He said, Look, the most vexing problems

1344
01:08:59,240 --> 01:09:02,960
Speaker 1: in our world are standing at the intersections of disciplines,

1345
01:09:03,360 --> 01:09:05,760
Speaker 1: and so for us to really advance, we need this

1346
01:09:05,880 --> 01:09:10,040
Speaker 1: concilience's unification of knowledge very much resonated with me, and

1347
01:09:10,080 --> 01:09:12,439
Speaker 1: I truly believe that. And we talked about, you know,

1348
01:09:12,439 --> 01:09:16,360
Speaker 1: even Danny Khneman winning the Nobel Prize in economics even

1349
01:09:16,400 --> 01:09:19,400
Speaker 1: though he's never taught an economics class in his life, right,

1350
01:09:19,600 --> 01:09:22,559
Speaker 1: but what he's brought to bear something that's really useful

1351
01:09:22,600 --> 01:09:25,920
Speaker 1: for both psychologists and economists, right, So that intersection, and

1352
01:09:26,240 --> 01:09:28,960
Speaker 1: Dick Taylor, who will likely win the Nobel Prize, also

1353
01:09:29,840 --> 01:09:32,880
Speaker 1: operating in that intersection. But go on and on, I mean,

1354
01:09:32,920 --> 01:09:35,960
Speaker 1: what can you learn from a biologist? Offline? We were

1355
01:09:35,960 --> 01:09:37,639
Speaker 1: talking about some of the things you listen to about

1356
01:09:37,760 --> 01:09:40,679
Speaker 1: musicians or comedians. What can you learn from what those

1357
01:09:40,720 --> 01:09:43,960
Speaker 1: guys do their creative processes their businesses that it might

1358
01:09:44,080 --> 01:09:46,759
Speaker 1: might apply to your creative process in your business. Gotta

1359
01:09:46,800 --> 01:09:49,599
Speaker 1: be stuff that's relevant, right, And so to me, that

1360
01:09:49,680 --> 01:09:52,840
Speaker 1: whole way of thinking and reaching outside of your own

1361
01:09:52,960 --> 01:09:56,800
Speaker 1: little world for ideas that may apply. Um so, so

1362
01:09:56,960 --> 01:09:58,920
Speaker 1: Ed Wilson's book on that is probably you know, the

1363
01:09:59,360 --> 01:10:04,200
Speaker 1: great one. So my last two questions and and these

1364
01:10:04,240 --> 01:10:07,320
Speaker 1: are my two favorites. Some millennial comes up to you

1365
01:10:07,360 --> 01:10:10,719
Speaker 1: and says, hey, I'm thinking about getting into UH finance.

1366
01:10:10,800 --> 01:10:13,760
Speaker 1: You teach at Columbia so you must have a lot

1367
01:10:13,800 --> 01:10:17,439
Speaker 1: of students who occasionally say, I'm interested in finance. What

1368
01:10:17,560 --> 01:10:19,280
Speaker 1: sort of advice would you give them? You know, So

1369
01:10:19,320 --> 01:10:22,240
Speaker 1: there there are two sides this one night classical finance,

1370
01:10:22,240 --> 01:10:25,559
Speaker 1: of corporate finance, mergers and acquisitions and capital raising, all

1371
01:10:25,600 --> 01:10:28,320
Speaker 1: that stuff is likely to continue, so that if they're

1372
01:10:28,360 --> 01:10:31,200
Speaker 1: interested in that, that's fine. More of the questions I

1373
01:10:31,200 --> 01:10:33,680
Speaker 1: get about money management, And you know, Richard grin Old

1374
01:10:33,720 --> 01:10:36,599
Speaker 1: wrote this really fascinating paper twenty five years ago about

1375
01:10:36,640 --> 01:10:39,479
Speaker 1: the law of active management, and he basically said, we'll

1376
01:10:39,560 --> 01:10:43,200
Speaker 1: use English terms instead of Greek terms excess returns or

1377
01:10:43,200 --> 01:10:46,760
Speaker 1: a function of your skill times your opportunity set. And

1378
01:10:46,800 --> 01:10:48,439
Speaker 1: that's a really interesting way to think about this, right,

1379
01:10:48,439 --> 01:10:50,280
Speaker 1: because you can be the most skillful person in the world,

1380
01:10:50,320 --> 01:10:53,840
Speaker 1: but if your opportunity set is not very attractive, you're

1381
01:10:53,840 --> 01:10:56,000
Speaker 1: not gonna go very far with your access returns. So

1382
01:10:56,040 --> 01:10:57,880
Speaker 1: one thing I just asked to encourage the students to

1383
01:10:57,920 --> 01:11:00,599
Speaker 1: think about is where do you think the next twenty

1384
01:11:00,600 --> 01:11:03,639
Speaker 1: five years things are going to be a little more exciting? Right?

1385
01:11:03,720 --> 01:11:06,320
Speaker 1: Where where is the where the opportunity is going to reside?

1386
01:11:07,600 --> 01:11:13,280
Speaker 1: My guess is it's unlikely to be the dwive US

1387
01:11:13,360 --> 01:11:18,080
Speaker 1: value manager Large Camp. It's unlikely to be. It's much

1388
01:11:18,120 --> 01:11:21,200
Speaker 1: more likely to be somewhere in emerging markets, maybe even

1389
01:11:21,320 --> 01:11:26,080
Speaker 1: you know, uh, you know, Africa or parts of Asia.

1390
01:11:26,400 --> 01:11:27,840
Speaker 1: So to me, that would be the thing to think

1391
01:11:27,840 --> 01:11:30,360
Speaker 1: about is if you you're gonna lay out next years,

1392
01:11:30,360 --> 01:11:32,759
Speaker 1: where do you think that those, um, those excess returns

1393
01:11:32,760 --> 01:11:34,880
Speaker 1: are going to be. The other thing I would say

1394
01:11:35,000 --> 01:11:37,200
Speaker 1: is that and you know, I think you've talked a

1395
01:11:37,240 --> 01:11:41,160
Speaker 1: lot about this as well. We've really moved rapidly towards

1396
01:11:41,280 --> 01:11:45,040
Speaker 1: quantitative methods, and the other other thing to think about

1397
01:11:45,160 --> 01:11:51,000
Speaker 1: is are there ways that we can meld or advanced

1398
01:11:51,080 --> 01:11:54,240
Speaker 1: quantitative techniques so meld them with our without what we're

1399
01:11:54,280 --> 01:11:57,320
Speaker 1: doing fundamentally, So so some blend of those two techniques

1400
01:11:57,400 --> 01:12:00,760
Speaker 1: or advanced un quantitative So that if you said, where

1401
01:12:00,800 --> 01:12:02,760
Speaker 1: is the future going? To me, I think these quantitative

1402
01:12:02,800 --> 01:12:06,559
Speaker 1: techniques are certainly not going to uh be rolled back.

1403
01:12:06,600 --> 01:12:08,640
Speaker 1: I think we're going to continue to see that advancement.

1404
01:12:09,080 --> 01:12:11,800
Speaker 1: And our final question, what is it that you know

1405
01:12:11,880 --> 01:12:15,479
Speaker 1: about investing today that you wish you knew when you

1406
01:12:15,520 --> 01:12:18,840
Speaker 1: began in the nineteen eighties? Geez, A lot of things,

1407
01:12:18,960 --> 01:12:21,240
Speaker 1: But the first is I mean, we really have. The

1408
01:12:21,360 --> 01:12:25,200
Speaker 1: complexion of the market has changed a great deal. When

1409
01:12:25,200 --> 01:12:27,920
Speaker 1: I started thirty years ago, indexing was less than one

1410
01:12:27,960 --> 01:12:30,439
Speaker 1: percent of assets under management. By the way we looked

1411
01:12:30,439 --> 01:12:35,000
Speaker 1: this up, the equity US equity mutual fund industry assets

1412
01:12:35,080 --> 01:12:39,360
Speaker 1: under management were hundred and thirty five billion. Wow, that's amazing.

1413
01:12:39,600 --> 01:12:42,840
Speaker 1: It's just mind boggling. In thirties decent sized funds. Decent

1414
01:12:42,920 --> 01:12:46,040
Speaker 1: size fund today, Isn't it remarkable? So the whole complexion

1415
01:12:46,040 --> 01:12:47,639
Speaker 1: has changed a great deal. But there are a couple

1416
01:12:47,640 --> 01:12:50,040
Speaker 1: of things that I would note. One is that that phenomenon,

1417
01:12:50,040 --> 01:12:52,479
Speaker 1: so we've gone from basically a standstill to thirty five

1418
01:12:52,479 --> 01:12:55,960
Speaker 1: percentage something like that that's passive or index So that's

1419
01:12:55,960 --> 01:12:59,160
Speaker 1: a big change. The other fascinating change is that, um

1420
01:12:59,400 --> 01:13:04,800
Speaker 1: most fun were single managed by one person about years ago.

1421
01:13:04,840 --> 01:13:09,559
Speaker 1: That's now down about most teams. Yeah, their team run,

1422
01:13:09,920 --> 01:13:12,000
Speaker 1: which is it? Truly That's another really big change and

1423
01:13:12,040 --> 01:13:14,680
Speaker 1: what's going on. And I think the other thing is

1424
01:13:14,720 --> 01:13:16,679
Speaker 1: just the level of skill has gone up, and going

1425
01:13:16,680 --> 01:13:18,599
Speaker 1: back to our discussion on the paradox of skill, we've

1426
01:13:18,640 --> 01:13:21,240
Speaker 1: never seen more skill than we have today, and that

1427
01:13:21,360 --> 01:13:23,799
Speaker 1: has made it much more difficult to outperform the market.

1428
01:13:24,360 --> 01:13:27,200
Speaker 1: So I mean it's it's always exciting, as you point out,

1429
01:13:27,200 --> 01:13:29,880
Speaker 1: because there's always something going on, the world is always changing.

1430
01:13:30,479 --> 01:13:33,799
Speaker 1: What's deeply fascinating about this business is you've never figured

1431
01:13:33,840 --> 01:13:36,080
Speaker 1: it out right because the world is always changing and

1432
01:13:36,120 --> 01:13:38,040
Speaker 1: it requires you to keep up with what's going on.

1433
01:13:38,479 --> 01:13:40,600
Speaker 1: But those are some of the really big changes the

1434
01:13:40,680 --> 01:13:44,800
Speaker 1: backdrop that I think make it all so fascinating. We

1435
01:13:44,880 --> 01:13:49,240
Speaker 1: have been speaking with Michael Mobison of Credit Swiss. Mike,

1436
01:13:49,320 --> 01:13:51,160
Speaker 1: thanks for being so generous with your time. This has

1437
01:13:51,200 --> 01:13:56,320
Speaker 1: been absolutely fascinating. If you enjoy this conversation, be showing

1438
01:13:56,400 --> 01:13:58,479
Speaker 1: look Up an Inch or Down an Inch on Apple

1439
01:13:58,560 --> 01:14:02,000
Speaker 1: iTunes and you could see any of the other one

1440
01:14:02,120 --> 01:14:06,120
Speaker 1: hundred uh such episodes that we have had. UH be

1441
01:14:06,240 --> 01:14:09,800
Speaker 1: sure and check out Mike's white papers, books, et cetera.

1442
01:14:09,960 --> 01:14:13,800
Speaker 1: You can find that at Michael Mobison dot com. I

1443
01:14:13,880 --> 01:14:17,680
Speaker 1: would be remiss if I did not thank uh the

1444
01:14:17,760 --> 01:14:20,920
Speaker 1: hard work of the team who helped put this together. Uh.

1445
01:14:20,960 --> 01:14:25,000
Speaker 1: Taylor Riggs is my producer, booker Charlie Bohmer, and Today

1446
01:14:25,040 --> 01:14:30,880
Speaker 1: Guests recording engineer David Uh. Mike Batnick is our head

1447
01:14:30,880 --> 01:14:36,560
Speaker 1: of research. We love your comments feedbacks, questions and suggestions.

1448
01:14:37,120 --> 01:14:40,280
Speaker 1: Be sure and write to us at our new email address,

1449
01:14:41,120 --> 01:14:46,760
Speaker 1: m IB podcast at bloomberg dot net. I'm Barry Ritults.

1450
01:14:46,800 --> 01:14:50,439
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio