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Speaker 1: This is Mesters in Business with Very Results on Bloomberg Radio.

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

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Speaker 1: Adam Parker. What a fascinating career UH, top ranked institutional analysts,

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Speaker 1: semiconductor analyst, head of research at Sanford Bernstein, head of

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Speaker 1: US Equities at Morgan Stanley. Really a master class and

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Speaker 1: how to think about creating frameworks for investing, for thinking

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Speaker 1: about how to apply quantitative research along with macro and

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Speaker 1: fundamental data UH in order to create a differentiated research product.

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Speaker 1: Just absolutely a master class in thinking about stocks, and

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Speaker 1: thinking about sectors, and thinking about where is the crowd

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Speaker 1: wrong and how to come up with a UM very

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Speaker 1: outlier perspective, many of which have been giant moneymakers and

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Speaker 1: really UH fascinating market calls. I found this conversation to

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Speaker 1: be brilliant and insightful, and I think you will also

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Speaker 1: with no further ado my conversation with Trivariant Researches Adam Parker.

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Speaker 1: This is mesters in Business with Very Results on Bloomberg Radio.

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Speaker 1: My special guest this week is Adam Parker. He is

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Speaker 1: the founder of Trivariant Research. Previously, he was Global director

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Speaker 1: of Research and US equity strategist at Sanford ci Bernstein.

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Speaker 1: He was the number one institutional investor ranked analyst in

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Speaker 1: semiconductors before he became Morgan Stanley's chief US equity strategist

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Speaker 1: and director of Global quant Research. Adam Parker, Welcome to Bloomberg.

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Speaker 1: Thanks thanks for having me here. I've been looking forward

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Speaker 1: to having this conversation for a while, and I have

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Speaker 1: to start with your very interesting academic background. You have

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Speaker 1: three agrees and stats, uh not just undergraduate at Michigan,

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Speaker 1: but a PhD from Boston University. And in the middle,

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Speaker 1: you've got a master's in biostatistics at UNC Chapel Hill.

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Speaker 1: Tell us about that. Yeah, well, back then statistics wasn't

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Speaker 1: as cool as it is now. Very so I didn't

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Speaker 1: know thirty years ago I was going to turn into

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Speaker 1: the all the rage and that everyone was kind of

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Speaker 1: one a major in you know, data science and analytics.

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Speaker 1: It just I was always more of a math guy,

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Speaker 1: and I liked having problem sets and then going and

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Speaker 1: playing sports, and I didn't want to have to read

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Speaker 1: Chaucer or whatever all the other miserable people were doing.

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Speaker 1: So kind of motivated me to be a little bit

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Speaker 1: more analytical. So so, but the question that raises biostatistics

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Speaker 1: is were you always planning on a career in finance

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Speaker 1: or was that you know, that was more of UM.

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Speaker 1: The biostatistics department was in the School Public Health at UNC,

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Speaker 1: and it's really you know, applied statistics applied that at

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Speaker 1: that age too, uh, mostly medical data, but it was

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Speaker 1: more about learning analytics and you know, programming and UM

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Speaker 1: and you can apply it to anything, complied to anything.

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Speaker 1: So like my my PhD thesis was about missing data

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Speaker 1: UM in a healthcare setting, but as you know, missing

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Speaker 1: data exists everywhere, including a finance so it turned out

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Speaker 1: to be pretty applicable. So how frustrating is it to

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Speaker 1: you to to see either newspaper headlines or social media

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Speaker 1: where people just lack of rudimentary understanding of basic statistics

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Speaker 1: and probability, you know. I think the big challenges is,

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Speaker 1: as you know, because you're good at this, is taking

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Speaker 1: things that are somewhat complicated and then making them sound

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Speaker 1: like they're simple and explaining them to everybody. I think

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Speaker 1: the average UM intellect of people watching and reading mainstream

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Speaker 1: media is still in the junior high or high school level.

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Speaker 1: So that's what you've got to resonate with. And I

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Speaker 1: romanticize the investment community is slightly above that, but it

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Speaker 1: probably is less above that than you think, right, So

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Speaker 1: so I love We'll talk about try variate a little later.

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Speaker 1: I love the name. I wrote a Bloomber column years ago. Um,

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Speaker 1: single variable analysis is for soccers or something like that,

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Speaker 1: And so I have to talk to you about But

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Speaker 1: but let's with all that stat background, how did you

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Speaker 1: get to Sanford CT Bernstein? You know in those days? Um,

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Speaker 1: you know, I finished my PhD um in the late nineties.

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Speaker 1: I you know, I had some buddies that seemed to

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Speaker 1: be getting rich on Wall Street and then I didn't

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Speaker 1: really know what they were doing. And one of my

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Speaker 1: best friends worked at Sanford Bernstein and and uh, they

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Speaker 1: were looking for somebody to write, um, you know, software

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Speaker 1: and do analysis called quant research on equities. And I

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Speaker 1: interviewed there and I loved it. This bunch of crazy,

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Speaker 1: you know, wild people who were brilliant and um, kind

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Speaker 1: of a little bit a little bit on the edge

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Speaker 1: of being unhinged as human beings. And it was just

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Speaker 1: kind of my jam, you know. And so, um, you're

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Speaker 1: so buttons up, you don't sound like a crazy but

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Speaker 1: it was. It was effort and enthusiasm or just like

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Speaker 1: getting the PhD berry, it's basically perseverance one percent intelligence.

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Speaker 1: And this was like you get in there and there

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Speaker 1: was just no rules, like find something interesting and write

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Speaker 1: about it. And so for me, you know, there's this

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Speaker 1: database of information on hundreds of stocks and you could

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Speaker 1: go in there and analyze as it reached conclusions. Oh way,

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Speaker 1: you know along the top market kept name and sure

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Speaker 1: these against it or do this or you know, it

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Speaker 1: just kind of empirically test everything. And there was a

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Speaker 1: bunch of incredibly brilliant people there. So I loved it. Um,

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Speaker 1: I loved the environment, and I didn't I didn't even

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Speaker 1: know what I was getting into, to be honest with you.

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Speaker 1: And then from quant work at Bernstein were you were

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Speaker 1: you an analyst and Semmi's there all yeah, so I

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Speaker 1: switched to being sem Look at that time, being late

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Speaker 1: nineties into the you know TMT bubble, what seemed cool

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Speaker 1: to young young Adam Parker was being an analyst. Oh man,

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Speaker 1: these tech analysts, they seem that seems like a great job.

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Speaker 1: And Bernstein in those days, you know, you were really

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Speaker 1: an expert you wrote, you know, a hundred hundred un

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Speaker 1: page black book. It was called on an Industry, and

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Speaker 1: you you could tear apart the p and ls of

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Speaker 1: the companies and you really understood, Um, you know, we

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Speaker 1: spend all our time on six to ten stocks, so

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Speaker 1: you really knew those companies, that management teams, the things

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Speaker 1: that impacted the volatility of the pan l you you

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Speaker 1: kind of became an expert. And so I really want

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Speaker 1: to do that. And I just got lucky that it

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Speaker 1: was semiconductors. Um. I basically just kept going in saying

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Speaker 1: I want to do this, I want to do this.

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Speaker 1: And the first sector they offered me very was European

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Speaker 1: electric utilities. So much fun. Yeah, and I I really

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Speaker 1: struggled with how I'm going to communicate to them. I'm

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Speaker 1: really on board with the fact that you you you're

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Speaker 1: allowing me to be an analyst, but I can't move

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Speaker 1: to London. Yeah, that's it. I can't move to London.

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Speaker 1: I just I just got engaged or whatever. So I

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Speaker 1: I enabled to sort of convince them, yes, thank you,

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Speaker 1: I'm an analysts, but no, I'll wait for the first

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Speaker 1: US one. And it could have been anything. It could

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Speaker 1: have been food it could have been I didn't really care.

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Speaker 1: And when Semi get you have you didn't have a

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Speaker 1: tech background. I have been engineering because a lot of

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Speaker 1: the analysts covering Semi's they're they're electric consigners, executor software design.

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Speaker 1: I used to, you know, and and Burnstein's hiring model

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Speaker 1: back then was basically get a mckensey guy who was

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Speaker 1: an expert on an industry or somebody who worked at

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Speaker 1: one of those companies. I was one of the rare

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Speaker 1: counter examples of you know, um from within. Yeah, I

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Speaker 1: think the PhD and statistics probably helped me. I used

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Speaker 1: to just say, look, I'm probably better accounting the chips

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Speaker 1: and knowing what they are, you know. And and it

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Speaker 1: turned out that in those days would really matter to

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Speaker 1: getting the stocks right was sort of a non consensus

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Speaker 1: in correct view of the gross margins six months forward,

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Speaker 1: and so that didn't really require the expertise on circuit

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Speaker 1: design and the like. In fact, you know this, but

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Speaker 1: sometimes those the people who work at the companies turned

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Speaker 1: out to be not very good at calling the stock

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Speaker 1: price of the owned company they worked at, because you

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Speaker 1: have all kinds of biases from the people you like,

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Speaker 1: and you don't like and that kind of stuff. So, um,

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Speaker 1: it worked to my advantage, but I think probably wouldn't

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Speaker 1: have happened if I didn't labor through that PhD. So

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Speaker 1: how do you get from Bernstein to Morgan Stanley? Yeah?

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Speaker 1: So after I did SEMIS for a few years, and

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Speaker 1: you know, that's a very competitive, you know business. You

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Speaker 1: get up every day and there's a there's a there's

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Speaker 1: a person at admiral and a person that you know, uh,

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Speaker 1: every there your competitors you want to like just you

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Speaker 1: want to make them look stupid on the conference calls,

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Speaker 1: and you want to ask the smartest question and you

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Speaker 1: want to be number one ranked, right, So you do that,

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Speaker 1: and you know very you know, once you get number

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Speaker 1: one a few times, all you think about is like,

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Speaker 1: am I gonna lose it? There's no joy in repeating

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Speaker 1: as number one, There's only the fear of losing it, right,

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Speaker 1: because then you're like, wait a minute, Like investors don't

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Speaker 1: like me as much as I used to. So you know,

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Speaker 1: I felt like I wasn't really incrementally you know, doing

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Speaker 1: that again wasn't going to drive me anymore. And um,

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Speaker 1: you know I was offered, um, you know, this position

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Speaker 1: to run research at Burnstein, and so I transitioned to

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Speaker 1: be the director of research for a while, which was

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Speaker 1: attracting and retaining, hiring, firing, that kind of stuff. Management position,

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Speaker 1: not research got me away from that. But the beautiful

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Speaker 1: for a year. But the beautiful part was I then

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Speaker 1: was helping other analysts RAMP and so I got to learn, Okay,

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Speaker 1: I'm gonna lauch the household products guy, I'm gonna launch

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Speaker 1: the capital equipment guy industrial and so in that year

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Speaker 1: I was helping kind of four or five analysts RAMP.

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Speaker 1: I started realizing like this kind of interesting, I'm kind

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Speaker 1: of I can apply what I know with Sammy's and

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Speaker 1: helped them. And so early in oh eight, at the

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Speaker 1: very beginning of a wait, the strategy and quant research

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Speaker 1: job opened up at Burnstein and that's how I transitioned

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Speaker 1: to being a little bit more quote unquote macro. So

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Speaker 1: I did that for a couple of years, and then

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Speaker 1: I transitioned to Morgan Stanley to be the strategist there.

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Speaker 1: And for people who may not remember this, in the nineties,

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Speaker 1: Bernstein's uh bevy of analysts were top top. So in

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Speaker 1: oh seven, when I was a director of research at Burnstein.

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Speaker 1: These the data UM Burnstein at twenty three u s

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Speaker 1: and also that were publishing eighteen were ranked in the

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Speaker 1: top three and eleven were number one. That's unbelievable. So

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Speaker 1: it was really a number one machine UM in terms

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Speaker 1: of the analysts UM that worked there. And you know,

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Speaker 1: I I, you know, was my job was get the

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Speaker 1: five that weren't in the top three in the top

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Speaker 1: three and hire a few more that will eventually be

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Speaker 1: you know, number one in the future, you know. And

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Speaker 1: and then that was in the US, and we also

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Speaker 1: were building a European business too, So so obvious obvious

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Speaker 1: after the fact question. Bernstein was substantial in size, But

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Speaker 1: they weren't you know, Gulman, Saxmom Stanley, Merrill, Lynch. What

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Speaker 1: was the secret of success? Why were they punching so

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Speaker 1: far outside of that weight class? I think they're you know, um,

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Speaker 1: it was the it was multiple things. But I'd say,

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Speaker 1: you know, you don't have a prime brokerage business, you

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Speaker 1: don't have a banking business. So there was this perception

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Speaker 1: of independence. You hire people who were you know, generally

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Speaker 1: were you know, experts in the industry. I was an exception,

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Speaker 1: but there were generally people who were running the Mackenzie

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Speaker 1: practice consulting the aerospace companies, and they would be hired

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Speaker 1: to cover Boeing or those kind of things. So kind

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Speaker 1: of the industry now, and I think the Bye side,

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Speaker 1: you know, relied on that is sort of a you know,

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Speaker 1: an external voice. When you interview the Bye side, they

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Speaker 1: tend to not care if the Cell side are good

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Speaker 1: stock pickers or not. They might blame them if they're bad,

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Speaker 1: but they're never gonna say I rely on the Cell

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Speaker 1: side for their stock selection skills. That's what they're supposed

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Speaker 1: to be doing. So I think what helped Bernstein gain

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Speaker 1: prominence was the fact that all right, we don't even

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Speaker 1: try to do that at an expert level, just try

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Speaker 1: to help people be smarter about the investment countries and

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Speaker 1: controversies and write detailed, um, you know, sensible issues on

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Speaker 1: those investment convers So that that was that was a

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Speaker 1: business model, and it really worked back through and at least,

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Speaker 1: you know, until maybe ten years ago. So so let

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Speaker 1: you raise such a fascinating question I want to ask

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Speaker 1: you about. And we're recording this UM late April, after Netflix,

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Speaker 1: which had fallen fifty from its October peak UM at

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Speaker 1: their earnings call, they announced a decrease in subscribers. The

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Speaker 1: stock falls another overnight the next day. There are all

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Speaker 1: these down grades from the major cell side shops, cut

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Speaker 1: cut to neutral, cut to hold, cut to and it

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Speaker 1: raises the question, and I'm sure lay people asked this

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Speaker 1: question to themselves all the time. Hey, the stock is

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Speaker 1: now down from where you told me to buy it.

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Speaker 1: What's the point of this down grade? Thanks for nothing? Yeah,

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Speaker 1: I mean the south side um defend the entire analysts

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Speaker 1: go okay, yeah, you know. I when I got to

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Speaker 1: Morgan Stanley in I'll answer this one. I got in

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Speaker 1: the late fall of I wondered if the research department

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Speaker 1: there generated any alpha with the recommendations, and so I

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Speaker 1: analyzed they had stored data from three to ten. There's

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Speaker 1: about thirty five stock recommendations that were kind of stored,

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Speaker 1: so you let the statistician loose on the data. And

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Speaker 1: about half were overweight rated half were equal underweight. So

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Speaker 1: I thought, all right, did the overweights beat the equal underweights?

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Speaker 1: Your exact question, I considered, So I didn't stock down

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Speaker 1: after mark it, and then they downgradeed. You do not

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Speaker 1: give them credit for that being a credit You lack

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Speaker 1: it by twenty four hours. You bade an adjusted meaning

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Speaker 1: you know, adjustable how much thing to be moved. And

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Speaker 1: it turned out, at least for the observations over seven years,

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Speaker 1: that they had about four percent average um alpha between

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Speaker 1: the overweights and equal underweights. So I published that as

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Speaker 1: a bar four So in other words, the stocks they

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Speaker 1: liked did four percent they didn't like, And then how

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Speaker 1: did it do versus so that basic indexing, Yeah, well

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Speaker 1: that was kind of a new market neutral, right, So

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Speaker 1: like you overweight longs and your short equal on the weights,

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Speaker 1: and then and then I had a quant model that

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Speaker 1: you know, the long top pointel beat the bottom by

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Speaker 1: nine percent, so I sort of said, look, I think

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Speaker 1: quantitative stuff probably you know, is a little bit better

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Speaker 1: than fundamental stock. But then the when the last bar

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Speaker 1: was thirteen percent, which was if you only bought the

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Speaker 1: overweight rated stocks at the model light and you only

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Speaker 1: sort of shorted the equal underweights and the model didn't like,

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Speaker 1: you get thirteens. The whole point of this was a

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Speaker 1: combination of something quantitative and maybe unemotional, combined with the

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Speaker 1: fundamentals would be superior to either discipline alone. And actually

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Speaker 1: i've spent most of my life since then, you know,

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Speaker 1: the last twelve years in that sort of combination sphere.

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Speaker 1: So I think I'm trying to defend it by saying, look,

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Speaker 1: I think there's some value in it, for sure, but

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Speaker 1: there's not value in changing the recommendation after it's happened.

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Speaker 1: My own personal opinion on Netflix, And I'm not a

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Speaker 1: fundamental analyst there, but I did write about it bury

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Speaker 1: it's interesting. Um, I've had two learning lessons of that.

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Speaker 1: This one applied to one. When things change, you have

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Speaker 1: to admit it, and this one I think has both

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Speaker 1: macro and micro changes. I think the macro would be,

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Speaker 1: you know, everyone bought too many streaming services during COVID,

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Speaker 1: and maybe it doesn't need they're out of their house again, right,

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Speaker 1: and so it's reopening and and And the micro is

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Speaker 1: they've got to think about pricing and maybe charging people

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Speaker 1: to or not charging the inverse for advertisements. So that's

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Speaker 1: kind of a business model change. And the other thing

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Speaker 1: so maybe you have to say to yourself, well, it's

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Speaker 1: not exactly the same fundamentally. Maybe you know, sometimes I

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Speaker 1: guess i'd answer your question by saying, sometimes the stocks

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Speaker 1: down twenty five but the fundamentals are worse than right

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Speaker 1: that maybe not in this case, but I'm saying in aggregate.

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Speaker 1: And the second learning lesson I've had from analyzing a

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Speaker 1: lot of behavior on the short selling it and running

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Speaker 1: my own fund is you make more money shorting stocks

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Speaker 1: down from highs than you do add highs. So it's

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Speaker 1: very tough to short stock add a high because you're

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Speaker 1: fighting positive price momentum. Right, So when the stocks down

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Speaker 1: and then you short it, I guarantee you make more

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Speaker 1: money shorting stocks down twenty from highs than you do

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Speaker 1: at high So it's not necessarily true that Netflix isn't

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Speaker 1: a short here um, But I'm not a fundamental an else,

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Speaker 1: and in that in that case, I'm not convinced that

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Speaker 1: it isn't worse. It's still trades in a hundred times

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Speaker 1: forward free cash flow. It's got a high correlation to

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Speaker 1: low quality and work from home. It's got a high

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Speaker 1: correlation to negative correlation to inflation. So I don't have growth.

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Speaker 1: You know stocks like that are gonna work, so you

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Speaker 1: know I don't. I don't know the fundamentals. And one

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Speaker 1: of one of my favorite things about having you who

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Speaker 1: is an independent research shop instead of a cell side analyst.

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Speaker 1: I'm not getting a phone call tomorrow from the PR

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Speaker 1: person begging me to take everything Adam said out about Netflix.

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Speaker 1: Can't he can't talk about you can talk. You'd go anywhere.

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Speaker 1: You could talk about anything that's right without friction. So

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Speaker 1: that leads to another question. How freeing is that that

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Speaker 1: you can actually say what's on your mind and you're

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Speaker 1: not thinking about what Obviously legal is important, but sometimes

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Speaker 1: compliance gets a little over enthusiastic, and PR even more stuff.

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Speaker 1: I would say, you know, I should look this up.

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Speaker 1: So this this is an exaggeration, but I would say

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Speaker 1: I would say maybe ten years ago when I worked

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Speaker 1: at Morgan Stanley, Um, I think there was fifty thousand

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Speaker 1: employees and ten thousand legal and compliance in ten thousand

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Speaker 1: and I t so that those are some but that's

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Speaker 1: something like that. So look, these are amazing firms, and

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Speaker 1: morganize an incredible firm with great people and a lot

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Speaker 1: of whom are closest. But what I'd say is that

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Speaker 1: there's positive and negative. The big firms have bigness disease,

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Speaker 1: and the taxes on your time become substantial. Right, you know,

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Speaker 1: you need a bunch of videos to money laundering and

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Speaker 1: a bunch of you know, every firm has this, you

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Speaker 1: know that you know, compliance stuff. You've got a bunch

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Speaker 1: of three sixty feedback M D and E D promotion,

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Speaker 1: the E S G diversity and include the number of

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Speaker 1: things you have to do just time taxes, time taxes.

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Speaker 1: It's a huge tax. And so for me, you know,

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Speaker 1: it's very freeing. We're not a broken deal or our

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Speaker 1: whole job is to write you know, interesting research that

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Speaker 1: makes people think. We sell data. We you know, create baskets.

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Speaker 1: We do a lot of outsource sort of chief chief

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Speaker 1: risk officer or work where people we signed onto sculture agreements.

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Speaker 1: People send us their portfolios and we we kind of

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Speaker 1: analyze them and try to give them some interesting thoughts

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Speaker 1: about it that aren't in you know, axioma or you know,

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Speaker 1: things they can get from other other vendors. So, um,

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Speaker 1: it's really freeing. It's it's really freeing. But you know,

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Speaker 1: you know you don't have the resources. Um, you don't

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Speaker 1: get the first class to uh, you know, you know

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Speaker 1: Beijing either, So there's some positive negatives. Wit you're flying commercial,

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Speaker 1: Come on, I always always fly commercial, man, So let's

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Speaker 1: talk semis they've been driving everything from the shortage of

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Speaker 1: automobiles to inflation. Give us the broad overview from your perspective. Yeah, well,

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Speaker 1: you know, one of the things that is tricky when

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Speaker 1: your investor barri is you know what what a cyclical

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Speaker 1: and what's structural, and you know you can confuse yourself

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Speaker 1: when something sicklal when you think it isn't, and when

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Speaker 1: the periodicity changes, and those kind of things. So uh love,

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Speaker 1: I love all this math talking amplitude. Pity, I'm so excited.

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Speaker 1: I'm back back in college. You know. I think what

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Speaker 1: you said is right though, that there are kind of

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Speaker 1: an important barometer um for a lot of broader issues.

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Speaker 1: The two things that I'm tracking right now really carefully

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Speaker 1: are a concept called book to bill, which is sort

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Speaker 1: of how much revenue did you ship out versus what

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Speaker 1: is your order flow look like? And is the order

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Speaker 1: high order flow higher than you shipped out? Book to

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Speaker 1: bill ratio generally that's still above one for most semi

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Speaker 1: conductor companies, meaning future demand looks a little bit better

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Speaker 1: than trailing demand. But that book to bill ratio has

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Speaker 1: come down from maybe one point one five to one

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Speaker 1: point eight to one point oh six so down due

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Speaker 1: to the supply as we finally get you know, supply

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Speaker 1: catching up, you know, post covid um. So you I

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Speaker 1: think when that if you think about it, it's a

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Speaker 1: weird way to think about it, but there's probably one

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Speaker 1: second where production equals consumption and then you're either about

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Speaker 1: to start overproducing consumption or you know you're about to

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Speaker 1: start underproducing. So I think will get to equilibrium in

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Speaker 1: the second half of this year in really most parts

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Speaker 1: of the semi conductors. Wow, that's that would be a

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Speaker 1: huge huge windfall fort by cars. Yeah, I think that's right.

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Speaker 1: And I think the second thing that's important related to

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Speaker 1: this is backlog. So you know, one of the things

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Speaker 1: that I think Bernstein was good about and is making

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Speaker 1: you think like you're the CEO as an analyst, so

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Speaker 1: think like your CEO, you know, stepping you know, kind

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Speaker 1: of stepping to the thought process that you're running the company.

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Speaker 1: So if you're the CEO of any industrial company, auto

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Speaker 1: home appliance, any real business, you've had trouble selling product

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Speaker 1: in the last eighteen months because you couldn't get the

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Speaker 1: supplies you need. So you go to your procurement officer

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Speaker 1: and you say, yo, how about stop bottlenecking my final revenue.

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Speaker 1: So what does that person do. It calls a semi

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Speaker 1: conductor supply chain. It says, I want two million eighteen

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Speaker 1: months from now, I want two million twelve months from now,

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Speaker 1: and by the way, I want to hundred million twenty

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Speaker 1: four months from now. And you start piling on the

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Speaker 1: backlog so that they know, hey, I'm gonna be there,

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Speaker 1: gonna be there for a while, ramp it up, right,

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Speaker 1: And so that has some interesting contagion in the economy,

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Speaker 1: right because these guys start planning their back law back um,

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Speaker 1: you know, their capacity as if that backlog is going

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Speaker 1: to be there. One of the very weird parts about

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Speaker 1: the semi connector industry that I don't think everyone understands

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Speaker 1: is there's zero penalty for backlog cancelation. So you and yeah,

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Speaker 1: you and I can if we want to go to

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Speaker 1: Noboot for sushi, we we're gonna pay twenty five bucks

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Speaker 1: if we cancel our reservation. But somehow I can order

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Speaker 1: two million of silicon and have zero penalty. It's very strange, right,

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Speaker 1: So if you get any whiff that backlog's got air

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Speaker 1: in it, meaning you know, when we get production going consumption.

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Speaker 1: Probably you're gonna call some of them like, you know what,

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Speaker 1: I probably only good for a hundred million eighteen months

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Speaker 1: from now. I don't need to twound a million. But

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Speaker 1: there's zero zero, zero penalty, right, and so I think, um,

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Speaker 1: that's a key. That's why I think backlog book to

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Speaker 1: bill are really important to watch. And if you get

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Speaker 1: any whiff that some of the backlog is is not real,

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Speaker 1: I think that causes fear. Now we've seen semis come

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Speaker 1: in a lot here because I think people know they're

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Speaker 1: over earning and they can see, you know where we

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Speaker 1: are six months from now, um, and so now I

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Speaker 1: think you're at the point where you're gonna pick winner

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Speaker 1: some losers. In a little bit more, as you can imagine,

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Speaker 1: some of semi conductor business does not have perishable pricing.

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Speaker 1: So the cancelation, yeah, they have inventorium, but they don't

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Speaker 1: have to cut the prices. So the Texas instruments and

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Speaker 1: all the devices of the world, their products really are imperishable.

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Speaker 1: Whereas you know some of the microprocesses that Intel an

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Speaker 1: m D make, or graphic processes that that n Video

428
00:20:14,720 --> 00:20:17,040
Speaker 1: an m D make, or you know obviously Micron with

429
00:20:17,119 --> 00:20:19,399
Speaker 1: memory like that stuff super perishable, right, so they make

430
00:20:19,440 --> 00:20:21,480
Speaker 1: excess the pricing comes out a lot, so you'll start

431
00:20:21,480 --> 00:20:23,640
Speaker 1: getting a little you know, discriminating between winners and losers,

432
00:20:23,640 --> 00:20:25,240
Speaker 1: a little bit more in that sector. But I think

433
00:20:25,280 --> 00:20:27,960
Speaker 1: the broad tenter of your question, barriers backlog and book

434
00:20:28,000 --> 00:20:30,119
Speaker 1: to bill are are are probably you know, in the

435
00:20:30,200 --> 00:20:33,080
Speaker 1: top ten interesting more macro barometers for people to focus on.

436
00:20:33,280 --> 00:20:35,960
Speaker 1: So from a macro perspective, one of the most interesting

437
00:20:36,080 --> 00:20:39,320
Speaker 1: questions that comes up over and over again is why

438
00:20:39,359 --> 00:20:42,520
Speaker 1: does it seem to take so long to reopen a

439
00:20:42,560 --> 00:20:47,040
Speaker 1: semiconductor fab after a prolonged shutdown? You know, it's a

440
00:20:47,119 --> 00:20:51,520
Speaker 1: number of issues, but you um may have excess capacity

441
00:20:51,560 --> 00:20:53,560
Speaker 1: and a factory, but you may take you several weeks

442
00:20:53,600 --> 00:20:56,600
Speaker 1: to start building it and ramping it up. UM. You know,

443
00:20:56,840 --> 00:20:59,520
Speaker 1: you may have tools that are idled, you may have

444
00:20:59,600 --> 00:21:03,840
Speaker 1: tools that are not assembled yet, right, so you're you

445
00:21:03,920 --> 00:21:07,119
Speaker 1: can't really turn on a dime your production as rapidly

446
00:21:07,200 --> 00:21:10,000
Speaker 1: as people think. UM it is is a lot more

447
00:21:10,080 --> 00:21:12,080
Speaker 1: automated now than it used to be, though in terms

448
00:21:12,160 --> 00:21:14,640
Speaker 1: of UM you know how it works inside a way

449
00:21:14,640 --> 00:21:18,680
Speaker 1: for fabrication people in bunny suits. I've exactly so you know,

450
00:21:18,760 --> 00:21:21,680
Speaker 1: you're you're, you're, you and I are the same vintage.

451
00:21:21,720 --> 00:21:22,920
Speaker 1: So you know, I've been in the bunny suit in

452
00:21:23,000 --> 00:21:25,600
Speaker 1: old factories and you know, if you think about they

453
00:21:25,680 --> 00:21:28,240
Speaker 1: used to talk a lot about yield, and some of

454
00:21:28,280 --> 00:21:30,840
Speaker 1: the yield was just like people's hair getting in the

455
00:21:30,920 --> 00:21:35,120
Speaker 1: stuff or you know, dropping dropping these things on the floor. Um,

456
00:21:35,280 --> 00:21:40,399
Speaker 1: and so that through multiple exactly now it's all you know,

457
00:21:40,600 --> 00:21:43,240
Speaker 1: synopsis and cadence and software and the stuff goes on

458
00:21:43,280 --> 00:21:45,399
Speaker 1: the ceiling on tracks and comes down to the right machine.

459
00:21:45,440 --> 00:21:47,840
Speaker 1: And I don't know if people can mentally imagine a

460
00:21:47,920 --> 00:21:50,760
Speaker 1: fab but away from fabrication facility, but they're like the

461
00:21:50,800 --> 00:21:54,920
Speaker 1: size of a football field and there's ten million dollar

462
00:21:55,040 --> 00:21:57,240
Speaker 1: machines as far as you can see in every direction.

463
00:21:57,640 --> 00:22:01,000
Speaker 1: So it's multiple, multiple billions of dollars, I think. I

464
00:22:01,080 --> 00:22:03,040
Speaker 1: think when I went to it's been many years now

465
00:22:03,119 --> 00:22:04,520
Speaker 1: since I covered Semmis, but when I went to one

466
00:22:04,560 --> 00:22:05,879
Speaker 1: of their state of the art fabs and intel in

467
00:22:05,960 --> 00:22:08,600
Speaker 1: Oregon many years ago, they had a sign up front

468
00:22:08,600 --> 00:22:11,159
Speaker 1: saying they had more steel than two Eiffel towers and

469
00:22:11,320 --> 00:22:13,679
Speaker 1: enough cement to go to Portland, from Portland to Seattle.

470
00:22:14,200 --> 00:22:16,399
Speaker 1: Like they're big facility, So I think it's just not

471
00:22:16,560 --> 00:22:18,680
Speaker 1: as easy to like quickly ramp up a bunch of

472
00:22:18,720 --> 00:22:21,520
Speaker 1: the capacity as people think. So so that raises a

473
00:22:21,640 --> 00:22:24,280
Speaker 1: question that UM a lot of people have been asking,

474
00:22:24,760 --> 00:22:30,600
Speaker 1: which is, how seriously can we reassure manufacturing facilities in

475
00:22:30,640 --> 00:22:33,280
Speaker 1: the US. Is that a real thing or is that

476
00:22:33,480 --> 00:22:36,320
Speaker 1: something that the politicians wave their hands about it. But

477
00:22:36,720 --> 00:22:38,960
Speaker 1: it's so much money and it's so much cheaper overseas,

478
00:22:39,040 --> 00:22:40,439
Speaker 1: it's not going to happen. I think there's a lot

479
00:22:40,520 --> 00:22:42,959
Speaker 1: of things that could change. That deglobalization team I think

480
00:22:43,080 --> 00:22:45,240
Speaker 1: is real. If I think about what's kind of changed

481
00:22:45,280 --> 00:22:48,480
Speaker 1: pre COVID to now, probably the deglobalization team you're talking

482
00:22:48,480 --> 00:22:51,240
Speaker 1: about it is one of the bigger actual changes. You

483
00:22:51,240 --> 00:22:53,439
Speaker 1: don't need a package and test every chip in Taiwan,

484
00:22:53,480 --> 00:22:55,159
Speaker 1: and there's some cheap areas here in the US, and

485
00:22:55,200 --> 00:22:58,000
Speaker 1: I think that's struct really changed. I know Intel's announced

486
00:22:58,040 --> 00:23:00,520
Speaker 1: some massive was an hundred billion CAPEX play over multiple

487
00:23:00,560 --> 00:23:02,520
Speaker 1: years to build some stuff in Arizona and other places.

488
00:23:02,600 --> 00:23:05,640
Speaker 1: So I think we're gonna onshore more than manufacturing UM,

489
00:23:06,080 --> 00:23:08,760
Speaker 1: and I think that part's real. Does the national security

490
00:23:08,760 --> 00:23:12,080
Speaker 1: issue security as well China make are the chips for

491
00:23:12,200 --> 00:23:15,520
Speaker 1: our F twenty two fighters is potentially and I think

492
00:23:15,560 --> 00:23:18,760
Speaker 1: there's a yeah, I think there's also Um, there's been

493
00:23:18,800 --> 00:23:21,560
Speaker 1: diminishing benefits to outsourcing it on the cost front as well.

494
00:23:22,040 --> 00:23:23,440
Speaker 1: Now maybe that doesn't mean, I don't I don't know

495
00:23:23,440 --> 00:23:24,960
Speaker 1: if that means Intel is gonna be a good stock.

496
00:23:25,119 --> 00:23:27,280
Speaker 1: Right just because they're you know, um, you're gonna spend

497
00:23:27,280 --> 00:23:29,880
Speaker 1: all that Capex doesn't mean it'll be So let's let's

498
00:23:29,920 --> 00:23:32,520
Speaker 1: talk about Intel. They've been criticized for lack of innovation,

499
00:23:33,000 --> 00:23:35,119
Speaker 1: for not keeping up with the in videos of the

500
00:23:35,160 --> 00:23:38,920
Speaker 1: world or even with Apple and there M one chips. I,

501
00:23:39,320 --> 00:23:43,000
Speaker 1: by the way, footnote, I got a new iMac in

502
00:23:43,680 --> 00:23:47,639
Speaker 1: December and the old machine is two years old. The

503
00:23:47,760 --> 00:23:51,440
Speaker 1: new one is like six times faster. It's insane. The

504
00:23:51,560 --> 00:23:54,320
Speaker 1: difference between the M one chip and the solid state

505
00:23:54,840 --> 00:23:59,200
Speaker 1: you know, no spending drives nothing. Um. So what happened

506
00:23:59,240 --> 00:24:02,280
Speaker 1: to Intel? How they seem to fall so far behind? Yeah,

507
00:24:02,320 --> 00:24:04,080
Speaker 1: that's a good question. I mean we looked at I

508
00:24:04,160 --> 00:24:07,240
Speaker 1: did a research note recently on UM capital spending in

509
00:24:07,400 --> 00:24:09,320
Speaker 1: R and D. It's in sort of R and D

510
00:24:09,400 --> 00:24:13,080
Speaker 1: intensity and capital spending intensity meaning relative to sales changes

511
00:24:13,119 --> 00:24:15,320
Speaker 1: in that what it means for subsequent returns. And our

512
00:24:15,359 --> 00:24:17,000
Speaker 1: work shows that Intel has been one of the biggest

513
00:24:17,080 --> 00:24:19,760
Speaker 1: destroyers or shareholder value of any company in the last

514
00:24:19,800 --> 00:24:22,560
Speaker 1: twenty years because they spend you know, tens and tens

515
00:24:22,640 --> 00:24:24,120
Speaker 1: of billions on this stuff and that hasn't really made

516
00:24:24,119 --> 00:24:25,960
Speaker 1: their stock go up. So if you think about it,

517
00:24:26,040 --> 00:24:29,680
Speaker 1: has it helped their sales and revenues? Yes, but we

518
00:24:29,800 --> 00:24:31,840
Speaker 1: don't really care. Like we're stock guys like I don't like,

519
00:24:32,040 --> 00:24:33,639
Speaker 1: you know, I want to buy a stock that goes up,

520
00:24:33,680 --> 00:24:35,080
Speaker 1: I don't really care if the revenue goes up in

521
00:24:35,080 --> 00:24:37,520
Speaker 1: the stock doesn't. So the stock has gotten cheaper, um,

522
00:24:37,760 --> 00:24:39,920
Speaker 1: and they've lost shared in major areas. So I I

523
00:24:40,080 --> 00:24:44,119
Speaker 1: think that, um, you know that that it may be

524
00:24:44,680 --> 00:24:46,960
Speaker 1: you know's a fruit list, but it may not be

525
00:24:47,080 --> 00:24:49,040
Speaker 1: a you know, high return on the investment. But maybe

526
00:24:49,080 --> 00:24:51,159
Speaker 1: it's just good for America. And there seems to be

527
00:24:51,240 --> 00:24:54,520
Speaker 1: bipartisan support for that as well. So let's talk about

528
00:24:54,600 --> 00:24:57,440
Speaker 1: a stock whose price has gone up. Probably the hottest

529
00:24:57,840 --> 00:25:00,520
Speaker 1: semi for years now is in video. It tell us

530
00:25:00,600 --> 00:25:04,879
Speaker 1: why their graphics engine is just kicking everybody. But they

531
00:25:04,960 --> 00:25:06,360
Speaker 1: did a lot of stuff, right, I mean, I'm look,

532
00:25:06,359 --> 00:25:09,760
Speaker 1: I dropped coverage semiconductors, you know, a dozen, you know,

533
00:25:09,840 --> 00:25:12,800
Speaker 1: more than fifteen years ago, actually General General seven. So

534
00:25:12,920 --> 00:25:15,520
Speaker 1: now you're up to data sign yeah, you know, and

535
00:25:15,600 --> 00:25:17,760
Speaker 1: it's I'm gonna get the like the danger zone of thinking,

536
00:25:17,800 --> 00:25:21,800
Speaker 1: I know stuff that's no longer relevant. Dunning Krueger presents. Yeah,

537
00:25:23,280 --> 00:25:24,760
Speaker 1: so I can tell you about you know, high school

538
00:25:24,760 --> 00:25:30,080
Speaker 1: in n also, but you know, I think that some

539
00:25:30,200 --> 00:25:32,359
Speaker 1: of us who have been around the block, you know,

540
00:25:32,480 --> 00:25:34,520
Speaker 1: probably missed at least the first half of the video

541
00:25:34,560 --> 00:25:37,280
Speaker 1: because we didn't trust the management team, you know. And

542
00:25:37,640 --> 00:25:40,159
Speaker 1: uh and I think, you know, a combination of lucky

543
00:25:40,280 --> 00:25:43,160
Speaker 1: and brilliant you know, not not all brilliant, but graphics

544
00:25:43,200 --> 00:25:44,840
Speaker 1: and crypto, and they got into a bunch of other

545
00:25:44,840 --> 00:25:48,400
Speaker 1: things that really writes it's been a monster. Now it's

546
00:25:48,400 --> 00:25:51,159
Speaker 1: been reset a lot because the valuation was high, you know,

547
00:25:51,320 --> 00:25:53,600
Speaker 1: And so I think people realize that these businesses back

548
00:25:53,640 --> 00:25:56,280
Speaker 1: to my original comments, yeah, the slope has been upward,

549
00:25:56,320 --> 00:25:58,080
Speaker 1: but they're also over earning at the same time, and

550
00:25:58,200 --> 00:25:59,840
Speaker 1: so that's why the stocks have come in so much.

551
00:26:00,160 --> 00:26:01,960
Speaker 1: I think it's probably still a little bit too early,

552
00:26:02,640 --> 00:26:05,080
Speaker 1: but I think as we get closer to production line

553
00:26:05,080 --> 00:26:07,520
Speaker 1: and consumption and the stocks correct. You know, maybe it's

554
00:26:07,560 --> 00:26:09,800
Speaker 1: time to get in again. And in the world needs semmis.

555
00:26:09,800 --> 00:26:11,600
Speaker 1: You can't reit produce anything without them. So I'm not

556
00:26:11,960 --> 00:26:14,040
Speaker 1: I'm kind of kind of a long term bull, but

557
00:26:14,520 --> 00:26:16,399
Speaker 1: kind of short to medium term. Just feel like this

558
00:26:16,480 --> 00:26:20,280
Speaker 1: correction needs to, you know, happen. So let's start talking

559
00:26:20,359 --> 00:26:25,200
Speaker 1: about you sitting on the investment committee at Morgan Stanley,

560
00:26:25,880 --> 00:26:29,399
Speaker 1: which is about two trillion dollars in client assets. I

561
00:26:29,400 --> 00:26:30,760
Speaker 1: don't know if it was that when you were there.

562
00:26:30,880 --> 00:26:32,199
Speaker 1: I think when I was there it was two. Who

563
00:26:32,240 --> 00:26:33,960
Speaker 1: knows what this each trade thing, it might be three.

564
00:26:34,000 --> 00:26:36,160
Speaker 1: I have no idea what that's a lot of would

565
00:26:36,240 --> 00:26:39,600
Speaker 1: tell us what it's like to be responsible for that

566
00:26:39,800 --> 00:26:43,040
Speaker 1: much money. Um, look, there was a seven person committee.

567
00:26:43,160 --> 00:26:46,920
Speaker 1: Everyone on there was hey one seven. There's still a

568
00:26:47,000 --> 00:26:50,640
Speaker 1: lot of money. Yeah, yeah, I don't know how much

569
00:26:50,680 --> 00:26:53,240
Speaker 1: of that, you know, I felt responsible for. I was

570
00:26:53,280 --> 00:26:55,360
Speaker 1: the you, I was the equity guy that were bond experts,

571
00:26:55,400 --> 00:26:57,840
Speaker 1: that were for you know, international experts and you know,

572
00:26:58,600 --> 00:27:01,639
Speaker 1: alternatives experts. But know, fortunately I was there during a

573
00:27:01,680 --> 00:27:04,560
Speaker 1: period where you know, it's right up. Yeah, I just

574
00:27:04,640 --> 00:27:07,120
Speaker 1: said and said look, you know, you guys can own

575
00:27:07,240 --> 00:27:09,240
Speaker 1: whatever you think makes sense. But I'll take twenty of

576
00:27:09,320 --> 00:27:11,160
Speaker 1: US growth stocks and I'll meet you in five years,

577
00:27:11,200 --> 00:27:14,119
Speaker 1: And basically that works. So um, I don't I can

578
00:27:14,200 --> 00:27:16,760
Speaker 1: look back and say I generally gave good investment advice

579
00:27:16,840 --> 00:27:18,920
Speaker 1: because I just felt like we were in by the

580
00:27:19,000 --> 00:27:22,120
Speaker 1: dip mode. You know, it was pretty clear that US

581
00:27:22,160 --> 00:27:24,200
Speaker 1: equities looked better than the other asset classes. Look, I

582
00:27:24,320 --> 00:27:27,480
Speaker 1: generally think that stillbury, which is that you know, I'm

583
00:27:27,520 --> 00:27:30,720
Speaker 1: getting to to an percent net buy back puss dividend.

584
00:27:30,840 --> 00:27:32,879
Speaker 1: I get, you know, some organic ornings growth of a

585
00:27:32,880 --> 00:27:34,640
Speaker 1: few percent. So I think the US equity market looks

586
00:27:34,640 --> 00:27:38,160
Speaker 1: like a six percent total return AUTORITHM. That look yeah normal,

587
00:27:38,200 --> 00:27:40,080
Speaker 1: And that looks a lot better than most of these

588
00:27:40,119 --> 00:27:42,920
Speaker 1: other things. And I never really understood the case for owning.

589
00:27:43,440 --> 00:27:44,840
Speaker 1: I mean, I got a little bit in trouble back

590
00:27:44,840 --> 00:27:46,479
Speaker 1: in the day went Morgan Salley when I would say

591
00:27:46,480 --> 00:27:49,560
Speaker 1: stuff like Europe is great for vacation, but not for stocks, uh,

592
00:27:50,280 --> 00:27:52,119
Speaker 1: you know, which has by the way, turned out to

593
00:27:52,160 --> 00:27:55,720
Speaker 1: be exactly acade. Yeah, and we had two year period

594
00:27:55,760 --> 00:27:57,040
Speaker 1: where it hasn't be good for vacation, But I think

595
00:27:57,040 --> 00:27:58,879
Speaker 1: it will be again this summer. But I think generally

596
00:27:59,280 --> 00:28:01,960
Speaker 1: that's been right, So I don't you know, um, I

597
00:28:02,320 --> 00:28:06,439
Speaker 1: felt like it's important to um hit on the importance

598
00:28:06,480 --> 00:28:09,160
Speaker 1: of your equities, but I don't really know, you know, today,

599
00:28:09,160 --> 00:28:10,840
Speaker 1: I think the problem would be different and more complex.

600
00:28:10,920 --> 00:28:13,160
Speaker 1: I think, you know, recently you've seen the news fidelities

601
00:28:13,160 --> 00:28:15,560
Speaker 1: say they're gonna offer crypto for retirement plans, and there's

602
00:28:15,600 --> 00:28:19,080
Speaker 1: other you know, there's other kind of diversifying things happening,

603
00:28:19,200 --> 00:28:21,800
Speaker 1: and I think alternatives. People have a different view now

604
00:28:21,840 --> 00:28:23,720
Speaker 1: than they did five six years ago, meaning you know,

605
00:28:23,960 --> 00:28:25,960
Speaker 1: maybe people now realize that some of private equity was

606
00:28:26,040 --> 00:28:29,520
Speaker 1: a levered uh rates call, and you know, so the

607
00:28:29,720 --> 00:28:32,000
Speaker 1: private markets have been a little bit more richie value

608
00:28:32,040 --> 00:28:34,679
Speaker 1: before they become public. You know, it's been some evolution

609
00:28:34,720 --> 00:28:36,560
Speaker 1: in the last five six years since I've been doing that.

610
00:28:36,680 --> 00:28:39,360
Speaker 1: But um you know, generally, I think I felt responsible

611
00:28:39,480 --> 00:28:42,680
Speaker 1: for making clear that US equities had a pretty important

612
00:28:42,680 --> 00:28:44,560
Speaker 1: and big place in the portfolio. And I think, is

613
00:28:44,640 --> 00:28:47,040
Speaker 1: you know better than me, much better than me, how

614
00:28:47,200 --> 00:28:49,480
Speaker 1: rich you are to start out with really impacts the

615
00:28:50,680 --> 00:28:53,840
Speaker 1: proper allocation. Of course, the question is that you're trying

616
00:28:53,880 --> 00:28:55,760
Speaker 1: to create wealth and preserve wealth, and that makes it

617
00:28:56,000 --> 00:28:58,200
Speaker 1: different huge. So I want to get a sense of

618
00:28:58,280 --> 00:29:00,720
Speaker 1: what it's like to be on a committee positible not

619
00:29:00,920 --> 00:29:04,600
Speaker 1: for two or three billion dollars, but for seven trillion dollars.

620
00:29:04,840 --> 00:29:08,320
Speaker 1: Is it all thirty thousand foot macro view or does

621
00:29:08,320 --> 00:29:11,920
Speaker 1: it get more granular? Did you dig into sectors stocks? How?

622
00:29:12,080 --> 00:29:16,320
Speaker 1: How how specific does that committee get? I think for me,

623
00:29:16,600 --> 00:29:18,920
Speaker 1: I've always been more about the industries of sectors, the

624
00:29:19,720 --> 00:29:21,480
Speaker 1: microstructure of the market. And it was hard for me

625
00:29:21,520 --> 00:29:23,560
Speaker 1: because I had to get had to get higher level because,

626
00:29:23,960 --> 00:29:26,760
Speaker 1: as you're pointing out correctly, UM, people are just trying

627
00:29:26,800 --> 00:29:28,680
Speaker 1: to get the mix of equities and bonds correct, their

628
00:29:28,720 --> 00:29:31,480
Speaker 1: mix of you know, us versus non us correct. UM.

629
00:29:31,600 --> 00:29:34,200
Speaker 1: I don't remember how much of that money is qualified

630
00:29:34,280 --> 00:29:37,000
Speaker 1: for alternatives, but you know that stuff obviously has a

631
00:29:37,000 --> 00:29:38,680
Speaker 1: bit of a different flavor to it, so it was

632
00:29:38,720 --> 00:29:42,600
Speaker 1: pretty high level stuff. UM. I'm not an economist, so UM,

633
00:29:42,720 --> 00:29:45,920
Speaker 1: I didn't really UM get into you know that, UM,

634
00:29:46,000 --> 00:29:47,640
Speaker 1: there are definitely some other fixing in come people who

635
00:29:47,680 --> 00:29:51,160
Speaker 1: focused on that. So I think generally, you know, at

636
00:29:51,240 --> 00:29:53,800
Speaker 1: least in the last decade, most people thought race were

637
00:29:53,800 --> 00:29:55,640
Speaker 1: going to back up and they've been wrong until the

638
00:29:55,720 --> 00:29:58,040
Speaker 1: last six or nine months, so you know they get there.

639
00:29:58,160 --> 00:30:02,000
Speaker 1: There was sort of pretty easy to like equities over ponds. Yeah,

640
00:30:02,000 --> 00:30:05,280
Speaker 1: it's very least. So let's talk a little bit about trivariate. Yeah,

641
00:30:05,400 --> 00:30:07,680
Speaker 1: starting with I love the name, tell us what it

642
00:30:07,760 --> 00:30:09,720
Speaker 1: means and now you can't. Yeah, it's totally a self

643
00:30:09,760 --> 00:30:12,120
Speaker 1: serving name. Um. So look, I was a number one

644
00:30:12,160 --> 00:30:13,880
Speaker 1: ranked then also have a PHC and statistics, and then

645
00:30:13,960 --> 00:30:17,640
Speaker 1: I did strategy. So I feel like the three buckets

646
00:30:17,680 --> 00:30:21,040
Speaker 1: of investing, the three variables investing UM, you know, quant fundamental,

647
00:30:21,120 --> 00:30:24,160
Speaker 1: and macro. So when I started a hedge fund, UM,

648
00:30:24,240 --> 00:30:26,680
Speaker 1: I called a Triviria Capital, just thinking that, you know,

649
00:30:26,720 --> 00:30:29,600
Speaker 1: I'll go tell allocators that I'm kind of considering quantitative

650
00:30:29,600 --> 00:30:31,280
Speaker 1: things and macro things and fundamental things as part of

651
00:30:31,280 --> 00:30:34,280
Speaker 1: my investment discipline. And we ran money a Trivita Capital

652
00:30:34,280 --> 00:30:36,960
Speaker 1: for a while. It closed down and converted into a

653
00:30:37,040 --> 00:30:41,120
Speaker 1: research firm UM in the middle of two thousand one,

654
00:30:41,240 --> 00:30:43,880
Speaker 1: and just kept the name. I had a fancy logo

655
00:30:44,240 --> 00:30:46,080
Speaker 1: that looks amazing, so I didn't want to repay for

656
00:30:46,160 --> 00:30:48,400
Speaker 1: a new logo. But uh, yeah, I know, it's just

657
00:30:48,440 --> 00:30:50,760
Speaker 1: I think we're we're approaching equities from the lens of

658
00:30:51,360 --> 00:30:55,000
Speaker 1: you know, systematic or quantitative um, some fundamental work and

659
00:30:55,080 --> 00:30:58,280
Speaker 1: then and then macro. Macro is more about where are

660
00:30:58,360 --> 00:31:00,200
Speaker 1: we and what to do about it? Meaning where we

661
00:31:00,680 --> 00:31:02,720
Speaker 1: where do we think we can pick stocks better or worse?

662
00:31:02,760 --> 00:31:04,520
Speaker 1: Where should we be able to generate more alpha? Which

663
00:31:04,560 --> 00:31:06,959
Speaker 1: parts of the market, you know, should we be able

664
00:31:07,000 --> 00:31:09,240
Speaker 1: to do that right now? Based on the conditions that exist.

665
00:31:09,320 --> 00:31:11,320
Speaker 1: So it's we're not doing macro from the standpoint of

666
00:31:11,360 --> 00:31:15,160
Speaker 1: forecasting rates or dour oil, but more recognizing where we

667
00:31:15,240 --> 00:31:17,560
Speaker 1: are and saying, okay, in this regime, we ought to

668
00:31:17,600 --> 00:31:19,480
Speaker 1: be able to pick winners from loses very well within

669
00:31:19,560 --> 00:31:21,800
Speaker 1: the industrial sector, but maybe not so well in general

670
00:31:21,840 --> 00:31:24,120
Speaker 1: bowls or things like that. So it's we're looking at

671
00:31:24,200 --> 00:31:27,080
Speaker 1: those three lenses to try to help, you know, people

672
00:31:27,120 --> 00:31:29,600
Speaker 1: who care about equities make money. So so let's talk

673
00:31:29,640 --> 00:31:33,440
Speaker 1: about the concept of outsourced chief risk officer. Tell us

674
00:31:33,480 --> 00:31:36,240
Speaker 1: a little bit about what you do in helping firms

675
00:31:36,800 --> 00:31:39,960
Speaker 1: manage their risk profiles. So when I left Morgan Stanley,

676
00:31:40,000 --> 00:31:41,440
Speaker 1: I left the cell side. I went to work at

677
00:31:41,440 --> 00:31:43,320
Speaker 1: a large hedge fund, and part of my role there

678
00:31:43,480 --> 00:31:46,560
Speaker 1: was to be much more analytically rigorous around risk management

679
00:31:47,280 --> 00:31:50,160
Speaker 1: UM and then also diagnosed trades to look for patterns

680
00:31:50,200 --> 00:31:52,320
Speaker 1: of behavior and the like. So I brought that sort

681
00:31:52,360 --> 00:31:54,680
Speaker 1: of risk framework into running my own hedge fund and

682
00:31:54,920 --> 00:31:58,320
Speaker 1: we have UM use that infrastructure now in the research

683
00:31:58,600 --> 00:32:00,680
Speaker 1: role to help firm. So I think we signed down

684
00:32:00,680 --> 00:32:04,600
Speaker 1: a little twentysothing nondisclosure agreements where firms they send us

685
00:32:04,640 --> 00:32:07,520
Speaker 1: their portfolio, we put it through our framework, and I

686
00:32:07,560 --> 00:32:09,520
Speaker 1: think they view me as sort of like an outsourced

687
00:32:09,600 --> 00:32:12,480
Speaker 1: cheap risk officer where we'll talk to them through things

688
00:32:12,520 --> 00:32:14,040
Speaker 1: that are not things they can get from the standard

689
00:32:14,160 --> 00:32:17,400
Speaker 1: risk vendors, So things like UM, you know, idisyncratic risk,

690
00:32:17,440 --> 00:32:20,120
Speaker 1: and maybe they differ for your lungs versus your shorts. UM.

691
00:32:20,360 --> 00:32:22,680
Speaker 1: You know, so you your bottom stock picker, but your

692
00:32:22,720 --> 00:32:24,720
Speaker 1: your lungs are pretty macro and your shorts are pretty

693
00:32:24,920 --> 00:32:28,280
Speaker 1: company specific or you know, as you know, as you know,

694
00:32:28,520 --> 00:32:30,959
Speaker 1: very like if risk didn't change, anyone could do risk management.

695
00:32:31,040 --> 00:32:33,600
Speaker 1: So most people know their growth value that are large small,

696
00:32:33,760 --> 00:32:35,600
Speaker 1: they're you know, So we have, like you know, in

697
00:32:35,600 --> 00:32:37,600
Speaker 1: the last two or three years, think about what's what's changed,

698
00:32:37,640 --> 00:32:40,400
Speaker 1: and we created a work from home basket and reopening basket,

699
00:32:40,440 --> 00:32:44,320
Speaker 1: and we look at every stocks correlation to low quality

700
00:32:44,360 --> 00:32:47,560
Speaker 1: work from home like Netflix or a high quality reopening

701
00:32:47,680 --> 00:32:49,080
Speaker 1: or whatever, and we kind of see, are you off

702
00:32:49,120 --> 00:32:51,440
Speaker 1: sides on your long short book on on those things,

703
00:32:51,600 --> 00:32:53,200
Speaker 1: or even on the long versus the index, or if

704
00:32:53,200 --> 00:32:57,280
Speaker 1: you're yeah, stuff like that, yeah yeah, or you can

705
00:32:57,320 --> 00:33:00,200
Speaker 1: see if they're off sides because they may not really lies.

706
00:33:00,240 --> 00:33:02,200
Speaker 1: They have UM that bet on as much as they

707
00:33:02,280 --> 00:33:06,840
Speaker 1: do UM, meaning they're unaware of the correlations. Yeah, maybe

708
00:33:06,920 --> 00:33:09,960
Speaker 1: unaware the correlations, may be unaware that they've got where

709
00:33:10,040 --> 00:33:11,800
Speaker 1: the real risks are. So what happens when you run

710
00:33:11,800 --> 00:33:13,120
Speaker 1: a fund is, let's say you decide, all right, I'm

711
00:33:13,120 --> 00:33:15,200
Speaker 1: a little bit nervous about my tech exposure a few

712
00:33:15,240 --> 00:33:18,640
Speaker 1: months ago. Uh yeah, they're expensive and more worried racer

713
00:33:18,680 --> 00:33:20,280
Speaker 1: can rise, so I'm gonna sell it, so I think

714
00:33:20,320 --> 00:33:22,200
Speaker 1: it practice. What happens is the c I O goes

715
00:33:22,280 --> 00:33:23,960
Speaker 1: to some of the analysts for PMS and said, Yo,

716
00:33:24,040 --> 00:33:25,760
Speaker 1: give me your at least two or three favorite tech names.

717
00:33:25,760 --> 00:33:27,800
Speaker 1: I'm gonna I'm gonna trim those out right, and you

718
00:33:27,920 --> 00:33:31,560
Speaker 1: trim them out, you sell five pips to tech and okay, great,

719
00:33:31,600 --> 00:33:33,600
Speaker 1: I'm proactive. I got the call right, but it may

720
00:33:33,640 --> 00:33:36,600
Speaker 1: not be that those names you trimmed with the risky ones, right.

721
00:33:36,680 --> 00:33:38,600
Speaker 1: So it's so we think about more from the risk

722
00:33:38,600 --> 00:33:41,360
Speaker 1: standpoint as much as the exposure um. And there's a

723
00:33:41,400 --> 00:33:43,520
Speaker 1: lot of so, you know, a lot of that goes

724
00:33:43,560 --> 00:33:45,040
Speaker 1: in there. So when we do our work, it's a

725
00:33:45,080 --> 00:33:50,560
Speaker 1: lot of you know, every single name's exposure to UH size, substance, style,

726
00:33:51,200 --> 00:33:57,440
Speaker 1: dollar rates, spreads, oil momentum, beta, a lot more than

727
00:33:57,560 --> 00:34:02,000
Speaker 1: just beta. This ownership. You know, we look at filing

728
00:34:02,120 --> 00:34:04,000
Speaker 1: data from sixty hedge funds that we track to do

729
00:34:04,120 --> 00:34:06,800
Speaker 1: deep fundamental researcher. We say, does anybody here have high

730
00:34:06,840 --> 00:34:08,440
Speaker 1: conviction in the name? Do they own three percent or

731
00:34:08,480 --> 00:34:09,960
Speaker 1: more of their assets in the name. How does it

732
00:34:10,000 --> 00:34:11,880
Speaker 1: differrom the broader population of funds? Is there a good

733
00:34:11,920 --> 00:34:13,239
Speaker 1: and bad crowd? And going on? I mean it's a

734
00:34:13,360 --> 00:34:15,360
Speaker 1: very you know, kind of differentiated system to try to

735
00:34:15,400 --> 00:34:18,520
Speaker 1: really help people understand, you know, what the true risk

736
00:34:18,560 --> 00:34:20,560
Speaker 1: of their portfolio. We we take the portfolio and we say,

737
00:34:20,560 --> 00:34:21,960
Speaker 1: how did this act in the last ten downturns of

738
00:34:22,000 --> 00:34:25,239
Speaker 1: ten percent or more? Where's a different today versus you

739
00:34:25,280 --> 00:34:26,839
Speaker 1: know that, so maybe you have names that you think

740
00:34:26,840 --> 00:34:29,440
Speaker 1: are defensive. You own Oracle and your own Walmart. You

741
00:34:29,440 --> 00:34:31,680
Speaker 1: think you're defensive, but they get much more correlated in

742
00:34:31,719 --> 00:34:33,520
Speaker 1: the downturns, and they look like in a steady state

743
00:34:33,640 --> 00:34:35,359
Speaker 1: or all those kind of things that try to help

744
00:34:35,360 --> 00:34:37,400
Speaker 1: people think through the risk of their portfolio. So you know,

745
00:34:37,520 --> 00:34:38,880
Speaker 1: I think we're good at that. We do a lot

746
00:34:38,920 --> 00:34:40,960
Speaker 1: of like hedge baskets. So you've got a big, long position,

747
00:34:41,040 --> 00:34:43,880
Speaker 1: you want to take out some of the you know,

748
00:34:44,040 --> 00:34:46,040
Speaker 1: kind of macro risk of it, so we can create

749
00:34:46,080 --> 00:34:47,440
Speaker 1: a basket to help you hedge it. So we do

750
00:34:47,480 --> 00:34:48,879
Speaker 1: a lot of that kind of risk work to help

751
00:34:49,360 --> 00:34:51,799
Speaker 1: funds think through um and And I think for us

752
00:34:51,840 --> 00:34:53,520
Speaker 1: it's great because I think people say, all right, well,

753
00:34:53,920 --> 00:34:55,920
Speaker 1: I can you know, I can hire hire trivarian and

754
00:34:56,480 --> 00:34:57,960
Speaker 1: um they can you know, help me once a quarter

755
00:34:58,000 --> 00:34:59,560
Speaker 1: think through the stuff or a big inflections and I

756
00:34:59,600 --> 00:35:02,439
Speaker 1: don't need uh um, you know, build a team here

757
00:35:02,520 --> 00:35:05,480
Speaker 1: to do do that same thing. Really interesting. So it's

758
00:35:06,520 --> 00:35:08,800
Speaker 1: I was gonna ask, you know, I know, back in

759
00:35:08,840 --> 00:35:11,480
Speaker 1: the days when you were Morgan Stanley, you were traveling

760
00:35:11,719 --> 00:35:15,879
Speaker 1: more than half the year and I I was away

761
00:35:15,960 --> 00:35:19,560
Speaker 1: for two days and I'm completely disoriented and it takes

762
00:35:19,600 --> 00:35:22,560
Speaker 1: a while to get my feet onto me. Again. I'm curious,

763
00:35:22,680 --> 00:35:25,120
Speaker 1: if you find now that you're not doing that sort

764
00:35:25,160 --> 00:35:28,120
Speaker 1: of travel, do you have the time to step back

765
00:35:28,239 --> 00:35:33,120
Speaker 1: and think deep thoughts and really organize how you're looking

766
00:35:33,160 --> 00:35:36,200
Speaker 1: at the world, not from airports and hotels. How does

767
00:35:36,239 --> 00:35:40,240
Speaker 1: that affect how you think? So? Yeah, Probably the smartest

768
00:35:40,280 --> 00:35:42,640
Speaker 1: person that ever worked with was a guy named Marty Leebowitz,

769
00:35:42,680 --> 00:35:45,280
Speaker 1: who Marty's an amazing human being is early to mid eighties,

770
00:35:45,440 --> 00:35:47,800
Speaker 1: is the most published person in the history of financial journals.

771
00:35:48,600 --> 00:35:51,520
Speaker 1: Um you know, worked I think with Mr Bloomberg and

772
00:35:51,520 --> 00:35:53,880
Speaker 1: at Solomon back in the day, and so just a

773
00:35:54,000 --> 00:35:57,120
Speaker 1: very connected and brilliant guy. And I think his wife

774
00:35:57,200 --> 00:35:59,760
Speaker 1: is a brain scientist. And we went to dinner together

775
00:36:00,160 --> 00:36:02,840
Speaker 1: with our wives, and I told his wife at dinner

776
00:36:02,880 --> 00:36:04,839
Speaker 1: that I spend five to ten minutes a day thinking.

777
00:36:05,000 --> 00:36:07,000
Speaker 1: This is when I worked at Morgan Stanley. And she

778
00:36:07,080 --> 00:36:10,560
Speaker 1: almost started crying about depressed of a level of thinking

779
00:36:10,600 --> 00:36:12,279
Speaker 1: I was able to do. And so all the thinking

780
00:36:12,320 --> 00:36:14,040
Speaker 1: I had to do was five thirty in the morning

781
00:36:14,120 --> 00:36:15,880
Speaker 1: to seven in the morning and then seven at night

782
00:36:15,960 --> 00:36:17,920
Speaker 1: on and on the weekends, which was fine, but it

783
00:36:18,040 --> 00:36:21,160
Speaker 1: wasn't you know, it's system and it's an amazing firm,

784
00:36:21,200 --> 00:36:24,360
Speaker 1: but traveling everywhere and and you know, getting fat, and

785
00:36:24,560 --> 00:36:26,920
Speaker 1: you know, just you know all that stuff. So, um,

786
00:36:27,320 --> 00:36:30,120
Speaker 1: I think it's freeing from the standpoint of you know,

787
00:36:30,239 --> 00:36:31,799
Speaker 1: A lot of that was just you know, you're flying

788
00:36:31,840 --> 00:36:34,120
Speaker 1: to conferences all around the world and and um, it's

789
00:36:34,120 --> 00:36:36,120
Speaker 1: a lot of airpoint time. I'm traveling some now, but

790
00:36:36,200 --> 00:36:38,000
Speaker 1: it's definitely more like one week a month, you know,

791
00:36:38,120 --> 00:36:40,759
Speaker 1: five six, um, you know, days of year to see

792
00:36:40,880 --> 00:36:42,719
Speaker 1: you know, clients and potential clients. And I find that

793
00:36:42,760 --> 00:36:44,600
Speaker 1: great because you want the human connection. Obviously, I'm glad

794
00:36:44,600 --> 00:36:47,600
Speaker 1: the world's reopening such that people are doing in person meetings. Um,

795
00:36:48,000 --> 00:36:49,759
Speaker 1: so you want to you want to do meetings to

796
00:36:49,800 --> 00:36:51,920
Speaker 1: talk to investors. What you don't want to do is

797
00:36:52,040 --> 00:36:54,640
Speaker 1: you know, fly to Jakarta for one one hour speech

798
00:36:54,680 --> 00:36:57,799
Speaker 1: on US equities if I turn on my back or whatever. Yeah.

799
00:36:57,880 --> 00:37:00,319
Speaker 1: So I think I think the answer to your question is, um,

800
00:37:00,840 --> 00:37:04,280
Speaker 1: you know, I'm thinking more, I'm talking to investors more often,

801
00:37:04,440 --> 00:37:07,080
Speaker 1: and I'm doing less you know, kind of push meeting

802
00:37:07,160 --> 00:37:09,799
Speaker 1: presentation of my you know, my material. So a lot

803
00:37:09,800 --> 00:37:12,440
Speaker 1: of those conferences Barry were sector conferences. We got a

804
00:37:12,440 --> 00:37:14,800
Speaker 1: Team T conference in San Francisco. The first thing of

805
00:37:14,840 --> 00:37:16,680
Speaker 1: the conference will be I talk about US equities, my

806
00:37:16,760 --> 00:37:18,600
Speaker 1: view of tech, and the Ansel picture ideas, and then

807
00:37:18,840 --> 00:37:20,880
Speaker 1: I move on right, so there's no push like that.

808
00:37:21,040 --> 00:37:23,560
Speaker 1: Now all right about tech because there's a big sell

809
00:37:23,600 --> 00:37:26,280
Speaker 1: off and I want to evaluate what signals and stocks

810
00:37:26,360 --> 00:37:28,440
Speaker 1: work after the sell off. For you know, it's it's

811
00:37:28,760 --> 00:37:31,520
Speaker 1: margin expansion and cash flow. Or I'll look at fang

812
00:37:31,640 --> 00:37:33,759
Speaker 1: am as a as a risk factor and say, you know,

813
00:37:34,040 --> 00:37:36,040
Speaker 1: should you really deviate from that or where should you?

814
00:37:36,080 --> 00:37:38,239
Speaker 1: So we'll do it where it's timely and relevant, not

815
00:37:38,440 --> 00:37:42,120
Speaker 1: just because there's a conference every March in Timbuctoo. But

816
00:37:42,200 --> 00:37:46,520
Speaker 1: it's fascinating that your job essentially is to think at

817
00:37:46,600 --> 00:37:49,759
Speaker 1: both places. But you could be the smartest guy in

818
00:37:49,800 --> 00:37:52,719
Speaker 1: the world if you're constantly running. You don't have Yeah

819
00:37:52,800 --> 00:37:55,400
Speaker 1: at a moment, yeah, you know, but they look in fairness,

820
00:37:55,560 --> 00:37:57,480
Speaker 1: I had like nine people in New York and five

821
00:37:57,520 --> 00:37:58,920
Speaker 1: people in India and my team when I was in

822
00:37:58,960 --> 00:38:01,000
Speaker 1: Morgan Stanley, and we we do not you know, we're

823
00:38:01,000 --> 00:38:03,279
Speaker 1: not doing that now. It's not it's not for lack

824
00:38:03,400 --> 00:38:06,520
Speaker 1: of brain power. It's you as my own personal time.

825
00:38:06,800 --> 00:38:09,040
Speaker 1: There's my own personal segmentation. But the team had a

826
00:38:09,040 --> 00:38:10,840
Speaker 1: lot of smart people working hard, Moore examly and you know,

827
00:38:10,920 --> 00:38:15,320
Speaker 1: we've got we've got you know, you know, five total

828
00:38:15,400 --> 00:38:18,040
Speaker 1: people at Triburiant. So we're keeping it tight and um

829
00:38:18,520 --> 00:38:20,880
Speaker 1: and and and that's because the gating factor is my

830
00:38:21,000 --> 00:38:23,920
Speaker 1: time and I want to be you know, um involved

831
00:38:23,920 --> 00:38:26,640
Speaker 1: in what we're writing and doing. So, Yeah, so let's

832
00:38:26,680 --> 00:38:28,239
Speaker 1: talk a little bit about what's going on in the

833
00:38:28,360 --> 00:38:31,880
Speaker 1: market today. Everything more or less seem to have peaked

834
00:38:32,400 --> 00:38:37,160
Speaker 1: back in October of one, and people are freaking out

835
00:38:37,239 --> 00:38:39,960
Speaker 1: about how this market is a bear and how terrible

836
00:38:40,000 --> 00:38:43,880
Speaker 1: it is. What do we eight nine from from the

837
00:38:43,960 --> 00:38:46,640
Speaker 1: peak that that's barely a draught down? What's going on

838
00:38:46,719 --> 00:38:49,960
Speaker 1: in US equities today? Yeah, I think that the sentiment

839
00:38:50,000 --> 00:38:52,719
Speaker 1: feels worse because a lot of people over index toward

840
00:38:52,760 --> 00:38:54,640
Speaker 1: growth in the previous few years, and a lot of

841
00:38:54,680 --> 00:38:57,560
Speaker 1: the growth stocks are down forty six if you're in

842
00:38:57,640 --> 00:39:01,520
Speaker 1: biotech or software. So I think the headline number is

843
00:39:01,560 --> 00:39:04,840
Speaker 1: probably less painful than some of the underlying carnage. And

844
00:39:04,920 --> 00:39:07,759
Speaker 1: I think that explains that disconnect between your high level

845
00:39:07,840 --> 00:39:11,719
Speaker 1: point and sentiment. Generally, I think I would describe the

846
00:39:11,840 --> 00:39:14,759
Speaker 1: last six months as huge change in the perception about

847
00:39:14,800 --> 00:39:17,560
Speaker 1: interest rates into a growth scare, and then we got

848
00:39:17,640 --> 00:39:20,480
Speaker 1: a war. So that's probably the the the cocktail that

849
00:39:20,719 --> 00:39:25,040
Speaker 1: that sort of caused the reset. UM. My own personal opinion,

850
00:39:25,360 --> 00:39:28,319
Speaker 1: UM is that the perception about rates has gotten too

851
00:39:28,360 --> 00:39:32,200
Speaker 1: hawkish and that they're unlikely to raise rates um as

852
00:39:32,360 --> 00:39:35,759
Speaker 1: much as as is now in the price UM. But

853
00:39:36,040 --> 00:39:37,359
Speaker 1: you know, I don't. I don't know that for sure.

854
00:39:37,600 --> 00:39:41,160
Speaker 1: But I only say that because as we talked about

855
00:39:41,200 --> 00:39:44,400
Speaker 1: semiconductors and other parts of the market, it's unclear to

856
00:39:44,520 --> 00:39:48,920
Speaker 1: me that raising rates will um expedite any of the

857
00:39:48,960 --> 00:39:51,600
Speaker 1: supply demand and balances. And cause, you know, if you

858
00:39:51,680 --> 00:39:53,480
Speaker 1: have a wheat shortage, I don't think you want to

859
00:39:53,560 --> 00:39:56,680
Speaker 1: crush demand for wheat to the point you get equilibrium.

860
00:39:56,719 --> 00:39:59,720
Speaker 1: I think you're just gonna have to lift live with wheat.

861
00:40:00,000 --> 00:40:03,040
Speaker 1: I see gaining share from something else, right, So UM,

862
00:40:03,840 --> 00:40:05,880
Speaker 1: I'm not sure the FED. I've taken the view of

863
00:40:05,880 --> 00:40:07,680
Speaker 1: the fete of the smart ones, and so therefore they're

864
00:40:07,680 --> 00:40:11,239
Speaker 1: not going to purposely create a recession. That seems to

865
00:40:11,320 --> 00:40:14,640
Speaker 1: becoming more and more of a consensus, and I thought

866
00:40:14,680 --> 00:40:16,920
Speaker 1: it was an outlier view. Hey, the FED wants to

867
00:40:16,960 --> 00:40:20,600
Speaker 1: get off zero and sort of normalize rates, But do

868
00:40:20,760 --> 00:40:23,160
Speaker 1: we really think they're going to tighten until there's a

869
00:40:23,200 --> 00:40:28,200
Speaker 1: recession in order to fight inflation? That is not um

870
00:40:28,800 --> 00:40:31,080
Speaker 1: interest rate based. And I know you're not an economist,

871
00:40:31,880 --> 00:40:34,080
Speaker 1: neither of mine. It seems a lot. It seems illogically

872
00:40:34,280 --> 00:40:36,840
Speaker 1: they do that. So I mean, how how is raising

873
00:40:36,920 --> 00:40:41,720
Speaker 1: rates going to affect we shortage is semi consummate, temparate

874
00:40:41,840 --> 00:40:44,480
Speaker 1: labor problems that you can't clean hotels, all those things.

875
00:40:44,520 --> 00:40:46,160
Speaker 1: So I don't think it will. And I think they'll

876
00:40:46,600 --> 00:40:48,840
Speaker 1: realize that and move a little bit more gingerly on

877
00:40:48,920 --> 00:40:50,799
Speaker 1: the path and and so the longer maybe the path

878
00:40:50,840 --> 00:40:53,560
Speaker 1: will be you know, lasts longer, which is which is fine.

879
00:40:53,680 --> 00:40:55,400
Speaker 1: I think the consumers are good shape. We did a

880
00:40:55,520 --> 00:40:58,399
Speaker 1: lot of research on that this year. Um. I think

881
00:40:58,440 --> 00:41:01,280
Speaker 1: the Ernie souseholds are strong. Yeah, the earning season in April.

882
00:41:01,280 --> 00:41:04,080
Speaker 1: If you really look at bank earnings and the comments

883
00:41:04,400 --> 00:41:08,360
Speaker 1: from them, master Trust credit card data thirty data linquencies

884
00:41:08,360 --> 00:41:10,759
Speaker 1: went down. Nine linquencies are at an all time low.

885
00:41:11,840 --> 00:41:17,320
Speaker 1: Retail sales, consumer confidence, wages, um jobs, everything looks fairly

886
00:41:17,400 --> 00:41:20,320
Speaker 1: good for the consumer. So I'm not saying it couldn't

887
00:41:20,320 --> 00:41:22,680
Speaker 1: slow material in six months with higher you know, oil

888
00:41:22,719 --> 00:41:24,560
Speaker 1: at the pump in the light. But I still see

889
00:41:25,040 --> 00:41:27,160
Speaker 1: the US consumer in pretty good shape. And so underneath that,

890
00:41:27,239 --> 00:41:28,680
Speaker 1: for me, like what I focus on is all right,

891
00:41:28,719 --> 00:41:31,279
Speaker 1: what's what's like along and what's a short? Wow? Like

892
00:41:31,480 --> 00:41:35,239
Speaker 1: growth staples are incredibly expensive and you know, yet you know,

893
00:41:35,480 --> 00:41:37,600
Speaker 1: like the value discretionary stocks look cheap, and so maybe

894
00:41:37,600 --> 00:41:39,360
Speaker 1: I can long sull and short the others or you know.

895
00:41:39,520 --> 00:41:41,880
Speaker 1: So I'm I think there's a lot, like I'm excited

896
00:41:41,920 --> 00:41:45,239
Speaker 1: about the long short opportunity within the equity market um

897
00:41:45,640 --> 00:41:47,640
Speaker 1: independent of what the FED does here. But I just

898
00:41:48,120 --> 00:41:49,880
Speaker 1: if you ask me, like what what I think is

899
00:41:49,960 --> 00:41:53,720
Speaker 1: like where there's the most excess capacity in the financial industry,

900
00:41:54,040 --> 00:41:56,719
Speaker 1: In an industry with massive excess capacity and every single

901
00:41:56,760 --> 00:41:59,160
Speaker 1: area of it, I would say the number of people

902
00:41:59,160 --> 00:42:02,120
Speaker 1: who watched the head and memorize everything they do and

903
00:42:02,200 --> 00:42:04,279
Speaker 1: have no idea what they're actually gonna do and are

904
00:42:04,360 --> 00:42:07,200
Speaker 1: never right. It's that that's where the excess capacity exists.

905
00:42:07,239 --> 00:42:10,439
Speaker 1: Short FED watchers, Oh my god, I would short short

906
00:42:10,560 --> 00:42:13,239
Speaker 1: hockey rinks of FED hockey rinks of FED watchers. I'm

907
00:42:13,280 --> 00:42:15,160
Speaker 1: with you on that. So so let's talk about a

908
00:42:15,239 --> 00:42:19,080
Speaker 1: couple of sectors. Oil and gas been a huge out performer.

909
00:42:19,560 --> 00:42:22,080
Speaker 1: Does this continue? Where? What do you look at? How

910
00:42:22,160 --> 00:42:24,640
Speaker 1: do you evaluate oil and gas when you have the

911
00:42:24,680 --> 00:42:28,760
Speaker 1: wild card of the war and the big booming reopening

912
00:42:28,960 --> 00:42:31,520
Speaker 1: fortunately for us, you know, and and I'm not you know,

913
00:42:31,560 --> 00:42:32,960
Speaker 1: I'm not like trying to break my own arm and

914
00:42:32,960 --> 00:42:34,759
Speaker 1: pounding myself on the back, but we had That's been

915
00:42:34,760 --> 00:42:36,920
Speaker 1: our biggest you know, call since we started the firm

916
00:42:36,960 --> 00:42:39,640
Speaker 1: a year ago, is to be over with energy. Yeah,

917
00:42:39,680 --> 00:42:41,680
Speaker 1: and that's why I asked you that. Thanks man, I

918
00:42:41,760 --> 00:42:44,000
Speaker 1: mean I can I can only hit THEES pitch. Um

919
00:42:44,600 --> 00:42:46,800
Speaker 1: you start start spinning it with me, and I'll be

920
00:42:46,880 --> 00:42:49,440
Speaker 1: in trouble now, you know. For me, Look, I think

921
00:42:49,480 --> 00:42:51,719
Speaker 1: it's really hard to forecast oil. So what I would

922
00:42:51,760 --> 00:42:53,560
Speaker 1: back up and say what attracted me to it was

923
00:42:53,800 --> 00:42:56,840
Speaker 1: what I call a triple crown of quand upward earnings revisions,

924
00:42:56,880 --> 00:43:00,239
Speaker 1: positive price momentum, cheap evaluation versus history. So I have

925
00:43:00,360 --> 00:43:04,279
Speaker 1: those three. You start digging in and you say, okay, well, um,

926
00:43:04,400 --> 00:43:06,920
Speaker 1: let's go talk about it with investors, right, And investors

927
00:43:07,080 --> 00:43:12,120
Speaker 1: gave me two sources of pushback. Right, one is, um, hey,

928
00:43:12,560 --> 00:43:14,160
Speaker 1: they don't say this way, but hey, Adam, like the

929
00:43:14,239 --> 00:43:16,840
Speaker 1: specter of US gathering assets under this thing called e

930
00:43:17,040 --> 00:43:19,920
Speaker 1: s G is far too great for us to you

931
00:43:20,000 --> 00:43:24,359
Speaker 1: know risk you know, yeah, the bigger firms, I think

932
00:43:24,400 --> 00:43:27,440
Speaker 1: that's the case. And then um, and there's a few

933
00:43:27,480 --> 00:43:29,000
Speaker 1: of those that that that might be the case for.

934
00:43:29,640 --> 00:43:31,680
Speaker 1: And then the second group. You know, I'm gonna say

935
00:43:31,680 --> 00:43:33,960
Speaker 1: it's more in the Hey, Adam, the terminal value of

936
00:43:34,000 --> 00:43:36,080
Speaker 1: oil is zero. And that's the part where I really

937
00:43:36,160 --> 00:43:39,200
Speaker 1: start getting on, you know, kind of unfamiliar with material

938
00:43:39,280 --> 00:43:42,560
Speaker 1: science and plastics. People don't. There's an old joke about

939
00:43:42,920 --> 00:43:46,920
Speaker 1: the Saudi prince who said to the American oil company,

940
00:43:47,000 --> 00:43:50,280
Speaker 1: I can't believe you guys burn the stuff. Yeah, totally.

941
00:43:50,520 --> 00:43:54,520
Speaker 1: So I'm smiling because, you know, as I pushed the thesis,

942
00:43:54,760 --> 00:43:56,839
Speaker 1: I think a lot of people just say, look, look,

943
00:43:57,360 --> 00:43:59,680
Speaker 1: I don't disagree that. As you get I think peak

944
00:43:59,719 --> 00:44:02,920
Speaker 1: oil demand from the experts, it looks like two something

945
00:44:02,960 --> 00:44:06,319
Speaker 1: plus or minus. Right, sixteen of new vehicle sales are

946
00:44:06,320 --> 00:44:09,040
Speaker 1: either electrical or hybrid. The installed basis eight percent cars

947
00:44:09,080 --> 00:44:10,800
Speaker 1: are born and then they die. There's no in between.

948
00:44:10,880 --> 00:44:13,160
Speaker 1: States so you can't. It's a lot of new car sales.

949
00:44:13,200 --> 00:44:16,799
Speaker 1: You need to get the installed base twelve years or whatever. Right,

950
00:44:16,880 --> 00:44:20,399
Speaker 1: So I don't see any way pico al demand isn't

951
00:44:20,440 --> 00:44:22,680
Speaker 1: in you know, in the in the next ten years. Okay.

952
00:44:23,120 --> 00:44:25,040
Speaker 1: And and remember you know where we live in our

953
00:44:25,080 --> 00:44:28,480
Speaker 1: cozy um you know lives here, but five million Indians

954
00:44:28,480 --> 00:44:30,520
Speaker 1: still deficating the street and three billion people don't have

955
00:44:30,560 --> 00:44:33,000
Speaker 1: air conditioning, and like it's not like when it's hot

956
00:44:33,040 --> 00:44:35,759
Speaker 1: out you um, and you've had experienced air conditioning, you

957
00:44:35,880 --> 00:44:39,000
Speaker 1: decide for the sake of humankind, I'm not gonna see

958
00:44:39,080 --> 00:44:40,800
Speaker 1: my place like. So that's the demand is gonna be

959
00:44:40,920 --> 00:44:43,680
Speaker 1: longer tailed than people think. As you know, toists are good,

960
00:44:43,680 --> 00:44:45,240
Speaker 1: and a C is good, and wife is good, electricity

961
00:44:45,280 --> 00:44:47,160
Speaker 1: is good, and so like oil consumption, like the people

962
00:44:47,200 --> 00:44:49,799
Speaker 1: have been the most protesting, you know, the terminal value

963
00:44:49,800 --> 00:44:51,680
Speaker 1: of zero argument or people who like fly private and

964
00:44:51,800 --> 00:44:54,360
Speaker 1: have nineteen houses like their own oil footprints massive. So

965
00:44:54,400 --> 00:44:57,080
Speaker 1: I just I don't understand where that disconnect is. And

966
00:44:57,160 --> 00:45:00,680
Speaker 1: I'm sure maybe there's like a pharmaceutical like patent cliff,

967
00:45:00,719 --> 00:45:02,799
Speaker 1: where I pay lower multiples for oil as I get

968
00:45:02,880 --> 00:45:04,880
Speaker 1: five years away from that peak or whatever. I get

969
00:45:04,960 --> 00:45:07,640
Speaker 1: how stocks work, but it seems awfully early if there's

970
00:45:07,680 --> 00:45:10,200
Speaker 1: e NPS free cash full yield to get too negative.

971
00:45:10,280 --> 00:45:13,759
Speaker 1: So I started getting aware it's sentiments negative, UM, you know,

972
00:45:13,960 --> 00:45:17,000
Speaker 1: and that bullsh really bullish. And if you look at

973
00:45:17,040 --> 00:45:19,480
Speaker 1: how a lot of funds work. We did a note

974
00:45:19,560 --> 00:45:23,000
Speaker 1: last year in June twenty one, UM called E s

975
00:45:23,080 --> 00:45:27,080
Speaker 1: G E t F s H forty uh nine percent

976
00:45:27,200 --> 00:45:30,680
Speaker 1: q q q xps sp X two percent E s G.

977
00:45:30,840 --> 00:45:33,360
Speaker 1: So the the idea was, these things are closet triple

978
00:45:33,440 --> 00:45:37,200
Speaker 1: ques now that energy is beaten the cues by plus.

979
00:45:37,280 --> 00:45:39,640
Speaker 1: In the last six months, we've heard a lot of

980
00:45:39,719 --> 00:45:42,239
Speaker 1: firms say, well, we're thinking about switching from a sustainability

981
00:45:42,360 --> 00:45:45,680
Speaker 1: level to buy a stock to a change in sustainability score,

982
00:45:45,760 --> 00:45:48,319
Speaker 1: meaning if they're improving on the sustainability from people can

983
00:45:48,360 --> 00:45:50,719
Speaker 1: buy it because you can't handle everyone's cool to buy

984
00:45:50,760 --> 00:45:52,759
Speaker 1: E s G stocks when they're outperforming because they're on

985
00:45:52,840 --> 00:45:56,239
Speaker 1: the queues, but when they lagged by fifty it's less cool, right,

986
00:45:56,320 --> 00:45:58,239
Speaker 1: So I think you could have a flow sting that's

987
00:45:58,239 --> 00:46:01,000
Speaker 1: positive for this group. Also, UM, and I know a

988
00:46:01,120 --> 00:46:03,080
Speaker 1: lot of smart people directly investing in resources and the

989
00:46:03,160 --> 00:46:05,399
Speaker 1: like you throw this Ukraine thing on, I'd say the one,

990
00:46:05,680 --> 00:46:08,040
Speaker 1: the one thing, and and you mentioned it earlier, and

991
00:46:08,080 --> 00:46:10,960
Speaker 1: I agree with it. I can't help but say, you know,

992
00:46:11,440 --> 00:46:14,000
Speaker 1: I don't. I'm talking about markets when there's massive and

993
00:46:14,080 --> 00:46:16,960
Speaker 1: horrible human implications, and I it's almost like you feel

994
00:46:17,000 --> 00:46:19,360
Speaker 1: awful doing that, but you have to mend. You have

995
00:46:19,480 --> 00:46:23,080
Speaker 1: to mentally separate for this and and just say, okay, well,

996
00:46:23,920 --> 00:46:26,040
Speaker 1: sure if we get any announcement of a ceasefire or

997
00:46:26,120 --> 00:46:30,160
Speaker 1: the Ukraine's winning, all its actually go down in a minute, right,

998
00:46:30,239 --> 00:46:32,600
Speaker 1: we get that. But I think I'm more in the

999
00:46:32,760 --> 00:46:34,879
Speaker 1: by the dip mode, believing that demand growth will seat

1000
00:46:34,880 --> 00:46:37,960
Speaker 1: supply growth. Sentiment's negative, their cheap upper revisions positive momentum

1001
00:46:38,040 --> 00:46:39,800
Speaker 1: than I am. It's the end of the it's the

1002
00:46:39,920 --> 00:46:41,800
Speaker 1: end of the day. So I think it's a pretty

1003
00:46:42,360 --> 00:46:44,160
Speaker 1: bullish set up for a couple of year of view,

1004
00:46:44,239 --> 00:46:46,680
Speaker 1: and it's not just a short term trade. So so

1005
00:46:46,800 --> 00:46:49,839
Speaker 1: you mentioned something that I'm kind of fascinated about. There's

1006
00:46:49,880 --> 00:46:53,200
Speaker 1: been a lot of pushback on E s G, and

1007
00:46:53,320 --> 00:46:57,280
Speaker 1: there's certainly been a lot of pushback on low carbon.

1008
00:46:57,640 --> 00:47:01,839
Speaker 1: Here's my beef with the low carbon portfolio. You're gonna

1009
00:47:01,840 --> 00:47:04,879
Speaker 1: take the SMP five hundred and you're gonna remove all

1010
00:47:04,920 --> 00:47:08,759
Speaker 1: the carbon producers, but you're gonna still invest in all

1011
00:47:08,840 --> 00:47:13,200
Speaker 1: the carbon consumers. It's the demand that's leading to these

1012
00:47:13,239 --> 00:47:17,880
Speaker 1: people producing carbon. How how does it make rational sense? Well,

1013
00:47:17,920 --> 00:47:20,400
Speaker 1: we're not going to buy oil or natural gas or

1014
00:47:20,440 --> 00:47:23,200
Speaker 1: cold companies, but we will buy all the companies that

1015
00:47:23,280 --> 00:47:25,920
Speaker 1: consume those problems. It's even more than that, I hear you.

1016
00:47:26,320 --> 00:47:27,960
Speaker 1: And it's even more than that, which is the solar

1017
00:47:28,000 --> 00:47:32,040
Speaker 1: and wind companies consume more energy um than anything else, right,

1018
00:47:32,080 --> 00:47:36,040
Speaker 1: I mean the plastics required to make the wind turbines

1019
00:47:36,080 --> 00:47:39,000
Speaker 1: and move them around and the producing But that's true

1020
00:47:39,080 --> 00:47:43,000
Speaker 1: for any new factory you're jenny, even a coal fire.

1021
00:47:43,600 --> 00:47:46,239
Speaker 1: You know, it takes X number years before their net

1022
00:47:46,400 --> 00:47:52,080
Speaker 1: energy neutral, Right. I don't know if um if you know,

1023
00:47:52,160 --> 00:47:56,640
Speaker 1: it makes sense from the planet's perspective to long solar

1024
00:47:56,920 --> 00:48:00,440
Speaker 1: and wind and short energy as a as a you know,

1025
00:48:00,680 --> 00:48:04,120
Speaker 1: convestment strategy, investment strategy. I don't think that makes any sense,

1026
00:48:04,200 --> 00:48:06,960
Speaker 1: to be honest with you. So there's a fascinating article

1027
00:48:07,160 --> 00:48:12,920
Speaker 1: in this week's Business Week about the rise of UM

1028
00:48:13,320 --> 00:48:18,799
Speaker 1: wind generation throughout all these supposedly red states. Because when

1029
00:48:18,840 --> 00:48:20,719
Speaker 1: you look at Oklahoma, in Texas, and you look in

1030
00:48:20,760 --> 00:48:23,960
Speaker 1: the Midwest where there's a ton of natural and fairly

1031
00:48:24,120 --> 00:48:29,480
Speaker 1: consistent geothermal movement, the wind and on all this farmland,

1032
00:48:30,080 --> 00:48:34,719
Speaker 1: the wind farms are giant money makers for these landowners. Right,

1033
00:48:35,360 --> 00:48:37,359
Speaker 1: it's just you know, just out of left. I don't

1034
00:48:37,360 --> 00:48:38,840
Speaker 1: know if it is for the people who produced the

1035
00:48:38,920 --> 00:48:41,600
Speaker 1: actual turbins and move them there. Though you know, you

1036
00:48:41,640 --> 00:48:45,400
Speaker 1: would think g capital who was funding these and ge

1037
00:48:45,760 --> 00:48:48,880
Speaker 1: wind power, that should be a giant home run business,

1038
00:48:48,960 --> 00:48:52,120
Speaker 1: and yet it doesn't seem to be. Yeah, well I don't.

1039
00:48:52,120 --> 00:48:54,040
Speaker 1: I don't. I think the tenor of your question I

1040
00:48:54,120 --> 00:48:57,359
Speaker 1: agree with, which is um you know, and it's kind

1041
00:48:57,400 --> 00:48:58,759
Speaker 1: of my point to which is I just don't think

1042
00:48:58,760 --> 00:49:01,440
Speaker 1: you can destroy demand, right, like you know, like my

1043
00:49:01,480 --> 00:49:03,840
Speaker 1: point about air conditioning, or you know, look at the

1044
00:49:04,040 --> 00:49:08,000
Speaker 1: entire movement to the Sun Belt that's because of air conditions.

1045
00:49:08,000 --> 00:49:10,400
Speaker 1: But there's conditioning and there's heart, and there's hundreds of

1046
00:49:10,400 --> 00:49:12,520
Speaker 1: millions of people on earth like this. You know. You know,

1047
00:49:12,600 --> 00:49:14,319
Speaker 1: it turns out that like a toilet is better than

1048
00:49:14,400 --> 00:49:16,840
Speaker 1: non toilet, it turns out that it turns out a

1049
00:49:16,920 --> 00:49:19,480
Speaker 1: BMW is better than a rickshaw. And I mean just

1050
00:49:19,560 --> 00:49:21,439
Speaker 1: go down the line. So like I don't, I don't.

1051
00:49:21,640 --> 00:49:23,799
Speaker 1: So it's gonna take a long time to destroy man

1052
00:49:23,880 --> 00:49:27,799
Speaker 1: demand for oil um and so speak oil. It's at

1053
00:49:27,920 --> 00:49:32,200
Speaker 1: least ten years from now and maybe yeah, and like

1054
00:49:32,440 --> 00:49:35,200
Speaker 1: maybe longer than Facebook US this or they you know

1055
00:49:35,360 --> 00:49:37,080
Speaker 1: or whatever you know, because there'll be something else school.

1056
00:49:37,080 --> 00:49:39,919
Speaker 1: I'm not you know, I'm not making a fundamental short

1057
00:49:39,920 --> 00:49:41,880
Speaker 1: theason on Facebook. I'm just saying, like, you know, two

1058
00:49:41,920 --> 00:49:43,840
Speaker 1: people talk about the terminal valley for oil, so I

1059
00:49:43,880 --> 00:49:45,640
Speaker 1: won't own the stocks, and like the terminal valley for

1060
00:49:45,840 --> 00:49:48,680
Speaker 1: Facebook is probably oil will last longer than Facebook. I

1061
00:49:48,719 --> 00:49:53,480
Speaker 1: would bet interesting, really interesting. Uh, last question before we

1062
00:49:53,640 --> 00:49:58,759
Speaker 1: get to our favorite questions. We are about to ramp

1063
00:49:58,880 --> 00:50:03,759
Speaker 1: up earning season. How does earning season play into the

1064
00:50:04,000 --> 00:50:06,680
Speaker 1: sort of research you do? How do your clients look

1065
00:50:06,760 --> 00:50:10,600
Speaker 1: at it um and how do you incorporate new data

1066
00:50:11,040 --> 00:50:14,440
Speaker 1: from you know, the key companies into your mind? Look,

1067
00:50:14,480 --> 00:50:16,840
Speaker 1: it's massive. So what we do is every day, for

1068
00:50:16,880 --> 00:50:19,520
Speaker 1: the top three thousand US equities, we download about five

1069
00:50:19,840 --> 00:50:22,719
Speaker 1: pieces of information and compute about five more and then

1070
00:50:22,719 --> 00:50:24,960
Speaker 1: we store that every day back for twenty plus years.

1071
00:50:25,040 --> 00:50:27,759
Speaker 1: So anytime somebody asked us a question. We can empirically

1072
00:50:27,840 --> 00:50:30,960
Speaker 1: test the distribution of subsequent returns, so hey, what happens

1073
00:50:31,000 --> 00:50:33,360
Speaker 1: when this happens, we go look and study it. So

1074
00:50:33,480 --> 00:50:35,839
Speaker 1: earnings is huge for us because we're getting balance sheet,

1075
00:50:35,880 --> 00:50:39,960
Speaker 1: income statement, cash flow, you know, ratings, changes in the analysts, downgrades,

1076
00:50:40,000 --> 00:50:42,800
Speaker 1: you know, insider buying and selling transactions, holdings, tons of

1077
00:50:42,840 --> 00:50:46,600
Speaker 1: stuff that's happening every day. Um, and so it changes

1078
00:50:46,719 --> 00:50:50,440
Speaker 1: you know, relative valuations and growth expectations and the like.

1079
00:50:50,680 --> 00:50:54,080
Speaker 1: So for us that's huge. Um. It Also, we have

1080
00:50:54,200 --> 00:50:57,279
Speaker 1: quantitative models that pretty subsequent stock performance, and the quant

1081
00:50:57,320 --> 00:50:59,600
Speaker 1: models use and ingest a lot of this data to

1082
00:51:00,000 --> 00:51:03,600
Speaker 1: form the forecast. So you know, my view of systematic

1083
00:51:03,680 --> 00:51:06,439
Speaker 1: stuff has always been, um that I romanticize something about

1084
00:51:06,440 --> 00:51:07,960
Speaker 1: the report of piano of the company matters to its

1085
00:51:08,040 --> 00:51:11,799
Speaker 1: ultimate value for the listeners. I think thirty of all

1086
00:51:12,400 --> 00:51:14,520
Speaker 1: money traded is two to five day holding period on

1087
00:51:14,600 --> 00:51:18,080
Speaker 1: price and liquidity. So it's yeah, so it's not you know,

1088
00:51:18,480 --> 00:51:20,960
Speaker 1: um a ten case and ques being processed. For us,

1089
00:51:21,040 --> 00:51:23,320
Speaker 1: that's a big part of what we do. Um. You know,

1090
00:51:23,400 --> 00:51:25,359
Speaker 1: it comes stay in cash flow, balance sheet, et cetera.

1091
00:51:25,400 --> 00:51:27,400
Speaker 1: It sounds like an inefficiency that a third of the

1092
00:51:27,480 --> 00:51:30,800
Speaker 1: market isn't paying attention to the fundamentals. Well, yeah, I

1093
00:51:31,239 --> 00:51:33,040
Speaker 1: think it's even more than that. That's just two to

1094
00:51:33,160 --> 00:51:34,560
Speaker 1: five day holding period. I think the guys who are

1095
00:51:34,560 --> 00:51:36,400
Speaker 1: doing a microsecond and stuff for a decent chunk of

1096
00:51:36,480 --> 00:51:39,520
Speaker 1: volume two. So I'm not saying there aren't plenty of

1097
00:51:39,560 --> 00:51:42,080
Speaker 1: really successful people that I've just personally never been intellectually

1098
00:51:42,160 --> 00:51:44,400
Speaker 1: interested in that. And I think what I've learned so

1099
00:51:44,480 --> 00:51:46,359
Speaker 1: far is that you're like going to probably be better

1100
00:51:46,400 --> 00:51:48,480
Speaker 1: at something you like doing than you don't, and so

1101
00:51:48,640 --> 00:51:50,560
Speaker 1: I just it doesn't really appeal to me to do that.

1102
00:51:50,760 --> 00:51:53,480
Speaker 1: I think you can only compete when you have the tech.

1103
00:51:53,600 --> 00:51:55,520
Speaker 1: You know, you need billions of dollars of tech to

1104
00:51:55,560 --> 00:51:57,279
Speaker 1: be able to compete in the microsecond space. And I

1105
00:51:57,360 --> 00:52:00,440
Speaker 1: think two to five holding is just price the adity, right,

1106
00:52:00,480 --> 00:52:02,319
Speaker 1: access to borrow, access to risk, and and am any

1107
00:52:02,440 --> 00:52:05,320
Speaker 1: other stuff that really isn't about what what we do.

1108
00:52:05,480 --> 00:52:08,640
Speaker 1: What we do is try to find big dislocations and

1109
00:52:08,719 --> 00:52:12,680
Speaker 1: opportunities like energy or metals or you know, when we

1110
00:52:12,760 --> 00:52:16,319
Speaker 1: go into each sector industry, where do we see you know, uh,

1111
00:52:16,440 --> 00:52:19,400
Speaker 1: interesting long short opportunities, So that that has to come

1112
00:52:19,440 --> 00:52:21,239
Speaker 1: from earning season and the updates there. And I think

1113
00:52:21,600 --> 00:52:23,600
Speaker 1: one of the things I've learned is like you don't

1114
00:52:23,640 --> 00:52:26,240
Speaker 1: anchor right, like you get like we talked about Netflix,

1115
00:52:26,320 --> 00:52:28,800
Speaker 1: like yeah, they told you the business models changing, Like

1116
00:52:28,960 --> 00:52:31,640
Speaker 1: that's not nothing. Maybe the stocks down too much, Maybe

1117
00:52:31,719 --> 00:52:33,520
Speaker 1: it isn't. I don't know the fundamental I as you know,

1118
00:52:33,600 --> 00:52:36,640
Speaker 1: could decide, but what I know is that it changed.

1119
00:52:37,200 --> 00:52:39,319
Speaker 1: So let's stay with that again, and let's look at

1120
00:52:39,400 --> 00:52:44,040
Speaker 1: technology where there is some dislocations. We're recording this before

1121
00:52:44,080 --> 00:52:48,040
Speaker 1: Apple reports, before Microsoft reports, So how do you look

1122
00:52:48,280 --> 00:52:51,680
Speaker 1: at the entire sector? Is a uniform or can you

1123
00:52:51,800 --> 00:52:57,360
Speaker 1: really segmented winners, losers, riskier valuation? What's the spectrum like

1124
00:52:57,400 --> 00:53:01,400
Speaker 1: in that space which has been clearly driving the market

1125
00:53:01,480 --> 00:53:04,319
Speaker 1: for the past decade. So look, we're more in the UM,

1126
00:53:04,600 --> 00:53:06,320
Speaker 1: you know, kind of maybe bucketing it too much. But

1127
00:53:06,360 --> 00:53:09,120
Speaker 1: when you think about earning season, a lot of things happen. Okay,

1128
00:53:09,440 --> 00:53:11,040
Speaker 1: did they beat on earnings, did they beat on margins?

1129
00:53:11,120 --> 00:53:13,839
Speaker 1: They beat on revenue? Did they guide to a change

1130
00:53:13,880 --> 00:53:16,399
Speaker 1: in earnings, margins and revenue? Did the stock T plus

1131
00:53:16,480 --> 00:53:18,759
Speaker 1: one tapus three go up or down relative to the

1132
00:53:18,800 --> 00:53:21,800
Speaker 1: market relatives to the pair group um? Did the implied

1133
00:53:21,840 --> 00:53:23,799
Speaker 1: guidance change because maybe they beat the quarter, didn't change

1134
00:53:23,800 --> 00:53:26,319
Speaker 1: the annual guidance, but the implied guidance is different, right,

1135
00:53:26,360 --> 00:53:28,640
Speaker 1: So like it's like thirty eight things that happened they report,

1136
00:53:28,680 --> 00:53:31,200
Speaker 1: whether you realize it or not. You know, Bloomberg is great,

1137
00:53:31,400 --> 00:53:33,960
Speaker 1: you know, here's what happened on revenue versus what the

1138
00:53:34,040 --> 00:53:36,240
Speaker 1: consensus they beat or not. But like there's there's eighteen

1139
00:53:36,280 --> 00:53:38,520
Speaker 1: things underneath that that happened. What about the cash flow

1140
00:53:38,560 --> 00:53:40,759
Speaker 1: versity earnings? Was there a disconnect? Wasn't a cruel? Was

1141
00:53:40,800 --> 00:53:43,440
Speaker 1: it capex? Was it inventory? Wasn't intangible? You know, like

1142
00:53:43,719 --> 00:53:45,879
Speaker 1: it's it's like an orgasmic amount of data that's coming

1143
00:53:45,920 --> 00:53:48,120
Speaker 1: in that you're just trying to figure out what's discounted.

1144
00:53:48,239 --> 00:53:51,000
Speaker 1: What isn't so like to me? You know, um, I

1145
00:53:51,320 --> 00:53:55,120
Speaker 1: I think that that's where the data will differentiate between

1146
00:53:55,200 --> 00:53:57,799
Speaker 1: you know, all the all the big tech companies, um.

1147
00:53:57,840 --> 00:53:59,200
Speaker 1: And then you can also pick up all the trends

1148
00:53:59,200 --> 00:54:00,680
Speaker 1: that are happening like when a second, So when I

1149
00:54:00,719 --> 00:54:03,239
Speaker 1: when I look recently, like transportation data is really rolled over,

1150
00:54:03,280 --> 00:54:06,000
Speaker 1: but industrial activity looks high. That's interesting, right, Like I'm

1151
00:54:06,000 --> 00:54:08,080
Speaker 1: not paying as much now for truckloads and van loads. Okay,

1152
00:54:08,120 --> 00:54:10,480
Speaker 1: so that's new the bank, the bank. Earnings comes in,

1153
00:54:10,640 --> 00:54:14,000
Speaker 1: match trust data comes in, you know, consumer behavior comes in,

1154
00:54:14,440 --> 00:54:17,040
Speaker 1: consumer demand commentary comes in, and you get the tech. Well,

1155
00:54:17,400 --> 00:54:19,040
Speaker 1: there's a lot of M and A happening. It's coming.

1156
00:54:19,080 --> 00:54:20,879
Speaker 1: It seems like a lot of kind of five ten

1157
00:54:21,040 --> 00:54:23,600
Speaker 1: fifteen billion market cap software companies now look attractive to

1158
00:54:23,640 --> 00:54:26,839
Speaker 1: the private markets. And what's Thomas Brabo doing or what's

1159
00:54:26,840 --> 00:54:28,359
Speaker 1: these guys who are they buying? And wait a minute,

1160
00:54:28,400 --> 00:54:30,480
Speaker 1: now a bunch of companies are below come down a lot.

1161
00:54:30,560 --> 00:54:33,200
Speaker 1: What about you know, biotech? Is there anything coming out

1162
00:54:33,200 --> 00:54:34,880
Speaker 1: of the pipeline there? Because those are an all time

1163
00:54:34,960 --> 00:54:37,560
Speaker 1: low and price to sales and maybe there's innovation on set.

1164
00:54:37,640 --> 00:54:39,239
Speaker 1: Like there's a lot of trends that happened in every

1165
00:54:39,239 --> 00:54:42,120
Speaker 1: sector during earnings that I think are interesting. Healthcare services,

1166
00:54:42,440 --> 00:54:44,160
Speaker 1: the costs are going up? What's going on there? Because

1167
00:54:44,520 --> 00:54:46,560
Speaker 1: all I know is um, you know, I I pay

1168
00:54:46,680 --> 00:54:49,440
Speaker 1: United Health like seven percent more every single year, no

1169
00:54:49,520 --> 00:54:53,720
Speaker 1: matter what happens, right, So like you yeah, yeah, exactly,

1170
00:54:53,800 --> 00:54:56,600
Speaker 1: So yeah that the single most gangster company I interact

1171
00:54:56,640 --> 00:54:59,520
Speaker 1: with is United Health. Uh. If people don't realize that's

1172
00:54:59,600 --> 00:55:01,800
Speaker 1: one of the biggest companies in the plot U n

1173
00:55:02,000 --> 00:55:05,560
Speaker 1: H Equity g P on your Bloomberg terminal and shocking yeah,

1174
00:55:05,640 --> 00:55:08,000
Speaker 1: bottom left, upper right. And one of my goals in

1175
00:55:08,040 --> 00:55:10,120
Speaker 1: life is to own enough UNH stock that it can

1176
00:55:10,200 --> 00:55:12,040
Speaker 1: offset the price increases they take on me and my

1177
00:55:12,080 --> 00:55:14,719
Speaker 1: employees each year to get the privet hedge because um,

1178
00:55:15,320 --> 00:55:17,560
Speaker 1: like door number one is hand what we're gonna raise you.

1179
00:55:17,760 --> 00:55:19,440
Speaker 1: You paste twenty grand now and then we won't raise

1180
00:55:19,480 --> 00:55:21,400
Speaker 1: everyone in your in your employees for a year. And

1181
00:55:21,440 --> 00:55:23,440
Speaker 1: door number two is we're just gonna raise all your employees.

1182
00:55:23,840 --> 00:55:25,359
Speaker 1: That's it. There's no three of Like you get a car,

1183
00:55:25,480 --> 00:55:27,719
Speaker 1: you get a car. But my point is and that's

1184
00:55:27,800 --> 00:55:29,480
Speaker 1: you know, it's kind of joking aside, like you want

1185
00:55:29,520 --> 00:55:30,960
Speaker 1: to look for pricing power. Like one of the biggest

1186
00:55:30,960 --> 00:55:33,160
Speaker 1: investment de bates right now is which companies are gonna

1187
00:55:33,160 --> 00:55:35,360
Speaker 1: have gross margin expansion six months from now, which aren't

1188
00:55:35,360 --> 00:55:37,480
Speaker 1: And is the gross s margin expectation achievable or not?

1189
00:55:37,640 --> 00:55:39,640
Speaker 1: So you get a lot of data points on where

1190
00:55:39,640 --> 00:55:42,759
Speaker 1: are we logistics, labor, you know, wages, where are we

1191
00:55:42,840 --> 00:55:45,239
Speaker 1: with um you know, input costs, oil, commodities, et cetera.

1192
00:55:45,280 --> 00:55:47,440
Speaker 1: Who's got the pricing power. Who doesn't you know, I

1193
00:55:47,520 --> 00:55:49,880
Speaker 1: think another interestinct trending earneys barriers, like you know your

1194
00:55:49,920 --> 00:55:52,080
Speaker 1: employee base is a US or non US, because most

1195
00:55:52,080 --> 00:55:54,160
Speaker 1: of the companies are telling you, and it's been subtle

1196
00:55:54,200 --> 00:55:56,759
Speaker 1: and not written about enough that all the wage issues

1197
00:55:56,800 --> 00:55:59,640
Speaker 1: are in the US, right, So maybe that U s

1198
00:55:59,680 --> 00:56:02,160
Speaker 1: non you labor mix is gonna matter for your margin profile.

1199
00:56:02,239 --> 00:56:04,080
Speaker 1: And so to me, there's just you know, so many

1200
00:56:04,120 --> 00:56:07,200
Speaker 1: things during earnings that are um kind of trends that

1201
00:56:07,239 --> 00:56:08,680
Speaker 1: you can pick up on. And there'll be at least

1202
00:56:08,719 --> 00:56:10,319
Speaker 1: ten or twelve things that happened and you know, kind

1203
00:56:10,360 --> 00:56:13,400
Speaker 1: of mid April through mid May that update you on

1204
00:56:13,640 --> 00:56:17,439
Speaker 1: and increase your decrease, increase your confidence on estimme achievability

1205
00:56:18,000 --> 00:56:20,200
Speaker 1: broadly and then within each industry going forward. So like

1206
00:56:20,360 --> 00:56:21,840
Speaker 1: when I give investiment mice of a lot of it

1207
00:56:21,920 --> 00:56:24,880
Speaker 1: is about relative estimate achievabilities six months from now. So

1208
00:56:25,080 --> 00:56:27,840
Speaker 1: I think energy, you know, okay, that's somewhat easier. Like

1209
00:56:27,960 --> 00:56:29,800
Speaker 1: the correlation between the change in the oil price and

1210
00:56:29,840 --> 00:56:31,440
Speaker 1: the change and the earnings of the income of the

1211
00:56:31,520 --> 00:56:34,439
Speaker 1: energy sector's point eight. So like it's it's well goes higher,

1212
00:56:34,440 --> 00:56:36,360
Speaker 1: Like they're gonna make more money. But there's more subtle

1213
00:56:36,400 --> 00:56:38,160
Speaker 1: things like we've been a little bit cautious on industrial

1214
00:56:38,280 --> 00:56:40,960
Speaker 1: machinery and capital goods because the estimates hockey stick. In

1215
00:56:41,000 --> 00:56:42,920
Speaker 1: the second half of the year, we saw the most

1216
00:56:42,960 --> 00:56:45,680
Speaker 1: downward visions of any sector in the marketing industrials and QUA,

1217
00:56:45,760 --> 00:56:48,080
Speaker 1: but the stocks didn't really underperform that much. So there

1218
00:56:48,120 --> 00:56:50,359
Speaker 1: seems to be this disconnect. You know, transportation is rolling over,

1219
00:56:50,400 --> 00:56:52,120
Speaker 1: so I'm trying to figure out, like, why do I

1220
00:56:52,160 --> 00:56:54,880
Speaker 1: have really high increminal margin expectations embedded in the industrial

1221
00:56:54,960 --> 00:56:57,200
Speaker 1: sector stocks, yet you know there's a bit of a

1222
00:56:57,239 --> 00:57:00,160
Speaker 1: slowdown and Martins have already recovered. So to me, those

1223
00:57:00,160 --> 00:57:02,560
Speaker 1: are the kind of dislocations that you get you should

1224
00:57:02,600 --> 00:57:04,520
Speaker 1: if you're being into actually honest, can increase or decrease

1225
00:57:04,520 --> 00:57:08,799
Speaker 1: conviction on during earnings. So you mentioned intangibles um your

1226
00:57:08,840 --> 00:57:12,680
Speaker 1: old shop, Morgan Stanley has a division called Counterpoint. Michael

1227
00:57:12,719 --> 00:57:16,280
Speaker 1: Mobison's the had a research there. He did a really

1228
00:57:16,480 --> 00:57:22,800
Speaker 1: interesting piece on intangibles and essentially technology holdings and how

1229
00:57:23,800 --> 00:57:29,600
Speaker 1: much much of the investment community has undervalued intangibles like

1230
00:57:29,760 --> 00:57:34,040
Speaker 1: software algorithms, brands go down the list, copyright patents, whatever,

1231
00:57:34,600 --> 00:57:38,360
Speaker 1: and that everybody has been looking at tech as overvalued

1232
00:57:38,400 --> 00:57:41,840
Speaker 1: for a decade. The market seems to have disagreed with

1233
00:57:42,000 --> 00:57:47,320
Speaker 1: that assessment. How do you view intangibles in that space? Yeah,

1234
00:57:47,600 --> 00:57:50,800
Speaker 1: so that's that's an interesting question. Um. I'll answer it

1235
00:57:50,880 --> 00:57:57,440
Speaker 1: purely quantitatively, UM, which is um identifying longs and identifying

1236
00:57:57,560 --> 00:58:01,200
Speaker 1: shorts use different signals. If I think about what people

1237
00:58:01,240 --> 00:58:02,720
Speaker 1: have been asking me the most in the last year,

1238
00:58:03,000 --> 00:58:05,720
Speaker 1: people will often say, hey, you know, Barry, They'll say,

1239
00:58:05,760 --> 00:58:08,480
Speaker 1: I want to buy compounders. I want to business that compounds.

1240
00:58:08,960 --> 00:58:10,320
Speaker 1: So we did a lot of research, and we do

1241
00:58:10,360 --> 00:58:12,480
Speaker 1: a lot of kind of frameworks like this at Trey Verry,

1242
00:58:12,520 --> 00:58:15,040
Speaker 1: where we'll say, okay, well, what is a compounder. Let's

1243
00:58:15,080 --> 00:58:18,080
Speaker 1: look at businesses with consistent gross margin expansion, consistent net

1244
00:58:18,160 --> 00:58:23,120
Speaker 1: income expansion, consistent earnings growth, consistent upward revisions, consistent price momentum.

1245
00:58:23,120 --> 00:58:25,120
Speaker 1: We'll take a bunch of signals and say which is

1246
00:58:25,120 --> 00:58:28,040
Speaker 1: associated with the best subsequent stock performance. And the answer

1247
00:58:28,320 --> 00:58:32,280
Speaker 1: was gross margin expansion. Okay, So we offer a screen

1248
00:58:32,360 --> 00:58:34,280
Speaker 1: and people can buy a basket of compounders that have

1249
00:58:34,640 --> 00:58:38,280
Speaker 1: consistent gross margin expansion and forecast a gross margin expansion

1250
00:58:38,320 --> 00:58:41,080
Speaker 1: going forward. Seems really important in this regime because of

1251
00:58:41,120 --> 00:58:43,520
Speaker 1: inflation and what we talked about. But on the short side,

1252
00:58:44,760 --> 00:58:48,080
Speaker 1: it isn't margin contraction. The question people were asking me

1253
00:58:48,160 --> 00:58:50,600
Speaker 1: last year was the inverse of compounder, was I want

1254
00:58:50,640 --> 00:58:52,360
Speaker 1: a short of melting ice cube. That seems to be

1255
00:58:52,440 --> 00:58:55,000
Speaker 1: the cool Wall Street phrase with short melting ice cubes, right,

1256
00:58:55,080 --> 00:58:56,960
Speaker 1: I want to long compounders and short melting ice cubes.

1257
00:58:56,960 --> 00:58:58,160
Speaker 1: So we didn't know what what the heck is a

1258
00:58:58,400 --> 00:59:03,080
Speaker 1: melting ice cube? And is interesting? Is the the thing

1259
00:59:03,240 --> 00:59:05,720
Speaker 1: that um mattered the most. The two things that matter

1260
00:59:05,800 --> 00:59:08,560
Speaker 1: the most were accruals, which would be disconnects me and

1261
00:59:08,560 --> 00:59:12,240
Speaker 1: anuras in cash flow, which were driven by cappex inventory

1262
00:59:12,440 --> 00:59:15,680
Speaker 1: or intangibles I'm getting to your intangible questions or bad

1263
00:59:15,760 --> 00:59:18,479
Speaker 1: price momentum, meaning actually the stock was just simply bad

1264
00:59:19,640 --> 00:59:23,920
Speaker 1: versus this industry peers. So the short ideas were businesses

1265
00:59:23,960 --> 00:59:26,800
Speaker 1: with the biggest intangible accruals in the last three quarters

1266
00:59:27,040 --> 00:59:30,640
Speaker 1: that also relatively underperformed their peers. That if you plotted

1267
00:59:30,760 --> 00:59:33,640
Speaker 1: that line versus the SMP, it materially lagged, and if

1268
00:59:33,680 --> 00:59:36,160
Speaker 1: you added on share loss and margic attraction, it didn't

1269
00:59:36,160 --> 00:59:39,240
Speaker 1: even help. So I think the fundamental analysts need to

1270
00:59:39,280 --> 00:59:42,520
Speaker 1: focus on this issue of whether the intangibles capex in

1271
00:59:42,560 --> 00:59:46,520
Speaker 1: inventory are obviously big, but the intangibles are are positive

1272
00:59:46,600 --> 00:59:48,640
Speaker 1: or not. My suspicion from Mobison's work is that there's

1273
00:59:48,680 --> 00:59:52,560
Speaker 1: some alpha spread in that group. Yeah, and um, I

1274
00:59:52,640 --> 00:59:54,160
Speaker 1: haven't seen I haven't seen that work, but I know

1275
00:59:54,280 --> 00:59:57,560
Speaker 1: he's an incredibly smart guy. So, um, but I'd say,

1276
00:59:58,200 --> 01:00:00,280
Speaker 1: I think when I'm looking for short ideas, I would

1277
01:00:00,320 --> 01:00:02,960
Speaker 1: start with do they have a higher cruel and as

1278
01:00:03,000 --> 01:00:05,920
Speaker 1: a stock acted bad? So you're describing hot stocks that

1279
01:00:05,960 --> 01:00:09,720
Speaker 1: have rolled over? Yeah, in some ways. In some ways, um,

1280
01:00:10,080 --> 01:00:11,760
Speaker 1: either hot that have rolled over, or they had a

1281
01:00:11,800 --> 01:00:14,200
Speaker 1: business model change where they had to increase your cap bacs,

1282
01:00:14,400 --> 01:00:16,920
Speaker 1: they built inventory and advance of recovery. They did a

1283
01:00:17,040 --> 01:00:19,960
Speaker 1: deal and it's uncertain about what the intangibles they acquired are.

1284
01:00:20,560 --> 01:00:23,640
Speaker 1: Something like that really fascinating, all right, So let's jump

1285
01:00:24,120 --> 01:00:27,760
Speaker 1: to our favorite questions that we ask all of our guests. Okay,

1286
01:00:28,040 --> 01:00:31,800
Speaker 1: starting with and we talked about Netflix before. Hey, we're

1287
01:00:31,960 --> 01:00:34,479
Speaker 1: past two years. Everybody's been streaming all sorts of stuff.

1288
01:00:34,520 --> 01:00:37,800
Speaker 1: Tell us what's been keeping you entertained? Oh boy, yeah,

1289
01:00:37,880 --> 01:00:41,920
Speaker 1: I'm I'm probably you know, um in in the bottom,

1290
01:00:42,040 --> 01:00:45,360
Speaker 1: you know, decile of culturally savvy people that you'll interview.

1291
01:00:45,680 --> 01:00:48,440
Speaker 1: I actually watched the David Rubinstein Show on Bloomberg. I

1292
01:00:48,600 --> 01:00:51,440
Speaker 1: like that show that counts. I think it's amazing. I

1293
01:00:51,480 --> 01:00:54,360
Speaker 1: think I think he's I think he's amazing. I think

1294
01:00:54,440 --> 01:00:56,920
Speaker 1: that show is incredible. We leave, um, we leave our

1295
01:00:56,960 --> 01:01:00,400
Speaker 1: TV on on Bloomberg TV in our office, and you

1296
01:01:00,480 --> 01:01:05,040
Speaker 1: know when that comes unbelievable guests. Yeah, smart quass and

1297
01:01:05,160 --> 01:01:08,360
Speaker 1: his perspective is so unique because he's walked in their shoes.

1298
01:01:08,440 --> 01:01:12,520
Speaker 1: He he's running multiviillion company. Not a lot of interviewers

1299
01:01:12,600 --> 01:01:14,920
Speaker 1: bring that ask great questions. So I I like, I

1300
01:01:15,040 --> 01:01:18,320
Speaker 1: like that. In terms of podcasts, you know, obviously yours

1301
01:01:18,480 --> 01:01:21,160
Speaker 1: is incredible. But yeah, but but but I think the

1302
01:01:22,480 --> 01:01:24,880
Speaker 1: truth is I'm not I'm more of a hodgepodge of

1303
01:01:24,960 --> 01:01:27,160
Speaker 1: people refer me stuff. You know. I I interviewed the

1304
01:01:27,240 --> 01:01:29,120
Speaker 1: freaking Omics guys before. I like them, so once in

1305
01:01:29,120 --> 01:01:30,800
Speaker 1: a while something that they said I think is interesting

1306
01:01:31,640 --> 01:01:34,440
Speaker 1: Jubnor Levitt, Yeah, so interesting guy. So it makes But

1307
01:01:34,520 --> 01:01:37,840
Speaker 1: I'm not really a consistent guy, and I'm definitely not

1308
01:01:37,960 --> 01:01:40,840
Speaker 1: a streamer. But I am. Um, if I look at

1309
01:01:40,880 --> 01:01:44,200
Speaker 1: the Parker Household. We probably paid twelve different streaming services.

1310
01:01:44,280 --> 01:01:47,400
Speaker 1: So I'm i'm I'm a I'm a revenue source, but

1311
01:01:47,520 --> 01:01:50,440
Speaker 1: I'm a high return on revenue for those that's research.

1312
01:01:50,520 --> 01:01:53,000
Speaker 1: So you gotta you can you Yeah, we gotta start

1313
01:01:53,040 --> 01:01:57,120
Speaker 1: cutting something. Yeah, exactly. So apparently lots of other folks

1314
01:01:57,600 --> 01:01:59,800
Speaker 1: have thought the same thing, and we've seen that reflected

1315
01:01:59,840 --> 01:02:03,640
Speaker 1: and yeah, yeah, exactly. Tell us about your mentors who

1316
01:02:03,720 --> 01:02:06,960
Speaker 1: helped shape your career. Yeah, so if Bernstein again, I

1317
01:02:07,040 --> 01:02:09,520
Speaker 1: think all of them came from Bernstein originally, honestly, so

1318
01:02:09,760 --> 01:02:12,200
Speaker 1: some of the original animals there, So people who followed

1319
01:02:12,240 --> 01:02:15,080
Speaker 1: Bernstein in the nineties and the early two thousands would

1320
01:02:15,160 --> 01:02:17,800
Speaker 1: know some of the minds there. But there's there's so

1321
01:02:17,920 --> 01:02:20,240
Speaker 1: many of them. But you know, um, people that I

1322
01:02:20,320 --> 01:02:24,000
Speaker 1: keep in touch with still, some of whom are still

1323
01:02:24,040 --> 01:02:27,600
Speaker 1: working on you know, on the streets. So um, you know,

1324
01:02:28,120 --> 01:02:31,000
Speaker 1: so i'd say, you know, probably Marty Leebertz, said Morgan Stanley,

1325
01:02:31,040 --> 01:02:34,040
Speaker 1: and then Lisa Shalott and Mark Mayer and Jonathan Gray

1326
01:02:34,240 --> 01:02:36,880
Speaker 1: and who's deceased but was probably the greatest analyst of

1327
01:02:36,920 --> 01:02:39,720
Speaker 1: all time. And yeah, existing animals there as well. So

1328
01:02:39,840 --> 01:02:42,240
Speaker 1: there's just so many mentors. I have people who taught

1329
01:02:42,280 --> 01:02:46,240
Speaker 1: me that it's effort, it's enthusiasm, it's creativity, you know, um,

1330
01:02:46,480 --> 01:02:49,560
Speaker 1: and it's a combination of analytics and communication. And you know,

1331
01:02:50,040 --> 01:02:52,800
Speaker 1: I can't imagine a more interesting job than you know,

1332
01:02:53,240 --> 01:02:54,880
Speaker 1: somebody told me once you always want to be talking

1333
01:02:54,920 --> 01:02:57,320
Speaker 1: to people in their thirties because they're not They're not

1334
01:02:57,520 --> 01:02:59,080
Speaker 1: so young that they're annoying to talk to, and they're

1335
01:02:59,080 --> 01:03:01,040
Speaker 1: not so old that technolog gen cool stuff has passed

1336
01:03:01,080 --> 01:03:03,880
Speaker 1: them by. And I think about the job job I

1337
01:03:03,920 --> 01:03:05,760
Speaker 1: have now I'm in I'm in my early fifties, and

1338
01:03:05,760 --> 01:03:06,720
Speaker 1: I think, yeah, I want to do this for the

1339
01:03:06,760 --> 01:03:09,000
Speaker 1: next thirty years. Like I want to write interesting research

1340
01:03:09,040 --> 01:03:11,240
Speaker 1: and I want to talk the smart, cool people about it,

1341
01:03:11,320 --> 01:03:13,080
Speaker 1: and a lot of them are in their thirties and forties,

1342
01:03:13,080 --> 01:03:15,240
Speaker 1: and that'll be that'll be an amazing place to spend

1343
01:03:15,240 --> 01:03:16,840
Speaker 1: the rest of my life doing so. It's and I

1344
01:03:16,920 --> 01:03:20,080
Speaker 1: tell you, it's you know my shop. You know the

1345
01:03:20,160 --> 01:03:23,840
Speaker 1: guys in my office. It like I am sort of

1346
01:03:23,960 --> 01:03:26,360
Speaker 1: between the Gen X and the boomers. I have a

1347
01:03:26,400 --> 01:03:30,240
Speaker 1: foot in each camp. And the millennials and the generation

1348
01:03:30,320 --> 01:03:33,880
Speaker 1: the gen wise they're absolutely cutting edge hip. They know

1349
01:03:34,000 --> 01:03:36,440
Speaker 1: everything that's going on, and I just want to avoid

1350
01:03:36,520 --> 01:03:39,480
Speaker 1: that whole okay boomer sort of thing, right, And uh,

1351
01:03:39,960 --> 01:03:43,720
Speaker 1: it's absolutely true. You know, sometimes experiences anti correlated with success, right,

1352
01:03:43,760 --> 01:03:45,760
Speaker 1: Like you sit there, like I mentioned that in video before,

1353
01:03:45,880 --> 01:03:48,040
Speaker 1: Like I admit, like I meant, I would have missed

1354
01:03:48,040 --> 01:03:50,240
Speaker 1: the first half of the video's appreciation because I was

1355
01:03:50,880 --> 01:03:55,120
Speaker 1: I was encumbered by irrelevant knowledge. Right, experts are experts

1356
01:03:55,160 --> 01:03:56,680
Speaker 1: in the way the world used to be, right, and

1357
01:03:56,800 --> 01:03:58,360
Speaker 1: so I think, you know, I I see that all

1358
01:03:58,440 --> 01:04:00,520
Speaker 1: the time because a lot of people were negative on

1359
01:04:00,560 --> 01:04:02,560
Speaker 1: the stock market are using Schilder p A or some

1360
01:04:02,720 --> 01:04:05,360
Speaker 1: Grantham view or stuff. You know, something that that was

1361
01:04:05,440 --> 01:04:08,480
Speaker 1: made sense cape and that made sense in the eighties,

1362
01:04:08,600 --> 01:04:10,520
Speaker 1: right when eight of the ten biggest equities were energy

1363
01:04:10,640 --> 01:04:13,440
Speaker 1: and and you know, capital intensity was higher. And now

1364
01:04:13,520 --> 01:04:15,280
Speaker 1: you look at it, you're like, wait a minute, of

1365
01:04:15,280 --> 01:04:18,000
Speaker 1: all companies don't even have any inventory dollars. Capital intensities

1366
01:04:18,000 --> 01:04:20,120
Speaker 1: an all time low for small one microcap Like I

1367
01:04:20,200 --> 01:04:23,160
Speaker 1: fang m is the it matters not not you know, um,

1368
01:04:23,440 --> 01:04:26,080
Speaker 1: you know mobile or whatever. So it's like a totally

1369
01:04:26,080 --> 01:04:27,680
Speaker 1: different business. So like to say, we're gonna mean we

1370
01:04:27,720 --> 01:04:29,640
Speaker 1: were back to something from foty years ago. Just you're

1371
01:04:29,720 --> 01:04:32,120
Speaker 1: encumbered by knowledge that's not relevant. And I think the

1372
01:04:32,200 --> 01:04:35,520
Speaker 1: thirties and forties crew is kind of right optimization on

1373
01:04:35,600 --> 01:04:37,840
Speaker 1: the curve. And so I want to be like hanging

1374
01:04:37,840 --> 01:04:40,080
Speaker 1: out with those people, and and what better job than

1375
01:04:40,120 --> 01:04:42,120
Speaker 1: it would be to do what I'm doing? If I remember,

1376
01:04:42,480 --> 01:04:47,080
Speaker 1: was an Adam Smith books talking about the new Adam Smith,

1377
01:04:47,200 --> 01:04:50,720
Speaker 1: not the original one, about all these funds that would

1378
01:04:50,840 --> 01:04:54,760
Speaker 1: hire young guns as traders because the guys who had

1379
01:04:54,840 --> 01:04:57,720
Speaker 1: the capital and the experience knew they couldn't buy the

1380
01:04:57,760 --> 01:05:01,040
Speaker 1: stuff the young guns were buying and would missed the opportunity.

1381
01:05:01,400 --> 01:05:04,720
Speaker 1: But you need some adult supervision overseeing them. I don't

1382
01:05:04,720 --> 01:05:07,880
Speaker 1: remember I was the money game or one of the places.

1383
01:05:08,200 --> 01:05:10,920
Speaker 1: But that's why risk management and alpha generation are different, right,

1384
01:05:11,000 --> 01:05:12,760
Speaker 1: Like the c I o's job and often is just

1385
01:05:12,800 --> 01:05:15,320
Speaker 1: some risk management, like you know, what can I tolerate?

1386
01:05:15,360 --> 01:05:17,280
Speaker 1: What have I experienced before? Maybe some of these guys

1387
01:05:17,320 --> 01:05:19,720
Speaker 1: don't haven't seen a cycle, you know, maybe they haven't

1388
01:05:19,760 --> 01:05:21,720
Speaker 1: seen rates go up or stuff like that, so I

1389
01:05:21,840 --> 01:05:24,360
Speaker 1: need to have, you know, some maybe maybe they don't

1390
01:05:24,400 --> 01:05:27,160
Speaker 1: realize that, you know, following a financial crisis, you don't

1391
01:05:27,200 --> 01:05:30,240
Speaker 1: short highly shorted stocks because they get squeezed or whatever

1392
01:05:30,400 --> 01:05:32,520
Speaker 1: like stuff that you know, some of us were writing

1393
01:05:32,520 --> 01:05:35,280
Speaker 1: about way before January one, because we knew that that

1394
01:05:35,360 --> 01:05:37,200
Speaker 1: was a risk, because we saw I saw that after

1395
01:05:37,240 --> 01:05:40,280
Speaker 1: the financial crisis, right, So I think that that. But

1396
01:05:40,440 --> 01:05:43,880
Speaker 1: you don't want to be um, you know, the the

1397
01:05:44,120 --> 01:05:46,840
Speaker 1: intractable guy who doesn't adapt, and I think these guys help.

1398
01:05:46,880 --> 01:05:51,640
Speaker 1: You're absolutely absolutely right on that. Let's talk about books.

1399
01:05:51,680 --> 01:05:53,440
Speaker 1: What are some of your favorites? What are you reading

1400
01:05:53,560 --> 01:05:56,120
Speaker 1: right now? Right now, I've got two books on the

1401
01:05:56,200 --> 01:06:00,120
Speaker 1: night stand. I've got um Maria Vanovitch's book, you know,

1402
01:06:00,240 --> 01:06:03,120
Speaker 1: Lessons from the Edge. So she was the US ambassador

1403
01:06:03,120 --> 01:06:06,640
Speaker 1: of the Ukraine, had incredibly interesting career. Her books, I'm

1404
01:06:06,680 --> 01:06:09,640
Speaker 1: only about halfway through, but it's crazy. Her life is crazy,

1405
01:06:10,360 --> 01:06:12,280
Speaker 1: um and obviously her I haven't gotten to the part

1406
01:06:12,280 --> 01:06:15,000
Speaker 1: of the book where the Trump induced exit happens yet,

1407
01:06:15,040 --> 01:06:17,440
Speaker 1: but incredible experience. You know, I always wondered what these

1408
01:06:17,480 --> 01:06:20,360
Speaker 1: foreign policy people do. So yeah, she's incredible. And then

1409
01:06:21,080 --> 01:06:23,640
Speaker 1: somebody gave me the all In book by Billy Jean King,

1410
01:06:23,800 --> 01:06:26,000
Speaker 1: and I'm definitely gonna read it. Um. You know, she's

1411
01:06:26,040 --> 01:06:28,280
Speaker 1: had an incredibly interesting life. Also, so I've got a

1412
01:06:28,360 --> 01:06:30,440
Speaker 1: stack and I rolled through. I am one of those

1413
01:06:30,440 --> 01:06:32,960
Speaker 1: people who, um, you know, probably needs to sleep a

1414
01:06:33,000 --> 01:06:35,520
Speaker 1: little bit more and so, um, I try to read

1415
01:06:35,600 --> 01:06:39,080
Speaker 1: to uh, you know, um get a little melotonin. Yeah,

1416
01:06:39,200 --> 01:06:41,040
Speaker 1: kind of. By the way, if you if you like

1417
01:06:41,160 --> 01:06:45,440
Speaker 1: the Billy Jean King book, someone recommended the Andre Agassi

1418
01:06:45,520 --> 01:06:49,320
Speaker 1: book called Open and It's absolutely fast. Yeah. Yeah, I

1419
01:06:49,360 --> 01:06:53,040
Speaker 1: read one of his originally years ago, but I didn't

1420
01:06:53,040 --> 01:06:55,440
Speaker 1: even know he had another one out. It's his um,

1421
01:06:55,760 --> 01:07:00,280
Speaker 1: it's really his life style. By um. What sort of

1422
01:07:00,320 --> 01:07:03,000
Speaker 1: advice would you give to a recent college grad who

1423
01:07:03,120 --> 01:07:07,640
Speaker 1: was interested in a career in investment finance becoming an analyst?

1424
01:07:07,880 --> 01:07:10,880
Speaker 1: What advice would you give them? Yeah, I guess the

1425
01:07:10,960 --> 01:07:13,480
Speaker 1: two things would be, you know, you know, assuming that

1426
01:07:13,560 --> 01:07:15,880
Speaker 1: they weren't born on you know, third base, or that

1427
01:07:15,960 --> 01:07:18,640
Speaker 1: they had to like organically earn it. I'd say one

1428
01:07:18,720 --> 01:07:22,480
Speaker 1: would be, Um, you need to differentiate your skill base,

1429
01:07:22,560 --> 01:07:23,960
Speaker 1: and the best way to do this through a computer

1430
01:07:24,080 --> 01:07:26,640
Speaker 1: science So you need to program all of the work

1431
01:07:26,720 --> 01:07:30,640
Speaker 1: we do. Barry is in Python, all of it. You know.

1432
01:07:30,720 --> 01:07:33,000
Speaker 1: You mentioned that you have some cool iMac that works,

1433
01:07:33,080 --> 01:07:35,240
Speaker 1: but I don't care because all of our we only

1434
01:07:35,320 --> 01:07:37,600
Speaker 1: use dummy terminals. All the competent storage is on a jore,

1435
01:07:38,040 --> 01:07:40,800
Speaker 1: like we don't really care. Um your ability, like the

1436
01:07:40,920 --> 01:07:43,680
Speaker 1: days of like you know, reading k's and like writing

1437
01:07:43,760 --> 01:07:46,880
Speaker 1: up a paragraph. Um, I don't want to say they're over,

1438
01:07:46,960 --> 01:07:49,760
Speaker 1: but like you can process information much more quickly with code.

1439
01:07:49,840 --> 01:07:52,320
Speaker 1: So like I think you need to have computer science

1440
01:07:52,320 --> 01:07:55,360
Speaker 1: skills now, and I would encourage people to, you know,

1441
01:07:55,520 --> 01:07:58,400
Speaker 1: get some skills and Python or are or you know, um,

1442
01:07:58,480 --> 01:08:01,720
Speaker 1: you know kind of database work because that's I think

1443
01:08:01,760 --> 01:08:04,640
Speaker 1: a growth industry, and um, you know, analytics and data

1444
01:08:04,680 --> 01:08:08,280
Speaker 1: are being you know, important considerations in every major industry.

1445
01:08:08,400 --> 01:08:10,720
Speaker 1: And and and I think in Wall Street in particular,

1446
01:08:10,800 --> 01:08:12,640
Speaker 1: so one computer science and two Like I've always been

1447
01:08:12,680 --> 01:08:14,760
Speaker 1: and people ask me all the time, what should I

1448
01:08:14,800 --> 01:08:16,240
Speaker 1: do with my career? What advice do you have? And

1449
01:08:16,560 --> 01:08:19,320
Speaker 1: you know, I look, I always encourage people to get

1450
01:08:19,360 --> 01:08:23,440
Speaker 1: more education because I think you can prove, um demographically

1451
01:08:23,520 --> 01:08:26,120
Speaker 1: that the distribution of people who get more education have

1452
01:08:26,280 --> 01:08:29,360
Speaker 1: more wealth right over time. And I think it's probably

1453
01:08:29,400 --> 01:08:31,160
Speaker 1: more differentiating. I know that if I didn't have a

1454
01:08:31,200 --> 01:08:33,479
Speaker 1: PhD in statistics, I wouldn't have gotten the jobs that

1455
01:08:33,520 --> 01:08:35,680
Speaker 1: I had at burned seeing the promotion and market stay

1456
01:08:35,720 --> 01:08:37,760
Speaker 1: on the etcetera. And so for me it's been huge.

1457
01:08:37,800 --> 01:08:39,320
Speaker 1: And my dad has a PhD from M I T.

1458
01:08:39,520 --> 01:08:41,640
Speaker 1: And he kind of told me, Adam, like, you get

1459
01:08:41,680 --> 01:08:43,920
Speaker 1: the pH d and then the bear cases you're you know,

1460
01:08:44,080 --> 01:08:46,639
Speaker 1: you're one of the most popular professors at the University

1461
01:08:46,640 --> 01:08:48,400
Speaker 1: of Michigan or something like, you know, so like that's

1462
01:08:48,439 --> 01:08:50,240
Speaker 1: the bearcase. And that's a pretty darn good bearcase. So

1463
01:08:50,640 --> 01:08:54,000
Speaker 1: I encourage the young guys every time get more education statistics,

1464
01:08:54,080 --> 01:08:56,800
Speaker 1: data science, computer science, something that is a differentiating skill

1465
01:08:56,880 --> 01:08:59,240
Speaker 1: because you know, just being like a basic NBA it

1466
01:08:59,360 --> 01:09:01,240
Speaker 1: was like, I like pick stocks and I can read

1467
01:09:01,320 --> 01:09:03,600
Speaker 1: k's and cues like I don't. I think that's just

1468
01:09:03,720 --> 01:09:06,600
Speaker 1: the differentiating of a skill. Um and and so I

1469
01:09:06,680 --> 01:09:09,840
Speaker 1: think if you can process information and and then you'll

1470
01:09:10,680 --> 01:09:13,080
Speaker 1: there's a bit of a you know, um and I

1471
01:09:13,160 --> 01:09:14,680
Speaker 1: should looking up at how many people get a PC

1472
01:09:14,760 --> 01:09:16,840
Speaker 1: and statistics every year in the country. There's a couple hundred,

1473
01:09:17,080 --> 01:09:20,519
Speaker 1: a few hundreds. So it's not I it's much more.

1474
01:09:20,560 --> 01:09:22,439
Speaker 1: I mean every major department has a few each year, right,

1475
01:09:22,479 --> 01:09:24,719
Speaker 1: So I don't do the math if there's a hundred

1476
01:09:24,720 --> 01:09:27,800
Speaker 1: real departments is a fut year. It's not if only

1477
01:09:27,800 --> 01:09:30,680
Speaker 1: I had access to a status stiffic. Yeah you do, well,

1478
01:09:30,760 --> 01:09:33,599
Speaker 1: we'll sell you our research it you'll problem And our

1479
01:09:33,640 --> 01:09:36,240
Speaker 1: final question, what do you know about the world of

1480
01:09:36,320 --> 01:09:39,280
Speaker 1: investing today that you wish you knew thirty or so

1481
01:09:39,479 --> 01:09:43,040
Speaker 1: years ago when you were first getting started? Yeah? Oh man, um,

1482
01:09:43,800 --> 01:09:46,080
Speaker 1: so much right because I published two pieces of research

1483
01:09:46,160 --> 01:09:49,680
Speaker 1: for eighteen years and we've started study and learned a lot.

1484
01:09:49,720 --> 01:09:53,040
Speaker 1: But I guess holistically, i'd say it's a very competitive

1485
01:09:53,080 --> 01:09:55,080
Speaker 1: business with a lot of incredibly smart people, and it's

1486
01:09:55,200 --> 01:09:58,200
Speaker 1: very humbling. So this idea that you know, you're used

1487
01:09:58,240 --> 01:10:00,120
Speaker 1: to being smarter than people because you've got to in

1488
01:10:00,600 --> 01:10:02,439
Speaker 1: math in high school and you're the smartest kid in

1489
01:10:02,479 --> 01:10:05,040
Speaker 1: your class. Like everybody is smart and everybody works hard,

1490
01:10:05,080 --> 01:10:07,920
Speaker 1: and so um, you have to have a you know,

1491
01:10:08,000 --> 01:10:11,760
Speaker 1: a differentiated, um, you know, way of thinking about the world.

1492
01:10:11,800 --> 01:10:14,320
Speaker 1: I think so, you know, I could have picked an

1493
01:10:14,360 --> 01:10:17,240
Speaker 1: easier industry to compete in for sure, to say the

1494
01:10:17,360 --> 01:10:20,560
Speaker 1: very least, and thank you for being so generous with

1495
01:10:20,600 --> 01:10:22,439
Speaker 1: your time. This really has been a lot of fun.

1496
01:10:22,960 --> 01:10:26,160
Speaker 1: We have been speaking with Adam Parker. He is the

1497
01:10:26,360 --> 01:10:31,160
Speaker 1: founder and CEO of tri Variant Research. If you enjoy

1498
01:10:31,280 --> 01:10:33,600
Speaker 1: this conversation, we'll be sure to check out any of

1499
01:10:33,640 --> 01:10:36,400
Speaker 1: the previous four hundred or so we've done over the

1500
01:10:36,439 --> 01:10:39,479
Speaker 1: past eight years. You can find them wherever you get

1501
01:10:39,560 --> 01:10:43,920
Speaker 1: your podcasts. We love your feedback and suggestions. Write us

1502
01:10:43,960 --> 01:10:47,720
Speaker 1: at m IB podcast at Bloomberg dot net. You can

1503
01:10:47,800 --> 01:10:50,800
Speaker 1: follow me on Twitter at rid Holts. Sign up for

1504
01:10:50,880 --> 01:10:54,120
Speaker 1: my daily reads at Ridlts dot com. I would be

1505
01:10:54,240 --> 01:10:56,280
Speaker 1: remiss if I did not thank the crack team that

1506
01:10:56,360 --> 01:11:00,879
Speaker 1: helps us put these conversations together each week. Mohammed Rumaui

1507
01:11:01,160 --> 01:11:04,840
Speaker 1: is my audio engineer, Paris Walden is my producer. Sean

1508
01:11:04,960 --> 01:11:09,479
Speaker 1: Russo is my director of research. I'm Barry Results. You've

1509
01:11:09,479 --> 01:11:12,639
Speaker 1: been listening to Masters and Business on Bloomberg Radio.