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Speaker 1: Hello, and welcome to What Goes Up a weekly markets podcast.

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Speaker 1: My name is Mike Reagan. I'm a senior editor at

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Speaker 1: Bloomberg and I'm Boldana Higher, across asset reporter with Bloomberg

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Speaker 1: at this week on the show, well, is the proverbial

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Speaker 1: fed put dead or is the strike price just so

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Speaker 1: low that it may as well be? That's the question

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Speaker 1: on everyone's mind this week after Jerome pal signaled that

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Speaker 1: the recent boltility in the stock market will not be

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Speaker 1: enough to force the Central Bank to amend its plans

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Speaker 1: to raise interest rates and shrink its balance sheet. What

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Speaker 1: does it mean for markets? We'll get into it with

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Speaker 1: the chief investment officer of a firm that focuses on

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Speaker 1: artificial intelligence. But first, the two things I have to

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Speaker 1: tell you, um. First of all, my crazy thing this

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Speaker 1: week is custom made for you, so I okay, I

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Speaker 1: expect big things from you on this Secondly, condolences UH

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Speaker 1: to you and Jeffrey Gunlock and all the other Buffalo

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Speaker 1: Bills fans out there. I know this is this is

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Speaker 1: a bad week, so bad that it's Veldona's turn to

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Speaker 1: hide under a blanket during the podcast this week. And Vildonna,

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Speaker 1: I think if there were like a pure play company

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Speaker 1: that makes folding tables, I would assorted them this week.

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Speaker 1: What do you think? Uh, yeah, it's it's a bad

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Speaker 1: time for Buffalo Bills fans for all of Western New York.

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Speaker 1: I was depressed on Sunday on Monday. I'm depressed today.

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Speaker 1: I don't know how I'm going to get through this answer,

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Speaker 1: but thank you. I appreciate it. At least at least

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Speaker 1: everybody watched the game was like record numbers of people

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Speaker 1: who watched it. And now everybody's discussing overtime rules for

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Speaker 1: the NFL, which is which is kind of cool. Yeah,

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Speaker 1: but by everyone, you mean that the team that lost,

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Speaker 1: the fans of the team that lost because of the

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Speaker 1: Hey I saw, I saw Larry David was talking about

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Speaker 1: it too. It is they've already amended it once. So

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Speaker 1: we'll see. We'll see. One can hope maybe they'll they'll

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Speaker 1: let them play that game over. But I'm excited about

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Speaker 1: this guest. Uh this week. We haven't talked to him

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Speaker 1: in a while. He's got a new firm he's involved with.

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Speaker 1: Tell us about him. Bring him into the show here,

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Speaker 1: let's get this going. Yeah, it's Max Kachman. He's the

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Speaker 1: chief investment officer at Alpha. Trey, and Max, I want

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Speaker 1: to welcome you back to the show. Thanks so much

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Speaker 1: for having me. It's awesome to be back. Max. It's

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Speaker 1: it's been a while, and I don't think i've talked

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Speaker 1: to you since you've started this new gig. And I'm

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Speaker 1: gonna I'm gonna confess something here. Max. Whenever I hear

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Speaker 1: someone talk about artificial investing or part of me artificial

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Speaker 1: intelligence in the investing world, you know what I do

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Speaker 1: is I kind of nod my head and I stroked

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Speaker 1: my chin as if I know what's going on. But

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Speaker 1: just between us, don't let this get out. I really

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Speaker 1: have no idea what they're talking about. I'm hoping you

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Speaker 1: can give us all your takes on the market in

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Speaker 1: the show. But but first let's get into that notion.

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Speaker 1: What are you guys up to. What is sort of

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Speaker 1: um going on with AI focused investing at the moment,

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Speaker 1: What are the applications and where do you see it

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Speaker 1: going uh in the future? And if you don't mind

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Speaker 1: the for dummies version, I think it would probably be

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Speaker 1: best for at least for me. I don't know about Baldonna. Sure, well,

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Speaker 1: I'm sure. I'm sure e Donna would understand the intricacies

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Speaker 1: of things like recursive neural networks and you know, transformers.

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Speaker 1: But Mike, for you, I'll dumb it down. Yeah, thank you.

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Speaker 1: I appreciate that. So, first of all, it's really important

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Speaker 1: to differentiate artificial intelligence from the broader quant ecosystem because

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Speaker 1: a lot of folks know about quant fund Systematic funds

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Speaker 1: have trillions of dollars um you know, a stematic strategies

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Speaker 1: have trillions of dollars under management. AI as in true

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Speaker 1: artificial intelligence is very rare, and in fact, that's why

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Speaker 1: I left a pretty strong position to to join this

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Speaker 1: plucky startup to lead the new frontier. True artificial intelligence

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Speaker 1: is really based on the concept of a neural network,

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Speaker 1: and a neural network is kind of what it sounds like.

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Speaker 1: It consists of neurons, just like your brain, and those

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Speaker 1: neurons fire in different ways to process information, kind of

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Speaker 1: the way human mind process it, but very differently from

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Speaker 1: how a traditional systematic strategy process it. The best way

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Speaker 1: to think about it is your typical quantitative strategy, even

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Speaker 1: if it uses machine learning, which is kind of part

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Speaker 1: of that AI phrase geology, but it's not quite what

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Speaker 1: we consider authentic AI. It has a formula, and that

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Speaker 1: formulas designed to systematize a way of thinking or an

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Speaker 1: economic theory. And then that formula has you know, a

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Speaker 1: bunch of factors, and those factors have coefficients, and basically

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Speaker 1: the machine learning part will at a high level kind

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Speaker 1: of adjust to coefficient so that we think that value

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Speaker 1: is going to do well, it will be a positive coefficient.

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Speaker 1: We think value is going to do not so well,

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Speaker 1: that could be in that g of coefficient, right, but

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Speaker 1: you're still basically making a decision. In that example of

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Speaker 1: that we go longer short value factor. A unsupervised deep

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Speaker 1: learning matterwork can actually create its own features. It can

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Speaker 1: create its own view on what a factor is. It

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Speaker 1: does not have to be this preconceived notion of value, large, momentum, quality, etcetera.

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Speaker 1: And so that's a key thing about true AI strategies

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Speaker 1: is they can adapt on their own without human intervention.

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Speaker 1: They're also going to be unbiased. And and this is

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Speaker 1: something that I think a lot of times when people

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Speaker 1: think about quant funds, they say, well, quant funds don't

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Speaker 1: have bias, and that's not quite true. They don't have

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Speaker 1: emotion as in a formula. As a formula, it doesn't

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Speaker 1: really care if the markets down fifty or up, you know,

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Speaker 1: but it will have a bias because it's based on

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Speaker 1: again systematizing a way of thinking. That thinking is inherently biased.

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Speaker 1: As as I said, it was created by a human.

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Speaker 1: An AI model actually does not have that bias in

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Speaker 1: it if it's an authentic a A model. And that's

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Speaker 1: something that I think is going to be even more

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Speaker 1: important as we get into these markets, which will of

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Speaker 1: course discuss where things are changing quite rapidly and we're

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Speaker 1: going to see patterns emerge that we haven't seen before.

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Speaker 1: You're gonna need something that can adapt on its own

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Speaker 1: and adapt quite quickly, and that is to me a

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Speaker 1: key hallmark of authentic artificial intelligence. So before we get

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Speaker 1: to talk about the market, Max, I really like always

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Speaker 1: talking with you because you're up for talking about lots

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Speaker 1: of different things, including cryptocurrencies, and I believe you guys

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Speaker 1: are working on an AI program for a crypto fund

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Speaker 1: if you can tell us a little bit about that. Yeah,

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Speaker 1: So this is currently kind of our cutting edge R

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Speaker 1: and D efforts that we are working on. We're we

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Speaker 1: think that AI and digital assets and I want to

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Speaker 1: go beyond just saying cryptocurrency, because I think that actually

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Speaker 1: is a arroware universe. So and I know you like

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Speaker 1: things like pudgy penguins, um, you know, and n f

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Speaker 1: t s. So those are all tradeable assets within the

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Speaker 1: digital asset ecosystem, and we think that AI is a

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Speaker 1: natural way to analyze those assets and trade them, in

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Speaker 1: part because it's a brand new asset class. This is

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Speaker 1: something that a lot of folks don't realize. I've seen

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Speaker 1: a lot of traders who were good in currencies, are

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Speaker 1: good in cross asset kind of jump into the crypto world.

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Speaker 1: But they bring in those same concepts and they don't

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Speaker 1: necessarily work in crypto space. Like, for instance, a lot

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Speaker 1: of resistance and support bands in the currency markets are

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Speaker 1: based on actually gentle bank purchasers or you know, companies

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Speaker 1: that are hedging their their cost of goods. That doesn't

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Speaker 1: exist in in crypto. So when you see our supported

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Speaker 1: resistance level, it's actually driven by different factors. When we

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Speaker 1: think about AI as as well, it really feeds on

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Speaker 1: data of course, Like that's that's a something pbably should

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Speaker 1: have said earlier, right, aies source of brain food is data.

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Speaker 1: The higher quality of a data a smarter b AI

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Speaker 1: will be able to be and data on the blockchain

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Speaker 1: is very clean and therefore highly nutritious to an artificial

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Speaker 1: intelligence engine. And addition to that, you have really low

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Speaker 1: efficiencies in the market, so high quality data, low efficiency,

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Speaker 1: and then sprinkling top about a really high volatility that

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Speaker 1: is kind of the most fertile you know, a sybal

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Speaker 1: if you will, for for AI to drive alpha from UM.

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Speaker 1: In addition to that, you've got really unique parts within

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Speaker 1: the crypto space, such as the actual code basis that UM.

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Speaker 1: A lot of these protocols are developed in and those

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Speaker 1: are things where you know, one bit of code, reading

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Speaker 1: another bit of code and making a decision on it

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Speaker 1: and analyzing it is actually something that opens up even

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Speaker 1: more doors. So we think there's a lot of really

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Speaker 1: amazing up tunities. I can't talk about too much on

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Speaker 1: the specifics, but next time I'm on I hopefully we'll

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Speaker 1: be able to share a lot more. But we do

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Speaker 1: think that AI and digital assets are a perfect marriage. Max.

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Speaker 1: Let's bring it into the present tense a little bit

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Speaker 1: with what we saw this week in the markets, and

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Speaker 1: I'm curious how AI would work with sort of an

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Speaker 1: event risk type of situation like we saw this week

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Speaker 1: with the fit. I mean, I know there's been a

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Speaker 1: lot of work done on sort of natural language processing

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Speaker 1: and try to you know, have computers listen or or

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Speaker 1: read text and and and make a decision based on that.

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Speaker 1: Where is that in in sort of the big scheme

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Speaker 1: of things with with Aiyes, it's still you know, is

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Speaker 1: that still a little too tricky to to do? Is

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Speaker 1: it something that they need sort of the uh, you know,

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Speaker 1: the the organic intelligence of a guy like you to

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Speaker 1: handle that end of it. I mean, you know, can

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Speaker 1: a I listened to Jerome palell and and pick up

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Speaker 1: the nuances and and that or is it just you know,

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Speaker 1: a matter of reacting to the market signals as he's speaking.

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Speaker 1: It's a great question. There's going to be different answers

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Speaker 1: depending on who you talk to. I know, on on

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Speaker 1: one end, there's one fund that thinks they can actually

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Speaker 1: train a camera. Well, they basically put a camera on

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Speaker 1: Powell live and they try to to construct the nuance

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Speaker 1: of his physical body language and the nu once of

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Speaker 1: his speech inflections to figure out what he's doing. Yeah, now,

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Speaker 1: I they when I asked him how well about work?

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Speaker 1: They kind of got quiet, so I'll just leave it

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Speaker 1: at that. Well, if Pal's got a pretty good poker face,

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Speaker 1: so he might not be the best, he's he's gotten

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Speaker 1: better since let's let's let's let's let's let's put it there.

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Speaker 1: I think I think Marrio track he might have been

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Speaker 1: a better test case so that you know a little

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Speaker 1: more out of me, Yeah, a little more. You know

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Speaker 1: his Italian. So there's there's that. But I do think

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Speaker 1: when it comes to things like n LP, it's a

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Speaker 1: question of the decay and what I mean by decays

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Speaker 1: between when the word is uttered and transcripted to when

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Speaker 1: the market reacts. What's the lag and can you actually

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Speaker 1: capture that? A lot of times the answer is no,

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Speaker 1: So we actually haven't found a lot of utility and

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Speaker 1: kind of real time n LP because a real trader,

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Speaker 1: who is you know, able to hit buy or sell

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Speaker 1: as soon as they here even like half of the

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Speaker 1: word is going to be a little bit quicker than

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Speaker 1: you know a program will be. It's fascinating area of

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Speaker 1: research because I feel like, you know, you could read

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Speaker 1: the text of a speech and think one thing where

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Speaker 1: you could see someone recite the same speech with different

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Speaker 1: inflection and sort of different emotions and and come away

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Speaker 1: with something something different. So it's a it's a fascinating

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Speaker 1: area of recent I think, yeah, and and I think

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Speaker 1: there's a lot of words that especially central bankers use

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Speaker 1: were almost dead of vocabulary is not a normal person's vocabulary.

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Speaker 1: And as FED watchers, like as economists, we kind of

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Speaker 1: know what they mean when they say words um, like

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Speaker 1: you know, we're gonna be humble um, or we're gonna

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Speaker 1: be um, you know, like persistent for for instance, like

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Speaker 1: different contexts that could be very hawkish are very dubbish.

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Speaker 1: And a traditional n LP model is just gonna say

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Speaker 1: this is positive or negative sentiment. It's not gonna be

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Speaker 1: quite as good unless you train And this is this

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Speaker 1: is actually where we get into some interesting stuff. And

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Speaker 1: I have seen this work where you train a model

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Speaker 1: on a specific person. You can do that of central bankers.

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Speaker 1: I actually had this idea originally back in I think,

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Speaker 1: and at the time it wasn't able to really figure

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Speaker 1: out how to do that. But I actually wasn't going

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Speaker 1: to train a model back then on Bernanky and uh

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Speaker 1: and and draggy and say okay, can we figure out

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Speaker 1: based on ner specific vocabulary they're specific mannerisms if they're

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Speaker 1: you know, being hawkish or dubbish. But back then it

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Speaker 1: definitely didn't work. Perhaps in the future, as compute increases,

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Speaker 1: we can you know, really isolate that and and that

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Speaker 1: will be a very interesting situation where you have actually

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Speaker 1: algorithms making those split second decisions. But we're not quite

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Speaker 1: there yet. So speaking about this week, I was hoping

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Speaker 1: you could sort of just go over briefly and very

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Speaker 1: quickly your takeaways from the FED meeting, and I wanted

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Speaker 1: to ask you if you think the market is correctly

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Speaker 1: interpreting what happened with Paul this week. I think the

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Speaker 1: market is been having a really hard time get getting

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Speaker 1: its sea legs this January, right. I mean, just look

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Speaker 1: at the action we experienced kind of right after the meeting,

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Speaker 1: we had the really big drop, then overnight we started

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Speaker 1: dropping further and then came back up. I don't think

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Speaker 1: Powell said anything really dramatically new. In fact, you and

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Speaker 1: I were talking to about that um right right after

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Speaker 1: the meeting. To me, it seemed like just the hope

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Speaker 1: is gone, right, that was the initial reaction, like, okay,

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Speaker 1: but the Powell put is out of the money. It

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Speaker 1: may be completely off the table. And that was that

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Speaker 1: Initially like oh crap, like we're the training wheels are

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Speaker 1: off uh situation for for investors. But then overnight, all

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Speaker 1: of a sudden, you know, we got some some I

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Speaker 1: guess newfound hope or newfound um confidence that the markets

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Speaker 1: can do well on their own. So if you think

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Speaker 1: of like again with the training wheels example, it almost

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Speaker 1: seemed like you know, you can take your your kid.

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Speaker 1: You kind of sent them down the hill and they

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Speaker 1: start really wobbly and they think they're going to fall,

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Speaker 1: and then they eventually they start peddling and they're like, oh,

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Speaker 1: look at me, I'm going But at no point that

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Speaker 1: pow will say anything to me. That was different. That

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Speaker 1: was you know, unexpected. They've done a really good jap

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Speaker 1: of telegraphing what they were going to do. And the

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Speaker 1: thing that I always watched for is kind of the

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Speaker 1: speed speakers score on the fringes either the most hawkish

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Speaker 1: or the most ubblish, and see if do they start

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Speaker 1: changing their tune, And they did the most dubbish. Fet

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Speaker 1: speakers UM and FMC members got a lot more hawkish

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Speaker 1: in the you know kind of weeks coming up to

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Speaker 1: this meeting, So it shouldn't have been a surprise that

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Speaker 1: the Fed said, Yep, we're gonna hike in March, we're

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Speaker 1: gonna end QE, and we're gonna look at tightening. That's

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Speaker 1: pretty much what I think everyone should have expected. Yeah,

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Speaker 1: and maybe that's you know, maybe they did, and that's

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Speaker 1: why we saw such such altility before the meeting, even

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Speaker 1: you know, kind of priced it in. But Maxell, well,

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Speaker 1: how are you thinking about the rest of the year now, um,

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Speaker 1: especially in context of well, if if treasuries and stocks

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Speaker 1: are selling off together, you know, uh, that's sixty forty

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Speaker 1: portfolio maybe is not as diversified as it once was.

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Speaker 1: You know, I think this is a issue with people

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Speaker 1: have been talking about for a year, year or more now,

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Speaker 1: but it certainly seems to be an urgent topic now,

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Speaker 1: you know. Or is it possible we'll see sort of

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Speaker 1: assimule tenious weakness in both the bond and the stock

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Speaker 1: market And how do you how do you play that? Yeah, So,

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Speaker 1: first of all, the answer is yes, I think that's likely.

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Speaker 1: That's actually my base cases that we will see weakness

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Speaker 1: in stocks for at least some part of this year.

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Speaker 1: I do think we'll um, eventually be a little bit

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Speaker 1: higher than where we started, but we will likely see

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Speaker 1: a bowl correction and we'll also see rates go up.

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Speaker 1: And you know, the last time when rates were in

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Speaker 1: a secular uptrend, I think Bill, Donna and I weren't

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Speaker 1: even born yet. So Mike, Mike had been around for

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Speaker 1: you for decades. But full disclosure, I was asked to

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Speaker 1: uh to give Mike some some singers before this show started.

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Speaker 1: I'll try to get him in where where I can.

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Speaker 1: I remember putting my money in the bank and earning

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Speaker 1: something on it that was for the Those were the days.

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Speaker 1: What what's such bank? Is that like like like a

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Speaker 1: physical ethereum wallet. I would I would roll up the

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Speaker 1: quarters and nickels and dimes in the little paper wrappers

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Speaker 1: from my paper routing and ride my bike down and

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Speaker 1: on a little paper to deposit slip and the interest

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Speaker 1: was good. Those were the days. Bring back bring back

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Speaker 1: those you know, five percent savings deposit accounts. Max's he's

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Speaker 1: talking about the early Yeah, I I think it's kind

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Speaker 1: of turn up the sanctuary, you know, yeah, novel there

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Speaker 1: that that Mike was was spinning a little little Oliver twist. Yeah,

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Speaker 1: artificial intelligence back then was like if you got the

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Speaker 1: cheap code to Donkey Kong. You know that that was

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Speaker 1: that was about as far as it went. I I

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Speaker 1: do think with that you may be careful what you

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Speaker 1: wish for, because I think we could get those five

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Speaker 1: percent interest rates, but that also, you know, I believe

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Speaker 1: came with like a twelve percent mortgage. That's fair, so

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Speaker 1: you know, fair point. Those of us who locked in

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Speaker 1: you know, low mortgages are are pretty happy. Probably at

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Speaker 1: this point, I do think we are going to see

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Speaker 1: this secular rising rate environment come back, and that really

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Speaker 1: posts the challenge not only for sixty forty, but the

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Speaker 1: thing that kind of supplanted sixty four for a lot

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Speaker 1: of institutional portfolis, which is risk parity. And while you

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Speaker 1: know risk parity folks will tell you about it's very sophisticated.

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Speaker 1: Um having run risparity strategies before, I can tell you

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Speaker 1: that generally speaking, it still relies on the crucial concept

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Speaker 1: of bonds go up when stocks go down, and duration

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Speaker 1: risk diversifies equity risk. If that no longer is the case,

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Speaker 1: you need to create something different. I think it's gonna

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Speaker 1: be really important to be more dynamic. It's gonna be

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Speaker 1: what I mean about its dynamic in terms of asset classes.

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Speaker 1: So the concept of a balanced portfolio is really important.

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Speaker 1: I think if we break it down to its really

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Speaker 1: building blocks, it's a risk asset that can go up

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Speaker 1: and produce capital gains, and then a diversifying outset that

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Speaker 1: maybe produces a little bit of income and steady returns,

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Speaker 1: but primarily is there to hedge the risky asset. So

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Speaker 1: what those two components are. I think that's gonna be

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Speaker 1: more dynamic going forward, and the new old Weather strategies

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Speaker 1: are going to be playing with those concepts. So they

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Speaker 1: may hold equities and bonds like current strategies do. They

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Speaker 1: may also hold some amount of um commodities like some

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Speaker 1: resperity strategies, but they may at various points hold totally

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Speaker 1: different things. They may actually hold some amount of crypto

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Speaker 1: and some amount of you know, loans, and some amount

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Speaker 1: of stocks, and those assequences will have to keep varying.

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Speaker 1: It's gonna be a bit of a musical chair strategy.

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Speaker 1: And I know it sounds a lot more complex than

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Speaker 1: it is, but the unfortunate byproduct of our reality is

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Speaker 1: things do get more complex with time, and if you

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Speaker 1: stick to you know your traditional approaches, I think you're

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Speaker 1: more liable to actually suffer long term and not achieve

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Speaker 1: your objectives as an institutional investor. Do you do, uh,

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Speaker 1: commodities play a role in in diversification these days, there's

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Speaker 1: that chip already say out of it, you already missed

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Speaker 1: that boat. I I think they do. But again it's tricky.

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Speaker 1: You know. I think a lot of times people say, oh,

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Speaker 1: commodities are a good inflation hedge, and in my research

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Speaker 1: I found that commodities are good as a hedge against

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Speaker 1: inflation shocks, but there not necessarily a hedge against steady

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Speaker 1: rising inflation. And there's a lot of factors that figure

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Speaker 1: into commodities, especially once you started actively trading around them,

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Speaker 1: that are very idiosyncratic. So you know, OPEC doesn't necessarily

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Speaker 1: care about your inflation hedge, your your income story when

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Speaker 1: they decide, however, to raise or lower supplies. And you know,

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Speaker 1: when people in OPEC cheat and actually ignore the supply constraints,

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Speaker 1: then you know, it creates a whole different situation. And

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Speaker 1: you know, then we have things like, well, if we

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Speaker 1: get the infrastructure bill pass, that will certainly send some

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Speaker 1: commodities up higher. But if Conversely, people stops, you know,

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Speaker 1: spending as much on restaurants, that will send other commodities down.

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Speaker 1: So so it's a more complex ecosystem, and I think

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Speaker 1: looking at it at a very high level, like as

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Speaker 1: just a single as a class, is not a good idea.

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Speaker 1: I think commodities are pretty idiosyncratic. So talking about the

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Speaker 1: high level, I wanted to ask you about some maybe

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Speaker 1: positive catalysts for the stock market, because the idea is, UH,

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Speaker 1: sort of the Powell and the FED can move sooner

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Speaker 1: and quicker in terms of hikes this year because the

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Speaker 1: economic backgroup backdrop is strong, right. And then the second

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Speaker 1: part of this is, and I've heard Gina Martin Adams

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Speaker 1: talking about this on different interviews that she was giving

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Speaker 1: this week, is earnings and how that might sort of

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Speaker 1: help support stocks at least in the near term. Yeah,

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Speaker 1: I think that the FED starting earlier is good. There's

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Speaker 1: obviously some folks who think that OFF is starting late,

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Speaker 1: and I I personally don't think they're starting late. I

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Speaker 1: think they're starting at the right time, and I think

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Speaker 1: they're signaling like signaling it well, So kudos to UH

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Speaker 1: to share Powell on I think navigating this about as

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Speaker 1: well as possible. I do think that earnings are also

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Speaker 1: going to be a very important catalyst, and it's a

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Speaker 1: question of not so much what the street expects, but

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Speaker 1: what investors expected. We've seen a bit of a divergence there.

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Speaker 1: So if we look at the most recent kind of

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Speaker 1: you know, Q four earnings that have been released so far,

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Speaker 1: I think um generally speaking, beats were punished a little

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Speaker 1: bit and losses were punished severely. And that's an important concept, right.

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Speaker 1: So that means even the beats on average or not

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Speaker 1: quite what investors expected. And that was my fear going

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Speaker 1: into this reporting season, is that the actual investor expectations

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Speaker 1: were higher than what we were going to see. So

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Speaker 1: then the question is, as we go further and as

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Speaker 1: we get into you know, like April, are Q one

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Speaker 1: expectations going to be more in line. That's gonna be

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Speaker 1: again using that training Wovels analogy, the market has to

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Speaker 1: peddle on its own. Now the fat is not going

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Speaker 1: to come in. I also think it's important to note

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Speaker 1: that fate works both ways, and I'm talking about the

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Speaker 1: flexible inflation targeting. So just as the fat was comfortable

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Speaker 1: with inflation running over the two percent line for a

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Speaker 1: while there will be actually be comfortable with inflation running

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Speaker 1: below that. So Powell made it very clear when he

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Speaker 1: spoke this week that he cares about the labor market

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Speaker 1: and not as surprises, asset prices inflating upwards a byproduct

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Speaker 1: of the stimulus that needed to be done to help

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Speaker 1: the labor market. And I think investors need to really

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Speaker 1: internalize that because because I don't think that's a blood

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Speaker 1: all right, Max, you know, Valdanna has a blanket on

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Speaker 1: her head for this podcast. I'm gonna put my tinfoil

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Speaker 1: hat on my head for a minute and uh and

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Speaker 1: tell me what you think of this, uh conspiracy theory

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Speaker 1: of mine. Actually it's not really a conspiracy theory. I

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Speaker 1: don't really believe this is a conspiracy, but I could

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Speaker 1: see sort of the monetary and political outcomes uh coming

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Speaker 1: out like this, and that is Um. Jerome pal comes

430
00:24:32,040 --> 00:24:35,639
Speaker 1: out this week. He's very hawkish. Um. We've obviously had

431
00:24:35,680 --> 00:24:38,520
Speaker 1: this correction in the stock market. The year goes on,

432
00:24:38,680 --> 00:24:41,920
Speaker 1: we get into the summer, we start lapping the comparisons

433
00:24:42,000 --> 00:24:44,800
Speaker 1: to last year, and we see that maybe inflation has

434
00:24:44,840 --> 00:24:47,440
Speaker 1: cooled off, at least come off the boil off the

435
00:24:47,520 --> 00:24:51,760
Speaker 1: seven percent. That allows pal to get a little more dubbish,

436
00:24:51,880 --> 00:24:54,800
Speaker 1: you know, a little less hawkish perhaps. Uh. In the

437
00:24:54,920 --> 00:25:00,880
Speaker 1: summer early fall, markets rallying again. Uh. By the time

438
00:25:00,920 --> 00:25:05,280
Speaker 1: the mid term election comes around, everyone's forgotten about this

439
00:25:05,480 --> 00:25:09,720
Speaker 1: ugly spell in the markets, and politically it's beneficial to

440
00:25:09,960 --> 00:25:13,240
Speaker 1: Biden and the Democrats. In theory. Again, I don't really

441
00:25:13,280 --> 00:25:15,200
Speaker 1: believe that's a conspiracy theory, but I could see it

442
00:25:15,280 --> 00:25:18,480
Speaker 1: playing out along those lines. But I'm just curious, you know,

443
00:25:18,880 --> 00:25:20,520
Speaker 1: what do you think of that scenario, and what do

444
00:25:20,600 --> 00:25:22,720
Speaker 1: you think about the mid terms in general? I know

445
00:25:22,840 --> 00:25:26,640
Speaker 1: they're traditionally uh, kind of a weak year for equities. Um,

446
00:25:27,200 --> 00:25:29,240
Speaker 1: you know, is that going to be the case this year?

447
00:25:29,320 --> 00:25:31,399
Speaker 1: Is there is there political risk that we're gonna have

448
00:25:31,440 --> 00:25:34,640
Speaker 1: to deal with later in the year or what? Well,

449
00:25:34,760 --> 00:25:37,639
Speaker 1: I think the second part of your question is the easiest. Yes, well,

450
00:25:37,680 --> 00:25:40,800
Speaker 1: you will have to deal with political risk with midterms around.

451
00:25:41,240 --> 00:25:44,080
Speaker 1: They are very important mid terms. I don't want to

452
00:25:44,119 --> 00:25:46,479
Speaker 1: be that guy who comes on you know every time

453
00:25:46,560 --> 00:25:48,399
Speaker 1: versus elections, save this it's the most important election of

454
00:25:48,640 --> 00:25:51,399
Speaker 1: our time, because you know, it's always easy to make

455
00:25:51,480 --> 00:25:53,359
Speaker 1: that comment. I don't think this is this is the

456
00:25:53,400 --> 00:25:56,080
Speaker 1: most important midterm election of our time. I do think

457
00:25:56,520 --> 00:26:00,240
Speaker 1: it's an interesting one as a someone you know who's

458
00:26:00,240 --> 00:26:03,040
Speaker 1: a bit of a political wonk and also market participant

459
00:26:03,080 --> 00:26:05,720
Speaker 1: who trades on that political information. I think there's some

460
00:26:05,960 --> 00:26:08,680
Speaker 1: unique features which are well known, which is one, you

461
00:26:08,760 --> 00:26:12,880
Speaker 1: obviously have a very tight margin and that margin needs

462
00:26:12,920 --> 00:26:16,359
Speaker 1: to be maintained by the Democrats. And I think with

463
00:26:16,600 --> 00:26:18,840
Speaker 1: that you also have these you know, two outliers in

464
00:26:18,920 --> 00:26:22,400
Speaker 1: the Senate who need to come in line at some point,

465
00:26:22,520 --> 00:26:25,119
Speaker 1: and I think they will because I think at the

466
00:26:25,240 --> 00:26:27,840
Speaker 1: end of the day, there are going to choose their

467
00:26:28,000 --> 00:26:31,760
Speaker 1: party and they're gonna want to keep that majority because

468
00:26:31,760 --> 00:26:33,480
Speaker 1: they know if they don't have friends on the other side,

469
00:26:33,520 --> 00:26:37,159
Speaker 1: even though they've skewed pretty close to it, and so

470
00:26:37,560 --> 00:26:40,359
Speaker 1: you know, I think Mansion and Cinema are going to

471
00:26:40,560 --> 00:26:43,840
Speaker 1: eventually support build Back Better, and I think the timing

472
00:26:43,920 --> 00:26:45,440
Speaker 1: of that is going to be important. We know that

473
00:26:45,560 --> 00:26:49,200
Speaker 1: in Washington a lot of times the theatrics are geared

474
00:26:49,280 --> 00:26:53,359
Speaker 1: towards giving you that like last minute conclusion. I think

475
00:26:53,400 --> 00:26:55,280
Speaker 1: that's probably what's gonna happen. And I actually still think

476
00:26:55,320 --> 00:26:58,040
Speaker 1: build Back Better passes that does, by the way, create

477
00:26:58,080 --> 00:27:01,520
Speaker 1: a little bit of a fist put in the markets

478
00:27:01,800 --> 00:27:05,119
Speaker 1: that I don't think it's totally priced in yet, but

479
00:27:05,960 --> 00:27:09,800
Speaker 1: just how much that gives us is a bit of

480
00:27:09,840 --> 00:27:12,040
Speaker 1: an open question. I think there we do want to

481
00:27:12,080 --> 00:27:15,960
Speaker 1: be careful, you know, I wouldn't start overweighing on industrials

482
00:27:15,960 --> 00:27:20,200
Speaker 1: and materials and financials just yet, but I do think

483
00:27:20,760 --> 00:27:24,240
Speaker 1: those are v sectors that can do better from from that,

484
00:27:24,440 --> 00:27:26,800
Speaker 1: and and beyond that, you know, there will be other

485
00:27:27,359 --> 00:27:30,840
Speaker 1: related midterm theatrics that are are going to start happening.

486
00:27:31,000 --> 00:27:33,080
Speaker 1: I think there we're going to start seeing that more

487
00:27:33,160 --> 00:27:38,600
Speaker 1: towards you know, kind of early third quarter UM is

488
00:27:38,680 --> 00:27:43,440
Speaker 1: when things will really start kind of getting interesting from

489
00:27:43,480 --> 00:27:47,240
Speaker 1: a political standpoint, the drama builds, the drama bills. Max.

490
00:27:47,320 --> 00:27:50,240
Speaker 1: I wanted to ask you about some geopolitical risks that

491
00:27:50,320 --> 00:27:53,080
Speaker 1: you're thinking about, because obviously we have a bunch of

492
00:27:53,080 --> 00:27:56,760
Speaker 1: stuff going around Russia and Ukraine, and so how should

493
00:27:56,800 --> 00:27:59,240
Speaker 1: investors be thinking about that. Do you think that some

494
00:27:59,359 --> 00:28:03,480
Speaker 1: of those risks are properly priced in? And what potentially

495
00:28:03,560 --> 00:28:06,919
Speaker 1: would would an escalation mean for the price of oil

496
00:28:07,000 --> 00:28:10,560
Speaker 1: and inflation and so on. Sure, So, so, one thing

497
00:28:10,920 --> 00:28:14,000
Speaker 1: that we know about Russias, they tend to be the

498
00:28:14,080 --> 00:28:18,159
Speaker 1: most aggressive in winter times because they control the heating

499
00:28:18,240 --> 00:28:22,440
Speaker 1: power for Europe and when Putin goes on, and you know,

500
00:28:22,520 --> 00:28:24,680
Speaker 1: this is one one area where it's nice to have

501
00:28:25,119 --> 00:28:27,280
Speaker 1: been born and raised in the country because I can

502
00:28:27,320 --> 00:28:31,679
Speaker 1: actually listen to Putin in Russian speak to Russian Russian public,

503
00:28:31,840 --> 00:28:34,960
Speaker 1: and he uses very colorful language. Let me just put

504
00:28:35,000 --> 00:28:37,320
Speaker 1: it that way. One that I I'm not going to

505
00:28:37,400 --> 00:28:40,600
Speaker 1: say on the podcast, but but he does talk about

506
00:28:40,600 --> 00:28:45,840
Speaker 1: how he can basically freeze all of the people in Europe,

507
00:28:45,920 --> 00:28:48,360
Speaker 1: and there's some truth about it. That's why you never

508
00:28:48,440 --> 00:28:53,000
Speaker 1: see very meaningful sanctions come out. Now. The biggest concern

509
00:28:53,080 --> 00:28:55,760
Speaker 1: for Putin, of course, is that Ukraine somehow becomes part

510
00:28:55,800 --> 00:28:59,480
Speaker 1: of NATO. So that's the that's really the gamble and

511
00:28:59,520 --> 00:29:03,560
Speaker 1: the risk. I do think that a conflict there is likely,

512
00:29:03,640 --> 00:29:07,400
Speaker 1: unfortunately in terms of a kinetic conflict, because you just

513
00:29:07,520 --> 00:29:10,760
Speaker 1: have this powder keg on both sides, and even though

514
00:29:10,800 --> 00:29:12,840
Speaker 1: it's cold, it's very dry, and in the sense that

515
00:29:12,920 --> 00:29:15,800
Speaker 1: anything could spark it off. I think for the markets

516
00:29:15,880 --> 00:29:17,560
Speaker 1: that's not going to be as big of a risk

517
00:29:18,320 --> 00:29:21,920
Speaker 1: as what's going on further out east um and there

518
00:29:22,200 --> 00:29:25,000
Speaker 1: I'm really thinking about China and Taiwan. So last time

519
00:29:25,640 --> 00:29:31,160
Speaker 1: China flew their jets times over Taiwanese airspace definitely a

520
00:29:31,320 --> 00:29:35,240
Speaker 1: very strong signal. I do think Taiwan has perhaps the

521
00:29:35,400 --> 00:29:39,120
Speaker 1: best defense forces of any small nation, and that defense

522
00:29:39,200 --> 00:29:45,240
Speaker 1: force is called t SMC. Taiwanese Semiconductor Company. Is probably

523
00:29:45,640 --> 00:29:49,880
Speaker 1: the best deterrent because it is so vital to all

524
00:29:50,040 --> 00:29:53,720
Speaker 1: other nations around the world that no one can actually

525
00:29:53,760 --> 00:29:58,040
Speaker 1: afford to risk China taking over t SMC. And for

526
00:29:58,200 --> 00:30:02,560
Speaker 1: that reason, you know, even as there kind of saber

527
00:30:02,640 --> 00:30:05,600
Speaker 1: rattling gets louder, I still think it's a big tail risk,

528
00:30:05,640 --> 00:30:07,440
Speaker 1: but it is also a tail risk that I don't

529
00:30:07,480 --> 00:30:10,520
Speaker 1: think we can fully discounts. So that to me is

530
00:30:10,800 --> 00:30:14,680
Speaker 1: kind of the biggest geopolitical risk. But to take your

531
00:30:14,800 --> 00:30:16,680
Speaker 1: question and look at it a little higher level, but

532
00:30:16,760 --> 00:30:18,880
Speaker 1: I think is driving a lot of geopolitical risk. And

533
00:30:18,920 --> 00:30:21,640
Speaker 1: the last i'd say, like five years or so is

534
00:30:21,720 --> 00:30:25,800
Speaker 1: de globalization. And that's a trend that's been pretty secular.

535
00:30:26,200 --> 00:30:28,120
Speaker 1: We've seen a lot of businesses move from just in

536
00:30:28,240 --> 00:30:32,000
Speaker 1: case inventor sorry, from just in time inventory to just

537
00:30:32,240 --> 00:30:34,640
Speaker 1: in case inventory. And what I mean by that is

538
00:30:34,840 --> 00:30:37,680
Speaker 1: it used to be that um, you could realize much

539
00:30:37,720 --> 00:30:40,200
Speaker 1: better margins if you just ordered whatever you needed. You

540
00:30:40,280 --> 00:30:44,560
Speaker 1: had want typically one plant, usually in China or you know,

541
00:30:44,640 --> 00:30:47,840
Speaker 1: another kind of low labor cost country that you'd get

542
00:30:47,880 --> 00:30:50,280
Speaker 1: all of your goods from, and that made for good

543
00:30:50,360 --> 00:30:53,920
Speaker 1: margins and it made for quick um inventory is talking,

544
00:30:53,960 --> 00:30:56,040
Speaker 1: so you didn't need to build up a lot. Well, now,

545
00:30:56,240 --> 00:30:59,840
Speaker 1: since tradewards really began and this deglobalization movement began, you've

546
00:31:00,160 --> 00:31:02,520
Speaker 1: to just in case inventory where you have a factory

547
00:31:02,520 --> 00:31:05,040
Speaker 1: in China, for instance, and then maybe a second factory

548
00:31:05,080 --> 00:31:08,800
Speaker 1: in Vietnam that's able to take on additional capacity necessary.

549
00:31:09,280 --> 00:31:12,320
Speaker 1: So that means companies are no longer as dependent on

550
00:31:12,480 --> 00:31:16,040
Speaker 1: a specific country. And while that can sound good, it

551
00:31:16,200 --> 00:31:22,280
Speaker 1: also lends a political or a politicians more leeway to

552
00:31:22,840 --> 00:31:26,080
Speaker 1: create more strenuous ties with those countries. And that's where

553
00:31:26,400 --> 00:31:29,800
Speaker 1: you know, trade wars as they progress can actually um

554
00:31:29,960 --> 00:31:36,680
Speaker 1: exacerbate because companies have created hedges, if you will, against

555
00:31:36,880 --> 00:31:38,880
Speaker 1: those situations. And and that to me is a more

556
00:31:39,120 --> 00:31:43,560
Speaker 1: concerning secular trend. Fascinating stuff, Max, you know, I it's

557
00:31:43,560 --> 00:31:47,360
Speaker 1: such a great point about Taiwan semiconductor, especially given the

558
00:31:47,400 --> 00:31:50,040
Speaker 1: state of the chip supply chain these days. And also

559
00:31:50,080 --> 00:31:52,360
Speaker 1: I didn't realize Pewton, you know, said they said the

560
00:31:52,400 --> 00:31:54,160
Speaker 1: quiet stuff out loud like that. I thought, you know,

561
00:31:54,200 --> 00:31:56,360
Speaker 1: I know, it's always assumed he could freeze everyone out

562
00:31:56,400 --> 00:31:58,520
Speaker 1: if he wanted to. I didn't realize he actually talked

563
00:31:58,560 --> 00:32:02,560
Speaker 1: about that. That's pretty that's such Tiden up your straight jackets.

564
00:32:02,720 --> 00:32:06,880
Speaker 1: It's time for the craziest things we saw in markets

565
00:32:07,200 --> 00:32:10,320
Speaker 1: this week. I think it's that time. It is that time.

566
00:32:10,600 --> 00:32:13,680
Speaker 1: As I said, I've Telor made my craziest thing for

567
00:32:14,080 --> 00:32:15,719
Speaker 1: the one in a evil Danta Hirich. But I want

568
00:32:15,760 --> 00:32:18,400
Speaker 1: to hear yours first. What's here. I appreciate that after

569
00:32:18,520 --> 00:32:22,040
Speaker 1: my tough bill's loss, but I want to first say

570
00:32:22,760 --> 00:32:26,240
Speaker 1: you and I never explained the blanket thing to our listeners.

571
00:32:26,320 --> 00:32:27,600
Speaker 1: So I want to give a shout out to our

572
00:32:27,640 --> 00:32:30,720
Speaker 1: producer Laura, who makes me and you hide under blankets

573
00:32:30,760 --> 00:32:33,760
Speaker 1: sometimes for better sound quality. So that's that's what we're

574
00:32:33,800 --> 00:32:36,120
Speaker 1: talking about here. I have a bunch of blankets over

575
00:32:36,240 --> 00:32:40,360
Speaker 1: my head. Yeah sometimes I just tied under them. You know. Yeah,

576
00:32:40,520 --> 00:32:42,480
Speaker 1: I know you did, because I'm feeling it, you know. Yeah,

577
00:32:42,840 --> 00:32:45,720
Speaker 1: that's that's fine, that's fine. I think that's okay. Well, okay,

578
00:32:45,760 --> 00:32:48,840
Speaker 1: so first or I guess second. After my my little

579
00:32:48,840 --> 00:32:51,840
Speaker 1: blanket thing, I want to give a second shout out

580
00:32:51,880 --> 00:32:55,400
Speaker 1: to Ben Emmons of Medley Global Advisors. He's a frequent

581
00:32:55,680 --> 00:32:58,400
Speaker 1: guest of the podcast, and he actually wrote something into

582
00:32:58,480 --> 00:33:00,800
Speaker 1: me that I wanted to read out loud. It's not

583
00:33:00,960 --> 00:33:04,920
Speaker 1: exactly markets related, but it's money related, so I'm gonna

584
00:33:05,000 --> 00:33:07,640
Speaker 1: allow it. He sent a USA Today story in the

585
00:33:07,720 --> 00:33:11,080
Speaker 1: headline is a woman finds out she won three million

586
00:33:11,240 --> 00:33:15,520
Speaker 1: dollar lottery prize after checking her email spam folder. So

587
00:33:15,720 --> 00:33:17,840
Speaker 1: thank you Ben for sending that in. I love that,

588
00:33:17,960 --> 00:33:20,120
Speaker 1: and I think it is markets related. I think lottery

589
00:33:20,200 --> 00:33:23,680
Speaker 1: tickets are a fine investment choice, you know. Yeah, sure,

590
00:33:23,760 --> 00:33:25,760
Speaker 1: it's kind of a tail risk fund. I'll all out,

591
00:33:25,880 --> 00:33:28,720
Speaker 1: all out. Yeah. And so that's just a reminder that

592
00:33:28,880 --> 00:33:31,360
Speaker 1: if anybody else has seen anything weird and wants us

593
00:33:31,360 --> 00:33:33,080
Speaker 1: to know about it, you can give us a call

594
00:33:33,160 --> 00:33:36,120
Speaker 1: on the Crazy Things hotline. That's six four six three

595
00:33:36,200 --> 00:33:39,840
Speaker 1: two four three nine zero, leave us a voicemail, hit

596
00:33:39,960 --> 00:33:42,360
Speaker 1: us up on Twitter, and maybe we'll play or talk

597
00:33:42,440 --> 00:33:45,720
Speaker 1: about your weird thing or crazy thing on the show.

598
00:33:46,760 --> 00:33:49,120
Speaker 1: And then for mine, I have a story courtesy of

599
00:33:49,160 --> 00:33:51,880
Speaker 1: another pod friend. It's Crystal Kim. She was on a

600
00:33:51,960 --> 00:33:54,840
Speaker 1: couple of weeks ago and she wrote about the FOMO

601
00:33:55,000 --> 00:33:57,640
Speaker 1: E t F the Fear of missing out E t F.

602
00:33:58,360 --> 00:34:00,280
Speaker 1: I don't know if you saw this story, but this

603
00:34:00,480 --> 00:34:04,480
Speaker 1: fund buys meme names and other sort of popular high

604
00:34:04,520 --> 00:34:10,200
Speaker 1: flying stocks except except right now, it has so many

605
00:34:10,360 --> 00:34:13,120
Speaker 1: dull names in it. But its own manager said the

606
00:34:13,200 --> 00:34:17,160
Speaker 1: strategy puts him to sleep. And the PHONEO et F

607
00:34:17,320 --> 00:34:21,080
Speaker 1: right now is almost in cash. It's the biggest holding

608
00:34:21,400 --> 00:34:24,759
Speaker 1: is Chevron. It has Campbell Soup in there. And then

609
00:34:24,800 --> 00:34:27,759
Speaker 1: I was checking it out and I look for Game

610
00:34:27,840 --> 00:34:30,719
Speaker 1: Stop in AMC and neither one are part of this

611
00:34:30,840 --> 00:34:34,239
Speaker 1: fund anymore. That is pretty fascinating. Well, they're they're not.

612
00:34:34,520 --> 00:34:37,080
Speaker 1: No fear of missing out on the drops in those stocks.

613
00:34:37,520 --> 00:34:43,680
Speaker 1: I guess that's that's pretty good. Yeah, fear of missing

614
00:34:43,760 --> 00:34:48,080
Speaker 1: out on capital preservation, I guess is the theme this week.

615
00:34:48,680 --> 00:34:50,719
Speaker 1: All right, Max, that's pretty good. What do you got first?

616
00:34:50,760 --> 00:34:54,000
Speaker 1: Have you seen anything crazy? I mean, so whenever I

617
00:34:54,080 --> 00:34:57,640
Speaker 1: look for something crazy on on short notice, I tend

618
00:34:57,719 --> 00:35:01,640
Speaker 1: to go to the metaverse. Now and uh, you know,

619
00:35:02,280 --> 00:35:05,360
Speaker 1: you'll you'll never stopped short of kind of fun figures.

620
00:35:05,400 --> 00:35:07,000
Speaker 1: So I'll start, I'll sort of that and then I'll

621
00:35:07,080 --> 00:35:09,200
Speaker 1: drill down to the actual thing that I thought was crazy.

622
00:35:09,760 --> 00:35:11,920
Speaker 1: They just released the data for the fourth quarter of

623
00:35:12,080 --> 00:35:19,719
Speaker 1: virtual land sales in metaverse three million dollars. So that

624
00:35:19,920 --> 00:35:22,239
Speaker 1: was interesting. And you know, we we we know about

625
00:35:22,280 --> 00:35:24,359
Speaker 1: how seeing inflation has been going on in the real world.

626
00:35:24,520 --> 00:35:31,320
Speaker 1: But Sandbox virtual real estate one last month, two lands

627
00:35:31,760 --> 00:35:34,200
Speaker 1: in the center land went for over two point three

628
00:35:34,280 --> 00:35:37,719
Speaker 1: million dollars. So um, I I don't know if that

629
00:35:37,800 --> 00:35:39,759
Speaker 1: means and that's just for the land or you you

630
00:35:39,880 --> 00:35:41,360
Speaker 1: build on them, and if you have to hire like

631
00:35:41,480 --> 00:35:44,960
Speaker 1: virtual developers to you know, come in and build stuff.

632
00:35:45,480 --> 00:35:49,200
Speaker 1: But it seems expensive to me. But but but the

633
00:35:49,360 --> 00:35:54,320
Speaker 1: thing that really got me was someone bought a yacht

634
00:35:54,920 --> 00:35:58,440
Speaker 1: in Sandbox. So it's not land, it's just really a

635
00:35:58,920 --> 00:36:01,680
Speaker 1: virtual yacht that guess you can sail around this virtual world.

636
00:36:02,200 --> 00:36:04,640
Speaker 1: You guys have any guests for how much they paid

637
00:36:04,640 --> 00:36:07,480
Speaker 1: for his virtual yacht? Oh? Well, I think I saw this.

638
00:36:08,000 --> 00:36:10,920
Speaker 1: I want to say half a billion. Well, if if

639
00:36:10,960 --> 00:36:13,040
Speaker 1: that's Mike's guests, I'll go with that too, because I

640
00:36:13,080 --> 00:36:15,759
Speaker 1: did not see this story, and I'm notoriously about at

641
00:36:15,800 --> 00:36:19,759
Speaker 1: guessing did you say half a billion. Yeah, it was

642
00:36:19,960 --> 00:36:22,040
Speaker 1: well that that that would be a lot um No,

643
00:36:22,200 --> 00:36:24,600
Speaker 1: it was half a half a million. It would be

644
00:36:24,680 --> 00:36:30,680
Speaker 1: closer six or so. Yeah, that's by a few zeros there. Yeah,

645
00:36:32,239 --> 00:36:35,960
Speaker 1: so pretty affordable then, uh you know, I guess and yeah,

646
00:36:36,120 --> 00:36:39,400
Speaker 1: and that's like a bargain now actually cheaper than I

647
00:36:39,440 --> 00:36:42,239
Speaker 1: guess a real yacht. So relative to you know, buying

648
00:36:42,400 --> 00:36:44,279
Speaker 1: two point four million dollars for a plot of land,

649
00:36:44,320 --> 00:36:46,480
Speaker 1: maybe that's what's that's what we should be doing now,

650
00:36:46,560 --> 00:36:50,560
Speaker 1: is buying U n F T yachts in uh the metaverse? Max?

651
00:36:50,840 --> 00:36:54,080
Speaker 1: Are you locking in low rates in the metaverse? Buying

652
00:36:54,760 --> 00:36:56,920
Speaker 1: purchasing land over there? I don't know. I mean, I

653
00:36:57,239 --> 00:37:00,560
Speaker 1: I kind of feel like someone does need to do.

654
00:37:00,640 --> 00:37:03,839
Speaker 1: But I'm going to train the AI to figure out

655
00:37:04,000 --> 00:37:06,799
Speaker 1: how to arbitrage different land plots and maybe we can

656
00:37:06,840 --> 00:37:10,160
Speaker 1: become just a virtual landlord, you know. Um, I don't know.

657
00:37:10,960 --> 00:37:13,320
Speaker 1: Didn't someone pay up, pay up big to be like

658
00:37:13,440 --> 00:37:17,000
Speaker 1: Snoop Dogg's neighbor or something like that? There was there

659
00:37:17,200 --> 00:37:20,160
Speaker 1: was a couple of stories about that last year. Um

660
00:37:20,520 --> 00:37:23,239
Speaker 1: so so yeah, and you know it's funny to me.

661
00:37:23,360 --> 00:37:25,719
Speaker 1: So when I was just starting out like writing code.

662
00:37:25,760 --> 00:37:27,400
Speaker 1: It was one of this thing around this is this

663
00:37:27,440 --> 00:37:29,880
Speaker 1: will now date me, Mike, so I'll join you the

664
00:37:29,960 --> 00:37:33,120
Speaker 1: old guy camp. There's a thing called vermal or virtual

665
00:37:33,200 --> 00:37:36,040
Speaker 1: reality markup language. It was. It was kind of started

666
00:37:36,120 --> 00:37:39,600
Speaker 1: like in the mid nineties, and it looked a lot

667
00:37:39,719 --> 00:37:42,120
Speaker 1: like the metaverse. It never took off. No one cared

668
00:37:42,120 --> 00:37:43,759
Speaker 1: about it. It was kind of silly, but you could

669
00:37:43,800 --> 00:37:47,320
Speaker 1: create these three dimensional virtual worlds by just writing like

670
00:37:47,440 --> 00:37:51,000
Speaker 1: kind of very basic code. Well, I mean, maybe you

671
00:37:51,080 --> 00:37:53,279
Speaker 1: know that should have been something I pursued further. I

672
00:37:53,320 --> 00:37:57,160
Speaker 1: don't know, but yeah, yeah, I get that dust off

673
00:37:57,239 --> 00:38:00,360
Speaker 1: those old ploppy disks. I guess. I see kids in

674
00:38:00,400 --> 00:38:03,200
Speaker 1: the in the coffee shops now and they're all wearing

675
00:38:03,239 --> 00:38:06,320
Speaker 1: like nineties clothes, and I feel like, hey, that's my generation.

676
00:38:06,560 --> 00:38:09,880
Speaker 1: You can't wear rape jeans like that and band shirts

677
00:38:09,960 --> 00:38:12,560
Speaker 1: that you know weren't around, uh or haven't been around

678
00:38:12,600 --> 00:38:14,960
Speaker 1: for like twenty years. That's pretty good. Well, the only

679
00:38:15,000 --> 00:38:17,320
Speaker 1: coating I ever did was as a kid. It was basic,

680
00:38:17,600 --> 00:38:20,840
Speaker 1: and it would be like line ten print Mike is cool,

681
00:38:21,600 --> 00:38:24,560
Speaker 1: and then line twenty go to ten and it would

682
00:38:24,560 --> 00:38:26,560
Speaker 1: just scroll rout and then I'd save it on too

683
00:38:26,640 --> 00:38:29,839
Speaker 1: literally a cassette tape on my there's a radio shack.

684
00:38:29,920 --> 00:38:33,839
Speaker 1: I think computer ride. Anyway, I've really dated myself there, boy.

685
00:38:34,880 --> 00:38:38,399
Speaker 1: All right, as loyal listeners will know you're you're what's

686
00:38:38,440 --> 00:38:40,920
Speaker 1: known as a potter Head, not a pothead. I do

687
00:38:41,040 --> 00:38:44,240
Speaker 1: know some potheads, but a potter Head. I love Harry Potter.

688
00:38:44,760 --> 00:38:48,320
Speaker 1: A big fan of Harry Potter. So J. K. Rowling's

689
00:38:48,400 --> 00:38:51,520
Speaker 1: first book, Harry Potter and the Philosopher's Stone, is heading

690
00:38:51,560 --> 00:38:55,200
Speaker 1: to auction. It's a very special edition, one of just

691
00:38:55,400 --> 00:39:01,759
Speaker 1: five hardbacks printed in so it's are you rare gem

692
00:39:01,920 --> 00:39:05,600
Speaker 1: In fact, her name is listed, I believe is Joanne Rolands,

693
00:39:05,680 --> 00:39:08,319
Speaker 1: not even j K. So that's going up for sale.

694
00:39:08,480 --> 00:39:10,600
Speaker 1: Now I want to put you two to the test.

695
00:39:11,000 --> 00:39:13,480
Speaker 1: There's something else going up for sale or went up

696
00:39:13,520 --> 00:39:16,920
Speaker 1: for sale, actually, and that is the hat Milannia Trump

697
00:39:17,200 --> 00:39:21,440
Speaker 1: wore to her first state dinner when her in President

698
00:39:21,480 --> 00:39:25,120
Speaker 1: Trump had the macrons from France over for dinner. That

699
00:39:25,280 --> 00:39:30,239
Speaker 1: sold at auction. Unfortunately, it's sold in soul the Salona

700
00:39:30,360 --> 00:39:34,880
Speaker 1: blockchain currency that that's not doing so well. So so

701
00:39:35,280 --> 00:39:38,920
Speaker 1: here's the prices right for you. Vill Donna, which had

702
00:39:38,960 --> 00:39:41,880
Speaker 1: a higher value the expected value of the Harry Potter

703
00:39:43,200 --> 00:39:48,080
Speaker 1: hard back hardcover or Milannia Trump's big floppy hat that

704
00:39:48,239 --> 00:39:51,520
Speaker 1: she wore to a state dinner UH with with the

705
00:39:51,920 --> 00:39:55,000
Speaker 1: leaders of France UH and his wife. Give me a

706
00:39:55,080 --> 00:39:58,359
Speaker 1: value on each. Okay, you know I told you I've

707
00:39:58,400 --> 00:40:01,520
Speaker 1: been I've been watching Antiques Brow Show recently because I'm

708
00:40:01,600 --> 00:40:05,000
Speaker 1: so bad at guessing this, so it's like preparation for me.

709
00:40:05,200 --> 00:40:07,239
Speaker 1: And they see all the things that come up on

710
00:40:07,280 --> 00:40:09,120
Speaker 1: the show, and I try to guess and I'm always

711
00:40:09,320 --> 00:40:12,160
Speaker 1: way off. Anyway, I'm going with Harry Potter. I have

712
00:40:12,360 --> 00:40:17,080
Speaker 1: to I know what the hat was supposed to go for.

713
00:40:17,320 --> 00:40:21,040
Speaker 1: I believe I think it was something like five hundred

714
00:40:21,239 --> 00:40:25,400
Speaker 1: thousand dollars. I honestly could be misremembering that, but it

715
00:40:25,520 --> 00:40:27,040
Speaker 1: was part of an n f T collection, which you

716
00:40:27,120 --> 00:40:29,480
Speaker 1: forgot to mention. It was like all a big package,

717
00:40:29,480 --> 00:40:32,920
Speaker 1: I believe. But I'm going with Harry Potter. Okay, I'm

718
00:40:32,960 --> 00:40:36,200
Speaker 1: gonna I'm gonna keep a poker face here. Max. As

719
00:40:36,440 --> 00:40:39,680
Speaker 1: a a student of political culture, what do you think

720
00:40:39,880 --> 00:40:45,080
Speaker 1: Philosopher's stone or Melani's hat? Great question, Um, so you're

721
00:40:45,080 --> 00:40:47,200
Speaker 1: gonna get some AI. I want an AI model on

722
00:40:47,280 --> 00:40:49,160
Speaker 1: this stuff. If you can get an AI model on

723
00:40:49,520 --> 00:40:52,480
Speaker 1: my alternative assets, then we're going to be billionaires. There

724
00:40:52,560 --> 00:40:54,880
Speaker 1: is a way to do that again when when you

725
00:40:54,920 --> 00:40:56,359
Speaker 1: guys have me on next time, I might be able

726
00:40:56,400 --> 00:40:59,000
Speaker 1: to share a few more insights. So I'll just leave

727
00:40:59,000 --> 00:41:01,560
Speaker 1: it as a foreshouting with for now. I'll keep my

728
00:41:02,120 --> 00:41:08,560
Speaker 1: you know, human brain working. So I think that if

729
00:41:08,600 --> 00:41:11,360
Speaker 1: we're not hedging for the fact that it was sold

730
00:41:11,520 --> 00:41:17,120
Speaker 1: on Soul, then I would echo what val Donna said

731
00:41:17,239 --> 00:41:21,680
Speaker 1: and go with Harry Potter. I would think that you

732
00:41:21,760 --> 00:41:25,239
Speaker 1: can get seven figures. There's enough Potter heads out there

733
00:41:25,280 --> 00:41:29,839
Speaker 1: who've done well, and I think they could build that up. Conversely,

734
00:41:31,080 --> 00:41:36,319
Speaker 1: despite how successful Trump's spack was initially, I don't think

735
00:41:36,400 --> 00:41:39,640
Speaker 1: there's as many folks who would want the hat and

736
00:41:39,880 --> 00:41:42,840
Speaker 1: also not on n F T S. Yeah, it's it's tricky.

737
00:41:42,920 --> 00:41:46,120
Speaker 1: You have to sort of uh, you know, engauge the

738
00:41:47,120 --> 00:41:51,080
Speaker 1: enthusiasm of Harry Potter fans versus fans of the Trump

739
00:41:51,640 --> 00:41:55,960
Speaker 1: both very enthusiastic fan basis. I would say, um, so

740
00:41:56,280 --> 00:41:59,360
Speaker 1: I will say that Milania's hat was was a disappointment.

741
00:41:59,480 --> 00:42:03,520
Speaker 1: She wanted to think in Soul. Of course, Soul crashed.

742
00:42:04,239 --> 00:42:06,360
Speaker 1: She only got a hundred and seventy thousand dollars for

743
00:42:06,440 --> 00:42:10,000
Speaker 1: the hat, but the Harry Potter book is only expected

744
00:42:10,040 --> 00:42:13,480
Speaker 1: to sell for thirty thousand pounds, so about forty dollars.

745
00:42:14,880 --> 00:42:18,920
Speaker 1: So so, but I kind of agree with both of you.

746
00:42:19,000 --> 00:42:22,080
Speaker 1: I think the Potter heads uh will show up in force.

747
00:42:22,160 --> 00:42:25,239
Speaker 1: And that's just you know, the hat already sold, the

748
00:42:26,120 --> 00:42:30,040
Speaker 1: book has not sold so very well. Could be could

749
00:42:30,080 --> 00:42:35,280
Speaker 1: be a push, but us as far as expected value,

750
00:42:35,640 --> 00:42:38,000
Speaker 1: L I think you should bid on that one, uh

751
00:42:39,160 --> 00:42:43,919
Speaker 1: and see how it goes. Yeah, it's time for me too. Yep.

752
00:42:44,400 --> 00:42:46,520
Speaker 1: All right with that said, I think that is all

753
00:42:46,560 --> 00:42:49,319
Speaker 1: the time we have. Max. Always a pleasure to catch

754
00:42:49,400 --> 00:42:51,040
Speaker 1: up with you, and good luck with the new venture.

755
00:42:51,160 --> 00:42:53,120
Speaker 1: We definitely are gonna have to catch up again and

756
00:42:53,160 --> 00:42:55,600
Speaker 1: have you back on and see how all how it's

757
00:42:55,640 --> 00:42:59,680
Speaker 1: all going. Thanks so much, guys. Always great to catch

758
00:42:59,760 --> 00:43:10,960
Speaker 1: up with you as well. Thank you, Max. What Goes

759
00:43:11,040 --> 00:43:13,120
Speaker 1: Up will be back next week. Until then, you can

760
00:43:13,120 --> 00:43:16,040
Speaker 1: find us on the Bloomberg Terminal website and app or

761
00:43:16,280 --> 00:43:18,920
Speaker 1: wherever you get your podcasts. We'd love it if you

762
00:43:19,000 --> 00:43:20,759
Speaker 1: took the time to rate and review the show on

763
00:43:20,840 --> 00:43:24,120
Speaker 1: Apple Podcasts so more listeners can find us and you

764
00:43:24,160 --> 00:43:27,480
Speaker 1: can find us on Twitter, follow me at Reaganonymous, Wildonta

765
00:43:27,560 --> 00:43:32,000
Speaker 1: Hirich is at Wildonta. Hirich also followed Bloomberg Podcasts at

766
00:43:32,080 --> 00:43:36,040
Speaker 1: Podcasts and thank you to Charlie Pelletta. Bloomberg Radio What

767
00:43:36,200 --> 00:43:38,840
Speaker 1: Goes Up is produced by Laura Carlson. The head of

768
00:43:38,840 --> 00:43:42,360
Speaker 1: Bloomberg Podcast is francesco Leavie. Thanks for listening, See you

769
00:43:42,440 --> 00:44:00,919
Speaker 1: next time, um,