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Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney alongside

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Speaker 1: my co host Matt Miller.

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Speaker 2: Every business day we bring you interviews from CEOs, market pros,

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Speaker 2: and Bloomberg experts, along with essential market moving news.

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Speaker 1: Find the Bloomberg Markets Podcast on Apple Podcasts or wherever

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Speaker 1: you listen to podcasts, and at Bloomberg dot com slash podcast.

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Speaker 1: Let's bringing Alison Williams. She's the pro on this. She's

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Speaker 1: our senior analyst Bloomberg Intelligence covering the bank. She's also

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Speaker 1: the co director of America's Research for Bloomberg Intelligence. So

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Speaker 1: she's got a bunch of people that report to her

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Speaker 1: rely on her for the management of that biz.

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Speaker 3: So she's a busy person.

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Speaker 1: We appreciate getting some time here in the Bloomberg in

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Speaker 1: our active broker studio. Allison, let's start with Goldman Sachs here.

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Speaker 1: You know, from my perspective, I haven't competing against these

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Speaker 1: guys forever. Look at you know, really admiring the franchise.

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Speaker 1: There a little bit of a miss here this quarter

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Speaker 1: on the fix training, the fixed income, commodities and credit

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Speaker 1: and all that kind of stuff.

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Speaker 3: From your perspective, what happened so.

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Speaker 4: I think, you know, obviously not benefiting from some of

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Speaker 4: the areas of the other banks, And just to put

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Speaker 4: that in context, so they missed on fixed they sort

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Speaker 4: of made it up with equities and fees, but net

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Speaker 4: the other banks posted big positive surprises. They have more

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Speaker 4: momentum going on. Yes, Goldman probably benefited last year. Well,

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Speaker 4: we're pretty sure they benefited last year from commodities, which

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Speaker 4: is a business that they have a lot more relative

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Speaker 4: strength than the other peers. But the bottom line is

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Speaker 4: there's just less momentum in that business going forward. They

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Speaker 4: earn more from the other banks reporting today from that

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Speaker 4: business that's a negative. Bank of America is really the

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Speaker 4: interesting one. They've been gaining share in equities and a

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Speaker 4: fixed income and you know, part of that is some

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Speaker 4: investments that they've made, but I think they're also benefiting

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Speaker 4: from some of the troubles at Credit Sue. If you

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Speaker 4: think about where Bank of America has some of their strengths,

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Speaker 4: the securitization business, those spread type products, those were businesses

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Speaker 4: that Credit Sweez is relative strong and and much of

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Speaker 4: that business has sort of gone away from them, and

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Speaker 4: perhaps going to Bank America.

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Speaker 2: Is there a best fixed income trading business on the street.

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Speaker 2: Is there a bank that's known for its fixed income trading? Like,

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Speaker 2: if I have a multi billion dollar fund and I

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Speaker 2: want to do work with one big firm, is there

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Speaker 2: one I would obviously choose?

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Speaker 4: JP Morgan.

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Speaker 3: Yeah, okay, I was just oh, I would said Goldman opinion.

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Speaker 4: Yeah, so you're thinking, so I guess that the reason

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Speaker 4: why you're probably thinking more of Goldman. Goldman has you know,

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Speaker 4: really strength with the hedge fund customers, the asset management customers,

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Speaker 4: you know that that that research frequently deals with. I

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Speaker 4: think JP Morgan has an additional leg up with the

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Speaker 4: corporate UH type customers, and so I think, you know,

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Speaker 4: they are the biggest in that business. Goldman has made,

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Speaker 4: i mean some pretty remarkable market share gains in the years,

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Speaker 4: which again might be why you're why are I just

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Speaker 4: going to them? But JP Morgan is still the biggest

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Speaker 4: of that business.

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Speaker 1: So be ay, give me a sense of where they

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Speaker 1: stack up against the JP Morgans. I guess, against the Goldmans,

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Speaker 1: against the Morgan, Stanley's and some of those capital markets businesses,

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Speaker 1: because again the guts of Bank of America on that

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Speaker 1: side is Merrill Lynch. Where where are they now, you know,

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Speaker 1: ten years on from this this merger, so.

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Speaker 4: I think they've they've definitely had some fits and starts,

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Speaker 4: and I think if you look at their business over

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Speaker 4: the long term, it's it's not been it. You know,

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Speaker 4: it doesn't have sort of the same volatility as some

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Speaker 4: of the other bigger names that we're talking about, JP,

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Speaker 4: Morgan and Goldman. You know, they've sort of kept that

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Speaker 4: business relatively steady. You know, Bank of America and UBS

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Speaker 4: as well, just you know, tend to be sort of

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Speaker 4: quarter and quarter out generating sort of the same amounts

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Speaker 4: of revenue. But in the you know, in the last

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Speaker 4: couple of years, and I think you know, this has

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Speaker 4: also been helped by some people exiting prime brokerage, right,

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Speaker 4: they sort of stepped up one of their capital requirements

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Speaker 4: and they figured, you know, we we're going to add

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Speaker 4: some more balance sheet to this trading business. So they

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Speaker 4: invested in prime brokerage. They invested in macro, which is

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Speaker 4: rates and currencies. So, as I said, they're good at

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Speaker 4: those credit spread securitization type businesses that Credit Sweep was

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Speaker 4: good at, but they've made investments in the interest rate business,

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Speaker 4: the currency's business, like that's sort of the business that

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Speaker 4: we're relatively more bullish on, you know, this year and

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Speaker 4: the years ahead, just because we think you have all

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Speaker 4: these trillions of dollars still sitting on central bank balance sheets.

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Speaker 4: There's going to be uncertainty. There's going to be you know,

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Speaker 4: this is an unprecedented situation, and we think that the

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Speaker 4: interest rate uncertainty extends.

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Speaker 2: We're in for a world of volatility for well, we're

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Speaker 2: in it already and it's got to continue with with

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Speaker 2: all of those variables. In terms of the investment banking business,

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Speaker 2: investment banking revenue, how do they do.

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Speaker 4: So, you know, Goldman did a little bit better, All

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Speaker 4: the banks did a little bit better. But you know,

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Speaker 4: we're looking at twenty to twenty five percent declines and

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Speaker 4: calling that a win because last year we're down fifty percent.

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Speaker 4: But things are still weak there and you're starting to

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Speaker 4: see these headlines about you know, cuts in the investment

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Speaker 4: banking business. You know, the CEOs have the CEOs of

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Speaker 4: the investment banks have talked about their clients, you know,

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Speaker 4: still not really adjusting to the newer valuation. So you know,

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Speaker 4: as we know, tech has had a few good months,

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Speaker 4: but we're just not back to where we were, and

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Speaker 4: some of those companies waiting to go public may not

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Speaker 4: have adjusted their expectations. The other thing that the investment

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Speaker 4: banks CEOs have said is, look, when the FED stops

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Speaker 4: raising rates, we think that that could really put a

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Speaker 4: flood of issuance. You know, that remains to be seen

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Speaker 4: when that happens. Again, We're we're sort of in a

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Speaker 4: pretty remarkable period where Jamie Diamond's interesting right, Like they

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Speaker 4: talked about FED cuts are being baked into their interesting

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Speaker 4: income guidance. But then he said everybody should prepare for

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Speaker 4: higher rates for longer, and so you see that you're

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Speaker 4: getting those contradicting messages.

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Speaker 1: So, Allison, you've been following these companies for a long time.

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Speaker 1: You've seen management teams come and go.

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Speaker 3: How was the street?

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Speaker 1: How were investors viewing David Solomon, the chairman and cy

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Speaker 1: of Goldman Sex.

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Speaker 4: I mean, I think for Solomon it's still a little

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Speaker 4: bit weight. And see I know that there's you know

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Speaker 4: a lot of the negative press and criticism have come

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Speaker 4: around the consumer business. I would keep in mind that

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Speaker 4: even though that's that's sort of being done away with

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Speaker 4: under Solomon, it was really started underwind Fly. That was

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Speaker 4: a huge deviation for Goldman. You know, parts of that

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Speaker 4: business remain. You know, we're hearing a lot about this

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Speaker 4: new Apple product on the deposit side of things, but

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Speaker 4: the Marcus loans. You know, frankly, I don't think any

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Speaker 4: investors really ever believed in that business. I always thought

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Speaker 4: the strategy was sort of like a me too from

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Speaker 4: like the nineties.

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Speaker 2: I'm going to say I like that strategy. I know you,

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Speaker 2: I know you have hated it forever, and Paul also

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Speaker 2: has never understood the consumer. To me, it's just high

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Speaker 2: end super luxury if you're doing your banking with Golden.

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Speaker 4: But it's not going but it's it's the it's the

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Speaker 4: strategy with which they went after these loans, which was

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Speaker 4: competing on price. Well they failed, so I was the

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Speaker 4: loan book, right, So they're still trying to go after

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Speaker 4: those deposits. Yeah, they're trying to go after it with

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Speaker 4: using the Apple premium brand.

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Speaker 5: You know.

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Speaker 4: They they're going after CDs with premium pricing in their market.

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Speaker 1: And they always had this, not not the loans, and

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Speaker 1: they always had and they still have. But one of

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Speaker 1: their real selling points for decades has been you sell

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Speaker 1: your company a will advise you take the fee there

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Speaker 1: and then when you have that the proceeds in your pocket. Oh,

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Speaker 1: let me introduce you to my private wealth manager in Dallas,

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Speaker 1: Texas or in San Francisco, and he'll he or she

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Speaker 1: will help you manage this money.

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Speaker 3: We'll make fees there too. So they've always had that.

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Speaker 3: But Marcus was more.

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Speaker 4: Marcus was more, you know, let's broaden out. Let's broaden

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Speaker 4: out the net, right, like, are we leaving all this

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Speaker 4: white space behind? And you know, let's try to do

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Speaker 4: a few different things there. You know, as we as

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Speaker 4: we just discussed the deposits, I think is interesting and

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Speaker 4: getting getting those customers in the door. But the credit

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Speaker 4: card lending strategy was really like a price a pricing strategy,

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Speaker 4: and that's you know, I don't know any bank where

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Speaker 4: it's one over the long term.

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Speaker 1: Eventually that all right, Morgan Stanley, before the open tomorrow

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Speaker 1: is up what we have?

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Speaker 4: So Morgan Stanley, Uh, people I think are going to

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Speaker 4: be listening. I'm going to be listening to find out.

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Speaker 4: You know, Andy c left Merrill. He's going over to

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Speaker 4: City to be the head of their global wealth management.

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Speaker 4: Is there going to be more competition in the us

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Speaker 4: keeping mind, Morgan Stanley bought that's you know, Smith Barty business.

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Speaker 4: They're a wealth powerhouse. Part of that came from city

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Speaker 4: a long time ago. Equities trading, you know, has been

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Speaker 4: generally been disappoint So that's not great for a city.

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Speaker 4: I mean, that's not great for Morgan Stanley. Excuse me.

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Speaker 4: We'll see how their fixed income holds up, all right, and.

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Speaker 2: We'll see if anyone else says, you know, AI can

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Speaker 2: reduce headcamps.

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Speaker 3: Yes exactly. We like that because that was a good

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Speaker 3: line from mois the morning Hand.

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Speaker 1: Yeah, morning Hand on Top Live. So that was you know,

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Speaker 1: if you're you know, working on wall streets, I think,

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Speaker 1: oh great, now I got another. I gotta worry about

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Speaker 1: these stupid machines taking over my job. All right, Allison Williams,

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Speaker 1: as always, thank you so much for finding a few

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Speaker 1: minutes in your busy scheduled to come in and get

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Speaker 1: us up to date on some of these numbers coming

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Speaker 1: out of Bank of America and Goldman Sachs. A little

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Speaker 1: bit of a miss for Goldman Sacks. It doesn't happen

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Speaker 1: very often, but so the stock's off just a little

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Speaker 1: bit here. But we'll continue with these big banks, big

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Speaker 1: investment banks reporting. We've got Morgan Stanley tomorrow before the

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Speaker 1: open and Alison will be all over that and her team.

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Speaker 6: You're listening to the team.

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Speaker 5: Can's are live program Bloomberg Markets weekdays at ten am

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Speaker 5: Eastern on Bloomberg dot Com, the iHeartRadio app and the

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Speaker 5: Bloomberg Business App, or listen on demand wherever you get

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Speaker 5: your podcast.

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Speaker 1: If you're Fox, Fox Communications, Fox Corporation, Rupert Murdock all

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Speaker 1: the Fox Network, You're in a little bit of a

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Speaker 1: legal suit here with dominion voting systems and the potential

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Speaker 1: risks there are pretty material, so believe it or not,

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Speaker 1: Bloomberg Intelligence has a bunch of litigation analysts for just

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Speaker 1: this type of analysis. Matt Sheltenham joins as Caesar, senior

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Speaker 1: Litigation analyst at Bloomberg Intelligence, and I happen to know

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Speaker 1: that he is in Vegas as we speak at the

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Speaker 1: National Association of Broadcasters conference. It's my first question, Matt,

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Speaker 1: most important, and this is important, so get it right.

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Speaker 3: Where are you staying?

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Speaker 7: I am at the Aria Hotel Nice.

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Speaker 3: All right, that's new school, that's new school, very good.

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Speaker 1: I'm a Bellagio guy, so you know, go to the

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Speaker 1: high rollers crafts table.

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Speaker 2: Where do they have the the fake Paris and the

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Speaker 2: Venice Yeah, and all that stuff that's.

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Speaker 3: Right there on the strip. Oh I thought it was

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Speaker 3: Paris the hotel. No, Paris is the hotel? Oh I see?

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Speaker 5: Yeah?

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Speaker 1: And the Venetian is that and they are both awesome properties.

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Speaker 1: All right, Matt, talk to us about Fox and Dominion

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Speaker 1: voting Systems. What is the lawsuit and where are we now?

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Speaker 7: Yeah, so we're at the very beginning of this, this

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Speaker 7: lawsuit heading to trial. Now, this is a suit about

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Speaker 7: Fox's election coverage over about a month from November into

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Speaker 7: December after the election, when Dominion's name kept coming up

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Speaker 7: in interviews that that Fox held and Dominions this voting

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Speaker 7: software company. Its software was used in about thirty states

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Speaker 7: during the election, and a number of allegations were made

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Speaker 7: suggesting that Dominion had had kind of rigged the election.

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Speaker 7: And so now this is a defamation claim brought against

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Speaker 7: Fox News claiming that you basically have wiped out our

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Speaker 7: business by these false statements that you made. And it

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Speaker 7: seeks initially they saw one point six billion dollars in

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Speaker 7: their complaint. I think that's come down a little bit

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Speaker 7: but it's a big, big deal, a serious financial threat

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Speaker 7: to the box.

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Speaker 2: Why one point said, you know, when I sue somebody,

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Speaker 2: typically it's for a million or so. Oh yeah, how

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Speaker 2: do they get to one point six billion dollars?

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Speaker 7: Yeah? So I think that's that's one of the most

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Speaker 7: important things to watch going forward, is how credible is

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Speaker 7: that number? Is that damage's number? And so they put

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Speaker 7: one point six billion in their complaint, and as I said,

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Speaker 7: I think it's come down a little bit. What they're

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Speaker 7: looking at is one, what is our lost profits because

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Speaker 7: of this? And two what is our lost value as

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Speaker 7: an enterprise? And I think it's that second category that's

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Speaker 7: going to be the focus here. They have an expert

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Speaker 7: who comes in that says, when we look at our

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Speaker 7: value as an enterprise over the next ten years, and

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Speaker 7: we're basically so damaged as a business that you've wiped

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Speaker 7: us out, we value that is at about nine hundred

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Speaker 7: and twenty million dollars. Now Fox comes back and says,

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Speaker 7: that's crazy. You you independently were valued as a company

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Speaker 7: in twenty two for about three hundred million dollars and

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Speaker 7: your business is actually doing better after the election. So

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Speaker 7: I think I think dominion has a strong case about defamation.

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Speaker 7: I don't know that it has a real strong case

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Speaker 7: to get to that billion dollar figure. But one other

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Speaker 7: footnote on that, though punitive damages are also in play here,

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Speaker 7: the judge refused to take them off the table, and

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Speaker 7: so it's not just that direct harm to the business.

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Speaker 7: The jury has the ability to come in and effectively

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Speaker 7: punish Fox. And sometimes punitive damages can go as much

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Speaker 7: as five times the economic damages. So that's where you

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Speaker 7: really get to big deal numbers.

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Speaker 3: It gets you there.

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Speaker 1: Yes, hey, Matt, if I'm Fox, why can I just

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Speaker 1: claim First Amendment protection?

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Speaker 7: Yeah, So that's what they've been doing. And they brought

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Speaker 7: in Paul Clement, you know, the noted First Amendment Supreme

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Speaker 7: Court lawyer, to defend them here, and and they tried

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Speaker 7: that defense, but the court has mostly shot it down.

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Speaker 7: That Fox, you know, Fox makes the First Amendment argument.

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Speaker 7: This was the most important story of the day. The

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Speaker 7: President of the United States is saying that the election

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Speaker 7: was rigged. How can we not report on that? And

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Speaker 7: and and so. But but the problem is there is real, no,

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Speaker 7: there really is no defense in in in in the

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Speaker 7: First Amendment that lets you air allegations that you know

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Speaker 7: to be false or that you strongly believe to be false,

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Speaker 7: and and so the so that the judge so far

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Speaker 7: is pretty much said, now, we're not going.

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Speaker 6: To go there.

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Speaker 7: I already have decided these are false statements that you made.

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Speaker 7: And so the only question in this trial now isn't

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Speaker 7: the First Amendment. It's it's it's whether you knew they

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Speaker 7: were false or whether you had a high probability to

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Speaker 7: believe they were false.

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Speaker 2: If television news can't make stuff up anymore than where

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Speaker 2: are we as a country?

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Speaker 3: Well, the first Amendment was for like mistakes.

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Speaker 5: You know that.

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Speaker 2: The The issue for me is do we get to

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Speaker 2: see Rupert Murdoch, Do we get to see Alan John Hannity,

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Speaker 2: Do we get to see Sean Hannity and Tucker Carlson testify.

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Speaker 2: We've already seen some of their you know, SMS text

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Speaker 2: messages and it didn't look good, right.

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Speaker 7: Absolutely, And I think that right now, you know, I

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Speaker 7: still think a settlement is a pretty good bet over

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Speaker 7: the next couple of months before a jury actually announces

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Speaker 7: its decision. But I wouldn't be at all surprised to

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Speaker 7: see this trial advance pretty far. So I think it's

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Speaker 7: more likely than not you are going to see those

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Speaker 7: witnesses called to the stand. And I think Rupert Rupert

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Speaker 7: Murdoch is pretty early on the list, potentially in the

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Speaker 7: next couple of days.

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Speaker 3: All Right, Matt, thank you so much for joining us.

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Speaker 1: Matt Sheltenham, he's a senior litigation analysts Bloomberg Intelligence.

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Speaker 3: He's in Vegas.

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Speaker 1: Matt, make sure you go over to the high end

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Speaker 1: crafts table Blajo use my name.

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Speaker 3: They'll take care of you over there.

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Speaker 1: So Matt Sheltenham, he covers all that in litigation stuff

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Speaker 1: for Bloomberg Intelligence, so it's great to get his informed

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Speaker 1: legal opinion when you get some of these big legal

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Speaker 1: cases surrounding certain companies.

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Speaker 5: You're listening to the tape Cat's are live program Bloomberg

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Speaker 5: Markets weekdays at ten am Eastern on Bloomberg Radio, the

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Speaker 5: tune it app, Bloomberg dot Com, and.

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Speaker 6: The Bloomberg Business App.

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Speaker 5: You can also listen live on Amazon Alexa from our

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Speaker 5: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

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Speaker 1: Let's turn to real estate, commercial real estate, and when

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Speaker 1: you want to do that, because you know people aren't

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Speaker 1: coming back to the office. That's how it is, particularly

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Speaker 1: here in Midtown Manhattan, but maybe it's different in other

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Speaker 1: parts of the world. But Jeff Langbound joins us. He's

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Speaker 1: a senior reat analyst with Bloomberg Intelligence. Jeff, talk to

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Speaker 1: us about I'd love to get your view on commercial

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Speaker 1: real estate, office real estate in particular, how do you

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Speaker 1: think office real estate is going to evolve in this country,

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Speaker 1: you know, over the next several years, because boy, it's

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Speaker 1: changed since pre pandemic levels.

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Speaker 6: Yeah.

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Speaker 8: Right, Well, first I want to say that nobody may

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Speaker 8: be buying equities broadly, but definitely nobody's buying reads right now.

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Speaker 8: So that's that's something we're definitely keeping an eye on.

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Speaker 8: So the office is definitely a very different environment than

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Speaker 8: it was pre pandemic, and right now we are in

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Speaker 8: a very long kind of figure it out period. I

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Speaker 8: think where we're going to figure we're going to have

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Speaker 8: to see how much how much space tenants need, if any,

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Speaker 8: We're going to have to figure out what properties are

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Speaker 8: actually worth, and then we're gonna have to figure out

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Speaker 8: what can be refinanced once mortgages start coming due. So

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Speaker 8: it's going to be a long period of time where

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Speaker 8: we have to figure out what the office environment is

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Speaker 8: going to look like.

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Speaker 2: What does the I mean I'm assuming everybody refinanced at

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Speaker 2: near zero when they could. So when does that wall

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Speaker 2: of debt? When's the first wall come due for maturities?

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Speaker 8: It's starting, it's it's this year. You know, there's there's

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Speaker 8: a big chunk, big chunk coming of debt that was

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Speaker 8: issued in the twenty eighteen timeframe. And like you said,

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Speaker 8: not only are interest rates at lows, but asset values

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Speaker 8: were at highs and cash flow. You know, everyone was

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Speaker 8: projecting continuous rise and cash flows. So you have loan

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Speaker 8: to values that you know are going to be elevated

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Speaker 8: if you start marking values down. You have interest rates

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Speaker 8: that you know, if it was floating debt has already

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Speaker 8: risen and it's starting to present a problem. If it's

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Speaker 8: not floating debt, then the refi is going to present

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Speaker 8: a problem. So you know, but like I said, it's

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Speaker 8: going to be a long term process to figure out

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Speaker 8: how this is going to work because lenders don't want

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Speaker 8: to take over office buildings that are you know, difficult

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Speaker 8: to operate in this environment, and so landlords or borrowers

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Speaker 8: are you know, kind of trying to figure out what

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Speaker 8: they can can negotiate in order to get properties refinanced.

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Speaker 1: So I'm just looking at the Bloomberg Property Reate Office

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Speaker 1: Property Index. It peaked in October of twenty twenty, and

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Speaker 1: it's now down sixty percent sixty zero percent from that

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Speaker 1: peak in February of twenty twenty. And Jeff, I don't

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Speaker 1: even feel like I can go in and buy the

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Speaker 1: bottom here because, like you mentioned, A, I don't know

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Speaker 1: don't know what my cash flows are going to because

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Speaker 1: I don't know how much space my tenants are really

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Speaker 1: going to need longer term, and too, I don't think

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Speaker 1: I can finance it.

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Speaker 3: At these rates.

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Speaker 1: So, I mean, I don't see is this is this

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Speaker 1: just the new normal and everybody's got to adjust.

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Speaker 8: No, I don't think so. I think it's a little

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Speaker 8: bit more nuanced than that.

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Speaker 6: You know.

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Speaker 8: So, the first wave of declining prices was you know, speculation,

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Speaker 8: declining prices in the office rate Index and in the

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Speaker 8: shares themselves was you know, speculation over how much property

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Speaker 8: values were actually falling. You've gotten some pressure recently as

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Speaker 8: the banks have started to struggle, where you know, there's

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Speaker 8: real concern over the over the financing, But I don't

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Speaker 8: think that it's a catch all for every property and

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Speaker 8: every owner of properties. There's going to be a differentiation

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Speaker 8: between quality. The owners of better quality assets are going

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Speaker 8: to be able to attract tenants, uh, and the lower

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Speaker 8: quality assets are going to need to find some alter

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Speaker 8: use as a polite way of saying, they are obsolete

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Speaker 8: as office buildings. And it's going to take some time

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Speaker 8: for that to get worked out. And so you know,

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Speaker 8: you may not want to buy the entire index, but

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Speaker 8: there may be some names that have over corrected to

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Speaker 8: figure out where the values actually are.

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Speaker 3: Right, amazing stuff.

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Speaker 1: We're gonna next time you're in New York, Jeff, let

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Speaker 1: us know we're gonna get you in the studio here

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Speaker 1: Jeff Langbaum Senior read analys for Bloomberg Intelligence, given the

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Speaker 1: latest on this property index property market out there.

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Speaker 5: You're listening to the team Can't Live program Bloomberg Markets

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Speaker 5: weekdays at ten am Eastern on Bloomberg dot Com, the

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Speaker 5: iHeartRadio app, and the Bloomberg Business App, or listen.

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Speaker 6: On demand wherever you get your podcasts.

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Speaker 1: Tesla's got some earnings coming up, and that's always something

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Speaker 1: to pay attention to.

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Speaker 3: A if you're Tesla shareholder or creditor. But this is

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Speaker 3: if you're interested in this whole ev issue. Transition from that.

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Speaker 1: I know Matt all Over, but Joe Levington Joints us

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Speaker 1: he's a direct or Credit Research of Bloomberg Intelligency Joints.

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Speaker 1: Here in our Bloomberg Interactive Broker Studio, Joe, you cover

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Speaker 1: the debt of Tesla. What's the what's your view of

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Speaker 1: kind of how the company's evolving here from you know,

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Speaker 1: you've been following this company since the beginning. How do

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Speaker 1: you view kind of where they are right now and

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Speaker 1: kind of what their growth plans are?

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Speaker 9: Sure Apoulo, Yeah, they really moved from the hunter to

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Speaker 9: the hunt ad at this point. And really for them,

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Speaker 9: they have a couple of strategic weapons that others don't.

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Speaker 9: One is a dominant cost position. You know, if you

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Speaker 9: think about their profitability, they're going to have about fourteen

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Speaker 9: percent margins this year. If you look at their peer group,

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Speaker 9: it's about eight percent. And so they can use that

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Speaker 9: in terms of pricing, and you're seeing them cut prices aggressively.

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Speaker 9: Some say it's because there's no demand, That's.

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Speaker 3: What I say.

438
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Speaker 1: But you're saying, but the other the bull case is

439
00:21:46,520 --> 00:21:49,800
Speaker 1: it's from Hey, it's a position of strength. They've got

440
00:21:49,800 --> 00:21:52,560
Speaker 1: the margin and they can drive market share and that's good.

441
00:21:52,720 --> 00:21:52,960
Speaker 6: Yeah.

442
00:21:53,000 --> 00:21:55,560
Speaker 9: I mean at the end of the day, whether you

443
00:21:55,600 --> 00:21:57,399
Speaker 9: know it might be the looking at the at the

444
00:21:57,400 --> 00:22:00,440
Speaker 9: same picture just slightly differently, the fact is that the

445
00:22:00,480 --> 00:22:03,000
Speaker 9: grow thirty eight percent, right, and your peers are growing

446
00:22:03,000 --> 00:22:03,919
Speaker 9: three or four percent.

447
00:22:04,040 --> 00:22:05,000
Speaker 10: So whether you're say in.

448
00:22:05,080 --> 00:22:07,159
Speaker 2: Terms of what top line sales, yeah.

449
00:22:07,040 --> 00:22:09,600
Speaker 9: So you know, like one can say, hey, there there's

450
00:22:09,640 --> 00:22:12,280
Speaker 9: not enough demand to hit their fifty percent, but relative

451
00:22:12,320 --> 00:22:15,000
Speaker 9: to peers, they're blowing them away, and they have the

452
00:22:15,040 --> 00:22:19,840
Speaker 9: ability to use that and scale on their margins and

453
00:22:19,920 --> 00:22:23,000
Speaker 9: still gain share at the same time. So whether you

454
00:22:23,040 --> 00:22:25,159
Speaker 9: look at it as hey, they're missing their fifty percent

455
00:22:25,359 --> 00:22:28,400
Speaker 9: or hey, they're you know, doing much better than their peers,

456
00:22:28,640 --> 00:22:31,440
Speaker 9: it's going in the right direction for them, certainly from

457
00:22:31,440 --> 00:22:34,280
Speaker 9: a credit perspective, and because it has been that way,

458
00:22:34,560 --> 00:22:37,439
Speaker 9: their balance sheet has a huge amount of cash and

459
00:22:37,520 --> 00:22:39,440
Speaker 9: very little debt at this point, so they can continue

460
00:22:39,480 --> 00:22:39,880
Speaker 9: to do that.

461
00:22:40,320 --> 00:22:44,360
Speaker 2: I saw I saw a funny headline this morning Mercedes

462
00:22:45,040 --> 00:22:47,760
Speaker 2: Benz aims to double EV sales this year, and at

463
00:22:47,760 --> 00:22:48,280
Speaker 2: first I.

464
00:22:48,200 --> 00:22:50,359
Speaker 3: Was like, wow, that's a big deal. But if you

465
00:22:50,440 --> 00:22:53,960
Speaker 3: only sold like twenty five evs last year, it's not

466
00:22:54,040 --> 00:22:54,680
Speaker 3: that difficult.

467
00:22:54,760 --> 00:22:57,080
Speaker 2: Now Tesla were to double ev sales, that would be

468
00:22:57,119 --> 00:23:01,480
Speaker 2: a major feat because what they're selling like, well, well,

469
00:23:01,520 --> 00:23:03,000
Speaker 2: over a million cars a year.

470
00:23:03,040 --> 00:23:04,359
Speaker 10: Now, that's totally right.

471
00:23:04,440 --> 00:23:06,560
Speaker 9: And you know, you could go back and forth on

472
00:23:06,600 --> 00:23:09,399
Speaker 9: whether fifty percent growth a year is doable or not.

473
00:23:09,840 --> 00:23:12,159
Speaker 10: To me, that sounds like a very very aggressive scale.

474
00:23:12,520 --> 00:23:13,840
Speaker 10: But what it's really telling you.

475
00:23:13,920 --> 00:23:16,159
Speaker 2: Except for when you tell me that they have thirty

476
00:23:16,200 --> 00:23:19,640
Speaker 2: eight percent growth and fourteen percent margins, I instantly think,

477
00:23:19,680 --> 00:23:23,240
Speaker 2: well they could have margins and boost that to over

478
00:23:23,280 --> 00:23:25,560
Speaker 2: fifty no problama for sure more.

479
00:23:26,440 --> 00:23:28,080
Speaker 9: And I will tell you, Matt, like the real thing

480
00:23:28,119 --> 00:23:31,480
Speaker 9: for them is kind of becoming the Toyota evs, meaning

481
00:23:31,800 --> 00:23:34,000
Speaker 9: as you move your price point down, the amount of

482
00:23:34,080 --> 00:23:37,960
Speaker 9: consumers that can afford your vehicles grows very, very significantly.

483
00:23:38,359 --> 00:23:40,320
Speaker 9: And that's really where they're going is to become the

484
00:23:40,359 --> 00:23:43,680
Speaker 9: affordable car, which was really you know, like master planned

485
00:23:43,720 --> 00:23:45,560
Speaker 9: part one from Tesla.

486
00:23:46,440 --> 00:23:50,840
Speaker 1: So the balance sheet, how does the credit market view Tesla?

487
00:23:50,920 --> 00:23:53,600
Speaker 1: I mean, if they wanted to go raise more capital,

488
00:23:53,640 --> 00:23:55,880
Speaker 1: could they do that at a reasonable rate?

489
00:23:55,960 --> 00:23:56,800
Speaker 10: Spread oh, one.

490
00:23:56,720 --> 00:24:00,680
Speaker 9: Hundred percent, when you know, it's amazing that in twenty

491
00:24:00,760 --> 00:24:04,240
Speaker 9: eighteen Elon Musk is talking about being in manufacturing hell

492
00:24:04,680 --> 00:24:07,520
Speaker 9: and just trying to survive. Today, they have over twenty

493
00:24:07,520 --> 00:24:10,600
Speaker 9: two billion dollars of cash on hand, They've been upgraded

494
00:24:10,640 --> 00:24:12,560
Speaker 9: seven times in the last two and a half years.

495
00:24:12,600 --> 00:24:15,080
Speaker 9: I've never seen that happen for another automobial company.

496
00:24:15,119 --> 00:24:17,719
Speaker 1: And look at the north of twenty percent ebitdam margins.

497
00:24:17,760 --> 00:24:20,240
Speaker 1: I mean, that's I could lend against that exactly.

498
00:24:20,320 --> 00:24:22,399
Speaker 9: So you know, like right now, you could probably do

499
00:24:22,440 --> 00:24:24,840
Speaker 9: a green bond for ten years at under four and

500
00:24:24,840 --> 00:24:27,639
Speaker 9: a half percent, so really a very.

501
00:24:27,520 --> 00:24:28,400
Speaker 10: Cost efficient way.

502
00:24:28,400 --> 00:24:30,720
Speaker 9: If they wanted to, say, build out a captive finance unit,

503
00:24:30,800 --> 00:24:34,040
Speaker 9: which a lot of auto companies do as another revenue stream.

504
00:24:34,200 --> 00:24:38,960
Speaker 2: Does it matter that they've had I mean, you're a

505
00:24:38,960 --> 00:24:41,720
Speaker 2: credit guy, but Paul and I are too dumb and

506
00:24:41,760 --> 00:24:45,280
Speaker 2: we just focus on the stock. So they had a

507
00:24:45,440 --> 00:24:49,120
Speaker 2: drop in market cap from like one point two trillion

508
00:24:49,200 --> 00:24:52,000
Speaker 2: down to six hundred billion, which sounds like, well, that's

509
00:24:52,160 --> 00:24:55,800
Speaker 2: that's a horrible wipeout, But to be fair, you know,

510
00:24:55,840 --> 00:24:58,080
Speaker 2: they're just back where they were two years ago. So

511
00:24:58,080 --> 00:25:01,320
Speaker 2: they had an incredible climb during the pandem and then

512
00:25:01,359 --> 00:25:04,280
Speaker 2: a drop back to normal levels.

513
00:25:04,359 --> 00:25:06,480
Speaker 3: There's still a behemoth, oh for sure.

514
00:25:06,480 --> 00:25:09,399
Speaker 9: And keep in mind how rates have extended during that

515
00:25:09,440 --> 00:25:14,080
Speaker 9: period two and for companies that are long Greasian cash

516
00:25:14,119 --> 00:25:16,280
Speaker 9: flow companies like them, when you have a big growth,

517
00:25:16,320 --> 00:25:18,080
Speaker 9: you're going to have that in a up cycle in

518
00:25:18,160 --> 00:25:22,040
Speaker 9: terms of breeds. But in that period Matt their CDs

519
00:25:22,119 --> 00:25:24,480
Speaker 9: is outperformed. So what it's telling you is that from

520
00:25:24,480 --> 00:25:27,280
Speaker 9: a risk perspective, people think that it actually has less

521
00:25:27,320 --> 00:25:29,679
Speaker 9: risk than it did two years ago, even though the

522
00:25:29,720 --> 00:25:31,080
Speaker 9: market cap is half the size.

523
00:25:31,359 --> 00:25:33,280
Speaker 1: That's interesting, all right, So how about the other auto

524
00:25:33,280 --> 00:25:36,280
Speaker 1: companies for GM, I mean all the other big public

525
00:25:36,320 --> 00:25:39,240
Speaker 1: ones that on that have public debt outsetting that you cover.

526
00:25:39,960 --> 00:25:43,800
Speaker 1: This industry is making this transition like we've never seen before.

527
00:25:44,160 --> 00:25:47,120
Speaker 1: If I'm a creditor, how nervous am I'm about going

528
00:25:47,119 --> 00:25:50,480
Speaker 1: from the traditional internal combustion engine, which I can model,

529
00:25:50,520 --> 00:25:52,359
Speaker 1: I know what the profits are and I know what

530
00:25:52,359 --> 00:25:53,960
Speaker 1: the growth is to this EV thing.

531
00:25:54,760 --> 00:25:57,280
Speaker 2: And the other car makers make a ton of margin

532
00:25:57,560 --> 00:26:01,399
Speaker 2: on the big ice cars, right, nothing on electrics.

533
00:26:01,560 --> 00:26:01,960
Speaker 10: That's right.

534
00:26:02,240 --> 00:26:05,640
Speaker 9: In fact, Ford, its model E business or its TV

535
00:26:05,800 --> 00:26:08,440
Speaker 9: business is expected to lose three billion dollars this year,

536
00:26:09,760 --> 00:26:13,320
Speaker 9: just to put that in perspective. But really for Tesla,

537
00:26:13,440 --> 00:26:16,920
Speaker 9: lart a very unique place because they have these other companies,

538
00:26:17,400 --> 00:26:20,280
Speaker 9: you know, in a very type grip. Obviously, any sort

539
00:26:20,280 --> 00:26:22,960
Speaker 9: of pricing pressure that they put on them just means

540
00:26:23,000 --> 00:26:26,840
Speaker 9: more opportunity for them and a tougher capability for Ford

541
00:26:27,359 --> 00:26:29,560
Speaker 9: and GM. But what I would say Paul to your

542
00:26:29,760 --> 00:26:33,479
Speaker 9: specific question is that if you're a creditor, you're probably

543
00:26:33,520 --> 00:26:36,600
Speaker 9: not that scared of the situation, despite the fact that

544
00:26:36,640 --> 00:26:40,240
Speaker 9: this evolution is very unique. And that's really because at

545
00:26:40,280 --> 00:26:42,359
Speaker 9: the end of the day, the average bond in the

546
00:26:42,359 --> 00:26:45,280
Speaker 9: sector is about three and a half years, and there'll

547
00:26:45,320 --> 00:26:47,560
Speaker 9: still be plenty of ice engines sold over the next

548
00:26:47,560 --> 00:26:49,560
Speaker 9: three and a half years to delay the bills.

549
00:26:49,560 --> 00:26:51,400
Speaker 3: So the big car companies don't go out with ten

550
00:26:51,480 --> 00:26:52,440
Speaker 3: year twenty year paper.

551
00:26:52,760 --> 00:26:52,800
Speaker 6: No.

552
00:26:53,000 --> 00:26:54,760
Speaker 9: Most of the debt, I would say about seventy five

553
00:26:54,760 --> 00:26:57,160
Speaker 9: percent of the debt is attached to the captain finance companies,

554
00:26:57,160 --> 00:26:59,520
Speaker 9: So if you think about the leaser alone, that's really

555
00:26:59,600 --> 00:27:01,879
Speaker 9: where the debt is finance. There's very little at the

556
00:27:01,920 --> 00:27:05,440
Speaker 9: manufacturing units because they're so cyclical. You really can't put

557
00:27:05,520 --> 00:27:06,440
Speaker 9: much leverage on them.

558
00:27:06,720 --> 00:27:12,240
Speaker 2: You wrote about Ferrari's tiny financial services unit. I always

559
00:27:12,240 --> 00:27:15,080
Speaker 2: find this part of the business interesting because it's a

560
00:27:15,119 --> 00:27:19,520
Speaker 2: little bit opaque to people focused on the manufacturers and

561
00:27:19,840 --> 00:27:23,159
Speaker 2: especially the stock side. Who's in the best position in

562
00:27:23,240 --> 00:27:25,800
Speaker 2: terms of a finance unit, Because as rates ries, you

563
00:27:25,800 --> 00:27:28,399
Speaker 2: want to have a very strong finance unit, you do.

564
00:27:28,359 --> 00:27:30,320
Speaker 9: And really it's the ones that have the best credit

565
00:27:30,440 --> 00:27:32,560
Speaker 9: quality that are in the best position. So a company

566
00:27:32,600 --> 00:27:36,080
Speaker 9: like a Toyota or Mercedes really or a BMW, they

567
00:27:36,119 --> 00:27:38,679
Speaker 9: really stand out above the others. A company like Ford

568
00:27:38,960 --> 00:27:41,200
Speaker 9: and I would say that they have as an operator

569
00:27:41,240 --> 00:27:42,480
Speaker 9: are a very good operator.

570
00:27:42,840 --> 00:27:44,520
Speaker 10: Their interest costs will go.

571
00:27:44,520 --> 00:27:47,520
Speaker 9: Up about eight hundred million dollars this year just because

572
00:27:47,560 --> 00:27:50,560
Speaker 9: of rates, and that is hard to pass through when

573
00:27:50,640 --> 00:27:53,720
Speaker 9: your average car has increased about twenty five or thirty

574
00:27:53,760 --> 00:27:55,159
Speaker 9: percent of the last couple of years.

575
00:27:56,119 --> 00:27:58,960
Speaker 10: Affordability really is the word of the year for autos.

576
00:27:59,200 --> 00:28:01,720
Speaker 2: I think of GS and Folkswagen, we probably talk about

577
00:28:01,720 --> 00:28:04,680
Speaker 2: them the most. Is the biggest competitors to Tesla, right

578
00:28:04,720 --> 00:28:08,560
Speaker 2: because Folkswagen has invested so much money in building out

579
00:28:08,600 --> 00:28:12,240
Speaker 2: its evs general motors as well, seems like they're in

580
00:28:12,240 --> 00:28:16,160
Speaker 2: a position to just explode in terms of EV sales.

581
00:28:16,440 --> 00:28:19,080
Speaker 2: How do they look from credit quality perspective?

582
00:28:20,040 --> 00:28:22,520
Speaker 9: Well, I'm a fan of Volkswagen, have ben it for

583
00:28:22,520 --> 00:28:25,560
Speaker 9: a long time. I do think with both of those companies.

584
00:28:25,600 --> 00:28:28,760
Speaker 9: One of the key differences between Volkswagen and in general

585
00:28:28,800 --> 00:28:30,879
Speaker 9: motors versus the Tesla is the.

586
00:28:30,760 --> 00:28:31,960
Speaker 10: Amount of products that they have.

587
00:28:32,359 --> 00:28:34,560
Speaker 9: There's a reason why Tesla only has a handful of

588
00:28:34,560 --> 00:28:36,439
Speaker 9: products and they scale the heck out of them as

589
00:28:36,520 --> 00:28:39,760
Speaker 9: much as they can. Versus having a very wide broad range,

590
00:28:39,800 --> 00:28:42,320
Speaker 9: you can't get the same scale and obviously you have

591
00:28:42,360 --> 00:28:44,360
Speaker 9: to have more marketing on top of it. So again,

592
00:28:44,400 --> 00:28:46,920
Speaker 9: from your cost position, you're much better off being in

593
00:28:46,920 --> 00:28:49,520
Speaker 9: the Tesla spot than one of the other peers. Despite

594
00:28:49,520 --> 00:28:51,920
Speaker 9: both of them being you know, like very very strong.

595
00:28:51,760 --> 00:28:54,240
Speaker 3: Their size hurts them. As what you're saying it does.

596
00:28:54,400 --> 00:28:54,760
Speaker 10: It does.

597
00:28:54,920 --> 00:28:57,360
Speaker 1: Hey, if I'm buying a Ferrari or Lamborghini, I'm not

598
00:28:57,480 --> 00:29:00,080
Speaker 1: finits in it. I'm just throwing down the AMEX.

599
00:28:59,840 --> 00:29:01,000
Speaker 3: Right pretty much.

600
00:29:01,080 --> 00:29:02,600
Speaker 10: Yeah, I don't think you even bring in the Amex

601
00:29:02,800 --> 00:29:03,680
Speaker 10: just you know, like.

602
00:29:07,280 --> 00:29:09,680
Speaker 2: I mean, I just walk in and you know, why

603
00:29:09,720 --> 00:29:11,600
Speaker 2: do they have a financial services unit?

604
00:29:11,640 --> 00:29:13,920
Speaker 3: What does Ferrari do with the financial services unit.

605
00:29:14,080 --> 00:29:16,520
Speaker 9: Yeah, they have a very small one, just for for

606
00:29:16,960 --> 00:29:19,040
Speaker 9: a few folks in the US. It's very different than

607
00:29:19,120 --> 00:29:22,040
Speaker 9: let's say Porsche, which has a much, much bigger and

608
00:29:22,120 --> 00:29:25,720
Speaker 9: more meaningful finance company. So there is a difference in

609
00:29:25,840 --> 00:29:28,760
Speaker 9: customer between a Porsche owner and a Ferrari owner. If

610
00:29:28,800 --> 00:29:32,160
Speaker 9: you're a Ferrari, you're like mister Sweeney or yourself, You're

611
00:29:32,600 --> 00:29:33,560
Speaker 9: you're living large.

612
00:29:35,200 --> 00:29:36,360
Speaker 3: I wish that were the case.

613
00:29:36,640 --> 00:29:38,560
Speaker 1: Yeah, I can't imagine if I'm again, if I'm in

614
00:29:38,600 --> 00:29:42,000
Speaker 1: the if I'm a Lamborghini Ferrari kind of buyer.

615
00:29:43,840 --> 00:29:44,200
Speaker 3: Costs.

616
00:29:44,280 --> 00:29:46,920
Speaker 2: I mean, you're looking at a base price of over

617
00:29:47,000 --> 00:29:50,480
Speaker 2: four hundred thousand dollars, well over four hundred thousand dollars,

618
00:29:50,520 --> 00:29:51,680
Speaker 2: and when you add options.

619
00:29:52,120 --> 00:29:54,040
Speaker 1: You know, I was just aut in Carmel and there

620
00:29:54,040 --> 00:29:57,520
Speaker 1: are invented doors parked on the street. That's I mean,

621
00:29:57,520 --> 00:29:59,240
Speaker 1: it's just incredible. I would never take it out of

622
00:29:59,240 --> 00:30:02,760
Speaker 1: the garage, but but that's that's the level of wealth out.

623
00:30:02,640 --> 00:30:03,000
Speaker 3: There, all right.

624
00:30:03,040 --> 00:30:05,880
Speaker 1: Joel, thanks so much for joining us. Joe Lovington, he's

625
00:30:05,880 --> 00:30:08,479
Speaker 1: a director of credit research for Bloomberg Intelligence, joining us

626
00:30:08,520 --> 00:30:11,840
Speaker 1: live here in the Bloomberg Interactive Broker studio. He doesn't

627
00:30:11,960 --> 00:30:14,200
Speaker 1: mail it in, slash phone it in like other directors

628
00:30:14,200 --> 00:30:16,080
Speaker 1: of research at BIS, so we appreciate.

629
00:30:15,680 --> 00:30:18,600
Speaker 3: Him coming into the office. It's good stuff. Don't get

630
00:30:18,640 --> 00:30:22,240
Speaker 3: him in trouble exactly. He gets in trouble all on

631
00:30:22,280 --> 00:30:22,600
Speaker 3: his own.

632
00:30:23,440 --> 00:30:27,080
Speaker 5: You're listening to the tape Catcher live program Bloomberg Markets

633
00:30:27,120 --> 00:30:30,520
Speaker 5: weekdays at ten am Eastern on Bloomberg Radio, the tune

634
00:30:30,560 --> 00:30:33,520
Speaker 5: in app, Bloomberg dot Com, and the Bloomberg Business App.

635
00:30:33,560 --> 00:30:36,360
Speaker 5: You can also listen live on Amazon Alexa from our

636
00:30:36,400 --> 00:30:41,440
Speaker 5: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

637
00:30:42,520 --> 00:30:43,280
Speaker 4: Matt, here's the story.

638
00:30:43,320 --> 00:30:45,479
Speaker 3: I have no idea what to do with. Here's the headline.

639
00:30:45,600 --> 00:30:51,840
Speaker 3: Chat GPT, can dcode fed speak predict stock moves from headlines?

640
00:30:52,240 --> 00:30:53,480
Speaker 3: Where are we going in the world?

641
00:30:53,880 --> 00:30:56,920
Speaker 2: I thought it was a great story, but it does

642
00:30:58,760 --> 00:31:01,400
Speaker 2: we do say in that story? Or Justina Lisa, as

643
00:31:01,400 --> 00:31:04,000
Speaker 2: she wrote the story for us at Bloomberg News that,

644
00:31:04,360 --> 00:31:10,640
Speaker 2: of course, there already are fairly intelligent algorithms out there

645
00:31:10,720 --> 00:31:13,040
Speaker 2: that decode this kind of stuff every day, right, I mean,

646
00:31:13,080 --> 00:31:19,200
Speaker 2: Bloomberg publishes stories that are intended for machines to consume,

647
00:31:19,600 --> 00:31:24,000
Speaker 2: to read and then parse. So this is just I

648
00:31:24,040 --> 00:31:27,280
Speaker 2: think from what I'm reading here a little bit of

649
00:31:27,320 --> 00:31:30,680
Speaker 2: progress in terms of the ability of these AI programs.

650
00:31:30,720 --> 00:31:33,520
Speaker 1: All right, Justina Lee, Bloomberg News, the reporter on this story,

651
00:31:33,600 --> 00:31:37,960
Speaker 1: joins us now from our London studio. Justina, fascinating story

652
00:31:38,000 --> 00:31:40,160
Speaker 1: you have here. Tell us kind of what's going on.

653
00:31:41,560 --> 00:31:44,560
Speaker 11: Yeah, I mean, like every other field, finance has seen

654
00:31:44,600 --> 00:31:48,480
Speaker 11: an avalanche of research about what chat GPT means for everyone,

655
00:31:49,000 --> 00:31:52,280
Speaker 11: and we're starting to see some preliminary results here And

656
00:31:52,320 --> 00:31:55,360
Speaker 11: this time my story is based on two academic papers.

657
00:31:55,680 --> 00:31:58,280
Speaker 11: I mean, one argues that you could use chat GPT

658
00:31:58,520 --> 00:32:01,920
Speaker 11: to classify if a sentence and a Federal Reserve statement

659
00:32:02,040 --> 00:32:05,080
Speaker 11: is dubvish and or hawkish, and in this case they

660
00:32:05,120 --> 00:32:09,720
Speaker 11: compare it to what a human analyst might classify. And

661
00:32:09,800 --> 00:32:12,400
Speaker 11: in the other case, they asked chat GPT if a

662
00:32:12,440 --> 00:32:15,360
Speaker 11: certain corporate news headline is good or bad for a stock,

663
00:32:15,440 --> 00:32:18,800
Speaker 11: and then they found that that answer correlated with how

664
00:32:18,840 --> 00:32:20,840
Speaker 11: the stock actually subsequently performed.

665
00:32:21,360 --> 00:32:24,239
Speaker 2: So yeah, I thought the FED paper was interesting. They

666
00:32:24,280 --> 00:32:30,840
Speaker 2: compare chat GPT's translation to that of Bryson. I guess

667
00:32:30,920 --> 00:32:33,040
Speaker 2: like an intern at the FED. He's like a twenty

668
00:32:33,080 --> 00:32:36,680
Speaker 2: four year old kid known for his intelligence and curiosity.

669
00:32:37,120 --> 00:32:41,000
Speaker 2: Did did chat GPT beat Bryson or did Bryson do better?

670
00:32:42,160 --> 00:32:42,400
Speaker 12: Well?

671
00:32:42,760 --> 00:32:46,160
Speaker 11: In this case, they used Bryson as a benchmark. So

672
00:32:46,240 --> 00:32:50,520
Speaker 11: the idea isn't necessarily that chat GPT is better than humans,

673
00:32:50,520 --> 00:32:54,240
Speaker 11: but that chat GPT was better than prior technologies and

674
00:32:54,320 --> 00:32:58,320
Speaker 11: coming close to human thought. And one amazing thing about

675
00:32:58,400 --> 00:33:01,680
Speaker 11: chat GPT is not only can it classify if the

676
00:33:01,720 --> 00:33:05,480
Speaker 11: sentence is devish or hawkish, it can even like explain

677
00:33:05,560 --> 00:33:08,840
Speaker 11: why it classified the sentence that way. And in this

678
00:33:08,880 --> 00:33:11,080
Speaker 11: particular case, they actually found that a lot of the

679
00:33:11,120 --> 00:33:15,320
Speaker 11: time chat GPT's explanations were very similar to Bryson, who

680
00:33:16,120 --> 00:33:19,600
Speaker 11: Google tells me is an actual research analyst at the

681
00:33:19,720 --> 00:33:23,440
Speaker 11: Richmond FED. So the two explanations were actually pretty similar.

682
00:33:23,680 --> 00:33:26,480
Speaker 3: So where does this technology go from here? Justina?

683
00:33:26,520 --> 00:33:29,120
Speaker 1: I mean, is this something that is just over time

684
00:33:29,240 --> 00:33:31,280
Speaker 1: every day it's the iterations are going to make it

685
00:33:31,320 --> 00:33:32,440
Speaker 1: better and better.

686
00:33:33,600 --> 00:33:35,960
Speaker 11: Yeah, I mean it does feel that way. I mean

687
00:33:36,000 --> 00:33:39,920
Speaker 11: currently it's very commonplace in hedge funds to use machines

688
00:33:40,000 --> 00:33:43,560
Speaker 11: to read earnings transcripts and tweets and headlines and to

689
00:33:43,680 --> 00:33:46,680
Speaker 11: kind of incorporate those into trading models. But I think

690
00:33:46,720 --> 00:33:49,200
Speaker 11: what all this research is telling us is that chat

691
00:33:49,240 --> 00:33:52,280
Speaker 11: GPT is a lot better than the early generation of

692
00:33:52,320 --> 00:33:56,400
Speaker 11: the technology in parsing nuance and context, and it can

693
00:33:56,560 --> 00:33:59,880
Speaker 11: kind of understand a lot of financial news even if

694
00:34:00,040 --> 00:34:03,000
Speaker 11: it hasn't been specifically trained for that purpose. So I

695
00:34:03,000 --> 00:34:05,240
Speaker 11: think what this tells is this there's probably going to

696
00:34:05,280 --> 00:34:08,600
Speaker 11: be even broader use of chat GPT for financial purposes,

697
00:34:09,000 --> 00:34:10,840
Speaker 11: and also that it's going to get a lot better

698
00:34:10,880 --> 00:34:11,880
Speaker 11: and a lot more accurate.

699
00:34:12,040 --> 00:34:15,600
Speaker 2: What's the difference between chat GPT and the technology that

700
00:34:16,320 --> 00:34:21,040
Speaker 2: you know, traders already use on a daily basis. You

701
00:34:21,160 --> 00:34:24,000
Speaker 2: talk to any humans in this market and they'll tell

702
00:34:24,040 --> 00:34:27,000
Speaker 2: you that the algorithms are taken over and it's just

703
00:34:27,080 --> 00:34:30,560
Speaker 2: computers that are doing all the trading at very high

704
00:34:30,600 --> 00:34:35,480
Speaker 2: frequencies often as well. So is chat GPT going to

705
00:34:35,480 --> 00:34:38,960
Speaker 2: be able to replace those or are those computer models

706
00:34:39,520 --> 00:34:43,200
Speaker 2: you know, made more specific to their jobs.

707
00:34:44,239 --> 00:34:44,479
Speaker 12: Yeah.

708
00:34:44,520 --> 00:34:46,720
Speaker 11: I think when it comes to you know, the first

709
00:34:46,760 --> 00:34:49,560
Speaker 11: generation of this technology, it was very much based on

710
00:34:49,760 --> 00:34:54,000
Speaker 11: understanding particular words and so sometimes it couldn't really understand

711
00:34:54,000 --> 00:34:58,120
Speaker 11: the context very well. Whereas chat GPT uses this technology

712
00:34:58,239 --> 00:35:01,399
Speaker 11: that kind of manages to understand and where the focus

713
00:35:01,440 --> 00:35:04,640
Speaker 11: of a piece of textas But I think if you

714
00:35:04,680 --> 00:35:07,200
Speaker 11: ask people who have been following this technology for years,

715
00:35:07,200 --> 00:35:10,799
Speaker 11: they would say it's not necessarily a huge breakthrough if

716
00:35:10,840 --> 00:35:14,040
Speaker 11: you're ready familiar with you know, for instance, Google's prior

717
00:35:14,120 --> 00:35:17,080
Speaker 11: model called bert. But I think what kind of chat

718
00:35:17,120 --> 00:35:20,040
Speaker 11: GPT has done is it's kind of opened up access

719
00:35:20,239 --> 00:35:23,799
Speaker 11: to this tech that makes it potentially possible for a

720
00:35:23,880 --> 00:35:27,399
Speaker 11: lot more financial firms to start doing this tech as well.

721
00:35:27,800 --> 00:35:31,160
Speaker 1: Is there the expectation that there will be some commercialization

722
00:35:31,520 --> 00:35:34,359
Speaker 1: of this type of technology so that if a hedge

723
00:35:34,360 --> 00:35:37,000
Speaker 1: fund wants to do it, I can go to you know,

724
00:35:37,120 --> 00:35:40,680
Speaker 1: chat Gptfinance dot com or something and you know, get

725
00:35:40,680 --> 00:35:41,719
Speaker 1: it from Amazon or something.

726
00:35:42,600 --> 00:35:42,799
Speaker 12: Yeah.

727
00:35:42,840 --> 00:35:45,080
Speaker 11: I mean obviously currently any one of us can go

728
00:35:45,120 --> 00:35:47,840
Speaker 11: on chat GPT and ask them to interpret a sentence

729
00:35:47,880 --> 00:35:49,719
Speaker 11: for us. But if you really kind of want to

730
00:35:49,800 --> 00:35:52,320
Speaker 11: use it on an industrial scale for a hedge fund,

731
00:35:52,520 --> 00:35:54,680
Speaker 11: I think you do need to reach out to the

732
00:35:54,719 --> 00:35:57,400
Speaker 11: firm for a license and something like that. And I

733
00:35:57,400 --> 00:36:00,319
Speaker 11: think that is kind of something that a lot of

734
00:36:00,360 --> 00:36:04,560
Speaker 11: Hetchman's are now considering, because then you know, chat GPD

735
00:36:04,680 --> 00:36:06,840
Speaker 11: is not open source and so in a way you

736
00:36:06,920 --> 00:36:09,840
Speaker 11: kind of need to give over your data to that software,

737
00:36:10,440 --> 00:36:12,920
Speaker 11: and a lot of firms will be pondering whether they

738
00:36:12,960 --> 00:36:15,600
Speaker 11: should do that or whether they should, you know, rely

739
00:36:15,719 --> 00:36:17,760
Speaker 11: on their own internally developed models.

740
00:36:17,800 --> 00:36:21,600
Speaker 2: What's the competition like right now, you mentioned Google's uh

741
00:36:22,000 --> 00:36:24,520
Speaker 2: chat or whatever we call it, Google's GPT.

742
00:36:24,680 --> 00:36:26,080
Speaker 3: What does GPT even stand for?

743
00:36:28,800 --> 00:36:32,440
Speaker 11: It stands for I'm forgetting GP but T is transformer.

744
00:36:32,640 --> 00:36:35,040
Speaker 3: I just nobody. I mean, it's funny.

745
00:36:35,040 --> 00:36:35,200
Speaker 11: You know.

746
00:36:35,280 --> 00:36:37,360
Speaker 2: Elon Musk was on Fox News and he said he

747
00:36:37,400 --> 00:36:40,200
Speaker 2: wants to start his own called truth GPT, and I

748
00:36:40,239 --> 00:36:42,440
Speaker 2: think he's trolling obviously.

749
00:36:42,960 --> 00:36:44,720
Speaker 3: But why do we have to put GPT?

750
00:36:45,000 --> 00:36:47,000
Speaker 2: I always wonder at the end of all these at

751
00:36:47,080 --> 00:36:50,160
Speaker 2: least Bert doesn't do that. What are the other competitors

752
00:36:50,200 --> 00:36:51,479
Speaker 2: to to open AI?

753
00:36:52,120 --> 00:36:54,040
Speaker 11: I just googled it. By the way, it's general to

754
00:36:54,320 --> 00:36:56,560
Speaker 11: pre trained transformer.

755
00:36:56,800 --> 00:36:58,120
Speaker 10: No wonder, no wonder.

756
00:36:58,280 --> 00:37:01,279
Speaker 11: Yes, yeah, I mean the last this major breakthrough is

757
00:37:01,320 --> 00:37:04,360
Speaker 11: Bert from Google. I kind of mentioned this at the

758
00:37:04,440 --> 00:37:06,960
Speaker 11: end of story more as a disclaimer. But Bloomberg now

759
00:37:07,120 --> 00:37:12,000
Speaker 11: has NP you know, a GPT as well. And the

760
00:37:12,080 --> 00:37:15,400
Speaker 11: significant thing about you know, Bloomberg's large language model is

761
00:37:15,400 --> 00:37:17,480
Speaker 11: that is specifically trained for finance.

762
00:37:17,800 --> 00:37:21,680
Speaker 2: But do we use. Have we licensed this from open ai?

763
00:37:22,360 --> 00:37:24,799
Speaker 2: Is this our own chat GPT? That seems to be

764
00:37:24,840 --> 00:37:27,719
Speaker 2: what everyone's doing, and I know they offer that with

765
00:37:27,760 --> 00:37:28,319
Speaker 2: GPT for.

766
00:37:29,360 --> 00:37:32,120
Speaker 11: Yeah, I think it's Bloomberg's own thing and it's very new.

767
00:37:32,560 --> 00:37:35,400
Speaker 11: They release the academic paper on it, I think, just

768
00:37:35,520 --> 00:37:36,440
Speaker 11: at the end of March.

769
00:37:37,200 --> 00:37:37,840
Speaker 3: Interesting stuff.

770
00:37:37,880 --> 00:37:39,759
Speaker 1: So I guess the word, you know, I think we're

771
00:37:39,760 --> 00:37:42,360
Speaker 1: going to see more and more of academic support for

772
00:37:42,400 --> 00:37:44,840
Speaker 1: this and maybe driving it forward because it just seems

773
00:37:44,880 --> 00:37:45,400
Speaker 1: like it's.

774
00:37:45,840 --> 00:37:49,640
Speaker 2: Well in Microsoft, which obviously is heavily invested in open ai,

775
00:37:49,880 --> 00:37:52,359
Speaker 2: has been using it for being and now I all

776
00:37:52,400 --> 00:37:54,760
Speaker 2: of a sudden find being being a lot more useful

777
00:37:54,800 --> 00:37:55,360
Speaker 2: than Google.

778
00:37:55,680 --> 00:37:58,319
Speaker 3: Yeah, I mean, that's that's Google's taking a hit on that.

779
00:37:58,480 --> 00:38:02,439
Speaker 1: So Justina, what should we look for next from chat

780
00:38:02,480 --> 00:38:04,120
Speaker 1: GPT in finance?

781
00:38:05,160 --> 00:38:07,960
Speaker 11: Yeah, I mean there's been so many papers on this subject,

782
00:38:08,480 --> 00:38:11,759
Speaker 11: you know. I've read one where the researchers started using

783
00:38:11,840 --> 00:38:15,879
Speaker 11: chat GPT to even design an investing strategy, and they

784
00:38:15,920 --> 00:38:19,080
Speaker 11: said that it was better than random, which is sort

785
00:38:19,120 --> 00:38:22,200
Speaker 11: of a low bar, but at least there's that. But

786
00:38:22,280 --> 00:38:24,759
Speaker 11: I think generally, you know, people have talked about how

787
00:38:24,880 --> 00:38:28,000
Speaker 11: it could really jump start a lot of financial research

788
00:38:28,120 --> 00:38:30,680
Speaker 11: is a very good at summarizing thing. So, if anything,

789
00:38:30,680 --> 00:38:33,160
Speaker 11: it will speed up a lot of process good stuff.

790
00:38:33,160 --> 00:38:35,400
Speaker 2: If it's better than a monkey throwing darts, that's actually

791
00:38:35,400 --> 00:38:36,160
Speaker 2: a stretty high bar.

792
00:38:36,320 --> 00:38:39,040
Speaker 1: That is Justina Lee. She's a market's quant reporter for

793
00:38:39,080 --> 00:38:41,120
Speaker 1: Bloomberg News. She's based in London and she's got this

794
00:38:41,200 --> 00:38:44,040
Speaker 1: great story out on the Bloomberg terminal. To check it

795
00:38:44,080 --> 00:38:48,400
Speaker 1: out talking about chart GPT can de code fed speak.

796
00:38:48,400 --> 00:38:48,920
Speaker 3: How about that?

797
00:38:49,280 --> 00:38:52,440
Speaker 5: You're listening to the tape Cat's are live program Bloomberg

798
00:38:52,480 --> 00:38:56,080
Speaker 5: Markets weekdays at ten am Eastern on Bloomberg Radio, the

799
00:38:56,120 --> 00:38:58,160
Speaker 5: tune in app, Bloomberg dot Com.

800
00:38:57,920 --> 00:38:59,359
Speaker 6: And the Bloomberg Business App.

801
00:38:59,400 --> 00:39:02,200
Speaker 5: You can also and live on Amazon Alexa from our

802
00:39:02,239 --> 00:39:08,080
Speaker 5: flagship New York station just Say Alexa playing Bloomberg eleven thirty.

803
00:39:07,760 --> 00:39:10,840
Speaker 1: Apple Computer make it a big splash or continuing to

804
00:39:10,880 --> 00:39:13,040
Speaker 1: increase their investments in India.

805
00:39:13,160 --> 00:39:14,680
Speaker 3: They actually open their first door in India.

806
00:39:14,719 --> 00:39:15,120
Speaker 10: I'm surprised.

807
00:39:15,120 --> 00:39:16,839
Speaker 1: I would have thought they would have had a store there,

808
00:39:16,840 --> 00:39:18,480
Speaker 1: but they opened their first door. And Tim Cook I

809
00:39:18,480 --> 00:39:21,280
Speaker 1: saw some video of him in India greeting folks anaa

810
00:39:21,320 --> 00:39:23,440
Speaker 1: Grana senior techanas Bloomberg Intelligence.

811
00:39:23,480 --> 00:39:26,440
Speaker 3: He's been following Apple for a long time. So, Honurra,

812
00:39:26,960 --> 00:39:27,399
Speaker 3: what's the.

813
00:39:27,360 --> 00:39:30,800
Speaker 1: Strategy here for for Apple and India broadly defined?

814
00:39:32,200 --> 00:39:36,560
Speaker 12: So, Paul, it's so when it comes to Apple in India, India,

815
00:39:36,719 --> 00:39:39,239
Speaker 12: Apple doesn't have such high market share in India, and

816
00:39:39,560 --> 00:39:42,080
Speaker 12: the reason for that is these phones are fairly expensive

817
00:39:42,440 --> 00:39:45,000
Speaker 12: and flankbet you know, even ninety percent of don't even

818
00:39:45,040 --> 00:39:48,120
Speaker 12: qualify for you know, Apples to be competing in that

819
00:39:48,239 --> 00:39:50,880
Speaker 12: market because they are all sub three hundred dollars phones.

820
00:39:51,320 --> 00:39:53,960
Speaker 12: But as you know, you and I've discussed in previous

821
00:39:54,520 --> 00:39:58,799
Speaker 12: discussions that as this middle class becomes more rich, they

822
00:39:58,840 --> 00:40:01,840
Speaker 12: are more inclined to buy a luxury product like Apple

823
00:40:02,120 --> 00:40:04,800
Speaker 12: compared to a lower and smartphone.

824
00:40:05,120 --> 00:40:08,680
Speaker 2: So I can't believe that this is their first store

825
00:40:09,520 --> 00:40:12,479
Speaker 2: in India. I mean I feel like people, yeah, more

826
00:40:12,680 --> 00:40:15,359
Speaker 2: well over a billion people, right, Honor rag and talk

827
00:40:15,400 --> 00:40:17,600
Speaker 2: to us about the size of the rising middle class.

828
00:40:17,600 --> 00:40:19,120
Speaker 3: What are we talking about in terms of numbers?

829
00:40:20,120 --> 00:40:22,760
Speaker 12: Yeah, So, first and foremost, I mean, you can buy

830
00:40:22,840 --> 00:40:25,759
Speaker 12: a you know, Apple phone in India through partners and

831
00:40:25,800 --> 00:40:28,360
Speaker 12: through online channels. So it's not as if that an

832
00:40:28,560 --> 00:40:31,520
Speaker 12: Indian consumer cannot buy it it was the first Apple

833
00:40:31,600 --> 00:40:35,799
Speaker 12: loan store right there. So so making that distinction in

834
00:40:35,880 --> 00:40:37,759
Speaker 12: terms of the market size, you know, this is a

835
00:40:38,000 --> 00:40:40,600
Speaker 12: contribute over three hundred and fifty million people or somewhere

836
00:40:40,640 --> 00:40:44,799
Speaker 12: in that range of mid middle class and extremely you

837
00:40:44,800 --> 00:40:47,680
Speaker 12: know grow that that particular portion is growing at a

838
00:40:47,760 --> 00:40:50,800
Speaker 12: much faster pace in terms of the purchasing power parity,

839
00:40:51,280 --> 00:40:55,040
Speaker 12: largely thanks to a very large you know, technology boom

840
00:40:55,080 --> 00:40:58,600
Speaker 12: and the software ecosystem with most of the you know,

841
00:40:58,760 --> 00:41:02,560
Speaker 12: technology companies in the West have very large development centers

842
00:41:02,600 --> 00:41:06,040
Speaker 12: in India. And it's those young people who are driving

843
00:41:06,680 --> 00:41:08,960
Speaker 12: you know, the spending of luxury products, and I think

844
00:41:08,960 --> 00:41:11,439
Speaker 12: that is the real big opportunity in our view.

845
00:41:11,600 --> 00:41:14,360
Speaker 2: So it's I think it's over one point four billion

846
00:41:14,400 --> 00:41:18,920
Speaker 2: people in total, right, three hundred fifty million in the

847
00:41:18,920 --> 00:41:19,520
Speaker 2: middle class.

848
00:41:19,640 --> 00:41:21,799
Speaker 3: That's more people than in all of America.

849
00:41:22,600 --> 00:41:27,360
Speaker 2: And yes, and they care this, this giant middle class.

850
00:41:27,400 --> 00:41:31,400
Speaker 2: They care about high tech products, right, they want this stuff.

851
00:41:32,560 --> 00:41:34,919
Speaker 12: No, I agree, But you know, I would also say

852
00:41:34,960 --> 00:41:37,280
Speaker 12: that they have been a lot more cost conscious about

853
00:41:37,480 --> 00:41:39,960
Speaker 12: the products because if they can find a you know,

854
00:41:40,040 --> 00:41:43,520
Speaker 12: Android product from Samsung or another firm or another company

855
00:41:43,520 --> 00:41:46,080
Speaker 12: that is equally or better you know, they are not

856
00:41:46,360 --> 00:41:49,120
Speaker 12: just bound by the brand itself. But you know, Apple

857
00:41:49,239 --> 00:41:51,680
Speaker 12: is a luxury brand. It is somebody something that people

858
00:41:51,800 --> 00:41:54,920
Speaker 12: aspire for. And I think, you know, Apple will build

859
00:41:55,000 --> 00:41:57,680
Speaker 12: products in India, and I think that way, you know,

860
00:41:57,719 --> 00:41:59,680
Speaker 12: it is possible down the road the products may be

861
00:41:59,760 --> 00:42:02,040
Speaker 12: slightly cheaper if they are made in India.

862
00:42:02,640 --> 00:42:03,879
Speaker 3: And they'll be building them there.

863
00:42:03,920 --> 00:42:09,000
Speaker 2: Though I think, what's the output of iPhones out of

864
00:42:09,000 --> 00:42:12,000
Speaker 2: India was like seven times more this year than they

865
00:42:12,040 --> 00:42:13,239
Speaker 2: made two years ago.

866
00:42:13,920 --> 00:42:16,440
Speaker 12: So Apple doesn't disclose that information, and you know you'll

867
00:42:16,480 --> 00:42:19,320
Speaker 12: have to rely from a lot of media, you know, channels,

868
00:42:19,320 --> 00:42:22,280
Speaker 12: but you know, we think it's still less than five percent,

869
00:42:22,440 --> 00:42:25,160
Speaker 12: a lot less than that at this point because ninety

870
00:42:25,160 --> 00:42:27,000
Speaker 12: eight percent of the phones as of last year were

871
00:42:27,000 --> 00:42:29,879
Speaker 12: assembled in China. Now that is going to change over

872
00:42:29,920 --> 00:42:32,560
Speaker 12: the next decade. But you know, one of the things

873
00:42:32,560 --> 00:42:34,399
Speaker 12: I would say is if if a phone is built

874
00:42:34,440 --> 00:42:37,239
Speaker 12: in India, I think in the long run, that would

875
00:42:37,280 --> 00:42:40,239
Speaker 12: be more palatable for the Indian audience because it will

876
00:42:40,280 --> 00:42:43,120
Speaker 12: not have export duties and other things that go with it.

877
00:42:43,719 --> 00:42:49,080
Speaker 1: An maybe two or three iPhone upgrade cycles ago, Apple introduced,

878
00:42:49,320 --> 00:42:52,960
Speaker 1: you know, a lower price model, specifically to appeal towards

879
00:42:53,320 --> 00:42:56,520
Speaker 1: you know, some of these emerging markets that never really

880
00:42:56,560 --> 00:42:58,560
Speaker 1: took off, do they do they still have that that

881
00:42:58,640 --> 00:43:00,520
Speaker 1: they still want to attack them a market that way?

882
00:43:00,560 --> 00:43:01,799
Speaker 1: Are they waiting for people.

883
00:43:01,560 --> 00:43:04,880
Speaker 2: To just what's that cominie or the SE something the

884
00:43:05,000 --> 00:43:06,400
Speaker 2: ion are which one is that?

885
00:43:07,120 --> 00:43:07,960
Speaker 8: Yeah, it is the s C.

886
00:43:08,160 --> 00:43:10,600
Speaker 12: But you know, Paul, if I look at the smartphone

887
00:43:10,600 --> 00:43:13,080
Speaker 12: install based in India, it's let's say about six hundred

888
00:43:13,120 --> 00:43:16,680
Speaker 12: million units out there. Ninety percent of them fall below

889
00:43:16,719 --> 00:43:19,719
Speaker 12: the three hundred you know, dollars price band. So even

890
00:43:19,760 --> 00:43:22,719
Speaker 12: the SE doesn't qualify for you know what you're saying,

891
00:43:22,760 --> 00:43:25,520
Speaker 12: because even that falls into that you know, thirty five

892
00:43:25,520 --> 00:43:28,879
Speaker 12: percent bucket or so, thirty five million unit bucket or so.

893
00:43:29,680 --> 00:43:32,000
Speaker 12: And I think that really is going to change over

894
00:43:32,080 --> 00:43:35,040
Speaker 12: time as this middle class becomes a little more affluent.

895
00:43:35,280 --> 00:43:37,560
Speaker 12: You know. We think, for example, the current you know,

896
00:43:37,640 --> 00:43:40,280
Speaker 12: revenue run rate in India is is is roughly around

897
00:43:40,280 --> 00:43:43,520
Speaker 12: six billion just on the on the iPhone itself. We think,

898
00:43:43,680 --> 00:43:46,520
Speaker 12: you know, that could grow at somewhere around sixteen seventeen

899
00:43:46,560 --> 00:43:49,799
Speaker 12: percent over the next decade, you know, reaching about thirty

900
00:43:49,840 --> 00:43:51,080
Speaker 12: billion by twenty thirty two.

901
00:43:51,960 --> 00:43:53,839
Speaker 3: Some good numbers there, buddy, that's some you can make

902
00:43:53,880 --> 00:43:54,400
Speaker 3: some money on that.

903
00:43:54,520 --> 00:43:56,840
Speaker 1: Off of that on ur RANA do in the research

904
00:43:56,880 --> 00:44:01,239
Speaker 1: analysis with the numbers on RANA Senior Techs for Bloomberg Intelligence.

905
00:44:01,239 --> 00:44:04,160
Speaker 1: Of course, you can find all of Bloomberg Intelligence research

906
00:44:04,520 --> 00:44:07,120
Speaker 1: really simple, b I, go boom.

907
00:44:07,160 --> 00:44:07,880
Speaker 3: It gets you all there.

908
00:44:07,920 --> 00:44:09,920
Speaker 1: Then you just click on industry, click on analysts, whatever

909
00:44:09,960 --> 00:44:12,239
Speaker 1: you want to do, stick in a ticker symbol, and

910
00:44:12,400 --> 00:44:15,760
Speaker 1: get all the research on their equity research, credit research.

911
00:44:15,760 --> 00:44:18,759
Speaker 1: And as we learned today, we've got litigation research there.

912
00:44:18,840 --> 00:44:20,680
Speaker 1: So if you want to bone up on some of

913
00:44:20,680 --> 00:44:24,880
Speaker 1: the litigation against certain companies out there, like Fox and

914
00:44:25,000 --> 00:44:27,839
Speaker 1: its defamation suit that it's going through starting today, that's

915
00:44:27,840 --> 00:44:29,040
Speaker 1: where you get all that research.

916
00:44:29,560 --> 00:44:30,480
Speaker 3: B I go on the.

917
00:44:30,480 --> 00:44:34,040
Speaker 1: Bloomberg Terminal exclusively for Bloomberg Terminal customers.

918
00:44:34,239 --> 00:44:37,840
Speaker 5: You're listening to the tape Kensur Live program Bloomberg Markets

919
00:44:37,920 --> 00:44:41,319
Speaker 5: weekdays at ten am Eastern on Bloomberg Radio, the tune

920
00:44:41,360 --> 00:44:43,040
Speaker 5: in app, Bloomberg dot Com, and.

921
00:44:43,000 --> 00:44:44,319
Speaker 6: The Bloomberg Business app.

922
00:44:44,360 --> 00:44:47,160
Speaker 5: You can also listen live on Amazon Alexa from our

923
00:44:47,200 --> 00:44:52,160
Speaker 5: flagship New York station. Just say Alexa play Bloomberg eleven thirty.

924
00:44:53,880 --> 00:44:57,200
Speaker 1: I was just showing Matt the Richmond Spider kind of

925
00:44:57,239 --> 00:44:59,320
Speaker 1: little hand signal.

926
00:44:59,080 --> 00:45:01,200
Speaker 3: And then you explain it to me. But I obviously

927
00:45:01,239 --> 00:45:03,640
Speaker 3: get it, and you've got it. Okay, thank you. Some

928
00:45:03,680 --> 00:45:05,880
Speaker 3: people don't, but you're on top of that stuff. All right,

929
00:45:05,960 --> 00:45:06,960
Speaker 3: let's talk markets here.

930
00:45:07,600 --> 00:45:12,120
Speaker 1: We're gonna welcome Karen pie In pie Pay parent Pay,

931
00:45:12,200 --> 00:45:13,960
Speaker 1: thank you, I am pay, I am pay. Got you

932
00:45:14,040 --> 00:45:18,000
Speaker 1: designed the Guggenheim thing and the pyramid in front of

933
00:45:18,040 --> 00:45:20,359
Speaker 1: the oh the loose right, that's right, all right here,

934
00:45:20,440 --> 00:45:23,279
Speaker 1: just so wordly, Karen Pay, head of portfolio management and

935
00:45:23,360 --> 00:45:27,160
Speaker 1: Equities at Fiduciary Trust International. Karen, we're kind of getting

936
00:45:27,160 --> 00:45:30,080
Speaker 1: into the thick of earning season. I'm gonna for like

937
00:45:30,200 --> 00:45:32,920
Speaker 1: fifteen seconds. I'm gonna get away from the Fed. What

938
00:45:32,960 --> 00:45:35,279
Speaker 1: are you looking for in earnings? How concerned are you

939
00:45:35,280 --> 00:45:37,080
Speaker 1: about earnings risk in this market?

940
00:45:37,760 --> 00:45:40,080
Speaker 13: It's a great question. So we've been saying that the

941
00:45:40,120 --> 00:45:43,160
Speaker 13: markets should be shifting their attention to fundamentals as opposed

942
00:45:43,200 --> 00:45:47,920
Speaker 13: to the FED, which continues too. Yes, but it continues

943
00:45:47,960 --> 00:45:50,239
Speaker 13: to be a driving force and continues to be a

944
00:45:50,239 --> 00:45:53,160
Speaker 13: theme in the market. But in terms of earnings, we

945
00:45:53,239 --> 00:45:57,759
Speaker 13: are concerned that profit margins are going to get squeeze.

946
00:45:58,360 --> 00:46:00,680
Speaker 13: You know, companies have been able to raise prices over

947
00:46:00,719 --> 00:46:04,520
Speaker 13: the past year. They've done really a good job, especially

948
00:46:04,600 --> 00:46:07,640
Speaker 13: the companies that have pricing power. But we are seeing

949
00:46:07,719 --> 00:46:09,800
Speaker 13: that companies are going to struggle a little bit in

950
00:46:09,920 --> 00:46:12,960
Speaker 13: terms of maintaining their their margins. And we are hearing

951
00:46:13,000 --> 00:46:16,680
Speaker 13: about companies talking about cost cutting. And you know, even

952
00:46:16,719 --> 00:46:20,440
Speaker 13: when we look at the banks earning, so far, margins

953
00:46:20,440 --> 00:46:23,360
Speaker 13: have been held up, but they're doing so with the

954
00:46:23,440 --> 00:46:28,879
Speaker 13: combination of better revenues, better better income interest income from

955
00:46:28,920 --> 00:46:32,120
Speaker 13: the banks, but also there's a lot of discussions about

956
00:46:32,160 --> 00:46:35,320
Speaker 13: cost reduction and cost containment. I think that will continue

957
00:46:35,320 --> 00:46:36,319
Speaker 13: to be a theme this year.

958
00:46:36,400 --> 00:46:40,840
Speaker 2: We heard someone today suggest lowering headcount by using AI

959
00:46:42,160 --> 00:46:44,359
Speaker 2: on Wall Street. That's at one of the biggest banks

960
00:46:44,360 --> 00:46:45,480
Speaker 2: in Charlotte.

961
00:46:45,640 --> 00:46:45,839
Speaker 3: Right.

962
00:46:46,560 --> 00:46:51,560
Speaker 2: So, in terms of the fed's impact, everyone seems to

963
00:46:51,560 --> 00:46:55,920
Speaker 2: be pricing in, you know, one hike and then a pause. Okay,

964
00:46:56,440 --> 00:47:00,000
Speaker 2: if you look at futures and options, you could see

965
00:47:00,160 --> 00:47:02,400
Speaker 2: that they're pricing in cuts, but I don't think a

966
00:47:02,440 --> 00:47:03,839
Speaker 2: lot of investors really buy that.

967
00:47:03,840 --> 00:47:04,680
Speaker 10: What's your outlook?

968
00:47:05,360 --> 00:47:08,200
Speaker 13: Sure, So, markets I think are backing in about eighty

969
00:47:08,239 --> 00:47:13,440
Speaker 13: five percent probability of another increase in May. So, but

970
00:47:13,640 --> 00:47:17,319
Speaker 13: I think, you know, there has been some expectation of

971
00:47:17,440 --> 00:47:20,879
Speaker 13: rate cuts for this year that I think helped drive

972
00:47:20,960 --> 00:47:25,200
Speaker 13: markets up during this first quarter. Our outlook is that

973
00:47:25,320 --> 00:47:28,560
Speaker 13: we probably won't see that occur until maybe next year.

974
00:47:29,200 --> 00:47:32,680
Speaker 13: And you know, my view is that inflation probably will

975
00:47:32,680 --> 00:47:35,239
Speaker 13: be a bigger challenge for the FED. I think, you know,

976
00:47:35,280 --> 00:47:38,080
Speaker 13: the label markets have been very strong so far, it's

977
00:47:38,120 --> 00:47:42,560
Speaker 13: going to be a gradual grind lower and that means

978
00:47:42,560 --> 00:47:45,399
Speaker 13: that inflation isn't going to come down as quickly as

979
00:47:45,480 --> 00:47:48,920
Speaker 13: maybe the markets expect. And so as far as rate

980
00:47:49,000 --> 00:47:52,319
Speaker 13: cuts go, I don't see that happening anytime soon.

981
00:47:53,160 --> 00:47:53,440
Speaker 10: Karen.

982
00:47:53,480 --> 00:47:56,200
Speaker 1: You know, I think what a lot of investors, you know,

983
00:47:56,400 --> 00:47:59,000
Speaker 1: really for the last twelve thirteen years, they've been used

984
00:47:59,040 --> 00:48:01,720
Speaker 1: to this technology that big tech companies leading the way,

985
00:48:02,400 --> 00:48:03,480
Speaker 1: and that.

986
00:48:03,360 --> 00:48:06,120
Speaker 3: Didn't work in twenty twenty two. It's kind of come back.

987
00:48:06,000 --> 00:48:08,799
Speaker 1: Here in twenty three, here the big tech names are

988
00:48:08,840 --> 00:48:11,400
Speaker 1: leading the way. How important is it for you and

989
00:48:11,520 --> 00:48:13,959
Speaker 1: just as you look at the broader market for tech

990
00:48:14,040 --> 00:48:16,240
Speaker 1: to be a leader, or can this market move higher

991
00:48:16,600 --> 00:48:19,359
Speaker 1: if it's something else industrials or small cap or something

992
00:48:19,360 --> 00:48:20,719
Speaker 1: like that, how do you think about leadership.

993
00:48:20,920 --> 00:48:23,919
Speaker 13: Sure. Yeah, So I think that market's in the very

994
00:48:23,920 --> 00:48:28,560
Speaker 13: short time period can sort of disconnect from fundamentals. So

995
00:48:28,719 --> 00:48:34,319
Speaker 13: last year technology stocks didn't do well because they were

996
00:48:34,320 --> 00:48:36,560
Speaker 13: fighting the tape, the FEDS tape in terms of rising

997
00:48:36,600 --> 00:48:39,839
Speaker 13: interest rates. You know, it's the long duration trade that

998
00:48:39,880 --> 00:48:43,840
Speaker 13: didn't work last year, given that technology companies have a

999
00:48:43,840 --> 00:48:46,680
Speaker 13: lot of future growth that had to be discounted back

1000
00:48:46,719 --> 00:48:49,920
Speaker 13: at higher rates. Valuation is a big part of it,

1001
00:48:49,960 --> 00:48:52,920
Speaker 13: and I think that in this market we have to

1002
00:48:52,920 --> 00:48:56,360
Speaker 13: pay attention to valuation in the very short term, like

1003
00:48:56,400 --> 00:48:58,600
Speaker 13: what happened in the first quarter because of the shift

1004
00:48:58,640 --> 00:49:02,640
Speaker 13: and rate expectations. That helped some of the valuation recovery

1005
00:49:02,680 --> 00:49:05,960
Speaker 13: in the tech stocks. But from a fundamental standpoint, we

1006
00:49:06,040 --> 00:49:10,400
Speaker 13: are seeing, for example, companies talking about reducing CAPAC spending.

1007
00:49:11,360 --> 00:49:15,280
Speaker 13: Taiwan Semi actually just this week talked about reducing CAPAX.

1008
00:49:15,800 --> 00:49:18,680
Speaker 13: So we're sort of on the other side of a

1009
00:49:18,719 --> 00:49:23,080
Speaker 13: lot of the spend that happened during the pandemic when

1010
00:49:23,080 --> 00:49:25,920
Speaker 13: it comes to companies you know, going all digital, right

1011
00:49:25,960 --> 00:49:28,560
Speaker 13: so there was a lot of accelerated spending in technology.

1012
00:49:28,880 --> 00:49:30,920
Speaker 13: I think we're on the other side of that right now.

1013
00:49:31,160 --> 00:49:33,879
Speaker 13: So it's going to be difficult as a longer term

1014
00:49:33,920 --> 00:49:38,839
Speaker 13: trend to see sustainable rallies in technology until either the

1015
00:49:38,840 --> 00:49:42,759
Speaker 13: FED pivots where we start to see rate cuts, or

1016
00:49:42,840 --> 00:49:47,680
Speaker 13: until we start to see bottoming in terms of fundamentals

1017
00:49:47,960 --> 00:49:51,400
Speaker 13: and we start to see maybe some increases in growth

1018
00:49:51,440 --> 00:49:53,320
Speaker 13: expectations or capac spending.

1019
00:49:53,360 --> 00:49:55,680
Speaker 2: How do you feel about the consumer because a lot

1020
00:49:55,680 --> 00:49:58,719
Speaker 2: of tension has been paid to bank balances. You know,

1021
00:49:58,760 --> 00:50:03,040
Speaker 2: previously we were all concerned with how much money was

1022
00:50:03,120 --> 00:50:06,920
Speaker 2: in savings accounts and checking accounts just to gauge the

1023
00:50:06,920 --> 00:50:12,839
Speaker 2: health of the consumer his cup overfloweth right, But now

1024
00:50:12,880 --> 00:50:16,040
Speaker 2: we were all just looking at you know, deposit outflows

1025
00:50:16,040 --> 00:50:18,040
Speaker 2: to gauge the health of the banks. I think we've

1026
00:50:18,040 --> 00:50:20,319
Speaker 2: gotten back. We've put that banking issue in the rear

1027
00:50:20,400 --> 00:50:24,000
Speaker 2: view mirror, and now we're concerned about the consumer credit

1028
00:50:24,080 --> 00:50:25,920
Speaker 2: use and the possibility of a credit crunch.

1029
00:50:26,960 --> 00:50:29,760
Speaker 13: All great questions. So I think a lot of about

1030
00:50:29,920 --> 00:50:34,000
Speaker 13: cash flow, and you know, I think we just talked

1031
00:50:34,040 --> 00:50:37,080
Speaker 13: about CAPEX and companies are in a position where they

1032
00:50:37,200 --> 00:50:40,000
Speaker 13: probably have less cash flow. We saw that impact some

1033
00:50:40,080 --> 00:50:42,640
Speaker 13: of the you know, private equity world and some of

1034
00:50:42,640 --> 00:50:45,360
Speaker 13: the venture companies, which caused the problems that you know

1035
00:50:45,440 --> 00:50:49,400
Speaker 13: we saw at Silicon Valley Bank. In terms of the consumer,

1036
00:50:49,480 --> 00:50:51,759
Speaker 13: I think that consumers are also at a point where

1037
00:50:51,800 --> 00:50:55,200
Speaker 13: as you pointed out low savings rate right we also

1038
00:50:55,320 --> 00:50:58,520
Speaker 13: have a cliff that is happening in terms of the

1039
00:50:58,560 --> 00:51:02,279
Speaker 13: support that consumers see from the government, So there's not

1040
00:51:02,360 --> 00:51:05,880
Speaker 13: going to be those tailwinds for the consumer. I think

1041
00:51:06,440 --> 00:51:08,799
Speaker 13: the one positive for the consumer right now is that

1042
00:51:08,840 --> 00:51:11,759
Speaker 13: the labor market is still very strong, and so we're

1043
00:51:11,760 --> 00:51:15,040
Speaker 13: not expecting the consumer to createor here, but we do

1044
00:51:15,120 --> 00:51:19,080
Speaker 13: expect consumer spending to slow. I think it also takes

1045
00:51:19,080 --> 00:51:22,120
Speaker 13: a long time for behavior to change. So everyone's still

1046
00:51:22,200 --> 00:51:24,799
Speaker 13: kind of on a high from the reopening of the

1047
00:51:25,320 --> 00:51:28,480
Speaker 13: economy right now, and there's still a lot of spend

1048
00:51:28,480 --> 00:51:30,840
Speaker 13: on travel and restaurants, and we see all of that,

1049
00:51:31,320 --> 00:51:35,919
Speaker 13: but the lower income consumer is spending on credit card

1050
00:51:35,960 --> 00:51:39,279
Speaker 13: debt right now. The higher end consumer still has a

1051
00:51:39,320 --> 00:51:45,480
Speaker 13: wherewithal to spend. But we expect that the pressures will

1052
00:51:45,520 --> 00:51:48,719
Speaker 13: be coming forth in the next several quarters that there

1053
00:51:48,719 --> 00:51:51,400
Speaker 13: would be more pressure on the consumer.

1054
00:51:52,280 --> 00:51:54,040
Speaker 1: Karen, I see kind of it in your notes here.

1055
00:51:54,080 --> 00:51:58,080
Speaker 1: You have an ESG investing license now, Matt and I

1056
00:51:58,239 --> 00:52:00,920
Speaker 1: kind of share I think what is a growing skepticism

1057
00:52:01,000 --> 00:52:04,200
Speaker 1: of ESG investing. We're not like the state of Florida

1058
00:52:04,280 --> 00:52:06,520
Speaker 1: or anything, but you know, it's it's it's there, under

1059
00:52:06,560 --> 00:52:09,400
Speaker 1: the under the under the surface here. How do you

1060
00:52:09,440 --> 00:52:12,760
Speaker 1: guys at Fiduciary Trust think about ESG.

1061
00:52:13,200 --> 00:52:17,560
Speaker 13: Sure, it certainly is a topic of interest. I would

1062
00:52:17,640 --> 00:52:20,720
Speaker 13: say that a lot, a lot more of our clients

1063
00:52:20,760 --> 00:52:25,680
Speaker 13: were interested in ESG investing and impact investing. I think

1064
00:52:25,719 --> 00:52:29,080
Speaker 13: some of that maybe definitely last year took a little

1065
00:52:29,120 --> 00:52:32,319
Speaker 13: bit of a turn in terms of you know, ESG

1066
00:52:33,080 --> 00:52:38,279
Speaker 13: investing has been somewhat correlated to growth investing and correlated

1067
00:52:38,400 --> 00:52:42,760
Speaker 13: with technology. The way that we think about ESG investing

1068
00:52:42,880 --> 00:52:46,160
Speaker 13: is we actually do believe that when you look at

1069
00:52:46,800 --> 00:52:52,160
Speaker 13: the ESG factors like environmental factors, social factors and incorporate

1070
00:52:52,200 --> 00:52:56,480
Speaker 13: that into your thinking, incorporate that into your analysis, it

1071
00:52:56,560 --> 00:53:01,120
Speaker 13: actually should help yield longer term better results. And the

1072
00:53:01,200 --> 00:53:03,759
Speaker 13: reason for that, and you know, sort of kind of

1073
00:53:03,760 --> 00:53:07,880
Speaker 13: fold into our investment philosophy, is that we believe companies

1074
00:53:07,920 --> 00:53:11,720
Speaker 13: that are forward thinking and think about their business risks

1075
00:53:11,719 --> 00:53:14,359
Speaker 13: and to understand the trends that are occurring in the

1076
00:53:14,400 --> 00:53:19,680
Speaker 13: marketplace will be in a better position to handle those

1077
00:53:19,800 --> 00:53:22,640
Speaker 13: risks and to capture the opportunities when it comes to

1078
00:53:22,760 --> 00:53:26,719
Speaker 13: changing consumer behavior, changing consumer trends, and even some of

1079
00:53:26,760 --> 00:53:30,279
Speaker 13: the you know, risks that might be coming forth from

1080
00:53:30,280 --> 00:53:34,400
Speaker 13: a regulatory standpoint or just from what's happening from a

1081
00:53:34,440 --> 00:53:36,759
Speaker 13: business standpoint. So when you do it right, it can

1082
00:53:36,840 --> 00:53:40,800
Speaker 13: be very beneficial. I think the issue is that you

1083
00:53:41,200 --> 00:53:44,839
Speaker 13: do you know. I think what raises your question is

1084
00:53:45,000 --> 00:53:49,279
Speaker 13: are people labeling ESG investing accurately?

1085
00:53:49,680 --> 00:53:49,880
Speaker 12: You know?

1086
00:53:49,920 --> 00:53:52,600
Speaker 13: And I think it's important for investors to really understand

1087
00:53:52,920 --> 00:53:55,080
Speaker 13: what's underneath the ESG labels.

1088
00:53:55,640 --> 00:53:55,839
Speaker 3: Yeah.

1089
00:53:55,920 --> 00:53:59,680
Speaker 2: Well, I mean some people will put big integrated oil

1090
00:54:00,160 --> 00:54:04,520
Speaker 2: producers in their ESG basket, which I can understand. You

1091
00:54:04,560 --> 00:54:08,839
Speaker 2: can kind of do some mental gymnastics and justify it,

1092
00:54:08,920 --> 00:54:11,439
Speaker 2: but at the end of the day, I mean they're

1093
00:54:11,520 --> 00:54:13,080
Speaker 2: mining for hydrocarbons, right.

1094
00:54:13,800 --> 00:54:16,920
Speaker 13: Yeah, So it's important to really understand again, you know,

1095
00:54:16,960 --> 00:54:19,960
Speaker 13: to your point, what is in the portfolio, what is

1096
00:54:20,000 --> 00:54:25,120
Speaker 13: in the investment strategy? And ESG investing doesn't necessarily mean

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Speaker 13: that you exclude fossil fuels or exclude energy stocks, but

1098
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Speaker 13: it's about finding the highest quality companies and to understand

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Speaker 13: the level of admissions and work with clients and for

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Speaker 13: those investors to understand what level of tolerance that they're

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Speaker 13: willing to take and what the opportunities are.

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Speaker 3: Karen, thank you so much for joining us. Really appreciate it.

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Speaker 1: Karen Peg, head of Portfolio Management Equities for Fiduciary Trust International,

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Speaker 1: joining us in the Bloomberg.

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Speaker 3: INARCT Approper Studios. You get a gold star for showing up.

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Speaker 2: Thanks for listening to the Bloomberg Markets podcast.

1107
00:54:59,160 --> 00:54:59,960
Speaker 3: You can subscribe.

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00:55:00,000 --> 00:55:03,200
Speaker 2: I've been listening to interviews at Apple Podcasts or whatever

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Speaker 2: podcast platform you prefer. I'm Matt Miller. I'm on Twitter

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Speaker 2: at Matt Miller nineteen seventy three.

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Speaker 3: And I'm Paul Sweeney. I'm on Twitter at pt Sweeney.

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Speaker 1: Before the podcast, you can always catch us worldwide at

1113
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Speaker 1: Bloomberg Radio