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Speaker 1: Welcome to the Bloomberg Surveillance Podcast. I'm Tom Keane Jay Ley.

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Speaker 1: We bring you insight from the best in economics, finance, investment,

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Speaker 1: and international relations. Find Bloomberg Surveillance on Apple Podcasts, SoundCloud,

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Speaker 1: Bloomberg dot Com, and of course on the Bloomberg. There's

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Speaker 1: no one better to talk to about the quickness that

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Speaker 1: we are all living and Sevida Supermannian Bank of America

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Speaker 1: ahead of US equity quantitative strategy. Tell me about the

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Speaker 1: Greek letters Cevita, What does Gamma? What does Delta say

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Speaker 1: about this unusual time? You know, I think we've had

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Speaker 1: a fast and furious comeback in the market. I think

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Speaker 1: that's really interesting to look at today is just the

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Speaker 1: valuation of the market. Um, so, two things have happened

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Speaker 1: over the last month. The market has gone down and

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Speaker 1: then up pretty quickly, and then you've also seen earnings

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Speaker 1: revised down pretty aggressively. So what's happened is the pe

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Speaker 1: of the market. The price to earnings ratio has has

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Speaker 1: actually risen to almost the same highs that we were

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Speaker 1: at in February, which means the market is again very

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Speaker 1: very expensive, and this time it's because earnings are lower

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Speaker 1: and the prices back to uh to to you know,

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Speaker 1: pretty aggressive levels. So so I guess when we look

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Speaker 1: at the market today, we think, okay, it looks just

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Speaker 1: as expensive as it did about a month ago. And

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Speaker 1: we're not out of the woods yet. So we've got

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Speaker 1: you know, we've got employment likely to get worse before

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Speaker 1: it gets better. We've got um, you know, a lot

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Speaker 1: of uncertainty around timing uh in terms of business resumption. Um,

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Speaker 1: we've got you know, kind of a very muddled earning

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Speaker 1: season where a lot of companies are just shutting down

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Speaker 1: guidance because they have, you know, kind of a no

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Speaker 1: visibility in terms of what's going to happen. I don't know.

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Speaker 1: When I look at this, I think, okay, we've come

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Speaker 1: back pretty quickly. But do we really have the underpinnings

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Speaker 1: of fundamental underpinnings of a of a strong bowlmarket from here?

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Speaker 1: And I think that's the big question mark for me. Yeah,

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Speaker 1: And when you talk about questions, we used to say

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Speaker 1: pay attention to fundamentals, and now people are saying numbers

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Speaker 1: don't matter. We're flying blind. It's bad. We don't know

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Speaker 1: how bad. It doesn't matter how bad, as long as

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Speaker 1: we get a sense of when it actually starts to

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Speaker 1: come back. What's your compass right now for deciding whether

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Speaker 1: to buy or sell? Yeah, I think it's a really

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Speaker 1: good question. And I think the way we're thinking about

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Speaker 1: things is more just the short term is so fraught

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Speaker 1: with uh, with uncertainty, that it might just make more

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Speaker 1: sense to think about a normalized earnings approach, just you know,

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Speaker 1: what are earning is gonna look like over the next

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Speaker 1: few years. Does the does the global pandemic change a

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Speaker 1: lot for the earnings composition of the SMP five And

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Speaker 1: I think there are puts and takes, you know, So

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Speaker 1: I think if you think about travel and commercial real estate,

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Speaker 1: we might be less inclined to UM to see those uh,

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Speaker 1: those areas continue to to receive the amounts of to

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Speaker 1: continue to generate the amounts of revenue they have to

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Speaker 1: date um as you know this this sort of hastens

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Speaker 1: the work from home less reliance on on office space

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Speaker 1: aspect of the economy. But on the flip side, if

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Speaker 1: companies are paying less for office space and corporate travel,

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Speaker 1: then maybe that's good for margins UM. So I do

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Speaker 1: think that there are you know, kind of offsets positive

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Speaker 1: offsets to normalize learnings. But here's our take. We think

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Speaker 1: that normalized learnings is going to be about tempers that

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Speaker 1: lower than what what we were expecting prior to COVID

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Speaker 1: nineteen UM And you know, and I think that that's

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Speaker 1: that's not so bad. I think stocks are still offering

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Speaker 1: a relatively competitive return to most other fixed income assets.

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Speaker 1: And you know, kind of thinking about our quant models, uh,

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Speaker 1: you know, I think one of the things that we

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Speaker 1: find interesting is that valuation doesn't really matter for the

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Speaker 1: short term, but it does seem to matter quite strongly

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Speaker 1: for the long term. The R squared on on you know,

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Speaker 1: the pe ratio of the market over the next ten

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Speaker 1: years is above six. So that means that evaluation is

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Speaker 1: really important in terms of describing what the market's going

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Speaker 1: to look like over a ten year time horizon. And

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Speaker 1: returns right now look like they could be in the

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Speaker 1: you know, three to four to five percent range over

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Speaker 1: the next ten years. If you add on a two

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Speaker 1: to three percent dividend yield, that's you know, seven percent returns.

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Speaker 1: It's not bad in an environment where where interest rates

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Speaker 1: are super low and most fixed income assets aren't going

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Speaker 1: to offer that type of quality adjusted return. So I

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Speaker 1: think longer term stocks still look good to me, but

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Speaker 1: I think they've come back a little bit too quickly

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Speaker 1: to help us understand. Then, with all of this in mind,

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Speaker 1: how useful earning season is, Yeah, I mean it's a

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Speaker 1: it's a great question. So I think that earning season

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Speaker 1: is a little bit of a guessing game. A lot

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Speaker 1: of companies aren't going to give you guidance. Um, if

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Speaker 1: you look at the dispersion of analysts estimates, we've reached

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Speaker 1: all time highs. Nobody knows what's going on. So I

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Speaker 1: think that you know what you want to pay attention

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Speaker 1: to this earning season are surprises. One of the things

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Speaker 1: we found interesting was that in in prior periods when

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Speaker 1: dispersion of analysts estimates was this high, surprises were actually

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Speaker 1: rewarded more than usual. They were rewarded three times as

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Speaker 1: much as usual. So I think that earnings beats in

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Speaker 1: this environment are going to be very sparse, but but

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Speaker 1: they will definitely see much bigger rewards than what we've

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Speaker 1: what we've experienced historically. So I think that's what we

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Speaker 1: want to pay attention to this earning season. I don't

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Speaker 1: know if we really want to pay attention to guidance

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Speaker 1: because I think companies just like you and me don't

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Speaker 1: really have a good sense of how this year is

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Speaker 1: going to shape up. I'm not sure who's still providing guidance.

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Speaker 1: Saveta are always great to cash out with you. Thanks

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Speaker 1: for the half work from the team, especially the time

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Speaker 1: like the Savita SUPERMANI in there of Bank America on

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Speaker 1: this pandemic. We have been thrilled by our medical coverage.

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Speaker 1: We really might try to make it come to speak

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Speaker 1: to experts on this weeks and weeks and weeks ago,

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Speaker 1: and we want thrilled, thrilled to have an agreement with

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Speaker 1: Johns Hopkins to bring you their best and brightest across

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Speaker 1: all of their facilities, including the Bloomberg School of Public Health,

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Speaker 1: of course, with the philanthropy there of our founder Michael Bloomberg,

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Speaker 1: founder of Bloomberg Gelpi, and of course this television and

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Speaker 1: radio operation as well. But there's much more at the

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Speaker 1: Johns Hopkins University, and that includes a world class nursing program.

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Speaker 1: Jason Farley is a doctor at the nursing program. He

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Speaker 1: is expert on the people in the trenches and we

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Speaker 1: spoke today about the dynamics of this nation and nursing

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Speaker 1: here is Professor Farley. So we know that when we're

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Speaker 1: looking at how many people become critically yell and we're

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Speaker 1: still looking at approximately of people hospitalized UH needing some

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Speaker 1: form of acute care, and of that group, approximately half

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Speaker 1: will need some form of mechanical ventilation. Now needing mechanical

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Speaker 1: ventilation tend to be tend to skew toward our older population.

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Speaker 1: And we're still seeing that data in the United States

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Speaker 1: consistent with what we've seen around the world. Does our

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Speaker 1: response team in the US and in other parts of

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Speaker 1: the world have enough equipment, have enough personal protection to

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Speaker 1: deal with the virus in the coming weeks and months.

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Speaker 1: So so, the the administration has finally started to UM

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Speaker 1: offer support to the states in trying to get more PPE.

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Speaker 1: There have been herculean efforts by various governors across the

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Speaker 1: United States to UM bring in more in maps, to

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Speaker 1: bring in more gowns and gloves, to bring in face

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Speaker 1: shields for our frontline healthcare workers. UH. That has been

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Speaker 1: different across states, and as you've seen recording UM, different

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Speaker 1: governors across the United States have had to basically barter

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Speaker 1: decal in you know, for lack of a better word. UM,

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Speaker 1: personal protective equipment from various agencies, including going overseas to

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Speaker 1: obtain you know, maths from China and their supports mass

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Speaker 1: from other countries. UM. It's also in my home state

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Speaker 1: of Maryland, the governor has launched a in ninety reprocessing center,

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Speaker 1: one of the largest in the country, to facilitate the

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Speaker 1: reuse and cleaning of IN nine mass, which is something

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Speaker 1: that's unheard of. We would never typically reuse those types

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Speaker 1: of maps, so we're being very resilient in our efforts

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Speaker 1: to try to make sure we have enough ppe. Dr Farrow,

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Speaker 1: you are expert in the epidemiology of infectious diseases, and

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Speaker 1: your Johns Hopkins is flat out done the best job

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Speaker 1: of a regional, a city, almost a nationwide epidemiology and

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Speaker 1: study of these statistics. The statistics, the slopes, the second

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Speaker 1: derivatives for some of these regions California, Florida, they're really

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Speaker 1: not very good. New New Jersey just it's not happening.

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Speaker 1: Tell us about the diffusement of a virus from hot

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Speaker 1: spots which get all the media attention out into the

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Speaker 1: greater public. What is the experience you have of infectious

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Speaker 1: disease from a hot spots out to broader geographies. Sure,

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Speaker 1: so when we talk about hot spots, it's important for

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Speaker 1: everyone to understand that where we have ongoing you know,

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Speaker 1: replication of the virus and transmission of the virus UM.

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Speaker 1: You know, when a when we know what the effectivity

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Speaker 1: uh potential of the virus is, and that's what we

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Speaker 1: call are not um In this case, it's between two

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Speaker 1: and three. So that means for every person that you uh,

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Speaker 1: in fact, that person with COVID infects, they're gonna in

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Speaker 1: fact approximately two to two and a half to three

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Speaker 1: people UM with the virus. And so hot spots allow

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Speaker 1: that propagation or that that transmissibility to occur um ongoing

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Speaker 1: in an ongoing basis. Now the trickle effect, you know,

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Speaker 1: it's bleeding out into other locales and locations. It's the

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Speaker 1: perfect example in the United States right now in the

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Speaker 1: Rhode Island. It's been getting cases coming in from New York,

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Speaker 1: from New York State as well as in from Connecticut.

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Speaker 1: And it would not have deemed a hotspot, but because

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Speaker 1: of you know, migration, because of contact, because of UM,

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Speaker 1: you know, people's movement, it has now started to see

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Speaker 1: bleed over. Jason Farley Johns Hopkins University thrilled to have

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Speaker 1: him on today with their School of Nursing Mishall Meyer.

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Speaker 1: Where the Bank of America, Michelle, I guess I did

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Speaker 1: math there. I don't know if it's a Bank of

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Speaker 1: America quality, but I took twenty two million divided by

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Speaker 1: a hundred and fifty five million employed. That's really ugly.

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Speaker 1: How do you fold that into a guestimate of where

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Speaker 1: the unemployment rate will ahead? Um? Good morning, Good morning

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Speaker 1: Tom Coom morning John so. Um. Yes, absolutely, those are

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Speaker 1: disturbing numbers, UM, and your math is correct. Um, it's

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Speaker 1: about fourteen per suddenly before that have already lost jobs

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Speaker 1: in an extremely short order amount of time, So the

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Speaker 1: unemployment is already in double digits. It's um with these numbers,

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Speaker 1: if you assume one to one translation to the household

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Speaker 1: survey UM from the BLS, which is probably not quite right.

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Speaker 1: Probably there's some wiggle room there, but it would suggest

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Speaker 1: you're probably already at about a fourteen percent fifteen percent

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Speaker 1: unemployment rate. What do you think it's going to eventually

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Speaker 1: end up being given the pace that we're seeing, Given

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Speaker 1: the lack of clarity as far as the ability to

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Speaker 1: process these claims versus the demand for unemployment benefits right now,

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Speaker 1: So you know, the bulk of the loss is happening

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Speaker 1: right now. I mean, that's this what this recession looks like.

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Speaker 1: It's an acute crisis. It's it's shutting down of parts.

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Speaker 1: They come all happens very very suddenly. So UM, you know,

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Speaker 1: we certainly should assume some moderation going forward. UM. I

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Speaker 1: don't think we've returned anything that you know as kind

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Speaker 1: of pre COVID levels for a really long time. When

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Speaker 1: it comes to claims, UM, they will remain elevated, UM,

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Speaker 1: but coming up they will come off of these extraordinary

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Speaker 1: levels of five six million a week UM. Nonetheless, you know,

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Speaker 1: April jobs report will be clearly very ugly with what

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Speaker 1: we're seeing, you know, millions of of of jobs lost. UM.

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Speaker 1: They will probably also be very weak because you have

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Speaker 1: some residual weakness there companies that had tried to to

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Speaker 1: to stay along UM, but you know, at some point

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Speaker 1: kind of decided that the math doesn't make sense anymore

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Speaker 1: and their employees would probably be better if they do

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Speaker 1: go on unemployment insurance. So you know, there's some laggers

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Speaker 1: there as well. UM, And I do think that it's

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Speaker 1: going to take some time to work through all of that, Michelle.

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Speaker 1: In four weeks, we've taken out ten years of jobs

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Speaker 1: growth in just four weeks, and quite clear it's easier

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Speaker 1: quicker to get rid of jobs than it is to

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Speaker 1: add them. Have you got any projections whatsoever at this

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Speaker 1: point about how long it's going to take to heal

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Speaker 1: these wounds that are deep into this labor market in

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Speaker 1: just four weeks, You're absolutely right. It's a lot easier

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Speaker 1: to shut the lights sticks off and put it back on,

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Speaker 1: especially in this environment where you know it's it's a dramatic,

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Speaker 1: quick shutdown, a lockdown, um from the shelter at home

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Speaker 1: orders and then it's going to be a very slow

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Speaker 1: and properly partial reopening. UM. So the one thing to

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Speaker 1: look at when you talk about the job cuts is

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Speaker 1: the percent that are reportedly considered temporarily unemployed. UM. You know, Esthmus,

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Speaker 1: I've seen suggested to that half of the current flow

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Speaker 1: and two unemployed are considered temp. So if that is

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Speaker 1: the case, those workers in theories should be more attached

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Speaker 1: to their employers um and they should be able to

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Speaker 1: have a clearer trajectory as to when they'll be hired

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Speaker 1: back on what that might look like. UM, so you'll

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Speaker 1: see an initial You know, once once we go to

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Speaker 1: the point where companies can start opening, businesses can start

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Speaker 1: coming back, even partially, they will bring their workforce back

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Speaker 1: to some content where it gets really sticky and where

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Speaker 1: you see the frictions. Is that next phase. Um, So

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Speaker 1: you bring back your essential workers and then what comes next,

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Speaker 1: It's going to be very slow because you describing you

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Speaker 1: improve your rise and claimed folks with this Michelle Meyer

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Speaker 1: Bank of America h this morning, your your rise to

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Speaker 1: economic acclaim. Michelle was founded on the housing market and

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Speaker 1: that dying to ask you this, give us the Michelle

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Speaker 1: Meyer Rent Dynamic Housing, sales dynamic housing, build dynamic. If

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Speaker 1: you had to write a three page essay right now

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Speaker 1: on housing amid this unemployment rate, how would you frame it?

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Speaker 1: Housing is under stress? Very simply? Um. You know, housing

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Speaker 1: is a sector that's heavily debt financed. So um, you

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Speaker 1: know it's reliant on the ability to get leverage and

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Speaker 1: the willingness suspend that debt. Both I would argue or

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Speaker 1: challenged right now from the household and from the builder perspective,

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Speaker 1: small builders UM and it also as respector that requires

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Speaker 1: quite a lot of confidence to go out and to

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Speaker 1: purchase the new property. Um to spend the money and

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Speaker 1: the time and the effort in terms of making that

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Speaker 1: your home of your dreams at all requires having confidence

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Speaker 1: about your current and future income. And I would argue

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Speaker 1: that that's certainly not the key today either. So you're

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Speaker 1: going to see quite a big drowsivity. Then what's the

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Speaker 1: elasticity of rent or house price? I mean, do you

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Speaker 1: do we finally get how you know, we're addicted to

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Speaker 1: housing always going up? Yeah, right, we learned in a

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Speaker 1: seven that doesn't work. But should we anticipate now flatness

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Speaker 1: or even declining cost of us living in rent or homes?

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Speaker 1: So you know it? First I'll take the un rents.

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Speaker 1: I mean on either rents are housing prices, they tend

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Speaker 1: to be sticky, so they don't adjust immediately. They you

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Speaker 1: first have to see transactions adjust er. You find the

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Speaker 1: market clearing price, and then you see the price data

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Speaker 1: actually um adjust. So but but what what's interesting on

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Speaker 1: the rent side is that you could see in theory,

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Speaker 1: you know, landlords across the boards, they look we're going

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Speaker 1: to give a reduction in in in rent very quickly.

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Speaker 1: Given the unprecedented nature of this shock. UM, you've seen

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Speaker 1: a number of nfc s trying to push for mandated

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Speaker 1: you know, rent reductions at least for a period of

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Speaker 1: time to ease the burden UM on individuals. So it depends.

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Speaker 1: I mean, I think given the unprecedented nature of the shock,

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Speaker 1: you could see a faster reduction perhaps UM in the

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Speaker 1: cost of living UM, given how much income has been reduced.

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Speaker 1: But typically UM rent and home praises tend to lack.

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Speaker 1: You first need to see the move in terms of transactions,

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Speaker 1: and then you see it in terms of the price variable. Michell,

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Speaker 1: there's a lot of people really struggling in this moment.

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Speaker 1: You know that everybody listening in this very moment knows that.

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Speaker 1: And I'm just wondering if this anything else we can

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Speaker 1: do on the policy side to help. Is there anything left?

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Speaker 1: I do think that there's more that probably will be done.

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Speaker 1: I mean, the response has been aggressive, it's been targeted UM,

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Speaker 1: but there are, naturally, you know, some frictions you know

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Speaker 1: there the clock is ticking, particularly when it comes to

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Speaker 1: small businesses who are forced to cut workers as we're

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Speaker 1: seeing this morning. UM. So the quicker that funds will

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Speaker 1: be distributed better. Now it's a really hard thing to

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Speaker 1: do is to turn around and all of a sudden

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Speaker 1: get all the money to where it needs to go.

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Speaker 1: In the private sector, it's not an easy task. And

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Speaker 1: naturally there's going to be some operational challenges and and

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Speaker 1: and issues there, which is what what what looks to

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Speaker 1: be happening to some extent, But UM I suspect we

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Speaker 1: will see additional funds be allocated, so they're likely will

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Speaker 1: be another round of stimulus, probably in pretty short order.

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Speaker 1: UM targeting smaller meme sized businesses and trying to create

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Speaker 1: the ray incentives to keep their workers on the books. Michelle, this,

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Speaker 1: these numbers are brutal. They're really really depressing to see

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Speaker 1: this scope of Americans lose their jobs and frankly, people

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Speaker 1: worldwide I'd lose their jobs. And yet we're looking at

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Speaker 1: SMP futures that are up a half percent there, Uh,

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Speaker 1: they actually climbed after this data came out. Can you

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Speaker 1: look to any positive economic developments that could be edifying

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Speaker 1: the sort of positive sentiment that we're seeing bleed out

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Speaker 1: in stocks today? So there's always a question what's priced

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Speaker 1: in UM and I think. You know, presumably very weak

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Speaker 1: data has been priced in, right, there's an awareness that

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Speaker 1: UM with a shutdowns being enforced, you're going to see

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Speaker 1: these level sets down in the data, which is exactly

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Speaker 1: what's coming through UM. But no, obviously the data on

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Speaker 1: the headline is absolutely UM is absolutely stunning in terms

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Speaker 1: of its degree of weakness. UM. The other thing probably

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Speaker 1: driving Marcus presumably is what we just talked about around stimulus.

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Speaker 1: You know, how much of an offset will there be? Uh?

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Speaker 1: There seems to be a large willingness from the policy

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Speaker 1: side to try to counter the weakness in the private

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Speaker 1: economy and try to upset the shock from the COVID pandemic.

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Speaker 1: So you know, I would imagine investors there as a

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Speaker 1: paying very close attention to that as well. What have

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Speaker 1: the degree of stimulus and how that bleeds into the

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Speaker 1: broader co Michelle quite to get your thoughts as always.

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Speaker 1: Michelle Meither of Thank America Internet of Trades joins with

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Speaker 1: Data Partners. I can't keep track, Henrietta the alphabet soup,

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Speaker 1: but one big small business pot has already been used up.

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Speaker 1: Is anybody in Washington aware that is a percent of

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Speaker 1: g d P there a third or maybe halfway to

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Speaker 1: where they're going to go. It's interesting. I think that

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Speaker 1: they are aware of that, but they are slowly being

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Speaker 1: becoming more comfortable with making it take as long as

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Speaker 1: possible to get each of these stimulus bills out, so

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Speaker 1: that the complicated way of saying, they get it, and

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Speaker 1: they know that there's more stimulus coming. Fourth bill, a

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Speaker 1: fifth bill, a sixth bill. But I've been tracking the

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Speaker 1: duration of time it takes them to agree to these

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Speaker 1: pieces of legislation, the first three and now three point five,

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Speaker 1: which hopefully we'll get by the end of this weekend. Um,

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Speaker 1: and it keeps taking them just a little bit longer

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Speaker 1: each time. So they're comfortable with the spending, but they're

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Speaker 1: getting more dug in politically about what points they want

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Speaker 1: to make. UM. Whether they sense any kind of urgency,

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Speaker 1: they it's almost like they know it's coming, so they're

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Speaker 1: willing to take it slow. Do you sense urgency at

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Speaker 1: the moment um? I don't sense urgency right now. No,

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Speaker 1: And I think that Treasury sec Reminution tried to create

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Speaker 1: some urgency starting last Wednesday, and now here we are

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Speaker 1: more than a week later, and we still don't even

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Speaker 1: have this urgent stimulus to the point five bill. UM.

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Speaker 1: I think Republicans are trying to make a political point

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Speaker 1: in the Senate, and Democrats still are the minority in

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Speaker 1: the House, so eventually you're gonna have to have partisan,

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Speaker 1: bipartisan conversation, and they're just not doing that at this

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Speaker 1: time despite the urgent calls from Treasury. So ultimately we'll

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Speaker 1: get this money spent. I think the three point five

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Speaker 1: bill will be about five billion dollars, and then the

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Speaker 1: fourth stimulus bill could easily get you into the one

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Speaker 1: point five to two trillion dollar package. They're seeing this

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Speaker 1: macro data come in. They know it's a horror show

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Speaker 1: out there, and there are still political games being played. Um,

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Speaker 1: and that decondo a sense that they know it's coming,

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Speaker 1: but it's not urgent. And we have said that's shocking

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Speaker 1: to me that in nine minutes time will have another

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Speaker 1: ugly jobless claims print. And for someone like yourself who

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Speaker 1: has to read the room in Washington and you don't

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Speaker 1: sense urgency, I mean, how disappointing is that, in a

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Speaker 1: moment like this that you don't sense that at all.

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Speaker 1: It's it's really painful. It hurts my feelings. If we're

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Speaker 1: being honest. I mean, there are some staff who are

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Speaker 1: deeply entrenched in this and understand the daily goings on

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Speaker 1: on the market, but that is not the norm. Um.

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Speaker 1: And you also have you also have the problem of

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Speaker 1: having these members spread across the nation, you know, so

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Speaker 1: not physically in DC capable of generating a momentum for

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Speaker 1: passing any specific legislation. So UM, I think we're seeing

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Speaker 1: that really play out. And while the President keeps holding

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Speaker 1: these daily press briefings, Um, it's almost like no one

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Speaker 1: can get a word in edgewise because she sort of

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Speaker 1: sucks the acute out of the room, So nobody is

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Speaker 1: able to make their individual political points even if they

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Speaker 1: wanted to. Let's let's take a little bit deeper into

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Speaker 1: the urgency of this. We've seen figures that of the

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Speaker 1: Small Business Business Administration lending facility has been already extended,

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Speaker 1: it has not been delivered yet, the cash not necessarily

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Speaker 1: in the hands of the businesses, but promised out. Now

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Speaker 1: there's another wave of funding being requested from even smaller businesses.

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Speaker 1: How urgent is it that Congress re ups the amount

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Speaker 1: of money to this program in order to stave off

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Speaker 1: another round of bankruptcies. I think that's exactly the question.

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Speaker 1: We don't have any oversight er data about what Treasury

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Speaker 1: is doing, who's getting these loans, how effective it's been,

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Speaker 1: its stemming payroll cuts, UM, and I think a lot

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Speaker 1: of members want that information and they want to see

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Speaker 1: it roll out. What the Democrats are trying to do

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Speaker 1: is not necessarily delay the PPP from being replenished, but

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Speaker 1: they want to steer the funding into specific baskets, which

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Speaker 1: members like Marco Rubio have an issue with. So, for instance,

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Speaker 1: if you can't oversee exactly who's getting these loans and

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Speaker 1: you don't know if it's really reaching the hands of

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Speaker 1: the folks who need it, what you can do is

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Speaker 1: you can streamline and say, all right, we want, you know,

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Speaker 1: a hundred and fifty billion dollars this money directly to

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Speaker 1: go to businesses with fewer than five people UM in

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Speaker 1: specific locations, UM, minority owned, women owned, you know, kind

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Speaker 1: of try to direct it in that regard. And that's

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Speaker 1: what Democrats have tried to put into their pieces of legislation.

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Speaker 1: But the Republicans are essentially saying, look, we just need

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Speaker 1: to get this money out there, and we don't have

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Speaker 1: time for these games. Let's just put the money in

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Speaker 1: this basket and get it spent um. So it's it's

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Speaker 1: a competing view of how to control where this money

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Speaker 1: is delivered and how effectively it's used. And when you

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Speaker 1: see the Fed and Treasury can saying we're just trying

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Speaker 1: to shovel money out the door right now, it's really

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Speaker 1: difficult to exert oversight in a specific time of crisis.

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Speaker 1: That should really come later, and it will. Is there

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Speaker 1: more urgency with respect to coming up with a plan

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Speaker 1: to reopen the economy. The plan to reopen the economy

432
00:24:16,960 --> 00:24:21,000
Speaker 1: is really just a mess um, if we're being honest.

433
00:24:21,200 --> 00:24:24,879
Speaker 1: There is a really haphazard effort going on at the

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00:24:24,920 --> 00:24:28,240
Speaker 1: administration level, and almost nothing in that main going on

435
00:24:28,280 --> 00:24:30,760
Speaker 1: to bet the House or the Senate. Obviously, we have

436
00:24:30,880 --> 00:24:33,480
Speaker 1: some select governors who are in states you've been deeply

437
00:24:33,560 --> 00:24:36,000
Speaker 1: hit and are trying to coordinate amongst each other to

438
00:24:36,160 --> 00:24:40,119
Speaker 1: try to bring their states out of this shutdown. But

439
00:24:40,240 --> 00:24:44,320
Speaker 1: the administration is creating a situation where people have different

440
00:24:44,400 --> 00:24:46,880
Speaker 1: senses of different dates. So May first is the thing,

441
00:24:47,000 --> 00:24:49,400
Speaker 1: May seventh is a thing. Jam onest is the thing.

442
00:24:49,440 --> 00:24:53,399
Speaker 1: There's no cohesion and the you know, quote unquote opening

443
00:24:53,400 --> 00:24:58,040
Speaker 1: our city Council that the administration has been um, you know,

444
00:24:58,080 --> 00:25:00,920
Speaker 1: sort of dangling in front of us. How as no members,

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00:25:01,280 --> 00:25:04,000
Speaker 1: It has no concrete plan, it is not unified. A

446
00:25:04,640 --> 00:25:08,160
Speaker 1: most importantly, it has nothing to do um with providing

447
00:25:08,200 --> 00:25:10,280
Speaker 1: testing to the extent that we need. You know, no

448
00:25:10,440 --> 00:25:12,200
Speaker 1: state has been able to test one in two point

449
00:25:12,240 --> 00:25:14,679
Speaker 1: seven of its population. That's just not going to cut it.

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00:25:15,160 --> 00:25:16,760
Speaker 1: You know, if you're President Trump and you can walk

451
00:25:16,800 --> 00:25:20,240
Speaker 1: down to that and you shoot somebody, that's different. Henrietta,

452
00:25:20,400 --> 00:25:23,119
Speaker 1: that's the statistical theday. Thank you for bringing us that

453
00:25:23,240 --> 00:25:27,160
Speaker 1: wisdom of under three percent testing right now. Certainly that's

454
00:25:27,200 --> 00:25:32,000
Speaker 1: what everyone's talking about this Thursday morning. Henrietta trace with

455
00:25:32,119 --> 00:25:46,120
Speaker 1: Veta Partners. Well, in the United States we have Uber

456
00:25:46,280 --> 00:25:49,399
Speaker 1: we have left for our rides sharing. In China, they

457
00:25:49,440 --> 00:25:52,240
Speaker 1: have a company by the name of d D. David

458
00:25:52,280 --> 00:25:56,199
Speaker 1: Rubinstein Carlisle co chairman, sat down with the president of

459
00:25:56,320 --> 00:25:58,840
Speaker 1: d D and part of his latest peer to peer conversations,

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00:25:58,920 --> 00:26:02,840
Speaker 1: let's take a listen. Uber is now publicly traded and

461
00:26:02,920 --> 00:26:05,720
Speaker 1: it's losing a fair amount of money every year, a

462
00:26:05,760 --> 00:26:09,840
Speaker 1: billion dollars plus a year or something like that or more. Um,

463
00:26:10,240 --> 00:26:12,480
Speaker 1: are you thinking of going public and are you losing

464
00:26:12,520 --> 00:26:16,439
Speaker 1: money or are you making money? Well, um, we do

465
00:26:16,560 --> 00:26:19,040
Speaker 1: have a specific type of timetable has put it that

466
00:26:19,080 --> 00:26:21,920
Speaker 1: way and back to the upper point. I'm sure it's

467
00:26:21,920 --> 00:26:24,320
Speaker 1: temporary and they would go through it. There is very

468
00:26:24,320 --> 00:26:28,359
Speaker 1: diligent CEO and a very experience for us. We think

469
00:26:28,760 --> 00:26:32,840
Speaker 1: profitability is natural result of the value usually create. And

470
00:26:32,880 --> 00:26:35,199
Speaker 1: there are two things in China very different from the

471
00:26:35,240 --> 00:26:39,720
Speaker 1: other market. First, the right share is cheaper than car ownership,

472
00:26:40,240 --> 00:26:43,800
Speaker 1: so that's the huge value creation you provide to your users.

473
00:26:44,119 --> 00:26:48,560
Speaker 1: And secondly, in China we're going through a transition that people,

474
00:26:49,600 --> 00:26:53,439
Speaker 1: you know, people urged for better life quality, better lives.

475
00:26:54,760 --> 00:26:58,080
Speaker 1: Jean Lou of d D. David Rubinstein joins us SNOW

476
00:26:58,640 --> 00:27:01,080
Speaker 1: and of course he's pure to pure. Conversations are just

477
00:27:01,240 --> 00:27:06,119
Speaker 1: superb and they're always names we know, except not now, David.

478
00:27:06,160 --> 00:27:09,560
Speaker 1: I want you to sell miss Lou to our audience.

479
00:27:09,720 --> 00:27:13,960
Speaker 1: Why do we care about this interesting investor from China.

480
00:27:14,920 --> 00:27:17,959
Speaker 1: It's a very interesting woman. She's educated in China. Her

481
00:27:18,000 --> 00:27:21,080
Speaker 1: father is a prominent business person. He he started Lenovo,

482
00:27:21,119 --> 00:27:24,639
Speaker 1: which is a major computer factor. She later went to

483
00:27:24,680 --> 00:27:26,919
Speaker 1: Harvard got a computer science degree and then did what

484
00:27:27,000 --> 00:27:29,800
Speaker 1: many Chinese educated United States do. They went to work

485
00:27:29,840 --> 00:27:32,000
Speaker 1: in Wall Street. She went to work for Goldman Sachs

486
00:27:32,119 --> 00:27:35,320
Speaker 1: in in Hong Kong and um she became an investor

487
00:27:35,359 --> 00:27:37,240
Speaker 1: and she wanted to invest in a company called d

488
00:27:37,359 --> 00:27:39,960
Speaker 1: D and they wouldn't take her money Goldman sachses money.

489
00:27:40,240 --> 00:27:43,000
Speaker 1: Eventually she said, let me just join the company. She did.

490
00:27:43,119 --> 00:27:45,360
Speaker 1: Now she rose up to be the president. We think

491
00:27:45,400 --> 00:27:47,000
Speaker 1: in the United States that were the center of the

492
00:27:47,080 --> 00:27:50,680
Speaker 1: universe very often. But d D is bigger than Uber. Now.

493
00:27:50,760 --> 00:27:53,240
Speaker 1: D D is not publicly traded, so we don't know

494
00:27:53,320 --> 00:27:56,800
Speaker 1: completely the value. But it has more customers than Uber does,

495
00:27:57,119 --> 00:28:00,640
Speaker 1: and it just has a bigger base than than Uber does.

496
00:28:00,680 --> 00:28:04,119
Speaker 1: Get people there. That scream you heard last night, uh

497
00:28:04,359 --> 00:28:08,000
Speaker 1: David Rubinstein was Paul Sweeney screaming at Uber left or

498
00:28:08,000 --> 00:28:10,359
Speaker 1: one of them in New Jersey as well. What's the

499
00:28:10,440 --> 00:28:15,520
Speaker 1: ability to bring d D over to America. Well, interestingly,

500
00:28:15,560 --> 00:28:18,159
Speaker 1: many of the Chinese companies that are the dominant in

501
00:28:18,240 --> 00:28:20,320
Speaker 1: China don't really do that well in the United States

502
00:28:20,359 --> 00:28:23,080
Speaker 1: are having become major presences here. So like Ali Bob

503
00:28:23,160 --> 00:28:25,560
Speaker 1: was not a major presence here, at least not yet.

504
00:28:25,880 --> 00:28:29,000
Speaker 1: Uh d D actually is owned in part by Huber.

505
00:28:29,119 --> 00:28:30,919
Speaker 1: Uber is an investorent at Uber tried to be a

506
00:28:30,960 --> 00:28:34,000
Speaker 1: major presence in China and ultimately couldn't beat d D,

507
00:28:34,200 --> 00:28:36,840
Speaker 1: so it basically invested in d D. A d D

508
00:28:37,119 --> 00:28:39,840
Speaker 1: is uh is really the dominant partner there and Uber

509
00:28:39,880 --> 00:28:42,080
Speaker 1: will not really compete in China with him. Whether d

510
00:28:42,160 --> 00:28:44,000
Speaker 1: D will come to the United States, I think right

511
00:28:44,000 --> 00:28:46,920
Speaker 1: now probably they've got their hands full with China. So, David,

512
00:28:47,080 --> 00:28:49,520
Speaker 1: what did ms lou suggest to you, as you know

513
00:28:49,600 --> 00:28:54,000
Speaker 1: the key challenge for continuing the growth of d D. Well,

514
00:28:54,040 --> 00:28:56,800
Speaker 1: of course this was done before the coronavirus. The interview

515
00:28:56,880 --> 00:28:59,640
Speaker 1: was done a little while ago. Um, now everybody has

516
00:28:59,680 --> 00:29:01,760
Speaker 1: the channel. Is that nobody's traveling though though China is

517
00:29:01,800 --> 00:29:06,440
Speaker 1: coming back online and therefore China is um more using

518
00:29:06,480 --> 00:29:09,360
Speaker 1: these kind of divide these kind of things than they

519
00:29:09,400 --> 00:29:11,200
Speaker 1: are in the United States right now because people are

520
00:29:11,240 --> 00:29:12,960
Speaker 1: going back to work. So it's not like the United

521
00:29:12,960 --> 00:29:16,160
Speaker 1: States where nobody's traveling. D D basically is a company

522
00:29:16,200 --> 00:29:19,480
Speaker 1: like Ali Baba, which has become a major presence in China.

523
00:29:19,720 --> 00:29:22,720
Speaker 1: Everybody knows it, everybody likes it. And interestingly, what she

524
00:29:22,800 --> 00:29:25,480
Speaker 1: does very often is she drives the car herself. She

525
00:29:25,560 --> 00:29:27,880
Speaker 1: wants to see what our customers are thinking. So she

526
00:29:28,040 --> 00:29:31,000
Speaker 1: drives the car and very often, uh, you know, she

527
00:29:31,040 --> 00:29:32,960
Speaker 1: gets comments from people saying, you're not a very good driver,

528
00:29:33,000 --> 00:29:34,800
Speaker 1: I'm gonna report you to the company or something like that,

529
00:29:34,920 --> 00:29:38,400
Speaker 1: or sometimes people say she's very good. So what's what's

530
00:29:38,440 --> 00:29:41,600
Speaker 1: the competitive landscape in China for the ride sharing business?

531
00:29:41,600 --> 00:29:43,400
Speaker 1: Here in the States, We've got Uber, We've got Liver

532
00:29:43,880 --> 00:29:47,280
Speaker 1: duking it out. When this when when they started, when

533
00:29:47,400 --> 00:29:49,840
Speaker 1: he was started in China, there were roughly thirty of

534
00:29:49,880 --> 00:29:51,840
Speaker 1: these companies and in the end they got down to

535
00:29:51,920 --> 00:29:54,200
Speaker 1: it just a few and d D is the dominant

536
00:29:54,200 --> 00:29:56,960
Speaker 1: one there, just says Uber Withlift or the two dominant

537
00:29:56,960 --> 00:29:59,720
Speaker 1: ones here. Um, but DD has a much bigger presence

538
00:29:59,760 --> 00:30:01,800
Speaker 1: than Huber really has in the United States. It's much

539
00:30:01,840 --> 00:30:04,800
Speaker 1: more dominant and many more customers. And I just think

540
00:30:04,840 --> 00:30:07,440
Speaker 1: it's likely to expand in the other areas. And it's

541
00:30:07,520 --> 00:30:10,320
Speaker 1: it's it's extremely well respected for their service and their

542
00:30:10,360 --> 00:30:13,520
Speaker 1: their ability to kind of take care of customers various needs.

543
00:30:14,760 --> 00:30:17,640
Speaker 1: David Rubinstein, thank you so much, greatly appreciated the Carlisle

544
00:30:17,680 --> 00:30:21,200
Speaker 1: Cole Chairman and of course UH interviewer extraordinaire. I can't

545
00:30:21,200 --> 00:30:23,640
Speaker 1: say enough folks about the David Rubinstein Show. Peer to

546
00:30:23,760 --> 00:30:29,720
Speaker 1: Peer Conversation, seven pm Friday in New York. Thanks for

547
00:30:29,840 --> 00:30:34,200
Speaker 1: listening to the Bloomberg Surveillance podcast. Subscribe and listen to

548
00:30:34,360 --> 00:30:40,120
Speaker 1: interviews on Apple Podcasts, SoundCloud, or whichever podcast platform you prefer.

549
00:30:40,680 --> 00:30:44,000
Speaker 1: I'm on Twitter at Tom Keane before the podcast. You

550
00:30:44,040 --> 00:31:00,480
Speaker 1: can always catch us worldwide. I'm Bloomberg Radio