1 00:00:02,520 --> 00:00:13,760 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is the Bloomberg 2 00:00:13,840 --> 00:00:17,920 Speaker 1: Surveillance Podcast. Catch us live weekdays at seven am Eastern 3 00:00:18,200 --> 00:00:22,000 Speaker 1: on Apple CarPlay or Android Auto with the Bloomberg Business App. 4 00:00:22,360 --> 00:00:25,680 Speaker 1: Listen on demand wherever you get your podcasts, or watch 5 00:00:25,760 --> 00:00:27,680 Speaker 1: us live on YouTube. 6 00:00:30,000 --> 00:00:33,280 Speaker 2: She's more important than the Secretary of Treasury. Constance Hunter 7 00:00:33,400 --> 00:00:38,160 Speaker 2: joins now from EIU Concerts, you take your inflation guestimate 8 00:00:38,640 --> 00:00:42,400 Speaker 2: to two decimal points? Can you do that? Can we 9 00:00:42,520 --> 00:00:46,680 Speaker 2: actually guess to two decimal points on a change in inflation? 10 00:00:47,920 --> 00:00:50,080 Speaker 3: Well, you bring up a really great point, Tom, And 11 00:00:50,200 --> 00:00:54,600 Speaker 3: especially with regards to goods, what we've discovered is that 12 00:00:55,080 --> 00:00:58,600 Speaker 3: we really don't know the price elasticity of demand of 13 00:00:58,680 --> 00:01:01,760 Speaker 3: a number of these goods that are going to be impacted. 14 00:01:02,280 --> 00:01:08,319 Speaker 3: So we're triangulating all sorts of different data sources and 15 00:01:08,560 --> 00:01:10,720 Speaker 3: really kind of trying to get under the hood. But 16 00:01:11,360 --> 00:01:13,760 Speaker 3: your point is well taken, right, What is even the 17 00:01:13,800 --> 00:01:17,440 Speaker 3: point of going out to two decimal points if you, uh, 18 00:01:17,640 --> 00:01:21,480 Speaker 3: if you have this level of uncertainty. Nevertheless, we try 19 00:01:21,520 --> 00:01:24,080 Speaker 3: to be as precise as possible so we can learn 20 00:01:24,640 --> 00:01:26,240 Speaker 3: from action. 21 00:01:26,520 --> 00:01:32,360 Speaker 2: Do you see substitution of products going on because of tariffs. 22 00:01:33,360 --> 00:01:36,399 Speaker 3: Oh absolutely, I mean, and I think what will be 23 00:01:36,520 --> 00:01:39,720 Speaker 3: interesting to see is is again how price sensitive are 24 00:01:39,920 --> 00:01:43,440 Speaker 3: consumers once these price increases really start to feed through. 25 00:01:43,800 --> 00:01:47,760 Speaker 3: Let's remember we had an extraordinary amount of front loading 26 00:01:49,040 --> 00:01:51,840 Speaker 3: in the in the beginning of the year and really 27 00:01:51,840 --> 00:01:54,280 Speaker 3: at the end of the fourth quarter last year, where 28 00:01:54,840 --> 00:02:01,000 Speaker 3: consumers pre bought and suppliers pre bought ahead of these tears, right, 29 00:02:01,080 --> 00:02:04,000 Speaker 3: and so we're still waiting for that pig in the 30 00:02:04,040 --> 00:02:07,440 Speaker 3: python to move its way through Constince. 31 00:02:07,480 --> 00:02:08,919 Speaker 4: What do you think we'll see that? Is that something 32 00:02:09,080 --> 00:02:11,800 Speaker 4: that we're going to see maybe today or we're not 33 00:02:11,840 --> 00:02:14,160 Speaker 4: going to see any inflation impact if there is any, 34 00:02:14,400 --> 00:02:15,800 Speaker 4: and a lot of folks say that there may not. 35 00:02:16,400 --> 00:02:17,760 Speaker 4: Is that something that we may have to wait to 36 00:02:17,800 --> 00:02:19,359 Speaker 4: the fall to see maybe in the data. 37 00:02:20,080 --> 00:02:20,280 Speaker 2: You know. 38 00:02:20,480 --> 00:02:22,320 Speaker 3: I think you're right, Paul. I think it's I think 39 00:02:22,360 --> 00:02:24,799 Speaker 3: it's taking a lot longer. And what we see from 40 00:02:24,840 --> 00:02:29,280 Speaker 3: some alternative sources like Cavallo for example, which really it 41 00:02:29,320 --> 00:02:34,160 Speaker 3: does work on real time prices and they gather information 42 00:02:34,320 --> 00:02:36,359 Speaker 3: from four different retailers. We can guess who some of 43 00:02:36,440 --> 00:02:41,240 Speaker 3: those retailers are Amazon, Walmart. And when you think about 44 00:02:41,280 --> 00:02:45,440 Speaker 3: Amazon and the tros of PhD economists they have working 45 00:02:45,520 --> 00:02:49,400 Speaker 3: on how to price goods, right, they probably have better 46 00:02:49,440 --> 00:02:52,520 Speaker 3: information about the price elasticitute than anybody else right now 47 00:02:52,560 --> 00:02:56,560 Speaker 3: in the US economy. And what they show is actually 48 00:02:56,639 --> 00:02:59,560 Speaker 3: that domestic prices, while they increase in the beginning, have 49 00:02:59,680 --> 00:03:03,760 Speaker 3: really leveled off. So domestic suppliers are aware of that 50 00:03:03,840 --> 00:03:07,440 Speaker 3: price sensitivity, I think, and are keeping their prices competitive 51 00:03:07,880 --> 00:03:09,320 Speaker 3: so that they can gain market share. 52 00:03:09,600 --> 00:03:12,440 Speaker 2: Cons it's just to get narrow here. Forty five seconds. 53 00:03:12,720 --> 00:03:16,680 Speaker 2: Auto insurance is three percent of our inflation picture. Is 54 00:03:16,720 --> 00:03:21,000 Speaker 2: there anybody listening who's paying less and auto insurance? I 55 00:03:21,040 --> 00:03:21,560 Speaker 2: don't think so. 56 00:03:21,760 --> 00:03:26,200 Speaker 3: Constance, No, And you talked earlier about all the flash 57 00:03:26,240 --> 00:03:29,400 Speaker 3: floods and rain that you know, the New York area 58 00:03:29,520 --> 00:03:34,559 Speaker 3: experienced over the last twenty four hours, and that destroys automobiles. 59 00:03:34,600 --> 00:03:36,680 Speaker 3: If you get stuck in a flash flood, your car 60 00:03:36,920 --> 00:03:41,160 Speaker 3: is totaled, right, And so we look at the incidents 61 00:03:41,160 --> 00:03:45,000 Speaker 3: of perils and they're increasing, and then what happens is 62 00:03:45,040 --> 00:03:49,760 Speaker 3: that that gets transferred to the entire auto market. Right, 63 00:03:49,840 --> 00:03:54,520 Speaker 3: everybody auto insures price so that they make up losses 64 00:03:54,640 --> 00:03:58,520 Speaker 3: in areas where they're experiencing higher catastrophes. 65 00:03:58,640 --> 00:04:01,160 Speaker 2: We're trying to give constance thirty two seconds here to 66 00:04:01,200 --> 00:04:01,640 Speaker 2: look at. 67 00:04:01,520 --> 00:04:03,040 Speaker 5: The Yeah, plenty of. 68 00:04:04,640 --> 00:04:08,040 Speaker 2: You in the ninety second glance. You've had Constance. What 69 00:04:08,040 --> 00:04:08,640 Speaker 2: do you see. 70 00:04:09,560 --> 00:04:12,400 Speaker 3: Well, it's not showing up in goods, right if you 71 00:04:12,440 --> 00:04:15,360 Speaker 3: go to the commodities less food and energy. You're not 72 00:04:15,440 --> 00:04:18,320 Speaker 3: seeing it in apparel, which was down month over month. 73 00:04:18,360 --> 00:04:21,840 Speaker 3: You're not seeing it in new vehicles, which you know 74 00:04:22,320 --> 00:04:24,920 Speaker 3: it was also down. Used cars and trucks have a 75 00:04:24,960 --> 00:04:29,840 Speaker 3: little more complicated pricing in them. Medical care commodities were 76 00:04:29,880 --> 00:04:32,640 Speaker 3: up zero point six month over month, but you're. 77 00:04:32,480 --> 00:04:33,480 Speaker 2: Really not seeing it. 78 00:04:33,640 --> 00:04:36,919 Speaker 3: Tobacco and smoking products are up zero point eight, but 79 00:04:37,800 --> 00:04:40,640 Speaker 3: I don't know if that constitutes a trend. And I 80 00:04:41,520 --> 00:04:43,440 Speaker 3: you know, I don't know how much we import of. 81 00:04:43,480 --> 00:04:46,080 Speaker 2: The dancer is before we let you go, I don't 82 00:04:46,120 --> 00:04:48,920 Speaker 2: mean to interrupt, but this is too important. After hearing 83 00:04:49,000 --> 00:04:52,840 Speaker 2: from bestent than Hunter, the President's going to come out 84 00:04:52,880 --> 00:04:55,640 Speaker 2: and say, show me the inflation in tariffs. 85 00:04:55,760 --> 00:04:59,760 Speaker 3: Right, of course he is, Yes, that's that is the issue, 86 00:05:00,040 --> 00:05:03,039 Speaker 3: and the reality is it is not in the current data. 87 00:05:03,440 --> 00:05:05,719 Speaker 3: We do know, of course, that inflation is somewhat of 88 00:05:05,720 --> 00:05:06,719 Speaker 3: a lagging indicator. 89 00:05:06,800 --> 00:05:06,920 Speaker 4: Right. 90 00:05:06,960 --> 00:05:09,719 Speaker 3: If you're a firm, You're going to wait as long 91 00:05:09,760 --> 00:05:12,840 Speaker 3: as you can to raise prices because you don't want 92 00:05:12,880 --> 00:05:14,920 Speaker 3: to lose market share. So we just need to keep 93 00:05:14,960 --> 00:05:17,320 Speaker 3: in mind this is a lagging indicator. 94 00:05:18,600 --> 00:05:22,159 Speaker 4: Is there a scenario where the companies actually buy and 95 00:05:22,240 --> 00:05:24,680 Speaker 4: large do ticket in the margin and we don't see 96 00:05:24,680 --> 00:05:27,320 Speaker 4: it necessarily at the consumer level. Is that a scenario. 97 00:05:27,400 --> 00:05:29,480 Speaker 4: I know a lot of folks are suggesting that might 98 00:05:29,520 --> 00:05:30,000 Speaker 4: be the case. 99 00:05:30,680 --> 00:05:32,720 Speaker 3: Well, there was an interesting article I think it was 100 00:05:32,760 --> 00:05:35,040 Speaker 3: in the Wall Street Journal from my morning scan of 101 00:05:35,080 --> 00:05:38,800 Speaker 3: the news how Levi's is reducing the number of skews 102 00:05:39,120 --> 00:05:42,000 Speaker 3: in order to manage their costs so that they don't 103 00:05:42,040 --> 00:05:46,400 Speaker 3: have to pass on the price increases, right, So they're 104 00:05:46,440 --> 00:05:50,280 Speaker 3: reducing the variety of things that they're producing. And so 105 00:05:50,480 --> 00:05:52,760 Speaker 3: I think firms are really you know, when you think 106 00:05:52,760 --> 00:05:56,279 Speaker 3: about the data and analytics that's been deployed over the 107 00:05:56,360 --> 00:06:01,440 Speaker 3: last six years in companies have a lot more capacity 108 00:06:01,480 --> 00:06:05,440 Speaker 3: to control their supply chain and to control their product mix. 109 00:06:05,880 --> 00:06:09,599 Speaker 3: And we're seeing some of that protivity game from that 110 00:06:09,839 --> 00:06:12,880 Speaker 3: those data enhancements coming to bear as firms try to 111 00:06:12,960 --> 00:06:14,160 Speaker 3: manage through this keas. 112 00:06:14,279 --> 00:06:16,240 Speaker 2: So thank you so much, just wonderful to have you 113 00:06:16,320 --> 00:06:19,400 Speaker 2: here at eight thirty for the National inflation for a 114 00:06:19,520 --> 00:06:21,120 Speaker 2: constance hunter with Ei. 115 00:06:21,720 --> 00:06:30,920 Speaker 1: You you're listening to the Bloomberg Surveillance podcast. Catch us 116 00:06:30,960 --> 00:06:34,279 Speaker 1: Live weekday afternoons from seven to ten am Eastern. Listen 117 00:06:34,360 --> 00:06:37,919 Speaker 1: on Applecarplay and Android Auto with the Bloomberg Business app, 118 00:06:38,080 --> 00:06:39,840 Speaker 1: or watch US live on YouTube. 119 00:06:40,040 --> 00:06:42,080 Speaker 2: We turned to banks. It's been a helter skelter today 120 00:06:42,120 --> 00:06:45,240 Speaker 2: with the Treasury Secretary. This is a more important conversation 121 00:06:45,560 --> 00:06:50,000 Speaker 2: for Global Wall Street. Allison Williams joins us with Bloomberg Intelligence. Allison, 122 00:06:50,560 --> 00:06:53,080 Speaker 2: the way I read it as a complete hack is 123 00:06:53,160 --> 00:06:57,000 Speaker 2: we go there's four big banks. No, there's JP Morgan 124 00:06:57,480 --> 00:06:59,960 Speaker 2: and there's these three other banks. Am I off the mark? 125 00:07:01,000 --> 00:07:04,279 Speaker 6: Well, there's JP Morgan. I always say covering JP Morgan 126 00:07:04,360 --> 00:07:07,279 Speaker 6: is like covering you know, four big companies, right, or 127 00:07:07,920 --> 00:07:10,040 Speaker 6: you know, depending on how they they've done their division. 128 00:07:10,080 --> 00:07:13,840 Speaker 6: I guess it's three big companies now, so you know, 129 00:07:14,000 --> 00:07:18,720 Speaker 6: really the net interest income guidance very strong, about six 130 00:07:18,760 --> 00:07:21,560 Speaker 6: hundred million upside to the course shy sick. 131 00:07:21,600 --> 00:07:24,600 Speaker 2: I went back and forth with Valerie Titell London. They 132 00:07:24,640 --> 00:07:28,120 Speaker 2: did a tangible equity of twenty percent plus. Am I right? 133 00:07:28,240 --> 00:07:29,320 Speaker 2: City Groups half that. 134 00:07:30,840 --> 00:07:33,760 Speaker 6: Uh, you are right, I mean if you look at 135 00:07:33,960 --> 00:07:35,640 Speaker 6: you know, so if you look at City Group, it's 136 00:07:35,640 --> 00:07:38,360 Speaker 6: a very different story. Right. So City Group is still 137 00:07:38,360 --> 00:07:42,760 Speaker 6: in a story of transition. We've been talking about that transition, 138 00:07:43,080 --> 00:07:46,200 Speaker 6: as we say, ever since Sandy built the house. It's 139 00:07:46,200 --> 00:07:49,360 Speaker 6: been being taken apart. And I think Jane Fraser is 140 00:07:49,400 --> 00:07:52,360 Speaker 6: making a lot of tough decisions there and really solid 141 00:07:52,600 --> 00:07:55,640 Speaker 6: quarter out of City Group as well. You know, they're 142 00:07:55,720 --> 00:07:58,480 Speaker 6: upping their net interesting income guidance. They're fixed income trading 143 00:07:58,560 --> 00:08:01,239 Speaker 6: up twenty percent, so that's a that you know, JP 144 00:08:01,320 --> 00:08:04,320 Speaker 6: Morgan and Wells were up thirteen to fourteen percent. You know, 145 00:08:04,680 --> 00:08:08,880 Speaker 6: City Group has an amazing rates and currencies trading business, 146 00:08:08,920 --> 00:08:12,080 Speaker 6: as you know, Paul, and that is really where the 147 00:08:12,080 --> 00:08:15,040 Speaker 6: strength is this quarter. That's where the volatility is and 148 00:08:15,080 --> 00:08:20,880 Speaker 6: they are really executing in that business. Again, Corn at 149 00:08:20,960 --> 00:08:23,400 Speaker 6: interest income going up for both of those banks. Wells 150 00:08:23,400 --> 00:08:25,840 Speaker 6: Fargo a little softer, but Wells Fargo is looking at 151 00:08:25,880 --> 00:08:29,240 Speaker 6: the overall number. Keep in mind that there is corenet 152 00:08:29,320 --> 00:08:32,560 Speaker 6: interest income and then there's a number that includes trading. 153 00:08:32,679 --> 00:08:34,760 Speaker 6: And so the trading that interest income tends to be 154 00:08:34,800 --> 00:08:38,559 Speaker 6: off set in fees. I think, but from a headline perspective, 155 00:08:38,679 --> 00:08:41,080 Speaker 6: people are sort of going to be less positive on 156 00:08:41,120 --> 00:08:45,040 Speaker 6: those Wells Fargo results. Wells Fargo also not getting this 157 00:08:45,160 --> 00:08:47,360 Speaker 6: huge benefit. So as I said, they saw some growth 158 00:08:47,360 --> 00:08:51,840 Speaker 6: and fixed income trading, but well, you know JP Morgan 159 00:08:51,840 --> 00:08:56,960 Speaker 6: and City much stronger in those businesses. JP Morgan delivering 160 00:08:56,960 --> 00:09:00,600 Speaker 6: a billion dollars of upside in their trading and fee 161 00:09:00,600 --> 00:09:03,080 Speaker 6: you I haven't done the calculation yet for our City group, 162 00:09:03,120 --> 00:09:06,200 Speaker 6: but the trends also better for City are. 163 00:09:06,200 --> 00:09:08,840 Speaker 4: What are the banks saying about loan origination? Are they 164 00:09:08,880 --> 00:09:11,560 Speaker 4: writing loans? We're just talking to christ Van seen us 165 00:09:11,600 --> 00:09:15,160 Speaker 4: at all relinement about private credit, and I just think 166 00:09:15,200 --> 00:09:16,600 Speaker 4: back to my Chase Manhattan days. 167 00:09:16,600 --> 00:09:17,599 Speaker 2: We were killing it. 168 00:09:17,920 --> 00:09:21,319 Speaker 4: They're on the leverage loan business, making money upfront, making 169 00:09:21,360 --> 00:09:24,440 Speaker 4: money on the spread, syndicating by risk out to every 170 00:09:24,440 --> 00:09:27,720 Speaker 4: other bank around the country. What are they making loans 171 00:09:27,720 --> 00:09:28,160 Speaker 4: these days? 172 00:09:28,200 --> 00:09:30,680 Speaker 6: So they are making loans, I mean you know that 173 00:09:30,679 --> 00:09:33,240 Speaker 6: that the card business, which is probably not your concern 174 00:09:33,360 --> 00:09:37,120 Speaker 6: back then or now, but the card business is very 175 00:09:37,120 --> 00:09:39,400 Speaker 6: profitable for these banks and that's where the loan growth 176 00:09:39,400 --> 00:09:43,200 Speaker 6: has been. And again that's benefiting City and JP Morgan. 177 00:09:43,280 --> 00:09:45,440 Speaker 6: We have seen a little bit of pickup in the 178 00:09:45,520 --> 00:09:48,720 Speaker 6: commercial numbers in uh, you know, in the FED data. 179 00:09:48,720 --> 00:09:51,920 Speaker 6: We're going to hear what Bank America says tomorrow on that. 180 00:09:52,640 --> 00:09:56,920 Speaker 6: I think with regard to privates, you know, it is 181 00:09:57,120 --> 00:10:00,760 Speaker 6: a change. But I think if you look at, for example, 182 00:10:00,800 --> 00:10:03,720 Speaker 6: Goldman's business, So Paul, you know Goldman, you know their 183 00:10:03,840 --> 00:10:06,920 Speaker 6: merchant banking business. You know that they, you know, back 184 00:10:06,960 --> 00:10:10,199 Speaker 6: in the day, made a lot of money from making 185 00:10:10,240 --> 00:10:15,480 Speaker 6: these proprietary investments. Vulgar rule came along and said, please 186 00:10:15,480 --> 00:10:17,560 Speaker 6: don't do that on your balance sheet. By the way, 187 00:10:17,600 --> 00:10:20,960 Speaker 6: investors don't like the balance sheet, right because when you 188 00:10:21,040 --> 00:10:24,600 Speaker 6: make those kind of investments and you're recording gains, people 189 00:10:24,600 --> 00:10:26,760 Speaker 6: don't really give you a lot of credit because they 190 00:10:26,840 --> 00:10:30,120 Speaker 6: view them as violatil et cetera. But you have someone 191 00:10:30,160 --> 00:10:32,320 Speaker 6: like Goldman who's now said I'm going to be an 192 00:10:32,320 --> 00:10:35,240 Speaker 6: alternative asset manager, and now instead of putting my balance 193 00:10:35,240 --> 00:10:39,160 Speaker 6: sheet we're at risk, I'm gonna take the client's money 194 00:10:39,679 --> 00:10:42,600 Speaker 6: so and I'm going to earn fees. And investors like that. 195 00:10:42,640 --> 00:10:45,720 Speaker 6: They like asset management. It's a high multiple business and 196 00:10:45,800 --> 00:10:48,400 Speaker 6: so it's a higher return on equity business. And that's 197 00:10:48,440 --> 00:10:51,400 Speaker 6: been a long term story at Goldman. So I think 198 00:10:51,440 --> 00:10:54,079 Speaker 6: the banks are fine, and so that's a Goldman specific 199 00:10:54,160 --> 00:10:55,760 Speaker 6: but I think all the banks are doing that. 200 00:10:56,200 --> 00:10:58,120 Speaker 2: Okay, I want to go to the gossip of the moment. 201 00:10:58,120 --> 00:11:01,000 Speaker 2: Folks on the Hampton's over a beverage for choice. Allison 202 00:11:01,040 --> 00:11:05,319 Speaker 2: Williams is getting hammered by fancy people over the reorg 203 00:11:05,559 --> 00:11:09,600 Speaker 2: under Mary Erdos at JP Morgan. First of all, I'm 204 00:11:09,600 --> 00:11:13,000 Speaker 2: going to say they're the most successful bank asset manager, 205 00:11:13,240 --> 00:11:16,480 Speaker 2: the only one really competing with black Rock and the others. 206 00:11:17,000 --> 00:11:21,040 Speaker 2: So Mary's built a franchise and now they're saying geography 207 00:11:21,160 --> 00:11:24,240 Speaker 2: doesn't matter anymore. The Wall Street Journal had this great 208 00:11:24,280 --> 00:11:27,720 Speaker 2: summary of it. Explain to us how JP Morgan and 209 00:11:27,760 --> 00:11:31,920 Speaker 2: Mary Erdos is blowing up asset management private wealth. 210 00:11:32,240 --> 00:11:35,600 Speaker 6: Well, I think that you know, Mary Aerdos is first 211 00:11:35,640 --> 00:11:39,199 Speaker 6: and foremost thoughtful about the talent across her business, and 212 00:11:39,720 --> 00:11:44,120 Speaker 6: you know, is consistently talking about the strength they have 213 00:11:44,320 --> 00:11:48,480 Speaker 6: as an active manager. But one of the more interesting 214 00:11:49,080 --> 00:11:51,560 Speaker 6: things that Eric Belchunis is, as you know, who's the 215 00:11:51,600 --> 00:11:56,520 Speaker 6: expert on eats, has been talking about, is that they 216 00:11:56,600 --> 00:12:00,439 Speaker 6: are really gaining share in the active etf business and 217 00:12:00,520 --> 00:12:03,120 Speaker 6: so so that's not your that's not your question, but 218 00:12:03,160 --> 00:12:05,240 Speaker 6: that is something that I want to highlight that I 219 00:12:05,240 --> 00:12:06,320 Speaker 6: think is something happening. 220 00:12:07,000 --> 00:12:10,040 Speaker 2: We're in a bull market, they're minting more money than God, 221 00:12:10,320 --> 00:12:12,920 Speaker 2: and JP Morgan is saying, we got a problem. We 222 00:12:13,040 --> 00:12:16,360 Speaker 2: got to blow up private Wealth Management. Why are they 223 00:12:16,360 --> 00:12:18,000 Speaker 2: blowing it up and what's the goal? 224 00:12:18,559 --> 00:12:23,079 Speaker 6: Well, again, I don't know. I think that Mary Erdos 225 00:12:23,200 --> 00:12:27,280 Speaker 6: is a very thoughtful leader, and I think that again, 226 00:12:27,520 --> 00:12:30,160 Speaker 6: it's it's sort of like as we're as we're going 227 00:12:30,280 --> 00:12:33,040 Speaker 6: through the future. We've been through a long period of 228 00:12:33,040 --> 00:12:38,360 Speaker 6: what we call us exceptionalism. But I think that JP 229 00:12:38,480 --> 00:12:42,480 Speaker 6: Morgan has always focused on the world at large, focused 230 00:12:42,520 --> 00:12:47,000 Speaker 6: on emerging markets, focused on these different growth opportunities, and 231 00:12:47,040 --> 00:12:49,160 Speaker 6: I think it's just a way and by the way, 232 00:12:49,280 --> 00:12:52,199 Speaker 6: JP Morgan, I think is always as I alluded to earlier, 233 00:12:52,800 --> 00:12:56,160 Speaker 6: you know, looking at things differently. When you're a global organization, 234 00:12:56,360 --> 00:12:59,600 Speaker 6: you have products and you have regions. I think sometimes 235 00:13:00,080 --> 00:13:02,840 Speaker 6: you have to find a way to focus on both. 236 00:13:03,440 --> 00:13:06,160 Speaker 6: And again I just I go back to the fact 237 00:13:06,160 --> 00:13:10,480 Speaker 6: that they're very focused on talent, and I think that 238 00:13:11,080 --> 00:13:13,720 Speaker 6: you know, they're thoughtful about the way they run their business. 239 00:13:13,840 --> 00:13:16,320 Speaker 4: We're seeing so many good results coming out of these 240 00:13:16,800 --> 00:13:20,600 Speaker 4: US global banks. It just makes me think next week 241 00:13:20,640 --> 00:13:23,439 Speaker 4: we're going to hear from the European banks and they're 242 00:13:23,480 --> 00:13:27,520 Speaker 4: just nowhere, nowhere relative to where the US banks are. 243 00:13:28,440 --> 00:13:32,120 Speaker 4: When will Europe say we need to allow consolidation here? 244 00:13:32,559 --> 00:13:36,880 Speaker 4: I mean, they fall so far behind every single year, 245 00:13:36,920 --> 00:13:38,960 Speaker 4: it seems is it. 246 00:13:38,880 --> 00:13:39,600 Speaker 2: Not an issue for them? 247 00:13:39,640 --> 00:13:39,920 Speaker 5: They do? 248 00:13:40,000 --> 00:13:42,920 Speaker 6: And I think that. I mean, I think the biggest 249 00:13:43,360 --> 00:13:45,560 Speaker 6: change Paul over the last you know, let's let's call 250 00:13:45,640 --> 00:13:48,760 Speaker 6: it a decade or a couple of decades, right time flies, 251 00:13:49,360 --> 00:13:51,280 Speaker 6: is that you know, first of all, the US has 252 00:13:51,480 --> 00:13:54,560 Speaker 6: benefited from a virtuous cycle of having sort of a 253 00:13:54,559 --> 00:13:57,400 Speaker 6: better economy for a long period of time. But really, 254 00:13:57,920 --> 00:14:01,640 Speaker 6: you know, they've they've been from the virtuous cycle of investment. 255 00:14:01,640 --> 00:14:04,080 Speaker 6: They had the money to invest, they invested the money, 256 00:14:04,120 --> 00:14:07,480 Speaker 6: so that's led to better revenue, better market share, et cetera. 257 00:14:07,559 --> 00:14:10,440 Speaker 6: Whereas I think for the Europeans, you know, they try 258 00:14:10,480 --> 00:14:13,240 Speaker 6: to sort of cut their way to profitability and that 259 00:14:13,440 --> 00:14:17,120 Speaker 6: ended up you know, as you know, investment, banking and 260 00:14:17,160 --> 00:14:20,440 Speaker 6: trading these are not businesses where you should go bargain shopping, right, 261 00:14:20,560 --> 00:14:25,360 Speaker 6: and so I think that they didn't invest that hurt them. 262 00:14:25,800 --> 00:14:29,200 Speaker 6: They've had to exit businesses, et cetera. But it's difficult 263 00:14:29,320 --> 00:14:35,360 Speaker 6: because each of the everyone agrees we really should consolidate, 264 00:14:35,560 --> 00:14:37,960 Speaker 6: merge some of the best ones together. But no one 265 00:14:38,000 --> 00:14:40,400 Speaker 6: wants to be the seller. Everyone wants to be the buyer. 266 00:14:40,560 --> 00:14:45,360 Speaker 6: Everybody wants the champion in their home market. That's that's 267 00:14:45,400 --> 00:14:46,160 Speaker 6: the difficulty. 268 00:14:46,960 --> 00:14:49,000 Speaker 2: Are we at the point where the shock of that 269 00:14:49,120 --> 00:14:51,880 Speaker 2: later this year quickly here Allison, as we get one 270 00:14:51,880 --> 00:14:55,000 Speaker 2: big industry roll up because they find price, and as 271 00:14:55,040 --> 00:14:56,440 Speaker 2: the sellers say enough, let's go. 272 00:14:57,320 --> 00:14:59,840 Speaker 6: I mean, you know, we could have had this conversation 273 00:15:00,240 --> 00:15:01,560 Speaker 6: fourteen a million. 274 00:15:01,680 --> 00:15:02,520 Speaker 5: You know, I just. 275 00:15:02,640 --> 00:15:09,480 Speaker 6: Think that those political sensitivities, it's it's very difficult and 276 00:15:09,520 --> 00:15:14,280 Speaker 6: it's hard to say whatever would would change their mind 277 00:15:15,600 --> 00:15:18,720 Speaker 6: makes that makes sense on paper, you know, politics get 278 00:15:18,760 --> 00:15:19,120 Speaker 6: in the way. 279 00:15:19,240 --> 00:15:22,400 Speaker 2: Ten seconds tomorrow, Golden Sacks, Morgan Stanley, they do better than. 280 00:15:22,240 --> 00:15:25,240 Speaker 6: Good I you know, based on the training and feed 281 00:15:25,280 --> 00:15:27,600 Speaker 6: members we saw today, based on all the trends we 282 00:15:27,640 --> 00:15:29,720 Speaker 6: saw in the quarner, we think that it's going to 283 00:15:29,800 --> 00:15:32,960 Speaker 6: be a solid quarter for trading. We're going to want 284 00:15:33,000 --> 00:15:35,680 Speaker 6: to hear what their what their CEO clients are saying 285 00:15:35,880 --> 00:15:38,000 Speaker 6: in regard to the thank you. 286 00:15:37,960 --> 00:15:40,600 Speaker 2: So much for this brief, particularly on you know where 287 00:15:40,600 --> 00:15:45,840 Speaker 2: we are on the reorg of JP Morgan in private management, 288 00:15:45,880 --> 00:15:48,360 Speaker 2: and of course over to the other banks, particular City Bank. 289 00:15:49,560 --> 00:15:53,480 Speaker 1: This is the Bloomberg Surveillance Podcast. Listen live each weekday 290 00:15:53,520 --> 00:15:56,520 Speaker 1: starting at seven am Eastern on Apple Corplay and Android 291 00:15:56,560 --> 00:15:59,560 Speaker 1: Auto with the Bloomberg Business app. You can also listen 292 00:15:59,640 --> 00:16:02,920 Speaker 1: live on Amazon Alexa from our flagship New York station, 293 00:16:03,480 --> 00:16:07,040 Speaker 1: Just say Alexa play Bloomberg eleven thirty where you get. 294 00:16:06,920 --> 00:16:09,200 Speaker 2: The private credit here with Hu von Steinas. Paul's going 295 00:16:09,240 --> 00:16:10,960 Speaker 2: to bring that in, but I got to first do 296 00:16:11,040 --> 00:16:13,960 Speaker 2: an audible here, folks, it's a surveillance audible in a 297 00:16:14,000 --> 00:16:17,960 Speaker 2: two hour conversation with Hugh von Steinas. You go, Okay, 298 00:16:18,000 --> 00:16:20,640 Speaker 2: where did this guy come from? And it's not Oliver 299 00:16:20,760 --> 00:16:22,880 Speaker 2: Wyman or it's worked with the Bank of England with 300 00:16:23,200 --> 00:16:25,920 Speaker 2: at the time Governor Karney Or he did this as 301 00:16:26,160 --> 00:16:30,200 Speaker 2: ubs No Da da da da years ago. I think 302 00:16:30,320 --> 00:16:33,680 Speaker 2: Queen Anne Hugh von Steinas was at Morgan Stanley and 303 00:16:33,760 --> 00:16:39,080 Speaker 2: he was absolutely definitive on EU finance joining us now 304 00:16:39,280 --> 00:16:43,080 Speaker 2: with Oliver Wyman, Hu von steinas audible, I saw the 305 00:16:43,200 --> 00:16:47,800 Speaker 2: French Trustsaur, not the Oat, Hugh, the Trustsaur roll over 306 00:16:47,960 --> 00:16:53,360 Speaker 2: yesterday with acceleration. How fragile is French debt given mister 307 00:16:53,440 --> 00:16:55,440 Speaker 2: Trump Shenanigans. 308 00:16:56,040 --> 00:16:58,480 Speaker 7: Oh, Tom, thanks for having me on again. Look, I 309 00:16:58,520 --> 00:17:01,240 Speaker 7: think there's a lot of pressure from invests for on 310 00:17:01,080 --> 00:17:04,000 Speaker 7: the on the French situation. A lot of fixed income 311 00:17:04,040 --> 00:17:08,160 Speaker 7: investors are struggling with the lack of debt consolidation. And look, 312 00:17:08,200 --> 00:17:10,159 Speaker 7: you know, let's play the history here, Tom. You know, 313 00:17:10,240 --> 00:17:12,840 Speaker 7: I think the French shrout the last thirty years have 314 00:17:12,920 --> 00:17:15,679 Speaker 7: somewhat struggled with reigning and public debt. In fact, if 315 00:17:15,680 --> 00:17:18,000 Speaker 7: you go back to nineteen eighty one eighty three, you know, 316 00:17:18,040 --> 00:17:21,080 Speaker 7: actually Meterol had to do a huge pivot because he 317 00:17:21,200 --> 00:17:23,679 Speaker 7: just wasn't controlling the public debts back there. And so 318 00:17:23,680 --> 00:17:25,800 Speaker 7: I think there's there's real pressure here. And look, the 319 00:17:25,840 --> 00:17:28,840 Speaker 7: fact that Spread's already trading outside Portugal and Spain and 320 00:17:28,840 --> 00:17:30,679 Speaker 7: there's a lot of debate about whether they trade outside 321 00:17:30,760 --> 00:17:35,360 Speaker 7: lately in Greece tells you how stress and unnervous folks are. 322 00:17:35,520 --> 00:17:38,880 Speaker 2: The Jurgon he's our jargon. I mean, he was a 323 00:17:38,920 --> 00:17:42,639 Speaker 2: state dinner and he brought the conversation or Holt with 324 00:17:42,720 --> 00:17:45,800 Speaker 2: his jargon. What he's saying there is French credits, trading 325 00:17:45,880 --> 00:17:49,439 Speaker 2: worser Folks and Portugal, Greece and all here. One final 326 00:17:49,560 --> 00:17:53,000 Speaker 2: question on this and beginning to see real yields inflation 327 00:17:53,080 --> 00:17:57,399 Speaker 2: adjusting yields in Europe come up as well, can President Trump, 328 00:17:57,440 --> 00:18:01,320 Speaker 2: with his tariff debate pull up e who really yields 329 00:18:01,359 --> 00:18:03,200 Speaker 2: to bring Europe to a standstill? 330 00:18:05,320 --> 00:18:08,359 Speaker 7: It's a great question. It's certainly possible. I think, look, 331 00:18:08,480 --> 00:18:10,800 Speaker 7: isn't the issue with taris at the moment that the 332 00:18:10,840 --> 00:18:13,160 Speaker 7: short term pain is not there, and it's a question 333 00:18:13,240 --> 00:18:16,600 Speaker 7: more about how it impacts investments and the longer term business. 334 00:18:16,640 --> 00:18:18,520 Speaker 7: And just like you've seen the resilience in the States, 335 00:18:18,760 --> 00:18:21,840 Speaker 7: you're seeing real resilience in the European earnings and particularly 336 00:18:21,880 --> 00:18:24,440 Speaker 7: the opening of the fiscal that so I'm not sure 337 00:18:24,480 --> 00:18:27,720 Speaker 7: that it would necessarily drag it up immediately, but I 338 00:18:27,720 --> 00:18:30,679 Speaker 7: think that tension over a one to three year period, 339 00:18:31,119 --> 00:18:33,040 Speaker 7: that's where you're going to start seeing, is they're going 340 00:18:33,119 --> 00:18:36,119 Speaker 7: to be more friction in the system from lack of competitiveness, 341 00:18:36,280 --> 00:18:38,320 Speaker 7: and let's hope certainly by then we're going to see 342 00:18:38,320 --> 00:18:40,840 Speaker 7: a deal. So I'm not as convinced on that, but 343 00:18:40,880 --> 00:18:41,960 Speaker 7: you know, anything's possible. 344 00:18:42,560 --> 00:18:45,440 Speaker 4: You were starting to hear from the big banks today 345 00:18:45,480 --> 00:18:48,359 Speaker 4: in one of the areas of questioning for the banks 346 00:18:48,359 --> 00:18:52,000 Speaker 4: will be kind of how they view private credit visa v. 347 00:18:52,119 --> 00:18:55,800 Speaker 4: Their core lending business. How do you guys view what 348 00:18:55,880 --> 00:18:58,640 Speaker 4: has been just a phenomenal growth story and global wallstree, 349 00:18:58,680 --> 00:19:00,160 Speaker 4: which is private credit. 350 00:19:01,920 --> 00:19:05,399 Speaker 7: Look, so, I think the private credit train continues, and 351 00:19:05,480 --> 00:19:08,600 Speaker 7: as we've discussed many times, you know that sector is 352 00:19:08,600 --> 00:19:12,080 Speaker 7: growing twenty to twenty five percent compound because it's not 353 00:19:12,240 --> 00:19:15,680 Speaker 7: just about leverage lending and mid market lending. It's also 354 00:19:15,800 --> 00:19:20,080 Speaker 7: now about replacing investment grade, either through asset back lending, 355 00:19:20,160 --> 00:19:22,960 Speaker 7: so you know, so like private securitizations, or it's going 356 00:19:22,960 --> 00:19:26,159 Speaker 7: for these jumbo deals, you know, these really large complex deals, 357 00:19:26,320 --> 00:19:30,280 Speaker 7: and under current bank regulations, if it's risky, if it's 358 00:19:30,640 --> 00:19:33,640 Speaker 7: very large or very complex, it goes to the private 359 00:19:33,680 --> 00:19:36,600 Speaker 7: markets rather than the public. So I think there's two 360 00:19:36,640 --> 00:19:38,920 Speaker 7: games here. I think the first is the banks are 361 00:19:39,000 --> 00:19:41,639 Speaker 7: clearly fighting back, and you saw the news from one 362 00:19:41,640 --> 00:19:43,919 Speaker 7: of the big banks yesterday which you ran showing a 363 00:19:43,960 --> 00:19:46,480 Speaker 7: new group to try and come up with solutions for 364 00:19:46,600 --> 00:19:49,880 Speaker 7: more complex deals. Maybe the bank takes part. Maybe it's 365 00:19:49,880 --> 00:19:52,080 Speaker 7: sliced up and gives part to private credit, but the 366 00:19:52,119 --> 00:19:54,600 Speaker 7: banks are very much back. But I think the other story, 367 00:19:54,600 --> 00:19:56,200 Speaker 7: and I think you'll see it through this earning season, 368 00:19:56,280 --> 00:20:00,479 Speaker 7: is banks love steep yield curves, and so the interest 369 00:20:00,520 --> 00:20:02,840 Speaker 7: income for the banks I think will continue to beat 370 00:20:02,920 --> 00:20:06,200 Speaker 7: and beat and beat because these steep curves will transferrate 371 00:20:06,359 --> 00:20:09,480 Speaker 7: through to higher earnings. And therefore, even though they're seeding 372 00:20:09,520 --> 00:20:12,680 Speaker 7: ground to private credit, the core engines are still doing 373 00:20:12,720 --> 00:20:13,200 Speaker 7: really well. 374 00:20:13,840 --> 00:20:17,479 Speaker 4: So for the private credit business, are there opportunities for 375 00:20:18,240 --> 00:20:20,919 Speaker 4: retail investors to get exposure to this asset class? 376 00:20:23,240 --> 00:20:25,280 Speaker 7: Yeah, this is a great question. So we've been digging 377 00:20:25,280 --> 00:20:27,520 Speaker 7: into this. So there's been a two and a half 378 00:20:27,560 --> 00:20:31,199 Speaker 7: fold increase in the investments by wealthy in private credit 379 00:20:31,240 --> 00:20:34,880 Speaker 7: in the last three years. That's four times faster than 380 00:20:34,880 --> 00:20:39,200 Speaker 7: from traditional institutional investors. We think something like three hundred 381 00:20:39,240 --> 00:20:42,639 Speaker 7: and fifty billion dollars, or about twelve percent of private 382 00:20:42,640 --> 00:20:45,840 Speaker 7: credit assets now come from the wealthy. Now the wealthy, 383 00:20:45,880 --> 00:20:49,119 Speaker 7: though it's still it's the ultra wealthy. And one of 384 00:20:49,119 --> 00:20:52,880 Speaker 7: the biggest areas of innovation in partnerships and new products 385 00:20:53,200 --> 00:20:55,560 Speaker 7: is trying to think about expanding this to a much 386 00:20:55,600 --> 00:20:59,479 Speaker 7: broader range of clients through Evergreen Funds, and you know, 387 00:20:59,560 --> 00:21:03,960 Speaker 7: just once at Evergreen Funds are probably the single fastest 388 00:21:03,960 --> 00:21:07,399 Speaker 7: growing part of the investment universe at the moment, interval 389 00:21:07,440 --> 00:21:09,919 Speaker 7: grums are growing seventy percent from a low base. So 390 00:21:09,960 --> 00:21:12,040 Speaker 7: I think this is a narrow where there's there's quite 391 00:21:12,080 --> 00:21:15,040 Speaker 7: a lot of room to run from here with. 392 00:21:15,200 --> 00:21:17,640 Speaker 2: Us, Hugh van Stinas, with Oliver Wyman, he's too madest. 393 00:21:17,680 --> 00:21:19,480 Speaker 2: Let me put it to you. It's a it's a 394 00:21:19,600 --> 00:21:22,320 Speaker 2: really I hate him. There's a twenty one page document 395 00:21:22,440 --> 00:21:27,200 Speaker 2: Oliver Wyman. Private credit is reshaping wealth portfolios. Get it 396 00:21:27,280 --> 00:21:31,000 Speaker 2: through Oliver Wyman. We do protect the copyright of all 397 00:21:31,040 --> 00:21:34,000 Speaker 2: of our guests, Hu von Steinas, Ben Phillips, Laura Watkin 398 00:21:34,080 --> 00:21:37,960 Speaker 2: and parth Aggerwall as well. What's the risk factor here? 399 00:21:38,040 --> 00:21:40,920 Speaker 2: A wise one? Is it too much money chasing too 400 00:21:41,000 --> 00:21:44,800 Speaker 2: few private credit opportunities? Is that the quiet quality is 401 00:21:44,840 --> 00:21:48,080 Speaker 2: going to go down? Is the ir ar GONFC? I 402 00:21:48,160 --> 00:21:50,760 Speaker 2: did that. It's impressive, so I'll get out here. What's 403 00:21:50,800 --> 00:21:52,320 Speaker 2: the major risk factor here? 404 00:21:53,440 --> 00:21:56,080 Speaker 7: Look, it's great, well, I'd say so. In the net 405 00:21:56,400 --> 00:22:00,520 Speaker 7: so neartum. The biggest threat is just too much money chasing, 406 00:22:00,560 --> 00:22:03,680 Speaker 7: and credit spreads continue to depress and compress. And look, 407 00:22:03,680 --> 00:22:05,680 Speaker 7: we've already gone from you know, if you were trying 408 00:22:05,680 --> 00:22:09,160 Speaker 7: to buy a single a private credit bond two years ago, 409 00:22:09,359 --> 00:22:11,800 Speaker 7: you might be getting two percent more than a public 410 00:22:11,960 --> 00:22:14,240 Speaker 7: single A last year, it's probably one hundred and fifty 411 00:22:14,280 --> 00:22:16,680 Speaker 7: one hundred and seventy five bases over basis points. Over 412 00:22:16,840 --> 00:22:18,840 Speaker 7: today it's probably only a point. So it's probably the 413 00:22:19,240 --> 00:22:22,479 Speaker 7: excess spread is probably halved in less than twenty four months, 414 00:22:22,560 --> 00:22:24,159 Speaker 7: and so there might be if there's a lot of 415 00:22:24,160 --> 00:22:27,679 Speaker 7: money coming through. So therefore the constraint is really about 416 00:22:27,720 --> 00:22:32,520 Speaker 7: originating more assets, and so you're seeing more deals with 417 00:22:32,760 --> 00:22:35,840 Speaker 7: private credit team up with banks for partnerships, but also 418 00:22:35,880 --> 00:22:38,680 Speaker 7: looking to generate their own assets. And I think that's 419 00:22:38,760 --> 00:22:40,960 Speaker 7: the gate for me. If they don't originate enough assets, 420 00:22:41,200 --> 00:22:44,159 Speaker 7: then the spreads will compress. I think longer term, clearly 421 00:22:44,160 --> 00:22:46,080 Speaker 7: there's always going to be a credit cycle and so 422 00:22:46,200 --> 00:22:47,800 Speaker 7: that you know, and there'll be some good decisions and 423 00:22:47,800 --> 00:22:49,600 Speaker 7: bad decisions. But I think at the moment it's the 424 00:22:49,600 --> 00:22:50,800 Speaker 7: weight of money Q. 425 00:22:51,000 --> 00:22:55,280 Speaker 4: I mean, the dollars are getting just really substantial there. 426 00:22:55,280 --> 00:22:58,000 Speaker 4: Over the last decade or so, we're starting to get 427 00:22:58,000 --> 00:23:00,879 Speaker 4: more and more retail exposure there, so there need to 428 00:23:00,920 --> 00:23:03,480 Speaker 4: be some type of regulation on the private credit business, do. 429 00:23:03,440 --> 00:23:03,960 Speaker 1: You think. 430 00:23:05,480 --> 00:23:05,640 Speaker 2: So? 431 00:23:05,720 --> 00:23:08,320 Speaker 7: I think it depends what kind of regulation. So I 432 00:23:08,359 --> 00:23:11,400 Speaker 7: don't see the sector as systemic. I mean, let's remember 433 00:23:11,880 --> 00:23:15,159 Speaker 7: banks get regulated partly because they hold everyone's deposits, but 434 00:23:15,240 --> 00:23:19,160 Speaker 7: also they're very leveraged. And so when credit leaves a bank, 435 00:23:19,200 --> 00:23:21,800 Speaker 7: which it may be is ten times levered, maybe going 436 00:23:21,840 --> 00:23:25,120 Speaker 7: to an institutional account, which may be zero to one 437 00:23:25,160 --> 00:23:28,840 Speaker 7: times levered, less leverage means it's less risky when you 438 00:23:28,880 --> 00:23:31,840 Speaker 7: hit a speedbump. So I don't see a systemic But 439 00:23:31,960 --> 00:23:36,280 Speaker 7: the more you put private credit into retail portfolios, the 440 00:23:36,280 --> 00:23:40,120 Speaker 7: more that then, you know, the sec and regulators looking 441 00:23:40,200 --> 00:23:43,040 Speaker 7: at the products and protecting investors will need to think 442 00:23:43,080 --> 00:23:46,679 Speaker 7: about is this the right product? Have we got regular marks? 443 00:23:46,680 --> 00:23:46,840 Speaker 2: You know? 444 00:23:47,000 --> 00:23:48,399 Speaker 7: And I think we discussed this last time I was 445 00:23:48,440 --> 00:23:50,800 Speaker 7: in New York, the pricing conventions, So the way you 446 00:23:50,840 --> 00:23:53,359 Speaker 7: think about pricing private credit, you can't just do it 447 00:23:53,359 --> 00:23:57,040 Speaker 7: once a quarter every month, and if not more frequently. 448 00:23:57,119 --> 00:23:59,720 Speaker 7: So there's a lot of regulatory stuff which we need 449 00:23:59,760 --> 00:24:00,080 Speaker 7: to play. 450 00:24:00,560 --> 00:24:03,800 Speaker 2: I promise Dan Tannebaum, I'd read your private credit report here, 451 00:24:03,840 --> 00:24:05,840 Speaker 2: I'll get a PIMS in my hand and read that 452 00:24:05,960 --> 00:24:09,679 Speaker 2: this weekend, huvon Stein is definitive on private credit with 453 00:24:10,000 --> 00:24:16,120 Speaker 2: Oliver Wyman. 454 00:24:16,800 --> 00:24:20,679 Speaker 1: This is the Bloomberg Surveillance Podcast. Listen live each weekday 455 00:24:20,720 --> 00:24:23,719 Speaker 1: starting at seven am Eastern on Apple Corplay and Android 456 00:24:23,760 --> 00:24:26,800 Speaker 1: Auto with the Bloomberg Business app. You can also watch 457 00:24:26,840 --> 00:24:29,800 Speaker 1: us live every weekday on YouTube and always on the 458 00:24:29,840 --> 00:24:30,920 Speaker 1: Bloomberg terminal. 459 00:24:31,160 --> 00:24:33,159 Speaker 2: If we're not going to turn this into a history lesson, 460 00:24:33,560 --> 00:24:37,280 Speaker 2: but the bottom line is at LC in the fifties, 461 00:24:37,840 --> 00:24:41,040 Speaker 2: Carl Popper sat with a young student named George Soros 462 00:24:41,440 --> 00:24:44,240 Speaker 2: and said, the way we think about history, the way 463 00:24:44,280 --> 00:24:48,800 Speaker 2: we digest past tense data, is maybe not as productive 464 00:24:48,840 --> 00:24:52,080 Speaker 2: as it could be with something called reflexivity, which is 465 00:24:52,119 --> 00:24:55,960 Speaker 2: a different approach. If AI is about a large language 466 00:24:55,960 --> 00:24:59,520 Speaker 2: model data dump, how are we going to move artificial 467 00:24:59,560 --> 00:25:03,240 Speaker 2: intelli eins onto what Soorrols and Popper we're talking about. 468 00:25:04,600 --> 00:25:06,480 Speaker 5: That's a great question. First of all, thank you for 469 00:25:06,520 --> 00:25:09,160 Speaker 5: having me on the show. I think as what we're 470 00:25:09,160 --> 00:25:13,080 Speaker 5: seeing currently, what is really exciting about the technology that 471 00:25:13,160 --> 00:25:15,919 Speaker 5: we're witnessing right in front of us is that it 472 00:25:16,000 --> 00:25:19,919 Speaker 5: has this capability to digest huge amounts of data and 473 00:25:20,040 --> 00:25:22,280 Speaker 5: start to make sense of it, help us make sense 474 00:25:22,280 --> 00:25:24,040 Speaker 5: of it. And I think that we have always looked 475 00:25:24,080 --> 00:25:26,840 Speaker 5: to the past to try to understand the future because 476 00:25:26,880 --> 00:25:29,679 Speaker 5: that's the only data that we had right And what 477 00:25:29,920 --> 00:25:33,639 Speaker 5: is different this time versus before is that we seem 478 00:25:33,680 --> 00:25:36,800 Speaker 5: to have been accumulating more and more data but didn't 479 00:25:36,800 --> 00:25:41,160 Speaker 5: have increased bandwidth to process it. In my mind, AI 480 00:25:41,240 --> 00:25:42,119 Speaker 5: is going to resolve this. 481 00:25:42,320 --> 00:25:44,960 Speaker 2: Okay, it's going to resolve it, but it's just nothing 482 00:25:45,000 --> 00:25:48,760 Speaker 2: more than a richer, deeper back test. Everybody did beck 483 00:25:48,800 --> 00:25:51,040 Speaker 2: to us on a difference equation. We went back to 484 00:25:51,080 --> 00:25:54,919 Speaker 2: T minus one or T minus twenty. Whatever is AI 485 00:25:55,040 --> 00:25:59,040 Speaker 2: going to change? How Global Wall Street, the pros listening 486 00:25:59,080 --> 00:26:00,040 Speaker 2: to the show. 487 00:26:00,000 --> 00:26:02,840 Speaker 5: How they back test it is going to change it? 488 00:26:02,880 --> 00:26:06,080 Speaker 5: Because actually, I'll let you know that the experience that 489 00:26:06,119 --> 00:26:08,800 Speaker 5: we have had so Reflexity platform is used by a 490 00:26:08,880 --> 00:26:10,919 Speaker 5: number of top head truants and what we have found 491 00:26:10,960 --> 00:26:14,000 Speaker 5: as they start to incorporate this into their workflow is 492 00:26:14,040 --> 00:26:16,479 Speaker 5: that they are amazed by the speed with which the 493 00:26:17,040 --> 00:26:19,840 Speaker 5: system is able to answer their questions by going straight 494 00:26:19,880 --> 00:26:22,919 Speaker 5: to the data. In fact, often they run out of 495 00:26:23,000 --> 00:26:25,800 Speaker 5: questions because you were not used to being able to 496 00:26:25,840 --> 00:26:29,560 Speaker 5: tap into the data so powerfully and so quickly, and 497 00:26:29,640 --> 00:26:32,760 Speaker 5: so we seem to now be limited only by our imagination. 498 00:26:32,880 --> 00:26:36,439 Speaker 5: So the way I think about AI helping investors on 499 00:26:36,480 --> 00:26:39,880 Speaker 5: Wall Street is giving them this superpower where they can 500 00:26:40,000 --> 00:26:43,199 Speaker 5: keep thinking about scenarios that can unfold and have the 501 00:26:43,280 --> 00:26:44,320 Speaker 5: AI back that. 502 00:26:44,359 --> 00:26:46,320 Speaker 2: Up or dispute it through the data. 503 00:26:46,400 --> 00:26:47,679 Speaker 5: It's a very objective arbiter. 504 00:26:47,920 --> 00:26:49,679 Speaker 4: Can you give us, and this is so new to 505 00:26:49,800 --> 00:26:53,240 Speaker 4: so many of us, our listeners, our viewers, what is 506 00:26:53,359 --> 00:26:56,080 Speaker 4: a typical query that you would get from an investor 507 00:26:56,359 --> 00:26:58,239 Speaker 4: client that maybe AI could be helpful with. 508 00:26:58,920 --> 00:27:02,200 Speaker 5: Well, it started with things that were extremely basic in nature. 509 00:27:02,240 --> 00:27:04,040 Speaker 5: Somebody might just say like, oh, you know, I'd like 510 00:27:04,080 --> 00:27:07,639 Speaker 5: to really understand when somebody talks about the inverted yield curve, 511 00:27:08,119 --> 00:27:11,920 Speaker 5: how accurate has that actually been in predicting recessions. It 512 00:27:12,000 --> 00:27:15,359 Speaker 5: then became a lot more complex when you might say, 513 00:27:16,119 --> 00:27:19,399 Speaker 5: look at all of financials for a specific company, be 514 00:27:19,400 --> 00:27:22,679 Speaker 5: a Tesla, Microsoft, jpm organ, whatever, tell me what is 515 00:27:22,680 --> 00:27:26,680 Speaker 5: the market not appreciating? And suddenly it's pulling together things. 516 00:27:26,720 --> 00:27:30,919 Speaker 5: It's able to glean from endless reports, news outlets and 517 00:27:31,000 --> 00:27:34,320 Speaker 5: financials and say, well, actually here are some parts of 518 00:27:34,359 --> 00:27:37,520 Speaker 5: the business that are growing extremely fast but get no 519 00:27:37,680 --> 00:27:40,119 Speaker 5: mention from the analysts, and so you could have a 520 00:27:40,160 --> 00:27:43,280 Speaker 5: rebrating the dream here is that And this is what 521 00:27:43,359 --> 00:27:47,399 Speaker 5: Reflexivity reports to do. Is if you were the first 522 00:27:47,400 --> 00:27:49,440 Speaker 5: to know that AWS was going to be a big 523 00:27:49,440 --> 00:27:52,639 Speaker 5: deal for Amazon, you would have made a lot of money. 524 00:27:53,520 --> 00:27:53,760 Speaker 2: Wow. 525 00:27:53,880 --> 00:27:59,320 Speaker 4: So this is such an amazing part of the investment process. 526 00:28:00,040 --> 00:28:02,520 Speaker 4: How are your clients using it today? I mean, how 527 00:28:02,600 --> 00:28:05,719 Speaker 4: is that evolving? Because I'm assuming this is an education 528 00:28:05,960 --> 00:28:09,119 Speaker 4: process for some pretty smart investors, some savvy investors, but 529 00:28:09,240 --> 00:28:12,840 Speaker 4: maybe they don't understand the logic behind artificial intelligence. How 530 00:28:12,840 --> 00:28:14,880 Speaker 4: are they using it today? And how do you want 531 00:28:14,920 --> 00:28:15,560 Speaker 4: them to use it? 532 00:28:17,240 --> 00:28:20,600 Speaker 5: Honestly, our first advice to the pioneering clients have come 533 00:28:20,640 --> 00:28:22,480 Speaker 5: to us and start using it, and there have been 534 00:28:23,200 --> 00:28:26,280 Speaker 5: an increasing number in the past few weeks, is just 535 00:28:26,600 --> 00:28:28,760 Speaker 5: dip their toes into a try it. I think trial 536 00:28:28,800 --> 00:28:30,879 Speaker 5: and error is very important because I think working with 537 00:28:31,000 --> 00:28:34,280 Speaker 5: AI is unlike any other software that you would have 538 00:28:34,359 --> 00:28:37,520 Speaker 5: used before. Right, where does it get data, how does 539 00:28:37,520 --> 00:28:38,880 Speaker 5: it calculate, how does it reason? 540 00:28:38,920 --> 00:28:39,440 Speaker 2: And so on? 541 00:28:39,440 --> 00:28:41,840 Speaker 5: Once you get the hang of it, however, it becomes 542 00:28:41,880 --> 00:28:44,360 Speaker 5: extremely exciting, and you can see people just get engaged 543 00:28:44,400 --> 00:28:44,520 Speaker 5: with it. 544 00:28:44,560 --> 00:28:46,720 Speaker 2: Okay, you grew up in a milieu of Harvard, MIT 545 00:28:46,960 --> 00:28:50,560 Speaker 2: and all the fancy people up in Boston. Black Shoals 546 00:28:50,640 --> 00:28:53,040 Speaker 2: came out, and I never met Fisher Black, to my 547 00:28:53,120 --> 00:28:56,160 Speaker 2: great regret, he died tragically early. But what a joy 548 00:28:56,200 --> 00:28:59,320 Speaker 2: I've had in discussing myrone Shoals and picking up the 549 00:28:59,360 --> 00:29:04,200 Speaker 2: pieces of nineteen ninety eight. How does a modeling that's new, 550 00:29:04,600 --> 00:29:09,520 Speaker 2: wrapped around reflexivity, How does this tested model work given 551 00:29:09,680 --> 00:29:15,880 Speaker 2: substantial leverage shocks the discontinuities that are brought out by leverage, 552 00:29:16,080 --> 00:29:17,160 Speaker 2: as a pro would. 553 00:29:17,040 --> 00:29:23,280 Speaker 5: Use So again, I think here the calculations that the 554 00:29:23,320 --> 00:29:27,760 Speaker 5: system will do do fall back on the traditional statistical methods. 555 00:29:27,840 --> 00:29:30,480 Speaker 5: It's not reinventing the way you're calculating things or the 556 00:29:30,480 --> 00:29:33,600 Speaker 5: way you're calculating correlations. What it's able to do, however, 557 00:29:34,320 --> 00:29:38,200 Speaker 5: is understand the analysis that you're trying to achieve and 558 00:29:38,240 --> 00:29:41,320 Speaker 5: then potentially do that analysis in three or four different ways. 559 00:29:41,600 --> 00:29:43,880 Speaker 2: Which side of the log normal curves aside. I can 560 00:29:43,880 --> 00:29:46,959 Speaker 2: see you with slide fern Rogoff in some PhD belllet 561 00:29:47,120 --> 00:29:51,560 Speaker 2: going Gaussi into lognormal toplasson distribution on a vanilla log 562 00:29:51,640 --> 00:29:55,080 Speaker 2: normal distribution, Kyl, and I'm doing this for you. It's jargon. 563 00:29:55,480 --> 00:29:59,120 Speaker 2: On a log normal distribution, the short side's more difficult 564 00:29:59,160 --> 00:30:03,800 Speaker 2: to manage alongside. Do you get better alpha pickup short 565 00:30:03,920 --> 00:30:05,720 Speaker 2: in the market versus a long market? 566 00:30:06,280 --> 00:30:08,720 Speaker 5: Actually, I think it's quite symmetric. It's true that generally 567 00:30:08,760 --> 00:30:13,080 Speaker 5: when you look at how investors perform in buying versus 568 00:30:13,200 --> 00:30:15,760 Speaker 5: getting out of positions, they're generally worse at getting out 569 00:30:15,760 --> 00:30:18,240 Speaker 5: of positions. They're quite good at picking them, but not 570 00:30:18,400 --> 00:30:20,400 Speaker 5: so good at getting rid of them. They typically do 571 00:30:20,480 --> 00:30:22,880 Speaker 5: it when they have to. So here the AI can 572 00:30:22,960 --> 00:30:26,440 Speaker 5: be helpful because it's able to actively This is what 573 00:30:26,600 --> 00:30:29,760 Speaker 5: reflexivity does spot something in the market. Let's say that 574 00:30:29,880 --> 00:30:32,000 Speaker 5: interest rates are starting to move much faster than they 575 00:30:32,080 --> 00:30:34,560 Speaker 5: have been, and you'll point to your banking stocks and 576 00:30:34,640 --> 00:30:37,400 Speaker 5: say you should pay attention to this because it could 577 00:30:37,480 --> 00:30:39,720 Speaker 5: be that there is a lot more downside races than 578 00:30:39,760 --> 00:30:40,240 Speaker 5: you realize. 579 00:30:40,320 --> 00:30:43,160 Speaker 2: This is great, thank you, So please don't be a stranger. 580 00:30:43,400 --> 00:30:46,760 Speaker 2: Yan Silagi with us with reflexivity. This is a tool 581 00:30:46,880 --> 00:30:49,880 Speaker 2: that's being used for a lot of sophisticated investors to 582 00:30:49,920 --> 00:30:52,680 Speaker 2: get out front of mind create an edge. His work 583 00:30:52,720 --> 00:30:56,440 Speaker 2: in academics with Ken Roe Gough Harvard is loaded. 584 00:30:56,920 --> 00:31:01,160 Speaker 1: This is the Bloomberg Surveillance Podcast. Listen each weekday starting 585 00:31:01,200 --> 00:31:04,160 Speaker 1: at seven am Eastern on Apple Corplay and Android Auto 586 00:31:04,320 --> 00:31:07,240 Speaker 1: with the Bloomberg Business app. You can also listen live 587 00:31:07,360 --> 00:31:10,880 Speaker 1: on Amazon Alexa from our flagship New York station. Just 588 00:31:10,960 --> 00:31:14,880 Speaker 1: say Alexa Play Bloomberg eleven thirty at the newspapers. 589 00:31:15,160 --> 00:31:18,520 Speaker 2: It's a truncated version day. She came in a little parched. 590 00:31:18,640 --> 00:31:20,880 Speaker 2: I think you out, I mean what I should say? Yeah, 591 00:31:21,160 --> 00:31:24,360 Speaker 2: after the floods yesterday, Lisa Matero Trooper made it into 592 00:31:24,480 --> 00:31:27,080 Speaker 2: work today. If you serious floods in New Jersey? What 593 00:31:27,160 --> 00:31:27,479 Speaker 2: do you got? 594 00:31:27,720 --> 00:31:29,880 Speaker 8: Okay, Paul, you have to still had that big European 595 00:31:29,920 --> 00:31:32,240 Speaker 8: trip coming up, right, Okay, Okay, So this one's for you. 596 00:31:32,320 --> 00:31:33,920 Speaker 8: I saw this on the Wall Street Journal. It stood 597 00:31:33,960 --> 00:31:37,880 Speaker 8: out American travelers have less buying power this summer because 598 00:31:37,880 --> 00:31:40,080 Speaker 8: of the weekening dollar. So the Wall Street Journal said 599 00:31:40,120 --> 00:31:42,360 Speaker 8: the dollar week in thirteen percent against a euro this 600 00:31:42,480 --> 00:31:45,880 Speaker 8: year six percent against the Japanese yen. But the thing is, 601 00:31:45,960 --> 00:31:48,960 Speaker 8: it's not stopping travelers from booking trips, as you know. Okay, 602 00:31:49,120 --> 00:31:52,800 Speaker 8: people say they just might buy less while they're there. Probably, 603 00:31:52,960 --> 00:31:54,920 Speaker 8: I don't know if you plan to buy less while 604 00:31:54,960 --> 00:31:55,320 Speaker 8: you're there. 605 00:31:55,400 --> 00:31:57,120 Speaker 4: I may go down a little bit lower on the 606 00:31:57,200 --> 00:31:59,600 Speaker 4: wine list. Maybe when I'm in Italy, I might not 607 00:31:59,680 --> 00:32:03,680 Speaker 4: go for the highest end. But we'll be fine, We'll 608 00:32:03,720 --> 00:32:04,040 Speaker 4: be fine. 609 00:32:04,200 --> 00:32:05,680 Speaker 8: Yeah, And then they to also point out to you 610 00:32:05,760 --> 00:32:07,880 Speaker 8: on the bright side, it's made kind of exports cheaper, 611 00:32:07,920 --> 00:32:10,080 Speaker 8: open up opportunities for Americans to invest in those foreign 612 00:32:10,160 --> 00:32:12,280 Speaker 8: stocks as well. But an interesting take on the weekends. 613 00:32:12,280 --> 00:32:14,360 Speaker 4: And I'm hearing, you know, hear from the airlines. I 614 00:32:14,400 --> 00:32:16,360 Speaker 4: mean that, you know, the airlines took up their guidance 615 00:32:16,400 --> 00:32:20,320 Speaker 4: and reintroduced their guidance, saying that the air travels strong, 616 00:32:20,520 --> 00:32:24,560 Speaker 4: including us to Europe. So you know, if you talk 617 00:32:24,600 --> 00:32:27,080 Speaker 4: to our friends in London or Paris, they're like, they're 618 00:32:27,080 --> 00:32:29,800 Speaker 4: Americans everywhere. I'm not sure they're happy about it. 619 00:32:29,960 --> 00:32:32,200 Speaker 2: I mean, I looked at the Bulgary menu with lots 620 00:32:32,240 --> 00:32:35,720 Speaker 2: hurras of the deck. They're overlooking the famous new monuments. 621 00:32:35,880 --> 00:32:41,160 Speaker 2: They just reopened the Carpaccio di Spignola on Alio Lemon. 622 00:32:42,000 --> 00:32:44,480 Speaker 2: I can't read the rest. Twenty two euros. I mean, well, 623 00:32:44,560 --> 00:32:46,840 Speaker 2: that's that's what you expected the bulgari that's going to 624 00:32:46,920 --> 00:32:48,640 Speaker 2: cost you a couple dollars more. 625 00:32:48,760 --> 00:32:49,440 Speaker 1: I know it is. 626 00:32:49,840 --> 00:32:50,800 Speaker 2: We'll be there, We'll be there. 627 00:32:51,240 --> 00:32:53,120 Speaker 8: Do you cut back on the Sylvan years? Maybe you 628 00:32:53,160 --> 00:32:54,560 Speaker 8: don't get presents for everyone? 629 00:32:54,800 --> 00:32:54,920 Speaker 1: Right? 630 00:32:55,160 --> 00:32:59,560 Speaker 2: Exactly, there's a you come down to Spanish steps and 631 00:32:59,640 --> 00:33:06,040 Speaker 2: there's a a souvenir stores called like Gucciz. It's a 632 00:33:06,760 --> 00:33:09,480 Speaker 2: that's a you're looking at next. 633 00:33:09,840 --> 00:33:14,800 Speaker 8: Okay, Starbucks corporate managers who are working remotely currently, they're 634 00:33:14,840 --> 00:33:17,160 Speaker 8: gonna have to pack their bags relocate. They have one 635 00:33:17,240 --> 00:33:19,960 Speaker 8: year to do it. They have to go to Seattle 636 00:33:20,120 --> 00:33:23,240 Speaker 8: or Toronto within twelve months. So they put out this 637 00:33:23,720 --> 00:33:27,320 Speaker 8: back to Starbucks plan. Corporate employees are now having to 638 00:33:27,360 --> 00:33:29,480 Speaker 8: come back four days in the office a week, up 639 00:33:29,520 --> 00:33:32,600 Speaker 8: from three days starting September twenty ninth. 640 00:33:33,160 --> 00:33:35,720 Speaker 4: But what's iron days they were still on? 641 00:33:35,880 --> 00:33:37,760 Speaker 1: I know they were still on. 642 00:33:37,800 --> 00:33:38,360 Speaker 8: The three days. 643 00:33:39,960 --> 00:33:40,120 Speaker 2: Yeah. 644 00:33:40,160 --> 00:33:42,959 Speaker 8: Actually, But the thing that was ironic to me too 645 00:33:43,160 --> 00:33:45,960 Speaker 8: was that Starbucks CEO Brian Nicol like he wasn't required 646 00:33:46,040 --> 00:33:49,280 Speaker 8: to really when he first started, but the company says 647 00:33:49,400 --> 00:33:52,280 Speaker 8: he does have a residence and an office in Seattle now, 648 00:33:52,520 --> 00:33:54,680 Speaker 8: so so yeah, so he has come back. 649 00:33:55,040 --> 00:33:57,400 Speaker 2: Are they are we still doing Are they doing well? 650 00:33:57,640 --> 00:33:58,880 Speaker 4: I mean no, they're not. 651 00:33:59,000 --> 00:34:01,480 Speaker 8: The thing is like sa else they posted five consecutive 652 00:34:01,560 --> 00:34:02,640 Speaker 8: quarters of safe store sales. 653 00:34:02,880 --> 00:34:05,480 Speaker 2: Line stock has going nowhere during the pandemic. 654 00:34:05,680 --> 00:34:09,040 Speaker 8: Essentially, he's trying like they're trying to overhaul the cafes, 655 00:34:09,120 --> 00:34:11,240 Speaker 8: make them more welcoming, trying different things. 656 00:34:11,360 --> 00:34:13,600 Speaker 4: But I mean I have seen a change. I mean 657 00:34:13,680 --> 00:34:17,960 Speaker 4: they're actually writing on the cups again. Yes, yes, that's 658 00:34:17,960 --> 00:34:22,320 Speaker 4: a big I have a sophisticated analysis there, so you 659 00:34:22,360 --> 00:34:25,359 Speaker 4: know it's not just but people are just the drive 660 00:34:25,480 --> 00:34:28,640 Speaker 4: through line is out the and there's nobody in the store. 661 00:34:28,840 --> 00:34:31,279 Speaker 2: I could tell you they do Costa's ben wall. They're 662 00:34:31,320 --> 00:34:35,480 Speaker 2: not right, exactly where do you go the next one? 663 00:34:35,560 --> 00:34:37,360 Speaker 8: Okay, you're gonna have to help me with this. 664 00:34:37,480 --> 00:34:37,719 Speaker 3: Okay. 665 00:34:37,960 --> 00:34:39,800 Speaker 8: So the NC double A right, they want to expand 666 00:34:39,880 --> 00:34:42,880 Speaker 8: March Madness, but the tournament might not be enough to 667 00:34:42,920 --> 00:34:44,920 Speaker 8: pull in more money by growing it. So they have 668 00:34:45,000 --> 00:34:48,240 Speaker 8: another option on the table, and that is going to booze. 669 00:34:48,320 --> 00:34:51,840 Speaker 8: We're talking beer, wine, alcohol. These are things that the 670 00:34:51,960 --> 00:34:54,719 Speaker 8: NC DOUBLEA has kind of steered away from. But here's 671 00:34:54,719 --> 00:34:56,480 Speaker 8: where it gets a little confusing for me too. But 672 00:34:56,840 --> 00:35:00,320 Speaker 8: the NC double A can't sell those sponsorships themselves, so 673 00:35:00,440 --> 00:35:03,759 Speaker 8: they have to persuade the broadcasters CBS and Turner to 674 00:35:03,840 --> 00:35:07,000 Speaker 8: do it and then increase their own annual payment to 675 00:35:07,040 --> 00:35:09,920 Speaker 8: the NC double A. So it's this weird position that 676 00:35:10,040 --> 00:35:10,360 Speaker 8: they're in. 677 00:35:10,760 --> 00:35:13,600 Speaker 2: So we're going to have I mean, Paul Dricks Budweiser folks. Yeah, 678 00:35:13,800 --> 00:35:16,520 Speaker 2: the seventeen year old new superstar do cass. They have 679 00:35:16,600 --> 00:35:18,960 Speaker 2: a Budweiser and a magic patch. 680 00:35:20,360 --> 00:35:25,040 Speaker 4: So I guess CBS will you know, start taking ads 681 00:35:25,120 --> 00:35:26,440 Speaker 4: for booze and stuff like that they're on. 682 00:35:27,360 --> 00:35:29,439 Speaker 8: It's weird because they're tied to this contract through twenty 683 00:35:29,560 --> 00:35:32,080 Speaker 8: thirty two, you know, fixed payments. 684 00:35:32,239 --> 00:35:34,880 Speaker 4: So but I think they're going to expand the NC DOUBLEA. 685 00:35:34,960 --> 00:35:36,879 Speaker 4: The march men is to like more teams and everything. 686 00:35:36,960 --> 00:35:38,879 Speaker 2: So they're going to go to be on sixty four teams. 687 00:35:38,960 --> 00:35:40,839 Speaker 8: They want to go to seventy six was what they're 688 00:35:40,880 --> 00:35:41,920 Speaker 8: saying from this article. 689 00:35:41,960 --> 00:35:44,239 Speaker 2: But does it, Paul help me here? That doesn't sound 690 00:35:44,320 --> 00:35:46,560 Speaker 2: like a dumb idea. I mean, well it's not. I 691 00:35:46,600 --> 00:35:50,080 Speaker 2: don't know what the right numbers like the NHL or MLB, 692 00:35:50,520 --> 00:35:53,800 Speaker 2: but yeah, just more teams. I mean, I guess, I guess. 693 00:35:53,960 --> 00:35:56,480 Speaker 8: Any more exciting. I don't. I think it kind of 694 00:35:56,560 --> 00:35:57,440 Speaker 8: waters it. I don't know. 695 00:35:57,560 --> 00:35:59,759 Speaker 2: Yeah, I like it at the beginning, am I right? Well, 696 00:36:00,000 --> 00:36:02,000 Speaker 2: I like it at the beginning of the tournament because 697 00:36:02,040 --> 00:36:04,200 Speaker 2: I haven't been thrown out. Right. I never watched a 698 00:36:04,239 --> 00:36:06,040 Speaker 2: final four because I don't know there's nothing in it. 699 00:36:06,120 --> 00:36:08,320 Speaker 4: But that first weekend is what it's all about. You know, 700 00:36:09,280 --> 00:36:12,160 Speaker 4: all the team's playing, and that's what makes March Madness great. 701 00:36:12,360 --> 00:36:13,799 Speaker 2: Lisa, you can be able to get home. We get 702 00:36:13,880 --> 00:36:16,600 Speaker 2: rain tomorrow, rain Thursday. 703 00:36:17,960 --> 00:36:19,680 Speaker 8: I just got to keep checking myself. Pumped. 704 00:36:19,719 --> 00:36:21,560 Speaker 2: I go to the basement and is it still working? 705 00:36:21,880 --> 00:36:23,359 Speaker 8: Kick that thing, make sure it's still going. 706 00:36:24,320 --> 00:36:26,760 Speaker 2: The newspapers thank you so much this morning. 707 00:36:27,400 --> 00:36:32,200 Speaker 1: This is the Bloomberg Surveillance podcast, available on Apple, Spotify, 708 00:36:32,360 --> 00:36:36,120 Speaker 1: and anywhere else you get your podcasts. Listen live each 709 00:36:36,200 --> 00:36:39,759 Speaker 1: weekday seven to ten am Easter and on Bloomberg dot Com, 710 00:36:40,160 --> 00:36:43,919 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 711 00:36:44,280 --> 00:36:47,360 Speaker 1: You can also watch us live every weekday on YouTube 712 00:36:47,680 --> 00:36:49,640 Speaker 1: and always on the Bloomberg terminal