1 00:00:02,160 --> 00:00:06,000 Speaker 1: This is Bloomberg Intelligence with Alex Steel and Paulsweenye. 2 00:00:06,080 --> 00:00:09,319 Speaker 2: The real app performance has been the US corporate high yield. 3 00:00:09,480 --> 00:00:11,840 Speaker 3: Are the companies lean enough? Have they trimmed all the fats? 4 00:00:11,880 --> 00:00:15,680 Speaker 2: The semiconductor business is a really cyclical business. 5 00:00:15,200 --> 00:00:18,759 Speaker 1: Breaking market headlines and corporate news from across the globe. 6 00:00:18,840 --> 00:00:21,439 Speaker 3: Do investors like the M and A that we've seen? 7 00:00:21,680 --> 00:00:24,720 Speaker 2: These are two big time blue chip companies. 8 00:00:25,040 --> 00:00:29,760 Speaker 3: Window between the peak and cut changing super fast Bloomberg. 9 00:00:29,280 --> 00:00:33,600 Speaker 1: Intelligence with Alex Steel and Paulsweenye on Bloomberg Radio. 10 00:00:34,840 --> 00:00:37,479 Speaker 2: On Today's Bloomberg Intelligence Show, we dig inside the big 11 00:00:37,520 --> 00:00:39,920 Speaker 2: business stories impacting Wall Street and the global markets. 12 00:00:40,040 --> 00:00:41,960 Speaker 3: Each and every week we provide in depth research and 13 00:00:42,040 --> 00:00:43,879 Speaker 3: data on some of the two thousand companies in one 14 00:00:43,960 --> 00:00:46,760 Speaker 3: hundred and thirty industries our analysts cover worldwide. 15 00:00:47,000 --> 00:00:48,800 Speaker 2: Today, we'll break down a merger between two of the 16 00:00:48,800 --> 00:00:50,680 Speaker 2: world's largest advertising groups PLAUS. 17 00:00:50,680 --> 00:00:53,400 Speaker 3: We'll discuss why shares of the computer tech company Oracle 18 00:00:53,479 --> 00:00:54,760 Speaker 3: fell by the most in a year. 19 00:00:55,160 --> 00:00:57,639 Speaker 2: But first we dive into how I planned US supermarket 20 00:00:57,720 --> 00:01:00,920 Speaker 2: merger between Kroger and Albertson's crumbled. And this came after 21 00:01:00,960 --> 00:01:02,400 Speaker 2: Albertson terminated their. 22 00:01:02,240 --> 00:01:05,240 Speaker 3: Packed Albertson's also set a file the lawsuit against Kroger, 23 00:01:05,280 --> 00:01:08,880 Speaker 3: claiming the company failed to exercise best efforts to secure 24 00:01:08,920 --> 00:01:10,040 Speaker 3: regulatory approval. 25 00:01:10,280 --> 00:01:12,720 Speaker 2: The lawsuit comes after our federal judge blocked the deal, 26 00:01:12,800 --> 00:01:15,280 Speaker 2: saying the merger with lesson competition and raise prices for 27 00:01:15,360 --> 00:01:16,360 Speaker 2: your shoppers. For more. 28 00:01:16,400 --> 00:01:19,600 Speaker 3: We are joined by Jen Ree Bloomberg Intelligence senior litigation analyst. 29 00:01:19,800 --> 00:01:22,400 Speaker 2: We first asked Jen if it's common for merger partners 30 00:01:22,400 --> 00:01:24,040 Speaker 2: on a broken deal to suit each other. 31 00:01:24,360 --> 00:01:26,600 Speaker 4: It happens once in a while. Well, I'll say it's 32 00:01:26,640 --> 00:01:29,720 Speaker 4: pretty rare, but it has happened. I mean, think about 33 00:01:29,760 --> 00:01:32,160 Speaker 4: Anthemin Signa. I don't know if you remember that deal 34 00:01:32,200 --> 00:01:35,080 Speaker 4: from back in twenty fifteen. It fell apart also, and 35 00:01:35,160 --> 00:01:37,280 Speaker 4: there was litigation for a couple of years after the 36 00:01:37,319 --> 00:01:39,520 Speaker 4: deal was terminated in Delaware. And you know, they both 37 00:01:39,560 --> 00:01:41,480 Speaker 4: came out with nothing. Spent a lot of money and 38 00:01:41,520 --> 00:01:42,479 Speaker 4: both came out with nothing. 39 00:01:42,920 --> 00:01:45,039 Speaker 3: Is it true that you think that Kroger didn't do 40 00:01:45,160 --> 00:01:47,080 Speaker 3: enough here? Like, do they have a leg to stand 41 00:01:47,120 --> 00:01:47,720 Speaker 3: on Alberson? 42 00:01:48,080 --> 00:01:51,240 Speaker 4: I think a breach of contract claim based on you know, 43 00:01:51,440 --> 00:01:55,160 Speaker 4: failing to abide by sufficiently defending against the antitrust claims 44 00:01:55,240 --> 00:01:57,520 Speaker 4: is very difficult because you know, this is really very 45 00:01:57,560 --> 00:01:59,960 Speaker 4: subjective and to some extent, when companies go to court 46 00:02:00,360 --> 00:02:02,440 Speaker 4: and they have a remedy, they're sort of rolling the dice. 47 00:02:02,480 --> 00:02:04,880 Speaker 4: They're hoping they'll convince the judge, even though they didn't 48 00:02:04,920 --> 00:02:07,200 Speaker 4: convince the FTC. And I'm not so sure that you 49 00:02:07,200 --> 00:02:09,880 Speaker 4: can think of that as a breach in this case though, 50 00:02:10,000 --> 00:02:13,120 Speaker 4: having meant a trial, the remedy really was deficient. I mean, 51 00:02:13,200 --> 00:02:16,320 Speaker 4: it was very difficult to understand how a judge, after 52 00:02:16,360 --> 00:02:19,079 Speaker 4: the FDC presented their case on how this deal could 53 00:02:19,080 --> 00:02:21,639 Speaker 4: cause harm in thousands of markets, how a judge could 54 00:02:21,680 --> 00:02:24,280 Speaker 4: accept the remedy that had been put forward by Kroger, 55 00:02:24,320 --> 00:02:27,800 Speaker 4: which was really very piecemeal, very complicated and difficult, and 56 00:02:27,840 --> 00:02:30,079 Speaker 4: didn't really have the best buyer. So you know, we're 57 00:02:30,080 --> 00:02:31,600 Speaker 4: gonna have to see what happens here with this. 58 00:02:31,960 --> 00:02:35,239 Speaker 2: I mean, so again, I kind of thought that's kind 59 00:02:35,240 --> 00:02:38,600 Speaker 2: of what a partially what a breakup fee kind of 60 00:02:38,680 --> 00:02:41,280 Speaker 2: covers there. I mean, if this deal doesn't go through 61 00:02:41,360 --> 00:02:44,560 Speaker 2: for whatever reason, you guys wanted to buyas you initiated 62 00:02:44,560 --> 00:02:46,600 Speaker 2: the deal, if it doesn't go through, right, you got 63 00:02:46,600 --> 00:02:47,720 Speaker 2: to compensate. 64 00:02:47,280 --> 00:02:50,680 Speaker 5: Me right, right, that's exactly right. The seller is really 65 00:02:50,919 --> 00:02:51,240 Speaker 5: has a. 66 00:02:51,200 --> 00:02:54,040 Speaker 4: Difficult time during that interim period, right, they lose employees. 67 00:02:54,080 --> 00:02:56,680 Speaker 4: They can always enter new supply contracts, So this is 68 00:02:56,720 --> 00:02:59,520 Speaker 4: really intended to make that seller whole, you know, in 69 00:02:59,560 --> 00:03:01,840 Speaker 4: this case, I think now Kroger's turned around and said, no, 70 00:03:01,919 --> 00:03:04,960 Speaker 4: it was it was Albertsons that breached the contract, you know, 71 00:03:05,000 --> 00:03:06,799 Speaker 4: And of course they're probably doing that to get a 72 00:03:06,800 --> 00:03:09,640 Speaker 4: little bit of leverage here. Maybe the hopes would be 73 00:03:09,800 --> 00:03:12,120 Speaker 4: that they could settle for something less than that six 74 00:03:12,240 --> 00:03:14,600 Speaker 4: hundred million breakup fee in order to make all the 75 00:03:14,639 --> 00:03:17,320 Speaker 4: litigation go away. Maybe maybe that's what's happening here, just 76 00:03:17,360 --> 00:03:18,480 Speaker 4: a little bit of leverage. 77 00:03:18,760 --> 00:03:20,640 Speaker 5: But you know, again, we'll have to see what happens. 78 00:03:20,680 --> 00:03:22,639 Speaker 4: It would be a shame if they continue to litigate 79 00:03:22,720 --> 00:03:24,960 Speaker 4: this and spend millions more dollars. 80 00:03:25,000 --> 00:03:26,120 Speaker 6: Good for the lawyers. 81 00:03:26,160 --> 00:03:28,880 Speaker 7: A deal, dumb question? Is a deal really done? 82 00:03:29,080 --> 00:03:29,240 Speaker 5: Oh? 83 00:03:29,320 --> 00:03:31,320 Speaker 7: Yes, they killed for sure, it's done. 84 00:03:31,560 --> 00:03:35,080 Speaker 4: So Albertson's is already terminated. They exercised their right under 85 00:03:35,120 --> 00:03:36,880 Speaker 4: the agreement to terminate, so it is done. 86 00:03:36,880 --> 00:03:37,280 Speaker 5: The deal. 87 00:03:37,520 --> 00:03:40,360 Speaker 4: Agreement is no longer good. So if they even wanted 88 00:03:40,400 --> 00:03:42,640 Speaker 4: to do another deal, they'd have to enter a new agreement. 89 00:03:43,160 --> 00:03:45,360 Speaker 4: And now obviously there's antagonism between the. 90 00:03:45,320 --> 00:03:48,560 Speaker 2: Companies, all right, in your world of antitrust, What is 91 00:03:48,600 --> 00:03:54,360 Speaker 2: the expectation of change given a new administration, given you 92 00:03:54,600 --> 00:03:57,720 Speaker 2: a Republican control House and Senate, is there any expectation 93 00:03:58,360 --> 00:03:59,440 Speaker 2: that things will get easier? 94 00:03:59,520 --> 00:04:00,760 Speaker 5: I think it get better. 95 00:04:00,840 --> 00:04:03,040 Speaker 4: I think some of the exuberance on Wall Street is 96 00:04:03,040 --> 00:04:07,040 Speaker 4: maybe a little overdone. These huge deals between competitors are 97 00:04:07,080 --> 00:04:09,320 Speaker 4: still going to get challenged. And we have to remember 98 00:04:09,520 --> 00:04:12,000 Speaker 4: that the two Republican FTC commissioners who will still be 99 00:04:12,040 --> 00:04:15,280 Speaker 4: there on the Commission both voted yes to sue the 100 00:04:15,280 --> 00:04:17,520 Speaker 4: Tapestry could pre deal and to sue the temper See 101 00:04:17,560 --> 00:04:20,680 Speaker 4: Le Mattress Firm deal. They both said yes we should sue, 102 00:04:20,800 --> 00:04:23,719 Speaker 4: so they are aligned in some restricts with suing the 103 00:04:23,720 --> 00:04:25,640 Speaker 4: deals that could cause harm. I do think what we're 104 00:04:25,640 --> 00:04:27,520 Speaker 4: going to see though, is an uptaking deals that can 105 00:04:27,520 --> 00:04:29,920 Speaker 4: close with settlements. We didn't have any of that in Biden. 106 00:04:30,120 --> 00:04:33,040 Speaker 4: The Biden enforcers did not want to settle deals if 107 00:04:33,080 --> 00:04:35,080 Speaker 4: the deal was bad. They wanted to challenge the deal, 108 00:04:35,080 --> 00:04:37,039 Speaker 4: and that's what they did. I think in this case, 109 00:04:37,279 --> 00:04:39,320 Speaker 4: once we have a majority of the FTC and new 110 00:04:39,320 --> 00:04:41,279 Speaker 4: people at the DOJ. We're going to see that list 111 00:04:41,279 --> 00:04:43,320 Speaker 4: of settled deals that then go on to close grow. 112 00:04:43,600 --> 00:04:46,119 Speaker 3: What do you make of the potential new FTC people 113 00:04:46,160 --> 00:04:47,279 Speaker 3: coming in, Well. 114 00:04:47,160 --> 00:04:47,880 Speaker 5: I'll just say this. 115 00:04:48,160 --> 00:04:52,480 Speaker 4: If Lenakon's mission was to stop consolidation and revitalize antitrust, 116 00:04:52,680 --> 00:04:54,960 Speaker 4: their mission is going to be to stop with they 117 00:04:54,960 --> 00:04:58,960 Speaker 4: perceive as censorship of conservative viewpoints by big tech platforms. 118 00:04:59,000 --> 00:05:01,360 Speaker 4: There has been a lot of talk about that, even 119 00:05:01,400 --> 00:05:04,440 Speaker 4: suggesting that there could be collusion amongst big tech platforms 120 00:05:04,440 --> 00:05:08,000 Speaker 4: to censor conservative viewpoints and that the FTC should be 121 00:05:08,080 --> 00:05:10,480 Speaker 4: going after these companies under the anti trust laws to 122 00:05:10,520 --> 00:05:12,279 Speaker 4: stop that. And I think that's going to be a 123 00:05:12,279 --> 00:05:13,000 Speaker 4: big focus. 124 00:05:13,480 --> 00:05:18,000 Speaker 2: So what's the timing there at the FTC and the DOJ. 125 00:05:18,160 --> 00:05:20,920 Speaker 2: When do the new sheriffs, if you will, kind of 126 00:05:21,320 --> 00:05:21,920 Speaker 2: get in town. 127 00:05:22,520 --> 00:05:26,080 Speaker 4: The FTC could be DOJ will be quick, these people 128 00:05:26,080 --> 00:05:28,839 Speaker 4: will leave. The appointees by Biden will leave in January, 129 00:05:29,360 --> 00:05:31,600 Speaker 4: and that is the expectation also that Lena Kon at 130 00:05:31,600 --> 00:05:34,600 Speaker 4: the FTC will leave in January. As soon as Trump 131 00:05:34,720 --> 00:05:38,600 Speaker 4: is inaugurated January twenty, then Andrew Ferguson will become the chair. 132 00:05:38,680 --> 00:05:40,800 Speaker 4: If Lena Khan is still there, she'll then just become 133 00:05:40,800 --> 00:05:43,320 Speaker 4: a commissioner, but she will probably leave and then at 134 00:05:43,320 --> 00:05:45,360 Speaker 4: that point he just has to get his new appointment 135 00:05:45,360 --> 00:05:48,760 Speaker 4: mark meter through the Senate confirmation process. It could take 136 00:05:48,760 --> 00:05:51,159 Speaker 4: a few months. He has a majority in the Senate 137 00:05:51,200 --> 00:05:52,560 Speaker 4: in the House. I don't think it's going to be 138 00:05:52,560 --> 00:05:54,479 Speaker 4: too difficult. So it'll be a few months and in 139 00:05:54,520 --> 00:05:56,960 Speaker 4: the meantime it'll be a two to two FTC. Probably 140 00:05:57,279 --> 00:05:59,960 Speaker 4: the DJ will change over more quickly, probably in January. 141 00:06:00,400 --> 00:06:03,080 Speaker 3: So if I'm a company and I'm interested in buying 142 00:06:03,120 --> 00:06:05,359 Speaker 3: another company, do I get on the list now? Do 143 00:06:05,440 --> 00:06:07,840 Speaker 3: I wait until all this stuff is cleared and then 144 00:06:07,880 --> 00:06:10,120 Speaker 3: I get on the waiting list to get my deal done? 145 00:06:10,200 --> 00:06:11,280 Speaker 3: Like what's my strategy? 146 00:06:11,640 --> 00:06:13,960 Speaker 4: I think if you know that it has some issues, 147 00:06:14,040 --> 00:06:16,320 Speaker 4: if you know you're probably going to get investigated, you 148 00:06:16,360 --> 00:06:18,359 Speaker 4: can go ahead and file it now because you have 149 00:06:18,480 --> 00:06:20,640 Speaker 4: eight months ahead of you and the new people will 150 00:06:20,640 --> 00:06:21,520 Speaker 4: be the decision maker. 151 00:06:21,520 --> 00:06:22,520 Speaker 5: So if you have a deal you. 152 00:06:22,440 --> 00:06:24,520 Speaker 4: Think has no issues and could get through in thirty days, 153 00:06:24,520 --> 00:06:27,720 Speaker 4: maybe you wait right because you have a greater hope 154 00:06:27,720 --> 00:06:29,520 Speaker 4: of just getting get cleared in thirty days once the 155 00:06:29,560 --> 00:06:30,800 Speaker 4: new people are in all right. 156 00:06:30,800 --> 00:06:33,640 Speaker 3: Thanks to Jen Rey, Bloomberg Intelligence Senior Litigation analyst. 157 00:06:34,040 --> 00:06:36,360 Speaker 2: This week we focused on a Bloomberg Big Take story 158 00:06:36,520 --> 00:06:40,520 Speaker 2: entitled Jane Fraser stares down skeptics ahead of City's critical year. 159 00:06:40,760 --> 00:06:43,000 Speaker 3: You can find it on Bloomberg dot Com and the Terminal, 160 00:06:43,000 --> 00:06:46,279 Speaker 3: and the story looks at extensive reporting and an exclusive 161 00:06:46,320 --> 00:06:49,600 Speaker 3: interview with Citygroup CEO Jane Fraser, and it illustrates her 162 00:06:49,640 --> 00:06:51,440 Speaker 3: five year plan to turn the bank around. 163 00:06:51,640 --> 00:06:51,960 Speaker 8: For more. 164 00:06:52,000 --> 00:06:54,200 Speaker 2: We were joined by the story's author, Todd Gillespie, a 165 00:06:54,240 --> 00:06:55,359 Speaker 2: Bloomberg Finance reporter. 166 00:06:55,600 --> 00:06:57,600 Speaker 3: We first asked Todd to break down his reporting and 167 00:06:57,600 --> 00:06:59,000 Speaker 3: how Citygroup has done recently. 168 00:06:59,360 --> 00:07:02,000 Speaker 9: Thanks generally speaking as a sector have done really well 169 00:07:02,000 --> 00:07:04,279 Speaker 9: this year. You know, Citi's kind of sitting the middle 170 00:07:04,320 --> 00:07:06,480 Speaker 9: of the pack of the top six major US banks, 171 00:07:06,480 --> 00:07:07,760 Speaker 9: so they're doing okay. 172 00:07:09,000 --> 00:07:09,240 Speaker 8: You know. 173 00:07:09,520 --> 00:07:12,680 Speaker 9: Mark Mason, the CFO, said that the bank is set 174 00:07:12,720 --> 00:07:14,840 Speaker 9: to hit the top end of its revenue guidance for 175 00:07:14,880 --> 00:07:17,960 Speaker 9: the end of this year. So after years of disappointing people, 176 00:07:18,160 --> 00:07:21,360 Speaker 9: you know, investors, analysts, slowly maybe starting to think that 177 00:07:21,400 --> 00:07:22,600 Speaker 9: she can turn the ship around. 178 00:07:22,880 --> 00:07:24,000 Speaker 6: But it's a huge task. 179 00:07:24,440 --> 00:07:26,440 Speaker 3: Why is the ship so big and why is it 180 00:07:26,520 --> 00:07:28,040 Speaker 3: so hard to turn Well. 181 00:07:28,120 --> 00:07:31,200 Speaker 9: City Group is you know, was once before the financial crisis, 182 00:07:31,200 --> 00:07:35,160 Speaker 9: the world's largest financial company. It was the US's largest bank. 183 00:07:35,960 --> 00:07:38,080 Speaker 9: This is a company that is probably the most global 184 00:07:38,080 --> 00:07:40,680 Speaker 9: bank in the world. It's the only bank, for instance, 185 00:07:40,720 --> 00:07:43,400 Speaker 9: that has a significant presence in Lebanon, where it had 186 00:07:43,440 --> 00:07:45,480 Speaker 9: to evacuate staff earlier this year. 187 00:07:46,280 --> 00:07:47,840 Speaker 6: We reported that earlier this year. 188 00:07:47,880 --> 00:07:50,080 Speaker 9: And yeah, so you know, it's one of those banks 189 00:07:50,080 --> 00:07:52,520 Speaker 9: that is so sprawling, which is one of its advantages 190 00:07:52,880 --> 00:07:55,080 Speaker 9: because it gets, you know, it enables it to have 191 00:07:55,160 --> 00:07:57,720 Speaker 9: this incredible global network of payment systems and m and 192 00:07:57,760 --> 00:08:00,680 Speaker 9: a advice and you know X in all sorts of 193 00:08:00,680 --> 00:08:01,280 Speaker 9: different countries. 194 00:08:01,320 --> 00:08:02,480 Speaker 6: But it's also a big hindrance. 195 00:08:03,280 --> 00:08:05,080 Speaker 9: And one of the things that Jane Fraser has tried 196 00:08:05,080 --> 00:08:08,000 Speaker 9: to do is pull in exit you know, thirteen of 197 00:08:08,080 --> 00:08:11,120 Speaker 9: its retail markets where it has retail banking presence. It's 198 00:08:11,120 --> 00:08:15,320 Speaker 9: about IPO, it's retail business in Mexico by twenty twenty six. 199 00:08:16,000 --> 00:08:18,720 Speaker 9: So it's really kind of trying to straighten, you know, 200 00:08:19,040 --> 00:08:21,640 Speaker 9: downsize in the right ways she's saying, and also increase 201 00:08:21,640 --> 00:08:24,840 Speaker 9: its feed businesses like wealth management and banking as well. 202 00:08:25,080 --> 00:08:27,200 Speaker 2: What does City want to be when it grows up? 203 00:08:27,240 --> 00:08:30,160 Speaker 2: Does it want to be JP Morgan? Is that the 204 00:08:30,320 --> 00:08:33,560 Speaker 2: aspiration for City or they have a different strategy. 205 00:08:34,520 --> 00:08:35,920 Speaker 9: I mean, it's hard to com bear, but yeah, I 206 00:08:35,960 --> 00:08:38,400 Speaker 9: mean I probably say JP Morgan is a bank. I 207 00:08:38,400 --> 00:08:40,120 Speaker 9: mean i'd say small steps, you know, I think Bank 208 00:08:40,200 --> 00:08:42,360 Speaker 9: for America. The way that Bank of America came out 209 00:08:42,400 --> 00:08:44,199 Speaker 9: of the financial crisis was something that City could have 210 00:08:44,240 --> 00:08:45,080 Speaker 9: only hoped to emulate. 211 00:08:45,200 --> 00:08:45,280 Speaker 8: Right. 212 00:08:45,400 --> 00:08:48,200 Speaker 9: City has really been a laggard since the financial crisis 213 00:08:48,880 --> 00:08:50,520 Speaker 9: in a way that Bank of America was able to 214 00:08:50,559 --> 00:08:54,520 Speaker 9: suddenly turn that ship around fairly strongly. City is only 215 00:08:54,559 --> 00:08:57,200 Speaker 9: just managing to maybe start to start do that. 216 00:08:57,880 --> 00:08:58,080 Speaker 8: You know. 217 00:08:58,520 --> 00:09:02,520 Speaker 9: It has very large servicesiness payment system, just like JP Morgan. 218 00:09:03,400 --> 00:09:07,160 Speaker 9: It's obviously it's banking business has been traditionally one of 219 00:09:07,200 --> 00:09:09,840 Speaker 9: the smaller on the M and A side, but larger 220 00:09:09,880 --> 00:09:12,480 Speaker 9: on the DCM and ECM side. So it's trying to 221 00:09:13,000 --> 00:09:14,560 Speaker 9: trying to see if it can kind of even out 222 00:09:14,600 --> 00:09:20,319 Speaker 9: those businesses as well. So yeah, it's unclear. I mean, yeah, 223 00:09:20,360 --> 00:09:22,360 Speaker 9: maybe if Jamie Diamond had stayed at City Group back 224 00:09:22,360 --> 00:09:23,640 Speaker 9: in the day, it. 225 00:09:23,679 --> 00:09:26,119 Speaker 6: Would have had a very different trajectory In talking. 226 00:09:25,840 --> 00:09:29,200 Speaker 3: To shareholders and talking to analysts, what's the vibe on 227 00:09:29,320 --> 00:09:31,319 Speaker 3: Jane Fraser, like, is she doing a good job? 228 00:09:31,480 --> 00:09:31,640 Speaker 6: Is it? 229 00:09:31,679 --> 00:09:35,000 Speaker 3: I realize it's only halfway through, but what's the confidence level? 230 00:09:35,080 --> 00:09:37,280 Speaker 9: Yeah, well she's passed halfway through now. I mean she 231 00:09:37,360 --> 00:09:39,680 Speaker 9: set out these targets in March twenty twenty two. She 232 00:09:39,720 --> 00:09:41,800 Speaker 9: says that by the end of twenty twenty six she 233 00:09:41,880 --> 00:09:44,559 Speaker 9: can get to a return on tangible common equity of 234 00:09:44,720 --> 00:09:48,439 Speaker 9: eleven to twelve percent. They're sitting rather unhappily just under 235 00:09:48,520 --> 00:09:50,200 Speaker 9: seven percent right now, which is one of the key 236 00:09:50,240 --> 00:09:53,280 Speaker 9: metrics that investors an analyst, are quite unhappy with, to 237 00:09:53,280 --> 00:09:56,080 Speaker 9: be honest. But people like her as a CEO, you know, 238 00:09:56,120 --> 00:10:02,480 Speaker 9: people respect her. She's pretty forthright, she's got empathy, you know, regulators, 239 00:10:03,000 --> 00:10:05,000 Speaker 9: you know folks. I think people like dealing with her 240 00:10:05,160 --> 00:10:08,160 Speaker 9: as a person. But obviously, when you take a bank 241 00:10:08,200 --> 00:10:11,000 Speaker 9: through this massive change, you know, right, they announced twenty 242 00:10:11,040 --> 00:10:13,120 Speaker 9: thousand job cuts at the beginning of this year, nine 243 00:10:13,200 --> 00:10:15,720 Speaker 9: thousand of those still to go in the next two years. 244 00:10:16,760 --> 00:10:19,280 Speaker 9: It's it's, you know, you're obviously going to have enemies 245 00:10:19,400 --> 00:10:21,439 Speaker 9: and people who really don't like the way that you're 246 00:10:21,640 --> 00:10:23,199 Speaker 9: you're moving this thing around. 247 00:10:23,360 --> 00:10:25,840 Speaker 2: What's next for city in their turner REMP plant. Is 248 00:10:25,840 --> 00:10:28,240 Speaker 2: there a mile post that investors are saying for this 249 00:10:28,280 --> 00:10:29,520 Speaker 2: is the next thing we need to see. 250 00:10:29,720 --> 00:10:32,200 Speaker 9: The next thing everyone needs to see is positive operating 251 00:10:32,280 --> 00:10:35,440 Speaker 9: leverage that really consistently comes through. That's you know, that's 252 00:10:35,520 --> 00:10:39,679 Speaker 9: that's revenues outpacing growth and revenues outpacing expenses for them 253 00:10:39,720 --> 00:10:41,600 Speaker 9: in the next twelve months. And they also really want 254 00:10:41,600 --> 00:10:46,440 Speaker 9: to see strong growth towards that RTC that returns target 255 00:10:46,800 --> 00:10:49,200 Speaker 9: every quarter, quarter by quarter, and that's the kind of 256 00:10:49,240 --> 00:10:52,280 Speaker 9: consistency that city has lacked for the past decade that 257 00:10:52,400 --> 00:10:55,080 Speaker 9: Jane Fraser is really trying to bring back to the 258 00:10:55,120 --> 00:10:58,920 Speaker 9: business and show investors that they can believe the targets 259 00:10:59,120 --> 00:11:02,040 Speaker 9: that she and her CFO Mark Mason set out and 260 00:11:02,080 --> 00:11:06,000 Speaker 9: actually stick to those so that they have that confidence 261 00:11:06,040 --> 00:11:08,720 Speaker 9: in the market, they can get rid of those regulatory 262 00:11:09,000 --> 00:11:12,440 Speaker 9: burdens that have kneecapped them for so long, and that 263 00:11:12,520 --> 00:11:15,120 Speaker 9: city can really come out of this stronger, more streamlined, 264 00:11:15,160 --> 00:11:19,160 Speaker 9: and finally, you know, put behind it this bloat that 265 00:11:19,200 --> 00:11:20,200 Speaker 9: has really weighed it down. 266 00:11:20,520 --> 00:11:23,760 Speaker 3: You also mentioned in the piece, but how they do 267 00:11:23,840 --> 00:11:25,760 Speaker 3: good numbers, They deliver good numbers, they don't get the 268 00:11:25,760 --> 00:11:26,760 Speaker 3: credit for it and earnings. 269 00:11:26,760 --> 00:11:27,360 Speaker 7: What's up with that? 270 00:11:27,840 --> 00:11:28,160 Speaker 6: Totally? 271 00:11:28,240 --> 00:11:30,679 Speaker 9: I mean every you know, every quarter there. You know, 272 00:11:31,120 --> 00:11:33,920 Speaker 9: if they post strong revenues, it's the ROTC number that's 273 00:11:33,960 --> 00:11:36,319 Speaker 9: not quite right. If the ROTC number is good, it's 274 00:11:36,400 --> 00:11:38,560 Speaker 9: revenues that aren't quite right, or it's something like that. 275 00:11:39,280 --> 00:11:42,320 Speaker 9: And you know these are often battles really on earning schools. 276 00:11:42,320 --> 00:11:44,400 Speaker 9: These are longer arning schools than most banks have, and 277 00:11:44,440 --> 00:11:46,880 Speaker 9: you can really listen to those yourself. They do get 278 00:11:46,960 --> 00:11:49,240 Speaker 9: quite colorful and quite lengthy, so you know you need 279 00:11:49,280 --> 00:11:52,000 Speaker 9: to have an extra shot of espresso while they're going through. 280 00:11:52,840 --> 00:11:55,320 Speaker 2: Thanks to Todd Gillespie, Bloomberg Finance. 281 00:11:55,040 --> 00:11:57,760 Speaker 3: Reporter, coming up well, break down the carbon market and 282 00:11:57,760 --> 00:12:00,720 Speaker 3: why it's a key part to transforming the energy landscape. 283 00:12:00,760 --> 00:12:03,440 Speaker 2: You listening to Bloomberg Intelligence on Bloomberg Radio, providing in 284 00:12:03,480 --> 00:12:05,640 Speaker 2: depth research and data on two thousand companies in one 285 00:12:05,679 --> 00:12:08,600 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 286 00:12:08,640 --> 00:12:09,600 Speaker 2: Bigo in the terminal. 287 00:12:09,640 --> 00:12:12,400 Speaker 3: I'm Paul Sweeney and a Malex Steel, and this is Bloomberg. 288 00:12:16,840 --> 00:12:20,720 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 289 00:12:20,800 --> 00:12:24,120 Speaker 1: weekdays at ten am Eastern on Afocarplay and Android Auto 290 00:12:24,240 --> 00:12:27,120 Speaker 1: with the Bloomberg Business app, Listen on demand wherever you 291 00:12:27,200 --> 00:12:31,360 Speaker 1: get your podcasts, or watch us live on YouTube. 292 00:12:31,800 --> 00:12:34,200 Speaker 7: We move next to a big deal in the media space. 293 00:12:34,240 --> 00:12:36,959 Speaker 3: This week we heard that Onakrom Group is acquiring inner 294 00:12:36,960 --> 00:12:40,000 Speaker 3: Public Group to create the world's largest advertising company. 295 00:12:40,280 --> 00:12:43,240 Speaker 2: The deal values into Public at thirteen point three billion 296 00:12:43,320 --> 00:12:45,240 Speaker 2: dollars and the merger has seen as a response to 297 00:12:45,280 --> 00:12:46,960 Speaker 2: the changing advertising landscape. 298 00:12:47,000 --> 00:12:47,319 Speaker 8: For more. 299 00:12:47,320 --> 00:12:50,080 Speaker 3: We are joined by Githermongana than Bloomberg intelligence analysts on 300 00:12:50,160 --> 00:12:50,760 Speaker 3: US Media. 301 00:12:51,160 --> 00:12:54,079 Speaker 2: We first asked Githa exactly why this deal happened. 302 00:12:54,640 --> 00:12:57,400 Speaker 10: Over the past ten to fifteen years, advertising has been 303 00:12:57,440 --> 00:13:01,120 Speaker 10: completely disrupted by the likes of you know, Google and 304 00:13:01,200 --> 00:13:04,120 Speaker 10: Meta where we've seen a complete shift of ad budgets 305 00:13:04,160 --> 00:13:07,360 Speaker 10: from traditional mediums like you know, TV and radio and 306 00:13:07,400 --> 00:13:11,439 Speaker 10: newspaper to really all digital forms of advertising to the Internet. 307 00:13:12,080 --> 00:13:15,280 Speaker 10: And so there has been a lot of disintermediation with 308 00:13:15,400 --> 00:13:18,720 Speaker 10: the ad agency holding companies all of these you know 309 00:13:18,800 --> 00:13:22,480 Speaker 10: Madison Avenue type of agency companies that are only really 310 00:13:22,520 --> 00:13:24,920 Speaker 10: a handful of them left. So you have the big 311 00:13:24,960 --> 00:13:28,560 Speaker 10: four omnicommon Interpublic are of course two US based agencies, 312 00:13:28,600 --> 00:13:32,240 Speaker 10: but the bigger ones are their European rivals, Publicies and WPP. 313 00:13:32,840 --> 00:13:35,000 Speaker 10: And really what we're trying, what they're trying to do 314 00:13:35,080 --> 00:13:38,480 Speaker 10: here is, you know, it's become such a data driven business. 315 00:13:38,480 --> 00:13:41,520 Speaker 10: There is so much competition from big tech and now 316 00:13:41,520 --> 00:13:44,000 Speaker 10: of course with the advent of AI, you know, this 317 00:13:44,120 --> 00:13:47,480 Speaker 10: threat of disintermediation is only growing more and more. And 318 00:13:47,520 --> 00:13:49,960 Speaker 10: I think this is absolutely the right time for consolidation. 319 00:13:50,000 --> 00:13:52,480 Speaker 10: And this really just tells us about the amount of 320 00:13:52,520 --> 00:13:55,760 Speaker 10: secular pressures in this industry, very very similar to the 321 00:13:56,080 --> 00:13:57,480 Speaker 10: linear TV model in a way. 322 00:13:57,840 --> 00:14:00,880 Speaker 6: How cost intensive is this business? How much do they 323 00:14:00,920 --> 00:14:01,640 Speaker 6: spend on labor? 324 00:14:01,679 --> 00:14:05,360 Speaker 10: For instance, sixty percent, sixty percent of all of their 325 00:14:05,400 --> 00:14:08,760 Speaker 10: costs are are on labor. So really, this merger, in 326 00:14:08,840 --> 00:14:11,720 Speaker 10: so many ways, John is really about saving costs. They 327 00:14:11,720 --> 00:14:15,199 Speaker 10: already outlined about seven hundred and fifty million dollars in 328 00:14:15,600 --> 00:14:17,600 Speaker 10: cost energies, and of course that is likely to go 329 00:14:17,720 --> 00:14:21,040 Speaker 10: up as they kind of eliminate redundancies. And if you 330 00:14:21,120 --> 00:14:22,640 Speaker 10: just kind of look at it over the course of 331 00:14:22,680 --> 00:14:24,480 Speaker 10: the next ten years, I mean there have been multiple 332 00:14:24,520 --> 00:14:27,040 Speaker 10: studies that have been conducted that have said that you know, 333 00:14:27,080 --> 00:14:30,560 Speaker 10: with AI being able to create campaigns. Being able to 334 00:14:30,600 --> 00:14:34,040 Speaker 10: create all of these the creative elements of what an 335 00:14:34,080 --> 00:14:38,480 Speaker 10: ad agency does, almost thirty three thousand jobs or post 336 00:14:38,480 --> 00:14:42,080 Speaker 10: to almost forty percent of the ad agency sector's jobs 337 00:14:42,280 --> 00:14:45,160 Speaker 10: would have anyway been eliminated. So it is really going 338 00:14:45,200 --> 00:14:46,920 Speaker 10: to come down to saving on on labor. 339 00:14:47,320 --> 00:14:47,640 Speaker 1: Wow. 340 00:14:48,000 --> 00:14:52,640 Speaker 2: So in this world where it's it's Facebook and it's YouTube, 341 00:14:52,680 --> 00:14:55,240 Speaker 2: and if I'm Coca Cola and I'm Ford Motor Company 342 00:14:55,280 --> 00:14:57,440 Speaker 2: and I'm going to do a big advertising buy on 343 00:14:57,440 --> 00:15:00,400 Speaker 2: one of these digital platforms, how different is is than 344 00:15:01,080 --> 00:15:04,120 Speaker 2: forty years ago when I was just advertising on network television. 345 00:15:04,400 --> 00:15:07,680 Speaker 10: Yeah, it's really different now because all of these small 346 00:15:08,040 --> 00:15:11,760 Speaker 10: businesses actually now just go directly to Google and Meta. 347 00:15:11,920 --> 00:15:13,880 Speaker 10: So the way that it is now working is all 348 00:15:13,920 --> 00:15:17,040 Speaker 10: of the small mom and pop stores, medium sized businesses 349 00:15:17,480 --> 00:15:20,000 Speaker 10: are not going to these ad agency networks anymore. So 350 00:15:20,040 --> 00:15:23,640 Speaker 10: it's really only a handful of these big brands that 351 00:15:23,720 --> 00:15:26,920 Speaker 10: are really still you know, having their campaigns kind of 352 00:15:27,000 --> 00:15:30,640 Speaker 10: designed by the big you know, ad agency networks. And 353 00:15:30,680 --> 00:15:33,600 Speaker 10: even in those big brands, even brands like p ANDNG, 354 00:15:33,720 --> 00:15:37,200 Speaker 10: even brands like Apple are really kind of going more 355 00:15:37,280 --> 00:15:40,080 Speaker 10: and more in house. So it has become really difficult 356 00:15:40,240 --> 00:15:43,880 Speaker 10: for the ad agency companies to kind of survive in 357 00:15:43,920 --> 00:15:46,040 Speaker 10: this changing world, and they have to keep adapting, and 358 00:15:46,080 --> 00:15:49,280 Speaker 10: they have to keep acquiring more data centric businesses. They 359 00:15:49,320 --> 00:15:53,160 Speaker 10: have to keep investing more in AI really and in data. 360 00:15:53,200 --> 00:15:55,320 Speaker 10: And if you kind of just look at omnicommon Interpublic 361 00:15:55,440 --> 00:15:58,760 Speaker 10: versus their European rivals, they're actually lagging behind a little 362 00:15:58,800 --> 00:16:01,320 Speaker 10: bit in terms of AI and tech investments. And I 363 00:16:01,400 --> 00:16:04,000 Speaker 10: think this deal was kind of prompted a little bit 364 00:16:04,040 --> 00:16:04,880 Speaker 10: by that as well. 365 00:16:05,080 --> 00:16:09,840 Speaker 6: Can AI really deliver a creative execution for these firms? 366 00:16:10,240 --> 00:16:14,720 Speaker 10: Yes, it can, maybe not at the highest level, but 367 00:16:14,760 --> 00:16:17,480 Speaker 10: I think as you know, the algorithms kind of keep 368 00:16:17,520 --> 00:16:20,680 Speaker 10: getting refined, we are definitely going to see a huge 369 00:16:20,760 --> 00:16:26,000 Speaker 10: level of disintermediation. And even today, you know, we talk about, 370 00:16:26,200 --> 00:16:28,720 Speaker 10: you know, these companies talk about how AI is helping 371 00:16:28,760 --> 00:16:32,280 Speaker 10: them kind of refine their campaigns, kind of refine the messaging, 372 00:16:32,880 --> 00:16:35,320 Speaker 10: get to better targeting. So AI is definitely a very 373 00:16:35,400 --> 00:16:37,000 Speaker 10: very useful tool. And if you just kind of look 374 00:16:37,040 --> 00:16:40,120 Speaker 10: at even Omnicom and Interpublic over the past year, one 375 00:16:40,120 --> 00:16:42,760 Speaker 10: of the reasons why Interpublic has kind of been so 376 00:16:43,480 --> 00:16:46,080 Speaker 10: keen to for the sale is because it has been 377 00:16:46,160 --> 00:16:49,240 Speaker 10: losing so many clients. They lost a very very important 378 00:16:49,280 --> 00:16:53,560 Speaker 10: client with Amazon, and really a whole string of account 379 00:16:53,720 --> 00:16:57,640 Speaker 10: losses kind of precipitated this whole sale process. And on 380 00:16:57,680 --> 00:16:59,600 Speaker 10: the other hand, if you look at Omnicom, they've actually 381 00:16:59,640 --> 00:17:03,760 Speaker 10: have an AI assist tool called Omni which has helped 382 00:17:03,800 --> 00:17:06,520 Speaker 10: them kind of really win a lot of clients. So 383 00:17:06,560 --> 00:17:09,400 Speaker 10: we've kind of seen the two agency groups have very 384 00:17:09,520 --> 00:17:11,159 Speaker 10: differing performances all right. 385 00:17:11,160 --> 00:17:14,679 Speaker 3: Thanks to Keith Raganath and Bloomberg Intelligence Analysts on US media, 386 00:17:14,800 --> 00:17:15,320 Speaker 3: each week. 387 00:17:15,200 --> 00:17:18,160 Speaker 2: We look at research from Bloomberg and EF previously known 388 00:17:18,200 --> 00:17:19,439 Speaker 2: as New Energy Finance. 389 00:17:19,600 --> 00:17:21,760 Speaker 3: They're the team at Bloomberg that tracks and analyzes the 390 00:17:21,880 --> 00:17:25,840 Speaker 3: energy transition from commodities to power, transport, industries, buildings, and 391 00:17:25,920 --> 00:17:27,040 Speaker 3: agricultural sectors. 392 00:17:27,200 --> 00:17:29,440 Speaker 2: This week, we took a look at the carbon market, 393 00:17:29,520 --> 00:17:32,679 Speaker 2: a key part of transforming the energy landscape for more. 394 00:17:32,880 --> 00:17:35,880 Speaker 2: We were joined by bo Chin b n EF, head 395 00:17:35,880 --> 00:17:38,240 Speaker 2: of America's Environmental Markets and Weather analysts. 396 00:17:38,280 --> 00:17:40,400 Speaker 3: We first asked bo to discuss what she's currently seeing 397 00:17:40,440 --> 00:17:42,480 Speaker 3: in the US carbon market and what she expects in 398 00:17:42,520 --> 00:17:43,159 Speaker 3: the year ahead. 399 00:17:43,480 --> 00:17:47,320 Speaker 11: The US carbon market is now going through ups and downs, 400 00:17:47,359 --> 00:17:50,200 Speaker 11: so this year has been super exciting year for US 401 00:17:50,280 --> 00:17:54,480 Speaker 11: carbon markets. We have hit record highs. The most interesting 402 00:17:54,520 --> 00:17:57,560 Speaker 11: carbon markets in US are on the coasts, so we 403 00:17:57,640 --> 00:18:01,440 Speaker 11: have on the West coast California linked with Quebec, and 404 00:18:01,480 --> 00:18:04,720 Speaker 11: then on the East Coast we have Regional Greenhouse Gas Initiative, 405 00:18:04,800 --> 00:18:08,960 Speaker 11: which is a power sector specific carbon market focused on 406 00:18:09,040 --> 00:18:14,480 Speaker 11: ten states. Both of these markets have seen price records. 407 00:18:14,920 --> 00:18:19,159 Speaker 11: So looking at just West Coast, the California carbon price 408 00:18:19,320 --> 00:18:22,760 Speaker 11: went up forty percent since twenty twenty three, hitting a 409 00:18:22,920 --> 00:18:26,800 Speaker 11: record forty four dollars per ton, and we have seen 410 00:18:26,880 --> 00:18:30,320 Speaker 11: this price is actually, unfortunately this year, come down thirty 411 00:18:30,320 --> 00:18:35,200 Speaker 11: percent this year, and this is because of the reforms 412 00:18:35,200 --> 00:18:38,960 Speaker 11: that have been proposed and also the delays of these reforms. 413 00:18:39,359 --> 00:18:41,640 Speaker 11: On the East Coast we have a similar story. So 414 00:18:42,000 --> 00:18:45,159 Speaker 11: it was a very polish story up till September with 415 00:18:45,320 --> 00:18:48,280 Speaker 11: prices moving up seventy percent to twenty eight dollars per 416 00:18:48,320 --> 00:18:51,320 Speaker 11: short ton, and then prices have since also come down 417 00:18:51,440 --> 00:18:53,600 Speaker 11: twenty five percent because of their reforms. 418 00:18:54,000 --> 00:18:55,680 Speaker 7: But we do expect next. 419 00:18:55,480 --> 00:18:59,440 Speaker 11: Year to be a very exciting year as these reforms 420 00:18:59,480 --> 00:19:06,000 Speaker 11: get concluded and we see more policy certainty within these markets. 421 00:19:06,480 --> 00:19:08,040 Speaker 2: Explain it to me like I'm a five year old. 422 00:19:08,160 --> 00:19:10,040 Speaker 2: What drives the price of carbon? 423 00:19:10,200 --> 00:19:13,000 Speaker 7: I was going to ask that to you. Actually, yeah, 424 00:19:13,119 --> 00:19:15,520 Speaker 7: carbon markets really is like commodity. 425 00:19:15,960 --> 00:19:19,879 Speaker 2: Yeah, commodity, so demand, Yeah, it's demand. 426 00:19:20,000 --> 00:19:23,879 Speaker 11: It's basically think it as a farmer's market where polluters 427 00:19:23,920 --> 00:19:27,120 Speaker 11: go to buy permits and then abaters go to sell 428 00:19:27,200 --> 00:19:30,719 Speaker 11: their permits. It depends how big family are feeding, how 429 00:19:30,800 --> 00:19:35,480 Speaker 11: much demand is for that abatement, then that drives the price. 430 00:19:36,320 --> 00:19:39,320 Speaker 11: In our world, we call that demand, and the price 431 00:19:39,400 --> 00:19:42,000 Speaker 11: is a marginal abatement cost curve. So if you have 432 00:19:42,119 --> 00:19:46,760 Speaker 11: a strong demand or a strong climate ambition and a 433 00:19:47,680 --> 00:19:51,080 Speaker 11: very limited marginal abayment cost curve, that would lead to 434 00:19:51,280 --> 00:19:54,359 Speaker 11: a high carbon price. And then vice versa. If you 435 00:19:54,480 --> 00:19:57,639 Speaker 11: have low demand or low climate action and a very 436 00:19:58,200 --> 00:20:03,040 Speaker 11: significant marginalbayment cost curve, meaning that you have cheap abatement 437 00:20:03,520 --> 00:20:06,160 Speaker 11: and a vast amount of it, then you would have 438 00:20:06,359 --> 00:20:08,000 Speaker 11: typically a lower carbon price. 439 00:20:08,200 --> 00:20:10,960 Speaker 3: So to me, also the idea what the carbon price is, 440 00:20:12,040 --> 00:20:14,560 Speaker 3: If you get it high enough, then companies are going 441 00:20:14,600 --> 00:20:18,080 Speaker 3: to want to store their carbon because they're going to 442 00:20:18,119 --> 00:20:20,359 Speaker 3: be able to then trade that credit and get money 443 00:20:20,400 --> 00:20:22,720 Speaker 3: for it. Right, So the higher it is, the better 444 00:20:22,800 --> 00:20:24,919 Speaker 3: it is for the environment because companies will be more 445 00:20:25,000 --> 00:20:28,199 Speaker 3: incentivized to store their carbon or not produce it or 446 00:20:28,240 --> 00:20:28,720 Speaker 3: to sell it. 447 00:20:28,800 --> 00:20:31,760 Speaker 7: Right, Yeah, but what is that price? 448 00:20:32,840 --> 00:20:36,520 Speaker 11: It really depends on the supply and demand assets a commodity. 449 00:20:37,000 --> 00:20:39,560 Speaker 3: But like every talking about like eighty dollars, would it 450 00:20:39,600 --> 00:20:40,800 Speaker 3: really be a good incentive? 451 00:20:40,880 --> 00:20:41,600 Speaker 7: Is it one hundred? 452 00:20:41,640 --> 00:20:44,439 Speaker 3: I mean any commodity is going to have that incentive price? 453 00:20:45,040 --> 00:20:48,240 Speaker 11: Yeah, exactly. It depends on what technology you're looking at. 454 00:20:48,280 --> 00:20:51,040 Speaker 11: What is the trigger point for a trigger price? So 455 00:20:51,160 --> 00:20:54,040 Speaker 11: if you're like we typically say that the cheapest is 456 00:20:54,080 --> 00:20:57,200 Speaker 11: the call to gas field switch in, and that also 457 00:20:57,280 --> 00:21:00,720 Speaker 11: depends on the market, So in a Europe that typically 458 00:21:00,800 --> 00:21:04,480 Speaker 11: is higher than US where gas is already very low. 459 00:21:05,280 --> 00:21:08,680 Speaker 11: We also looked into this that not only a power 460 00:21:08,720 --> 00:21:10,960 Speaker 11: sector now is the ones that are being trigger but 461 00:21:11,000 --> 00:21:17,159 Speaker 11: also transport where you tilt from ice vehicle to a 462 00:21:17,200 --> 00:21:20,159 Speaker 11: EV and a carbon price can help you to do that. 463 00:21:20,760 --> 00:21:23,439 Speaker 11: And also carbon price doesn't work alone, so there's a 464 00:21:23,440 --> 00:21:26,520 Speaker 11: lot of complementary policies. We are pretty much the yeah, 465 00:21:27,160 --> 00:21:31,080 Speaker 11: final defense. So if other complementary policies didn't deliver the 466 00:21:31,240 --> 00:21:34,080 Speaker 11: results we wanted to see, then carbon price can do 467 00:21:34,200 --> 00:21:34,960 Speaker 11: the final lift. 468 00:21:35,080 --> 00:21:39,919 Speaker 2: Hopefully interesting, So real quickly change in administrations coming up. 469 00:21:39,920 --> 00:21:41,639 Speaker 2: What does that mean for your world? 470 00:21:41,960 --> 00:21:45,199 Speaker 11: Yeah, it's really exciting actually for US. Maybe for the 471 00:21:45,400 --> 00:21:49,320 Speaker 11: clean energy sector it's maybe not such positive news as 472 00:21:49,640 --> 00:21:55,360 Speaker 11: potentially some subsidies like Inflation Reduction Act and other complementary 473 00:21:55,359 --> 00:22:00,000 Speaker 11: policies for renewables and clean transport will be now reduced 474 00:22:00,480 --> 00:22:04,360 Speaker 11: and there will be more support for oil and gas industry. 475 00:22:04,400 --> 00:22:06,320 Speaker 11: But for carbon market this could be a good thing 476 00:22:06,440 --> 00:22:10,560 Speaker 11: because we would have more emissions that would support carbon prices. 477 00:22:10,840 --> 00:22:13,520 Speaker 11: But also a lot of states could now really double 478 00:22:13,600 --> 00:22:18,879 Speaker 11: down on carbon market as a policy tool to support prices. Also, 479 00:22:18,920 --> 00:22:22,639 Speaker 11: for investors, carbon price could become an attractive tool to 480 00:22:23,119 --> 00:22:28,680 Speaker 11: invest because carbon markets, particularly those compliance carbon markets, can 481 00:22:28,840 --> 00:22:32,720 Speaker 11: hedge inflation, which could become a topic now with the 482 00:22:32,800 --> 00:22:36,919 Speaker 11: immigration rules and twists being changed with the new admin quickly. 483 00:22:37,000 --> 00:22:38,719 Speaker 3: Is it a bad thing that it's so fragmented? 484 00:22:40,480 --> 00:22:40,840 Speaker 6: Yes? 485 00:22:41,840 --> 00:22:43,719 Speaker 3: Do we ever get to a place where there's like 486 00:22:43,800 --> 00:22:45,040 Speaker 3: a US carbon price? 487 00:22:45,720 --> 00:22:46,560 Speaker 6: We do hope. 488 00:22:46,640 --> 00:22:47,400 Speaker 10: So for. 489 00:22:49,200 --> 00:22:52,840 Speaker 11: National level carbon price, it will most likely not become 490 00:22:52,960 --> 00:22:55,679 Speaker 11: a realistic thing in the next few years, but we 491 00:22:55,720 --> 00:23:00,320 Speaker 11: could see more states linking like even right now we 492 00:23:00,400 --> 00:23:05,560 Speaker 11: are seeing that Washington, which survived the repeal actually. 493 00:23:05,320 --> 00:23:06,200 Speaker 7: In November as well. 494 00:23:06,280 --> 00:23:08,960 Speaker 11: So now there's more opportunity for Washington state to be 495 00:23:09,000 --> 00:23:10,080 Speaker 11: linked with a California. 496 00:23:10,520 --> 00:23:12,000 Speaker 7: There are also other states that. 497 00:23:12,000 --> 00:23:16,640 Speaker 11: Are talking about having a compliance carbon market. Actually right 498 00:23:16,640 --> 00:23:20,680 Speaker 11: here in New York, very excited about Niki. I'm excited 499 00:23:20,720 --> 00:23:24,280 Speaker 11: to make the sun O Mikid become a Hey Nikki. 500 00:23:25,520 --> 00:23:27,200 Speaker 7: So New York is going to. 501 00:23:27,119 --> 00:23:28,680 Speaker 11: Be a economy wide and it's going to be a 502 00:23:28,680 --> 00:23:32,520 Speaker 11: similar size to California. So if these all get linked, 503 00:23:32,560 --> 00:23:35,040 Speaker 11: then we could have a like maybe not an official 504 00:23:35,720 --> 00:23:38,000 Speaker 11: national but a benchmark price our. 505 00:23:37,960 --> 00:23:40,480 Speaker 2: Thanks to bo Chin b n Ef, head of America's 506 00:23:40,560 --> 00:23:42,280 Speaker 2: Environmental Markets and weather analysts. 507 00:23:42,480 --> 00:23:44,520 Speaker 3: Coming up on the program, we'll discuss why the department 508 00:23:44,520 --> 00:23:46,600 Speaker 3: store chain may See has trimmed its profit outlook for 509 00:23:46,680 --> 00:23:46,960 Speaker 3: the year. 510 00:23:47,200 --> 00:23:50,000 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 511 00:23:50,080 --> 00:23:52,159 Speaker 2: depth research and data on two thousand companies from one 512 00:23:52,200 --> 00:23:55,200 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 513 00:23:55,240 --> 00:23:57,480 Speaker 2: bi go on the terminal, Paul Swinging and am Alex 514 00:23:57,520 --> 00:23:59,119 Speaker 2: Steel and this is Bloomberg. 515 00:24:05,440 --> 00:24:09,320 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 516 00:24:09,400 --> 00:24:12,920 Speaker 1: weekdays at ten am Eastern on Applecar Play and Android 517 00:24:12,960 --> 00:24:15,720 Speaker 1: Auto with the Bloomberg Business App. You can also listen 518 00:24:15,840 --> 00:24:18,960 Speaker 1: live on Amazon Alexa from our flagship New York station. 519 00:24:19,320 --> 00:24:21,320 Speaker 1: Just say Alexa Play Bloomberg. 520 00:24:21,359 --> 00:24:24,600 Speaker 3: Eleven thirty We moved next to the computer tech company Oracle. 521 00:24:24,640 --> 00:24:26,399 Speaker 7: This week's shares of Oracle fell by the most in 522 00:24:26,440 --> 00:24:26,760 Speaker 7: a year. 523 00:24:27,040 --> 00:24:29,480 Speaker 2: That's after the company reported second quarter profit and revenue 524 00:24:29,520 --> 00:24:31,240 Speaker 2: that missed analysts expectations from more. 525 00:24:31,280 --> 00:24:34,360 Speaker 3: We are joined by anaag Rana, Bloomberg Intelligence technology analyst. 526 00:24:34,680 --> 00:24:37,600 Speaker 2: First, ask on Arov what Oracle's results say about its business. 527 00:24:37,880 --> 00:24:39,960 Speaker 8: See, when you look at the constant currency growth for 528 00:24:40,040 --> 00:24:43,360 Speaker 8: a cloud infrastructure, it was fifty two percent. I think 529 00:24:43,359 --> 00:24:46,200 Speaker 8: consensus was fifty one, so a slight beat, but the 530 00:24:46,280 --> 00:24:49,760 Speaker 8: reaction tells me it was not good enough for byside investors. 531 00:24:50,400 --> 00:24:53,760 Speaker 8: You know, from our side, there's no problem in terms 532 00:24:53,800 --> 00:24:57,639 Speaker 8: of future growth prospects for that business because backlock's very strong. 533 00:24:57,960 --> 00:25:01,320 Speaker 8: The demand is there. And for Oracle, they're actually getting 534 00:25:01,359 --> 00:25:04,680 Speaker 8: work from Microsoft because Microsoft is leasing their data centers 535 00:25:05,359 --> 00:25:08,040 Speaker 8: because it is having a huge backlog of work on 536 00:25:08,080 --> 00:25:08,520 Speaker 8: their end. 537 00:25:08,800 --> 00:25:10,560 Speaker 3: So does that mean this is just a Hey, the 538 00:25:10,600 --> 00:25:13,639 Speaker 3: stock could run up really fast. Therefore, the expectator, just 539 00:25:13,640 --> 00:25:15,480 Speaker 3: the expectation game, is that what I can read into 540 00:25:15,480 --> 00:25:16,280 Speaker 3: the equity. 541 00:25:16,320 --> 00:25:19,440 Speaker 8: I would absolutely say that because right now this has 542 00:25:19,520 --> 00:25:23,280 Speaker 8: been the trade for twenty twenty four has been the 543 00:25:23,280 --> 00:25:27,159 Speaker 8: infrastructure that's related to AI, so whether that's the chips, 544 00:25:27,200 --> 00:25:30,080 Speaker 8: even the numbers were pretty good, and twenty twenty five 545 00:25:30,119 --> 00:25:32,400 Speaker 8: could be a year where you will see the downstream 546 00:25:32,440 --> 00:25:36,320 Speaker 8: impact of some of these AI infrastructures, which is cloud 547 00:25:36,400 --> 00:25:40,000 Speaker 8: companies or other software companies coming up with applications that 548 00:25:40,040 --> 00:25:43,240 Speaker 8: are based on some of these AI infrastructures. So we 549 00:25:43,320 --> 00:25:45,920 Speaker 8: have a long way to go before we realize the 550 00:25:45,960 --> 00:25:49,320 Speaker 8: true value of AI spending across the entire value chain. 551 00:25:50,119 --> 00:25:52,280 Speaker 8: For this year, we just saw it in the you know, 552 00:25:52,320 --> 00:25:54,240 Speaker 8: the cloud and the chips part of it. 553 00:25:54,560 --> 00:25:57,360 Speaker 2: The demand is so robust on a rock that I've 554 00:25:57,359 --> 00:26:01,000 Speaker 2: heard companies talk about maybe even some shortage of some 555 00:26:01,160 --> 00:26:03,600 Speaker 2: components of some of the things they want to buy. 556 00:26:03,720 --> 00:26:05,280 Speaker 2: Talk to us about that. What did Oracle say? 557 00:26:05,720 --> 00:26:09,480 Speaker 8: Yeah, Oracle is no different than other companies because even 558 00:26:09,480 --> 00:26:12,240 Speaker 8: for Microsoft, they cannot get up data centers at the 559 00:26:12,240 --> 00:26:15,320 Speaker 8: same rate as they're seeing demand coming from open AI. 560 00:26:15,480 --> 00:26:19,200 Speaker 8: They are not They are everybody's having struggled to procure 561 00:26:19,760 --> 00:26:22,639 Speaker 8: in video GPUs to train some of these models. And 562 00:26:22,680 --> 00:26:26,040 Speaker 8: that's the case. Oracle's backlock is so strong. But you 563 00:26:26,080 --> 00:26:28,520 Speaker 8: know there has been supply challenges. They are spending a 564 00:26:28,520 --> 00:26:32,479 Speaker 8: lot of money on capital expenditure. Last year approximately, they 565 00:26:32,520 --> 00:26:35,879 Speaker 8: spent about seven billion dollars in capex. This year is 566 00:26:35,920 --> 00:26:39,400 Speaker 8: going to be double of that fourteen billion dollars. So 567 00:26:39,480 --> 00:26:43,200 Speaker 8: I mean it we see that when these companies spend 568 00:26:43,200 --> 00:26:46,320 Speaker 8: money to expand their capacity, we know that demand is 569 00:26:46,359 --> 00:26:46,880 Speaker 8: behind it. 570 00:26:47,280 --> 00:26:49,520 Speaker 7: So this is like the software stuff. 571 00:26:49,520 --> 00:26:53,240 Speaker 3: So Nvidia supplies the chips for Oracle, who then uses 572 00:26:53,440 --> 00:26:57,280 Speaker 3: builds the software stuff for companies like Eccentric or Amazon 573 00:26:57,320 --> 00:26:58,840 Speaker 3: AM I is that correct? 574 00:26:59,480 --> 00:27:02,280 Speaker 8: Yeah, it's you got it right. In this case, what's 575 00:27:02,280 --> 00:27:07,920 Speaker 8: happening is companies like Microsoft, Google, AWS, and Oracle are 576 00:27:07,920 --> 00:27:12,000 Speaker 8: building an infrastructure on which companies can build applications. So 577 00:27:12,080 --> 00:27:14,760 Speaker 8: these companies could be accentual, it could be JP Morgan, 578 00:27:15,040 --> 00:27:18,320 Speaker 8: it could be Citybank, et cetera. These companies will go 579 00:27:18,400 --> 00:27:21,640 Speaker 8: out and create their own AI apps that can then 580 00:27:21,680 --> 00:27:24,639 Speaker 8: be used internally by the companies. And I mean you 581 00:27:24,680 --> 00:27:27,400 Speaker 8: can do this in house also by buying AI servers 582 00:27:27,680 --> 00:27:30,040 Speaker 8: but cloud is usually a better and each cheaper way 583 00:27:30,080 --> 00:27:31,560 Speaker 8: to go because you can turn it off if you 584 00:27:31,600 --> 00:27:34,200 Speaker 8: don't like it. So that's what we are seeing right. 585 00:27:34,119 --> 00:27:38,000 Speaker 2: Now is peak AI anywhere on the horizon there on 586 00:27:38,119 --> 00:27:38,440 Speaker 2: a rug. 587 00:27:39,400 --> 00:27:42,080 Speaker 8: That's a very very good and tough question, frankly, because 588 00:27:42,320 --> 00:27:44,840 Speaker 8: the question is going to be will people get bored 589 00:27:44,880 --> 00:27:47,479 Speaker 8: in the AI infrastructure space, and they will will they 590 00:27:47,520 --> 00:27:50,080 Speaker 8: move to somewhere else? We saw that in the last 591 00:27:50,119 --> 00:27:53,719 Speaker 8: three months suddenly the interest in Salesforce, for example, and 592 00:27:53,760 --> 00:27:56,880 Speaker 8: that is because Salesforce is the second leg of this equation. 593 00:27:57,200 --> 00:27:59,960 Speaker 8: One is the infrastructure we talked about. Second is the application. 594 00:28:00,760 --> 00:28:04,359 Speaker 8: Salesforce created some AI application that can help you do 595 00:28:04,720 --> 00:28:10,680 Speaker 8: you know, fix your online orders, automatically, do some referns, 596 00:28:10,720 --> 00:28:13,359 Speaker 8: et cetera. So that's the application part of it. We 597 00:28:13,520 --> 00:28:16,200 Speaker 8: think next yer is going to be a lot of 598 00:28:16,520 --> 00:28:20,040 Speaker 8: Salesforce like applications that will come to the market by 599 00:28:20,080 --> 00:28:23,440 Speaker 8: different software companies that will help people be more productive. 600 00:28:23,720 --> 00:28:25,479 Speaker 8: And then the third and the final layer would be 601 00:28:25,680 --> 00:28:28,879 Speaker 8: the consulting companies that are helping in the implementation of 602 00:28:28,880 --> 00:28:32,480 Speaker 8: that that goes in tandem with software companies. But we 603 00:28:32,560 --> 00:28:34,960 Speaker 8: think there are multiple years before we see the true 604 00:28:35,000 --> 00:28:37,640 Speaker 8: realization of the funding that's happening right now. 605 00:28:37,760 --> 00:28:38,800 Speaker 7: All right, quickly on a rag. 606 00:28:39,120 --> 00:28:41,720 Speaker 3: Adobe is a competitor Oracle and what do we learn 607 00:28:41,760 --> 00:28:43,680 Speaker 3: from Oracle as relates to Adobe. 608 00:28:43,880 --> 00:28:48,120 Speaker 8: Yeah, from a from Wells. They're both software companies, but 609 00:28:48,160 --> 00:28:51,240 Speaker 8: they do slightly different things. For Adobe, the big question 610 00:28:51,360 --> 00:28:54,560 Speaker 8: is going to be is their business being cannibalized by 611 00:28:54,880 --> 00:28:58,080 Speaker 8: open source AI tools. We don't think that's the case 612 00:28:58,160 --> 00:29:00,320 Speaker 8: right now, but that's a big argument in this space. 613 00:29:00,600 --> 00:29:03,080 Speaker 8: If you look at the entire software space right now, 614 00:29:03,520 --> 00:29:06,280 Speaker 8: Adobe's probably the one where do you have the biggest 615 00:29:06,400 --> 00:29:09,000 Speaker 8: argument whether they're going to be the net beneficiaries of 616 00:29:09,040 --> 00:29:11,160 Speaker 8: AI or whether they're going to get hurt by I. 617 00:29:11,640 --> 00:29:14,160 Speaker 8: For Adobe, we expect them to go out and come 618 00:29:14,160 --> 00:29:16,600 Speaker 8: out and say that the adoption rate of their own 619 00:29:16,640 --> 00:29:19,120 Speaker 8: AI tools is picking up, and that's really what we 620 00:29:19,160 --> 00:29:20,400 Speaker 8: are looking for. All right. 621 00:29:20,400 --> 00:29:23,520 Speaker 3: Thanks to Anna Agrana Bloomberg Intelligence and your technology analyst. 622 00:29:23,800 --> 00:29:26,760 Speaker 2: We move next to the retail space and Macy's. This week, 623 00:29:26,840 --> 00:29:29,240 Speaker 2: the department store chain said it had trimmed its profit 624 00:29:29,320 --> 00:29:30,120 Speaker 2: outlook for the year. 625 00:29:30,440 --> 00:29:33,600 Speaker 3: This comes after Macy's concluded its investigation into an employee 626 00:29:33,600 --> 00:29:36,240 Speaker 3: that hid millions of dollars and expenses. The discovery of 627 00:29:36,240 --> 00:29:38,800 Speaker 3: the accounting eras led Macy's to delay its full earnings 628 00:29:38,840 --> 00:29:39,920 Speaker 3: report back in November. 629 00:29:40,160 --> 00:29:42,280 Speaker 2: For more, we were joined by Mary Ross Gilbert Bloomberg 630 00:29:42,320 --> 00:29:45,120 Speaker 2: Intelligence senior Ecuadanalysts. We first asked Mary to walk us 631 00:29:45,160 --> 00:29:46,719 Speaker 2: through Macy's recent investigation. 632 00:29:47,160 --> 00:29:50,640 Speaker 12: What happened here is that they had a single employee 633 00:29:50,960 --> 00:29:55,000 Speaker 12: who basically hid small package delivery expenses of about one 634 00:29:55,080 --> 00:29:58,200 Speaker 12: hundred and fifty one million. That was over a three 635 00:29:58,320 --> 00:30:01,680 Speaker 12: year period, so were the year to date. I think 636 00:30:01,720 --> 00:30:04,560 Speaker 12: it was about nine million is the effect in the 637 00:30:05,640 --> 00:30:08,600 Speaker 12: for that period. So the company, of course had to 638 00:30:08,640 --> 00:30:13,360 Speaker 12: lower their guidance, and the gross margin figures anyps of 639 00:30:13,400 --> 00:30:15,920 Speaker 12: course because it drops all the way down there, but 640 00:30:16,240 --> 00:30:20,080 Speaker 12: it doesn't affect cash. So that's the good news and 641 00:30:20,120 --> 00:30:23,920 Speaker 12: it's put behind him. What they have done is they've 642 00:30:23,920 --> 00:30:28,640 Speaker 12: improved their financial controls. They've added some additional checks and 643 00:30:28,720 --> 00:30:31,880 Speaker 12: balances there. So I think this is now behind them, 644 00:30:32,280 --> 00:30:35,320 Speaker 12: and I think the real focus here is on the 645 00:30:35,360 --> 00:30:39,040 Speaker 12: go forward operations. And I say go forward meaning let's 646 00:30:39,120 --> 00:30:41,440 Speaker 12: exclude the one hundred and fifty stores that they're going 647 00:30:41,520 --> 00:30:44,680 Speaker 12: to close. Sixty five of those stores will close in 648 00:30:44,720 --> 00:30:47,959 Speaker 12: the fourth quarter. That's ahead of the original fifty they 649 00:30:48,000 --> 00:30:51,400 Speaker 12: slated to close, and then the other eighty five maybe 650 00:30:51,400 --> 00:30:53,840 Speaker 12: that'll get done by next year instead of the next 651 00:30:53,880 --> 00:30:57,040 Speaker 12: two years. Nonetheless, if you put those aside and you 652 00:30:57,080 --> 00:30:59,760 Speaker 12: look at what's going on with the go forward business, 653 00:31:00,160 --> 00:31:05,440 Speaker 12: we're seeing sequentially sequential improvement with the first fifty stores 654 00:31:05,480 --> 00:31:10,440 Speaker 12: that they've completely revamped actually posting a positive increase. They 655 00:31:10,440 --> 00:31:12,719 Speaker 12: were up one point nine percent in the third quarter, 656 00:31:13,000 --> 00:31:16,520 Speaker 12: and that's the third quarter of positive comp improven there. 657 00:31:16,800 --> 00:31:21,520 Speaker 12: So we're actually seeing signs that the plans, the initiatives 658 00:31:21,520 --> 00:31:25,920 Speaker 12: that they're employing are working. So we see some green 659 00:31:25,960 --> 00:31:27,880 Speaker 12: shoots in there. What does it let me think this 660 00:31:28,160 --> 00:31:30,320 Speaker 12: accounting issue is behind them all right? 661 00:31:30,480 --> 00:31:33,840 Speaker 2: What does a revamped store look like? What's the strategy 662 00:31:33,960 --> 00:31:35,720 Speaker 2: behind a revamped store? 663 00:31:36,840 --> 00:31:38,160 Speaker 5: Paul, That's a good question. 664 00:31:38,640 --> 00:31:41,160 Speaker 12: So what that means is is they've really up their 665 00:31:41,200 --> 00:31:44,520 Speaker 12: game in terms of merchandising. They brought in new brands 666 00:31:44,600 --> 00:31:51,560 Speaker 12: like Lafitte, La Levec Fee, Donna, Karen, Carl Lagerfeld, Steve Madden. 667 00:31:51,920 --> 00:31:56,800 Speaker 12: These brands offer great style and value to the company. 668 00:31:57,240 --> 00:32:01,280 Speaker 12: Their private label program has been completely revamped. Now that's 669 00:32:01,320 --> 00:32:04,040 Speaker 12: only about fifteen percent of sales. So the biggest part 670 00:32:04,120 --> 00:32:07,040 Speaker 12: is really bringing me in these other brands. But not 671 00:32:07,120 --> 00:32:09,920 Speaker 12: only that, they've uped their game in terms of service. 672 00:32:10,240 --> 00:32:13,560 Speaker 12: They now have salespeople in the shoe department and then 673 00:32:13,600 --> 00:32:16,800 Speaker 12: they have runners. So what happens is a salesperson will 674 00:32:16,800 --> 00:32:19,480 Speaker 12: be working directly with a client and they'll say, hey, 675 00:32:19,560 --> 00:32:22,920 Speaker 12: I need size eight, size eight and a half in this, 676 00:32:23,240 --> 00:32:25,640 Speaker 12: and then they run and go get them. And then 677 00:32:25,680 --> 00:32:29,840 Speaker 12: also they have service in handbags and in women's ready 678 00:32:29,880 --> 00:32:34,720 Speaker 12: to wear. So having boosted the service levels also improving 679 00:32:34,760 --> 00:32:38,320 Speaker 12: the overall appearance throughout the stores. We've been in our 680 00:32:38,400 --> 00:32:42,080 Speaker 12: channel checks watching this happen, and we see the improvement. 681 00:32:42,160 --> 00:32:44,160 Speaker 12: We see how that they got rid of some brands 682 00:32:44,160 --> 00:32:47,440 Speaker 12: that were less relevant, brought in these newer ones. And 683 00:32:47,760 --> 00:32:52,520 Speaker 12: we also saw during that Black Friday, we saw this 684 00:32:52,640 --> 00:32:55,840 Speaker 12: year more traffic in the stores than we saw in the. 685 00:32:55,840 --> 00:32:56,960 Speaker 5: Prior two years. 686 00:32:57,400 --> 00:33:00,240 Speaker 12: And of course when you look at the data, their 687 00:33:00,280 --> 00:33:03,200 Speaker 12: sales were better this year. They saw an increase in 688 00:33:03,280 --> 00:33:06,720 Speaker 12: the Black Friday through Cyber Monday and the consumer transaction data, 689 00:33:07,120 --> 00:33:10,280 Speaker 12: and that's something the company mentioned on the call today 690 00:33:10,320 --> 00:33:13,520 Speaker 12: that yes, they're seeing strong you know, or sort of 691 00:33:13,560 --> 00:33:16,959 Speaker 12: sequential improvement versus the third quarter into the fourth quarter. 692 00:33:17,720 --> 00:33:20,560 Speaker 5: So I think those are some of the part. 693 00:33:20,520 --> 00:33:23,760 Speaker 3: You're describing Mary. The revamped stores. It sounds a little 694 00:33:23,760 --> 00:33:25,200 Speaker 3: bit like Bloomingdale's Light. 695 00:33:27,320 --> 00:33:27,800 Speaker 5: You know what. 696 00:33:28,040 --> 00:33:31,640 Speaker 12: That's exactly if you look at a let's say, one 697 00:33:31,640 --> 00:33:34,600 Speaker 12: of their better what what they would call that, one 698 00:33:34,600 --> 00:33:37,000 Speaker 12: of their flagships. So we have one here right next 699 00:33:37,000 --> 00:33:41,080 Speaker 12: door in Century City, it does very much look like 700 00:33:41,320 --> 00:33:42,240 Speaker 12: a Bloomingdale's. 701 00:33:42,280 --> 00:33:44,440 Speaker 5: The store is interesting. It's brand new. 702 00:33:44,560 --> 00:33:47,000 Speaker 12: I mean they've remodeled it four or five times within 703 00:33:47,040 --> 00:33:49,360 Speaker 12: the last few years. What I mean by that is 704 00:33:49,400 --> 00:33:52,800 Speaker 12: they've expanded beauty. So you walk in, it's an incredible 705 00:33:52,840 --> 00:33:54,000 Speaker 12: showcase of beauty. 706 00:33:54,320 --> 00:33:56,680 Speaker 5: Also includes Blue Mercury, of course. 707 00:33:56,880 --> 00:34:00,880 Speaker 12: So we we've seen just and the stores so much brighter. 708 00:34:00,920 --> 00:34:04,640 Speaker 12: But even in a tired old mall down in southern 709 00:34:04,720 --> 00:34:08,720 Speaker 12: California that I will go to, even that store looks 710 00:34:08,800 --> 00:34:13,120 Speaker 12: much better, even though it's sort of a tired location. 711 00:34:14,200 --> 00:34:15,640 Speaker 5: They've really improved it. 712 00:34:16,200 --> 00:34:20,320 Speaker 12: The better brands that they've incorporated, making these little Calvin 713 00:34:20,400 --> 00:34:24,560 Speaker 12: Klein they look like little boutique shops, Michael Core's, Calvin Kleine, 714 00:34:25,080 --> 00:34:29,960 Speaker 12: Tommy Hillfigure those are all in there and beautifully appointed. 715 00:34:30,000 --> 00:34:34,360 Speaker 12: It's much more open. There's not just a sea of merchandise. 716 00:34:34,560 --> 00:34:38,600 Speaker 12: It just makes more sense. So that's been extended across 717 00:34:38,640 --> 00:34:41,279 Speaker 12: another hundred stores, so not just a fifty, but another 718 00:34:41,280 --> 00:34:41,720 Speaker 12: one hundred. 719 00:34:41,880 --> 00:34:45,960 Speaker 2: All right, So what's the company saying just about holiday 720 00:34:46,080 --> 00:34:49,279 Speaker 2: sales so far? It is kind of confirmed what you've seen. 721 00:34:50,320 --> 00:34:51,560 Speaker 5: Yes, they did. 722 00:34:51,600 --> 00:34:54,000 Speaker 12: They said that they saw a good start to the 723 00:34:54,000 --> 00:34:56,839 Speaker 12: holiday season, but of course we still have more to go. 724 00:34:57,520 --> 00:35:02,160 Speaker 12: So no, and they act actually raised their guidance ATAD 725 00:35:03,520 --> 00:35:07,640 Speaker 12: so I thought that was also an indication of what 726 00:35:07,680 --> 00:35:10,360 Speaker 12: they're seeing, and so they could potentially beat the guidance 727 00:35:10,400 --> 00:35:13,720 Speaker 12: because when they put things the guidance out, they really 728 00:35:14,160 --> 00:35:17,120 Speaker 12: attempt to exceed those figures. 729 00:35:17,600 --> 00:35:21,080 Speaker 3: Right. Thanks to Mary Ross Gilbert, Bloomberg Intelligence Senior Equity Analyst. 730 00:35:21,160 --> 00:35:25,680 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apples, Spotify, 731 00:35:25,880 --> 00:35:29,080 Speaker 1: and anywhere else you get your podcasts. Listen live each 732 00:35:29,120 --> 00:35:32,480 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot Com, 733 00:35:32,600 --> 00:35:36,000 Speaker 1: the iHeartRadio app tune In, and the Bloomberg Business app. 734 00:35:36,120 --> 00:35:39,120 Speaker 1: You can also watch us live every weekday on YouTube 735 00:35:39,360 --> 00:35:41,240 Speaker 1: and always on the Bloomberg terminal