1 00:00:02,400 --> 00:00:09,119 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. You're listening to the 2 00:00:09,119 --> 00:00:13,320 Speaker 1: Bloomberg Intelligence podcast. Catch us live weekdays at ten am 3 00:00:13,320 --> 00:00:16,320 Speaker 1: Eastern on applecar Playing and broud Otto with the Bloomberg 4 00:00:16,360 --> 00:00:19,720 Speaker 1: Business app. Listen on demand wherever you get your podcasts, 5 00:00:19,920 --> 00:00:22,280 Speaker 1: or watch us live on YouTube. 6 00:00:22,960 --> 00:00:24,560 Speaker 2: One of the stock stories of the day we've been 7 00:00:24,560 --> 00:00:30,440 Speaker 2: reporting Macy's. They are disclosed that a employee may have 8 00:00:30,520 --> 00:00:32,360 Speaker 2: hit as much as one hundred and fifty four million 9 00:00:32,400 --> 00:00:34,479 Speaker 2: dollars of delivery expenses, so they don't know what's going 10 00:00:34,520 --> 00:00:37,479 Speaker 2: on with their financials. They delayed their earnings release, so 11 00:00:37,640 --> 00:00:40,519 Speaker 2: a lot of issues going on at macy Stocks off 12 00:00:40,520 --> 00:00:42,560 Speaker 2: three and a half three point twenty five percent today 13 00:00:42,600 --> 00:00:44,479 Speaker 2: on the news. Let's check in with the analysts who 14 00:00:44,520 --> 00:00:48,280 Speaker 2: follows this company. Mary Ross Gilbert for Bloomberg Intelligence. He's 15 00:00:48,440 --> 00:00:51,680 Speaker 2: senior equity analyst, Mary. What have you heard from the company? 16 00:00:51,720 --> 00:00:54,000 Speaker 2: What's going on there? Really is an odd story. 17 00:00:55,960 --> 00:00:58,920 Speaker 3: It is an odd story and something kind of wild 18 00:00:58,920 --> 00:01:03,120 Speaker 3: to wake up on a Monday before Thanksgiving and Black Friday. 19 00:01:03,200 --> 00:01:09,360 Speaker 3: But as you know, Macy's reported that one employee had 20 00:01:09,520 --> 00:01:12,240 Speaker 3: hidden one hundred and thirty two to one hundred and 21 00:01:12,240 --> 00:01:18,039 Speaker 3: fifty four million of cumulative costs. But when you look 22 00:01:18,040 --> 00:01:20,080 Speaker 3: at it's over about a three year period, so it's 23 00:01:20,080 --> 00:01:23,120 Speaker 3: about twenty cents a share impact, meaning that they might 24 00:01:23,200 --> 00:01:26,560 Speaker 3: have to restate their earnings by about twenty cents. It's 25 00:01:26,680 --> 00:01:30,120 Speaker 3: not a huge number in that respect, it's huge in 26 00:01:30,120 --> 00:01:33,320 Speaker 3: this sense of well, what happened with the financial controls here. 27 00:01:34,160 --> 00:01:37,240 Speaker 3: On the other hand, I think they can move past this, 28 00:01:37,760 --> 00:01:41,480 Speaker 3: and I think if there were some positive snippets in 29 00:01:41,520 --> 00:01:43,679 Speaker 3: the press release that came out this morning with their 30 00:01:43,760 --> 00:01:48,120 Speaker 3: third quarter preliminary sales results. So they reported that their 31 00:01:48,320 --> 00:01:52,640 Speaker 3: comparable sales on an owned plus license plus marketplace basis 32 00:01:53,160 --> 00:01:56,280 Speaker 3: was down one point three percent for the company, and 33 00:01:56,360 --> 00:01:59,120 Speaker 3: that was a little better than what analysts were looking for. 34 00:01:59,160 --> 00:02:00,520 Speaker 3: They were looking for a while one and a half 35 00:02:00,520 --> 00:02:03,960 Speaker 3: percent decrease. And if you look at the go forward stores, 36 00:02:04,200 --> 00:02:06,800 Speaker 3: so this excludes the one hundred and fifty stores they 37 00:02:06,760 --> 00:02:09,720 Speaker 3: are planning to close, the comp sales on that same 38 00:02:09,800 --> 00:02:13,680 Speaker 3: basis were down just zero point nine percent. And in 39 00:02:13,720 --> 00:02:17,160 Speaker 3: the third quarter, most apparel retailers have been negatively impacted 40 00:02:17,200 --> 00:02:24,760 Speaker 3: by unseasonable warm weather related to in September primarily but 41 00:02:24,840 --> 00:02:28,440 Speaker 3: also in October, and so when you look at the results. 42 00:02:28,600 --> 00:02:31,800 Speaker 3: We're already seeing November Macy's reported, and we've seen this 43 00:02:31,840 --> 00:02:36,120 Speaker 3: with other retailers. Sales are actually performing better than what 44 00:02:36,240 --> 00:02:38,520 Speaker 3: we saw on the third quarter because the weather, of 45 00:02:38,560 --> 00:02:41,360 Speaker 3: course has turned cooler. So we think those are some 46 00:02:41,480 --> 00:02:43,919 Speaker 3: of the snippets of good news, including the luxury side 47 00:02:43,960 --> 00:02:46,760 Speaker 3: of their business. With Bloomingdale's comp sales up three point 48 00:02:46,840 --> 00:02:50,000 Speaker 3: two percent and Bloomergray up three point three percent. So 49 00:02:50,080 --> 00:02:53,720 Speaker 3: I think that kind of outweighs this because that employee's 50 00:02:53,800 --> 00:02:57,280 Speaker 3: now gone. And of course we'll learn more once this 51 00:02:57,480 --> 00:03:00,640 Speaker 3: investigation is complete and they can report and their earnings. 52 00:03:00,680 --> 00:03:05,200 Speaker 3: Call what's happened in terms of the financial controls. 53 00:03:06,000 --> 00:03:08,600 Speaker 4: Talk to me about what we know about inventory for 54 00:03:08,800 --> 00:03:11,560 Speaker 4: Macy's and any discounting. I'm just asking for a friend, 55 00:03:11,639 --> 00:03:13,239 Speaker 4: particularly for Bloomingdale's. 56 00:03:15,200 --> 00:03:18,320 Speaker 3: Yeah, well, of course going into Black Friday, and the 57 00:03:18,320 --> 00:03:20,920 Speaker 3: one thing that we learned from a consumer survey that 58 00:03:20,960 --> 00:03:26,880 Speaker 3: we recently completed, discounts and sales anything like that is 59 00:03:27,360 --> 00:03:31,680 Speaker 3: a primary driver for sales, and the other one is weather. 60 00:03:32,040 --> 00:03:34,480 Speaker 3: That comes second, is a change in weather, and that 61 00:03:34,520 --> 00:03:40,480 Speaker 3: prompts apparel sales. So I think that we're already seeing 62 00:03:40,560 --> 00:03:43,960 Speaker 3: pre Black Friday sales. Macy's is always top of mind. 63 00:03:44,000 --> 00:03:46,760 Speaker 3: They're known for their promotions, known for their sales, so 64 00:03:46,800 --> 00:03:50,160 Speaker 3: they're already out there with their Black Friday sales going on. 65 00:03:50,280 --> 00:03:53,040 Speaker 3: And of course they're going to open early on Friday 66 00:03:53,560 --> 00:03:57,800 Speaker 3: on Black Friday, so we'll actually be there in the stores. 67 00:03:57,480 --> 00:04:02,040 Speaker 2: At that time. That Yeah, absolutely, all right, Mary, thank 68 00:04:02,080 --> 00:04:04,280 Speaker 2: you so much. We appreciate that. Mary Ross Gilbert, senior 69 00:04:04,280 --> 00:04:07,720 Speaker 2: equity analysts covering retail for Bloomberg Intelligence. Again, a wacky 70 00:04:07,760 --> 00:04:10,360 Speaker 2: story or there Macy's. Macy's delays earnings after employee had 71 00:04:10,400 --> 00:04:13,480 Speaker 2: millions and expenses. For me, it would just be you know, 72 00:04:13,560 --> 00:04:16,760 Speaker 2: the the you know, the audit and control function for 73 00:04:16,880 --> 00:04:19,279 Speaker 2: the company. Can I trust it? The other numbers that 74 00:04:19,320 --> 00:04:21,640 Speaker 2: are out there? So that's kind of typically. 75 00:04:21,680 --> 00:04:23,120 Speaker 5: Do you think it takes for that to sort of 76 00:04:23,160 --> 00:04:24,000 Speaker 5: clear its way out? 77 00:04:25,200 --> 00:04:27,520 Speaker 2: Well, I think what's most troubling is that it was 78 00:04:27,560 --> 00:04:30,320 Speaker 2: over a multi year period. Yeah, you know, that's kind 79 00:04:30,360 --> 00:04:32,520 Speaker 2: of goes to the quality of the controls both internally 80 00:04:33,240 --> 00:04:35,200 Speaker 2: of the company and then from their auditor. I'd can 81 00:04:35,279 --> 00:04:36,920 Speaker 2: be looking at my order saying, dude, I mean that's 82 00:04:36,960 --> 00:04:38,680 Speaker 2: what we pay it for to kind of go through 83 00:04:38,680 --> 00:04:42,160 Speaker 2: our statements. Make sure this stuff is, you know, in 84 00:04:42,240 --> 00:04:43,200 Speaker 2: fine shapes, so we'll see. 85 00:04:43,279 --> 00:04:45,040 Speaker 4: I cannot argue with Paul Sweeney on this one. 86 00:04:45,279 --> 00:04:46,080 Speaker 5: Sorry, I can't do it. 87 00:04:47,600 --> 00:04:51,479 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 88 00:04:51,560 --> 00:04:54,600 Speaker 1: weekdays at ten am Eastern on Apple car Play and 89 00:04:54,600 --> 00:04:57,520 Speaker 1: Android Auto with the Bloomberg Business app. You can also 90 00:04:57,600 --> 00:05:00,800 Speaker 1: listen live on Amazon Alexa from our flo New York 91 00:05:00,839 --> 00:05:03,799 Speaker 1: station just say Alexa playing Bloomberg eleven. 92 00:05:05,120 --> 00:05:06,279 Speaker 5: Let's get brought our take on the market. 93 00:05:06,360 --> 00:05:10,400 Speaker 4: Kati Kaminski, chief research strategist and portfolio manager at Alpha Simplex, 94 00:05:10,920 --> 00:05:14,120 Speaker 4: joins us. All right, Katie, this record high here, potentially 95 00:05:14,120 --> 00:05:17,719 Speaker 4: another record high on the SMP. How long until we 96 00:05:17,720 --> 00:05:21,000 Speaker 4: start to see re upgrading the S ANDB forecast for 97 00:05:21,040 --> 00:05:21,880 Speaker 4: twenty twenty five. 98 00:05:23,560 --> 00:05:26,040 Speaker 6: I mean this is exciting. I mean it's a positive day. 99 00:05:26,120 --> 00:05:27,440 Speaker 6: I was actually surprised. 100 00:05:27,480 --> 00:05:30,360 Speaker 7: So I think, you know, the last week or two 101 00:05:30,400 --> 00:05:32,680 Speaker 7: we've seen a little bit of retraction, and I think 102 00:05:33,000 --> 00:05:36,800 Speaker 7: in general the market is still parsing through some of 103 00:05:36,839 --> 00:05:38,920 Speaker 7: the aftermath of the elections. 104 00:05:38,920 --> 00:05:39,880 Speaker 6: So I do think that. 105 00:05:39,960 --> 00:05:42,880 Speaker 7: People are forecasting and I'm seeing more and more positive 106 00:05:42,920 --> 00:05:47,680 Speaker 7: sentiment about equities US equities in particular, are going into 107 00:05:47,760 --> 00:05:48,200 Speaker 7: your end. 108 00:05:49,520 --> 00:05:51,960 Speaker 2: So when you woke up the day after the election, 109 00:05:52,040 --> 00:05:54,039 Speaker 2: did you and your team did you sit down and say, 110 00:05:54,680 --> 00:05:56,680 Speaker 2: we got to redo our models here, we got to 111 00:05:56,760 --> 00:06:00,800 Speaker 2: change some inputs. Did the market outlook materially changed for 112 00:06:00,800 --> 00:06:01,320 Speaker 2: you guys. 113 00:06:02,520 --> 00:06:05,400 Speaker 7: No, not at all, actually, And what's strange is that, 114 00:06:05,480 --> 00:06:10,960 Speaker 7: as quantitative traders or trend followers, we systematically follow where 115 00:06:11,000 --> 00:06:12,040 Speaker 7: prices are moving. 116 00:06:12,560 --> 00:06:13,800 Speaker 6: And I think what was the. 117 00:06:13,720 --> 00:06:17,320 Speaker 7: Most interesting to me about the election is the Trump trade. 118 00:06:17,600 --> 00:06:23,080 Speaker 7: So for example, long equities, short fixed income, long dollar 119 00:06:23,839 --> 00:06:26,720 Speaker 7: was actually playing out quite a few weeks before the election, 120 00:06:26,920 --> 00:06:27,159 Speaker 7: and it. 121 00:06:27,320 --> 00:06:28,960 Speaker 6: Just extended after the fact. 122 00:06:29,000 --> 00:06:31,560 Speaker 7: It's not really working today, but it has you know 123 00:06:31,760 --> 00:06:35,400 Speaker 7: pretty much what pre and post. 124 00:06:34,800 --> 00:06:37,240 Speaker 4: Do you think that US exceptionalism, if would just call 125 00:06:37,279 --> 00:06:39,159 Speaker 4: it like that, does that keep working? 126 00:06:41,320 --> 00:06:41,800 Speaker 6: Hopefully? 127 00:06:41,839 --> 00:06:44,560 Speaker 7: So, I mean, obviously since that's something we're seeing in 128 00:06:44,600 --> 00:06:46,800 Speaker 7: the data and we're seeing in momentum signals. 129 00:06:47,240 --> 00:06:48,880 Speaker 6: But it has been an interesting month. 130 00:06:48,920 --> 00:06:51,280 Speaker 7: I mean, look at this month, like the Russell ahead 131 00:06:51,920 --> 00:06:56,119 Speaker 7: US strongly ahead of em and Europe. So that's been 132 00:06:56,400 --> 00:06:59,360 Speaker 7: sort of a US centric theme this month, and you're 133 00:06:59,360 --> 00:07:03,640 Speaker 7: seeing that coupling from the US and other areas. So 134 00:07:03,839 --> 00:07:06,080 Speaker 7: it does seem to have some steam so far. 135 00:07:06,760 --> 00:07:09,159 Speaker 2: And how about the US dollar here, because that was 136 00:07:09,560 --> 00:07:13,160 Speaker 2: a pillar of the Trump trade long US dollar? How 137 00:07:13,200 --> 00:07:13,840 Speaker 2: do you think about that? 138 00:07:15,160 --> 00:07:17,960 Speaker 7: So the US dollar, I mean, that's probably one of 139 00:07:18,040 --> 00:07:20,360 Speaker 7: the bigger movers that I think a lot of investors 140 00:07:21,160 --> 00:07:24,240 Speaker 7: don't realize how incredibly strong that move has been. We've 141 00:07:24,280 --> 00:07:27,520 Speaker 7: had new highs on the dollar for eight weeks. It 142 00:07:27,600 --> 00:07:31,040 Speaker 7: is selling off today for various reasons related to some 143 00:07:31,120 --> 00:07:33,080 Speaker 7: of the you know, we can talk about that later, 144 00:07:33,200 --> 00:07:36,960 Speaker 7: but basically the dollar has been an overall extremely strong 145 00:07:37,840 --> 00:07:40,640 Speaker 7: especially versus the Euro. I think that it's up six 146 00:07:40,680 --> 00:07:43,960 Speaker 7: point five percent versus the Euro and the last you know, 147 00:07:44,560 --> 00:07:46,480 Speaker 7: two months, which is pretty huge. 148 00:07:46,800 --> 00:07:49,040 Speaker 4: Yeah, it was one oh four we were talking about 149 00:07:49,200 --> 00:07:53,520 Speaker 4: on Friday before we saw everything kind of calm down today. Okay, 150 00:07:53,680 --> 00:07:57,080 Speaker 4: there is I keep getting notes though, to not discount Europe, 151 00:07:57,440 --> 00:07:59,560 Speaker 4: in part because the economy could get better over in 152 00:07:59,560 --> 00:08:01,360 Speaker 4: Europe because the ECB is going to have to cut 153 00:08:01,360 --> 00:08:03,360 Speaker 4: more aggressively, and that that's going to be good for 154 00:08:03,400 --> 00:08:05,800 Speaker 4: European equities, and that we can finally I've heard the 155 00:08:05,800 --> 00:08:08,640 Speaker 4: story before, can finally sort of outshine the US a 156 00:08:08,680 --> 00:08:11,320 Speaker 4: little bit. Are you seeing any trends or flows or 157 00:08:11,360 --> 00:08:14,000 Speaker 4: momentum into that kind of trade? 158 00:08:15,320 --> 00:08:18,080 Speaker 7: So I agree with that, but I also think that 159 00:08:18,200 --> 00:08:19,200 Speaker 7: has two sides. 160 00:08:19,400 --> 00:08:21,760 Speaker 6: If the ECB keeps cutting, that's going. 161 00:08:21,760 --> 00:08:24,800 Speaker 7: To put more pressure on the euro, which is not 162 00:08:24,920 --> 00:08:27,800 Speaker 7: going to be helpful. The one story that we will 163 00:08:27,920 --> 00:08:30,200 Speaker 7: be looking for that we haven't seen so far is 164 00:08:30,280 --> 00:08:33,520 Speaker 7: perhaps stronger growth at some point in Europe, So there 165 00:08:33,559 --> 00:08:37,280 Speaker 7: will be potential for that rotation if we can see 166 00:08:37,280 --> 00:08:40,160 Speaker 7: stronger growth. I think the euro trade is still a 167 00:08:40,240 --> 00:08:43,200 Speaker 7: little bit more questionable because of what I said that. 168 00:08:43,600 --> 00:08:46,200 Speaker 7: You know, if ECB continues to cut through next year 169 00:08:46,280 --> 00:08:48,680 Speaker 7: and we see a more steady FED, that's actually going 170 00:08:48,760 --> 00:08:51,880 Speaker 7: to be pro dollar. So I think there's going to 171 00:08:51,920 --> 00:08:53,840 Speaker 7: be it's going to require quite a bit, especially with 172 00:08:53,880 --> 00:08:54,559 Speaker 7: the weaker euro. 173 00:08:55,440 --> 00:08:58,280 Speaker 2: Are you concerned about inflation here at the US economy 174 00:08:58,360 --> 00:09:00,000 Speaker 2: or do you feel like the Fed's got that under control, 175 00:09:00,200 --> 00:09:02,520 Speaker 2: Because I feel like I'm hearing more and more people 176 00:09:02,559 --> 00:09:05,160 Speaker 2: say twenty twenty five that might be a thing in 177 00:09:05,240 --> 00:09:06,400 Speaker 2: terms of researching inflation. 178 00:09:07,880 --> 00:09:12,400 Speaker 7: So we definitely do see cross asset themes and movements 179 00:09:12,440 --> 00:09:16,200 Speaker 7: to show some indication of concern for inflation. I think 180 00:09:16,280 --> 00:09:21,040 Speaker 7: Initially post selection, there was a lot of concern. Today 181 00:09:21,200 --> 00:09:23,120 Speaker 7: is a day where you're seeing some of that abate 182 00:09:23,400 --> 00:09:26,280 Speaker 7: because given some of the you know, the choice of 183 00:09:26,400 --> 00:09:32,040 Speaker 7: US Treasury Secretary by the incoming president suggests that, you know, 184 00:09:32,080 --> 00:09:35,080 Speaker 7: there's a little bit more pro business, and recent commentary 185 00:09:35,160 --> 00:09:38,760 Speaker 7: has also focused on maybe tariffs won't be as aggressive. 186 00:09:39,160 --> 00:09:43,000 Speaker 7: So I think the general digesting of that information is 187 00:09:43,040 --> 00:09:46,560 Speaker 7: about figuring out, you know, how much inflation could we 188 00:09:46,640 --> 00:09:49,199 Speaker 7: actually have as a result of a change in policy. 189 00:09:49,920 --> 00:09:52,760 Speaker 7: There's definitely still a good chance that that's something on 190 00:09:52,800 --> 00:09:55,640 Speaker 7: the longer term, especially with the deficit as high as 191 00:09:55,640 --> 00:09:55,920 Speaker 7: it is. 192 00:09:56,120 --> 00:09:57,960 Speaker 4: This gonna be a really dumb question, but does better 193 00:09:58,120 --> 00:10:03,240 Speaker 4: US economic growth than mean more inflation in that? Yeah? No, 194 00:10:03,320 --> 00:10:04,679 Speaker 4: I mean, I mean just that, like in order to 195 00:10:04,720 --> 00:10:06,640 Speaker 4: small caps and mid caps to really work, in order 196 00:10:06,640 --> 00:10:08,440 Speaker 4: for value to really work, you really need a stronger 197 00:10:08,520 --> 00:10:10,440 Speaker 4: US economy. But then that comes with demand, and then 198 00:10:10,480 --> 00:10:12,280 Speaker 4: you add tariffs into that, and then that leads to 199 00:10:12,360 --> 00:10:13,040 Speaker 4: higher prices. 200 00:10:14,480 --> 00:10:16,760 Speaker 7: Yes, And I think that's part of the narrative that 201 00:10:16,840 --> 00:10:19,880 Speaker 7: people are concerned about, is if we do have that 202 00:10:20,120 --> 00:10:24,000 Speaker 7: coupled with high debt or high deficit, then at some 203 00:10:24,160 --> 00:10:26,480 Speaker 7: point you have to have higher prices, which is kind 204 00:10:26,480 --> 00:10:30,880 Speaker 7: of what happened post COVID, which is why people, especially us, 205 00:10:30,920 --> 00:10:32,800 Speaker 7: we think a lot about what's going to happen to 206 00:10:32,840 --> 00:10:35,840 Speaker 7: the yield curve because that's probably where you're going to 207 00:10:35,840 --> 00:10:40,240 Speaker 7: see those price expectations for inflation baked in. It is 208 00:10:40,280 --> 00:10:42,720 Speaker 7: interesting to see also how much gold has gone up 209 00:10:42,720 --> 00:10:43,600 Speaker 7: this year, because that. 210 00:10:43,559 --> 00:10:45,080 Speaker 6: Tends to be a play on inflation. 211 00:10:45,600 --> 00:10:48,280 Speaker 7: So I think there are definitely enough people out there 212 00:10:48,400 --> 00:10:52,240 Speaker 7: that have significant concerns about inflation longer term and the 213 00:10:52,280 --> 00:10:53,400 Speaker 7: potential impact of that. 214 00:10:53,800 --> 00:10:56,079 Speaker 2: Katie, thank you so much for joining us. Always appreciate 215 00:10:56,120 --> 00:10:58,760 Speaker 2: getting a few minutes of your time. Katie Kaminski, Chief 216 00:10:58,800 --> 00:11:01,560 Speaker 2: Research Strategy is important only a manatured alpha simplex up 217 00:11:01,600 --> 00:11:03,239 Speaker 2: there in Cambridge. 218 00:11:03,280 --> 00:11:03,720 Speaker 8: Massive. 219 00:11:05,000 --> 00:11:08,880 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 220 00:11:08,960 --> 00:11:12,079 Speaker 1: weekdays at ten am Eastern on Affocarplay and then Broid 221 00:11:12,080 --> 00:11:15,200 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 222 00:11:15,240 --> 00:11:19,079 Speaker 1: you get your podcasts, or watch us live on YouTube. 223 00:11:19,960 --> 00:11:23,400 Speaker 4: Alex still here alongside Paul Sweenie. This is Bloomberg Intelligence Radio. 224 00:11:23,480 --> 00:11:25,360 Speaker 4: We bring you all the tap news and business economics 225 00:11:25,400 --> 00:11:27,720 Speaker 4: and finance through our lens of our Bloomberg Intelligence folks. 226 00:11:27,720 --> 00:11:29,880 Speaker 4: And because I love energy, we talk a lot about energy. 227 00:11:29,960 --> 00:11:32,559 Speaker 4: We'll just throw that in there too, why not. So 228 00:11:32,720 --> 00:11:34,839 Speaker 4: one company that you may not have heard of is 229 00:11:34,880 --> 00:11:38,839 Speaker 4: called Bloom Energy. They are a fuel cell maker. They 230 00:11:38,920 --> 00:11:40,599 Speaker 4: made a lot of headlines in the last couple of 231 00:11:40,640 --> 00:11:42,839 Speaker 4: weeks and hid an all time high on November twenty 232 00:11:42,880 --> 00:11:46,280 Speaker 4: second after it made a deal with American Electric Power. 233 00:11:46,320 --> 00:11:47,560 Speaker 5: American Electric Power. 234 00:11:47,400 --> 00:11:49,960 Speaker 4: Is going to use their fuel cells to bring electricity 235 00:11:50,000 --> 00:11:53,360 Speaker 4: to say hyperscalers AEP is going to use up to 236 00:11:53,400 --> 00:11:56,959 Speaker 4: one gigawatt of Bloom's fuel cells. Now, something that's quite 237 00:11:56,960 --> 00:11:58,720 Speaker 4: interesting in this is that a lot of companies in 238 00:11:58,720 --> 00:12:02,000 Speaker 4: the energy space do do cool stuff, but syncing up 239 00:12:02,040 --> 00:12:04,480 Speaker 4: with the companies that could actually use their cool stuff 240 00:12:04,760 --> 00:12:07,440 Speaker 4: isn't always easy. In fact, it is quite difficult. So 241 00:12:07,440 --> 00:12:09,560 Speaker 4: we wanted to get an idea of how this came about. 242 00:12:09,920 --> 00:12:10,240 Speaker 8: K R. 243 00:12:10,320 --> 00:12:13,560 Speaker 4: Shweththar is CEO of Bloom Energy and he joins us 244 00:12:13,559 --> 00:12:15,800 Speaker 4: now from Silicon Valley in California. 245 00:12:15,880 --> 00:12:17,880 Speaker 5: K Or how did this deal come about? 246 00:12:19,640 --> 00:12:22,959 Speaker 9: So we have been working with data centers for a 247 00:12:23,040 --> 00:12:26,680 Speaker 9: very long time. Now we have over three hundred megawarts 248 00:12:26,720 --> 00:12:29,720 Speaker 9: in multiple data centers across the country. These are the 249 00:12:29,800 --> 00:12:32,959 Speaker 9: smaller data centers called the edge data centers that are 250 00:12:32,960 --> 00:12:35,439 Speaker 9: located where customers are somewhere in. 251 00:12:35,400 --> 00:12:38,440 Speaker 8: The five to ten megawat range in a particular site. 252 00:12:38,600 --> 00:12:41,640 Speaker 9: So we have transacted close to three hundred megawards, so 253 00:12:41,679 --> 00:12:44,880 Speaker 9: we are a known player to data centers. Now with 254 00:12:44,960 --> 00:12:48,920 Speaker 9: the hyperscalers, what's the difference, it's a much larger data centers. 255 00:12:49,440 --> 00:12:51,920 Speaker 8: These are now particularly. 256 00:12:51,320 --> 00:12:54,720 Speaker 9: More important in terms of growth because of AI and 257 00:12:54,760 --> 00:12:59,360 Speaker 9: the amount of power they need. And currently these hyperscalers, 258 00:12:59,400 --> 00:13:03,600 Speaker 9: as they had growth going very fast, the transmission distribution 259 00:13:04,200 --> 00:13:07,440 Speaker 9: is not able to keep up with providing those hundreds 260 00:13:07,440 --> 00:13:09,920 Speaker 9: of megawats of power right at the site where you 261 00:13:10,000 --> 00:13:13,000 Speaker 9: need it within the time that you need it maybe five. 262 00:13:12,840 --> 00:13:13,440 Speaker 8: To six years. 263 00:13:13,480 --> 00:13:15,520 Speaker 9: They may be able to provide the power, but the 264 00:13:15,600 --> 00:13:18,559 Speaker 9: data center really wants it today, they want it now. 265 00:13:19,200 --> 00:13:24,040 Speaker 9: So we are a perfect solution under those circumstances because 266 00:13:24,160 --> 00:13:26,840 Speaker 9: our Bloom Energy servers can be deployed in a matter 267 00:13:26,920 --> 00:13:31,600 Speaker 9: of months right where the customer is, thereby not worrying 268 00:13:31,640 --> 00:13:37,160 Speaker 9: about the transmission distribution gridlock and providing that reliable, clean, 269 00:13:37,400 --> 00:13:39,720 Speaker 9: always on power to the data center. 270 00:13:40,200 --> 00:13:42,160 Speaker 8: So that's the reason this happened. 271 00:13:42,640 --> 00:13:48,640 Speaker 9: And here what happened is the electricity provider AEP said, 272 00:13:48,800 --> 00:13:52,920 Speaker 9: we don't make nuclear power plants, we don't make gas turbance, 273 00:13:53,240 --> 00:13:54,440 Speaker 9: we don't make fuel cells. 274 00:13:54,720 --> 00:13:57,320 Speaker 8: We're agnostic. We'll buy your systems. 275 00:13:57,480 --> 00:14:00,720 Speaker 9: And similar to us using those other power sources to 276 00:14:00,760 --> 00:14:03,640 Speaker 9: provide power to the customer. Here we can take your 277 00:14:03,640 --> 00:14:05,880 Speaker 9: fuel cells and take the power you produce and give 278 00:14:05,920 --> 00:14:06,920 Speaker 9: it to the data center. 279 00:14:07,320 --> 00:14:09,120 Speaker 8: However, the big advantage here. 280 00:14:09,080 --> 00:14:12,599 Speaker 9: Is we can put these fuel cells right where the 281 00:14:12,679 --> 00:14:16,199 Speaker 9: data center is, thereby avoiding the transmission distribution issue. 282 00:14:16,400 --> 00:14:18,400 Speaker 2: So carry I mean, just you know, I didn't know 283 00:14:18,480 --> 00:14:20,640 Speaker 2: much about your company before, so just reading up here, 284 00:14:20,720 --> 00:14:24,480 Speaker 2: it's like right company, right place, at the right time, 285 00:14:24,600 --> 00:14:27,840 Speaker 2: with the right technology, and boom. Talk to us about 286 00:14:28,360 --> 00:14:31,560 Speaker 2: how good your fuel cells are. How would I know 287 00:14:31,600 --> 00:14:34,760 Speaker 2: whether your fuel cell is better more productive than say 288 00:14:34,760 --> 00:14:35,400 Speaker 2: a competitor. 289 00:14:37,040 --> 00:14:37,920 Speaker 8: That's a great question. 290 00:14:38,840 --> 00:14:41,720 Speaker 9: So let me go away from fuel cells just into 291 00:14:41,760 --> 00:14:44,080 Speaker 9: electricity for the customer. At the end of the day, 292 00:14:44,520 --> 00:14:48,360 Speaker 9: we all provide a service or a product to our 293 00:14:48,480 --> 00:14:52,680 Speaker 9: end customer. That electricity that a data center takes has 294 00:14:52,720 --> 00:14:55,960 Speaker 9: to be clean, it has to be always on and 295 00:14:56,040 --> 00:14:59,240 Speaker 9: reliable twenty four to seven. It needs to have a 296 00:14:59,360 --> 00:15:03,880 Speaker 9: pay su grow characteristic, and it needs to be future proofed. 297 00:15:03,920 --> 00:15:08,080 Speaker 9: In terms of sustainability. Bloom Energy is one of those 298 00:15:08,120 --> 00:15:11,600 Speaker 9: solutions that offer all of the above no rs. 299 00:15:11,840 --> 00:15:13,960 Speaker 8: It's the genius of end So. 300 00:15:14,040 --> 00:15:17,880 Speaker 9: We are the most efficient way of taking natural gas 301 00:15:18,360 --> 00:15:22,400 Speaker 9: and making electricity out of it without combusting. Because we 302 00:15:22,480 --> 00:15:26,160 Speaker 9: don't combust, there is no knock stocks particulates anything going 303 00:15:26,160 --> 00:15:28,840 Speaker 9: into the atmosphere, so there is no local air pollution. 304 00:15:29,560 --> 00:15:32,800 Speaker 9: And if you look at our system they're like lego blocks. 305 00:15:32,840 --> 00:15:35,040 Speaker 9: You put many of these lego blocks, hundreds of them 306 00:15:35,480 --> 00:15:37,880 Speaker 9: to be able to provide power to a data center. 307 00:15:38,040 --> 00:15:40,400 Speaker 9: If any one of them has to be serviced, you 308 00:15:40,440 --> 00:15:43,160 Speaker 9: can just hot swap them in and out. So the 309 00:15:43,280 --> 00:15:46,240 Speaker 9: reliability and the resiliency of our systems are very. 310 00:15:46,160 --> 00:15:47,200 Speaker 8: High and. 311 00:15:48,800 --> 00:15:52,400 Speaker 9: You can pay as you grow data centers. Even though 312 00:15:52,400 --> 00:15:55,960 Speaker 9: they build a big data center, don't start that entire 313 00:15:56,040 --> 00:15:58,160 Speaker 9: data center on day one. They may do one third 314 00:15:58,200 --> 00:16:00,480 Speaker 9: of the load, and then a few months later they 315 00:16:00,480 --> 00:16:03,240 Speaker 9: may add additional load. As they are adding the load, 316 00:16:03,400 --> 00:16:05,680 Speaker 9: they can add more and more of our fuel cells. 317 00:16:05,960 --> 00:16:08,280 Speaker 9: You can't do that with the gas turbine. You can't 318 00:16:08,320 --> 00:16:10,760 Speaker 9: do that to the nuclear power plant. So we bring 319 00:16:10,840 --> 00:16:14,160 Speaker 9: all these attributes in so I would say we are 320 00:16:14,240 --> 00:16:17,320 Speaker 9: ideally suited for this AI data central market. 321 00:16:17,760 --> 00:16:20,840 Speaker 4: Oh, now the financial terms were not disclosed. I appreciate that, 322 00:16:21,080 --> 00:16:23,120 Speaker 4: So I'm going to ask about the money a different way. 323 00:16:23,440 --> 00:16:26,400 Speaker 4: How easy was it or difficult was it to come 324 00:16:26,440 --> 00:16:29,040 Speaker 4: to an agreement on price with AEP. 325 00:16:30,400 --> 00:16:33,160 Speaker 9: In this particular case, it was fairly easy to come 326 00:16:33,200 --> 00:16:37,600 Speaker 9: to that agreement. Nothing is easy, but relatively speaking, And 327 00:16:37,680 --> 00:16:42,640 Speaker 9: here is why. There are three parties involved, actually four, 328 00:16:43,240 --> 00:16:49,520 Speaker 9: the data center, customer, AP, the public at large where. 329 00:16:49,320 --> 00:16:52,920 Speaker 8: This is being installed, and blow energy we. 330 00:16:52,720 --> 00:16:56,720 Speaker 9: Were able to put together When when for all four 331 00:16:56,840 --> 00:16:58,560 Speaker 9: of these stakeholders, why is. 332 00:16:58,520 --> 00:16:59,720 Speaker 8: That number one? 333 00:16:59,760 --> 00:17:03,920 Speaker 9: Let's started the public at large with other kind of 334 00:17:03,960 --> 00:17:08,520 Speaker 9: provisions that were being contemplated. The fear of the rate 335 00:17:08,600 --> 00:17:11,480 Speaker 9: payer was because the hyperscaler is going to get a 336 00:17:11,520 --> 00:17:15,400 Speaker 9: large amount of power from the transmission distribution company, they 337 00:17:15,440 --> 00:17:20,080 Speaker 9: will end up carrying the bill. In this construct, AEP 338 00:17:20,280 --> 00:17:23,840 Speaker 9: made sure none of the costs associated with putting these 339 00:17:23,880 --> 00:17:27,359 Speaker 9: fuel cells and providing that clean power to the data 340 00:17:27,359 --> 00:17:30,760 Speaker 9: center will cost the rate payer any money. So that 341 00:17:30,880 --> 00:17:33,399 Speaker 9: was a win for the ratepayer Number two for the 342 00:17:33,480 --> 00:17:37,119 Speaker 9: data center. For the data center, the price of not 343 00:17:37,280 --> 00:17:41,160 Speaker 9: having power on time is significantly greater than the cost 344 00:17:41,200 --> 00:17:41,679 Speaker 9: of power. 345 00:17:42,119 --> 00:17:43,399 Speaker 8: If you just think about. 346 00:17:43,160 --> 00:17:46,960 Speaker 9: The race in AI and who has to get there competitively. 347 00:17:47,440 --> 00:17:50,679 Speaker 9: So time to power was the key metric, and they 348 00:17:50,680 --> 00:17:53,560 Speaker 9: would pay a slight premium to be able to get that, 349 00:17:53,920 --> 00:17:58,320 Speaker 9: and that penciled out for AP. They were able to 350 00:17:58,359 --> 00:18:02,880 Speaker 9: grow their customer base, give them their growth needs without 351 00:18:02,920 --> 00:18:06,000 Speaker 9: disintermediating them and having them go to some of their 352 00:18:06,040 --> 00:18:08,919 Speaker 9: stake which is what has been happening in places like 353 00:18:09,000 --> 00:18:12,320 Speaker 9: Virginia where data centers are moving away from there because 354 00:18:12,359 --> 00:18:14,280 Speaker 9: there is not enough power. So it was a win 355 00:18:14,400 --> 00:18:17,639 Speaker 9: for AEP in retaining their customer and taking care of 356 00:18:17,640 --> 00:18:21,439 Speaker 9: their customer and making money for Bloom. Whether we are 357 00:18:21,440 --> 00:18:23,600 Speaker 9: in front of the meter or behind the meter, it 358 00:18:23,680 --> 00:18:26,119 Speaker 9: is the same thing whether we sell it to AEP 359 00:18:26,760 --> 00:18:29,240 Speaker 9: who then provides the power for the data center, or 360 00:18:29,320 --> 00:18:31,440 Speaker 9: we sell to the data center and they provide it 361 00:18:31,960 --> 00:18:34,639 Speaker 9: and they take their own power. You know, for us, 362 00:18:34,680 --> 00:18:37,080 Speaker 9: we are agnostic because we get to make the sale 363 00:18:37,400 --> 00:18:39,240 Speaker 9: and we get the gross margins and the product. 364 00:18:39,480 --> 00:18:41,480 Speaker 8: So that's how it was constructed. 365 00:18:41,000 --> 00:18:42,199 Speaker 2: Alex another story. 366 00:18:42,280 --> 00:18:42,720 Speaker 8: I missed. 367 00:18:43,040 --> 00:18:46,520 Speaker 2: Bloom Energy stocks up seventy two per year today, all 368 00:18:46,560 --> 00:18:50,000 Speaker 2: time high today up ninety on a trolley twelve month basis, 369 00:18:50,040 --> 00:18:52,359 Speaker 2: Where were you? Where was the story for me? Like 370 00:18:52,880 --> 00:18:53,800 Speaker 2: fifty dollars ago? 371 00:18:53,920 --> 00:18:56,359 Speaker 5: Yeah, sorry, man, missed that one for you. Yeah, my bad. 372 00:18:56,640 --> 00:18:58,680 Speaker 4: All right, great to see you. Thank you so much 373 00:18:58,680 --> 00:19:02,879 Speaker 4: for the great story. Archer reydar a CEO of Bloom Energy, 374 00:19:03,000 --> 00:19:05,720 Speaker 4: and just sort of the idea that how you pair 375 00:19:05,800 --> 00:19:08,480 Speaker 4: all these things together, whether you're an energy butt provider 376 00:19:08,520 --> 00:19:11,440 Speaker 4: and then electricity provider and then a hyperscaler and getting 377 00:19:11,480 --> 00:19:13,760 Speaker 4: all of that to match up sounds like it could 378 00:19:13,800 --> 00:19:17,360 Speaker 4: be really simple, and sometimes it is, as Kara was saying, 379 00:19:17,359 --> 00:19:19,000 Speaker 4: and sometimes it's really not. 380 00:19:19,320 --> 00:19:22,080 Speaker 2: It doesn't, but it sounds like, again, they have a 381 00:19:22,119 --> 00:19:25,719 Speaker 2: great product for this part of you know, the AI 382 00:19:26,200 --> 00:19:28,800 Speaker 2: energy provision kind of scenario. 383 00:19:28,960 --> 00:19:31,280 Speaker 5: Wow, you said, right time, right place. 384 00:19:31,119 --> 00:19:34,600 Speaker 2: And again in San Jose, California. I mean, just the 385 00:19:35,040 --> 00:19:37,720 Speaker 2: amount of innovation is in all parts of the industry 386 00:19:37,800 --> 00:19:40,000 Speaker 2: in that part of the country is just amazing. 387 00:19:40,160 --> 00:19:40,440 Speaker 8: Yeah. 388 00:19:40,600 --> 00:19:42,960 Speaker 2: Fortunately, my youngest goes to college there, so I'm like, dude, 389 00:19:43,320 --> 00:19:44,000 Speaker 2: get a job. 390 00:19:44,440 --> 00:19:47,880 Speaker 4: You're like, just feel the feel the innovation, Get the innovation. 391 00:19:49,520 --> 00:19:53,440 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 392 00:19:53,520 --> 00:19:56,560 Speaker 1: weekdays at ten am Eastern on Apple car Play and 393 00:19:56,560 --> 00:19:59,760 Speaker 1: Android Auto with the Bloomberg Business. You can also listen 394 00:20:00,160 --> 00:20:03,040 Speaker 1: I have on Amazon Alexa from our flagship New York station, 395 00:20:03,400 --> 00:20:06,440 Speaker 1: Just Say Alexa playing Bloomberg eleven thirty. 396 00:20:07,520 --> 00:20:10,040 Speaker 2: Dallas Steele, Paul Swingey. We're live here on a Bloomberg 397 00:20:10,040 --> 00:20:13,119 Speaker 2: Interactive Brooker Studio and are streaming live on YouTube as well. 398 00:20:13,160 --> 00:20:15,560 Speaker 2: So head over to YouTube dot com search Bloomberg Podcast 399 00:20:15,600 --> 00:20:17,440 Speaker 2: and that's where you'll find us. A part of the 400 00:20:17,480 --> 00:20:20,480 Speaker 2: Trump trade was to sell bonds, and the market certainly did. 401 00:20:20,480 --> 00:20:22,080 Speaker 2: We had rached shoot up. We got a little bit 402 00:20:22,080 --> 00:20:24,720 Speaker 2: of a pullback today, as Charlie was pointing out, But 403 00:20:25,440 --> 00:20:27,520 Speaker 2: see what's happening in the world of fixing. Come today 404 00:20:27,560 --> 00:20:29,199 Speaker 2: to do that. We check in with r J. Gallows, 405 00:20:29,200 --> 00:20:32,360 Speaker 2: senior portfolio manager over the fixed income group at federatedt Hermes. 406 00:20:33,000 --> 00:20:37,080 Speaker 2: They're in the great city of Pittsburgh, PA. RJ. When 407 00:20:37,080 --> 00:20:40,120 Speaker 2: you and your team woke up the day after election Day, 408 00:20:40,200 --> 00:20:42,400 Speaker 2: you sat down, you got to your next strategy meeting 409 00:20:42,400 --> 00:20:44,800 Speaker 2: with your fixed income folks. Did you guys, change your 410 00:20:44,840 --> 00:20:47,120 Speaker 2: outlook at all, or maybe how you approach the market. 411 00:20:49,400 --> 00:20:50,600 Speaker 10: Yeah, we did. 412 00:20:51,880 --> 00:20:53,879 Speaker 11: I know, plenty of people didn't want to bet on 413 00:20:53,920 --> 00:20:56,359 Speaker 11: the election, and I would put us in that camp. 414 00:20:56,359 --> 00:20:59,160 Speaker 11: I would say that the sharp increase in yields from 415 00:20:59,240 --> 00:21:03,000 Speaker 11: mid September all the way up pretty much to election day, 416 00:21:03,800 --> 00:21:07,080 Speaker 11: in part reflected that the betting markets had moved sharply 417 00:21:07,560 --> 00:21:10,760 Speaker 11: in Trump's favor, and so the market was advancing to 418 00:21:10,920 --> 00:21:13,160 Speaker 11: start pricing in a Trump trade even before the election 419 00:21:13,240 --> 00:21:16,800 Speaker 11: outcome was known. We you know, we were a little 420 00:21:16,800 --> 00:21:20,680 Speaker 11: cautious on duration during that period. You know, probably booked 421 00:21:20,680 --> 00:21:23,480 Speaker 11: a few basis points of excess returners, was all. We 422 00:21:23,480 --> 00:21:25,840 Speaker 11: didn't make a big bet a little bit for the 423 00:21:25,880 --> 00:21:30,040 Speaker 11: actual election itself. The you know, the polls were closed, 424 00:21:30,080 --> 00:21:32,680 Speaker 11: the betting markets weren't. As it turned out, the betting 425 00:21:32,720 --> 00:21:36,639 Speaker 11: markets were much more accurate. And then following the election, 426 00:21:36,760 --> 00:21:39,880 Speaker 11: we also got a little short duration, thinking that we 427 00:21:39,920 --> 00:21:43,480 Speaker 11: would maybe test four fifty on the tenure, and that's 428 00:21:43,520 --> 00:21:46,160 Speaker 11: exactly what happened. But as we looked this morning, we're 429 00:21:46,160 --> 00:21:47,680 Speaker 11: back to four thirty pretty much. 430 00:21:47,960 --> 00:21:50,480 Speaker 4: So yeah, so what does that mean then for the 431 00:21:50,520 --> 00:21:52,879 Speaker 4: long end? Is it path of Lee's resistance lower, do 432 00:21:52,880 --> 00:21:53,600 Speaker 4: we stay sticky? 433 00:21:55,800 --> 00:21:58,280 Speaker 11: I think that this is sort of a tactical replacement. 434 00:21:58,480 --> 00:22:00,320 Speaker 11: I think that the moves of four to fifty was 435 00:22:00,359 --> 00:22:03,440 Speaker 11: pricing in as much as one could the idea of 436 00:22:03,480 --> 00:22:08,240 Speaker 11: the Trump trade, a broader fiscal deficit, stimulative suite of policies, 437 00:22:08,320 --> 00:22:11,800 Speaker 11: especially deregulation, and the prospect of tariffs and trade war 438 00:22:11,840 --> 00:22:14,919 Speaker 11: which could boost inflation in the short run. That's what 439 00:22:15,040 --> 00:22:18,480 Speaker 11: drove rates in part to where they were. I think 440 00:22:18,520 --> 00:22:21,000 Speaker 11: the fact that we've had a retlacement that the market 441 00:22:21,040 --> 00:22:23,640 Speaker 11: was sort of consolidating as we wait to see how 442 00:22:23,720 --> 00:22:28,920 Speaker 11: all these policy plans actually get implemented, how successful would 443 00:22:28,920 --> 00:22:32,440 Speaker 11: the Trump administration be in implementing them, How open to 444 00:22:32,560 --> 00:22:34,480 Speaker 11: negotiation are they on tariffs. 445 00:22:34,960 --> 00:22:37,080 Speaker 10: The fact that the market's responding with a bit of. 446 00:22:37,000 --> 00:22:40,119 Speaker 11: A rally today yields sharply lower about almost ten eleven 447 00:22:40,160 --> 00:22:42,959 Speaker 11: basis points in the tenure. It seems that the market 448 00:22:43,040 --> 00:22:45,560 Speaker 11: views best int as a pretty conventional pick. I think 449 00:22:45,600 --> 00:22:48,159 Speaker 11: I would agree with that. I wouldn't say it eliminates 450 00:22:48,200 --> 00:22:51,560 Speaker 11: the Trump trade. It's just the next iteration of the 451 00:22:51,640 --> 00:22:54,800 Speaker 11: unfolding of the actual Trump outcome, which is going to 452 00:22:54,800 --> 00:22:55,800 Speaker 11: take time to see. 453 00:22:56,600 --> 00:22:59,720 Speaker 2: So let's talk about this US economy here, man, how 454 00:22:59,720 --> 00:23:01,639 Speaker 2: do you think that'll be reflected in the rates market. 455 00:23:01,680 --> 00:23:03,879 Speaker 2: It seems some of the economic Now this is going 456 00:23:03,920 --> 00:23:06,680 Speaker 2: to be a busy week for economic data, inflation data, 457 00:23:06,760 --> 00:23:10,879 Speaker 2: jobs data. In terms of claims your thought on the 458 00:23:10,920 --> 00:23:13,520 Speaker 2: economy and how that might be affecting this federal reserve. 459 00:23:15,359 --> 00:23:19,760 Speaker 11: The economy has held up remarkably well. The once expected 460 00:23:19,840 --> 00:23:23,959 Speaker 11: recession a year ago never showed up. The economy had 461 00:23:24,000 --> 00:23:27,439 Speaker 11: a lot of tailwinds. Credit markets have done well. 462 00:23:27,880 --> 00:23:28,040 Speaker 8: You know. 463 00:23:28,200 --> 00:23:31,280 Speaker 11: The recipe for our performance and fixed income has been 464 00:23:31,320 --> 00:23:34,240 Speaker 11: to own lower quality credit risk over higher quality credit risk. 465 00:23:34,840 --> 00:23:37,560 Speaker 11: I'm glad to say broad fixed income indicies of all 466 00:23:37,560 --> 00:23:42,200 Speaker 11: sorts have generated positive returns. The economic outlook from here 467 00:23:42,359 --> 00:23:43,840 Speaker 11: is still relatively supportive. 468 00:23:44,440 --> 00:23:45,480 Speaker 10: I worry a little. 469 00:23:45,280 --> 00:23:49,000 Speaker 11: Bit that the overall suite of Trump policies that I've 470 00:23:49,000 --> 00:23:53,359 Speaker 11: described previously might actually end up being stimulative in the 471 00:23:53,400 --> 00:23:57,360 Speaker 11: short run, but at a meaningful cost in the longer run, 472 00:23:57,400 --> 00:24:00,359 Speaker 11: in the sense that you'll have larger deficits, more debt, 473 00:24:01,160 --> 00:24:03,520 Speaker 11: and the bond market might be left to be the 474 00:24:03,560 --> 00:24:07,520 Speaker 11: source of discipline in terms of reacting to that outcome. 475 00:24:08,000 --> 00:24:12,160 Speaker 11: With higher yields as we go into further into twenty 476 00:24:12,200 --> 00:24:14,679 Speaker 11: twenty five, but a lot remains to be seen. We 477 00:24:14,760 --> 00:24:17,639 Speaker 11: have to see how serious are is the Trump administration 478 00:24:17,720 --> 00:24:19,119 Speaker 11: about the broader tear of threat? 479 00:24:19,440 --> 00:24:20,800 Speaker 10: Is it in fact a negotiation. 480 00:24:21,760 --> 00:24:24,960 Speaker 11: I do think that the Feds the lesser expectations for 481 00:24:25,000 --> 00:24:28,640 Speaker 11: FED easing are in fact rational as the fiscal policy 482 00:24:28,680 --> 00:24:31,439 Speaker 11: expansion is pushing in the other direction in terms of 483 00:24:31,440 --> 00:24:33,080 Speaker 11: monetary policy expectations. 484 00:24:33,680 --> 00:24:37,439 Speaker 4: So fiscal policy chain expand and the FED gets looser. 485 00:24:38,760 --> 00:24:41,600 Speaker 11: No, no, no, the Fed gets less loose those but 486 00:24:41,640 --> 00:24:45,320 Speaker 11: they're still loose. You look at so for futures, for example, 487 00:24:45,640 --> 00:24:48,919 Speaker 11: they gap sharply higher and implied rate in terms of 488 00:24:48,960 --> 00:24:50,399 Speaker 11: the terminal rate in this trough. 489 00:24:51,160 --> 00:24:53,800 Speaker 10: Upon the election outcome, Really. 490 00:24:53,600 --> 00:24:56,000 Speaker 11: As the market was moving to price in the Trump trade, 491 00:24:56,200 --> 00:25:00,680 Speaker 11: less FED easing was expected. That is very rational monetary 492 00:25:00,680 --> 00:25:04,520 Speaker 11: stimulus as the fiscal side opens up more with bigger deficits. 493 00:25:04,840 --> 00:25:07,040 Speaker 2: The best performance by far and fixed to come this 494 00:25:07,119 --> 00:25:09,879 Speaker 2: year RJ has been high yield and leverage loans in 495 00:25:09,920 --> 00:25:12,760 Speaker 2: the US, So I guess the market the market's comfortable 496 00:25:12,760 --> 00:25:13,359 Speaker 2: with risk. 497 00:25:13,440 --> 00:25:14,080 Speaker 8: I guess. 498 00:25:15,440 --> 00:25:15,600 Speaker 10: Well. 499 00:25:15,640 --> 00:25:17,919 Speaker 11: I think once it became clear that the economy had 500 00:25:17,960 --> 00:25:22,879 Speaker 11: significant tailwinds and corporate profits have held up relatively well. 501 00:25:23,200 --> 00:25:25,240 Speaker 11: It's been sort of risk on in terms of where 502 00:25:25,280 --> 00:25:27,439 Speaker 11: you put your capital up and down the credit quality 503 00:25:27,480 --> 00:25:31,840 Speaker 11: spectrum and spreads have tightened, and you've generated very very 504 00:25:31,880 --> 00:25:36,280 Speaker 11: favorable returns. To be frank in our multisector, you know, 505 00:25:36,680 --> 00:25:39,240 Speaker 11: a strategy here at Federated we've been a little more 506 00:25:39,280 --> 00:25:41,560 Speaker 11: cautious thinking that spreads had gotten too tight. 507 00:25:42,359 --> 00:25:43,960 Speaker 10: As a result, we've been a little underweight. 508 00:25:44,760 --> 00:25:46,520 Speaker 11: Fortunately, we've been able to make it up a little 509 00:25:46,520 --> 00:25:49,600 Speaker 11: bit more on curve positioning. Theyll curve is steep and sharply, 510 00:25:50,000 --> 00:25:52,399 Speaker 11: and a number of our strategies taking an active position 511 00:25:52,440 --> 00:25:55,240 Speaker 11: to benefit from that has really worked very well. We've 512 00:25:55,240 --> 00:25:58,760 Speaker 11: also been overweight mortgages, which have generated incremental outperformance. Not 513 00:25:58,800 --> 00:26:01,240 Speaker 11: as much as how you corporate, but there's a lot 514 00:26:01,280 --> 00:26:03,520 Speaker 11: of eras in the quiver of a fixed income manager 515 00:26:03,520 --> 00:26:05,399 Speaker 11: and we've been shooting some of them in the right direction. 516 00:26:05,760 --> 00:26:07,919 Speaker 11: The high yield one has been a little tough and 517 00:26:07,960 --> 00:26:10,880 Speaker 11: the rally has outpaced us. We're still a little cautious 518 00:26:10,880 --> 00:26:13,479 Speaker 11: that that might actually crack as we headed in next year, 519 00:26:13,560 --> 00:26:16,639 Speaker 11: especially if rates start to rise in a sort of 520 00:26:16,640 --> 00:26:19,760 Speaker 11: Bond benjel Ante theme around the Trump administration. 521 00:26:19,960 --> 00:26:22,320 Speaker 5: All right, our Jay, super appreciate it. Have a good week. 522 00:26:22,440 --> 00:26:22,600 Speaker 8: R J. 523 00:26:22,720 --> 00:26:26,200 Speaker 4: Gallows, Senior portfolio Manager, Fixed Income at Federated Hermes. 524 00:26:27,800 --> 00:26:31,680 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 525 00:26:31,760 --> 00:26:35,280 Speaker 1: weekdays at ten am Eastern on applecar Play and Android 526 00:26:35,320 --> 00:26:38,480 Speaker 1: Otto with the Bloomberg Business. You can also listen live 527 00:26:38,560 --> 00:26:41,760 Speaker 1: on Amazon Alexa from our flagship New York station Just 528 00:26:41,800 --> 00:26:44,440 Speaker 1: Say Alexa playing Bloomberg eleven thirty. 529 00:26:45,720 --> 00:26:47,800 Speaker 5: We have a great story out in the Bloomberg terminal. 530 00:26:48,000 --> 00:26:50,880 Speaker 4: It talks about their new head of Wealth, Andy Sigg, 531 00:26:51,200 --> 00:26:54,960 Speaker 4: and how the's revamping the wealth business and how that's going. 532 00:26:55,080 --> 00:26:56,879 Speaker 4: Basically at the end of the day, joining us now 533 00:26:56,880 --> 00:27:00,320 Speaker 4: from more Catherine Doherty, Bloomberg Finance reporter, joining ours on 534 00:27:00,520 --> 00:27:01,120 Speaker 4: that story. 535 00:27:01,280 --> 00:27:03,040 Speaker 5: So how is it going? 536 00:27:03,320 --> 00:27:06,000 Speaker 12: So the story kind of goes in different directions on 537 00:27:06,040 --> 00:27:09,960 Speaker 12: that exact answer, right. I mean, Andy is one year 538 00:27:10,160 --> 00:27:14,280 Speaker 12: into his new role, and he came into it with 539 00:27:14,560 --> 00:27:19,720 Speaker 12: a lot of difficulties, and the basic infrastructure that city 540 00:27:19,880 --> 00:27:23,600 Speaker 12: was was working with in their wealth division was something 541 00:27:23,680 --> 00:27:29,159 Speaker 12: that had been lagging peers. It was way behind in 542 00:27:29,280 --> 00:27:34,239 Speaker 12: terms of speed execution and really that was something that 543 00:27:34,640 --> 00:27:38,320 Speaker 12: Andy had focused on right when he came in, and 544 00:27:38,760 --> 00:27:42,239 Speaker 12: upgrading the technology was one thing, then bringing talent was 545 00:27:42,560 --> 00:27:46,040 Speaker 12: kind of the second pillar to improving the wealth division. 546 00:27:46,840 --> 00:27:50,720 Speaker 12: And keeping talent is the hardest part about building a 547 00:27:50,720 --> 00:27:54,040 Speaker 12: wealth business across Wall Street because you're it's a very 548 00:27:54,080 --> 00:27:58,560 Speaker 12: competitive business. The pay is oftentimes used as a way 549 00:27:58,600 --> 00:28:03,800 Speaker 12: to lure experience and advisors to competitors, and so not 550 00:28:03,840 --> 00:28:06,840 Speaker 12: only are you trying to focus on keeping that talent, 551 00:28:07,080 --> 00:28:09,880 Speaker 12: the talent is tied to the assets that they're managing 552 00:28:09,920 --> 00:28:13,640 Speaker 12: for their clients. So anytime you lose some big advisors 553 00:28:14,040 --> 00:28:17,760 Speaker 12: with millions or even billions in assets, that's going to 554 00:28:17,800 --> 00:28:19,640 Speaker 12: affect your business moving forward. 555 00:28:19,920 --> 00:28:22,800 Speaker 6: So Andy had a very big. 556 00:28:24,080 --> 00:28:26,119 Speaker 12: Agenda in front of him in terms of having to 557 00:28:26,160 --> 00:28:28,440 Speaker 12: clean up, and not just clean up, but then improve 558 00:28:29,520 --> 00:28:32,639 Speaker 12: City's wealth business so that it could truly compete with 559 00:28:33,000 --> 00:28:34,760 Speaker 12: the big peers on Wall Street. 560 00:28:34,840 --> 00:28:37,879 Speaker 2: Yeah, because like when I think of Morgan Stanley, I 561 00:28:37,880 --> 00:28:40,320 Speaker 2: think of wealth management I use, and that's different. When 562 00:28:40,320 --> 00:28:41,800 Speaker 2: I grew up in the business, I thought about them 563 00:28:41,800 --> 00:28:43,440 Speaker 2: as sales and trading and investment banking. 564 00:28:44,000 --> 00:28:44,800 Speaker 10: City I don't. 565 00:28:44,640 --> 00:28:48,400 Speaker 2: Necessarily think of wealth as part as a kind of 566 00:28:48,440 --> 00:28:50,560 Speaker 2: a growth driver for them. But I know in their 567 00:28:50,600 --> 00:28:53,880 Speaker 2: private bank, which generates two point three billion dollars in revenue, 568 00:28:54,280 --> 00:28:57,840 Speaker 2: it is high touch service for the wealthiest clients, including 569 00:28:57,920 --> 00:29:01,640 Speaker 2: a quarter of the world's billionaire minimum net worth twenty 570 00:29:01,680 --> 00:29:04,760 Speaker 2: five million dollars. So is this a situation for City 571 00:29:04,800 --> 00:29:06,560 Speaker 2: that Jane Fraser has to just put a flag in 572 00:29:06,600 --> 00:29:09,760 Speaker 2: a ground sake? This is a core business for City 573 00:29:09,800 --> 00:29:11,160 Speaker 2: and we will invest accordingly. 574 00:29:11,600 --> 00:29:14,480 Speaker 12: Private bank is definitely one of their money makers. You 575 00:29:14,480 --> 00:29:17,800 Speaker 12: can see that in the numbers. But also if you 576 00:29:17,920 --> 00:29:21,640 Speaker 12: think two tiers down, you have City Gold and this 577 00:29:21,760 --> 00:29:25,160 Speaker 12: is clients with average monthly balances of at least two 578 00:29:25,280 --> 00:29:29,000 Speaker 12: hundred thousand. So it's definitely not at the private bank tier. 579 00:29:29,520 --> 00:29:32,280 Speaker 12: The private bank tier. Why they're trying to keep that 580 00:29:32,720 --> 00:29:36,600 Speaker 12: part of the wealth business up is because that's where 581 00:29:36,640 --> 00:29:40,080 Speaker 12: the biggest assets are. That's where the revenue really gets generated. 582 00:29:40,160 --> 00:29:43,960 Speaker 12: When you have the wealthiest and it's not just Americans, 583 00:29:44,360 --> 00:29:48,800 Speaker 12: it's oftentimes billionaires in Asia. They're really focused on growing 584 00:29:48,840 --> 00:29:52,000 Speaker 12: their business outside of the US, which is different from 585 00:29:52,000 --> 00:29:55,000 Speaker 12: some of the other big Wall Street banks that are 586 00:29:55,080 --> 00:29:58,840 Speaker 12: more US focused in terms of building up their own 587 00:29:58,920 --> 00:30:03,640 Speaker 12: private bank assets and so City, their private bank, I 588 00:30:03,680 --> 00:30:06,040 Speaker 12: would say, was their strong suit and continues to be 589 00:30:06,080 --> 00:30:08,600 Speaker 12: their strong suit, but it doesn't mean that that's going 590 00:30:08,680 --> 00:30:11,240 Speaker 12: to stay that way. So they really have to remain 591 00:30:11,320 --> 00:30:15,920 Speaker 12: competitive keep their advisors that are catering to the wealthiest individuals, 592 00:30:15,960 --> 00:30:17,959 Speaker 12: not just in the US, but across the globe. 593 00:30:18,000 --> 00:30:20,960 Speaker 4: Did they lose advisors because they were posed or because 594 00:30:20,960 --> 00:30:22,800 Speaker 4: they let them go because they were revamping it, and. 595 00:30:23,440 --> 00:30:27,360 Speaker 12: So it's a combination. Just last week we reported that 596 00:30:27,720 --> 00:30:32,360 Speaker 12: two of City's former private bank advisors defected to go 597 00:30:32,400 --> 00:30:35,680 Speaker 12: to Bank of America actually in their private bank, which 598 00:30:35,680 --> 00:30:38,160 Speaker 12: is interesting because that's where Andy sig had come. He 599 00:30:38,200 --> 00:30:40,480 Speaker 12: worked in the Meryl division, but still it's under the 600 00:30:40,480 --> 00:30:44,680 Speaker 12: Bank of America umbrella. And those two advisors they brought 601 00:30:45,360 --> 00:30:48,240 Speaker 12: seven billion of assets that they managed. So that was 602 00:30:48,800 --> 00:30:52,200 Speaker 12: a big kick to City. And so when you see that, 603 00:30:52,280 --> 00:30:55,600 Speaker 12: but it's not i would say unique just to City. 604 00:30:56,360 --> 00:30:59,040 Speaker 12: There's stories like that all the time of some of 605 00:30:59,080 --> 00:31:02,800 Speaker 12: these really big, big advisors bringing their team to a 606 00:31:02,960 --> 00:31:07,680 Speaker 12: competitor and oftentimes they're just looking for uh, it's it's opportunity, 607 00:31:08,040 --> 00:31:11,000 Speaker 12: and most of the time that opportunity is translated and 608 00:31:11,080 --> 00:31:12,680 Speaker 12: in pay and in compensation. 609 00:31:12,880 --> 00:31:15,480 Speaker 2: Another risk to the talent is something that Alex and 610 00:31:15,520 --> 00:31:18,760 Speaker 2: I work with Commonwealth people, those platforms that are non 611 00:31:18,880 --> 00:31:22,360 Speaker 2: wire house investment banks. They say, hey, why are you 612 00:31:22,440 --> 00:31:24,480 Speaker 2: working for Merrill Lynch or think you know, we have 613 00:31:24,520 --> 00:31:27,080 Speaker 2: to sell their progress. Go on your own. We'll support 614 00:31:27,120 --> 00:31:29,040 Speaker 2: you with all the technology, all the back office stuff, 615 00:31:29,120 --> 00:31:31,080 Speaker 2: and you go run your business like you want to 616 00:31:31,160 --> 00:31:34,320 Speaker 2: run your business. And that is also another risk to 617 00:31:34,760 --> 00:31:37,520 Speaker 2: you know, the you know all these big wirehouses. So great, 618 00:31:37,520 --> 00:31:41,200 Speaker 2: great story. Not surprisingly it's the second most read story 619 00:31:41,200 --> 00:31:45,640 Speaker 2: in the entire Bloomberg terminalism. You talk about our terminal 620 00:31:45,720 --> 00:31:48,280 Speaker 2: users and their businesses in their futures. They read it. 621 00:31:48,440 --> 00:31:53,760 Speaker 1: Oh yeah, you're listening to the Bloomberg Intelligence Podcast. Catch 622 00:31:53,840 --> 00:31:57,200 Speaker 1: us live weekdays at ten am Eastern on applecar Play 623 00:31:57,240 --> 00:32:00,680 Speaker 1: and Android Auto with the Bloomberg Business and also listen 624 00:32:00,800 --> 00:32:03,840 Speaker 1: live on Amazon Alexa from our flagship New York station, 625 00:32:04,280 --> 00:32:07,040 Speaker 1: Just say Alexa playing Bloomberg eleven thirty. 626 00:32:08,120 --> 00:32:10,720 Speaker 4: Happy Monday, everybody and Alexia alongside Paulus. We need this 627 00:32:10,760 --> 00:32:13,640 Speaker 4: a Bloomberg Intelligence Radio. We are broadcasting to live from 628 00:32:13,640 --> 00:32:16,680 Speaker 4: Interactive Brooker Studio right here in Midtown Manhattan. You can 629 00:32:16,680 --> 00:32:19,920 Speaker 4: also check us out on YouTube as well. We also 630 00:32:20,000 --> 00:32:22,560 Speaker 4: at this round this time every Monday, we tap our 631 00:32:22,600 --> 00:32:29,600 Speaker 4: wonderful Bloomberg bn EF folks. They do amazing research on commodities, power, transport, industry, buildings, 632 00:32:29,680 --> 00:32:33,480 Speaker 4: AG sectors, all in the terms of helping businesses and 633 00:32:33,600 --> 00:32:38,680 Speaker 4: finances transition to green energy to the energy transition. That 634 00:32:38,760 --> 00:32:41,160 Speaker 4: cover great stuff for US and Davies is a Bloomberg 635 00:32:41,160 --> 00:32:44,200 Speaker 4: b andn EF's head of renewable fuels and she joins 636 00:32:44,280 --> 00:32:46,440 Speaker 4: US now and has worn many hats also over at 637 00:32:46,480 --> 00:32:49,800 Speaker 4: b and EF over time. And what kind of renewable 638 00:32:49,800 --> 00:32:52,760 Speaker 4: fuel projects are there, Let's just say in the US, 639 00:32:52,880 --> 00:32:54,320 Speaker 4: and let's at the stage because then we want to 640 00:32:54,320 --> 00:32:56,040 Speaker 4: know what's going to look like four years from now. 641 00:32:56,400 --> 00:32:58,840 Speaker 13: Sure, so there's a good number of projects in the 642 00:32:58,920 --> 00:33:02,120 Speaker 13: US and needs to be here, and there's a wide range. 643 00:33:02,160 --> 00:33:03,960 Speaker 13: The US is the biggest market at the moment, so 644 00:33:04,040 --> 00:33:06,520 Speaker 13: a lot of the projects are based here, a lot 645 00:33:06,520 --> 00:33:08,560 Speaker 13: of renewable fields. When we talk about reneable fuels, we're 646 00:33:08,560 --> 00:33:12,240 Speaker 13: really talking about basically biofuels at the moment made from 647 00:33:12,320 --> 00:33:15,560 Speaker 13: oils like soybean oil or even use cooking oil like 648 00:33:15,600 --> 00:33:18,640 Speaker 13: the excess grease from your Fryer. The key here is 649 00:33:18,640 --> 00:33:22,600 Speaker 13: that unlike ethanol or biodiesel or biofuels you usually think about, 650 00:33:23,000 --> 00:33:25,400 Speaker 13: renewable fuels are a special term for ones that are 651 00:33:25,480 --> 00:33:28,520 Speaker 13: drop in ready, which means they produce a diesel molecule 652 00:33:28,600 --> 00:33:31,040 Speaker 13: or a jet fuel molecule that's basically the same as 653 00:33:31,160 --> 00:33:33,560 Speaker 13: fossil dieseler jet fuel, So you could just blend it 654 00:33:33,600 --> 00:33:36,280 Speaker 13: one for one into your dieseler jet fuel pool. There's 655 00:33:36,280 --> 00:33:39,640 Speaker 13: no blending limit. It can just go in however much 656 00:33:39,640 --> 00:33:41,800 Speaker 13: you have, so it's really cool in that regard. A 657 00:33:41,880 --> 00:33:44,800 Speaker 13: lot of the projects in the US are based, especially 658 00:33:44,840 --> 00:33:47,080 Speaker 13: out of California. You could take an old oil refinery 659 00:33:47,400 --> 00:33:51,320 Speaker 13: and convert that to produce a biooil like a biofeedstock 660 00:33:51,360 --> 00:33:54,120 Speaker 13: instead of a crude feedstock, so that's really common. There's 661 00:33:54,120 --> 00:33:56,920 Speaker 13: also a lot of projects being developed to use other feedstocks, 662 00:33:56,960 --> 00:34:00,400 Speaker 13: things like corn ethanol, because if you think about passenger 663 00:34:00,440 --> 00:34:03,440 Speaker 13: vehicle fleet going electric, you're going to have less demand 664 00:34:03,520 --> 00:34:06,080 Speaker 13: for gasoline or ethanol, so this could be another use 665 00:34:06,120 --> 00:34:08,240 Speaker 13: for that ethanol in the world today. 666 00:34:08,640 --> 00:34:10,799 Speaker 2: Got to ask the question everybody wants to ask, how 667 00:34:10,800 --> 00:34:14,720 Speaker 2: will the Trump administration impact renewable fuel business? 668 00:34:15,080 --> 00:34:18,279 Speaker 13: That is the million dollar question. Probably for every clean 669 00:34:18,400 --> 00:34:22,200 Speaker 13: energy sector. I'd say that renewable fuels, compared to a 670 00:34:22,200 --> 00:34:24,799 Speaker 13: lot of the other sectors of clean energy, might be 671 00:34:24,800 --> 00:34:28,480 Speaker 13: a bit better insulated than most because this is a 672 00:34:28,480 --> 00:34:31,879 Speaker 13: sector where it really promotes the agriculture industry by using 673 00:34:32,000 --> 00:34:34,960 Speaker 13: biofuels as a new demand source. It also is a 674 00:34:35,000 --> 00:34:36,799 Speaker 13: way for the oil industry to get a second life 675 00:34:36,800 --> 00:34:39,160 Speaker 13: because they can't take this old refinery and convert it 676 00:34:39,160 --> 00:34:42,200 Speaker 13: into something new. So in that regard, I don't think 677 00:34:42,200 --> 00:34:44,839 Speaker 13: it's going to be completely in the crosshairs. There are ways, though, 678 00:34:44,840 --> 00:34:46,960 Speaker 13: that the Trump administration might have a big impact on 679 00:34:47,000 --> 00:34:48,560 Speaker 13: this sector. One of the big ones is if he 680 00:34:48,640 --> 00:34:51,520 Speaker 13: puts a tariff on used cooking oil from China or 681 00:34:51,520 --> 00:34:51,879 Speaker 13: some of the. 682 00:34:51,800 --> 00:34:53,880 Speaker 2: Feet support use cooking oil from China. 683 00:34:53,960 --> 00:34:54,239 Speaker 9: We do. 684 00:34:54,400 --> 00:34:56,160 Speaker 13: China produces a lot of used cooking oil, a lot 685 00:34:56,160 --> 00:34:59,759 Speaker 13: of fried food. They export the cooking oil to California, 686 00:34:59,760 --> 00:35:02,080 Speaker 13: which they can then blend into these refineries. Use cooking 687 00:35:02,120 --> 00:35:05,760 Speaker 13: oil is a really popular feedstock for renewable fuels because 688 00:35:05,800 --> 00:35:08,160 Speaker 13: it has a low carbon intensity. Otherwise it's just wasted 689 00:35:08,520 --> 00:35:10,759 Speaker 13: right you throw it out, it has to be collected. 690 00:35:12,719 --> 00:35:15,040 Speaker 13: It was collected into dumped in probably a dump. 691 00:35:15,680 --> 00:35:16,759 Speaker 5: But so we don't have. 692 00:35:16,840 --> 00:35:18,920 Speaker 4: Enough fuse cooking oil here in the US as supplement 693 00:35:19,800 --> 00:35:22,320 Speaker 4: if everything, we. 694 00:35:22,239 --> 00:35:24,400 Speaker 13: Have a lot, but if you think about replacing you know, 695 00:35:24,440 --> 00:35:26,480 Speaker 13: the diesel or the jet fuel pool, you can always 696 00:35:26,560 --> 00:35:26,879 Speaker 13: use more. 697 00:35:27,000 --> 00:35:27,520 Speaker 8: I see. 698 00:35:27,920 --> 00:35:29,959 Speaker 4: So to that point, what is the price spread between 699 00:35:29,960 --> 00:35:32,360 Speaker 4: renewable fuels and traditional fuels right now? 700 00:35:32,600 --> 00:35:37,360 Speaker 13: TI renewable fuels, their cheapest are probably two to four times, 701 00:35:37,360 --> 00:35:39,760 Speaker 13: which is a big range, but cheaper than more expensive 702 00:35:39,840 --> 00:35:42,920 Speaker 13: sorry than fossil like jet fuel. And if you talk 703 00:35:42,960 --> 00:35:45,400 Speaker 13: about some of the novel technologies, So one of the 704 00:35:45,400 --> 00:35:48,880 Speaker 13: ways to make these renewable fuels is to take carbon 705 00:35:48,920 --> 00:35:51,440 Speaker 13: dioxide and green hydrogen, which is great because then you're 706 00:35:51,480 --> 00:35:54,200 Speaker 13: not using any biofeedstock that could be up to like 707 00:35:54,280 --> 00:35:56,560 Speaker 13: ten times as expensive as jet fuel. These are pricey 708 00:35:56,560 --> 00:35:58,160 Speaker 13: fuels and they're probably not going to get too much 709 00:35:58,200 --> 00:35:59,880 Speaker 13: cheaper because a lot of it is just the technology. 710 00:36:00,680 --> 00:36:04,160 Speaker 2: So but if as an airline am I'm mandated to 711 00:36:04,239 --> 00:36:06,799 Speaker 2: use a certain percentage of clean fuel. 712 00:36:06,640 --> 00:36:09,480 Speaker 13: Depends where you are in Europe starting next year, yes 713 00:36:09,800 --> 00:36:12,279 Speaker 13: you are mandated to blend it in in the US, 714 00:36:12,320 --> 00:36:14,960 Speaker 13: we don't have mandates yet. The biggest is we're doing 715 00:36:15,000 --> 00:36:17,520 Speaker 13: a lot of carrot incentives. So the Inflation Reduction Act 716 00:36:17,600 --> 00:36:21,720 Speaker 13: has a tax credit for producing renewable sustainable aviation fuel 717 00:36:22,000 --> 00:36:24,120 Speaker 13: that would give a discount of about a dollar dollar 718 00:36:24,200 --> 00:36:26,799 Speaker 13: twenty five to these producers. It's not enough to cover 719 00:36:26,840 --> 00:36:29,120 Speaker 13: that bridge that cost. It could bring it down closer. 720 00:36:29,200 --> 00:36:30,840 Speaker 4: And then if that goes away, then it makes it 721 00:36:30,840 --> 00:36:34,800 Speaker 4: even worse. So what's the best way to lower that gap? 722 00:36:34,960 --> 00:36:38,759 Speaker 4: Is it we need better technology, we need scalable technology, 723 00:36:38,880 --> 00:36:39,840 Speaker 4: or more sourcing. 724 00:36:40,920 --> 00:36:42,719 Speaker 13: I don't think technology is going to come down too 725 00:36:42,800 --> 00:36:44,680 Speaker 13: much in cost because a lot of the cost technology 726 00:36:44,719 --> 00:36:46,400 Speaker 13: is a big component, but the feedstock is a big 727 00:36:46,440 --> 00:36:50,719 Speaker 13: component too, and it's hard China exactly, so that could 728 00:36:50,760 --> 00:36:53,279 Speaker 13: make it worse. A lot of it is probably going 729 00:36:53,320 --> 00:36:56,440 Speaker 13: to be a bit mix of mandates and subsidies. So 730 00:36:57,120 --> 00:36:59,000 Speaker 13: if you have a mandate, then you just have to 731 00:36:59,000 --> 00:37:01,080 Speaker 13: blend a certain amount. That's going to cause you know, 732 00:37:01,160 --> 00:37:03,799 Speaker 13: maybe you put a premium on the cost of jet fuel, 733 00:37:03,800 --> 00:37:05,719 Speaker 13: a tax on jet fuel that can cause that price 734 00:37:05,800 --> 00:37:09,600 Speaker 13: gap to close, or if you offer an incentive to 735 00:37:09,760 --> 00:37:12,040 Speaker 13: produce these fuels that can bring it down. There's a 736 00:37:12,040 --> 00:37:14,680 Speaker 13: lot of research being done on nude feedstocks like something 737 00:37:14,719 --> 00:37:16,520 Speaker 13: like cover crops. If you have a fuel you could 738 00:37:16,520 --> 00:37:18,680 Speaker 13: just plant a new oil crop in the off season 739 00:37:18,960 --> 00:37:20,960 Speaker 13: that can help retain the soil. You can use the 740 00:37:21,000 --> 00:37:22,920 Speaker 13: same land. You don't have to have this issue of 741 00:37:22,960 --> 00:37:26,640 Speaker 13: food versus fuel land use. But yeah, that's an dar 742 00:37:26,800 --> 00:37:27,920 Speaker 13: question is how cheap can you get? 743 00:37:27,920 --> 00:37:29,920 Speaker 5: It? So interesting it is. 744 00:37:30,080 --> 00:37:31,200 Speaker 2: I'm glad we do this every week. 745 00:37:31,280 --> 00:37:32,440 Speaker 5: Yes see if we learn stuff? 746 00:37:32,440 --> 00:37:35,280 Speaker 4: All right, Anna Davis, thank you very much, really appreciate it. 747 00:37:35,520 --> 00:37:36,879 Speaker 5: No easy solutions is. 748 00:37:36,840 --> 00:37:38,680 Speaker 4: Basically at the end of the day, what we see, 749 00:37:38,840 --> 00:37:43,120 Speaker 4: Anna Davies is Bloomberg bnif's head of renewable fuels. Here's 750 00:37:43,120 --> 00:37:44,799 Speaker 4: something that caught my eye, apology, did you see this 751 00:37:44,840 --> 00:37:47,680 Speaker 4: one that some colleges are cutting their tuition by fifty 752 00:37:47,719 --> 00:37:50,240 Speaker 4: percent as Ivy's near one hundred thousand? 753 00:37:50,239 --> 00:37:50,719 Speaker 5: Did you see this? 754 00:37:50,920 --> 00:37:50,960 Speaker 8: No? 755 00:37:51,160 --> 00:37:53,240 Speaker 4: So, I mean okay, So basically you got some private 756 00:37:53,239 --> 00:37:56,000 Speaker 4: colleges like Bethel University in Minnesota, for example, cuts his 757 00:37:56,080 --> 00:37:58,920 Speaker 4: tuition price from forty four thousand to twenty five twenty 758 00:37:58,920 --> 00:38:02,200 Speaker 4: six thousand, effort to attract more students because other ones 759 00:38:02,200 --> 00:38:03,440 Speaker 4: are just so expensive. 760 00:38:03,440 --> 00:38:05,560 Speaker 5: But then can you survive if you slash your tuitions? 761 00:38:05,680 --> 00:38:05,759 Speaker 8: Right? 762 00:38:06,080 --> 00:38:08,920 Speaker 2: And small colleges that do not have an endowment. The 763 00:38:08,960 --> 00:38:14,239 Speaker 2: answer probably is no, because they fund so much of 764 00:38:14,520 --> 00:38:17,600 Speaker 2: their total funny comes from to tuition. They don't get 765 00:38:17,600 --> 00:38:20,719 Speaker 2: income off the endowment like bigger schools do, so that's 766 00:38:20,760 --> 00:38:23,520 Speaker 2: why they've always always had a very stretch of his 767 00:38:23,640 --> 00:38:27,080 Speaker 2: business model. But having been involved in higher education for 768 00:38:27,120 --> 00:38:29,279 Speaker 2: a long time at the board level, you got to 769 00:38:29,320 --> 00:38:31,880 Speaker 2: cut your costs dramatically. There's no will to do that. 770 00:38:31,960 --> 00:38:34,160 Speaker 4: You get to cut costs, you got to cut tuition, 771 00:38:34,280 --> 00:38:36,239 Speaker 4: you got to enroll more students, and you also got 772 00:38:36,280 --> 00:38:37,400 Speaker 4: to spend to attract the best. 773 00:38:38,000 --> 00:38:42,520 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apples, Spotify, 774 00:38:42,719 --> 00:38:45,640 Speaker 1: and anywhere else you will get your podcasts. Listen live 775 00:38:45,719 --> 00:38:49,320 Speaker 1: each weekday ten am to noon Eastern on Bloomberg dot com, 776 00:38:49,440 --> 00:38:52,799 Speaker 1: the iHeart Radio app, tune In, and the Bloomberg Business app. 777 00:38:52,960 --> 00:38:56,080 Speaker 1: You can also watch us live every weekday on YouTube 778 00:38:56,160 --> 00:38:58,040 Speaker 1: and always on the Bloomberg terminal.