1 00:00:02,640 --> 00:00:05,320 Speaker 1: Welcome to the Bloomberg Penel podcast. I'm Paul Swinge you, 2 00:00:05,360 --> 00:00:07,680 Speaker 1: along with my co host Lisa Brahma Waits. Each day 3 00:00:07,720 --> 00:00:10,240 Speaker 1: we bring you the most noteworthy and useful interviews for 4 00:00:10,280 --> 00:00:12,520 Speaker 1: you and your money, whether at the grocery store or 5 00:00:12,560 --> 00:00:15,480 Speaker 1: the trading floor. Find a Bloomberg Penl podcast on Apple 6 00:00:15,520 --> 00:00:17,960 Speaker 1: podcast or wherever you listen to podcasts, as well as 7 00:00:17,960 --> 00:00:21,840 Speaker 1: at Bloomberg dot com. The day started with some tweets 8 00:00:21,840 --> 00:00:24,440 Speaker 1: from President Trump saying that he planned to re implement 9 00:00:24,560 --> 00:00:27,680 Speaker 1: some tariffs on Argentina and Brazil having to do with 10 00:00:27,840 --> 00:00:31,200 Speaker 1: steal as a results of their devaluing their currencies. He also, though, 11 00:00:31,240 --> 00:00:34,559 Speaker 1: said that China was on board with getting some sort 12 00:00:34,600 --> 00:00:36,760 Speaker 1: of trade deal done, but if they didn't do it 13 00:00:37,000 --> 00:00:39,480 Speaker 1: that he would escalate tariffs. To try to make sense 14 00:00:39,520 --> 00:00:41,800 Speaker 1: of all of these cross winds, Damian Sasa are joining 15 00:00:41,840 --> 00:00:46,159 Speaker 1: us now, chief Emerging market credit strategist for Bloomberg Intelligence, 16 00:00:46,159 --> 00:00:49,320 Speaker 1: as well as Brendan Murray. The trades are for Bloomberg, 17 00:00:49,360 --> 00:00:52,480 Speaker 1: so let's head to our trades are first. Brendon lay 18 00:00:52,479 --> 00:00:55,040 Speaker 1: out the land for us in terms of trade headlines 19 00:00:55,080 --> 00:00:57,480 Speaker 1: this morning. Yes, so there was a lot to unpack 20 00:00:57,560 --> 00:01:01,400 Speaker 1: this morning, And at about six am Washington time, the 21 00:01:01,440 --> 00:01:06,040 Speaker 1: President tweeted, uh this stuff about Brazil and Argentina and 22 00:01:06,040 --> 00:01:09,840 Speaker 1: reinstating the steel and aluminum tariffs which had been uh 23 00:01:09,880 --> 00:01:12,759 Speaker 1: sort of uh lifted a little over a year ago 24 00:01:13,400 --> 00:01:16,760 Speaker 1: to those two countries. And his justification was that they 25 00:01:16,800 --> 00:01:20,600 Speaker 1: were uh they as he said, they were massive devaluations 26 00:01:20,600 --> 00:01:23,200 Speaker 1: of those currencies, which is not good for American farmers. 27 00:01:23,600 --> 00:01:26,840 Speaker 1: So he said he's restoring those uh tariffs on those 28 00:01:26,880 --> 00:01:31,200 Speaker 1: medals immediately, and uh and and the and the market 29 00:01:31,240 --> 00:01:34,200 Speaker 1: fallout from that was, you know that U S steel 30 00:01:34,240 --> 00:01:38,880 Speaker 1: companies shares rows and Brazilian steelmakers shares declined, and so 31 00:01:39,319 --> 00:01:43,280 Speaker 1: it was. It was another even by Trump standards, surprise 32 00:01:43,400 --> 00:01:46,160 Speaker 1: for for everyone this morning. So, Damian, let's focus on 33 00:01:46,240 --> 00:01:49,600 Speaker 1: some of those emerging markets, UM, Argentina and Brazil. What 34 00:01:49,640 --> 00:01:53,880 Speaker 1: does this news mean for those countries and just kind 35 00:01:53,920 --> 00:01:56,800 Speaker 1: of investments in those economies. Well, it's still very great 36 00:01:56,840 --> 00:01:59,400 Speaker 1: in terms of exactly what he's announced here. But I mean, Paul, 37 00:01:59,440 --> 00:02:00,960 Speaker 1: you make a good point. I mean, this is actually 38 00:02:01,000 --> 00:02:04,680 Speaker 1: the first time that Trump has actually um initiated tariffs 39 00:02:04,800 --> 00:02:07,919 Speaker 1: due to currency manipulation. Go figure, right, this is actually 40 00:02:07,920 --> 00:02:09,760 Speaker 1: the first time he's done it, so you know, let's 41 00:02:09,760 --> 00:02:11,800 Speaker 1: take that for what it's worth. But really, you know, 42 00:02:11,800 --> 00:02:14,120 Speaker 1: he didn't elaborate on the level of the tax. I mean, 43 00:02:14,120 --> 00:02:16,080 Speaker 1: if we just look backwards, yeah, okay, we can assume 44 00:02:16,120 --> 00:02:19,960 Speaker 1: maybe a tarafund steel, another ten percent on aluminium because 45 00:02:19,960 --> 00:02:21,960 Speaker 1: that's what we had earlier this year. But look, I 46 00:02:22,000 --> 00:02:24,160 Speaker 1: mean this hurts you as steelmakers who are relying on 47 00:02:24,240 --> 00:02:28,280 Speaker 1: Brazilian exporters for their components anyway, So it's really kind 48 00:02:28,280 --> 00:02:30,359 Speaker 1: of difficult to gauge who wins and who loses at 49 00:02:30,400 --> 00:02:33,120 Speaker 1: this point, especially when this is done in lieu of 50 00:02:33,160 --> 00:02:36,520 Speaker 1: the ongoing China U s trade discussions, And to me, 51 00:02:36,560 --> 00:02:38,079 Speaker 1: it just seems like a signal he's trying to send 52 00:02:38,120 --> 00:02:39,920 Speaker 1: to China more than anything else. Do you think that 53 00:02:39,960 --> 00:02:41,840 Speaker 1: he's going to reimplement these tariffs? I mean, is this 54 00:02:41,919 --> 00:02:44,120 Speaker 1: basically a done deal? Well, wilber Ross was just on 55 00:02:44,160 --> 00:02:47,440 Speaker 1: the tape saying December fifte is the deadline. He's reaffirmed that, 56 00:02:47,520 --> 00:02:50,040 Speaker 1: so I mean, it's it's anyone's guess as to how 57 00:02:50,080 --> 00:02:52,440 Speaker 1: serious Trump is. But you know, I mean, when when 58 00:02:52,440 --> 00:02:54,760 Speaker 1: wilber Ross does kind of you know, draw that line 59 00:02:54,800 --> 00:02:57,440 Speaker 1: in the Santa I think a lot of investors, myself included, 60 00:02:57,440 --> 00:02:59,680 Speaker 1: are shure to take note of it. So Brendan, give 61 00:02:59,760 --> 00:03:02,800 Speaker 1: us just sense of this on again, off again, you know, 62 00:03:02,960 --> 00:03:05,800 Speaker 1: just the tweet of the day. Is there a sense 63 00:03:05,919 --> 00:03:09,640 Speaker 1: that a Phase one deal is still something that both 64 00:03:09,639 --> 00:03:12,920 Speaker 1: sides can get to maybe at or near this December 65 00:03:13,080 --> 00:03:15,480 Speaker 1: fifte date. Let me just go back to the steel 66 00:03:15,520 --> 00:03:18,760 Speaker 1: aluminum terrorists. First. There is a question about this threat 67 00:03:18,800 --> 00:03:22,639 Speaker 1: that he uh he issued today, about whether it's even 68 00:03:22,720 --> 00:03:25,359 Speaker 1: legal that he can do that. So, um, you know, 69 00:03:25,840 --> 00:03:28,960 Speaker 1: once once, once the experts figure that out, um, you know, 70 00:03:29,000 --> 00:03:31,040 Speaker 1: then then I think we can assess what the damage 71 00:03:31,120 --> 00:03:34,079 Speaker 1: might be. As far as the China tariffs, Um, we've 72 00:03:34,120 --> 00:03:36,200 Speaker 1: been hearing for weeks now that this that this thing 73 00:03:36,240 --> 00:03:38,840 Speaker 1: was imminent, that you know, they're in the final stages, 74 00:03:39,480 --> 00:03:43,240 Speaker 1: and we've h and the latest we get is that 75 00:03:43,280 --> 00:03:45,800 Speaker 1: they're not quite there yet. So and they're still haggling 76 00:03:45,840 --> 00:03:48,960 Speaker 1: over the same basic things that they were haggling over 77 00:03:49,040 --> 00:03:53,040 Speaker 1: six months ago. So the longer time drags out here 78 00:03:53,040 --> 00:03:55,320 Speaker 1: and the closer we get to December fifteenth, for now, 79 00:03:55,360 --> 00:03:57,600 Speaker 1: you know, less than two weeks from there. Uh, you know, 80 00:03:57,640 --> 00:04:00,280 Speaker 1: the more I think investors are gonna worry that you 81 00:04:00,320 --> 00:04:02,480 Speaker 1: know this, this isn't coming together, and we are going 82 00:04:02,520 --> 00:04:04,520 Speaker 1: to get the tariffs that the President threatened, you know. 83 00:04:04,600 --> 00:04:06,800 Speaker 1: He he issued another tweet today which kind of went 84 00:04:06,880 --> 00:04:09,080 Speaker 1: under the radar screen that said, uh, you know, the 85 00:04:09,120 --> 00:04:12,200 Speaker 1: stock market is up twenty one since uh, you know, 86 00:04:12,320 --> 00:04:16,280 Speaker 1: March when I first started the tariffs. Um, you know, 87 00:04:16,440 --> 00:04:18,560 Speaker 1: and and so you wonder what he's kind of laying 88 00:04:18,600 --> 00:04:21,239 Speaker 1: the groundwork for here. Is it bad news or good news? 89 00:04:21,760 --> 00:04:23,559 Speaker 1: You know, you can make a case for either one. Really, 90 00:04:24,160 --> 00:04:29,200 Speaker 1: take me in our Argentina and Brazil manipulating their currency. Well, 91 00:04:29,279 --> 00:04:32,159 Speaker 1: let's start with Argentina. The answer there is assuredly no, 92 00:04:32,800 --> 00:04:36,040 Speaker 1: because the currency is so liquid, it's so it's capital constrained. 93 00:04:36,080 --> 00:04:38,000 Speaker 1: Now after the you know, everything that's gone on in 94 00:04:38,000 --> 00:04:40,479 Speaker 1: the country. I mean, they've they've lost complete control of it. 95 00:04:40,520 --> 00:04:42,520 Speaker 1: I mean I wouldn't. I would go so far as 96 00:04:42,520 --> 00:04:44,440 Speaker 1: to argue that the Argentine pace it wasn't worth the 97 00:04:44,440 --> 00:04:46,159 Speaker 1: paper it's written on, right, I mean, that's what most 98 00:04:46,160 --> 00:04:49,640 Speaker 1: investors think. No off shore overseas institutional investor has the 99 00:04:49,720 --> 00:04:52,520 Speaker 1: risk tolerance to move into pace of denominated assets at 100 00:04:52,520 --> 00:04:54,560 Speaker 1: this point in time. In my opinion, there's a's a 101 00:04:54,600 --> 00:04:57,120 Speaker 1: different story and Brazil. I mean, look, you've got to 102 00:04:57,120 --> 00:04:59,520 Speaker 1: be clear here, when when the rail kind of moved 103 00:04:59,520 --> 00:05:01,480 Speaker 1: through that for twenty threshold, which we saw just a 104 00:05:01,520 --> 00:05:03,920 Speaker 1: few weeks ago, you know, that was a pretty big move. 105 00:05:04,040 --> 00:05:06,679 Speaker 1: I don't think they wanted that to happen. In fact, 106 00:05:06,680 --> 00:05:08,279 Speaker 1: if you just look at some of their FX swap 107 00:05:08,320 --> 00:05:09,880 Speaker 1: activity and some of the things that they do to 108 00:05:10,040 --> 00:05:13,360 Speaker 1: sort of manage the volatility around the dollar rayale, you know, 109 00:05:13,600 --> 00:05:16,400 Speaker 1: they were pretty mindful. They kept defending that for twenty level. 110 00:05:16,440 --> 00:05:17,760 Speaker 1: So the fact that it went through, and I think 111 00:05:17,839 --> 00:05:19,640 Speaker 1: I don't think it has much to do with the 112 00:05:19,680 --> 00:05:21,840 Speaker 1: fact that they were allowing it to depreciate. I think 113 00:05:21,880 --> 00:05:24,159 Speaker 1: it was more than markets pushing back. And so right 114 00:05:24,240 --> 00:05:27,080 Speaker 1: now the rails off over eight percent year to date. UM, 115 00:05:27,120 --> 00:05:29,119 Speaker 1: I think it's a good time to probably start looking 116 00:05:29,120 --> 00:05:32,480 Speaker 1: at the local denominated debt in Brazil. I mean, it's 117 00:05:32,560 --> 00:05:34,760 Speaker 1: it's it's definitely attractive at these levels receiving in the 118 00:05:34,760 --> 00:05:37,920 Speaker 1: front end. Damien Sassi are chief Emerging Markets credit strategist 119 00:05:37,920 --> 00:05:41,640 Speaker 1: for Bloomberg Intelligence and Brendan Murray uh Bloomberg Trade reporter, 120 00:05:41,720 --> 00:05:44,080 Speaker 1: joining us from the London Damien here in our Bloomberg 121 00:05:44,120 --> 00:05:47,640 Speaker 1: Interactor Broker Studio. Thank you both for that those comments 122 00:05:47,680 --> 00:05:50,960 Speaker 1: on what has been a busy day on the trade 123 00:05:50,960 --> 00:05:55,279 Speaker 1: front tariffs Trump. President Trump reaginstating some tariffs on Brazil, 124 00:05:55,560 --> 00:05:59,839 Speaker 1: uh and Argentina, while still making some broadly positive comments 125 00:06:00,000 --> 00:06:03,880 Speaker 1: about the China trade negotiations that China wants a deal 126 00:06:03,960 --> 00:06:05,920 Speaker 1: and the US wants the right deal, so we will 127 00:06:05,920 --> 00:06:22,839 Speaker 1: continue to follow that story well. For quite a long time, 128 00:06:23,000 --> 00:06:26,560 Speaker 1: global interest rates have been at exceptionally low levels, including 129 00:06:26,600 --> 00:06:29,480 Speaker 1: the US. We even have a significant developed markets such 130 00:06:29,480 --> 00:06:32,839 Speaker 1: as Germany and Japan with negative interest rates. Is that 131 00:06:32,880 --> 00:06:36,040 Speaker 1: a good thing going forward long term for the global economy? 132 00:06:36,160 --> 00:06:37,840 Speaker 1: Our next guest, I think can help us answer that, 133 00:06:37,960 --> 00:06:40,400 Speaker 1: Dr Brendan Brown. He's a senior fellow at the Hudston 134 00:06:40,600 --> 00:06:44,599 Speaker 1: Institute and publisher of the newsletter Monetary Scenarios at the 135 00:06:44,680 --> 00:06:47,120 Speaker 1: Hudsdon Institute based in London, but joining us here in 136 00:06:47,120 --> 00:06:50,280 Speaker 1: our Bloomberg Interactive Broker Studio. Dr Brown, thanks so much 137 00:06:50,320 --> 00:06:54,000 Speaker 1: for joining us. Just help us put into contests this 138 00:06:54,120 --> 00:06:57,800 Speaker 1: interest rate environment, this easy money environment that we've seen 139 00:06:57,920 --> 00:07:01,960 Speaker 1: around the world and again most notably Germany with negative 140 00:07:01,960 --> 00:07:05,080 Speaker 1: interest rates in Japan. Is that a good thing or 141 00:07:05,160 --> 00:07:08,040 Speaker 1: a bad thing for the global economy? Well, I would say, 142 00:07:08,040 --> 00:07:10,760 Speaker 1: first of all, low interest rates in this radical monetary 143 00:07:10,800 --> 00:07:12,960 Speaker 1: policy has been a good thing for the masters of 144 00:07:13,000 --> 00:07:15,720 Speaker 1: the central banks governments. It's saved from a lot of 145 00:07:15,800 --> 00:07:20,600 Speaker 1: interest rate cost on massive budget deficits or overhang of 146 00:07:20,600 --> 00:07:23,480 Speaker 1: government debt. And they've been able to do this because 147 00:07:23,560 --> 00:07:27,960 Speaker 1: the fundamental background for global economy has been disinflationary, falling 148 00:07:28,400 --> 00:07:32,600 Speaker 1: tendency of prices due to technological change and globalization. I 149 00:07:32,600 --> 00:07:35,080 Speaker 1: would say, as far as you and I and general 150 00:07:35,120 --> 00:07:38,640 Speaker 1: prosperity goes, if they've been a bad thing there. In 151 00:07:38,840 --> 00:07:43,280 Speaker 1: several ways, this low interest rate environment has has held 152 00:07:43,320 --> 00:07:47,320 Speaker 1: back prosperity. It's encouraged a lot of malinvestment. It's created 153 00:07:47,320 --> 00:07:50,400 Speaker 1: a lot of uncertainty about a long term picture, which 154 00:07:50,400 --> 00:07:55,400 Speaker 1: has held investment back. It's spurred monopoly power by creating 155 00:07:55,400 --> 00:07:58,440 Speaker 1: desperation for yield and what do people like at the moment. 156 00:07:58,440 --> 00:08:02,160 Speaker 1: It's anything which claims it will have monopoly power in 157 00:08:02,200 --> 00:08:07,520 Speaker 1: the future. And all these things together have weighed on prosperity. 158 00:08:07,600 --> 00:08:09,960 Speaker 1: So when you look at for general picture now, I 159 00:08:09,960 --> 00:08:13,320 Speaker 1: don't believe investment spending or slow growth as a result 160 00:08:13,400 --> 00:08:16,040 Speaker 1: of the crash ten years ago. I believe it's a 161 00:08:16,080 --> 00:08:19,280 Speaker 1: result of cumulative malinvestment in all these forces I'm talking about. 162 00:08:19,520 --> 00:08:23,920 Speaker 1: What's monetary pessimism. Well, monitory pessimism has many strands. I've 163 00:08:23,960 --> 00:08:29,080 Speaker 1: talked of several here, with monopoly power, the the the 164 00:08:29,080 --> 00:08:32,920 Speaker 1: these radical monetary policies producing low growth and prosperity and 165 00:08:32,960 --> 00:08:36,200 Speaker 1: weighing on investment. I would save us two aspects of 166 00:08:36,200 --> 00:08:40,520 Speaker 1: monetary pessimism miss pessimism, which have been false. That is, 167 00:08:40,559 --> 00:08:43,200 Speaker 1: first of all, the belief that all this radical monetary 168 00:08:43,200 --> 00:08:45,640 Speaker 1: policy will create high inflation soon. That clearly isn't the 169 00:08:45,679 --> 00:08:49,160 Speaker 1: case in our disinflationary environment. And the other aspect of 170 00:08:49,200 --> 00:08:51,800 Speaker 1: monetary pessimism which has proved false has been the belief 171 00:08:51,880 --> 00:08:53,480 Speaker 1: that it's all going to fizzle out quite soon and 172 00:08:53,480 --> 00:08:55,640 Speaker 1: we're going to get a crash or recession. There's been 173 00:08:55,640 --> 00:08:59,320 Speaker 1: a lot of false crash and recession warnings. But what 174 00:08:59,440 --> 00:09:02,560 Speaker 1: you call that monetary optimism that we have in You 175 00:09:02,600 --> 00:09:05,240 Speaker 1: could say that, but I think for dilemmo investors and 176 00:09:05,280 --> 00:09:10,160 Speaker 1: everybody everybody now faces is the central banks and led 177 00:09:10,160 --> 00:09:14,840 Speaker 1: by the FED, have been a ministering alexas to the 178 00:09:14,880 --> 00:09:17,439 Speaker 1: business cycle now for a long time, making this for 179 00:09:17,559 --> 00:09:21,720 Speaker 1: longest cyclical expansion on record. But we all know that 180 00:09:22,320 --> 00:09:24,920 Speaker 1: the alex her is not going to create perpetual life 181 00:09:25,000 --> 00:09:27,679 Speaker 1: for the business cycle and that eventually there was an 182 00:09:27,760 --> 00:09:31,480 Speaker 1: endgame there. So when I look at Germany, you know, 183 00:09:31,520 --> 00:09:38,480 Speaker 1: the largest economy in Europe, manufacturing economy, export economy, is 184 00:09:38,520 --> 00:09:41,280 Speaker 1: it talked to us about the negative rate environment there? 185 00:09:41,280 --> 00:09:44,600 Speaker 1: Why are the rates negative there? And is there what 186 00:09:44,760 --> 00:09:48,439 Speaker 1: is the scenario for them to turn positive? But negative 187 00:09:48,520 --> 00:09:51,600 Speaker 1: rates in Germany are a key aspect of a fundamental 188 00:09:52,800 --> 00:09:57,719 Speaker 1: arrangement or coalition between Germany and the e c B 189 00:09:58,480 --> 00:10:03,040 Speaker 1: where Germany keeps keeps rates negative or allows rage to 190 00:10:03,040 --> 00:10:06,440 Speaker 1: be negative to help bail out Italian banks um but 191 00:10:06,600 --> 00:10:09,680 Speaker 1: at the same time that benefits for German export sector. 192 00:10:10,400 --> 00:10:13,720 Speaker 1: I think it's creating a very treacherous situation in Germany 193 00:10:13,720 --> 00:10:16,360 Speaker 1: because a lot of the people who are suffering from 194 00:10:16,440 --> 00:10:19,840 Speaker 1: negative rates at a moment think they're doing okay elsewhere 195 00:10:20,400 --> 00:10:25,160 Speaker 1: through asset market inflation. But when that turns round, we'll 196 00:10:25,160 --> 00:10:27,400 Speaker 1: find out if we didn't do any any good there, 197 00:10:27,400 --> 00:10:30,280 Speaker 1: And we've also lost in netfix investments. So there's a 198 00:10:30,320 --> 00:10:33,160 Speaker 1: story out on the Bloomberg today Peak private equity fears 199 00:10:33,160 --> 00:10:36,320 Speaker 1: are spreading across the pension world, and it talks about 200 00:10:36,440 --> 00:10:40,160 Speaker 1: how the fears are growing among the pension community that 201 00:10:40,320 --> 00:10:43,720 Speaker 1: as a prices in the private equity space have gotten 202 00:10:43,840 --> 00:10:47,280 Speaker 1: too high. Where do you see the biggest bubbles being 203 00:10:47,320 --> 00:10:49,160 Speaker 1: formed or if you don't want to use the word bubble, 204 00:10:49,520 --> 00:10:52,520 Speaker 1: excesses being formed as a result of some of these 205 00:10:52,600 --> 00:10:55,800 Speaker 1: negative rate policies. Well, in terms of a real investment, 206 00:10:55,800 --> 00:10:57,880 Speaker 1: I think the biggest question of all we face is 207 00:10:58,280 --> 00:11:03,520 Speaker 1: has been over invest ment and digitalization and this Third 208 00:11:04,120 --> 00:11:06,760 Speaker 1: Industrial Revolution. It's a bit like asking the question of 209 00:11:06,760 --> 00:11:09,199 Speaker 1: the ninete essentially was for over investment in the railroads, 210 00:11:09,200 --> 00:11:12,840 Speaker 1: and yes I was research. It has been done at 211 00:11:12,840 --> 00:11:15,839 Speaker 1: the University of Chicago by Fogel and Angerment showed that 212 00:11:16,040 --> 00:11:18,360 Speaker 1: there was over investment and the actual prosperity gain was 213 00:11:18,400 --> 00:11:20,720 Speaker 1: quite small. I think we're probably in something like that 214 00:11:20,760 --> 00:11:26,319 Speaker 1: with digitalization today. But in terms of financial investments and dangers, 215 00:11:26,360 --> 00:11:28,600 Speaker 1: I think you've highlighted the core of that, which is 216 00:11:28,679 --> 00:11:31,160 Speaker 1: private equity. See thing. Private equity is a space that's 217 00:11:31,160 --> 00:11:33,800 Speaker 1: most inflated at this point, definitely, And alongside that you 218 00:11:33,880 --> 00:11:36,560 Speaker 1: also mentioned insurance companies. You have to think of all 219 00:11:36,600 --> 00:11:39,800 Speaker 1: these European and insurance companies who have been going more 220 00:11:39,800 --> 00:11:44,240 Speaker 1: and more into risky products to produce returns, and if 221 00:11:44,320 --> 00:11:47,360 Speaker 1: eventually they are not there on a cumulative basis, there's 222 00:11:47,360 --> 00:11:49,880 Speaker 1: going to be a lot of disappointment and results among 223 00:11:49,920 --> 00:11:53,360 Speaker 1: households in Europe and just real quick over investing in 224 00:11:53,480 --> 00:11:56,120 Speaker 1: the digitalization. Does that mean that the Google's and the 225 00:11:56,120 --> 00:12:00,439 Speaker 1: apples are overvalued? Well, Google's and Apples of value on 226 00:12:00,480 --> 00:12:05,560 Speaker 1: the basis of them having monopoly part into the long run. 227 00:12:06,320 --> 00:12:10,360 Speaker 1: We know from economic history that monopolies do not generally 228 00:12:10,400 --> 00:12:13,160 Speaker 1: survive into a long run unless something really has changed here, 229 00:12:13,920 --> 00:12:17,640 Speaker 1: and so to value these on the basis of perpetual 230 00:12:17,640 --> 00:12:20,240 Speaker 1: monopoly streams or in some companies in the case of 231 00:12:20,240 --> 00:12:24,040 Speaker 1: monopoly streams still to come, is symptomatic of the general 232 00:12:24,520 --> 00:12:29,400 Speaker 1: effvescence of financial markets. Fascinating, especially as Amazon attracts all 233 00:12:29,440 --> 00:12:32,240 Speaker 1: of the attention on this cyber Monday. Thank you so 234 00:12:32,320 --> 00:12:35,160 Speaker 1: much for being with us. Brendan Brown, Senior Fellow. He 235 00:12:35,280 --> 00:12:37,520 Speaker 1: is joining us here senior fellow at the Hudson Institute 236 00:12:37,520 --> 00:12:40,840 Speaker 1: and publisher of the newsletter A Monetary Scenarios here in 237 00:12:40,880 --> 00:12:43,720 Speaker 1: our eleven three oh studios are interactive broker studios. Really 238 00:12:43,760 --> 00:12:48,160 Speaker 1: interesting to me the idea of comparing tech investment, digitalization 239 00:12:48,240 --> 00:12:52,760 Speaker 1: investment today to the investment in railroads years and years back, 240 00:12:53,000 --> 00:12:56,320 Speaker 1: and how yes that was revolutionary, but perhaps the amount 241 00:12:56,320 --> 00:12:58,760 Speaker 1: of money that went in was a little bit more 242 00:12:59,040 --> 00:13:17,480 Speaker 1: than the foot people we're going to get out. Time 243 00:13:17,520 --> 00:13:19,640 Speaker 1: to check in with Bloomberg Opinion. We're joined by Opinion 244 00:13:19,679 --> 00:13:24,680 Speaker 1: Calumnust Sour Halzac. It is cyber Monday, it was Black Friday. 245 00:13:24,720 --> 00:13:26,560 Speaker 1: Let's get a sense of how this is playing out. 246 00:13:26,800 --> 00:13:29,200 Speaker 1: Sarah covers All Things Retails. She joins us from the 247 00:13:29,200 --> 00:13:32,079 Speaker 1: Washington d C Bureau. So, Sarah, we've had a pretty 248 00:13:32,120 --> 00:13:35,320 Speaker 1: good look at what happened on Black Friday. We're starting 249 00:13:35,320 --> 00:13:37,320 Speaker 1: to get some data here on some of the cyber 250 00:13:37,360 --> 00:13:40,760 Speaker 1: trends Monday. What are your takeaways? Sure, So, the latest 251 00:13:40,840 --> 00:13:42,760 Speaker 1: data we have on Cyber Monday is it's off to 252 00:13:42,800 --> 00:13:46,680 Speaker 1: a strong star. As of nine am, seventy three million 253 00:13:47,040 --> 00:13:50,560 Speaker 1: in online sales, and according to Adobe, that puts us 254 00:13:50,600 --> 00:13:54,000 Speaker 1: on pace for it to achieve the projected nine point 255 00:13:54,040 --> 00:13:56,880 Speaker 1: four billion dollars in sales for the day overall. So 256 00:13:56,920 --> 00:13:59,800 Speaker 1: clearly the big spending hours still yet to come, but 257 00:14:00,080 --> 00:14:02,679 Speaker 1: a lot of momentum for the industry today. Um and 258 00:14:02,720 --> 00:14:04,760 Speaker 1: that sort of tracks with what we saw over the 259 00:14:04,800 --> 00:14:08,240 Speaker 1: holiday weekend overall, healthy online spending on Thanksgiving and Black 260 00:14:08,240 --> 00:14:11,200 Speaker 1: Friday as well. Sarah, this has been something everyone has 261 00:14:11,240 --> 00:14:14,199 Speaker 1: been talking about. In other words, people are shifting to 262 00:14:14,440 --> 00:14:17,840 Speaker 1: online shopping, and we did get a record amount of sales. 263 00:14:18,120 --> 00:14:20,840 Speaker 1: How out of the expectation is this? In other words, 264 00:14:21,120 --> 00:14:23,600 Speaker 1: what's going to be the takeaway the surprise from this 265 00:14:23,880 --> 00:14:27,280 Speaker 1: shopping series season? Yeah, one thing that I think is 266 00:14:27,640 --> 00:14:30,080 Speaker 1: perhaps surprising is there's all this conversation about how Black 267 00:14:30,120 --> 00:14:33,040 Speaker 1: Friday is dead, that these deals started, you know, as 268 00:14:33,080 --> 00:14:35,200 Speaker 1: it really is November one now, and so what's the 269 00:14:35,240 --> 00:14:37,960 Speaker 1: incentive to get off the block and shop over this 270 00:14:38,040 --> 00:14:40,240 Speaker 1: past weekend or even on Cyber Monday. And I think 271 00:14:40,240 --> 00:14:44,000 Speaker 1: what's interesting is that online Black Friday sales were up 272 00:14:44,120 --> 00:14:47,600 Speaker 1: nineteen point six percent Cyber Monday over last year. Uh, 273 00:14:47,640 --> 00:14:49,920 Speaker 1: Cyber Monday sales are expected to be up eighteen point 274 00:14:50,000 --> 00:14:52,400 Speaker 1: nine percent. That will make them two of the fastest 275 00:14:52,440 --> 00:14:55,520 Speaker 1: growing days of online shopping of the whole holiday season. 276 00:14:55,800 --> 00:14:58,320 Speaker 1: In other words, in the digital space, Black Friday and 277 00:14:58,320 --> 00:15:01,080 Speaker 1: Cyber Monday aren't getting less impor and they're actually getting 278 00:15:01,120 --> 00:15:04,040 Speaker 1: more important. More of our spending is concentrating in these 279 00:15:04,080 --> 00:15:06,440 Speaker 1: big deals events. So it's really critical for retailers to 280 00:15:06,800 --> 00:15:09,720 Speaker 1: stay in stock today, to not have some website outages, 281 00:15:09,920 --> 00:15:12,040 Speaker 1: make sure they have the merchandise that customers want. How 282 00:15:12,080 --> 00:15:14,800 Speaker 1: about for the Gen Z and the millennials, what are 283 00:15:14,800 --> 00:15:18,440 Speaker 1: we learning about how they are shopping here bricks and 284 00:15:18,440 --> 00:15:22,120 Speaker 1: mortar versus e commerce? So interestingly, I think we all 285 00:15:22,160 --> 00:15:25,520 Speaker 1: have this idea that Generation Z, you know, live their 286 00:15:25,560 --> 00:15:28,120 Speaker 1: life with their nose and their phones, and so probably 287 00:15:28,520 --> 00:15:31,080 Speaker 1: they are going to prefer that channel to in store shopping. 288 00:15:31,320 --> 00:15:35,320 Speaker 1: That's not exactly what we see. Uh. In fact, when 289 00:15:35,360 --> 00:15:38,080 Speaker 1: we looked at data heading into this long holiday we 290 00:15:38,560 --> 00:15:42,080 Speaker 1: uh long holiday weekend, it was Gen Z shoppers, the 291 00:15:42,120 --> 00:15:44,120 Speaker 1: ones who are going to stores were the ones who 292 00:15:44,160 --> 00:15:45,640 Speaker 1: are most likely to say, I'm going to be doing 293 00:15:45,720 --> 00:15:47,720 Speaker 1: it with family and friends. I'm gonna be doing it 294 00:15:47,720 --> 00:15:50,640 Speaker 1: in a social experiential way. I like getting out of 295 00:15:50,640 --> 00:15:54,480 Speaker 1: the house, I like trying trying garments on before I 296 00:15:54,520 --> 00:15:57,360 Speaker 1: buy them, or testing out electronics in person. And so 297 00:15:57,600 --> 00:15:59,920 Speaker 1: I think that there's not a lot of evidence out 298 00:16:00,040 --> 00:16:02,040 Speaker 1: or to suggest that gen Z is going to kill 299 00:16:02,080 --> 00:16:05,120 Speaker 1: the physical store. They might just continue to as other 300 00:16:05,120 --> 00:16:08,120 Speaker 1: generations have kind of change what we use the store 301 00:16:08,160 --> 00:16:10,280 Speaker 1: for and how frequently we go to it. Let's throw 302 00:16:10,360 --> 00:16:12,640 Speaker 1: some statistics out here as we speak with Sarah how Zach, 303 00:16:12,720 --> 00:16:15,920 Speaker 1: retail columnist for Bloomberg Opinion. Black Friday hit a record 304 00:16:16,040 --> 00:16:20,240 Speaker 1: seven point for billion dollars in US online sales people 305 00:16:20,560 --> 00:16:23,200 Speaker 1: buying a lot from their phones. Also is the second 306 00:16:23,200 --> 00:16:27,760 Speaker 1: biggest US online sales stay ever, behind Cyber Monday seven 307 00:16:27,800 --> 00:16:30,880 Speaker 1: point nine billion dollars. And we see a healthy clip today, 308 00:16:30,880 --> 00:16:34,000 Speaker 1: So perhaps will surpass it today? I'm wondering, Sarah, you 309 00:16:34,080 --> 00:16:36,480 Speaker 1: said that the bulk of the shopping was done in 310 00:16:36,520 --> 00:16:39,480 Speaker 1: this period. At this point people are concentrating their purchases. 311 00:16:39,880 --> 00:16:42,760 Speaker 1: How deep are the discounts, in other words, how much 312 00:16:42,800 --> 00:16:45,960 Speaker 1: are margins compressing for the retailers as they try to 313 00:16:46,000 --> 00:16:49,920 Speaker 1: lure in shoppers during this period. Yeah, so margins are 314 00:16:50,000 --> 00:16:52,840 Speaker 1: especially tricky, not just because of the deals, but because 315 00:16:53,040 --> 00:16:55,720 Speaker 1: as we make this migration to online shopping, we all know, 316 00:16:55,840 --> 00:16:58,800 Speaker 1: we've all come to expect free shipping is table stakes, right, 317 00:16:59,120 --> 00:17:01,280 Speaker 1: and so that's a lot of the margin pressure is 318 00:17:01,320 --> 00:17:03,720 Speaker 1: going to come in for retailers in this holiday season 319 00:17:04,119 --> 00:17:06,240 Speaker 1: is that two day shipping has become the standard, and 320 00:17:06,320 --> 00:17:09,240 Speaker 1: now Amazon has sort of stepped up our expectations even 321 00:17:09,280 --> 00:17:11,800 Speaker 1: more by offering one day shipping on a lot of products. 322 00:17:12,000 --> 00:17:14,280 Speaker 1: Walmart two is offering one day shipping on a lot 323 00:17:14,320 --> 00:17:16,560 Speaker 1: of products, and so that's going to be a huge 324 00:17:16,560 --> 00:17:19,679 Speaker 1: expense for them to shoulder and work through during the season. 325 00:17:20,160 --> 00:17:22,400 Speaker 1: And so a lot of these retailers have been warning 326 00:17:22,600 --> 00:17:25,439 Speaker 1: us that this shopping season would actually be about a 327 00:17:25,440 --> 00:17:28,720 Speaker 1: week less than last year. How does that can impact 328 00:17:28,800 --> 00:17:31,879 Speaker 1: kind of results? I'm skeptical that it will have a 329 00:17:31,880 --> 00:17:34,840 Speaker 1: big impact on results, frankly, because, as we mentioned earlier, 330 00:17:35,000 --> 00:17:36,800 Speaker 1: a lot of these deals have been in place since 331 00:17:36,800 --> 00:17:40,919 Speaker 1: early November. Folks have gotten their shopping started earlier in 332 00:17:40,960 --> 00:17:43,560 Speaker 1: the month. And we just if you look historically over 333 00:17:43,680 --> 00:17:46,920 Speaker 1: time at whether there's a correlation between the years when 334 00:17:46,960 --> 00:17:50,359 Speaker 1: Thanksgiving falls late uh and Thanksgiving falls early, and whether 335 00:17:50,440 --> 00:17:52,880 Speaker 1: or not holiday sales overall or strong, we just don't 336 00:17:52,920 --> 00:17:55,760 Speaker 1: really see a clear correlation there. So I have no 337 00:17:55,880 --> 00:17:57,920 Speaker 1: doubt that many retailers will trt that out as an 338 00:17:57,920 --> 00:18:02,280 Speaker 1: excuse when they report their holiday sales results in January 339 00:18:02,400 --> 00:18:05,840 Speaker 1: or February as a reason for their challenge, but I 340 00:18:05,840 --> 00:18:07,600 Speaker 1: don't think they should have a big impact on how 341 00:18:07,600 --> 00:18:09,840 Speaker 1: the season shapes up overall. I have to say I 342 00:18:09,840 --> 00:18:12,200 Speaker 1: don't want to be Debbie Downer, but I was reading 343 00:18:12,200 --> 00:18:15,520 Speaker 1: this column in the New York Times about how Amazon 344 00:18:15,600 --> 00:18:18,119 Speaker 1: wove itself into the life of an American city, and 345 00:18:18,160 --> 00:18:21,959 Speaker 1: I did see American Factory, this documentary about what's been 346 00:18:22,000 --> 00:18:24,640 Speaker 1: going on in a date in Ohio in particular, with 347 00:18:24,840 --> 00:18:27,080 Speaker 1: a Chinese factory coming in. And it's not so much 348 00:18:27,160 --> 00:18:30,520 Speaker 1: about trade wars and who is running the factory, but 349 00:18:30,560 --> 00:18:35,320 Speaker 1: there is a shift away from human expertise toward humans 350 00:18:35,400 --> 00:18:38,680 Speaker 1: just for now being better or at least more less 351 00:18:38,720 --> 00:18:42,280 Speaker 1: expensive than the machines. But at some point that will flip. 352 00:18:42,320 --> 00:18:44,480 Speaker 1: Can you give us a sense of who the big 353 00:18:44,480 --> 00:18:47,240 Speaker 1: winners and who the big losers are as we see 354 00:18:47,240 --> 00:18:50,159 Speaker 1: this shift unfold but before our eyes, Because basically the 355 00:18:50,200 --> 00:18:52,560 Speaker 1: data that we're getting out of Black Friday and Cyber 356 00:18:52,600 --> 00:18:56,920 Speaker 1: Monday is just confirming foot traffic falling in department stores, 357 00:18:57,160 --> 00:18:59,880 Speaker 1: and the focus on how fast you can ship sore 358 00:19:00,119 --> 00:19:02,640 Speaker 1: and to all these things that computers eventually in robots 359 00:19:02,640 --> 00:19:05,080 Speaker 1: will do. Sure, I think the winners and losers. Are 360 00:19:05,080 --> 00:19:06,840 Speaker 1: the ones who are are the winners, I should say, 361 00:19:06,840 --> 00:19:09,960 Speaker 1: are the ones who are figuring out how to manage 362 00:19:09,960 --> 00:19:12,439 Speaker 1: both the store portfolio and a digital portfolio. And I 363 00:19:12,480 --> 00:19:15,520 Speaker 1: think Walmart, Target, and Best Buy are probably clearly in 364 00:19:15,560 --> 00:19:18,080 Speaker 1: that winner circle. Um. They've made a lot of investments 365 00:19:18,080 --> 00:19:21,600 Speaker 1: and figuring out how to expand their online assortments, how 366 00:19:21,640 --> 00:19:24,679 Speaker 1: to not get clovered on price by Amazon in the 367 00:19:24,720 --> 00:19:28,399 Speaker 1: digital space, um, and in certain cases use their stores 368 00:19:28,440 --> 00:19:31,680 Speaker 1: as an asset. You know, best Buy says that its 369 00:19:31,680 --> 00:19:35,000 Speaker 1: online orders are picked up in stores. Uh, So, clearly 370 00:19:35,320 --> 00:19:37,440 Speaker 1: consumers like that click and collect model. It's a more 371 00:19:37,480 --> 00:19:39,879 Speaker 1: profitable model for them. And by figuring out how to 372 00:19:39,920 --> 00:19:42,560 Speaker 1: steer people to make that choice, they're figuring out how 373 00:19:42,560 --> 00:19:45,159 Speaker 1: to do online shopping in a more profitable way. And 374 00:19:45,200 --> 00:19:46,920 Speaker 1: so I suspect those will be to the folks that 375 00:19:46,960 --> 00:19:50,080 Speaker 1: come out as winners from this Thanksgiving A Cyber Monday 376 00:19:50,080 --> 00:19:52,880 Speaker 1: stretch from from the holiday season overall. A thirty seconds sir, 377 00:19:53,160 --> 00:19:54,680 Speaker 1: do we know what people are buying this year? Is 378 00:19:54,680 --> 00:19:59,080 Speaker 1: it just electronics? So? Lots of frozen two toys, ELSA 379 00:19:59,200 --> 00:20:02,640 Speaker 1: certainly having an impact, l O L Surprise and other 380 00:20:02,640 --> 00:20:05,000 Speaker 1: really popular toy property. And yes, it's always a big 381 00:20:05,000 --> 00:20:08,520 Speaker 1: weekend for electronics, UH is, particularly in the online space, 382 00:20:08,560 --> 00:20:12,080 Speaker 1: strong sales of Samson TVs and air pods, or some 383 00:20:12,119 --> 00:20:14,280 Speaker 1: of the reports we saw coming out of this weekend. Sarah, 384 00:20:14,280 --> 00:20:17,560 Speaker 1: did you buy lots of frozen two gear tons? Right, 385 00:20:17,760 --> 00:20:19,560 Speaker 1: you're going to show up on television. I'm gonna see 386 00:20:19,560 --> 00:20:22,960 Speaker 1: her wearing a little Elsa outfit. I'm sure. No, not 387 00:20:23,040 --> 00:20:25,760 Speaker 1: so much. Well, I'm expecting a girl baby, so maybe 388 00:20:25,760 --> 00:20:27,680 Speaker 1: I should get ahead of account on that and start 389 00:20:27,680 --> 00:20:31,040 Speaker 1: buying save Elsa stuff for sure. Congratulations, very exciting. I 390 00:20:31,760 --> 00:20:35,040 Speaker 1: expect great things. Uh so, Sarah, thank you so much 391 00:20:35,080 --> 00:20:37,240 Speaker 1: for being with us. Sarah, how Zach Bloomberg opinion retail 392 00:20:37,280 --> 00:20:54,760 Speaker 1: columnist joining us from Washington, D C. Well E. S 393 00:20:54,800 --> 00:20:57,320 Speaker 1: G investing is becoming a much bigger part of the 394 00:20:57,359 --> 00:21:01,679 Speaker 1: global institutional investing marketplace. S G, of course, stands for 395 00:21:01,880 --> 00:21:06,720 Speaker 1: environment UH, sustainability and governance. In fact, today Bloomberg and 396 00:21:06,920 --> 00:21:08,879 Speaker 1: our good friends at New Vine are co sponsoring a 397 00:21:08,920 --> 00:21:12,680 Speaker 1: global responsible Investing conference here at Bloomberg headquarters in New York. 398 00:21:13,080 --> 00:21:15,520 Speaker 1: If you get a sense of what this means for investors, investors, 399 00:21:15,560 --> 00:21:18,639 Speaker 1: we welcome my next guest. Steve Libertur lead fixed income 400 00:21:18,680 --> 00:21:21,840 Speaker 1: E s G portfolio manager at New Vine's based in Charlotte, 401 00:21:21,880 --> 00:21:23,680 Speaker 1: North Carolina, but he joins us here on our Bloomberg 402 00:21:23,680 --> 00:21:26,600 Speaker 1: Interactor Brokers studio. Steve, thanks so much for joining us again. 403 00:21:27,040 --> 00:21:31,480 Speaker 1: You manage ten billion dollars in core bond strategies at 404 00:21:31,800 --> 00:21:34,400 Speaker 1: t I double a Craft part of New Vine. How 405 00:21:34,400 --> 00:21:37,920 Speaker 1: do you use a portfolio manager UM really integrate E 406 00:21:38,119 --> 00:21:40,960 Speaker 1: s G aspects into your analysis? Sure, Paul, thanks for 407 00:21:41,000 --> 00:21:43,280 Speaker 1: having me UM. I think there's really two different ways 408 00:21:43,280 --> 00:21:45,359 Speaker 1: that we look at it. UM. E s G is 409 00:21:45,359 --> 00:21:48,520 Speaker 1: is really an application of trying to identify the best 410 00:21:48,560 --> 00:21:51,680 Speaker 1: operating to manage issuers UM in the market, and it's 411 00:21:51,680 --> 00:21:55,159 Speaker 1: not a way of trying to create value definitions or 412 00:21:55,200 --> 00:21:57,480 Speaker 1: value judgments. You know, what we're not saying don't invest 413 00:21:57,520 --> 00:21:59,280 Speaker 1: in the oil and gas issue, or what we're saying 414 00:21:59,400 --> 00:22:01,680 Speaker 1: is if going to invest in an oil and gas issue, 415 00:22:01,760 --> 00:22:04,040 Speaker 1: or what you want to look for those issuers that 416 00:22:04,119 --> 00:22:06,159 Speaker 1: have a good track record as far as taking care 417 00:22:06,200 --> 00:22:08,160 Speaker 1: of the environment. You know that you don't want to 418 00:22:08,200 --> 00:22:11,720 Speaker 1: invest with someone who consistently has spills or continually is 419 00:22:11,760 --> 00:22:13,960 Speaker 1: being fined because what they're doing is putting at risk 420 00:22:14,040 --> 00:22:16,439 Speaker 1: their ability to generate free cash flow in the future, 421 00:22:16,680 --> 00:22:20,000 Speaker 1: which obviously, as a as an unsecured creditor, that's really 422 00:22:20,000 --> 00:22:22,760 Speaker 1: what you're focused on getting repayment from. So we we 423 00:22:22,840 --> 00:22:25,520 Speaker 1: look at those issuers that we feel our our e 424 00:22:25,720 --> 00:22:27,800 Speaker 1: s G leaders and we feel are therefore are the 425 00:22:27,800 --> 00:22:30,719 Speaker 1: best operating and managed and less likely to blow up 426 00:22:30,760 --> 00:22:33,600 Speaker 1: over time. And we combine that with a view on 427 00:22:33,720 --> 00:22:38,400 Speaker 1: impact investing, where we're looking for very specific um proceed 428 00:22:38,520 --> 00:22:41,639 Speaker 1: utilization that is direct and measurable. So think of things 429 00:22:41,720 --> 00:22:44,800 Speaker 1: like the easy ones are so solar and wind farms, 430 00:22:44,800 --> 00:22:46,960 Speaker 1: for example, but it's much broader than that, where we 431 00:22:47,000 --> 00:22:50,280 Speaker 1: look at different types of things, whether it the affordable 432 00:22:50,320 --> 00:22:54,880 Speaker 1: housing issues or attempting to help with education opportunities, things 433 00:22:54,880 --> 00:22:57,399 Speaker 1: of that nature that allow our investors to have a 434 00:22:57,440 --> 00:23:01,520 Speaker 1: direct and measurable exposure to security that generates some type 435 00:23:01,560 --> 00:23:04,600 Speaker 1: of u UH impact in areas that they care about. 436 00:23:04,760 --> 00:23:08,680 Speaker 1: We've read about an increasing number of investors inquiring about 437 00:23:08,800 --> 00:23:11,119 Speaker 1: E s G strategies, but when you look at the 438 00:23:11,160 --> 00:23:14,880 Speaker 1: actual flows, it still is a fraction of the overall money. 439 00:23:14,920 --> 00:23:17,679 Speaker 1: I'm wondering what you're seeing on that front. Yeah, that's 440 00:23:17,680 --> 00:23:19,960 Speaker 1: a great question. I think, really what it is there's 441 00:23:19,960 --> 00:23:22,199 Speaker 1: a couple of things there. I think when when you 442 00:23:22,240 --> 00:23:24,560 Speaker 1: look at E s G investing, it's still fairly nascent 443 00:23:24,600 --> 00:23:26,960 Speaker 1: when you really think about it. Um. You know, it's 444 00:23:26,960 --> 00:23:28,640 Speaker 1: been going on in the equity market for a much 445 00:23:28,680 --> 00:23:31,359 Speaker 1: longer period of time than it certainly hasn't fixed. But 446 00:23:31,520 --> 00:23:35,040 Speaker 1: what we're starting to see is the asset management industry 447 00:23:35,040 --> 00:23:37,080 Speaker 1: as a whole had not been good at being able 448 00:23:37,119 --> 00:23:41,040 Speaker 1: to create products that performed well relative to common benchmarks 449 00:23:41,160 --> 00:23:43,520 Speaker 1: or to peer groups. UM. And that's really due to 450 00:23:43,560 --> 00:23:46,080 Speaker 1: how E s G was applied historically, which is really 451 00:23:46,119 --> 00:23:49,000 Speaker 1: more of an exclusionary screen perspective. You were good or 452 00:23:49,000 --> 00:23:51,200 Speaker 1: you were bad, and that was it. You wouldn't invest 453 00:23:51,240 --> 00:23:52,960 Speaker 1: in any oil and gas name. So when oil and 454 00:23:52,960 --> 00:23:57,200 Speaker 1: gas rallied, your portfolio was structurally deficient. You couldn't perform well. UM. 455 00:23:57,280 --> 00:23:59,360 Speaker 1: Now when you look at E s G application, it's 456 00:23:59,359 --> 00:24:01,959 Speaker 1: really more of a active industry class. You're looking to 457 00:24:02,000 --> 00:24:06,680 Speaker 1: invest in issuers, not necessarily excluding them solely. So when 458 00:24:06,720 --> 00:24:09,359 Speaker 1: you do that, you can build the broadly diversified portfolio. 459 00:24:09,440 --> 00:24:11,760 Speaker 1: And now you're seeing product that is able to perform 460 00:24:11,800 --> 00:24:14,960 Speaker 1: well against common benchmarks and against peer groups. So I 461 00:24:14,960 --> 00:24:17,840 Speaker 1: think it makes investors more comfortable that they can invest 462 00:24:17,880 --> 00:24:21,480 Speaker 1: in this way without dealing with the main the main 463 00:24:21,520 --> 00:24:24,240 Speaker 1: misperception in the space, which is you sacrifice performance. I 464 00:24:24,240 --> 00:24:26,359 Speaker 1: want to talk about the data behind E s G 465 00:24:26,520 --> 00:24:28,480 Speaker 1: investing because I know a lot of people that we've 466 00:24:28,480 --> 00:24:30,359 Speaker 1: spoken to about s G investing say, you know that 467 00:24:30,680 --> 00:24:32,879 Speaker 1: there's not that great data out there. Let you know, 468 00:24:32,960 --> 00:24:35,240 Speaker 1: it's just I can't just rely on income statement balance 469 00:24:35,280 --> 00:24:37,760 Speaker 1: and I need other data. Help me figure this out. Now. 470 00:24:37,800 --> 00:24:39,960 Speaker 1: On the Bloomberg terminal, in the FA function, which is 471 00:24:39,960 --> 00:24:42,480 Speaker 1: one of our most widely used functions, there is a 472 00:24:42,520 --> 00:24:44,720 Speaker 1: tab right there on the F A function for E 473 00:24:44,840 --> 00:24:47,160 Speaker 1: s G. So you know it's important to Bloomberg we're 474 00:24:47,160 --> 00:24:49,679 Speaker 1: collecting that data. What is your sense of kind of 475 00:24:49,840 --> 00:24:53,320 Speaker 1: where the data is today to allow you to really 476 00:24:53,880 --> 00:24:57,640 Speaker 1: use your analytical skills to factor it into your decision. Yeah, 477 00:24:57,680 --> 00:24:59,520 Speaker 1: it's really at this point in time, a little bit 478 00:24:59,560 --> 00:25:02,160 Speaker 1: all over the place there. There are certainly getting better 479 00:25:02,240 --> 00:25:04,760 Speaker 1: over time, and it gets better on a daily basis. 480 00:25:05,119 --> 00:25:08,119 Speaker 1: UM there are certainly efforts outstanding at the moment to 481 00:25:08,280 --> 00:25:13,080 Speaker 1: try to to UM provide centralized data that is common 482 00:25:13,200 --> 00:25:15,920 Speaker 1: for all issuers to provide. The one challenge, of course, 483 00:25:16,000 --> 00:25:18,359 Speaker 1: within e s G data for an issue is that 484 00:25:18,440 --> 00:25:21,320 Speaker 1: it's different for all of them. You know that environmental 485 00:25:21,359 --> 00:25:24,240 Speaker 1: issues may not apply as much to a technology companies, 486 00:25:24,320 --> 00:25:26,960 Speaker 1: let's say an oil and gas issue. So the key 487 00:25:27,040 --> 00:25:29,520 Speaker 1: is is really finding those factors that are material for 488 00:25:29,600 --> 00:25:32,080 Speaker 1: your particular sector and being able to report on those. 489 00:25:32,359 --> 00:25:35,040 Speaker 1: The really good thing is that issuers have become very 490 00:25:35,160 --> 00:25:38,240 Speaker 1: much focused on this particular topic and willing to try 491 00:25:38,240 --> 00:25:41,280 Speaker 1: to work with investors um when we engage with them, 492 00:25:41,280 --> 00:25:44,159 Speaker 1: to try to improve their overall quality of data and 493 00:25:44,240 --> 00:25:47,240 Speaker 1: provide a wider range of data that allows an investor 494 00:25:47,280 --> 00:25:50,200 Speaker 1: to utilize it in a way to build a portfolio. 495 00:25:50,440 --> 00:25:52,639 Speaker 1: There's a structural challenge to when you talk about e 496 00:25:52,800 --> 00:25:57,320 Speaker 1: s G, particularly environmental and social, and that is that 497 00:25:57,480 --> 00:26:01,560 Speaker 1: the universe of public equities is shrinking and a lot 498 00:26:01,600 --> 00:26:04,520 Speaker 1: of people who are looking to have an impact are 499 00:26:04,560 --> 00:26:09,040 Speaker 1: looking to alternative methods that are less tested, smaller investments, 500 00:26:09,119 --> 00:26:12,320 Speaker 1: more at U syncratic. How do you see the market 501 00:26:12,359 --> 00:26:15,680 Speaker 1: evolving based on that challenge? Now, that's a great point, 502 00:26:15,760 --> 00:26:18,120 Speaker 1: and that's why I really think that people are now 503 00:26:18,160 --> 00:26:20,240 Speaker 1: becoming more comfortable with the application at B s G 504 00:26:20,320 --> 00:26:23,280 Speaker 1: and impact in public fixed income because we have such 505 00:26:23,320 --> 00:26:27,080 Speaker 1: a wider array of investment opportunities, we're not looking for issuers. 506 00:26:27,200 --> 00:26:29,080 Speaker 1: We don't need a public issuer you know in the 507 00:26:29,119 --> 00:26:31,520 Speaker 1: fixed income market. But how we can utilize that as 508 00:26:31,560 --> 00:26:34,560 Speaker 1: we can get issuance at a sector, at a subsector 509 00:26:34,640 --> 00:26:37,880 Speaker 1: level or a subsidiary level for a corporate. But these 510 00:26:37,880 --> 00:26:41,199 Speaker 1: are companies that already are issuing bonds in the public markets, right, 511 00:26:41,200 --> 00:26:44,000 Speaker 1: I mean, this isn't necessarily a company that's trying to 512 00:26:44,000 --> 00:26:47,640 Speaker 1: put toilets in in villages in in you know, pick 513 00:26:47,680 --> 00:26:51,240 Speaker 1: your country, right, it depends actually from a corporate perspective 514 00:26:51,280 --> 00:26:53,439 Speaker 1: that is correct. But where we would have issues like 515 00:26:53,600 --> 00:26:56,200 Speaker 1: that for example you just described with be with super 516 00:26:56,200 --> 00:26:58,359 Speaker 1: sovereign issuers or n g O s that come to 517 00:26:58,480 --> 00:27:01,280 Speaker 1: the market. Um, we've done things from you know, looked 518 00:27:01,280 --> 00:27:05,520 Speaker 1: at deals where we provided vaccination financing, um, all the 519 00:27:05,520 --> 00:27:08,560 Speaker 1: way through looking at transactions that provided soul Wark cook 520 00:27:08,640 --> 00:27:12,720 Speaker 1: tops to sub Saharan African families. So there is because 521 00:27:12,760 --> 00:27:14,479 Speaker 1: I think the nature of the fixed income mark is 522 00:27:14,720 --> 00:27:18,119 Speaker 1: so much more diverse and wider that you have the 523 00:27:18,119 --> 00:27:22,920 Speaker 1: opportunity to look at NGO super sovereign's agency, securities, municipalities 524 00:27:22,920 --> 00:27:26,800 Speaker 1: and structured securities to find a growing opportunity set. Thank 525 00:27:26,840 --> 00:27:28,760 Speaker 1: you so much for being with us. Thanks for having me. 526 00:27:28,960 --> 00:27:32,040 Speaker 1: Steve Liberal Tour joining us here, lead fixed income E 527 00:27:32,200 --> 00:27:35,560 Speaker 1: s G portfolio manager with New Vine uh joining us 528 00:27:35,560 --> 00:27:38,480 Speaker 1: in our in our active broker's studios. Thanks for listening 529 00:27:38,520 --> 00:27:40,920 Speaker 1: to the Bloomberg P and L podcast. You can subscribe 530 00:27:40,920 --> 00:27:43,760 Speaker 1: and listen to interviews at Apple Podcasts or whatever podcast 531 00:27:43,760 --> 00:27:47,320 Speaker 1: platform you prefer. Paul Sweeney, I'm on Twitter at pt Sweeney. 532 00:27:47,359 --> 00:27:49,600 Speaker 1: I'm Lisa abram Woods. I'm on Twitter at Lisa A. 533 00:27:49,680 --> 00:27:52,479 Speaker 1: Bramwoits one before the podcast. You can always catch us 534 00:27:52,560 --> 00:27:54,159 Speaker 1: worldwide on Bloomberg Radio