1 00:00:00,800 --> 00:00:04,040 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney. Alongside 2 00:00:04,040 --> 00:00:06,920 Speaker 1: my co host Matt Miller. Every business day, we bring 3 00:00:06,960 --> 00:00:11,520 Speaker 1: you interviews from CEOs, market pros, and Bloomberg experts, along 4 00:00:11,520 --> 00:00:15,600 Speaker 1: with essential market moving news. Find the Bloomberg Markets Podcast 5 00:00:15,600 --> 00:00:18,439 Speaker 1: on Apple Podcasts or wherever you listen to podcasts, and 6 00:00:18,480 --> 00:00:23,320 Speaker 1: at Bloomberg dot com slash podcast. E s G Investing. 7 00:00:23,440 --> 00:00:26,360 Speaker 1: I first started hearing about it maybe ten years ago 8 00:00:26,440 --> 00:00:30,840 Speaker 1: from some of my institutional investor clients in Europe. First 9 00:00:31,280 --> 00:00:34,800 Speaker 1: they roll into it like, is Disney is environmental, social 10 00:00:34,880 --> 00:00:37,560 Speaker 1: and governance? And I said, I think Disney is okay. 11 00:00:37,600 --> 00:00:39,240 Speaker 1: That was my big stock pick of the day back 12 00:00:39,240 --> 00:00:42,440 Speaker 1: in the day. I think it's s d okay. I mean, 13 00:00:42,600 --> 00:00:44,839 Speaker 1: it's Mickey Mouse, right, I mean, how bad could it 14 00:00:44,880 --> 00:00:46,360 Speaker 1: be from the E s G perspective? But a lot 15 00:00:46,360 --> 00:00:49,760 Speaker 1: of pope folks wrong. No, I think I'm okay. I 16 00:00:49,760 --> 00:00:52,920 Speaker 1: think I'm okay. It's Mickey's pretty good. Mickey, Mickey, Mickey's 17 00:00:52,920 --> 00:00:55,040 Speaker 1: pretty good. But a lot of folks it's obviously issue 18 00:00:55,040 --> 00:00:57,440 Speaker 1: has become a major major trend. I think it's gone 19 00:00:57,480 --> 00:01:00,560 Speaker 1: two ways. It's gone. On the one hand, it's it's 20 00:01:00,760 --> 00:01:04,760 Speaker 1: been a good label to use for marketing, right, and 21 00:01:05,000 --> 00:01:07,920 Speaker 1: we've seen that takeoff and really sputter out over the 22 00:01:08,040 --> 00:01:09,840 Speaker 1: last couple of months because E s G E t 23 00:01:10,040 --> 00:01:12,360 Speaker 1: F s have now gone negative and let's look at somebody. 24 00:01:13,160 --> 00:01:15,040 Speaker 1: I was gonna say, it's and the the other way 25 00:01:15,240 --> 00:01:19,520 Speaker 1: it's gone, Um, people who are activists or is activists 26 00:01:19,560 --> 00:01:23,280 Speaker 1: the right term. Maybe people who actually do something with 27 00:01:23,440 --> 00:01:25,720 Speaker 1: their steaks, you know, put their money where their mouth 28 00:01:25,880 --> 00:01:27,720 Speaker 1: is and change the world. We have one of those 29 00:01:27,720 --> 00:01:30,120 Speaker 1: people in the office in the studio right now, Yasmin 30 00:01:30,200 --> 00:01:31,920 Speaker 1: Dia Bilder joins us. She's the head of E t 31 00:01:32,120 --> 00:01:35,000 Speaker 1: F S and managing director at Engine Number one, which 32 00:01:35,080 --> 00:01:38,600 Speaker 1: you know probably from last year's proxy battle that won 33 00:01:38,720 --> 00:01:41,680 Speaker 1: them three spots on the board. So it's been great 34 00:01:41,680 --> 00:01:43,960 Speaker 1: to have you in here. Um, talk to us about 35 00:01:44,280 --> 00:01:47,000 Speaker 1: the first of all, the kind of crash and burn 36 00:01:47,200 --> 00:01:49,920 Speaker 1: of the marketing side of E s G investing, because, 37 00:01:49,960 --> 00:01:53,400 Speaker 1: like I said, E t F flows have gone negative. Now, yeah, 38 00:01:53,440 --> 00:01:55,600 Speaker 1: it's a really interesting trend. You're scene right now, So 39 00:01:55,680 --> 00:01:57,720 Speaker 1: I should say off the bat, we don't see ourselves 40 00:01:57,840 --> 00:02:00,160 Speaker 1: as E s G providers, but let's talk definition. What 41 00:02:00,360 --> 00:02:03,640 Speaker 1: is E s G investing. You mentioned environmental, social, and 42 00:02:03,720 --> 00:02:06,960 Speaker 1: governance data. Ultimately, what most of these strategies are doing 43 00:02:07,000 --> 00:02:10,519 Speaker 1: they're trying to rate companies and rank companies. So ultimately 44 00:02:10,600 --> 00:02:15,360 Speaker 1: they're saying what companies look quote unquote best across environmental, social, 45 00:02:15,400 --> 00:02:17,799 Speaker 1: and governance issues and what look worst? And then many 46 00:02:17,880 --> 00:02:20,400 Speaker 1: strategies are built off the back of it. Um Typically 47 00:02:20,440 --> 00:02:23,040 Speaker 1: what you see is strategies that exclude the worst or 48 00:02:23,160 --> 00:02:25,680 Speaker 1: by the best. The challenge with the space and you're 49 00:02:25,680 --> 00:02:27,960 Speaker 1: seeing it sort of play out real time is first 50 00:02:28,040 --> 00:02:30,280 Speaker 1: and foremost, what is a good company and what's a 51 00:02:30,320 --> 00:02:33,680 Speaker 1: bad company? Right Like, many of these data providers actually 52 00:02:33,720 --> 00:02:37,600 Speaker 1: have no correlation across what where they actually rank companies. 53 00:02:37,600 --> 00:02:40,560 Speaker 1: In fact, Facebook is the best example. Some rank it 54 00:02:40,680 --> 00:02:43,360 Speaker 1: is a top quartel, some rank its bottom quartel. It's 55 00:02:43,400 --> 00:02:47,160 Speaker 1: a very personal discussion, insane. Now you see the performances. 56 00:02:47,200 --> 00:02:48,840 Speaker 1: The other thing though, which is it? It tends to 57 00:02:48,919 --> 00:02:52,200 Speaker 1: be that many of these portfolios are just overweight technology 58 00:02:52,520 --> 00:02:54,360 Speaker 1: and underweight oiling gas and so what are you seeing 59 00:02:54,400 --> 00:02:57,040 Speaker 1: the last couple? Because Exon is clearly a bad company 60 00:02:57,240 --> 00:02:59,480 Speaker 1: right from it. I don't know how they are in 61 00:02:59,639 --> 00:03:02,799 Speaker 1: terms of the social and governance side of things. But 62 00:03:02,880 --> 00:03:06,000 Speaker 1: in terms of environmental you just measure them by you know, 63 00:03:06,600 --> 00:03:10,120 Speaker 1: CEO two emissions. Surely X it's one of the worst 64 00:03:10,160 --> 00:03:12,880 Speaker 1: companies in the world. Right, we'll all talk about our 65 00:03:12,919 --> 00:03:15,280 Speaker 1: campaign and what we were hoping to see for X 66 00:03:15,360 --> 00:03:17,919 Speaker 1: on mobiles. It's the anniversary kind of it's right around 67 00:03:17,919 --> 00:03:19,959 Speaker 1: now is when we had what we were able to 68 00:03:20,000 --> 00:03:22,720 Speaker 1: successfully place three people on the board of directors of Exxon, 69 00:03:22,840 --> 00:03:25,240 Speaker 1: and we did that with owning just two basis points 70 00:03:25,280 --> 00:03:28,840 Speaker 1: in the company. It's very fashion. I mean, if I 71 00:03:28,919 --> 00:03:30,600 Speaker 1: were on that board. But but you know what was 72 00:03:30,680 --> 00:03:34,359 Speaker 1: interesting is we really focused our argument on the shareholder 73 00:03:34,440 --> 00:03:36,440 Speaker 1: argument for what we wanted to talk about, not the 74 00:03:36,520 --> 00:03:39,760 Speaker 1: morality climate argument. And what we were really focusing on 75 00:03:39,920 --> 00:03:42,160 Speaker 1: was a few things. First, it was all about governance. 76 00:03:42,560 --> 00:03:44,760 Speaker 1: It was about the fact that, whether you like it 77 00:03:44,880 --> 00:03:46,520 Speaker 1: or not, whether you believe in climate change or not, 78 00:03:47,200 --> 00:03:50,680 Speaker 1: energy is going through huge transformation, whether it's government regulation 79 00:03:50,920 --> 00:03:54,200 Speaker 1: or shifting consumer preferences, and we felt that the board 80 00:03:54,480 --> 00:03:58,640 Speaker 1: would benefit from additional folks who had real, true transformational 81 00:03:58,760 --> 00:04:02,760 Speaker 1: energy experience. It was also about their capital allocation strategy. 82 00:04:03,280 --> 00:04:05,200 Speaker 1: And so I think the real interesting thing from the 83 00:04:05,280 --> 00:04:07,520 Speaker 1: campaign for me was two fold one was you can 84 00:04:07,600 --> 00:04:09,720 Speaker 1: own a small part of a company, but if you 85 00:04:09,800 --> 00:04:13,160 Speaker 1: focus on the shareholder value argument, you can bring people 86 00:04:13,200 --> 00:04:15,960 Speaker 1: along with you. Um. And the other is that you know, 87 00:04:16,279 --> 00:04:19,560 Speaker 1: moving the conversation from ideology to economics, and that was 88 00:04:19,600 --> 00:04:23,520 Speaker 1: where we had our success. That's key, right, that's super key, 89 00:04:23,760 --> 00:04:29,400 Speaker 1: And in that sense you can easily understand why shareholders 90 00:04:29,440 --> 00:04:32,120 Speaker 1: would want you there. They've got to make this transformation anyway. 91 00:04:32,520 --> 00:04:35,120 Speaker 1: The guys they know on the board are probably a 92 00:04:35,640 --> 00:04:38,680 Speaker 1: all guys and be only know how to put holes 93 00:04:38,720 --> 00:04:42,000 Speaker 1: in the ground and pull out the Earth's precious resources, 94 00:04:42,080 --> 00:04:44,240 Speaker 1: and they need someone to show them the way. How 95 00:04:44,279 --> 00:04:47,760 Speaker 1: does the C suite react, Well, we are an engagement focus. 96 00:04:47,880 --> 00:04:51,400 Speaker 1: From the traditional theory of change in this space is divestment, 97 00:04:51,480 --> 00:04:53,200 Speaker 1: which is if you don't like it, don't own it. 98 00:04:53,320 --> 00:04:55,000 Speaker 1: And when I say this space, I mean broadly. It's 99 00:04:55,040 --> 00:04:57,880 Speaker 1: true for climate, it's true for social issues. A lot 100 00:04:57,880 --> 00:05:00,800 Speaker 1: of times e s g. People just sell you know, tobacco, 101 00:05:01,360 --> 00:05:05,840 Speaker 1: sell oil, sell everything that's bad, probably gunmakers, and that 102 00:05:05,920 --> 00:05:09,240 Speaker 1: doesn't really help, right because someone else just buys those 103 00:05:09,240 --> 00:05:12,240 Speaker 1: steaks and keeps making tobacco, oil and guns. Well, when 104 00:05:12,279 --> 00:05:16,040 Speaker 1: you think about I think carbon decarbonization in general. I 105 00:05:16,080 --> 00:05:18,279 Speaker 1: think there's a real strong case to make for own 106 00:05:18,360 --> 00:05:20,760 Speaker 1: and engage. I mean, energy is one place, but how 107 00:05:20,760 --> 00:05:26,000 Speaker 1: about global greenhouse gas emissions comes from energy, transportation and agriculture. 108 00:05:26,200 --> 00:05:28,600 Speaker 1: Like this is the heart of the transition. When we 109 00:05:28,680 --> 00:05:31,080 Speaker 1: talk about the energy transition, which is, by the way, 110 00:05:31,080 --> 00:05:33,839 Speaker 1: a big investment opportunity for investors. That's where it's happening. 111 00:05:34,240 --> 00:05:37,200 Speaker 1: And our theory of changes um not just that it's 112 00:05:37,240 --> 00:05:39,760 Speaker 1: not just that the small green technology companies are going 113 00:05:39,800 --> 00:05:41,560 Speaker 1: to be the ones driving it, but that actually the 114 00:05:41,680 --> 00:05:45,840 Speaker 1: big traditional incumbents have to transform themselves. So that's the automakers, 115 00:05:45,960 --> 00:05:48,560 Speaker 1: that's the big ad companies, and that's our That's where 116 00:05:48,560 --> 00:05:51,320 Speaker 1: we spend our time. And I will say, um, all 117 00:05:51,400 --> 00:05:54,480 Speaker 1: of our engagements post xcon have been very collaborative because 118 00:05:54,520 --> 00:05:56,559 Speaker 1: we're not the first people to tell the c suite 119 00:05:56,600 --> 00:05:59,520 Speaker 1: of these large companies, Hey, there's a lot of focus 120 00:05:59,680 --> 00:06:02,640 Speaker 1: on decarbonization. You need to have a strategy and so 121 00:06:02,720 --> 00:06:04,480 Speaker 1: in some ways, I think the fact that we approach 122 00:06:04,560 --> 00:06:07,720 Speaker 1: it from a shareholder value perspective allows us to have 123 00:06:07,839 --> 00:06:11,559 Speaker 1: a really good dialogue with companies. What's real quickly thirty seconds, 124 00:06:11,600 --> 00:06:14,040 Speaker 1: what's the next industry that you think is should get 125 00:06:14,120 --> 00:06:16,080 Speaker 1: some of attention from an E s G perspective. I 126 00:06:16,120 --> 00:06:19,000 Speaker 1: think the demand side of decarbonization is interesting. Everyone focused 127 00:06:19,000 --> 00:06:22,159 Speaker 1: on oil and gas, but transportation is a really interesting one, right. 128 00:06:22,200 --> 00:06:24,359 Speaker 1: It's it's a move to all electric vehicles. A lot 129 00:06:24,440 --> 00:06:26,560 Speaker 1: of the market is focused on that, but but you 130 00:06:26,680 --> 00:06:28,359 Speaker 1: have to think more deeply about it. It's what are 131 00:06:28,400 --> 00:06:31,960 Speaker 1: the commodity inputs like lithium and aluminum that are needed 132 00:06:32,000 --> 00:06:35,280 Speaker 1: for that. So it's a really interesting investment opportunity. Good stuff, 133 00:06:35,320 --> 00:06:37,120 Speaker 1: good stuff. Yasmin, thanks so much for coming into our 134 00:06:37,120 --> 00:06:40,280 Speaker 1: Bloomberg Interactor Broker studio. Yasmin daya build your head of 135 00:06:40,360 --> 00:06:43,000 Speaker 1: E T f s and a managing director at a 136 00:06:43,080 --> 00:06:46,400 Speaker 1: really cool firms called Engine Number one. Is probably a 137 00:06:46,400 --> 00:06:48,960 Speaker 1: story behind that name as well. We'll get that, uh 138 00:06:49,200 --> 00:06:55,840 Speaker 1: next time our next guest. In my notes, it says 139 00:06:56,440 --> 00:06:59,120 Speaker 1: it was two point two billion assets under management his fund. 140 00:06:59,200 --> 00:07:02,400 Speaker 1: He now has over nine of the firm's assets in cash, 141 00:07:02,920 --> 00:07:05,760 Speaker 1: with the remaining ten in defensive fixed income. I can 142 00:07:05,880 --> 00:07:08,120 Speaker 1: honestly say I've never seen that before. Let's check in 143 00:07:08,200 --> 00:07:11,040 Speaker 1: with Phil Toes, CEO of Toes Asset Management. Phil, thanks 144 00:07:11,040 --> 00:07:13,080 Speaker 1: so much for joining us really appreciate it. Talk to 145 00:07:13,200 --> 00:07:15,600 Speaker 1: us about your portfolio here and your your cash position. 146 00:07:16,240 --> 00:07:20,040 Speaker 1: Where is it? Our our notes correct? Are our notes correct? Yeah? 147 00:07:20,160 --> 00:07:22,320 Speaker 1: So we shifted slightly in the last couple of weeks 148 00:07:22,400 --> 00:07:24,480 Speaker 1: and when they move up in the markets, moved us 149 00:07:24,920 --> 00:07:29,560 Speaker 1: into more of a round a cash position relative to 150 00:07:29,640 --> 00:07:33,520 Speaker 1: our earlier this year. But what what drives us there 151 00:07:33,640 --> 00:07:36,360 Speaker 1: isn't isn't really are thinking about the markets, but rather 152 00:07:36,480 --> 00:07:40,160 Speaker 1: just algorithms that are trend following, algorithms that have the 153 00:07:40,240 --> 00:07:44,800 Speaker 1: ability to attempt to move us into either either fixed 154 00:07:44,840 --> 00:07:47,720 Speaker 1: income or cash instruments. And because fixed income has been 155 00:07:47,760 --> 00:07:50,600 Speaker 1: doing so poorly that we've largely just been in cash. 156 00:07:50,680 --> 00:07:53,840 Speaker 1: But that's been in the position that we've had primarily 157 00:07:53,920 --> 00:07:56,640 Speaker 1: since January thirteenth of this year. So it's been a 158 00:07:56,720 --> 00:07:59,239 Speaker 1: it's been a good thing to just not be playing. 159 00:07:59,680 --> 00:08:03,520 Speaker 1: But it's like, I mean, it reminds me of Pablo Escobar. 160 00:08:03,720 --> 00:08:06,080 Speaker 1: He also held most of his assets in cash, and 161 00:08:06,760 --> 00:08:09,840 Speaker 1: the rats ate about ten percent every year. You have 162 00:08:09,960 --> 00:08:14,080 Speaker 1: the same problem, right, don't you lose money through uh 163 00:08:14,520 --> 00:08:19,040 Speaker 1: just inflation? Yeah. So if if all you're doing is 164 00:08:19,080 --> 00:08:20,920 Speaker 1: putting money in cash and didn't have a plan to 165 00:08:21,000 --> 00:08:23,200 Speaker 1: come back in then then that's exactly what would happened, 166 00:08:23,240 --> 00:08:25,840 Speaker 1: and inflation would eat you. But if you think about 167 00:08:25,880 --> 00:08:28,240 Speaker 1: the way, so to our base cases that this will 168 00:08:28,320 --> 00:08:31,000 Speaker 1: be a continuing bear market, and what we've seen in 169 00:08:31,040 --> 00:08:34,600 Speaker 1: the last couple of weeks is a fair market trap. 170 00:08:35,280 --> 00:08:37,679 Speaker 1: So if that, if that scenario plays out, the way 171 00:08:37,720 --> 00:08:40,280 Speaker 1: the markets tend to work is they move down and 172 00:08:40,320 --> 00:08:43,240 Speaker 1: then they even pick up more volatility. UH. And And 173 00:08:43,400 --> 00:08:46,520 Speaker 1: what happens across our platform, across our different funds in 174 00:08:46,600 --> 00:08:50,079 Speaker 1: EATS is that we have a trend following algorithm and 175 00:08:50,160 --> 00:08:53,000 Speaker 1: just as we exited in the early phase of this decline, 176 00:08:53,559 --> 00:08:55,679 Speaker 1: they would be designed to attempt to re enter in 177 00:08:55,679 --> 00:08:58,160 Speaker 1: the early stage of a rebound. So then via zoom 178 00:08:58,200 --> 00:08:59,800 Speaker 1: out and look at let's let's say this is like 179 00:08:59,840 --> 00:09:02,360 Speaker 1: a typical bear market and it bottoms and somewhere around 180 00:09:02,400 --> 00:09:06,000 Speaker 1: October of this year, UH somewhere around thirty five percent lower. 181 00:09:06,480 --> 00:09:09,000 Speaker 1: At that point, having been in casual of it, it 182 00:09:09,120 --> 00:09:11,000 Speaker 1: would have been a fantastic thing. But then you have 183 00:09:11,200 --> 00:09:14,559 Speaker 1: the possibility for rebound, and that, frankly, is what it's 184 00:09:14,559 --> 00:09:17,679 Speaker 1: all about, which is positioning yourself. You know, it's always 185 00:09:17,679 --> 00:09:19,560 Speaker 1: so amusing to hear people talk about buying the dip, 186 00:09:19,640 --> 00:09:21,520 Speaker 1: and the real question is what are they buying the 187 00:09:21,600 --> 00:09:24,320 Speaker 1: dip with if they remain fully invest in the whole time. 188 00:09:24,559 --> 00:09:28,719 Speaker 1: So yeah, yeah, the idea is to not make a 189 00:09:28,800 --> 00:09:32,120 Speaker 1: market call necessarily, but to position yourselves in a way 190 00:09:32,280 --> 00:09:34,880 Speaker 1: that address was at least the contingency that this bear 191 00:09:34,960 --> 00:09:39,520 Speaker 1: market will continue, and then also positions yourself to potentially 192 00:09:39,600 --> 00:09:42,319 Speaker 1: capture the rebound. And that's you know, this is not 193 00:09:42,400 --> 00:09:44,720 Speaker 1: something new for us. We've been doing this since nineties six. 194 00:09:44,920 --> 00:09:47,319 Speaker 1: We we saw the same kind of thing happened during 195 00:09:47,400 --> 00:09:50,680 Speaker 1: the Internet double burst and the financial crisis, So we're 196 00:09:51,360 --> 00:09:53,560 Speaker 1: we've got a long history of trying of navigated. By 197 00:09:53,600 --> 00:09:58,000 Speaker 1: the way, speaking of bubbles, um, dot com and housing, 198 00:09:58,840 --> 00:10:01,679 Speaker 1: what do you think we're look at today, especially with 199 00:10:01,840 --> 00:10:03,920 Speaker 1: regards to I just bought a house for more money 200 00:10:03,960 --> 00:10:05,800 Speaker 1: than I thought it'd ever spend on any one thing 201 00:10:05,880 --> 00:10:09,000 Speaker 1: in my entire life, and I'm still skeptical that I 202 00:10:09,080 --> 00:10:12,880 Speaker 1: can actually afford it. Yeah, So I think it's really 203 00:10:13,000 --> 00:10:15,480 Speaker 1: fascinating because you get, you know, get conversations like this. 204 00:10:15,640 --> 00:10:19,080 Speaker 1: You have a decent because little rally in the stock market, 205 00:10:19,080 --> 00:10:21,160 Speaker 1: and everyone just thinks, well, we're back to to life 206 00:10:21,200 --> 00:10:23,960 Speaker 1: as normal. But I think we're anything but that. One 207 00:10:24,000 --> 00:10:26,440 Speaker 1: of the most compelling you know, you mentioned housing. One 208 00:10:26,480 --> 00:10:28,719 Speaker 1: of the most compelling data points I've seen in the 209 00:10:28,840 --> 00:10:33,560 Speaker 1: last six months is the you know, the Schiller Housing 210 00:10:33,679 --> 00:10:37,079 Speaker 1: Price Index, which you know, for a hundred years, that 211 00:10:37,240 --> 00:10:40,000 Speaker 1: index range between a hundred and a hundred and twenty 212 00:10:40,000 --> 00:10:43,959 Speaker 1: because it's a real, real prices inflation, justin prices for houses. 213 00:10:44,520 --> 00:10:47,680 Speaker 1: And then we saw that shocking increase of the index 214 00:10:47,800 --> 00:10:51,960 Speaker 1: up to two hundred during the during the two thousand 215 00:10:52,000 --> 00:10:54,640 Speaker 1: seven and then it came back down again. But right 216 00:10:54,720 --> 00:10:58,360 Speaker 1: now we're at two twenty. So in real terms, we're 217 00:10:58,480 --> 00:11:04,079 Speaker 1: higher with these historically, and so that that, you know, 218 00:11:04,160 --> 00:11:05,880 Speaker 1: compare that with the fact that we're in the top 219 00:11:06,000 --> 00:11:10,920 Speaker 1: quintile in stock valuations. But here's the big one. Here's 220 00:11:10,960 --> 00:11:13,280 Speaker 1: the big one, which is that the said has changed 221 00:11:13,280 --> 00:11:17,079 Speaker 1: his motivations and where primarily over the last you know, 222 00:11:17,120 --> 00:11:20,480 Speaker 1: the say, thirteen years, that said has actively tried to 223 00:11:20,600 --> 00:11:25,439 Speaker 1: avoid financial market declines. Now you can actually argue that 224 00:11:25,520 --> 00:11:29,360 Speaker 1: because of demand pull inflation, which effectively means that people 225 00:11:29,400 --> 00:11:31,880 Speaker 1: are buying so much stuff because they have so much money. 226 00:11:32,880 --> 00:11:35,880 Speaker 1: You know, Now the said, you know, maybe implicitly they're 227 00:11:35,880 --> 00:11:37,360 Speaker 1: not coming out and saying that, but they may have 228 00:11:37,440 --> 00:11:42,319 Speaker 1: a motivation to pop the bubble. And so that's a 229 00:11:42,360 --> 00:11:45,120 Speaker 1: completely different scenario, completely different world to live in. So 230 00:11:45,280 --> 00:11:47,280 Speaker 1: instead of it being like fourth quarter of two thousand 231 00:11:47,280 --> 00:11:52,079 Speaker 1: and eighteen where said they're saving the day, now the 232 00:11:52,160 --> 00:11:56,079 Speaker 1: said may uh, you know, have have some kind of 233 00:11:56,080 --> 00:11:59,520 Speaker 1: an incentive to actually see financial asset prices, home prices 234 00:11:59,559 --> 00:12:03,560 Speaker 1: come to more reasonable levels as a way of addressing inflation. Absolutely, 235 00:12:03,760 --> 00:12:06,160 Speaker 1: I feel just third thirty seconds, it feels to me 236 00:12:06,480 --> 00:12:09,320 Speaker 1: like your firm and your strategy tries to time the market. 237 00:12:09,640 --> 00:12:13,319 Speaker 1: Is that right, Well, so we we we do. We 238 00:12:13,400 --> 00:12:16,160 Speaker 1: can't extu the markets. We also use options hedging, but 239 00:12:16,360 --> 00:12:19,000 Speaker 1: we would say no, not really, because most market timers 240 00:12:19,040 --> 00:12:20,800 Speaker 1: are trying to make a market call and predict what's 241 00:12:20,840 --> 00:12:23,640 Speaker 1: going to happen, and that tends to not go well often. 242 00:12:24,320 --> 00:12:26,160 Speaker 1: So instead of doing that, all we're doing is just 243 00:12:26,400 --> 00:12:29,520 Speaker 1: falling trend following algorithms, and so that tends to be 244 00:12:29,880 --> 00:12:34,000 Speaker 1: in our history, a relatively reliable way of doing two things, 245 00:12:34,040 --> 00:12:35,719 Speaker 1: which is getting out of the way of the real 246 00:12:35,800 --> 00:12:39,560 Speaker 1: train wrecks, but also participating in what you know generally 247 00:12:39,679 --> 00:12:41,959 Speaker 1: is just rising markets, and that's what markets do nine 248 00:12:42,440 --> 00:12:45,120 Speaker 1: of the time. So the idea is is not not 249 00:12:45,320 --> 00:12:47,480 Speaker 1: making a market call, which is not market timing, but 250 00:12:47,559 --> 00:12:51,640 Speaker 1: actually positioning yourself in the way address contingencies. All right, 251 00:12:51,679 --> 00:12:53,920 Speaker 1: So great stuff, man, thanks for coming on and really 252 00:12:53,960 --> 00:12:58,400 Speaker 1: appreciate a really interesting kind of you, even behavior and investing, 253 00:12:58,440 --> 00:12:59,800 Speaker 1: which I wanted to get to. So I hope we 254 00:12:59,840 --> 00:13:01,520 Speaker 1: can get you on again, Phil, because I know you 255 00:13:01,559 --> 00:13:04,719 Speaker 1: were a founder of the Behavioral Investing Institute, and I 256 00:13:04,800 --> 00:13:07,280 Speaker 1: think that's that sounds pretty pretty interesting as well as 257 00:13:07,320 --> 00:13:12,560 Speaker 1: released to investing. All Right, I just typed an FB 258 00:13:12,880 --> 00:13:15,719 Speaker 1: equity go into my terminal for the Facebook and I 259 00:13:15,800 --> 00:13:19,400 Speaker 1: got nothing. Nothing. It says it changed it's symbol today 260 00:13:19,480 --> 00:13:23,439 Speaker 1: to meta, so not gonna type in meta equity go 261 00:13:23,840 --> 00:13:25,439 Speaker 1: and I get Okay, there we go. I got my 262 00:13:25,520 --> 00:13:27,880 Speaker 1: ticker there, and all thats two more letters. It used 263 00:13:27,920 --> 00:13:30,360 Speaker 1: to be, dude, You remember when you get smaller, Yes, 264 00:13:30,480 --> 00:13:32,360 Speaker 1: to get down to that, if you had one letter 265 00:13:32,800 --> 00:13:36,679 Speaker 1: that was the most coveted of all tickers, like if 266 00:13:36,679 --> 00:13:39,520 Speaker 1: you were t then people stopped calling you anything else. 267 00:13:39,559 --> 00:13:42,760 Speaker 1: They just called you t right ford, all that kind 268 00:13:42,760 --> 00:13:44,640 Speaker 1: of good stuff. All right, So I'm blaming Matt Bloxham. 269 00:13:44,760 --> 00:13:46,839 Speaker 1: He's a t M T anlost to Bloomberg Intelligence. He's 270 00:13:46,840 --> 00:13:49,559 Speaker 1: based in London, runs all of our t MT stuff 271 00:13:49,640 --> 00:13:52,880 Speaker 1: over there. Uh we snagged him about five years ago, 272 00:13:54,160 --> 00:13:59,240 Speaker 1: don't remember. Uh, telecoms, tech media and technology. That's right, 273 00:13:59,480 --> 00:14:01,960 Speaker 1: that people still use that moniker mat is that still? 274 00:14:02,160 --> 00:14:04,320 Speaker 1: I thought that was a thing from the nineties. They 275 00:14:04,360 --> 00:14:06,960 Speaker 1: still do, but I still every day you get the question, 276 00:14:07,080 --> 00:14:10,240 Speaker 1: what is what's what's that team and the team? That's right? 277 00:14:10,400 --> 00:14:12,640 Speaker 1: So so Matt talk to us about it. Let's just 278 00:14:12,679 --> 00:14:15,640 Speaker 1: start with with Facebook. This is a meta free studio, 279 00:14:15,679 --> 00:14:19,480 Speaker 1: so what we can still call it Facebook is the 280 00:14:19,760 --> 00:14:23,200 Speaker 1: market buying off on this transformation. Then Mr Zuckerberg wants 281 00:14:23,280 --> 00:14:26,280 Speaker 1: to take meta platforms through over the next I don't know, 282 00:14:26,400 --> 00:14:28,600 Speaker 1: five to ten years. Yeah, I guess not. You know, 283 00:14:28,640 --> 00:14:31,840 Speaker 1: when the share prices down over the last twelve months, 284 00:14:32,080 --> 00:14:35,760 Speaker 1: I guess not. Um And I guess you know they've 285 00:14:35,800 --> 00:14:38,160 Speaker 1: got bigger near term issues, you know that. I mean, 286 00:14:38,200 --> 00:14:40,560 Speaker 1: obviously Instagram has been the big growth engine for them, 287 00:14:40,600 --> 00:14:44,280 Speaker 1: and TikTok is adding huge amounts of pressure to the 288 00:14:44,360 --> 00:14:46,760 Speaker 1: ad revenues they can generate from that. So I think 289 00:14:46,800 --> 00:14:48,840 Speaker 1: they need to kind of sort out the current business first. 290 00:14:48,920 --> 00:14:50,720 Speaker 1: And you know, I think that the metaverse, if it 291 00:14:50,760 --> 00:14:53,200 Speaker 1: ever becomes more than just you know, the current kind 292 00:14:53,240 --> 00:14:56,520 Speaker 1: of gaming environment it really is today, UM, then there's 293 00:14:56,520 --> 00:14:57,720 Speaker 1: going to be a lot of rivals for it. And 294 00:14:57,760 --> 00:15:02,520 Speaker 1: I think your historically Facebook UM gained its success by 295 00:15:02,600 --> 00:15:06,240 Speaker 1: acquiring businesses and building them. I guess with the new 296 00:15:06,320 --> 00:15:09,360 Speaker 1: constraints around um M and A, it's a question mark 297 00:15:09,360 --> 00:15:10,960 Speaker 1: about whether they will be able to kind of follow 298 00:15:11,000 --> 00:15:14,560 Speaker 1: that and whether they can successfully execute an organic struct 299 00:15:14,760 --> 00:15:18,840 Speaker 1: in that area. I have questioned Facebook's ability to actually 300 00:15:18,960 --> 00:15:22,320 Speaker 1: be the main player in the metaverse ten twenty years 301 00:15:22,400 --> 00:15:24,160 Speaker 1: from now, And I got a lot of pushback on 302 00:15:24,200 --> 00:15:26,200 Speaker 1: Twitter from people who said, you know what, they have 303 00:15:26,280 --> 00:15:29,680 Speaker 1: invested a ton you know, they bought Oculus Rift, which 304 00:15:29,840 --> 00:15:33,320 Speaker 1: was the VR headset. You know, they've done stuff, and 305 00:15:33,360 --> 00:15:36,800 Speaker 1: they're spending money UM to be a major player. Do 306 00:15:36,840 --> 00:15:39,360 Speaker 1: you agree, UM? I mean, I agree that they've been 307 00:15:39,360 --> 00:15:41,120 Speaker 1: spending a ton of money, But then yeah, a lot 308 00:15:41,160 --> 00:15:42,800 Speaker 1: of other people are spending a ton of money too, 309 00:15:42,880 --> 00:15:45,760 Speaker 1: and it's you know, completely open field UM. And I 310 00:15:45,800 --> 00:15:48,640 Speaker 1: think we've seen with every big technology wave, it's been 311 00:15:48,680 --> 00:15:51,840 Speaker 1: a new player that nobody saw coming that's ended up 312 00:15:51,920 --> 00:15:54,280 Speaker 1: being the big player. I mean that that's essentially what 313 00:15:54,400 --> 00:15:57,440 Speaker 1: Facebook is. You know, it was the current the current 314 00:15:57,960 --> 00:16:00,400 Speaker 1: king of the last technology cycle. I mean, I think 315 00:16:00,440 --> 00:16:02,360 Speaker 1: what's going to be interesting for the metaverse as a 316 00:16:02,440 --> 00:16:04,960 Speaker 1: whole is what Apple does with this kind of room 317 00:16:05,000 --> 00:16:07,040 Speaker 1: with a r VR headset. And if it comes out 318 00:16:07,120 --> 00:16:09,640 Speaker 1: next year, you know Apple's weight. You know, we'll create 319 00:16:09,760 --> 00:16:13,280 Speaker 1: this I think so called halo effect for everybody. So 320 00:16:13,800 --> 00:16:15,880 Speaker 1: maybe that will help Facebook, but you know they're going 321 00:16:15,920 --> 00:16:19,920 Speaker 1: to face a lot of UM current and non existent 322 00:16:20,360 --> 00:16:22,960 Speaker 1: UM competitors, you know, even the next three to five years. 323 00:16:23,160 --> 00:16:27,200 Speaker 1: Think there's also I think I think there's a conception 324 00:16:27,280 --> 00:16:30,800 Speaker 1: of what the metaverse is that requires you to put 325 00:16:30,880 --> 00:16:34,200 Speaker 1: on a headset and something that blocks out the rest 326 00:16:34,240 --> 00:16:37,480 Speaker 1: of the world. But I feel like we've already gone 327 00:16:37,560 --> 00:16:40,440 Speaker 1: beyond dipping our toe into the pool of metaverse. Like 328 00:16:40,760 --> 00:16:44,000 Speaker 1: if you spend all day working from home, you're on Zoom, 329 00:16:44,280 --> 00:16:48,400 Speaker 1: you're on Slack or whatever, and then you spend your evenings, um, 330 00:16:48,560 --> 00:16:51,360 Speaker 1: you know, shooting people on call of duty, and all 331 00:16:51,400 --> 00:16:54,600 Speaker 1: of your frends are from forums You're watching other people 332 00:16:54,800 --> 00:16:57,960 Speaker 1: shoot people on call of duty, Like aren't we already 333 00:16:58,120 --> 00:17:00,520 Speaker 1: in a sense living in the metaverse. I think there 334 00:17:00,560 --> 00:17:04,280 Speaker 1: are multiple meta verses essentially in another areas dating. You know, 335 00:17:04,280 --> 00:17:06,600 Speaker 1: a lot of the big dating platforms are moving into 336 00:17:06,640 --> 00:17:10,440 Speaker 1: a kind of VR environment. Yeah, you don't even have 337 00:17:10,640 --> 00:17:13,520 Speaker 1: to go out of your house, and neither does she 338 00:17:14,040 --> 00:17:17,679 Speaker 1: or he or whatever the person identifies as. You can 339 00:17:17,760 --> 00:17:21,760 Speaker 1: spend some money on virtual gifts as you know. Crazy. 340 00:17:22,000 --> 00:17:24,200 Speaker 1: All right, I'm honest with you. I'm done with this 341 00:17:24,240 --> 00:17:27,840 Speaker 1: whole conversation. Let's talk music. Spotify. How to Capital Markets? 342 00:17:27,920 --> 00:17:29,960 Speaker 1: Day said they got their analysts, they got their investors 343 00:17:30,080 --> 00:17:33,159 Speaker 1: coming in. There's another stock. You know, you talk to 344 00:17:33,240 --> 00:17:37,240 Speaker 1: these high flying technology names. It's all fifty percent this year. 345 00:17:38,440 --> 00:17:40,600 Speaker 1: What's the story with Spotify? I mean, I thought the 346 00:17:40,680 --> 00:17:43,320 Speaker 1: future of music was kind of renting this stuff. Yeah, 347 00:17:43,359 --> 00:17:44,879 Speaker 1: and you know, I think it's I guess one of 348 00:17:44,920 --> 00:17:48,080 Speaker 1: the near term worreas that people have had is that, 349 00:17:48,320 --> 00:17:52,040 Speaker 1: in the wake of the whole Netflix subscribe by growth 350 00:17:52,119 --> 00:17:55,399 Speaker 1: slowing um that perhaps Spotify is a similar kind of 351 00:17:55,440 --> 00:17:58,880 Speaker 1: platform where there's going to be um a challenge there. 352 00:17:59,000 --> 00:18:03,080 Speaker 1: I think Spotify is a bit different because unlike video 353 00:18:03,320 --> 00:18:06,600 Speaker 1: subscription services, where essential you've got exclusive, unique content and 354 00:18:06,680 --> 00:18:09,320 Speaker 1: you've spent tons of months to build it. Essence, you 355 00:18:09,400 --> 00:18:12,360 Speaker 1: can get most music across all platforms. So if it's 356 00:18:12,400 --> 00:18:15,040 Speaker 1: more about the user interface and the kind of platform itself, 357 00:18:15,320 --> 00:18:19,040 Speaker 1: and your Spotify is a very strong, compelling platform. So 358 00:18:19,080 --> 00:18:21,800 Speaker 1: I think they don't face the same kind of stagnation 359 00:18:21,960 --> 00:18:26,160 Speaker 1: risks that we've seen with Netflix, and yesterday they were 360 00:18:26,240 --> 00:18:29,200 Speaker 1: incredibly bullish on the mid term. They've got, you know, 361 00:18:29,560 --> 00:18:31,880 Speaker 1: give or take half a billion users today and want 362 00:18:31,920 --> 00:18:34,440 Speaker 1: to get that to a billion over the long term. 363 00:18:34,720 --> 00:18:36,320 Speaker 1: I think you're one of the One of the bigger 364 00:18:36,400 --> 00:18:39,600 Speaker 1: issues that they've had recently is the drag on profits 365 00:18:39,720 --> 00:18:43,560 Speaker 1: from there for a into podcasts. They're not even making 366 00:18:43,600 --> 00:18:46,640 Speaker 1: a gross margin on that today, and they were trying 367 00:18:46,680 --> 00:18:48,560 Speaker 1: to reassure people that they'll get to some kind of 368 00:18:48,600 --> 00:18:51,800 Speaker 1: gross margin in the next year or two. Um. You know, 369 00:18:51,840 --> 00:18:54,119 Speaker 1: I think that the shares moved a little bit possibly yesterday, 370 00:18:54,160 --> 00:18:55,400 Speaker 1: but you know, I think there's still a long way 371 00:18:55,440 --> 00:18:58,480 Speaker 1: for them to go. To the podcasting business everybody in 372 00:18:58,560 --> 00:19:03,560 Speaker 1: their Jack introduced a new podcast yesterday, Dak Shepherd of 373 00:19:03,880 --> 00:19:05,720 Speaker 1: you Know, Armchair Expert, which is arguably one of the 374 00:19:05,760 --> 00:19:10,119 Speaker 1: best podcasts that there is with his wife Kristen bell Um, 375 00:19:10,240 --> 00:19:15,080 Speaker 1: the actress famous for Frozen among other things. Um, and 376 00:19:15,080 --> 00:19:17,720 Speaker 1: they're going to give relationship advice and do I pay 377 00:19:17,800 --> 00:19:19,399 Speaker 1: for this? Is just all that support you have to 378 00:19:19,440 --> 00:19:22,359 Speaker 1: be on Spotify together, that's the idea, right, And Armchair 379 00:19:22,400 --> 00:19:25,920 Speaker 1: Expert moved to Spotify. I think Obama is. I guess 380 00:19:25,960 --> 00:19:28,280 Speaker 1: he was on Armchair Expert, but he's also got a 381 00:19:28,359 --> 00:19:31,000 Speaker 1: deal with Spotify and he has a lot on there 382 00:19:31,080 --> 00:19:34,000 Speaker 1: beyond just music, right, yeah, exactly. But it's it's a 383 00:19:34,119 --> 00:19:36,320 Speaker 1: very different model where we can with the music. There's 384 00:19:36,359 --> 00:19:39,800 Speaker 1: that they have a pay away to the music platforms, 385 00:19:39,880 --> 00:19:42,040 Speaker 1: so there's kind of less risk around it, whereas you know, 386 00:19:42,400 --> 00:19:46,760 Speaker 1: the podcast taken them into purchase a kind of content acquisition, 387 00:19:46,800 --> 00:19:48,920 Speaker 1: if you like, so a bit more like a PATV platform, 388 00:19:49,320 --> 00:19:52,320 Speaker 1: and you're kind of risking, you know, hundreds of millions 389 00:19:52,359 --> 00:19:54,960 Speaker 1: of dollars essentially just to try and kind of generate 390 00:19:55,119 --> 00:19:58,679 Speaker 1: more users and keep people on the platform. So it's 391 00:19:58,760 --> 00:20:01,119 Speaker 1: quite a different business model, and I guess it's spook 392 00:20:01,200 --> 00:20:03,440 Speaker 1: people a bit, you know that that it's a drag 393 00:20:03,560 --> 00:20:08,560 Speaker 1: on earnings podcasting in general. How big is it is 394 00:20:08,600 --> 00:20:11,960 Speaker 1: that profitable business? Is it? Something that Um, well, I 395 00:20:12,000 --> 00:20:15,480 Speaker 1: think it is for some of the podcasters, right, made 396 00:20:15,800 --> 00:20:17,560 Speaker 1: a lot of money, and that's what pushed a lot. 397 00:20:17,600 --> 00:20:19,240 Speaker 1: In fact, I think, actually, this is what I was 398 00:20:19,280 --> 00:20:22,520 Speaker 1: thinking of. Obama has a deal with Spotify, but they're 399 00:20:22,640 --> 00:20:25,600 Speaker 1: ending it now because of Neil Young and all these 400 00:20:25,640 --> 00:20:27,960 Speaker 1: people who are mad about Joe Rogan saying like you 401 00:20:28,080 --> 00:20:33,560 Speaker 1: can feel COVID by drinking horsty Warmer. Yeah, yeah, that's 402 00:20:33,600 --> 00:20:34,920 Speaker 1: what I mean. As you said, I think that the 403 00:20:35,000 --> 00:20:38,480 Speaker 1: big winners, um right now, all the all the big, 404 00:20:38,720 --> 00:20:40,600 Speaker 1: the big podcast is actually I think there was a 405 00:20:40,680 --> 00:20:43,080 Speaker 1: story um earlier in the week that she's even some 406 00:20:43,200 --> 00:20:46,400 Speaker 1: of the smaller podcast is generating you know, relatively big 407 00:20:46,440 --> 00:20:51,720 Speaker 1: bucks all advertising. Yep, yep. Interesting. All right, Matt, don't 408 00:20:51,760 --> 00:20:53,720 Speaker 1: we have a podcast Bloomberg Market? We do? We should. 409 00:20:53,920 --> 00:20:56,640 Speaker 1: We should get some some big ad people on there. 410 00:20:57,359 --> 00:20:59,359 Speaker 1: I'll make a few phone calls. Get that going, all right, 411 00:20:59,400 --> 00:21:01,240 Speaker 1: Matt Blocks, and thanks so much for joining us here 412 00:21:01,240 --> 00:21:04,199 Speaker 1: in our Bloomberg Interactive Broker studio. It's in how long 413 00:21:04,200 --> 00:21:07,120 Speaker 1: are you here? I'm here until tomorrow, you think, PARTI, 414 00:21:07,720 --> 00:21:10,320 Speaker 1: And he flies back to London Queen Victoria Street, which 415 00:21:10,359 --> 00:21:14,000 Speaker 1: is our London office which is just so awesome. Um. 416 00:21:14,240 --> 00:21:16,119 Speaker 1: It's just an amazing right in the city of London, 417 00:21:16,200 --> 00:21:19,040 Speaker 1: right by the Bank of England, right smack down there. 418 00:21:19,200 --> 00:21:20,840 Speaker 1: And there's a Greg right down the street, so you 419 00:21:20,880 --> 00:21:23,920 Speaker 1: can always get good sausage roll. Yeah. There you go, 420 00:21:24,320 --> 00:21:26,520 Speaker 1: all right at Blocks and t mt Annals Bloomberg intelligence 421 00:21:26,520 --> 00:21:29,119 Speaker 1: student joining us year. We appreciate getting the latest on 422 00:21:29,200 --> 00:21:33,040 Speaker 1: all things tech media and telecom. Lots to talk about. 423 00:21:33,040 --> 00:21:39,640 Speaker 1: They're always June is Pride month and a month when 424 00:21:39,960 --> 00:21:43,080 Speaker 1: we're focusing on equality issues here at Bloomberg. Today we 425 00:21:43,119 --> 00:21:45,679 Speaker 1: speak with Jape Rice and chief economist at Wells far 426 00:21:45,720 --> 00:21:49,640 Speaker 1: going to talk about the economic influence of the LGBT community. 427 00:21:49,640 --> 00:21:52,600 Speaker 1: And I must note that Jay was educated down the 428 00:21:52,720 --> 00:21:55,760 Speaker 1: road from my Duke University at some school down and 429 00:21:56,160 --> 00:21:58,399 Speaker 1: I think it's Chapel Hill or something. The name escapes me, 430 00:21:58,520 --> 00:22:00,480 Speaker 1: but maybe we'll get to it at some point. Jay, 431 00:22:00,760 --> 00:22:03,160 Speaker 1: Thanks for joining us here talk to us about Wells 432 00:22:03,200 --> 00:22:06,760 Speaker 1: Fargo and how you guys or financial services in general 433 00:22:07,160 --> 00:22:11,159 Speaker 1: kind of thinks about the LGBT community. It's it's pretty big. Um. 434 00:22:11,280 --> 00:22:13,600 Speaker 1: I'm just wondering growing and growing and wonder if they 435 00:22:13,640 --> 00:22:16,440 Speaker 1: get you know, kind of the um, the focus for 436 00:22:16,520 --> 00:22:20,520 Speaker 1: maybe the financial services business. Well, that's that's a good question, Paul. 437 00:22:20,560 --> 00:22:22,479 Speaker 1: And and so, first of all, it is it has 438 00:22:22,560 --> 00:22:25,560 Speaker 1: been and growing. Um. So just put just a level 439 00:22:25,600 --> 00:22:28,520 Speaker 1: set here, um And and yes it's kind of vary, 440 00:22:28,640 --> 00:22:30,600 Speaker 1: but you know, if you look at the Gallop data, 441 00:22:30,800 --> 00:22:34,879 Speaker 1: it would uh be around seven percent or so of 442 00:22:35,000 --> 00:22:37,440 Speaker 1: the population today. Put that in perspective, and that was 443 00:22:37,480 --> 00:22:39,840 Speaker 1: only three and a half percent, you know, about a 444 00:22:39,920 --> 00:22:43,280 Speaker 1: decade ago. So it certainly is growing. And you know, 445 00:22:43,320 --> 00:22:45,320 Speaker 1: whether or not it gets the full attention of the 446 00:22:45,359 --> 00:22:48,000 Speaker 1: financial service community at this point, I think is an 447 00:22:48,080 --> 00:22:51,760 Speaker 1: open question. But this is also a young population and 448 00:22:51,880 --> 00:22:55,560 Speaker 1: as that um and well educated, and as that population 449 00:22:55,640 --> 00:22:59,199 Speaker 1: continues to grow, uh, with the young population, they're going 450 00:22:59,280 --> 00:23:02,160 Speaker 1: to be more financial services and you know, other sorts 451 00:23:02,200 --> 00:23:05,160 Speaker 1: of of goods and services as well. Well. It's also 452 00:23:05,440 --> 00:23:09,679 Speaker 1: an incredibly diverse group. I mean, there are a lot 453 00:23:09,760 --> 00:23:13,240 Speaker 1: of letters there, and each one person in this community 454 00:23:13,280 --> 00:23:16,720 Speaker 1: is an individual human. Is it hard to market to 455 00:23:17,280 --> 00:23:21,200 Speaker 1: or offer financial services to a group like this when 456 00:23:21,240 --> 00:23:26,280 Speaker 1: it's made up of such unique individuals? Yeah, I think 457 00:23:26,359 --> 00:23:29,320 Speaker 1: it is. And and and part of the part of 458 00:23:29,480 --> 00:23:32,600 Speaker 1: the issue I think is so not only is you know, 459 00:23:32,720 --> 00:23:37,959 Speaker 1: it's very diverse in terms of different um sex, sexual orientation, 460 00:23:38,280 --> 00:23:43,040 Speaker 1: but it's also the data isn't all that really um 461 00:23:43,359 --> 00:23:46,160 Speaker 1: good right now, the best day that we have really 462 00:23:46,200 --> 00:23:49,880 Speaker 1: would be on individuals who are lesbian, gay, and bisexual. 463 00:23:50,240 --> 00:23:53,840 Speaker 1: When it comes to you know, trans or queer or 464 00:23:54,000 --> 00:23:57,440 Speaker 1: other sorts of areas, that data isn't as good and 465 00:23:57,880 --> 00:24:00,600 Speaker 1: and probably because what you're looking at is you're looking 466 00:24:00,680 --> 00:24:05,640 Speaker 1: at self identification, and for many reasons, many of these 467 00:24:05,720 --> 00:24:08,720 Speaker 1: individuals don't really want to self identify as that. So 468 00:24:09,200 --> 00:24:13,520 Speaker 1: whereas you may have really good data on say white 469 00:24:13,640 --> 00:24:17,600 Speaker 1: males or or black females or et cetera, UM, the 470 00:24:17,720 --> 00:24:20,760 Speaker 1: data in this community is not quite as as robust 471 00:24:20,880 --> 00:24:25,280 Speaker 1: as it is in other areas. J From I guess 472 00:24:25,280 --> 00:24:28,600 Speaker 1: an economic demographic perspective, what do we know about the 473 00:24:28,800 --> 00:24:33,040 Speaker 1: LGBT population in terms of you know, educations and incomes 474 00:24:33,080 --> 00:24:37,080 Speaker 1: and things like that. Yeah, So so again, what we're 475 00:24:37,080 --> 00:24:39,320 Speaker 1: pot we looked at here and in the data we 476 00:24:39,440 --> 00:24:43,360 Speaker 1: have would be on individuals who identify as lesbian, gay, 477 00:24:43,600 --> 00:24:46,680 Speaker 1: and bisexual. And I would say that that that group 478 00:24:46,760 --> 00:24:51,800 Speaker 1: of people is more educated than the overall population. So well, 479 00:24:52,040 --> 00:24:54,440 Speaker 1: individuals have to be friend to get right, if you 480 00:24:54,720 --> 00:24:57,359 Speaker 1: if you have more income, you know that if you 481 00:24:57,520 --> 00:25:01,600 Speaker 1: feel secure enough in your environment that if you feel 482 00:25:01,680 --> 00:25:06,080 Speaker 1: safe enough to identify as one of these as part 483 00:25:06,119 --> 00:25:08,359 Speaker 1: of the group that's discriminated against like this from the 484 00:25:08,440 --> 00:25:12,160 Speaker 1: get go, you're automatically gonna be likely to have more 485 00:25:12,240 --> 00:25:16,200 Speaker 1: wealth and more education, right, I think I think that's right. Yeah, 486 00:25:16,600 --> 00:25:18,760 Speaker 1: And you know, again it's it's hard to parse all 487 00:25:18,880 --> 00:25:21,760 Speaker 1: that out. But what we do know are you know, 488 00:25:21,960 --> 00:25:25,000 Speaker 1: a lesbian, gay, and bisexual individuals. If you look at 489 00:25:25,040 --> 00:25:27,919 Speaker 1: the people who have a bachelor's degree, that's roughly twenty 490 00:25:28,040 --> 00:25:32,440 Speaker 1: seven of that community um. The overall population that's about 491 00:25:33,920 --> 00:25:36,639 Speaker 1: And then when you look at post bachelor so that 492 00:25:36,680 --> 00:25:40,919 Speaker 1: would be master's, pH d. Professional degree, you're looking at 493 00:25:41,119 --> 00:25:45,600 Speaker 1: more than twenty and that's five percentage points higher than 494 00:25:45,680 --> 00:25:50,600 Speaker 1: the overall population. And consequently, UH, these individuals tend to 495 00:25:50,680 --> 00:25:54,159 Speaker 1: have higher income UM in general than you know, the 496 00:25:54,280 --> 00:26:00,639 Speaker 1: overall population. JA any geographic diversify K should there. I mean, 497 00:26:00,760 --> 00:26:04,040 Speaker 1: I think the expectation or the supposition is that maybe 498 00:26:04,119 --> 00:26:07,640 Speaker 1: more on the coastal UH cities for example in New 499 00:26:07,720 --> 00:26:09,800 Speaker 1: York and San Francisco, might have a higher percentage is 500 00:26:10,000 --> 00:26:13,480 Speaker 1: that what your data shows. Yeah, so we didn't look 501 00:26:13,480 --> 00:26:16,880 Speaker 1: at it in terms of metropolitan statistical areas per set. 502 00:26:17,320 --> 00:26:19,159 Speaker 1: We looked at it in terms of states, and the 503 00:26:19,240 --> 00:26:21,199 Speaker 1: District of Columbia got it. And when you look at 504 00:26:21,240 --> 00:26:25,520 Speaker 1: it there Washington, d C. You know, so the overall 505 00:26:25,600 --> 00:26:30,320 Speaker 1: average again is somewhere seven eight percent outside of Washington. 506 00:26:30,440 --> 00:26:33,200 Speaker 1: You see, it's you know, these individuals are going to 507 00:26:33,240 --> 00:26:36,680 Speaker 1: be concentrated in you know, the West Coast, um, in 508 00:26:36,760 --> 00:26:41,399 Speaker 1: the Upper Midwest, in the Northeast. But that's said every 509 00:26:41,520 --> 00:26:45,840 Speaker 1: state in the in the United States, UM has individuals 510 00:26:45,880 --> 00:26:49,480 Speaker 1: who who are identified with this community. Great stuff, all right, Jay, 511 00:26:49,480 --> 00:26:51,320 Speaker 1: thanks so much for joining us. They're really fascinating and 512 00:26:51,359 --> 00:26:53,840 Speaker 1: that you guys have done a lot of economic research 513 00:26:54,280 --> 00:26:58,000 Speaker 1: on this community. Jay Brice and Managing director and chief 514 00:26:58,080 --> 00:27:01,360 Speaker 1: economist at Wells Fargo. But I think it's a community 515 00:27:01,480 --> 00:27:05,760 Speaker 1: that hasn't really been addressed by the financial industry. Right, 516 00:27:05,840 --> 00:27:08,919 Speaker 1: so there's a lot of money at stake there, UM 517 00:27:09,440 --> 00:27:13,520 Speaker 1: for the bank that can crack this nut first or 518 00:27:14,119 --> 00:27:17,680 Speaker 1: or best right UM. And Jay is a great uh 519 00:27:18,640 --> 00:27:21,280 Speaker 1: resource to have on because not only is he a 520 00:27:21,280 --> 00:27:23,640 Speaker 1: managing director in chief economist at Wells Fargo, but he's 521 00:27:23,680 --> 00:27:27,439 Speaker 1: also been an adjunct professor at a very regionally diverse 522 00:27:27,520 --> 00:27:32,000 Speaker 1: group of University of Alabama, at North Carolina, at Johns Hopkins, 523 00:27:32,040 --> 00:27:36,320 Speaker 1: and Georgetown. So great to get everything from Jay. Thanks 524 00:27:36,359 --> 00:27:39,760 Speaker 1: for listening to the Bloomberg Markets podcast. You can subscribe 525 00:27:39,840 --> 00:27:43,520 Speaker 1: and listen to interviews with Apple Podcasts or whatever podcast 526 00:27:43,600 --> 00:27:47,119 Speaker 1: platform you prefer. I'm Matt Miller. I'm on Twitter at 527 00:27:47,200 --> 00:27:50,760 Speaker 1: Matt Miller three pt on false Sweeney I'm on Twitter 528 00:27:50,880 --> 00:27:53,680 Speaker 1: at pt Sweeney Before the podcast. You can always catch 529 00:27:53,760 --> 00:27:55,320 Speaker 1: us worldwide at Bloomberg Radio