1 00:00:14,000 --> 00:00:17,240 Speaker 1: Hello, and welcome to What Goes Up, a weekly markets podcast. 2 00:00:17,480 --> 00:00:20,160 Speaker 1: My name is Mike Reagan. I'm a senior editor at Bloomberg, 3 00:00:20,560 --> 00:00:24,000 Speaker 1: and I'm gonna higher across Acid reporter with Bloomberg and 4 00:00:24,160 --> 00:00:27,080 Speaker 1: this week on the show. Well, if you've been anywhere 5 00:00:27,120 --> 00:00:29,640 Speaker 1: near the Internet in the last few months, you've probably 6 00:00:29,640 --> 00:00:32,280 Speaker 1: read a poem, or bits of a movie script, or 7 00:00:32,840 --> 00:00:35,800 Speaker 1: maybe even some dad jokes that were written by a 8 00:00:35,800 --> 00:00:40,040 Speaker 1: while a computer. Actually, the Experimental Chat Bought Chat GPT 9 00:00:40,200 --> 00:00:43,160 Speaker 1: has taken the world by storm since its launched in November, 10 00:00:43,640 --> 00:00:46,800 Speaker 1: sugaring a million questions about how this type of technology 11 00:00:46,880 --> 00:00:50,800 Speaker 1: can disrupt various industries and fueling a fresh wave of 12 00:00:50,840 --> 00:00:54,680 Speaker 1: interest in how artificial intelligence can be used by investors. 13 00:00:55,400 --> 00:00:57,480 Speaker 1: We're gonna get into it with the head of research 14 00:00:57,520 --> 00:00:59,560 Speaker 1: at a company that's been using AI for a few 15 00:00:59,640 --> 00:01:02,720 Speaker 1: years now to pick stocks for an almost two billion 16 00:01:02,720 --> 00:01:07,160 Speaker 1: dollar ETF. But uh, first, l donna I try to 17 00:01:07,200 --> 00:01:10,680 Speaker 1: go to chat GPT. I've I've, like everyone else, I've 18 00:01:10,680 --> 00:01:12,959 Speaker 1: fallen into the hype of this chat GPT, and I 19 00:01:13,000 --> 00:01:15,000 Speaker 1: went on trying to get it to write us an 20 00:01:15,000 --> 00:01:17,840 Speaker 1: intro to the podcast, but it's too busy. There are 21 00:01:17,880 --> 00:01:21,000 Speaker 1: too many people using it that they just turn me down. 22 00:01:22,480 --> 00:01:24,920 Speaker 1: I tried for you also, so that we can get 23 00:01:24,959 --> 00:01:28,440 Speaker 1: a nice fun intro from from the robot. And it 24 00:01:28,520 --> 00:01:30,800 Speaker 1: didn't work. And I even try to trick it. I said, 25 00:01:30,920 --> 00:01:33,839 Speaker 1: I have a very simple request and I'm on deadline. 26 00:01:33,880 --> 00:01:39,440 Speaker 1: Please can you help me out? No? Luck, No, robot 27 00:01:39,480 --> 00:01:41,760 Speaker 1: doesn't like me. I wonder the robot must have a 28 00:01:41,800 --> 00:01:46,280 Speaker 1: pr REP that maybe we could to complain. Yeah, it's 29 00:01:46,520 --> 00:01:51,280 Speaker 1: probably a robot pr rep. I don't know. But on 30 00:01:51,400 --> 00:01:55,160 Speaker 1: a pr rep who that is? Also? Yeah? Yeah, they'll 31 00:01:55,200 --> 00:01:59,400 Speaker 1: just tell me to go to hell. So is that 32 00:01:59,640 --> 00:02:04,080 Speaker 1: most of I'm sorry you some of them? Oh my gosh. 33 00:02:04,960 --> 00:02:07,040 Speaker 1: All right, well you taught me which one. I'll get 34 00:02:07,040 --> 00:02:10,560 Speaker 1: back to them. But I do think our guest is 35 00:02:10,560 --> 00:02:14,640 Speaker 1: the perfect guest to unpack this, uh this topic. So 36 00:02:14,680 --> 00:02:16,600 Speaker 1: why don't you bring him in? Yeah he is. It's 37 00:02:16,680 --> 00:02:19,640 Speaker 1: Matt Bartolini. He's the head of Spider America's research at 38 00:02:19,680 --> 00:02:23,040 Speaker 1: Stage Street Global Advisors. And Matt, thanks so much for 39 00:02:23,120 --> 00:02:26,120 Speaker 1: joining us. Yeah, thank you for having me. So we'll 40 00:02:26,160 --> 00:02:28,440 Speaker 1: get into one of your AI E t F s 41 00:02:28,440 --> 00:02:30,200 Speaker 1: in a bit, but maybe just to start, you can 42 00:02:30,200 --> 00:02:33,240 Speaker 1: give us sort of your journey into working with E 43 00:02:33,320 --> 00:02:36,200 Speaker 1: t F and what you do. Yeah. Sure, So my 44 00:02:36,280 --> 00:02:39,799 Speaker 1: journey in TTS is working at State Street Bank essentially 45 00:02:39,880 --> 00:02:42,280 Speaker 1: since the middle of two thousand's and work my way 46 00:02:42,360 --> 00:02:46,200 Speaker 1: up throughout the organization on the County team's portfolio management 47 00:02:46,200 --> 00:02:48,840 Speaker 1: teams and then landed within the E t F teams 48 00:02:49,320 --> 00:02:51,880 Speaker 1: helping to conduct some of the research on our products, 49 00:02:52,480 --> 00:02:57,040 Speaker 1: different portfolio construction topics, investment theses, market outlooks, and market commentaries. 50 00:02:57,720 --> 00:02:59,760 Speaker 1: Uh and that's really where my job is now at 51 00:02:59,760 --> 00:03:02,720 Speaker 1: the of Spider America's research. You know, our job is 52 00:03:02,760 --> 00:03:05,560 Speaker 1: to help makes sense of a complex world by using 53 00:03:05,680 --> 00:03:09,280 Speaker 1: data driven insights, and we write market commentaries, market outlooks, 54 00:03:09,919 --> 00:03:14,480 Speaker 1: provide some portfolio instruction discussions to end advisors and hopefully 55 00:03:14,480 --> 00:03:17,639 Speaker 1: help them, you know, select the right investment choice for them. 56 00:03:17,639 --> 00:03:20,120 Speaker 1: And if that's a Spider et F then I think 57 00:03:20,160 --> 00:03:22,519 Speaker 1: that's all great, But in some cases it doesn't. And 58 00:03:22,600 --> 00:03:25,880 Speaker 1: that's how we operate, trying to be fair and balanced. Yea, 59 00:03:26,480 --> 00:03:29,440 Speaker 1: and uh Ai is obviously one of your areas of research. 60 00:03:29,520 --> 00:03:34,160 Speaker 1: Match I'm curious, you know, this chat CHPT thing to me, 61 00:03:34,240 --> 00:03:35,560 Speaker 1: and I think to a lot of people, just can't 62 00:03:35,600 --> 00:03:37,080 Speaker 1: it seem to have come out of nowhere. You know, 63 00:03:37,120 --> 00:03:40,839 Speaker 1: it launched in November, and you know, granted I don't 64 00:03:40,840 --> 00:03:42,960 Speaker 1: follow the space that closely, but I think for a 65 00:03:43,000 --> 00:03:47,000 Speaker 1: lot of people it was just sort of dumbfounding how 66 00:03:47,040 --> 00:03:49,080 Speaker 1: good this thing is right at launched, you know it 67 00:03:49,120 --> 00:03:51,360 Speaker 1: to me, I would have expected sort of a to 68 00:03:51,360 --> 00:03:54,080 Speaker 1: see a cruder version of this that wasn't quite as impressive. 69 00:03:54,120 --> 00:03:57,000 Speaker 1: But how did you see it like it was? Were 70 00:03:57,040 --> 00:03:59,840 Speaker 1: you sort of as surprised as everyone else for the 71 00:04:00,080 --> 00:04:03,040 Speaker 1: you're sort of research into AI? Had it led you 72 00:04:03,080 --> 00:04:06,000 Speaker 1: to kind of know this type of thing was possible 73 00:04:06,160 --> 00:04:09,240 Speaker 1: and in the pipeline? Yeah, So a lot of the 74 00:04:09,320 --> 00:04:12,800 Speaker 1: AI work we've done is is within sort of portfolio 75 00:04:12,920 --> 00:04:16,120 Speaker 1: construction and index selection on some of our funds, so 76 00:04:16,160 --> 00:04:18,400 Speaker 1: we were aware of the ability to use things like 77 00:04:18,480 --> 00:04:22,000 Speaker 1: natural language processing, predictive text, but also even just in 78 00:04:22,040 --> 00:04:24,320 Speaker 1: our daily lives. I think some of the functions of 79 00:04:24,320 --> 00:04:27,719 Speaker 1: Czech GP two we've probably just been benefiting from just 80 00:04:27,800 --> 00:04:31,440 Speaker 1: in very small morsels, whether that is, you know, auto 81 00:04:31,720 --> 00:04:35,479 Speaker 1: correcting your text, or the predictive text nature within your 82 00:04:35,480 --> 00:04:37,719 Speaker 1: iPhone of what you might say next, like that's sort 83 00:04:37,760 --> 00:04:40,719 Speaker 1: of the same idea, or even you know, when when 84 00:04:40,760 --> 00:04:43,360 Speaker 1: we use the Bloomberg terminal and we asked the help desk, 85 00:04:43,400 --> 00:04:45,960 Speaker 1: we sometimes get a very automated response back that's all 86 00:04:46,000 --> 00:04:48,400 Speaker 1: sort of pieces of it. Uh. The first time I 87 00:04:48,440 --> 00:04:50,520 Speaker 1: saw it, you know, we were sort of playing around 88 00:04:50,520 --> 00:04:52,680 Speaker 1: with it of you know, write us a blog post 89 00:04:52,880 --> 00:04:57,279 Speaker 1: about the benefits of ETFs and it got it probably 90 00:04:57,320 --> 00:04:59,800 Speaker 1: eighty per correct, you know, how we would want to 91 00:05:00,000 --> 00:05:01,839 Speaker 1: structure of the argument. And I think that's sort of 92 00:05:01,839 --> 00:05:04,800 Speaker 1: where chat chypt is is that it kind of gives 93 00:05:04,800 --> 00:05:07,680 Speaker 1: you about an eight. And now sort of joking with 94 00:05:07,880 --> 00:05:09,880 Speaker 1: you know, some of my colleagues who have old their 95 00:05:09,960 --> 00:05:13,000 Speaker 1: kids that you know, chet chypt would probably be be 96 00:05:13,040 --> 00:05:16,159 Speaker 1: a B minus student if it only ever turned in 97 00:05:16,240 --> 00:05:19,680 Speaker 1: its homework, because that's kind of the surface level it gets. 98 00:05:20,040 --> 00:05:22,320 Speaker 1: And I have a friend who's a professor at a 99 00:05:22,400 --> 00:05:25,080 Speaker 1: college and they've actually started to work on how to 100 00:05:25,160 --> 00:05:28,960 Speaker 1: figure out you know, essays and reports that are written 101 00:05:29,440 --> 00:05:32,440 Speaker 1: through AI. And the big thing is you've got to 102 00:05:32,480 --> 00:05:35,520 Speaker 1: look at the nuance. And chat gypt doesn't really understand 103 00:05:35,600 --> 00:05:39,480 Speaker 1: the complexity of nuances, particularly for topics like ets where 104 00:05:39,480 --> 00:05:42,560 Speaker 1: there actually is a lot of operational nuance. You know, 105 00:05:43,120 --> 00:05:45,520 Speaker 1: as a B bonus student, myself. That explains why I 106 00:05:45,560 --> 00:05:48,320 Speaker 1: was so impressed. I think if you are a student 107 00:05:48,400 --> 00:05:50,200 Speaker 1: right now, you could use it to help boost your 108 00:05:50,200 --> 00:05:52,839 Speaker 1: grades a little bit. Yeah, well that's I think the 109 00:05:52,880 --> 00:05:55,080 Speaker 1: fear of everyone has. I have a nine year old 110 00:05:55,080 --> 00:05:57,520 Speaker 1: son who had to do a penguin project and he, 111 00:05:57,760 --> 00:06:01,200 Speaker 1: instead of looking in a book, he yelled out to Alexa, 112 00:06:01,480 --> 00:06:04,359 Speaker 1: you know how how fast you to penguin swim? And 113 00:06:04,360 --> 00:06:06,800 Speaker 1: I had to tell them they can't do that. So 114 00:06:08,120 --> 00:06:11,080 Speaker 1: the new reality that we're all living in, ask can 115 00:06:11,080 --> 00:06:16,159 Speaker 1: penguins swim? I don't know what the the ask Alexa, No, 116 00:06:16,279 --> 00:06:19,400 Speaker 1: don't ask Alexa. Well, no, my kids it's the same thing. 117 00:06:19,440 --> 00:06:22,320 Speaker 1: They're sitting there doing their homework and I hear them, y'all, hey, Google, 118 00:06:22,360 --> 00:06:25,440 Speaker 1: what's you know? Nine times thirty seven? And I mean, 119 00:06:25,480 --> 00:06:27,640 Speaker 1: in some ways, it's just a calculator, and I think 120 00:06:27,880 --> 00:06:30,520 Speaker 1: educators are gonna have to get used to it and 121 00:06:30,880 --> 00:06:32,960 Speaker 1: allow it in some to some degree, I don't know, 122 00:06:33,000 --> 00:06:35,480 Speaker 1: it's it's such a strange new world. But but Matt 123 00:06:35,520 --> 00:06:39,440 Speaker 1: talked to us about the spider Ken show New Economy ZTF, 124 00:06:39,480 --> 00:06:42,960 Speaker 1: which actually has been using AI to to pick stocks, 125 00:06:43,000 --> 00:06:45,880 Speaker 1: you know, for for sort of the Layman among us 126 00:06:45,920 --> 00:06:50,039 Speaker 1: South there. How how exactly does AI help in this, uh, 127 00:06:50,160 --> 00:06:54,400 Speaker 1: this stock picking effort. Yeah, So the artificial intelligence behind 128 00:06:54,440 --> 00:06:57,240 Speaker 1: it is natural language processing and this is run by 129 00:06:57,320 --> 00:07:00,040 Speaker 1: the index provider S and P. To actually start it 130 00:07:00,120 --> 00:07:02,480 Speaker 1: over the firm Ken Show there was a small startup 131 00:07:02,520 --> 00:07:06,320 Speaker 1: that was incubated out of Goldman Sachs. UH SMP bought 132 00:07:06,360 --> 00:07:08,760 Speaker 1: that firm and all of the I P along with it, 133 00:07:08,800 --> 00:07:12,520 Speaker 1: and that's our index provider for the fund. And you 134 00:07:12,560 --> 00:07:16,400 Speaker 1: know the NLP or natural natural Language process and what 135 00:07:16,520 --> 00:07:21,320 Speaker 1: it does. Scans through UH perspectives and other regulatory filings 136 00:07:21,320 --> 00:07:24,320 Speaker 1: from companies because you want to start with a strong source. 137 00:07:24,400 --> 00:07:28,040 Speaker 1: Regulatory filings have to be quite prescriptive and if you, 138 00:07:28,040 --> 00:07:31,880 Speaker 1: you know, make falsehoods about that, uh, there's penalties, right. UM. 139 00:07:32,080 --> 00:07:36,120 Speaker 1: So the scans through UH regulatory documents searching for key 140 00:07:36,280 --> 00:07:40,760 Speaker 1: terms to identify how these firms material operations correlate back 141 00:07:40,920 --> 00:07:45,480 Speaker 1: to areas of innovation, whether it's like enterprise collaboration, clean energy, 142 00:07:46,040 --> 00:07:51,000 Speaker 1: advanced transport systems, drones. So scan through all these UM 143 00:07:51,680 --> 00:07:55,400 Speaker 1: regulatory documents looking for the frequency of a term used, 144 00:07:55,440 --> 00:07:58,800 Speaker 1: but also the words around it, so you know if 145 00:07:58,840 --> 00:08:01,720 Speaker 1: a company is saying that drow own technology is incredibly 146 00:08:01,760 --> 00:08:04,440 Speaker 1: important for the future of growth of our business. That 147 00:08:04,520 --> 00:08:08,160 Speaker 1: really shows some emphasis towards that type of innovation. So 148 00:08:08,240 --> 00:08:12,160 Speaker 1: that would be scanned and recorded and classified appropriately into 149 00:08:12,280 --> 00:08:16,920 Speaker 1: twenty five different areas of innovation, and then from their 150 00:08:17,080 --> 00:08:20,080 Speaker 1: stocks are weighted and more of a modified equal weighted 151 00:08:20,360 --> 00:08:25,640 Speaker 1: structure where core firms to a specific innovation are overweighted 152 00:08:25,640 --> 00:08:28,640 Speaker 1: to non core firms. So basically, the you know, the 153 00:08:28,680 --> 00:08:31,600 Speaker 1: way we sort of describe it is that the AI 154 00:08:31,640 --> 00:08:34,960 Speaker 1: process selects the stocks and then there's a quantitative weighting 155 00:08:35,000 --> 00:08:38,679 Speaker 1: methodology to weight the stocks. But the reason why we 156 00:08:38,720 --> 00:08:41,160 Speaker 1: went down this path of using AI s that we 157 00:08:41,280 --> 00:08:45,360 Speaker 1: wanted something forward looking, something dynamic, because you know, back 158 00:08:45,360 --> 00:08:48,000 Speaker 1: in two thousand and eighteen, we understood that in the 159 00:08:48,040 --> 00:08:50,360 Speaker 1: E t F world, there weren't a lot of strategies 160 00:08:50,360 --> 00:08:55,320 Speaker 1: that were this forward looking innovative type paradigm. A lot 161 00:08:55,360 --> 00:08:58,080 Speaker 1: of it was based on revenue, and revenue was what 162 00:08:58,160 --> 00:09:01,720 Speaker 1: has already been realized that a backward looking approach, and 163 00:09:01,720 --> 00:09:03,960 Speaker 1: we wanted something that was more dynamic and a ford 164 00:09:03,960 --> 00:09:07,040 Speaker 1: looking approach in the AI process was able to deliver 165 00:09:07,120 --> 00:09:09,520 Speaker 1: that for us. Okay, so before you tell us more 166 00:09:09,559 --> 00:09:12,040 Speaker 1: about that I am interested in sort of the mechanics. 167 00:09:12,080 --> 00:09:16,559 Speaker 1: So once the AI runs through and chooses these companies 168 00:09:16,640 --> 00:09:19,640 Speaker 1: that it fits, that it thinks fits thinks I don't 169 00:09:19,640 --> 00:09:21,040 Speaker 1: know I thinks is the right word, but that it 170 00:09:21,120 --> 00:09:24,880 Speaker 1: chooses as fitting the right criteria, do you then have 171 00:09:24,960 --> 00:09:29,439 Speaker 1: a human go through the results and say, okay, this 172 00:09:29,640 --> 00:09:32,200 Speaker 1: actually sounds pretty good, or maybe we don't want to 173 00:09:32,280 --> 00:09:35,079 Speaker 1: have X y Z company as part of this portfolio. 174 00:09:35,840 --> 00:09:38,640 Speaker 1: So within the index methodology there is sort of a 175 00:09:38,720 --> 00:09:41,960 Speaker 1: human control element to it, most like a quality control. 176 00:09:42,040 --> 00:09:45,599 Speaker 1: So for instance, uh, if you know a company is 177 00:09:45,640 --> 00:09:50,600 Speaker 1: classified as innovating within Clean Energy UM, they use the 178 00:09:50,760 --> 00:09:54,000 Speaker 1: term your wind and solar are quite significantly that it 179 00:09:54,120 --> 00:09:57,200 Speaker 1: said it's part of the material operations. But when it 180 00:09:57,240 --> 00:10:00,480 Speaker 1: comes down to it, there's a check and balance from 181 00:10:00,480 --> 00:10:03,080 Speaker 1: the index committee to say, okay, well, does firm x 182 00:10:03,200 --> 00:10:06,319 Speaker 1: y Z offer a product and service in this category 183 00:10:06,920 --> 00:10:09,520 Speaker 1: or they just some sort of you know, this is 184 00:10:09,559 --> 00:10:11,480 Speaker 1: probably a bad term, but some sort of shell company 185 00:10:11,480 --> 00:10:15,080 Speaker 1: that doesn't actually provide a product or service. They just yeah, 186 00:10:15,080 --> 00:10:17,200 Speaker 1: this isn't what they do. They just say saying something 187 00:10:17,200 --> 00:10:19,840 Speaker 1: that doesn't correlate back to their actual products and services. 188 00:10:19,880 --> 00:10:21,680 Speaker 1: So that's where there's a little bit of a manual 189 00:10:21,800 --> 00:10:27,160 Speaker 1: quality check to ensure that these firms are actually engaged 190 00:10:27,600 --> 00:10:29,920 Speaker 1: in these areas of innovation and they are not just 191 00:10:30,120 --> 00:10:34,439 Speaker 1: talking about it sort of you know, extemporaneously. And the 192 00:10:34,760 --> 00:10:38,200 Speaker 1: other thing is too that helps in terms of you know, 193 00:10:38,559 --> 00:10:41,360 Speaker 1: get let's say perfect you know, we have a champagne 194 00:10:41,400 --> 00:10:44,360 Speaker 1: problem that this fund becomes a hundred and ninety billion 195 00:10:44,400 --> 00:10:46,760 Speaker 1: dollars and someone wants to get into it and they 196 00:10:46,800 --> 00:10:49,400 Speaker 1: just use the word drone a thousand times to game it. 197 00:10:49,760 --> 00:10:52,679 Speaker 1: That helps, right, that sort of oat manual overrides sort 198 00:10:52,679 --> 00:10:55,640 Speaker 1: of quality check. What I find fascinating about it something 199 00:10:55,679 --> 00:10:58,080 Speaker 1: like five sixty holdings, you know, so it's not not 200 00:10:58,160 --> 00:11:02,520 Speaker 1: a very concentrated fun and you know, when you're looking 201 00:11:02,520 --> 00:11:05,000 Speaker 1: for innovative sort of startup type of companies, a lot 202 00:11:05,000 --> 00:11:10,560 Speaker 1: of times that means really small even maybe microcap companies, uh, 203 00:11:10,679 --> 00:11:13,120 Speaker 1: that you have to dig through, which are not typically 204 00:11:14,559 --> 00:11:17,480 Speaker 1: very heavily followed by you know, the Wall Street analysts 205 00:11:17,520 --> 00:11:21,400 Speaker 1: class just by definition, you know, if there's thousands of them, um, 206 00:11:21,520 --> 00:11:25,000 Speaker 1: and this really surprised me. Uh. And you know what 207 00:11:25,080 --> 00:11:27,840 Speaker 1: you say about forty eight percent of the holdings have 208 00:11:28,000 --> 00:11:31,920 Speaker 1: less than ten analysts covering the stock. Is that almost 209 00:11:32,000 --> 00:11:35,080 Speaker 1: a benefit for this type of strategy that it helps 210 00:11:35,080 --> 00:11:39,200 Speaker 1: you sort of find these hidden gems that are maybe 211 00:11:39,640 --> 00:11:43,960 Speaker 1: being completely overlooked by by the masses out there. Yeah, 212 00:11:44,000 --> 00:11:47,080 Speaker 1: I mean, AI at its heart is to help increase 213 00:11:47,120 --> 00:11:51,440 Speaker 1: efficient efficiencies and productivity. And what this does is allows 214 00:11:51,520 --> 00:11:54,199 Speaker 1: us to cover the uncovered. So if you're using an 215 00:11:54,200 --> 00:11:57,880 Speaker 1: analyst recommendations, analysts can only cover so many stocks within 216 00:11:57,920 --> 00:12:01,120 Speaker 1: a given day. And there's candies some firms that are 217 00:12:01,200 --> 00:12:05,160 Speaker 1: quite innovative, they're you know, performing and producing some really 218 00:12:05,800 --> 00:12:09,200 Speaker 1: interesting things within our economy. You know, whether it's things 219 00:12:09,200 --> 00:12:13,360 Speaker 1: within advanced healthcare like wearables that aren't really covered by 220 00:12:13,679 --> 00:12:16,960 Speaker 1: Wall Street analysts because they might be smaller capitalization securities. 221 00:12:16,960 --> 00:12:18,760 Speaker 1: And we sort of just know this even from like 222 00:12:18,800 --> 00:12:22,880 Speaker 1: traditional finance, that the majority of analysts recommendations are in 223 00:12:22,920 --> 00:12:26,640 Speaker 1: that large caps space um and then small caps and 224 00:12:26,640 --> 00:12:29,360 Speaker 1: maycaps sort of do you not get as much notoriety 225 00:12:29,440 --> 00:12:33,120 Speaker 1: or coverage. And AI is basically is one way to 226 00:12:33,160 --> 00:12:37,000 Speaker 1: solve that problem, to give you a deeper breath of 227 00:12:37,040 --> 00:12:41,560 Speaker 1: opportunities and really broaden your scope of companies that may 228 00:12:41,600 --> 00:12:51,240 Speaker 1: be considered innovative. So I want to give a shout 229 00:12:51,280 --> 00:12:54,600 Speaker 1: out to Katie Greifield and Sam Potter on the Cross 230 00:12:54,640 --> 00:12:57,400 Speaker 1: has a team at Bloomberg, because they had this really 231 00:12:57,440 --> 00:13:01,040 Speaker 1: fascinating story that said something like, we asked chat GPT 232 00:13:01,320 --> 00:13:04,319 Speaker 1: to create an e t F for us, and here's 233 00:13:04,320 --> 00:13:06,760 Speaker 1: the results, and and actually it had done a really 234 00:13:06,800 --> 00:13:10,640 Speaker 1: good job putting something together. And you were part of 235 00:13:10,679 --> 00:13:13,360 Speaker 1: this story. And Katie and I were chatting about it afterwards, 236 00:13:13,400 --> 00:13:17,160 Speaker 1: and she said, Matt had all these insights into the 237 00:13:17,160 --> 00:13:21,199 Speaker 1: composition aspect of because you guys have your own AI 238 00:13:21,280 --> 00:13:23,800 Speaker 1: E t F. And I do wonder about that, like, 239 00:13:24,000 --> 00:13:27,560 Speaker 1: is the power of the AI being able to create 240 00:13:27,600 --> 00:13:29,640 Speaker 1: an E t F? Is the power? Does it lie 241 00:13:29,720 --> 00:13:32,079 Speaker 1: in the sheer amount of work that it can do, 242 00:13:32,480 --> 00:13:35,160 Speaker 1: whereas you might not be able to have like a 243 00:13:35,200 --> 00:13:39,240 Speaker 1: team of humans combing through so many different things to 244 00:13:39,280 --> 00:13:41,840 Speaker 1: the point where they get to an e t F 245 00:13:41,920 --> 00:13:46,040 Speaker 1: that has five and sixty components. Yeah, it's it's all 246 00:13:46,080 --> 00:13:50,200 Speaker 1: about creating efficiencies and being able to capture, you know, 247 00:13:51,120 --> 00:13:55,480 Speaker 1: undiscovered or unrepresented areas within the equity markets. You know, 248 00:13:55,520 --> 00:13:59,800 Speaker 1: even just looking in core portfolios, disruption happens further down 249 00:13:59,800 --> 00:14:03,680 Speaker 1: that cap spectrum. And that's why using something that is 250 00:14:03,720 --> 00:14:08,480 Speaker 1: able to explore data sets that are really unstructured because 251 00:14:08,520 --> 00:14:12,839 Speaker 1: revenue profiles balances those more structured data sets. But using 252 00:14:13,360 --> 00:14:17,880 Speaker 1: textual language processing to identify firms based on what their 253 00:14:17,920 --> 00:14:21,680 Speaker 1: material operations are saying is one way to help classify 254 00:14:21,840 --> 00:14:24,840 Speaker 1: them into these areas of innovation. And I think one 255 00:14:24,840 --> 00:14:27,600 Speaker 1: of the things about this fund in particular is that 256 00:14:28,240 --> 00:14:31,560 Speaker 1: we do understand that it is not innovation does not 257 00:14:31,720 --> 00:14:36,480 Speaker 1: just benefit the pure place, is that the ecosystem around 258 00:14:36,520 --> 00:14:39,240 Speaker 1: it can benefit. You know, the whole idea during the 259 00:14:39,640 --> 00:14:42,040 Speaker 1: gold rush of the eighteen hundreds of it would rather 260 00:14:42,120 --> 00:14:44,480 Speaker 1: mind for gold or sell the pick axes and the 261 00:14:44,560 --> 00:14:46,920 Speaker 1: tents to go along with it. You probably had a 262 00:14:46,920 --> 00:14:48,920 Speaker 1: pretty good business model if you're selling a lot of 263 00:14:48,920 --> 00:14:52,480 Speaker 1: pick axes in the eighteen hundreds. And that's sort of 264 00:14:52,480 --> 00:14:55,480 Speaker 1: the idea here is, you know, the ecosystem is also beneficial, 265 00:14:55,840 --> 00:14:59,080 Speaker 1: and how do you identify that ecosystem? Uh? You know, 266 00:14:59,120 --> 00:15:02,080 Speaker 1: affirm like Video for example, they make all of the 267 00:15:02,120 --> 00:15:06,240 Speaker 1: sensory technology with an autonomous vehicles. That's a supplier to 268 00:15:06,480 --> 00:15:10,320 Speaker 1: that ecosystem. And as autonomous vehicles take off, they're going 269 00:15:10,360 --> 00:15:14,360 Speaker 1: to benefit as well, so using AI to to detect 270 00:15:14,440 --> 00:15:20,080 Speaker 1: that can really help create a really targeted, but diversified 271 00:15:20,080 --> 00:15:23,680 Speaker 1: portfolio of innovative stocks. And basically this e t F 272 00:15:23,880 --> 00:15:27,520 Speaker 1: has many more components than it would if a team 273 00:15:27,560 --> 00:15:30,840 Speaker 1: of humans was putting it together. Right, yeah, so the 274 00:15:31,160 --> 00:15:35,320 Speaker 1: you know the statistic, they're over ten analysts. So let's 275 00:15:35,320 --> 00:15:37,320 Speaker 1: just say we use that as an example, like we 276 00:15:37,400 --> 00:15:40,680 Speaker 1: needsily at least be covered by ten ten analysts will 277 00:15:40,880 --> 00:15:43,440 Speaker 1: right then and there we lose half the portfolio and 278 00:15:43,560 --> 00:15:47,560 Speaker 1: tends not a big number. So if we were to 279 00:15:47,880 --> 00:15:50,120 Speaker 1: take more of a human based approach to it, it 280 00:15:50,160 --> 00:15:54,880 Speaker 1: would be far more concentrated with portfolio. And that's what 281 00:15:54,920 --> 00:15:57,240 Speaker 1: we see with the other broad innovation e t f 282 00:15:57,320 --> 00:16:00,200 Speaker 1: s out there, is that they're far more concentrated and 283 00:16:00,240 --> 00:16:04,000 Speaker 1: they're also far more geared towards large cap security. So 284 00:16:04,040 --> 00:16:08,240 Speaker 1: you do not get the differentiation that you would want 285 00:16:09,080 --> 00:16:12,560 Speaker 1: in something that is supposed to be innovative and you know, 286 00:16:12,640 --> 00:16:17,640 Speaker 1: not largely represented within core portfolio. Is right when this 287 00:16:17,760 --> 00:16:20,200 Speaker 1: fund was launched, I guess it was three or four 288 00:16:20,280 --> 00:16:24,840 Speaker 1: years ago. You know, growth stocks, innovative disruptive stocks were 289 00:16:25,280 --> 00:16:27,880 Speaker 1: you know, the hottest things going in the market, and 290 00:16:27,920 --> 00:16:30,160 Speaker 1: the funded great, you know, a few thousand and nineteen 291 00:16:30,200 --> 00:16:33,600 Speaker 1: up thirty seven, two thousand twenty up sixty one percent, 292 00:16:34,000 --> 00:16:37,760 Speaker 1: up about four twenty one. Then obviously last year was 293 00:16:38,040 --> 00:16:40,960 Speaker 1: kind of the rug pole out from under growth and 294 00:16:41,000 --> 00:16:44,720 Speaker 1: innovation down. So I'm wondering, you know, is there a 295 00:16:44,720 --> 00:16:48,240 Speaker 1: way to layer AI on top of a fund like 296 00:16:48,320 --> 00:16:51,800 Speaker 1: this to allow to sort of shift to a value 297 00:16:51,800 --> 00:16:56,440 Speaker 1: strategy or to kind of sniff out the market cycle 298 00:16:56,560 --> 00:16:59,640 Speaker 1: into what's kind of the the new hot factor to 299 00:16:59,720 --> 00:17:02,080 Speaker 1: get to UM. I know that's not the goal of 300 00:17:02,080 --> 00:17:03,720 Speaker 1: this fund, but I wonder if you think about that, 301 00:17:03,760 --> 00:17:05,439 Speaker 1: you know, is there is there a way to not 302 00:17:05,480 --> 00:17:08,920 Speaker 1: only pick the the individual stocks under a certain theme 303 00:17:09,000 --> 00:17:13,040 Speaker 1: or strategy like this, but so also have that strategy 304 00:17:13,280 --> 00:17:17,000 Speaker 1: sort of evolve over time and try to you know, 305 00:17:17,160 --> 00:17:20,040 Speaker 1: isolate the upcoming market cycle and what's gonna what's the 306 00:17:20,119 --> 00:17:23,280 Speaker 1: leadership is gonna be? Uh in case growth does have 307 00:17:23,480 --> 00:17:27,200 Speaker 1: a down draft like this, So I mean, that's when 308 00:17:27,240 --> 00:17:30,920 Speaker 1: that becomes just market timing, so to speak. Right, and 309 00:17:30,960 --> 00:17:34,600 Speaker 1: you're now you're now doing some former factor rotation. You know, 310 00:17:34,640 --> 00:17:38,720 Speaker 1: I think you could but perhaps create more of a 311 00:17:38,800 --> 00:17:44,080 Speaker 1: style style neutral innovative portfolio, but that becomes much harder 312 00:17:44,560 --> 00:17:48,840 Speaker 1: because then you're going to have in an optimization framework, 313 00:17:49,800 --> 00:17:52,359 Speaker 1: the optimizer is gonna be working really hard to mitigate 314 00:17:52,440 --> 00:17:55,680 Speaker 1: any of that small cap bias, so and then you're 315 00:17:55,720 --> 00:17:58,680 Speaker 1: just gonna basically look like you know, a large cap 316 00:17:58,720 --> 00:18:02,600 Speaker 1: growth tech exposure. So then it's always this trade off 317 00:18:02,640 --> 00:18:05,399 Speaker 1: of like, do I want to mitigate some of these 318 00:18:05,440 --> 00:18:10,639 Speaker 1: implicit style factors and get you know, sort of close 319 00:18:10,760 --> 00:18:16,320 Speaker 1: up that tracking risk to traditional benchmarks, or maintain the 320 00:18:16,359 --> 00:18:19,440 Speaker 1: purity of what we're trying to do of innovative exposures. 321 00:18:19,440 --> 00:18:21,320 Speaker 1: So you always try to find that balance. And if 322 00:18:21,320 --> 00:18:25,439 Speaker 1: you try to create more style neutral or or something 323 00:18:25,440 --> 00:18:30,360 Speaker 1: that is, you know, less impacted by market cyclical factors, 324 00:18:31,160 --> 00:18:32,800 Speaker 1: then you're gonna lose some of the purity of your 325 00:18:32,840 --> 00:18:35,879 Speaker 1: intended focus. And I think when we are having discussion 326 00:18:36,000 --> 00:18:39,639 Speaker 1: ground performance, we always just go back to attribution and 327 00:18:39,680 --> 00:18:43,840 Speaker 1: we will use you know, UH fundamental risk models. And 328 00:18:43,880 --> 00:18:47,919 Speaker 1: if we look at it, since inception, industry and stock 329 00:18:47,960 --> 00:18:51,439 Speaker 1: selection effects relative to UH, you know, the like the 330 00:18:51,520 --> 00:18:55,439 Speaker 1: SMP fIF for example, industry and stock selection effects have 331 00:18:55,520 --> 00:18:59,800 Speaker 1: been positive to UH. The funds return has been a 332 00:19:00,040 --> 00:19:03,800 Speaker 1: dative to performance. The industry party is interesting because there 333 00:19:03,840 --> 00:19:10,360 Speaker 1: are some industries like semiconductor software, um you uh, sort 334 00:19:10,400 --> 00:19:14,600 Speaker 1: of wearable technologies within healthcare, those industries are gonna be 335 00:19:14,600 --> 00:19:18,119 Speaker 1: more innovative than say some firms within like staples and 336 00:19:18,240 --> 00:19:23,000 Speaker 1: you're sort of consumer goods products. So industry effects byproduct 337 00:19:23,080 --> 00:19:26,760 Speaker 1: of the folks of innovation. Stock selection effects is by 338 00:19:26,840 --> 00:19:30,320 Speaker 1: product of the AI selection methodology and then the waiting 339 00:19:31,000 --> 00:19:36,560 Speaker 1: um process. The detractors of returns have been style factors, 340 00:19:36,880 --> 00:19:42,159 Speaker 1: namely higher volatility, lower quality, and high high growth you know, 341 00:19:42,280 --> 00:19:47,520 Speaker 1: since inception. But those factors are are implicit because it's 342 00:19:47,520 --> 00:19:50,600 Speaker 1: not what we're we're seeking to obtain. But they're also cyclical. 343 00:19:50,800 --> 00:19:58,760 Speaker 1: So high volatility, low quality, high growth were being famously rewarded, 344 00:19:59,240 --> 00:20:03,560 Speaker 1: uh star and through you know sort of mid right, 345 00:20:03,600 --> 00:20:06,280 Speaker 1: So that was as a tail wind two returns back then. 346 00:20:07,160 --> 00:20:09,360 Speaker 1: So that's how we always like to frame the performance 347 00:20:09,400 --> 00:20:13,760 Speaker 1: conversation is breaking those three components out, noting that the 348 00:20:13,840 --> 00:20:18,040 Speaker 1: style components are going to be cyclical and move in 349 00:20:18,040 --> 00:20:21,560 Speaker 1: and out based on market directions. I'm always curious how 350 00:20:21,640 --> 00:20:25,280 Speaker 1: E t F issuers decide on a theme or a 351 00:20:25,400 --> 00:20:28,919 Speaker 1: topic or you know, putting an e T F together. 352 00:20:29,080 --> 00:20:32,320 Speaker 1: So a couple of years ago. Was AI something that 353 00:20:32,400 --> 00:20:35,120 Speaker 1: you guys when you got together, we're thinking was going 354 00:20:35,160 --> 00:20:37,639 Speaker 1: to be a big deal in the coming years, or 355 00:20:37,720 --> 00:20:40,159 Speaker 1: is it sort of which I think this happens a 356 00:20:40,200 --> 00:20:42,280 Speaker 1: lot in the E T F space. Let's just put 357 00:20:42,320 --> 00:20:45,639 Speaker 1: it out there, give it a try, and see what happens. 358 00:20:45,680 --> 00:20:48,800 Speaker 1: So it's definitely not the ladder within our firm, We're 359 00:20:48,800 --> 00:20:51,920 Speaker 1: definitely not that. Yeah, we're we're not gonna be like, hey, 360 00:20:52,040 --> 00:20:53,920 Speaker 1: this is a hot dot, let's throw it out there 361 00:20:53,920 --> 00:20:56,639 Speaker 1: and see if it works. You know, that's just not 362 00:20:56,720 --> 00:21:01,600 Speaker 1: what we do UM with respect these funds. We have 363 00:21:01,640 --> 00:21:05,119 Speaker 1: a pretty strong heritage within sector and industry investing, and 364 00:21:05,200 --> 00:21:07,680 Speaker 1: we know that there are thematic investors out there. We 365 00:21:07,720 --> 00:21:10,200 Speaker 1: see it all the time within our traditional industry suite. 366 00:21:10,240 --> 00:21:12,920 Speaker 1: You know, someone that wants to play a rally and 367 00:21:13,000 --> 00:21:16,520 Speaker 1: oil stocks will go by xop our Oil and Gas 368 00:21:17,080 --> 00:21:19,840 Speaker 1: ETF and that's a thematic investor. And we knew that 369 00:21:19,880 --> 00:21:23,679 Speaker 1: thematic investing was was going to be UM on the 370 00:21:23,800 --> 00:21:29,480 Speaker 1: rise because there's some thematics like auntonomous vehicles or cybersecurity 371 00:21:29,560 --> 00:21:34,040 Speaker 1: or clean energy that are hard to to to gain 372 00:21:34,160 --> 00:21:37,840 Speaker 1: exposure to under a traditional GETS framework. Because some of 373 00:21:37,840 --> 00:21:41,720 Speaker 1: these firms are are operate across gig sectors. You know, 374 00:21:41,840 --> 00:21:45,840 Speaker 1: clean energy is a perfect example. You have firms within 375 00:21:45,880 --> 00:21:50,959 Speaker 1: the legacy energy sector, the utility sector, industrial sector, technology sector, 376 00:21:51,200 --> 00:21:54,440 Speaker 1: so you want to go across the sectors. So we 377 00:21:54,440 --> 00:21:57,040 Speaker 1: were like, well, how do you go about doing this again? 378 00:21:57,119 --> 00:21:59,040 Speaker 1: We wanted something that was forward looking. We knew that 379 00:21:59,080 --> 00:22:01,919 Speaker 1: revenue was back with game. So this is how we 380 00:22:02,000 --> 00:22:04,199 Speaker 1: landed on, you know, firm like Ken Show and then 381 00:22:04,280 --> 00:22:06,560 Speaker 1: later obviously S and P Ken Show as a combined 382 00:22:06,680 --> 00:22:10,960 Speaker 1: entity of having a really unique value proposition of using 383 00:22:11,400 --> 00:22:16,160 Speaker 1: natural language processing to detect firms that are listing out 384 00:22:16,200 --> 00:22:23,119 Speaker 1: these innovative UM services or innovative corporate designs as part 385 00:22:23,160 --> 00:22:26,840 Speaker 1: of their material operations. UM. So that's that was really 386 00:22:26,880 --> 00:22:28,520 Speaker 1: the impetus for it. And I think, you know, I 387 00:22:28,840 --> 00:22:32,320 Speaker 1: sort of remember one instance. It was I think it 388 00:22:32,440 --> 00:22:35,680 Speaker 1: was obviously before we launched, was probably time frame when 389 00:22:35,720 --> 00:22:38,919 Speaker 1: we're really starting to kick the tires on this. The 390 00:22:39,000 --> 00:22:45,680 Speaker 1: Pokemon vert augmented reality iPhone app was just really really popular. 391 00:22:45,680 --> 00:22:49,040 Speaker 1: I remember playing softball and seeing a bunch of people 392 00:22:49,119 --> 00:22:52,000 Speaker 1: like hanging out by the left field tree. We had 393 00:22:52,000 --> 00:22:54,879 Speaker 1: no idea why, and someone put a Pokemon stay. I 394 00:22:54,880 --> 00:22:57,679 Speaker 1: don't play this, so I have no idea, and I 395 00:22:57,800 --> 00:23:00,359 Speaker 1: remember talking to folks and internally like you would be 396 00:23:00,359 --> 00:23:03,720 Speaker 1: really interesting if we could have something that focused on 397 00:23:04,000 --> 00:23:07,840 Speaker 1: these type of firms, you know, innovating within virtual reality 398 00:23:07,840 --> 00:23:11,400 Speaker 1: and augmented reality coincided at the same time as we're 399 00:23:11,480 --> 00:23:14,280 Speaker 1: kicking the tires on on this process. And that's kind 400 00:23:14,280 --> 00:23:18,040 Speaker 1: of the idea now, owning twenty stocks and augmented reality, 401 00:23:18,119 --> 00:23:20,760 Speaker 1: is that, you know, pure play investment thesis for the 402 00:23:20,800 --> 00:23:23,359 Speaker 1: long term, probably not, but having it part of a 403 00:23:23,400 --> 00:23:26,520 Speaker 1: more diversified innovative exposure probably is. And that's sort of 404 00:23:26,520 --> 00:23:29,080 Speaker 1: where we ended up. They were looking for for for 405 00:23:29,119 --> 00:23:34,960 Speaker 1: Pokey Balls, I think, right, yeah, some rare Pokemon character 406 00:23:35,040 --> 00:23:37,080 Speaker 1: or something. I don't know. I've never played it either, 407 00:23:37,119 --> 00:23:42,080 Speaker 1: But do you remember people wandering around turning out their phones, 408 00:23:42,080 --> 00:23:44,560 Speaker 1: pumped into each other. It was, I think the kind 409 00:23:44,560 --> 00:23:46,199 Speaker 1: of cave and went though, which is weird, you know, 410 00:23:46,240 --> 00:23:49,360 Speaker 1: it's it's uh. I almost thought that type of gaming 411 00:23:49,680 --> 00:23:53,479 Speaker 1: would have caught on more, you know that using that 412 00:23:53,560 --> 00:23:57,680 Speaker 1: location element of your phone more. But who knows, maybe 413 00:23:57,720 --> 00:24:15,760 Speaker 1: maybe something is coming. I'm curious just if you can 414 00:24:16,160 --> 00:24:20,359 Speaker 1: give us kind of a thirty ft view of how 415 00:24:20,600 --> 00:24:23,320 Speaker 1: you're thinking about AI. Now. Like I said at the beginning, 416 00:24:23,320 --> 00:24:27,680 Speaker 1: you know this chat GPT does seem to to sort 417 00:24:27,680 --> 00:24:30,639 Speaker 1: of us layman like a big innovation, Like suddenly the 418 00:24:30,680 --> 00:24:35,160 Speaker 1: innovation in in AI has accelerated faster than I think 419 00:24:35,400 --> 00:24:40,080 Speaker 1: UM people realized. UM tell me if you agree with 420 00:24:40,119 --> 00:24:44,280 Speaker 1: that or disagree, But also UM, in general, where do 421 00:24:44,320 --> 00:24:47,000 Speaker 1: you see AI? What industries do you see being most 422 00:24:47,000 --> 00:24:51,080 Speaker 1: susceptible to disruption from AI going forward? Yeah, I mean 423 00:24:51,119 --> 00:24:55,520 Speaker 1: I think for the most part, you know, AI investment 424 00:24:55,560 --> 00:24:59,159 Speaker 1: I think is projected to increase something in respected like 425 00:24:59,200 --> 00:25:01,879 Speaker 1: a fift percent over the next three years. That, like, 426 00:25:02,240 --> 00:25:05,000 Speaker 1: the statistics around AI investment is astounding. You see it 427 00:25:05,000 --> 00:25:07,919 Speaker 1: every day, big numbers, big percentages. I think from an 428 00:25:07,960 --> 00:25:14,359 Speaker 1: industry perspective, something that like paralegal services could be something 429 00:25:14,400 --> 00:25:17,960 Speaker 1: like that UM research documentation. We're able to scan something 430 00:25:18,040 --> 00:25:20,439 Speaker 1: very quickly, and I think you can even see that 431 00:25:20,480 --> 00:25:23,040 Speaker 1: and some of the McKenzie studies that you talk about, 432 00:25:23,119 --> 00:25:26,679 Speaker 1: how you know upwards of the workforce we need to 433 00:25:26,760 --> 00:25:30,159 Speaker 1: change jobs as a result of advances in the artificial intelligence. 434 00:25:30,840 --> 00:25:34,680 Speaker 1: Legal requests are are likely to be one of those 435 00:25:34,720 --> 00:25:38,400 Speaker 1: because you know, going and pulling all of the specific 436 00:25:38,680 --> 00:25:41,600 Speaker 1: you know, court cases over the last fifty years. Really 437 00:25:41,720 --> 00:25:44,800 Speaker 1: the one topic you know that could be done quite 438 00:25:44,800 --> 00:25:47,880 Speaker 1: easily through natural language processing is you know, using predictive 439 00:25:47,880 --> 00:25:50,200 Speaker 1: tax searching for tax I think that's just one one 440 00:25:50,240 --> 00:25:53,040 Speaker 1: of those. Just even within my team, we're trying to 441 00:25:53,200 --> 00:25:58,000 Speaker 1: use some form of AI to help, you know, right, 442 00:25:58,080 --> 00:26:01,840 Speaker 1: weekly notes for us, it's something that you know, some 443 00:26:02,080 --> 00:26:04,639 Speaker 1: I put out on our plans for this year is 444 00:26:04,680 --> 00:26:07,000 Speaker 1: just you know, again creating more efficiencies and some of 445 00:26:07,040 --> 00:26:09,040 Speaker 1: the weekly notes are more about you know, fun flows 446 00:26:09,040 --> 00:26:12,600 Speaker 1: and market performance and you're having something easily done quicker 447 00:26:12,880 --> 00:26:16,080 Speaker 1: that there's also can be helped from a compliance perspective too, 448 00:26:16,680 --> 00:26:19,680 Speaker 1: because everything's rublespaced. But yeah, that's the legal one of 449 00:26:19,720 --> 00:26:22,240 Speaker 1: always ones that comes to mind any sort of documents search, 450 00:26:22,400 --> 00:26:26,119 Speaker 1: document retrieval, UM. Those that's where a I think is 451 00:26:26,160 --> 00:26:28,240 Speaker 1: some of the more low hung fruits. It doesn't sound 452 00:26:28,240 --> 00:26:32,760 Speaker 1: as flashy, but you know that's um that's one Well, 453 00:26:32,760 --> 00:26:35,639 Speaker 1: if you're a law firm, you're certainly gonna save a 454 00:26:35,640 --> 00:26:38,760 Speaker 1: boatload of money, uh if you can you know, hire 455 00:26:38,760 --> 00:26:41,040 Speaker 1: a fewer paralegals to do all that. That's sort of 456 00:26:41,720 --> 00:26:47,560 Speaker 1: leg work. But I think podcast hosts are totally fine, right, 457 00:26:47,840 --> 00:26:53,040 Speaker 1: don't chinx us, Yeah, don't chin us. I don't know 458 00:26:53,119 --> 00:26:55,720 Speaker 1: that chat JBT wrote wrote some pretty good dad jokes, 459 00:26:55,760 --> 00:26:59,280 Speaker 1: So I'm feeling threatened you might be out of that job. Yeah, 460 00:26:59,440 --> 00:27:02,600 Speaker 1: even kind of. I mean, who would have thought that, 461 00:27:02,760 --> 00:27:05,280 Speaker 1: you know, they would could create a young Luke Skywalker 462 00:27:05,320 --> 00:27:08,160 Speaker 1: and the most recent or last seasons of the Mandalorian. 463 00:27:08,280 --> 00:27:11,920 Speaker 1: You know, all of a sudden they can use you know, 464 00:27:12,400 --> 00:27:15,320 Speaker 1: AI and some of the other stuff to create different 465 00:27:15,359 --> 00:27:17,840 Speaker 1: voice structures. Who knows. I think podcast has have a go, 466 00:27:18,200 --> 00:27:20,600 Speaker 1: have a good chance of out last night for at 467 00:27:20,640 --> 00:27:23,639 Speaker 1: least the next twenty years. Fun fun podcast hosts. Maybe 468 00:27:23,640 --> 00:27:26,800 Speaker 1: just to bring it back to the market, I'm wondering, 469 00:27:26,880 --> 00:27:29,480 Speaker 1: like which sectors maybe can stand to benefit the most 470 00:27:29,480 --> 00:27:32,720 Speaker 1: from AI. That sort of tough because I think, you know, 471 00:27:32,760 --> 00:27:36,280 Speaker 1: obviously within technology, a lot of firms for already starting 472 00:27:36,320 --> 00:27:38,920 Speaker 1: to use AI and their processes. I would probably say 473 00:27:39,000 --> 00:27:45,120 Speaker 1: within the industrial sector for supply chain logistics, um, other 474 00:27:45,280 --> 00:27:50,800 Speaker 1: sort of you know, consumer oriented areas in terms of 475 00:27:51,280 --> 00:27:54,840 Speaker 1: consumer service. So you can obviously already see it with 476 00:27:54,880 --> 00:27:57,840 Speaker 1: Amazon and some of the way they interact with consumers 477 00:27:57,840 --> 00:28:01,880 Speaker 1: and using AI. Um, I would say probably those three 478 00:28:02,040 --> 00:28:05,600 Speaker 1: probably the biggest round industrials. You know, you can supply 479 00:28:05,720 --> 00:28:08,199 Speaker 1: chain and then consumer and then tech is just going 480 00:28:08,240 --> 00:28:14,160 Speaker 1: to benefit because they're the ones sort of creating the innovation. Yeah, yeah, 481 00:28:14,200 --> 00:28:16,879 Speaker 1: mat I know. So we've been talking all out about AI, 482 00:28:17,040 --> 00:28:19,439 Speaker 1: which is one of your focuses, but not not the 483 00:28:19,440 --> 00:28:21,360 Speaker 1: only ones. So I'm curious if you can just give 484 00:28:21,400 --> 00:28:25,560 Speaker 1: us kind of the state of play in the TF 485 00:28:25,680 --> 00:28:27,600 Speaker 1: market as a whole. You know, what, what kind of 486 00:28:27,600 --> 00:28:30,920 Speaker 1: flows are you seeing? Uh this year? You know, market 487 00:28:30,960 --> 00:28:34,680 Speaker 1: obviously off to this super strong start, growth and innovation 488 00:28:34,720 --> 00:28:36,919 Speaker 1: doing well again. Where are you seeing the flows? Are 489 00:28:36,960 --> 00:28:41,120 Speaker 1: people chasing that sort of rebound in innovation and growth 490 00:28:41,200 --> 00:28:43,680 Speaker 1: or they still going into value? What's uh? What do 491 00:28:43,760 --> 00:28:46,920 Speaker 1: the flows look like? Yeah, so thematic ETFs last year 492 00:28:46,920 --> 00:28:50,080 Speaker 1: in two actually had outflows, and they had outflows for 493 00:28:50,120 --> 00:28:52,920 Speaker 1: the first time since two thousand and thirteen. Now k 494 00:28:53,040 --> 00:28:56,320 Speaker 1: OMP actually had inflows. A little bit of a divergence there, 495 00:28:56,680 --> 00:29:00,400 Speaker 1: um maybe speaking to our efforts, but a lot of 496 00:29:00,400 --> 00:29:04,920 Speaker 1: it was for our performance related. Roughly of all thematic 497 00:29:04,960 --> 00:29:08,000 Speaker 1: ETFs on the et F industry beat the SP five 498 00:29:08,080 --> 00:29:10,880 Speaker 1: hundred last year. That's actually been different this year. This 499 00:29:10,960 --> 00:29:15,400 Speaker 1: year we're around eighties. Six of thematic ets are beating 500 00:29:15,400 --> 00:29:18,520 Speaker 1: the sp five hundred. Yet at the end of January 501 00:29:18,640 --> 00:29:22,840 Speaker 1: flows we're still negative for the broader category. So ETF 502 00:29:22,920 --> 00:29:25,760 Speaker 1: investors are still little skeptical, which I think is not 503 00:29:25,960 --> 00:29:31,000 Speaker 1: too surprising given the dour performance results from last year UM. 504 00:29:31,080 --> 00:29:33,520 Speaker 1: But you know, again within our suite we've actually seen 505 00:29:33,560 --> 00:29:37,120 Speaker 1: influence which you know, perhaps speaks to the efficacy of 506 00:29:37,160 --> 00:29:42,080 Speaker 1: the UM, the product type, the structure, the rationale on 507 00:29:42,120 --> 00:29:45,400 Speaker 1: the investor motivation. Matt, we can't let you go without 508 00:29:45,520 --> 00:29:48,960 Speaker 1: asking you about Spy, which is probably the best known 509 00:29:49,080 --> 00:29:51,960 Speaker 1: at F out there, and it just turned thirty years old. 510 00:29:52,040 --> 00:29:55,080 Speaker 1: So happy birthday, just SPI. I know you guys through 511 00:29:55,120 --> 00:29:57,160 Speaker 1: it a couple of birthday parties, but can you maybe 512 00:29:57,160 --> 00:29:59,480 Speaker 1: tell us about this like it's been around for thirty 513 00:29:59,560 --> 00:30:03,680 Speaker 1: years ow I think we had a story on Bloomberg saying, 514 00:30:03,680 --> 00:30:05,400 Speaker 1: you know, it's held the crown for so long, but 515 00:30:05,480 --> 00:30:08,040 Speaker 1: can it continue to hold on to this sort of 516 00:30:08,080 --> 00:30:10,560 Speaker 1: the crown of being the most prominent and well known 517 00:30:10,560 --> 00:30:12,240 Speaker 1: e t F. So maybe just tell us about by 518 00:30:12,360 --> 00:30:14,400 Speaker 1: a little bit, just because we have you here and 519 00:30:14,720 --> 00:30:18,200 Speaker 1: you're the sort of pre eminent figure I'm talking about this. Yeah, 520 00:30:18,240 --> 00:30:21,240 Speaker 1: so I mean Spy. Like I said, you know, without Spy, 521 00:30:21,400 --> 00:30:24,000 Speaker 1: there's a lot there's no k MP, but there's not 522 00:30:24,040 --> 00:30:25,440 Speaker 1: a lot of other e t f s out there. 523 00:30:25,440 --> 00:30:29,720 Speaker 1: It started the industry. U. The infrastructure that it has 524 00:30:30,040 --> 00:30:31,840 Speaker 1: is the reason why we do have ETFs, the ability 525 00:30:31,880 --> 00:30:34,720 Speaker 1: to in kind creation redemption. UH. And it's been time 526 00:30:34,760 --> 00:30:38,000 Speaker 1: tested throughout those past thirty years. And well, I think 527 00:30:38,040 --> 00:30:42,240 Speaker 1: this year taught us it. Spy had a record amount 528 00:30:42,320 --> 00:30:45,480 Speaker 1: of users come to that product in terms of it 529 00:30:45,520 --> 00:30:47,840 Speaker 1: had nine and a half trillion dollars of trading volume. 530 00:30:48,720 --> 00:30:52,480 Speaker 1: It had a record amount of overall shares traded and 531 00:30:52,840 --> 00:30:59,520 Speaker 1: you know, roughly uh is of all trading volume in 532 00:30:59,600 --> 00:31:03,680 Speaker 1: et fs was on it was on Spy. UM. So 533 00:31:03,720 --> 00:31:06,120 Speaker 1: I think that's just really you know, a good indicator 534 00:31:06,360 --> 00:31:10,239 Speaker 1: of UM how much usage it still gets even though 535 00:31:10,280 --> 00:31:13,120 Speaker 1: it's thirty years after its inception. And it was interesting 536 00:31:13,240 --> 00:31:16,240 Speaker 1: we were talking with jet Chat GPT earlier that when 537 00:31:16,280 --> 00:31:19,200 Speaker 1: you do ask chat GPT what is the best et F, 538 00:31:19,640 --> 00:31:22,120 Speaker 1: it does come back Spy. And I think it's with reason. 539 00:31:22,200 --> 00:31:24,080 Speaker 1: You know, it's it's the biggest, it's the most liquid, 540 00:31:24,160 --> 00:31:28,280 Speaker 1: it's the longest trajectory um and for that reason, chat 541 00:31:28,320 --> 00:31:31,880 Speaker 1: GPT recognizes being one of the better ets out in 542 00:31:31,920 --> 00:31:36,960 Speaker 1: the marketplace, bringing in full circle there. I like it. Oh, 543 00:31:37,040 --> 00:31:41,720 Speaker 1: Matt Bartolini's the head of Spider's America's research at State 544 00:31:41,760 --> 00:31:45,080 Speaker 1: Street Global Advisors. Great stuff. We really appreciate your time. 545 00:31:45,840 --> 00:31:48,680 Speaker 1: We cannot let you go though, and so uh we 546 00:31:48,800 --> 00:31:51,360 Speaker 1: hear the craziest thing you've seen in markets this week? 547 00:31:52,040 --> 00:31:54,719 Speaker 1: Data as always, why don't you get us started? Okay, 548 00:31:54,760 --> 00:31:58,800 Speaker 1: So mine is in crypto this week, and it's this 549 00:31:58,880 --> 00:32:01,280 Speaker 1: report by chain Alis this which I don't know if 550 00:32:01,320 --> 00:32:03,520 Speaker 1: you don't know about the analysis. They sort of do 551 00:32:03,680 --> 00:32:09,600 Speaker 1: like forensics basically of the blockchain and within the crypto space. 552 00:32:09,840 --> 00:32:12,400 Speaker 1: So it's interesting that such a report will come from 553 00:32:12,440 --> 00:32:16,040 Speaker 1: a crypto company specifically, But basically, they found that thieves 554 00:32:16,040 --> 00:32:19,600 Speaker 1: stole a record three point eight billion dollars worth of 555 00:32:19,640 --> 00:32:24,040 Speaker 1: cryptocurrency last year, and at North Korea itself, it's estimated 556 00:32:24,520 --> 00:32:29,080 Speaker 1: uh still one point seven billion dollars in up from 557 00:32:29,080 --> 00:32:32,280 Speaker 1: four million the year prior, which is just crazy amounts 558 00:32:32,320 --> 00:32:35,720 Speaker 1: of money. Because you know, it's people in crypto don't 559 00:32:35,720 --> 00:32:38,200 Speaker 1: like to talk about this aspect of crypto, but then 560 00:32:38,240 --> 00:32:40,360 Speaker 1: you have this crypto company actually coming out with this 561 00:32:40,440 --> 00:32:44,480 Speaker 1: report talking about if that's that's at current market prices 562 00:32:44,640 --> 00:32:47,040 Speaker 1: were at it Probably it's probably at the price of 563 00:32:48,400 --> 00:32:50,680 Speaker 1: the assets when they were stolen, I would imagine, But 564 00:32:50,880 --> 00:32:53,000 Speaker 1: I would think so, yeah, I would think so, But 565 00:32:53,120 --> 00:32:55,320 Speaker 1: I mean, yeah, but even if you think about where 566 00:32:55,320 --> 00:32:58,840 Speaker 1: bitcoin was yeah a couple of months ago versus not. Yes, 567 00:32:58,920 --> 00:33:01,480 Speaker 1: And they probably don't answer for this in that report. 568 00:33:01,560 --> 00:33:04,320 Speaker 1: But I wonder how much of that is sort of 569 00:33:04,360 --> 00:33:06,680 Speaker 1: trapped you have. Can you steal some crypto and it's 570 00:33:06,680 --> 00:33:08,440 Speaker 1: stuck in a while and everyone knows it's there, and 571 00:33:08,680 --> 00:33:12,200 Speaker 1: it's it's sometimes hard to launder that. I'd be curious 572 00:33:12,240 --> 00:33:14,360 Speaker 1: to see how much of that actually, you know, these 573 00:33:14,360 --> 00:33:17,760 Speaker 1: thieves are enjoying the benefits of that at the Yeah, 574 00:33:17,880 --> 00:33:22,120 Speaker 1: there are some companies, some crypto like researchers that look 575 00:33:22,160 --> 00:33:27,440 Speaker 1: into when sizeable sums of coins are moved, or like 576 00:33:27,920 --> 00:33:30,640 Speaker 1: nineteen thousand coins that hadn't moved in ten years or 577 00:33:30,640 --> 00:33:34,200 Speaker 1: some which I really interested. That's when you never know 578 00:33:34,240 --> 00:33:36,760 Speaker 1: where they're going with That's when the thirties always catch them. 579 00:33:36,760 --> 00:33:38,280 Speaker 1: Two is the minute you try to move it, and 580 00:33:38,320 --> 00:33:40,920 Speaker 1: the something else than the exactly the FBI is watching. 581 00:33:42,720 --> 00:33:45,080 Speaker 1: That's a pretty good How about you, Matt, you see 582 00:33:45,080 --> 00:33:48,160 Speaker 1: anything crazy recently? I mean, I actually one of the 583 00:33:48,200 --> 00:33:51,400 Speaker 1: craziest things the market reaction to the most recent Federal 584 00:33:51,440 --> 00:33:55,560 Speaker 1: Reserve great hike I didn't think would be that overwhelmingly positive. 585 00:33:56,120 --> 00:33:59,640 Speaker 1: Powell was still pretty persistent on the need to hike 586 00:33:59,720 --> 00:34:03,400 Speaker 1: rate um and right now you have a two year 587 00:34:03,480 --> 00:34:06,120 Speaker 1: yield that is roughly fifty basis points below what the 588 00:34:06,120 --> 00:34:08,439 Speaker 1: Fed funds is and that doesn't really happen. I think 589 00:34:08,480 --> 00:34:12,239 Speaker 1: that's pretty crazy. Is that, you know, borrowing money two 590 00:34:12,280 --> 00:34:17,160 Speaker 1: years out is cheaper than overnight rates at the Reserve. 591 00:34:17,440 --> 00:34:19,680 Speaker 1: So I think that's I'd be interesting what happens in 592 00:34:19,719 --> 00:34:23,560 Speaker 1: the ensuing days if that course corrects. Yeah, that is 593 00:34:23,680 --> 00:34:27,680 Speaker 1: a It is a bizarre upside down world. And uh, 594 00:34:27,840 --> 00:34:30,040 Speaker 1: I don't think the market reaction was anything what he 595 00:34:30,840 --> 00:34:33,560 Speaker 1: hadn't sended. I've joked that Palp should probably have a 596 00:34:33,600 --> 00:34:35,680 Speaker 1: Bloomberg terminal in front of him when he's giving the 597 00:34:35,880 --> 00:34:39,040 Speaker 1: press conference to it to amend his answers to to 598 00:34:39,120 --> 00:34:41,359 Speaker 1: have the desired effect, because I don't think I don't 599 00:34:41,360 --> 00:34:43,680 Speaker 1: think that's what he was after that day. But we 600 00:34:43,719 --> 00:34:46,359 Speaker 1: should send him. Yeah, I bet he. Well he I'm 601 00:34:46,360 --> 00:34:48,520 Speaker 1: assuming he has one. I know, I think he has one. 602 00:34:52,480 --> 00:34:54,839 Speaker 1: All right, we'll give you mine. Yeah, well do as 603 00:34:54,920 --> 00:34:57,080 Speaker 1: i've You know, I'm not really a car guy. I'm 604 00:34:57,120 --> 00:35:01,240 Speaker 1: more of a pedestrian. But I real. But what happened 605 00:35:01,239 --> 00:35:05,879 Speaker 1: to those four portions? Yeah, they're they're still imaginary. They 606 00:35:05,960 --> 00:35:09,920 Speaker 1: still are imaginary. I am into when people pay ridiculous 607 00:35:09,960 --> 00:35:13,960 Speaker 1: prices for collectible items. As you know though, So the 608 00:35:14,000 --> 00:35:16,800 Speaker 1: story's courtesy of CNN. So if you ever heard of 609 00:35:16,840 --> 00:35:21,200 Speaker 1: the car company Bugatti, they make these like hot rods, supercars. 610 00:35:21,200 --> 00:35:23,880 Speaker 1: They call them. Uh yeah, I heard you have two 611 00:35:23,920 --> 00:35:29,680 Speaker 1: of them? Yes, yes, matchbox size. But so Bugatti apparently 612 00:35:29,760 --> 00:35:34,640 Speaker 1: is transitioning to electric. They're gonna go hybrid first. Um, 613 00:35:34,640 --> 00:35:39,279 Speaker 1: but they're done making uh strictly gas powered cars. So 614 00:35:40,480 --> 00:35:46,279 Speaker 1: they recently produced the last pure gas line powered car 615 00:35:46,320 --> 00:35:48,959 Speaker 1: they're ever gonna make. It's a um, I'm probably gonna 616 00:35:49,239 --> 00:35:55,239 Speaker 1: butcher this pronunciation. The Bugatti cheron profably. I believe I 617 00:35:55,280 --> 00:35:57,799 Speaker 1: didn't take French, but I have something like that. So 618 00:35:57,880 --> 00:35:59,879 Speaker 1: when up for auction, they instead of just selling it, 619 00:36:00,000 --> 00:36:02,640 Speaker 1: they put it up for auctions with Southern beas I believe. 620 00:36:02,840 --> 00:36:04,960 Speaker 1: I'm just gonna tell you what it went for on auction. 621 00:36:05,080 --> 00:36:08,040 Speaker 1: It's boring to to name the price. Uh, ten points 622 00:36:08,040 --> 00:36:11,759 Speaker 1: ten point seven million. This car million, brand new car 623 00:36:11,800 --> 00:36:15,319 Speaker 1: set a record for the highest priced new car sold 624 00:36:15,360 --> 00:36:18,520 Speaker 1: at auction. But what I'm gonna make you guys square 625 00:36:18,560 --> 00:36:22,319 Speaker 1: off against each other in our game show is what 626 00:36:22,440 --> 00:36:26,120 Speaker 1: do you think the max speed is that this vehicle 627 00:36:26,239 --> 00:36:29,799 Speaker 1: is capable of reaching the fastest it can go? Oh 628 00:36:29,800 --> 00:36:34,360 Speaker 1: my gosh, in miles per hour for ten point seven 629 00:36:34,400 --> 00:36:37,319 Speaker 1: million dollar car? How fast do you think you get 630 00:36:37,360 --> 00:36:39,839 Speaker 1: to go in that car when you flo It's only 631 00:36:39,880 --> 00:36:43,880 Speaker 1: fair if we can name it in kilometers. All feel 632 00:36:43,920 --> 00:36:45,880 Speaker 1: free to do that, but you need to translate it 633 00:36:45,920 --> 00:36:50,680 Speaker 1: to miles for me, like like, oh my gosh, um, 634 00:36:50,719 --> 00:36:53,440 Speaker 1: I'm guessing it's not as high. But I really I 635 00:36:53,960 --> 00:36:55,960 Speaker 1: know nothing about cars. Am I going first? There's not 636 00:36:56,080 --> 00:36:58,920 Speaker 1: going first? I think you go first? Yeah, fine, I'm 637 00:36:58,960 --> 00:37:02,480 Speaker 1: gonna go with two sixty two d and sixty miles 638 00:37:02,520 --> 00:37:05,399 Speaker 1: an hour. I don't know. Is that a lot? That's 639 00:37:05,400 --> 00:37:07,400 Speaker 1: a lot that's way too much? A lot that's like 640 00:37:07,440 --> 00:37:11,640 Speaker 1: a plane here? I would say I would probably like too, 641 00:37:12,800 --> 00:37:14,719 Speaker 1: Oh my gosh, I think we uh I think we 642 00:37:14,800 --> 00:37:20,360 Speaker 1: have our first tie in the Prices Precise to thirty six. Wow, 643 00:37:21,200 --> 00:37:26,960 Speaker 1: so you guys are pretty close. Although traditional rules she 644 00:37:27,040 --> 00:37:28,759 Speaker 1: went over vill Donna's, I think we gotta give it 645 00:37:28,760 --> 00:37:31,080 Speaker 1: some run over. It's fine, the guests can win. That's 646 00:37:33,680 --> 00:37:38,200 Speaker 1: that's right here, Prices Precise rules. Don't get our lawyers involved. 647 00:37:38,200 --> 00:37:42,200 Speaker 1: This is called the Prices Precise Yet. Here is the 648 00:37:42,440 --> 00:37:46,040 Speaker 1: crazier thing, though, is that's not the fastest car got 649 00:37:46,040 --> 00:37:49,240 Speaker 1: he's ever sold. The fastest could go three hundred miles 650 00:37:49,239 --> 00:37:52,200 Speaker 1: an hour, they say, quote in theory, and I'm not 651 00:37:52,239 --> 00:37:54,640 Speaker 1: sure everyone anyone's ever managed to get it up to 652 00:37:54,640 --> 00:37:57,080 Speaker 1: three hundred. I don't know if you could. Tom Cruise 653 00:37:57,080 --> 00:37:59,799 Speaker 1: would if you gave him a chance for one of 654 00:37:59,840 --> 00:38:02,799 Speaker 1: his good It's probably has several of these, but I'm 655 00:38:02,800 --> 00:38:05,120 Speaker 1: not sure if you could. Theoretically, if you could drive 656 00:38:05,120 --> 00:38:06,920 Speaker 1: a car three hundred miles an hour, I feel like 657 00:38:06,960 --> 00:38:08,680 Speaker 1: it would take off like a rocket ship at that point, 658 00:38:08,760 --> 00:38:11,560 Speaker 1: like I would my heart would burst from it, like 659 00:38:12,760 --> 00:38:16,080 Speaker 1: I'd be so scared. You definitely have to live. If 660 00:38:16,120 --> 00:38:18,440 Speaker 1: you're driving that fast, you gotta listen to your podcasts 661 00:38:18,440 --> 00:38:20,120 Speaker 1: at double speed. I think so if we have any 662 00:38:20,120 --> 00:38:23,560 Speaker 1: boogotten drivers out there allow them to double speed us 663 00:38:23,680 --> 00:38:27,440 Speaker 1: two x X. Yeah, pretty good, though you guys are 664 00:38:27,440 --> 00:38:29,279 Speaker 1: both in the ballpark. I'm not sure what I would 665 00:38:29,280 --> 00:38:30,480 Speaker 1: have I would have guessed. I'm not sure if I 666 00:38:30,520 --> 00:38:32,520 Speaker 1: would have gone over two hundred. It just seems insane 667 00:38:32,560 --> 00:38:36,120 Speaker 1: to drive over two hundred miles an hour. But anyway, 668 00:38:36,360 --> 00:38:38,239 Speaker 1: you don't go. You don't go two hundred miles an 669 00:38:38,239 --> 00:38:40,600 Speaker 1: hour in the New Jersey Turnpike. Well, New Jersey Transit 670 00:38:40,640 --> 00:38:46,640 Speaker 1: I do. Yeah, that's the when we're late anyway, Matt 671 00:38:46,680 --> 00:38:50,719 Speaker 1: Partalini from State Treet Global ADVISORSS just real honor to 672 00:38:50,719 --> 00:38:53,120 Speaker 1: be able to pick your brain on all these topics. Uh, 673 00:38:53,400 --> 00:38:55,440 Speaker 1: wish you all the best and hopefully you'll come back 674 00:38:55,440 --> 00:38:58,600 Speaker 1: and talk to us again some day. Yeah, thanks, thanks Matt, 675 00:39:06,320 --> 00:39:08,360 Speaker 1: what goes up? We'll be back next week. And so 676 00:39:08,480 --> 00:39:10,799 Speaker 1: then you can find us on the Bloomberg Terminal website 677 00:39:10,800 --> 00:39:14,200 Speaker 1: and app or wherever you get your podcasts. We love 678 00:39:14,239 --> 00:39:16,000 Speaker 1: it if you took the time to rate and review 679 00:39:16,040 --> 00:39:19,120 Speaker 1: the show on Apple Podcasts, so more listeners can find us, 680 00:39:19,680 --> 00:39:21,880 Speaker 1: and you can find us on Twitter, follow me at 681 00:39:21,920 --> 00:39:26,360 Speaker 1: reag Anonymous, Bill Donna hierarch Is at Bildonna Hirach. You 682 00:39:26,360 --> 00:39:31,000 Speaker 1: can also follow Bloomberg Podcasts at Podcasts. What Goes Up 683 00:39:31,040 --> 00:39:33,960 Speaker 1: is produced by Stacy Wang. Thanks for listening, See you 684 00:39:34,000 --> 00:39:34,399 Speaker 1: next time.