1 00:00:13,960 --> 00:00:17,200 Speaker 1: Hello, and welcome to What Goes Up, a weekly markets podcast. 2 00:00:17,320 --> 00:00:19,400 Speaker 1: My name is Mike Kriegan. I'm a senior editor at 3 00:00:19,400 --> 00:00:20,239 Speaker 1: Bloomberg and. 4 00:00:20,200 --> 00:00:22,840 Speaker 2: I'm Aldana Hire, Across Acid reporter with Bloomberg. 5 00:00:23,079 --> 00:00:25,480 Speaker 1: And this week on the show, Well, the stock market 6 00:00:25,520 --> 00:00:27,680 Speaker 1: was running at full speed in the first half of 7 00:00:27,680 --> 00:00:30,320 Speaker 1: the year, then towards the end of July it seemed 8 00:00:30,320 --> 00:00:33,080 Speaker 1: to well just run out of steam. And whether what 9 00:00:33,159 --> 00:00:36,560 Speaker 1: we've seen since then is just normal seasonal weakness or 10 00:00:36,720 --> 00:00:39,800 Speaker 1: something more serious, that's yet to be seen. But it 11 00:00:39,880 --> 00:00:42,279 Speaker 1: is notable that among the hardest hit in this off 12 00:00:42,400 --> 00:00:45,159 Speaker 1: patch have been some of the most popular thematic trades, 13 00:00:45,360 --> 00:00:49,040 Speaker 1: from electric vehicles to travel stocks and even the darlings 14 00:00:49,080 --> 00:00:52,560 Speaker 1: of the artificial intelligence world. So what can we expect 15 00:00:52,600 --> 00:00:55,080 Speaker 1: for the rest of the year. Well, these themes snapped 16 00:00:55,120 --> 00:00:58,160 Speaker 1: back were is there a new regime in place? We'll 17 00:00:58,160 --> 00:01:00,520 Speaker 1: get into it with the co founder and chief investment 18 00:01:00,520 --> 00:01:02,920 Speaker 1: officer at a company that offers some of the most 19 00:01:02,920 --> 00:01:06,720 Speaker 1: well known thematic ETFs. But first of all, Donna, I 20 00:01:06,760 --> 00:01:09,880 Speaker 1: have to ask, September is the worst month in the 21 00:01:09,880 --> 00:01:11,840 Speaker 1: stock market? What's your show? 22 00:01:11,840 --> 00:01:14,800 Speaker 2: It was October, November, December, jen that's the joke, Right. 23 00:01:15,600 --> 00:01:19,440 Speaker 1: February my worst than what's your worst month personally? 24 00:01:19,640 --> 00:01:20,240 Speaker 2: February. 25 00:01:20,360 --> 00:01:21,520 Speaker 1: I was going to say February too. 26 00:01:21,680 --> 00:01:24,320 Speaker 2: I think it's everybody's worst month. Yeah, because you're depressed 27 00:01:24,360 --> 00:01:26,880 Speaker 2: by then, you know, the winter's been dragging on, you 28 00:01:27,040 --> 00:01:27,360 Speaker 2: like have. 29 00:01:27,360 --> 00:01:31,640 Speaker 1: Seasonal there's nothing seasonal affected this one, yes, yeah, and 30 00:01:31,680 --> 00:01:34,080 Speaker 1: there's nothing like Okay, there's the Super Bowls, all right, 31 00:01:34,080 --> 00:01:34,880 Speaker 1: that's a nice bright. 32 00:01:34,760 --> 00:01:36,760 Speaker 2: Spot, but that's only one day and the rest of 33 00:01:36,800 --> 00:01:40,360 Speaker 2: the time there's no sun. There's nothing like just so 34 00:01:40,520 --> 00:01:41,200 Speaker 2: dark and depressed. 35 00:01:41,200 --> 00:01:43,000 Speaker 1: All right. Well, we finally agree on one thing. 36 00:01:42,959 --> 00:01:46,199 Speaker 2: On one thing. Yeah, wow, we both hate February. Yeah, 37 00:01:46,240 --> 00:01:49,040 Speaker 2: we can pretend like it doesn't exist. I do want 38 00:01:49,040 --> 00:01:52,080 Speaker 2: to bring our guests in. It's Sylvia Jablonski, co founder 39 00:01:52,080 --> 00:01:55,880 Speaker 2: and chief investment officer at Defines ETFs. Sylvia, you've been 40 00:01:55,880 --> 00:01:57,600 Speaker 2: on the show before, and I'm so happy you could 41 00:01:57,880 --> 00:01:59,600 Speaker 2: join us again. Thanks so much for coming on. 42 00:02:00,040 --> 00:02:03,920 Speaker 3: Thanks so much for having me. What about Valentine's Day guys? 43 00:02:02,840 --> 00:02:06,920 Speaker 1: Oh no, thank you, that's fair. 44 00:02:07,360 --> 00:02:09,440 Speaker 2: Okay, I mean, I guess Parts. 45 00:02:09,120 --> 00:02:11,760 Speaker 3: And Stars chocolates, two little. 46 00:02:11,520 --> 00:02:14,240 Speaker 2: Bright spots in a very darkened dank. 47 00:02:14,440 --> 00:02:16,600 Speaker 1: We get that presence day weekend off. 48 00:02:17,160 --> 00:02:17,760 Speaker 2: That's true. 49 00:02:17,880 --> 00:02:18,120 Speaker 4: Fun. 50 00:02:18,200 --> 00:02:22,120 Speaker 2: Everybody's like, are we all redeeming February now? But Sylvia, 51 00:02:22,800 --> 00:02:25,160 Speaker 2: Mike had this great introduction of you, and you have 52 00:02:25,240 --> 00:02:27,480 Speaker 2: been on the show before, but maybe just to start 53 00:02:27,680 --> 00:02:29,799 Speaker 2: and just to give our audience a refresher, you can 54 00:02:29,840 --> 00:02:31,239 Speaker 2: just tell us a bit about your background. 55 00:02:31,440 --> 00:02:34,640 Speaker 3: Yeah. Sure. So I'm currently the CEO and CIO of 56 00:02:34,720 --> 00:02:37,720 Speaker 3: Defiance CTFs, but a large part of my background has 57 00:02:37,840 --> 00:02:41,280 Speaker 3: actually been in ETF. So I worked prior to Defiance, 58 00:02:41,400 --> 00:02:45,000 Speaker 3: I worked at a Levern NIVERSITYTF provider called Direction ETF 59 00:02:45,160 --> 00:02:48,600 Speaker 3: for a decade basically, and then out of college I 60 00:02:48,919 --> 00:02:51,840 Speaker 3: worked on an equity derivatives delta on trading desk, so 61 00:02:52,120 --> 00:02:53,840 Speaker 3: I sort of learned about the nuts and bolts and 62 00:02:53,880 --> 00:02:56,480 Speaker 3: trading them, how to actually kind of create and build 63 00:02:56,480 --> 00:03:00,840 Speaker 3: the ETFs, and then ended up working in all aspects 64 00:03:00,840 --> 00:03:04,560 Speaker 3: of ETFs, whether it's educating about their product development, research, 65 00:03:05,000 --> 00:03:08,079 Speaker 3: and of course I have a just general passion for markets, 66 00:03:08,080 --> 00:03:10,120 Speaker 3: so kind of studying markets and figuring out how they 67 00:03:10,120 --> 00:03:12,119 Speaker 3: all fit it is part of my day to day. 68 00:03:12,280 --> 00:03:14,920 Speaker 1: Solbia, let's talk about that idea. You know, we have 69 00:03:15,000 --> 00:03:18,120 Speaker 1: seen this soft patch in the market. It seems like, 70 00:03:18,400 --> 00:03:20,360 Speaker 1: as I mentioned in the intro, some of the hot 71 00:03:20,440 --> 00:03:23,880 Speaker 1: thematic ETFs are doing even worse than the market. Is 72 00:03:23,919 --> 00:03:26,400 Speaker 1: there just a beta there? How do you think about 73 00:03:27,000 --> 00:03:32,480 Speaker 1: how thematic ETFs, at least the defiance ETFs perform compared 74 00:03:32,480 --> 00:03:34,280 Speaker 1: to the market. I mean, is it just natural to 75 00:03:34,320 --> 00:03:37,840 Speaker 1: expect these themes to sort of do even better on 76 00:03:37,880 --> 00:03:40,119 Speaker 1: the upside and maybe a little worse on the downside. 77 00:03:40,400 --> 00:03:43,800 Speaker 1: What's the sort of your analysis of how they perform 78 00:03:44,320 --> 00:03:45,600 Speaker 1: in different market cycles. 79 00:03:46,000 --> 00:03:49,000 Speaker 3: Yeah, And that's actually a pretty fair way to look 80 00:03:49,040 --> 00:03:50,960 Speaker 3: at it, right, You do have beta exposure, and when 81 00:03:50,960 --> 00:03:55,240 Speaker 3: you think about thematic ETFs, oftentimes we're sort of, I think, 82 00:03:55,360 --> 00:03:57,680 Speaker 3: moving away from thematic and we think about it more 83 00:03:57,760 --> 00:04:01,040 Speaker 3: like tech growth innovator, so kind of what is the 84 00:04:01,520 --> 00:04:04,640 Speaker 3: tech of the future, what is innovation for the future, 85 00:04:05,000 --> 00:04:07,120 Speaker 3: And they're not so much like Kitschy themes, right, so 86 00:04:07,160 --> 00:04:10,960 Speaker 3: we just think about how different sectors will morph. But 87 00:04:11,280 --> 00:04:13,280 Speaker 3: it's a fair point because if you think about twenty 88 00:04:13,400 --> 00:04:16,360 Speaker 3: twenty two, all of the top kind of like growth 89 00:04:16,400 --> 00:04:19,920 Speaker 3: stocks and tech stocks really suffered a bear market, and 90 00:04:20,160 --> 00:04:23,000 Speaker 3: our ETF suffered alongside of that. And if you think 91 00:04:23,040 --> 00:04:26,760 Speaker 3: about why, I'll use like quantum ETF is a great example. 92 00:04:26,839 --> 00:04:32,000 Speaker 3: So so quantum gives you access to five G artificial intelligence, supercomputing, 93 00:04:32,520 --> 00:04:35,919 Speaker 3: quantum computing, right, and all of those themes. In twenty 94 00:04:35,960 --> 00:04:39,760 Speaker 3: twenty two, we're just kind of non existent. Tech stocks 95 00:04:39,760 --> 00:04:41,840 Speaker 3: were down, and what makes up that ETF or some 96 00:04:41,880 --> 00:04:45,280 Speaker 3: of the top movers like the Apple, Google, IBM, Amazon, 97 00:04:45,360 --> 00:04:48,840 Speaker 3: the video amts of the world. And then lo and behold, 98 00:04:48,880 --> 00:04:50,440 Speaker 3: as you said, the first part of the year, up 99 00:04:50,520 --> 00:04:53,960 Speaker 3: until July, these names sored and so ANYTF like that 100 00:04:54,160 --> 00:04:58,239 Speaker 3: kind of more than sorted outperformed the NADAQ one hundred 101 00:04:58,279 --> 00:05:01,520 Speaker 3: because of the same names a MD and such getting 102 00:05:01,520 --> 00:05:03,880 Speaker 3: a great tail and then five G same thing. It's 103 00:05:03,920 --> 00:05:07,560 Speaker 3: made up of sort of semiconductors technology. And if you 104 00:05:07,600 --> 00:05:10,720 Speaker 3: think about when the market pulls back, right when you 105 00:05:10,760 --> 00:05:14,320 Speaker 3: have kind of like these little bursts of panic because 106 00:05:14,440 --> 00:05:17,400 Speaker 3: whatever might be service ism is too hot or whatever 107 00:05:17,440 --> 00:05:19,760 Speaker 3: it is, we worry about the FED raising rates. Everyone 108 00:05:19,800 --> 00:05:22,000 Speaker 3: kind of panics, sells off tech sales, off growth and 109 00:05:22,080 --> 00:05:25,679 Speaker 3: goes back into cash equivalent staples and kind of the 110 00:05:25,720 --> 00:05:27,920 Speaker 3: defensive types of plays. But what I think is that 111 00:05:27,960 --> 00:05:30,360 Speaker 3: these are actually great opportunities, especially if you're a young 112 00:05:30,440 --> 00:05:34,080 Speaker 3: person investing for the long term, these are amazing opportunities 113 00:05:34,120 --> 00:05:36,320 Speaker 3: to dollar costs to average. Like That's that's how I 114 00:05:36,320 --> 00:05:41,480 Speaker 3: would characterize this market this year. It's not wholly volatile 115 00:05:41,600 --> 00:05:44,400 Speaker 3: where we have everybody kind of on the sidelines, like 116 00:05:44,400 --> 00:05:47,680 Speaker 3: like twenty twenty two complete panic highest savings rates ever, 117 00:05:47,800 --> 00:05:50,720 Speaker 3: I mean, we're still pretty pretty high, but I think 118 00:05:50,760 --> 00:05:52,400 Speaker 3: this year you're starting to see some of that come 119 00:05:52,440 --> 00:05:54,760 Speaker 3: off the sidelines and go into these products. So although 120 00:05:54,800 --> 00:05:57,520 Speaker 3: the performance is low or is getting hurt anyway by 121 00:05:57,520 --> 00:06:00,160 Speaker 3: these pullbacks, I think over time these ETFs are want 122 00:06:00,200 --> 00:06:02,200 Speaker 3: to outperform and the people who kind of buy them 123 00:06:02,200 --> 00:06:04,280 Speaker 3: on these pullbacks are going to be kind of very 124 00:06:04,279 --> 00:06:08,039 Speaker 3: happy about that. Right. The future is technology. Every single sector, 125 00:06:08,160 --> 00:06:10,719 Speaker 3: asset class depends on it. So I don't think that 126 00:06:10,800 --> 00:06:12,640 Speaker 3: these techniques are going anywhere. 127 00:06:12,800 --> 00:06:15,920 Speaker 2: Okay, So, Sylvia, you like investing around some of these themes, 128 00:06:15,960 --> 00:06:20,479 Speaker 2: including airlines, hotels, cruise companies, the EV trade, AI and 129 00:06:20,560 --> 00:06:22,800 Speaker 2: machine learning. So I'm just wondering what the reason is 130 00:06:22,839 --> 00:06:25,279 Speaker 2: behind that, whether or not you're thinking about the consumer 131 00:06:25,320 --> 00:06:26,560 Speaker 2: and the consumer staying strong. 132 00:06:26,839 --> 00:06:28,640 Speaker 3: Yeah, that's a great point, and I think that I 133 00:06:28,800 --> 00:06:32,040 Speaker 3: sort of have different reasons for the interest in different sectors. 134 00:06:32,040 --> 00:06:35,560 Speaker 3: So on the travel trade, the cruises, hotels and airlines. 135 00:06:35,960 --> 00:06:39,120 Speaker 3: You know, I think the big impetus there is that, yes, 136 00:06:39,160 --> 00:06:42,039 Speaker 3: the consumer remains strong and resilient. They have high levels 137 00:06:42,040 --> 00:06:45,680 Speaker 3: of savings, and that savings has gone essentially from spending 138 00:06:45,680 --> 00:06:48,240 Speaker 3: on goods to spending on services. And there are a 139 00:06:48,240 --> 00:06:50,680 Speaker 3: couple of factors here. So one is that in the 140 00:06:50,839 --> 00:06:53,680 Speaker 3: early winter it's the first time that the kind of 141 00:06:53,680 --> 00:06:56,599 Speaker 3: globe reopened to full travel, right, So you have the 142 00:06:56,600 --> 00:06:59,040 Speaker 3: connection of the East to west back up, you have 143 00:06:59,160 --> 00:07:01,960 Speaker 3: Asia to the US and Europe and kind of more 144 00:07:02,000 --> 00:07:05,240 Speaker 3: global travel happening. You have business travel picking up in 145 00:07:05,320 --> 00:07:07,919 Speaker 3: the spend picking up there. Cruises, some of the cruise 146 00:07:07,920 --> 00:07:10,640 Speaker 3: stocks are actually up over one hundred percent year today, right, 147 00:07:10,680 --> 00:07:13,679 Speaker 3: and I think we're coming off of some loan numbers 148 00:07:13,720 --> 00:07:16,679 Speaker 3: because of COVID. But you know, again, the consumer seems 149 00:07:16,720 --> 00:07:20,200 Speaker 3: to have shifted to spend into experiences and travel and 150 00:07:20,240 --> 00:07:23,520 Speaker 3: things like this. So Norwegian has been able to up 151 00:07:23,560 --> 00:07:26,640 Speaker 3: their prices and create these luxury experience same with Carnival 152 00:07:26,680 --> 00:07:29,360 Speaker 3: and Royal Caribbean. It's just they've caught a massive tailment 153 00:07:29,440 --> 00:07:31,800 Speaker 3: from that spent and of course hotels it fits all 154 00:07:31,840 --> 00:07:34,320 Speaker 3: of it, right, So you have again people traveling it's 155 00:07:34,320 --> 00:07:36,120 Speaker 3: that time of year, and then on top of that 156 00:07:36,160 --> 00:07:39,040 Speaker 3: business travel, all of the people flying everywhere obviously up 157 00:07:39,080 --> 00:07:42,280 Speaker 3: this stay somewhere, so you've seen some major pick up there. 158 00:07:42,680 --> 00:07:45,240 Speaker 3: And then during the earning season, the CEOs have just 159 00:07:45,320 --> 00:07:49,200 Speaker 3: really been super positive about current bookings, future bookings being 160 00:07:49,280 --> 00:07:52,040 Speaker 3: kind of back to where they were in twenty nineteen 161 00:07:52,120 --> 00:07:54,840 Speaker 3: pre pandemic. And I think that it's a sector that 162 00:07:55,000 --> 00:07:58,080 Speaker 3: really has been a great short term trade in terms 163 00:07:58,120 --> 00:08:01,440 Speaker 3: of why evs, I think EV's or where the puck 164 00:08:01,520 --> 00:08:03,840 Speaker 3: is going. You know, you look at the growth of 165 00:08:03,880 --> 00:08:06,120 Speaker 3: the EV market. It went from five percent of all 166 00:08:06,200 --> 00:08:09,160 Speaker 3: vehicles sold last year to fourteen percent this year. The 167 00:08:09,200 --> 00:08:12,400 Speaker 3: projected statistics of this is to kind of double in 168 00:08:12,440 --> 00:08:14,920 Speaker 3: the next five years and then at some point to 169 00:08:14,960 --> 00:08:18,440 Speaker 3: become a multi trillion dollar industry. So there's so many 170 00:08:18,440 --> 00:08:20,960 Speaker 3: reasons for that, right you have awareness of climate and 171 00:08:21,000 --> 00:08:23,040 Speaker 3: wanting to be a car but neutral these are obviously 172 00:08:23,080 --> 00:08:26,120 Speaker 3: better for the environment, and have the support of governments 173 00:08:26,240 --> 00:08:29,840 Speaker 3: globally around that Inflation Reduction Act, tax credits, all of 174 00:08:29,880 --> 00:08:32,199 Speaker 3: these sorts of things, and then if you just look 175 00:08:32,200 --> 00:08:34,559 Speaker 3: at the stats, right it's like one in three cars 176 00:08:34,559 --> 00:08:37,480 Speaker 3: in China's an electric vehicle. I think it's close to 177 00:08:37,640 --> 00:08:40,560 Speaker 3: sixty sixty percent of vehicles sold this year where EV's 178 00:08:40,559 --> 00:08:44,120 Speaker 3: in China. In terms of Norway, it's like ninety nine percent. 179 00:08:44,160 --> 00:08:46,560 Speaker 3: So these are real growth opportunities. 180 00:08:46,720 --> 00:08:49,160 Speaker 5: And that's really why I like regardless of what's happening 181 00:08:49,160 --> 00:08:51,280 Speaker 5: in the market today, I think that these are great 182 00:08:51,280 --> 00:08:59,880 Speaker 5: allocations for the next few years. 183 00:09:00,000 --> 00:09:05,120 Speaker 1: Thing Sylvia that's corresponded coincidentally with this drop in equities 184 00:09:05,559 --> 00:09:09,040 Speaker 1: the last few weeks, perhaps not coincidentally, is this surgeon 185 00:09:09,080 --> 00:09:11,760 Speaker 1: oil prices. And we have seen some of the airlines 186 00:09:11,800 --> 00:09:16,080 Speaker 1: come out Southwest Alaska Air another who I can't remember 187 00:09:16,360 --> 00:09:19,000 Speaker 1: warning about though the jet fuel prices are going to 188 00:09:19,000 --> 00:09:21,480 Speaker 1: be a little bit higher than expected. How big of 189 00:09:21,480 --> 00:09:24,439 Speaker 1: a threat is this oil price to that travel theme? 190 00:09:24,960 --> 00:09:28,040 Speaker 1: Was the consumer strong enough to handle even higher ticket 191 00:09:28,080 --> 00:09:31,640 Speaker 1: prices if airlines are forced to raise them because of energy? 192 00:09:31,720 --> 00:09:35,800 Speaker 1: How are you thinking about how the energy situation, the 193 00:09:35,920 --> 00:09:39,079 Speaker 1: OPEC plus supply cuts, how that all fits into the 194 00:09:39,400 --> 00:09:40,319 Speaker 1: travel them. 195 00:09:40,280 --> 00:09:42,400 Speaker 3: Yeah, and that's that's a great point. And obviously that 196 00:09:42,480 --> 00:09:45,360 Speaker 3: can that could certainly have an input negative impact on 197 00:09:45,400 --> 00:09:48,240 Speaker 3: the travel industry. But you know where where we see 198 00:09:48,320 --> 00:09:50,559 Speaker 3: oil and gas prices now, I don't think is going 199 00:09:50,600 --> 00:09:53,959 Speaker 3: to impact the bottom line for for airline companies. Right 200 00:09:54,000 --> 00:09:55,960 Speaker 3: if they continue to rise and we do have this 201 00:09:56,080 --> 00:09:59,600 Speaker 3: threat of increasing oil prices, then I do think that 202 00:10:00,080 --> 00:10:03,000 Speaker 3: it could potentially hamper the returns for these companies. But 203 00:10:03,400 --> 00:10:05,640 Speaker 3: I think there's sort of that sweet spot right now, 204 00:10:05,760 --> 00:10:08,800 Speaker 3: like where these companies can charge higher ticket prices, the 205 00:10:08,840 --> 00:10:11,280 Speaker 3: consumer can afford it, they're sort of not complaining about it. 206 00:10:11,480 --> 00:10:14,320 Speaker 3: If they go a little higher, it sustainable. But yeah, 207 00:10:14,320 --> 00:10:16,040 Speaker 3: it's certainly something that we would have to keep an 208 00:10:16,040 --> 00:10:18,280 Speaker 3: eye on in the longer term and the shorter term, though, 209 00:10:18,360 --> 00:10:20,920 Speaker 3: I think next two quarters of earnings for these companies 210 00:10:20,960 --> 00:10:23,000 Speaker 3: are going to continue to remain stellar. 211 00:10:23,440 --> 00:10:25,760 Speaker 2: A lot of the projections that people had for twenty 212 00:10:25,840 --> 00:10:30,600 Speaker 2: twenty three, including recession and whatever other negative impacts people 213 00:10:30,679 --> 00:10:33,280 Speaker 2: were thinking about on the economy, a lot of those 214 00:10:33,320 --> 00:10:36,800 Speaker 2: have been pushed out and pushed further and further into 215 00:10:36,920 --> 00:10:40,160 Speaker 2: the start of twenty twenty four, maybe even later. And 216 00:10:40,240 --> 00:10:42,439 Speaker 2: I think one of the thoughts that's going around now 217 00:10:42,559 --> 00:10:44,320 Speaker 2: is that the consumer is going to run out of 218 00:10:44,360 --> 00:10:47,560 Speaker 2: steam at the start of twenty twenty four. Do you 219 00:10:47,679 --> 00:10:50,800 Speaker 2: foresee the same or do you continue to see the 220 00:10:50,800 --> 00:10:51,880 Speaker 2: consumer staying strong. 221 00:10:52,040 --> 00:10:54,640 Speaker 3: I continue to see the consumer staying strong. I don't 222 00:10:54,679 --> 00:10:57,600 Speaker 3: think anything will sort of happen that quickly. Twenty twenty 223 00:10:57,640 --> 00:10:59,400 Speaker 3: four is really just a few months away. If we 224 00:10:59,440 --> 00:11:01,199 Speaker 3: think about it, and if we look at the data, 225 00:11:01,200 --> 00:11:02,839 Speaker 3: if we look at corporate earnings, if we look at 226 00:11:02,880 --> 00:11:06,080 Speaker 3: spending data, it is unlikely to fall off of a cliff, 227 00:11:06,240 --> 00:11:09,079 Speaker 3: especially because, as you said in your intro, September is 228 00:11:09,080 --> 00:11:11,040 Speaker 3: you should terrible. October is a little bit rocky too, 229 00:11:11,080 --> 00:11:14,600 Speaker 3: But November December tend to be positive months for the market, 230 00:11:14,600 --> 00:11:16,880 Speaker 3: positive months for retail, for spending and things like this. 231 00:11:17,040 --> 00:11:19,280 Speaker 3: I just don't see that happening. I think you get 232 00:11:19,280 --> 00:11:20,680 Speaker 3: some tail and went from that and that kind of 233 00:11:21,040 --> 00:11:23,360 Speaker 3: holds you over till next quarter. It depends on the 234 00:11:23,400 --> 00:11:25,240 Speaker 3: FED and what the FED does right and how that 235 00:11:25,320 --> 00:11:27,960 Speaker 3: impacts the economy. So I think that jobs are going 236 00:11:28,000 --> 00:11:29,760 Speaker 3: to level off. I think part of the reason why 237 00:11:29,800 --> 00:11:33,439 Speaker 3: jobs have remained strong is, you know, also because we've 238 00:11:33,480 --> 00:11:36,160 Speaker 3: seen some economic expansion, we've seen some growth, we've seen 239 00:11:36,200 --> 00:11:39,840 Speaker 3: some innovation and technology and things like this. Eventually that stabilizes, 240 00:11:39,920 --> 00:11:42,960 Speaker 3: We just stabilize, But you know, if the FED sort 241 00:11:42,960 --> 00:11:46,400 Speaker 3: of holds rates higher for longer, continues to raise rates, 242 00:11:46,480 --> 00:11:49,840 Speaker 3: I do think that impacts kind of a lot of 243 00:11:49,840 --> 00:11:53,200 Speaker 3: things in the market. It'll impact corporate America, It'll impact spending. 244 00:11:53,240 --> 00:11:55,240 Speaker 3: We'll kind of go back to this like huge risk 245 00:11:55,280 --> 00:11:58,199 Speaker 3: of recession fears and things like this. But if we 246 00:11:58,360 --> 00:12:00,840 Speaker 3: kind of are at a point where we think that 247 00:12:01,040 --> 00:12:03,559 Speaker 3: we're closer to the end of right hikes and potentially 248 00:12:03,960 --> 00:12:06,360 Speaker 3: thinking about kind of a reduction of rates in the 249 00:12:06,400 --> 00:12:08,640 Speaker 3: next year or so, you know, then I think we 250 00:12:09,120 --> 00:12:11,280 Speaker 3: pulled off that soft landing and I don't expect that 251 00:12:11,360 --> 00:12:14,679 Speaker 3: massive pullback to come. Of course, things have happened in 252 00:12:14,760 --> 00:12:17,920 Speaker 3: the last couple of years, right, Nobody anticipated COVID, Nobody 253 00:12:17,920 --> 00:12:21,520 Speaker 3: anticipated kind of like Russia and Ukraine and tensions with China. 254 00:12:21,559 --> 00:12:24,400 Speaker 3: So we always have to be aware of the geopolitical events. 255 00:12:24,400 --> 00:12:27,000 Speaker 3: But that aside, if the market continues holding up the 256 00:12:27,000 --> 00:12:29,400 Speaker 3: way it is, corporate America continues to hold up the 257 00:12:29,400 --> 00:12:32,160 Speaker 3: way it is, cutting jobs, becoming more efficient, then I 258 00:12:32,160 --> 00:12:34,480 Speaker 3: think the consumer will be justified and we avoid that 259 00:12:34,520 --> 00:12:35,280 Speaker 3: big recession. 260 00:12:35,720 --> 00:12:39,680 Speaker 1: Yeah, Sylvia, obviously we've all kind of talked ourselves blue 261 00:12:39,720 --> 00:12:42,600 Speaker 1: in the face over but AI such a big theme 262 00:12:42,880 --> 00:12:47,200 Speaker 1: this year. Artificial intelligence one of your newer products QTUM, 263 00:12:47,440 --> 00:12:51,840 Speaker 1: the Quantum and Artificial Intelligence ETF. What I find interesting 264 00:12:51,920 --> 00:12:56,120 Speaker 1: about the ETF is that so much focus has been 265 00:12:56,160 --> 00:13:00,920 Speaker 1: placed on the big megacap tech companies, you know, in Nvidia, Alphabet, 266 00:13:01,320 --> 00:13:04,400 Speaker 1: the Magnificent Seven as they're called, how they are sort 267 00:13:04,400 --> 00:13:07,600 Speaker 1: of at the sweet spot to be beneficiaries of the 268 00:13:07,760 --> 00:13:12,200 Speaker 1: AI craze. But your ETF has some more interesting small 269 00:13:12,200 --> 00:13:15,520 Speaker 1: cap companies that I don't think many people are necessarily 270 00:13:15,559 --> 00:13:21,560 Speaker 1: aware of, So companies like Ion C. Brighetti Computer also innovation. 271 00:13:21,920 --> 00:13:23,760 Speaker 1: I'm wondering if you could talk to us a little 272 00:13:23,760 --> 00:13:26,760 Speaker 1: bit about some of the lesser known names that are 273 00:13:26,760 --> 00:13:29,120 Speaker 1: held by this ETF and sort of what the thinking 274 00:13:29,360 --> 00:13:34,960 Speaker 1: is about being included in this Quantum and Artificial Intelligence ETF. 275 00:13:35,360 --> 00:13:37,640 Speaker 3: Yeah. Sure, I'll kind of give you the macro overview 276 00:13:37,640 --> 00:13:39,640 Speaker 3: of like why the smaller name is in general, and 277 00:13:39,720 --> 00:13:41,960 Speaker 3: like what kinds of things they do and why the 278 00:13:41,960 --> 00:13:44,520 Speaker 3: bigger names. Right, So, when we think about themes and 279 00:13:44,559 --> 00:13:47,880 Speaker 3: innovation and things like AI and machine learning, there is 280 00:13:47,920 --> 00:13:50,760 Speaker 3: a lot of hype about it, right, And so when 281 00:13:50,760 --> 00:13:53,319 Speaker 3: there's hype about something, but it's also coming to fruition 282 00:13:53,440 --> 00:13:56,160 Speaker 3: and you're starting to see revenue as a results from this. 283 00:13:56,240 --> 00:13:59,120 Speaker 3: You're starting to see the actual impact of AI and 284 00:13:59,160 --> 00:14:01,920 Speaker 3: how it's being You just quickly note that AI will 285 00:14:02,000 --> 00:14:05,320 Speaker 3: change every sector and kind of asset class. I think 286 00:14:05,360 --> 00:14:08,000 Speaker 3: out there for example biotech, right, you can use AI 287 00:14:08,160 --> 00:14:10,680 Speaker 3: for better data to create better drugs to have better 288 00:14:10,720 --> 00:14:14,200 Speaker 3: results and surgeries, to power robots. If it's defense, better 289 00:14:14,559 --> 00:14:19,680 Speaker 3: targets and information. If it's retail targeting more accurately what 290 00:14:19,760 --> 00:14:21,480 Speaker 3: you're going to buy and purchase and you know where 291 00:14:21,480 --> 00:14:22,760 Speaker 3: it should be in the store of things like that. 292 00:14:22,880 --> 00:14:25,520 Speaker 3: But we can go through every sector and find the 293 00:14:25,560 --> 00:14:30,200 Speaker 3: application of AI. And so the big companies that power 294 00:14:30,240 --> 00:14:34,480 Speaker 3: AI are the Navidia, the AMD, the Google and Amazon 295 00:14:34,520 --> 00:14:36,960 Speaker 3: companies are the companies that have money, they have budget 296 00:14:37,040 --> 00:14:40,000 Speaker 3: and they're going to be immediate benefactors of this and 297 00:14:40,040 --> 00:14:42,360 Speaker 3: we've seen that play out this year. And so you 298 00:14:42,440 --> 00:14:45,040 Speaker 3: want them to have a big place in an ETF 299 00:14:45,120 --> 00:14:48,240 Speaker 3: like this because you get quality, balance sheet stability, but 300 00:14:48,400 --> 00:14:51,040 Speaker 3: with innovation, they're going to be winners and losers, right, 301 00:14:51,040 --> 00:14:53,040 Speaker 3: And some of these smaller companies that you mentioned, you 302 00:14:53,080 --> 00:14:54,760 Speaker 3: know why we have the smaller companies in there because 303 00:14:54,840 --> 00:14:57,280 Speaker 3: you think about innovation, you don't know who the winners 304 00:14:57,320 --> 00:14:59,280 Speaker 3: and the losers are going to be. So some of 305 00:14:59,320 --> 00:15:01,400 Speaker 3: these smaller company these are chip companies. Some of them 306 00:15:01,440 --> 00:15:04,320 Speaker 3: are like Amberella that deal with the visuals and graphics 307 00:15:04,400 --> 00:15:07,080 Speaker 3: and we're getting and things like this. There are different 308 00:15:07,080 --> 00:15:10,400 Speaker 3: parts of AI. There's data parsing, there's the actual chips 309 00:15:10,440 --> 00:15:12,400 Speaker 3: part of it, there's a supercomputing part of it. And 310 00:15:12,840 --> 00:15:15,360 Speaker 3: we believe in the whole kind of ecosystem of AI, 311 00:15:15,440 --> 00:15:18,000 Speaker 3: and we think that having exposure to some of the 312 00:15:18,000 --> 00:15:21,400 Speaker 3: small caps that can become major players here and eventually 313 00:15:21,520 --> 00:15:23,920 Speaker 3: MNA targets, it's a good way to kind of diversify 314 00:15:23,960 --> 00:15:24,920 Speaker 3: your exposure there. 315 00:15:25,520 --> 00:15:28,280 Speaker 2: So a lot of attention, as Mike said, and as 316 00:15:28,280 --> 00:15:30,840 Speaker 2: you just said, has been paid to Navidia and some 317 00:15:30,880 --> 00:15:33,280 Speaker 2: of the other bigger ones. I spoke with Rob are 318 00:15:33,320 --> 00:15:36,240 Speaker 2: Not from research Affiliates a couple days ago, and he's 319 00:15:36,280 --> 00:15:40,320 Speaker 2: pointing to Navidia as being potentially in a bubble, wondering 320 00:15:40,360 --> 00:15:44,080 Speaker 2: if you are looking at some of these AI companies 321 00:15:44,080 --> 00:15:46,160 Speaker 2: and thinking the same that maybe potentially some of the 322 00:15:46,160 --> 00:15:48,280 Speaker 2: bigger ones are overvalued at this point. 323 00:15:48,480 --> 00:15:50,600 Speaker 3: I actually don't think that they're in the bubble because 324 00:15:50,600 --> 00:15:53,800 Speaker 3: I think that you know that once we get past 325 00:15:53,960 --> 00:15:57,720 Speaker 3: the kind of like the fat and the hype around 326 00:15:57,720 --> 00:16:00,760 Speaker 3: AI and we actually see the practical appation of AI 327 00:16:00,840 --> 00:16:04,320 Speaker 3: and how it's impacting top and bottom lines of major companies, 328 00:16:04,320 --> 00:16:07,000 Speaker 3: I think that these companies can only grow further. And 329 00:16:07,040 --> 00:16:09,760 Speaker 3: a Video, without a doubt, has set themselves to a 330 00:16:09,800 --> 00:16:13,120 Speaker 3: part to be the AI provider. The only way I 331 00:16:13,280 --> 00:16:15,600 Speaker 3: think that this doesn't sort of shake out for a 332 00:16:15,760 --> 00:16:19,480 Speaker 3: video to continue growing is if they can't actually supply 333 00:16:20,160 --> 00:16:23,080 Speaker 3: the chips that are that are needed. Justin Wong came 334 00:16:23,080 --> 00:16:26,560 Speaker 3: out and gave us his view of the twenty twenty 335 00:16:26,600 --> 00:16:31,200 Speaker 3: four forward looking orders and said that they're beyond kind 336 00:16:31,200 --> 00:16:34,320 Speaker 3: of his expectations and well beyond what the company expected. 337 00:16:34,400 --> 00:16:37,080 Speaker 3: So when you have the CEO that has actual clarity 338 00:16:37,160 --> 00:16:40,360 Speaker 3: into his orders or his chips, as long as they 339 00:16:40,360 --> 00:16:43,200 Speaker 3: can provide those chips, and barring any major there's so 340 00:16:43,280 --> 00:16:45,640 Speaker 3: much government intervention now with chips and China and all 341 00:16:45,680 --> 00:16:47,320 Speaker 3: these things, so there are things that can come up. 342 00:16:47,360 --> 00:16:52,120 Speaker 3: But barring any kind of like political event, it seems 343 00:16:52,200 --> 00:16:55,600 Speaker 3: that the demand will be there to justify the price 344 00:16:55,640 --> 00:17:14,320 Speaker 3: and perhaps the price appreciation. In my opinion, Sylvia. 345 00:17:14,400 --> 00:17:18,840 Speaker 1: One more interesting thematic ETF is the hydrogen ETF you 346 00:17:18,840 --> 00:17:21,240 Speaker 1: guys offer, And if you go back, I don't know 347 00:17:21,280 --> 00:17:23,919 Speaker 1: what decade or more, there was a lot of optimism 348 00:17:24,000 --> 00:17:27,960 Speaker 1: that sort of vehicles would have one day be hydrogen powered. 349 00:17:28,040 --> 00:17:30,920 Speaker 1: I mean, I guess there are some currently that are, 350 00:17:30,960 --> 00:17:34,280 Speaker 1: but it never quite reached that goal that they had 351 00:17:34,560 --> 00:17:38,479 Speaker 1: for hydrogen. Where is the growth for hydrogen hydrogen? What's 352 00:17:38,560 --> 00:17:41,760 Speaker 1: in the CTF that has you excited? What sort of 353 00:17:41,840 --> 00:17:47,320 Speaker 1: opportunity sets are there for the companies that are owned 354 00:17:47,359 --> 00:17:48,160 Speaker 1: by the CTF. 355 00:17:48,320 --> 00:17:50,199 Speaker 3: I think it goes back to the macro story of 356 00:17:50,240 --> 00:17:53,800 Speaker 3: alternative energy. We have to find better ways to power things, 357 00:17:53,840 --> 00:17:58,199 Speaker 3: whether it's vehicles, factories, or otherwise that are friendly to 358 00:17:58,240 --> 00:18:00,960 Speaker 3: the environment. So it's not to say get rid of 359 00:18:00,960 --> 00:18:05,000 Speaker 3: all fuel based cars, but EV is one way to 360 00:18:05,040 --> 00:18:07,840 Speaker 3: go about it, and hydrogen fuel cell power is another 361 00:18:07,880 --> 00:18:09,800 Speaker 3: way to go about it. So, although it's not super 362 00:18:09,840 --> 00:18:13,080 Speaker 3: well known in the US outside of California, California is 363 00:18:13,080 --> 00:18:17,280 Speaker 3: actually seeing great growth in hydrogen powered vehicles. But it 364 00:18:17,320 --> 00:18:21,400 Speaker 3: is actually something that's taken shape around Europe and Japan 365 00:18:21,560 --> 00:18:24,200 Speaker 3: and different places around the world. So in Japan, for example, 366 00:18:24,200 --> 00:18:27,240 Speaker 3: you see you see hydrogen powered buses, you see hydrogen 367 00:18:27,240 --> 00:18:30,280 Speaker 3: powered boats and ships and all sorts of vehicles and 368 00:18:30,320 --> 00:18:32,280 Speaker 3: things like this, and it's starting to play out in 369 00:18:32,280 --> 00:18:34,719 Speaker 3: Europe too with some of their mass transportation options. So 370 00:18:35,080 --> 00:18:37,840 Speaker 3: I do think that we've seen companies like UPS and 371 00:18:37,960 --> 00:18:42,800 Speaker 3: NASA and Amazon having fuel cell powered kind of like 372 00:18:42,840 --> 00:18:46,480 Speaker 3: a crane machine, but all of their kind of manufacturing 373 00:18:46,520 --> 00:18:49,600 Speaker 3: equipment is powered by hydrogen. And of course they have 374 00:18:49,640 --> 00:18:51,679 Speaker 3: that investment in ribbon too, so you see the electric 375 00:18:51,760 --> 00:18:54,359 Speaker 3: vehicle side there. So there is growth in the space. 376 00:18:54,440 --> 00:18:56,520 Speaker 3: I think that we believe that there will be enough 377 00:18:56,560 --> 00:18:58,280 Speaker 3: growth in the space where it will be an interesting 378 00:18:58,320 --> 00:19:03,440 Speaker 3: alternative energy asset class alongside electric vehicles. But you are right, 379 00:19:03,520 --> 00:19:06,520 Speaker 3: it hasn't seen necessarily that hyper growth that these have seen, 380 00:19:06,560 --> 00:19:08,560 Speaker 3: so I think that that takes some time to play out. 381 00:19:08,960 --> 00:19:12,000 Speaker 3: Interestingly enough, though, remaining one of our most popular ETFs, 382 00:19:12,000 --> 00:19:15,280 Speaker 3: it's getting a lot of that ESG type of alternative 383 00:19:15,400 --> 00:19:16,520 Speaker 3: energy investment. 384 00:19:17,320 --> 00:19:21,080 Speaker 1: Well, Sylvia Jablonski of Defiance ETFs really great to hear 385 00:19:21,119 --> 00:19:24,120 Speaker 1: your thoughts. Can't let you go quite yet, though. He's 386 00:19:24,160 --> 00:19:27,840 Speaker 1: a contestant for our craziest thing that we saw in 387 00:19:27,880 --> 00:19:31,240 Speaker 1: markets this week. Well, why do you get a started, Bildina. 388 00:19:31,640 --> 00:19:34,280 Speaker 2: You probably saw this headline and you definitely thought of me. 389 00:19:34,880 --> 00:19:36,359 Speaker 1: Sure, yeah, well let's hear it. 390 00:19:36,520 --> 00:19:40,240 Speaker 2: Taylor Swift's Eras to Our Concert film broke AMC Entertainment's 391 00:19:40,280 --> 00:19:43,280 Speaker 2: advanced ticket sales record in just three hours. 392 00:19:43,480 --> 00:19:43,840 Speaker 1: Wow. 393 00:19:43,920 --> 00:19:46,439 Speaker 2: So AMC announced that they're going to have like a 394 00:19:46,560 --> 00:19:51,640 Speaker 2: movie film version of her concert. They sold twenty six 395 00:19:51,680 --> 00:19:54,240 Speaker 2: million dollars worth of tickets on the first day, and 396 00:19:54,280 --> 00:19:57,719 Speaker 2: I think the stock rose like nine or ten percent 397 00:19:57,840 --> 00:20:01,800 Speaker 2: at first, and there was like so much excitement. I 398 00:20:01,840 --> 00:20:04,159 Speaker 2: didn't buy an advanced ticket yet, but I will definitely 399 00:20:04,200 --> 00:20:04,840 Speaker 2: go see. 400 00:20:04,600 --> 00:20:08,920 Speaker 1: It's so AMC is back stock. 401 00:20:09,080 --> 00:20:11,280 Speaker 2: No, but then they took the opportunity to do stock 402 00:20:11,320 --> 00:20:14,520 Speaker 2: offering like two days later. Yes, but the previous record 403 00:20:14,600 --> 00:20:17,800 Speaker 2: was like almost seventeen million by Spider Man, which like 404 00:20:17,960 --> 00:20:21,200 Speaker 2: is well known for breaking all kinds of records. 405 00:20:21,240 --> 00:20:23,720 Speaker 1: I want to know it. Will these be normally priced 406 00:20:23,800 --> 00:20:28,240 Speaker 1: tickets or like Taylor Swift sized ticket prices for this. 407 00:20:28,480 --> 00:20:30,560 Speaker 2: She loves the number thirteen, so I could see them 408 00:20:30,560 --> 00:20:33,760 Speaker 2: being like thirteen bucks or something. I know that they're 409 00:20:33,800 --> 00:20:39,240 Speaker 2: selling like themed popcorn buckets or soda cups or something 410 00:20:39,320 --> 00:20:39,560 Speaker 2: like that. 411 00:20:39,680 --> 00:20:42,120 Speaker 1: Yeah, well, I know I will be sending all three 412 00:20:42,119 --> 00:20:44,280 Speaker 1: of my daughters to see this at some point. You 413 00:20:44,320 --> 00:20:45,879 Speaker 1: can go with them, right, I'll go with them. What 414 00:20:45,920 --> 00:20:48,679 Speaker 1: the heck? All right, that's a good one, Sylvia. How 415 00:20:48,680 --> 00:20:51,399 Speaker 1: about you? Have you seen anything crazy lately in markets? 416 00:20:51,720 --> 00:20:55,760 Speaker 3: I think that the market itself has just been crazy. 417 00:20:55,880 --> 00:20:59,480 Speaker 3: Like if you this has been the most interesting market 418 00:20:59,560 --> 00:21:01,480 Speaker 3: that I've want for the entire year, and the last 419 00:21:01,520 --> 00:21:04,760 Speaker 3: week is no exception, one little piece of data that's 420 00:21:04,800 --> 00:21:08,000 Speaker 3: the whole thing off, child, right, So I think it's 421 00:21:08,160 --> 00:21:10,960 Speaker 3: just if you think about like classic investing, right, we're 422 00:21:10,960 --> 00:21:13,920 Speaker 3: supposed to buy low, sell high holds for a long time, 423 00:21:14,080 --> 00:21:17,080 Speaker 3: you know, minimize our taxes, all of these things. And 424 00:21:17,480 --> 00:21:19,800 Speaker 3: if you just look at the gyrations of what a 425 00:21:19,800 --> 00:21:22,679 Speaker 3: lot of retail investors are doing, they're buying high and 426 00:21:22,720 --> 00:21:26,439 Speaker 3: they're like, just get an ISM service number that's too 427 00:21:26,520 --> 00:21:28,560 Speaker 3: hot and selling the whole thing off and losing four 428 00:21:28,640 --> 00:21:30,320 Speaker 3: or five percent in a day. So it's just a 429 00:21:30,440 --> 00:21:33,560 Speaker 3: very erratic market. It's an emotionally charged market that is 430 00:21:33,640 --> 00:21:35,720 Speaker 3: very difficult to predict. And I think that that's the 431 00:21:35,760 --> 00:21:38,800 Speaker 3: weirdest thing about it, this ISM number and the reaction 432 00:21:39,119 --> 00:21:41,879 Speaker 3: by the market to me is the weirdest thing. I mean, 433 00:21:41,880 --> 00:21:43,800 Speaker 3: if that's not going to hike one percent this year, 434 00:21:43,840 --> 00:21:45,960 Speaker 3: like what are we not to say that things can't 435 00:21:46,160 --> 00:21:48,560 Speaker 3: kind of turn but in the next couple of months, 436 00:21:48,560 --> 00:21:49,160 Speaker 3: like what changed? 437 00:21:49,359 --> 00:21:52,439 Speaker 1: Yeah? Yeah, how much is the needle really moving compared 438 00:21:52,480 --> 00:21:53,320 Speaker 1: with expectations? 439 00:21:53,400 --> 00:21:53,600 Speaker 3: Right? 440 00:21:54,080 --> 00:21:58,639 Speaker 1: And it's funny because it is that notoriously seasonal viotal 441 00:21:58,720 --> 00:22:00,600 Speaker 1: time of the year, but it's it's kind of like 442 00:22:01,640 --> 00:22:04,439 Speaker 1: the drama surrounding interest rates and the fed and the 443 00:22:04,440 --> 00:22:08,240 Speaker 1: fundamentals are are sort of peaking right at the seasonal 444 00:22:08,520 --> 00:22:13,280 Speaker 1: vault all time. It's an interesting mix that we'll see 445 00:22:13,280 --> 00:22:16,439 Speaker 1: how the rest of September plays out. But yeah, I agree, 446 00:22:16,520 --> 00:22:19,359 Speaker 1: it's been It's been fascinating, all right, I'll give you 447 00:22:19,480 --> 00:22:23,040 Speaker 1: mine this once again. My favorite is the alternative asset 448 00:22:23,119 --> 00:22:25,560 Speaker 1: space for Donna. So Freddie Mercury Do you know who 449 00:22:25,640 --> 00:22:28,080 Speaker 1: he is? Freddie Murcer of course, Yeah, the lead singer 450 00:22:28,119 --> 00:22:32,399 Speaker 1: of Queen. Don't ask me his real name. Actually I 451 00:22:32,400 --> 00:22:34,240 Speaker 1: think I might be his real name. I don't know, No, 452 00:22:34,880 --> 00:22:39,159 Speaker 1: can't be anyway. They sold a bunch of his stuff 453 00:22:39,200 --> 00:22:44,600 Speaker 1: at auction Southeby's sold all sorts of stuff, lyrics, clothes, artwork, 454 00:22:44,880 --> 00:22:49,480 Speaker 1: but the star of the show was his Yamaha Grand Piano, 455 00:22:50,560 --> 00:22:53,600 Speaker 1: which it really hyped this up in the material for 456 00:22:53,680 --> 00:22:56,600 Speaker 1: the auction, saying, let me read it here from our 457 00:22:56,640 --> 00:23:03,360 Speaker 1: Bloomberg coverage, Yamaha Grand Piano that Sotheby's with uncharistic immodesty 458 00:23:03,440 --> 00:23:07,239 Speaker 1: called quote the instrument used to compose some of the 459 00:23:07,240 --> 00:23:10,840 Speaker 1: greatest songs of the twentieth century. Which, fair enough, it 460 00:23:10,920 --> 00:23:13,280 Speaker 1: is true, we know some good songs. I'll give that 461 00:23:13,720 --> 00:23:16,560 Speaker 1: time to play. The prices precise, Sylvia, this is this 462 00:23:16,600 --> 00:23:20,600 Speaker 1: is where this is your time to shine. Yeah. What? 463 00:23:21,400 --> 00:23:21,960 Speaker 4: Oh gosh? 464 00:23:22,160 --> 00:23:22,400 Speaker 3: Okay. 465 00:23:22,560 --> 00:23:27,080 Speaker 1: Freddie Mercury's Yamaha Grand Piano self war at auction by 466 00:23:27,160 --> 00:23:29,719 Speaker 1: sothebys will give you one hint. It was lower than 467 00:23:29,720 --> 00:23:30,720 Speaker 1: the expected range. 468 00:23:30,960 --> 00:23:33,600 Speaker 2: Oh damn. I was going to say I'm going high. 469 00:23:33,800 --> 00:23:37,080 Speaker 1: Yeah, not to say it was a low number, mind you. 470 00:23:37,480 --> 00:23:39,399 Speaker 1: It's like I said, it was considered to be the 471 00:23:39,440 --> 00:23:41,760 Speaker 1: centerpiece of this auction, but a little came in a 472 00:23:41,760 --> 00:23:42,840 Speaker 1: little under the rest. 473 00:23:43,119 --> 00:23:47,040 Speaker 2: Interesting pianos are expensive, no, okay, So then a famous 474 00:23:47,119 --> 00:23:51,960 Speaker 2: person's piano. I'm gonna go with three hundred and fifty. 475 00:23:52,119 --> 00:23:57,960 Speaker 1: Dollars, okay, oh pounds? What I'll accept either. I'll accept either. 476 00:23:58,000 --> 00:23:59,119 Speaker 1: I can do the conversion for you. 477 00:24:00,320 --> 00:24:02,720 Speaker 2: That's too low, isn't it. Can I revise? I'm gonna 478 00:24:02,720 --> 00:24:03,760 Speaker 2: go with two million. 479 00:24:04,080 --> 00:24:06,320 Speaker 1: All right, Sylvia? I don't know will Sylvia, Will you 480 00:24:06,359 --> 00:24:09,160 Speaker 1: let her revise her guests please? You're the other content. 481 00:24:09,440 --> 00:24:10,280 Speaker 3: I'll let her revise. 482 00:24:10,440 --> 00:24:14,320 Speaker 1: Okay, So now you gotta get you gotta give us 483 00:24:14,400 --> 00:24:14,880 Speaker 1: years now. 484 00:24:15,480 --> 00:24:20,520 Speaker 3: So I wonder what Taylor Swifts piano for there, I 485 00:24:20,560 --> 00:24:21,000 Speaker 3: say so. 486 00:24:21,240 --> 00:24:24,199 Speaker 4: I think if I think hers goes for five million, 487 00:24:24,280 --> 00:24:27,080 Speaker 4: I'll give him just because half of the world that 488 00:24:27,200 --> 00:24:29,280 Speaker 4: is alive now maybe it doesn't know who he is, 489 00:24:29,880 --> 00:24:31,960 Speaker 4: like two and a half. 490 00:24:32,520 --> 00:24:34,560 Speaker 1: Man, you guys more or less split the difference there. 491 00:24:34,640 --> 00:24:37,760 Speaker 3: I know who he is, but of course. 492 00:24:37,680 --> 00:24:40,560 Speaker 1: One point seven million British Browns, so two point two 493 00:24:40,600 --> 00:24:42,240 Speaker 1: something million, you guys are kind of I think we 494 00:24:42,280 --> 00:24:42,720 Speaker 1: have a draw. 495 00:24:43,040 --> 00:24:48,199 Speaker 2: No, I think I will. I definitely win. Hello. Sorry, Sylvia, I. 496 00:24:49,720 --> 00:24:51,600 Speaker 1: Don't know you because. 497 00:24:53,040 --> 00:24:56,359 Speaker 2: Once once I said it, it was so obviously too long. 498 00:24:56,280 --> 00:24:59,840 Speaker 1: Giving it to Sylvia. You defaulted seven to eight. 499 00:24:59,880 --> 00:25:03,440 Speaker 2: I what what do they expect? 500 00:25:03,800 --> 00:25:06,120 Speaker 1: They expected more than that? I don't know. I forget 501 00:25:06,200 --> 00:25:08,120 Speaker 1: the exact I think it was like two to three 502 00:25:08,359 --> 00:25:12,479 Speaker 1: million British pounds. So they got one point seven something 503 00:25:13,040 --> 00:25:18,399 Speaker 1: million British pounds. So I mean, who's got room for 504 00:25:18,440 --> 00:25:19,159 Speaker 1: a grand piano? 505 00:25:19,400 --> 00:25:26,479 Speaker 4: Yeah, paid two point two exactly million for it. 506 00:25:26,520 --> 00:25:29,600 Speaker 1: I tell you they never let you know who bought it. 507 00:25:29,600 --> 00:25:33,440 Speaker 1: Maybe Elton John bought it. That'd be my guest possible anyway, 508 00:25:33,560 --> 00:25:36,920 Speaker 1: So be a Jablonski of Defiance eats apps. Really pleasure 509 00:25:36,960 --> 00:25:39,359 Speaker 1: to catch up with you and hear your thoughts on 510 00:25:39,400 --> 00:25:41,439 Speaker 1: some of these big trends of the markets here. Hopefully 511 00:25:41,480 --> 00:25:42,560 Speaker 1: we can get you back again some day. 512 00:25:42,960 --> 00:25:43,800 Speaker 3: Thank you so much. 513 00:25:43,920 --> 00:25:52,560 Speaker 2: Thanks Thanks Sylvia. 514 00:25:53,480 --> 00:25:55,560 Speaker 1: What Goes Up will be back next week and so 515 00:25:55,680 --> 00:25:57,960 Speaker 1: then you can find us on the Bloomberg Terminal website 516 00:25:58,000 --> 00:26:01,240 Speaker 1: and app, or wherever you get your podc guests. We'd 517 00:26:01,280 --> 00:26:02,800 Speaker 1: love it if you took the time to rate and 518 00:26:02,880 --> 00:26:05,840 Speaker 1: review the show on Apple Podcasts. Some more listeners can 519 00:26:05,880 --> 00:26:08,760 Speaker 1: find us, and you can find us on Twitter, follow 520 00:26:08,800 --> 00:26:12,760 Speaker 1: me at rig Anonymous, Wildona Hirich is at Goldna Hirich. 521 00:26:13,440 --> 00:26:17,960 Speaker 1: You can also follow Bloomberg Podcasts at Podcasts. What Goes 522 00:26:18,040 --> 00:26:21,040 Speaker 1: Up is produced by Stacey Wong. Thanks for listening, See 523 00:26:21,080 --> 00:26:21,600 Speaker 1: you next time.