1 00:00:13,920 --> 00:00:17,360 Speaker 1: Welcome to Wako's Up, a weekly markets podcast. Humble Dona 2 00:00:17,440 --> 00:00:19,720 Speaker 1: Hire a market supporter with Bloomberg. 3 00:00:19,360 --> 00:00:21,799 Speaker 2: And I'm Katie Greifeld. Mike Reagan is off this week. 4 00:00:21,920 --> 00:00:23,079 Speaker 1: I'm so happy to have you here. 5 00:00:23,160 --> 00:00:23,960 Speaker 2: Yeah, thank god. 6 00:00:24,079 --> 00:00:26,479 Speaker 1: But first, Katie, it's been a really long time since 7 00:00:26,480 --> 00:00:29,920 Speaker 1: you've been on the podcast. I want to loll youoateral years, 8 00:00:29,960 --> 00:00:32,280 Speaker 1: not literal, No, it actually no literal. 9 00:00:32,400 --> 00:00:34,360 Speaker 2: But I'm thrilled to be here because it feels like 10 00:00:34,400 --> 00:00:35,440 Speaker 2: there's a lot to talk about. 11 00:00:35,720 --> 00:00:37,600 Speaker 1: There is, but I'm gonna put you on the spot first. 12 00:00:37,680 --> 00:00:38,040 Speaker 2: Okay. 13 00:00:38,479 --> 00:00:40,519 Speaker 1: I want you to tell the audience something about you 14 00:00:40,720 --> 00:00:44,360 Speaker 1: that nobody knows, not even your husband. 15 00:00:44,560 --> 00:00:48,199 Speaker 2: Oh no, I mean I don't think there's anything I 16 00:00:48,200 --> 00:00:51,720 Speaker 2: would say. I am extremely afraid of the dark. 17 00:00:52,520 --> 00:00:53,040 Speaker 3: I knew that. 18 00:00:53,400 --> 00:00:55,040 Speaker 1: Yeah, we are in a bit of a dark home. 19 00:00:55,080 --> 00:00:56,080 Speaker 1: It has black walls. 20 00:00:56,200 --> 00:00:59,120 Speaker 2: No, it's just like when you're in maybe a room 21 00:00:59,400 --> 00:01:02,040 Speaker 2: or your house saloon and the lights go out. I 22 00:01:02,160 --> 00:01:02,840 Speaker 2: hate that. 23 00:01:02,840 --> 00:01:03,960 Speaker 1: That's not a secret. 24 00:01:04,240 --> 00:01:06,959 Speaker 2: I'm media trained enough, don't you don't do. 25 00:01:06,959 --> 00:01:07,880 Speaker 1: That secret. 26 00:01:09,240 --> 00:01:09,640 Speaker 2: Anyway. 27 00:01:09,800 --> 00:01:12,959 Speaker 1: Artificial intelligence is all the hype on Wall Street these days, 28 00:01:13,040 --> 00:01:16,000 Speaker 1: and we have some strategists even seeing AI trends driving 29 00:01:16,000 --> 00:01:19,160 Speaker 1: further gains for stocks. Now one company is looking to 30 00:01:19,200 --> 00:01:24,080 Speaker 1: analyze US equity market trends using AI technology to project 31 00:01:24,080 --> 00:01:27,200 Speaker 1: in real time why stocks might be moving, and its 32 00:01:27,200 --> 00:01:31,240 Speaker 1: founder is joining us today. Gets Yen's Nordvic. He's the 33 00:01:31,280 --> 00:01:34,800 Speaker 1: co founder and CEO of Market Reader, and he's also 34 00:01:34,920 --> 00:01:38,279 Speaker 1: the founder and CEO of ex Anti Data. So, YenS, 35 00:01:38,360 --> 00:01:41,440 Speaker 1: thank you so much for joining us. I told so 36 00:01:41,520 --> 00:01:43,120 Speaker 1: many people you were going to be on the podcast, 37 00:01:43,160 --> 00:01:44,720 Speaker 1: and everybody's like, oh my god, I know him. 38 00:01:45,080 --> 00:01:46,640 Speaker 4: It was crazy that there was a line of people 39 00:01:46,640 --> 00:01:48,360 Speaker 4: when I came into the building. What's going on here? 40 00:01:48,520 --> 00:01:50,720 Speaker 1: Yeah, there was literally a lot of people. They were like, 41 00:01:50,720 --> 00:01:52,720 Speaker 1: I would like to meet him, and so that's why 42 00:01:52,720 --> 00:01:53,160 Speaker 1: we were like. 43 00:01:53,160 --> 00:01:55,240 Speaker 2: I was thrilled that Mike Reagan happened to be off 44 00:01:55,240 --> 00:01:58,160 Speaker 2: this week because I haven't talked to you in years. 45 00:01:58,200 --> 00:01:59,160 Speaker 2: But we go back. 46 00:01:59,440 --> 00:02:03,960 Speaker 4: I have been doing surveillance in the morning, probably forty times, 47 00:02:04,400 --> 00:02:07,720 Speaker 4: but I've not been in the office. This office I've 48 00:02:07,760 --> 00:02:10,919 Speaker 4: not been to I think since nineteen So it was weird, 49 00:02:11,000 --> 00:02:12,560 Speaker 4: but it's still the same building at least. 50 00:02:12,680 --> 00:02:14,920 Speaker 1: Oh yeah, it feels like Mike Reagan also hasn't been 51 00:02:14,960 --> 00:02:20,200 Speaker 1: in this office. Okay, so again, just to start, Katie 52 00:02:20,280 --> 00:02:22,560 Speaker 1: knows you. All these people I talk to know you, 53 00:02:22,639 --> 00:02:25,480 Speaker 1: but maybe just tell our audience a little bit about you. 54 00:02:25,800 --> 00:02:29,400 Speaker 4: I started my career being a currency strategist to Goldman 55 00:02:29,680 --> 00:02:32,600 Speaker 4: and then I was a currency strategist at Bridgeward Associates. 56 00:02:32,639 --> 00:02:34,079 Speaker 3: I was head of research. 57 00:02:33,680 --> 00:02:36,320 Speaker 4: At the More Securities, and then in sixteen I founded 58 00:02:36,320 --> 00:02:41,000 Speaker 4: a company called Exante Data where we advise institutional investors 59 00:02:41,120 --> 00:02:44,000 Speaker 4: on what's going on in the world using a lot 60 00:02:44,000 --> 00:02:48,359 Speaker 4: of data. And that's the sort of background. But really 61 00:02:48,200 --> 00:02:50,040 Speaker 4: the reason why I invite me in today is that 62 00:02:50,080 --> 00:02:53,160 Speaker 4: we have a new company called market Reader, where we 63 00:02:53,639 --> 00:02:57,760 Speaker 4: use latest technology to really crunch data in real time 64 00:02:58,360 --> 00:03:00,480 Speaker 4: and explain why the market is moving. So if you 65 00:03:00,520 --> 00:03:02,560 Speaker 4: have a stock that's gapping down, market readers should be 66 00:03:02,600 --> 00:03:05,840 Speaker 4: able to tell you why using all the different techniques 67 00:03:05,880 --> 00:03:08,240 Speaker 4: that like a top hedge fund would also be using, 68 00:03:08,240 --> 00:03:11,280 Speaker 4: but all programmed and a piece of software something that 69 00:03:11,360 --> 00:03:12,880 Speaker 4: everybody reader should be able to use. 70 00:03:13,000 --> 00:03:14,520 Speaker 1: And are you also afraid of the dark? 71 00:03:15,639 --> 00:03:15,679 Speaker 2: Not? 72 00:03:16,720 --> 00:03:19,640 Speaker 4: Not really, but this room is a little bit scary. 73 00:03:19,639 --> 00:03:21,240 Speaker 4: It is one of the blackest rooms I've seen. 74 00:03:21,639 --> 00:03:24,160 Speaker 2: Yeah, it's pretty cool. I don't know, it's just you know, 75 00:03:24,200 --> 00:03:27,040 Speaker 2: when the lights go out, you don't know where everything is. Yeah, 76 00:03:27,080 --> 00:03:30,040 Speaker 2: I like to know in any case, So market reader, 77 00:03:30,160 --> 00:03:33,400 Speaker 2: so you say that this is theoretically similar to what 78 00:03:33,880 --> 00:03:36,920 Speaker 2: a hedge fund would be doing. Who is your client then? 79 00:03:37,000 --> 00:03:37,880 Speaker 2: Is it hedge funds? 80 00:03:38,280 --> 00:03:41,120 Speaker 4: This is my biggest problem with the market reader that 81 00:03:41,240 --> 00:03:44,000 Speaker 4: I think it's literally for everybody, So everybody else wants to, Oh, 82 00:03:44,000 --> 00:03:45,320 Speaker 4: who's your target audience? 83 00:03:45,320 --> 00:03:45,840 Speaker 3: Who is it? 84 00:03:46,600 --> 00:03:48,880 Speaker 4: I think it could be used by a hedge fund 85 00:03:48,880 --> 00:03:51,600 Speaker 4: manager or an asset manager, but it could also be used 86 00:03:51,640 --> 00:03:54,800 Speaker 4: by somebody who has, you know, a savings account and 87 00:03:54,840 --> 00:03:57,680 Speaker 4: investing on their own and they've maybe lost some money 88 00:03:57,680 --> 00:03:59,640 Speaker 4: and made some money. They want to know why so 89 00:03:59,680 --> 00:04:04,119 Speaker 4: they can good decisions. So this could be used by everybody, 90 00:04:04,120 --> 00:04:07,040 Speaker 4: Like I have a company where we cater to like 91 00:04:07,120 --> 00:04:10,960 Speaker 4: a niche audience of institutional investors. Right, the whole goal 92 00:04:11,080 --> 00:04:14,400 Speaker 4: of market reader is to do something for millions of users. 93 00:04:14,160 --> 00:04:17,000 Speaker 3: Really like have a tool that everybody can use. 94 00:04:17,000 --> 00:04:20,120 Speaker 4: And I have to say the AI aspect actually has 95 00:04:20,200 --> 00:04:23,200 Speaker 4: turned out to be very important in that regard, right, 96 00:04:23,200 --> 00:04:28,400 Speaker 4: because we have complex models that generate the results, but 97 00:04:28,480 --> 00:04:32,000 Speaker 4: the AI, the language processing, really allows us to present 98 00:04:32,200 --> 00:04:35,320 Speaker 4: that in a very easy to understand way, right, a 99 00:04:35,400 --> 00:04:37,240 Speaker 4: couple of lines of text saying Okay, this is really 100 00:04:37,279 --> 00:04:40,080 Speaker 4: what you need to know about this stock or this ETF. 101 00:04:40,360 --> 00:04:42,320 Speaker 4: And if it's just like you know, model output, it 102 00:04:42,320 --> 00:04:45,440 Speaker 4: would actually be kind of hard to digest. So the 103 00:04:45,480 --> 00:04:48,520 Speaker 4: AI technology has really allowed us to present stuff in 104 00:04:48,560 --> 00:04:49,880 Speaker 4: an easier to digest way. 105 00:04:50,120 --> 00:04:53,080 Speaker 1: Okay, so talk more about the AI aspect, because that 106 00:04:53,120 --> 00:04:55,440 Speaker 1: part obviously is super interesting to a lot of people 107 00:04:55,480 --> 00:04:58,960 Speaker 1: these days. But how even does market reader work? And 108 00:04:59,000 --> 00:05:03,279 Speaker 1: I know you won't spill the secret sauce I suppose 109 00:05:03,320 --> 00:05:05,400 Speaker 1: I could call it, but just like, give us an 110 00:05:05,400 --> 00:05:07,680 Speaker 1: overview of how it works, what it does, and how 111 00:05:07,720 --> 00:05:08,599 Speaker 1: the AI plays it. 112 00:05:08,880 --> 00:05:11,720 Speaker 4: I will give an example. Right, So, one of the 113 00:05:11,760 --> 00:05:14,920 Speaker 4: inputs into our system is literally everything that's going on 114 00:05:14,920 --> 00:05:17,680 Speaker 4: on social media. We so ingest a lot of data, right, 115 00:05:18,120 --> 00:05:20,400 Speaker 4: but if you look at everything that's going on on Twitter, 116 00:05:20,440 --> 00:05:23,080 Speaker 4: for example, there's a lot of stuff that is not 117 00:05:23,120 --> 00:05:25,599 Speaker 4: that relevant, a lot of stuff that is fairly noisy. 118 00:05:25,680 --> 00:05:25,840 Speaker 3: Right. 119 00:05:25,920 --> 00:05:28,080 Speaker 4: So one of the ways we can apply AI is 120 00:05:28,080 --> 00:05:31,120 Speaker 4: to literally say, is this a high quality tweet that 121 00:05:31,160 --> 00:05:33,760 Speaker 4: we should consider? Is this a low quality maybe even 122 00:05:33,760 --> 00:05:41,600 Speaker 4: inappropriate tweet right, and that process is extremely powerful, extremely powerful. 123 00:05:41,800 --> 00:05:44,640 Speaker 4: So there's sort of analytical aspect, but there's also as 124 00:05:44,640 --> 00:05:47,200 Speaker 4: a set before in presentational aspect when you then have 125 00:05:47,360 --> 00:05:50,080 Speaker 4: all the results from all the different fundamental models, just 126 00:05:50,160 --> 00:05:54,640 Speaker 4: summarizing in a way where the human can actually understand. 127 00:05:54,080 --> 00:05:55,239 Speaker 3: It very quickly. 128 00:05:55,560 --> 00:05:57,960 Speaker 4: It's very hard to get like how should we call 129 00:05:58,040 --> 00:06:01,560 Speaker 4: it mechanical robot to do it? But the possibilities you 130 00:06:01,640 --> 00:06:05,159 Speaker 4: have within different AI tools such as chet GPT to 131 00:06:05,279 --> 00:06:08,200 Speaker 4: really generate text that is what should we call it, 132 00:06:08,240 --> 00:06:13,280 Speaker 4: like digestible but also appetizing is something new and give 133 00:06:13,360 --> 00:06:14,360 Speaker 4: some new opportunities. 134 00:06:14,520 --> 00:06:16,480 Speaker 2: And that's what I wanted to sort of hone in 135 00:06:16,880 --> 00:06:19,680 Speaker 2: on on the new part, because you know, describing you know, 136 00:06:19,720 --> 00:06:23,880 Speaker 2: scraping social media that necessarily isn't new, but it sounds 137 00:06:23,880 --> 00:06:27,040 Speaker 2: like it's the step further turning that into text that 138 00:06:27,640 --> 00:06:29,520 Speaker 2: kind of makes sense and you can read and is 139 00:06:30,000 --> 00:06:32,720 Speaker 2: pleasant to look at. Am I understanding that correctly. 140 00:06:32,760 --> 00:06:35,320 Speaker 4: There's many ways we use AI, but two good examples 141 00:06:35,360 --> 00:06:38,600 Speaker 4: would be the filtering itself, Like we can actually use 142 00:06:38,640 --> 00:06:41,320 Speaker 4: the language models to say, okay, is this the type 143 00:06:41,360 --> 00:06:43,320 Speaker 4: of content we want to look at it all? So 144 00:06:43,360 --> 00:06:45,960 Speaker 4: it can kind of look as a filtering mechanism, right. 145 00:06:46,279 --> 00:06:48,839 Speaker 4: And then there's a further step that is close to 146 00:06:48,880 --> 00:06:51,239 Speaker 4: where the user gets the output, where we essentially, okay, 147 00:06:51,600 --> 00:06:54,280 Speaker 4: present all this model output in a way where it's 148 00:06:54,320 --> 00:06:56,880 Speaker 4: easy to digest in one second. Right, there's those two 149 00:06:56,960 --> 00:06:57,680 Speaker 4: aspects of it. 150 00:06:57,839 --> 00:06:58,080 Speaker 3: Okay. 151 00:06:58,080 --> 00:07:01,359 Speaker 1: And then let's say I ran an analysis on some 152 00:07:01,839 --> 00:07:06,520 Speaker 1: stock and then I got the little paragraph, Right, what 153 00:07:06,600 --> 00:07:08,960 Speaker 1: do I then do with that information? What does the 154 00:07:08,960 --> 00:07:09,880 Speaker 1: typical client do? 155 00:07:10,520 --> 00:07:13,680 Speaker 4: It could be used by financial advisor, right, Like if 156 00:07:13,680 --> 00:07:16,960 Speaker 4: financial advisor has fifty stocks that his clients are following, 157 00:07:17,000 --> 00:07:19,680 Speaker 4: you can very quickly get a gist of Okay, that 158 00:07:19,680 --> 00:07:21,800 Speaker 4: client just lost five percent, and now I know why. 159 00:07:21,840 --> 00:07:23,760 Speaker 4: I can tell him if he calls, right, so it 160 00:07:23,760 --> 00:07:26,320 Speaker 4: can be used for that. Actually was last week nowadays 161 00:07:26,320 --> 00:07:29,400 Speaker 4: of passing fast, we had Madona was popping again. Like 162 00:07:29,440 --> 00:07:32,320 Speaker 4: in the old days vaccine stocks, and I used to 163 00:07:32,360 --> 00:07:34,200 Speaker 4: do a lot of COVID forecasting, but it's not a 164 00:07:34,200 --> 00:07:35,560 Speaker 4: big part of our process anymore. 165 00:07:35,680 --> 00:07:36,600 Speaker 3: It does move marketing. 166 00:07:36,840 --> 00:07:39,640 Speaker 4: Would Madanna moved ten percent very quickly, and it popped 167 00:07:39,680 --> 00:07:42,320 Speaker 4: up in our system, right, And the reason was there 168 00:07:42,400 --> 00:07:45,400 Speaker 4: was actually a new wave of COVID in China that 169 00:07:45,440 --> 00:07:48,160 Speaker 4: I had not known like, so even though I've been 170 00:07:48,160 --> 00:07:51,280 Speaker 4: doing this for three years now, it actually showed me 171 00:07:51,360 --> 00:07:53,120 Speaker 4: something where I said, Okay, go and look at that, 172 00:07:53,160 --> 00:07:55,120 Speaker 4: and then we want to analyze the COVID data as well. 173 00:07:55,200 --> 00:07:55,800 Speaker 3: There was a spike. 174 00:07:55,840 --> 00:07:58,040 Speaker 4: We didn't think it was enough to worry about, but 175 00:07:58,120 --> 00:08:00,560 Speaker 4: it can actually allert you to stuff happen that you're 176 00:08:00,560 --> 00:08:01,600 Speaker 4: otherwise not aware of. 177 00:08:02,560 --> 00:08:06,000 Speaker 1: Audience can't see us. But I went whow because I 178 00:08:06,040 --> 00:08:07,480 Speaker 1: hadn't heard that COVID news either. 179 00:08:07,680 --> 00:08:07,880 Speaker 3: Yeah. 180 00:08:08,000 --> 00:08:11,240 Speaker 4: No, and I like, we're known for literally like tracking 181 00:08:11,280 --> 00:08:14,160 Speaker 4: COVID like that. We have clients that came to us 182 00:08:14,640 --> 00:08:17,960 Speaker 4: in the COVID crisis, So it was kind of interesting 183 00:08:18,000 --> 00:08:20,320 Speaker 4: to see that example. Like obviously the Silicon Valley bank 184 00:08:22,040 --> 00:08:24,800 Speaker 4: situation that everybody knows about is another one where the 185 00:08:24,840 --> 00:08:28,360 Speaker 4: system was very helpful. Say okay on that Wednesday night, Okay, 186 00:08:28,600 --> 00:08:31,920 Speaker 4: there's something happening here. It's because of the announcement they 187 00:08:32,160 --> 00:08:34,640 Speaker 4: made about the bond says. By the way, First Republic 188 00:08:34,720 --> 00:08:36,560 Speaker 4: is also doing something it's not supposed to do on 189 00:08:36,559 --> 00:08:39,719 Speaker 4: Wednesday night. And those are instruments. I'm not used to 190 00:08:39,760 --> 00:08:43,320 Speaker 4: having those instruments on my screen. I'm traditionally currency guy. Right, 191 00:08:43,320 --> 00:08:45,760 Speaker 4: if there's something happening with the Euro, I'll have it 192 00:08:45,800 --> 00:08:48,560 Speaker 4: on my screen. But all those smallest stocks I don't have. 193 00:08:48,679 --> 00:08:48,880 Speaker 3: Right. 194 00:08:49,200 --> 00:08:52,120 Speaker 4: So having a system that can monitor everything that's going 195 00:08:52,120 --> 00:08:55,080 Speaker 4: on in the US equity market eleven thousand instruments and 196 00:08:55,200 --> 00:08:58,280 Speaker 4: pick out what should you focus on now gives you. 197 00:08:58,240 --> 00:09:00,440 Speaker 3: A really anability to absorb more information. 198 00:09:01,200 --> 00:09:03,960 Speaker 2: And so it's the US equity market, right, that's the 199 00:09:04,000 --> 00:09:05,200 Speaker 2: only asset class right now. 200 00:09:05,240 --> 00:09:07,160 Speaker 4: We want to do everything. So at the moment in 201 00:09:07,200 --> 00:09:10,839 Speaker 4: the system, we have US stocks ad rs like foreign 202 00:09:10,840 --> 00:09:14,800 Speaker 4: companies that are listed here ETFs that will cover the macroangle, right, 203 00:09:14,880 --> 00:09:17,559 Speaker 4: but we want to do literally every single financial instruments. 204 00:09:17,600 --> 00:09:19,280 Speaker 3: So give us a few weeks, few months, we will 205 00:09:19,320 --> 00:09:19,720 Speaker 3: have that done. 206 00:09:19,920 --> 00:09:22,040 Speaker 2: You have a few weeks, a few weeks, we'll give 207 00:09:22,080 --> 00:09:24,199 Speaker 2: you two weeks, yeah, two weeks, and then we'll check 208 00:09:24,240 --> 00:09:28,319 Speaker 2: back in. So it creates this text that you can 209 00:09:28,360 --> 00:09:33,160 Speaker 2: actually read. It doesn't necessarily issue like a buy sell recommendation. 210 00:09:33,679 --> 00:09:37,120 Speaker 4: No, So we call it market reader very intentionally, like 211 00:09:37,160 --> 00:09:42,040 Speaker 4: we want it to really generate information that describes what's 212 00:09:42,080 --> 00:09:45,199 Speaker 4: going on. I'm sure there's going to be quant funds 213 00:09:45,240 --> 00:09:47,200 Speaker 4: and so forth. That can use the data set that 214 00:09:47,240 --> 00:09:50,480 Speaker 4: we're generating to create their own signals and outph and 215 00:09:50,480 --> 00:09:52,520 Speaker 4: so forth. But we want this to be for a 216 00:09:52,520 --> 00:09:55,080 Speaker 4: lot of people, and the purpose of our company is 217 00:09:55,080 --> 00:09:58,520 Speaker 4: to explain in a very objective, very precise way what's 218 00:09:58,520 --> 00:09:58,840 Speaker 4: going on. 219 00:09:58,960 --> 00:10:09,079 Speaker 1: Right, Okay, So is it fortuitous or maybe lucky is 220 00:10:09,120 --> 00:10:11,600 Speaker 1: the right word that you're that you started using AI 221 00:10:11,720 --> 00:10:13,760 Speaker 1: to do this and now AI is the big thing? 222 00:10:14,000 --> 00:10:16,679 Speaker 1: Or was it the case that even a couple of 223 00:10:16,720 --> 00:10:19,120 Speaker 1: years ago you thought to yourself, AI is going to 224 00:10:19,120 --> 00:10:21,559 Speaker 1: be the next big thing, so I'm going to incorporate 225 00:10:21,600 --> 00:10:22,640 Speaker 1: it into my strategy. 226 00:10:22,760 --> 00:10:25,800 Speaker 4: I think what's happened this year is that actually applying 227 00:10:25,920 --> 00:10:29,760 Speaker 4: AI has become so much easier than it was six 228 00:10:29,840 --> 00:10:32,280 Speaker 4: months ago or twelve months ago, Right, So the hurdle 229 00:10:32,360 --> 00:10:35,760 Speaker 4: to really embedding it in a system has been lowered 230 00:10:35,760 --> 00:10:39,719 Speaker 4: so much so. Our original plan was more focused on 231 00:10:39,760 --> 00:10:43,880 Speaker 4: structural modeling, traditional fundamental modeling, but we've really seen how 232 00:10:43,920 --> 00:10:46,240 Speaker 4: this actually allows us to do stuff that we just 233 00:10:46,280 --> 00:10:47,720 Speaker 4: can't do with traditional models. 234 00:10:47,760 --> 00:10:51,160 Speaker 3: So it's very it's. 235 00:10:50,720 --> 00:10:52,800 Speaker 4: Very rewarding to actually see that you can take the 236 00:10:52,840 --> 00:10:53,959 Speaker 4: technology step further. 237 00:10:54,760 --> 00:10:56,920 Speaker 2: I haven't really curious. I mean, my question at the 238 00:10:57,320 --> 00:11:00,559 Speaker 2: outset was who is this for? I'm curious about how 239 00:11:00,559 --> 00:11:03,360 Speaker 2: you market something like this because obviously, with AI being 240 00:11:03,360 --> 00:11:05,960 Speaker 2: the hot topic, there's going to be a lot of 241 00:11:06,040 --> 00:11:08,360 Speaker 2: snake oil out there, and obviously you have a track 242 00:11:08,400 --> 00:11:13,080 Speaker 2: record well respected. But when you're you're pitching this, I mean, 243 00:11:13,280 --> 00:11:16,480 Speaker 2: what does that pitch sound like? Especially when I'm sure 244 00:11:16,800 --> 00:11:19,280 Speaker 2: there's a lot of pitches out there right now. 245 00:11:19,520 --> 00:11:22,160 Speaker 4: If I'm gonna describe it very simply, is that we 246 00:11:22,400 --> 00:11:28,199 Speaker 4: don't have any garbage. Like when you look at financial markets, 247 00:11:28,240 --> 00:11:31,400 Speaker 4: they are so complex, like trying to figure out how 248 00:11:31,480 --> 00:11:36,160 Speaker 4: they linked, how processed correlations work, like one thing moves 249 00:11:36,160 --> 00:11:39,000 Speaker 4: the other and so forth, it's very easy to get 250 00:11:39,000 --> 00:11:43,439 Speaker 4: information overload and get like false positives effectively. Right, So 251 00:11:43,760 --> 00:11:46,520 Speaker 4: I think what our system is very good at is 252 00:11:46,559 --> 00:11:48,960 Speaker 4: to really cut down to what really matters and have 253 00:11:49,040 --> 00:11:51,120 Speaker 4: almost no noise, no garbage. 254 00:11:50,679 --> 00:11:51,160 Speaker 3: In the feed. 255 00:11:51,640 --> 00:11:53,839 Speaker 4: And I think that's what we've seen, Like once in 256 00:11:53,920 --> 00:11:55,959 Speaker 4: a while there's an AI tool that pops up with 257 00:11:56,000 --> 00:11:59,160 Speaker 4: the headlines, oh that sounds like market reader, and then 258 00:11:59,200 --> 00:12:02,640 Speaker 4: we see what is surfaced. Right, if you're just letting 259 00:12:02,840 --> 00:12:08,720 Speaker 4: the AI technology lose without you know, strict control of it, 260 00:12:08,720 --> 00:12:10,839 Speaker 4: it's going to find a lot of garbage and it's 261 00:12:10,880 --> 00:12:13,320 Speaker 4: going to become from it. For a financial market participant, 262 00:12:13,320 --> 00:12:16,839 Speaker 4: it's going to become not just useless, but like a 263 00:12:17,040 --> 00:12:19,400 Speaker 4: total waste of time, right, because it gives you information 264 00:12:19,440 --> 00:12:22,400 Speaker 4: that you don't want. So getting rid of the garbage 265 00:12:22,440 --> 00:12:26,040 Speaker 4: is very important. Also, if you think about if you 266 00:12:26,080 --> 00:12:28,120 Speaker 4: want to have alerts coming out from this thing, like 267 00:12:28,160 --> 00:12:30,000 Speaker 4: you don't want to get woken up at night because 268 00:12:30,000 --> 00:12:32,360 Speaker 4: there's something happened that is totally relevant. 269 00:12:31,920 --> 00:12:35,120 Speaker 3: For your portfolios. So getting rid of the garbage is cool. 270 00:12:35,160 --> 00:12:37,439 Speaker 4: So if you feel, if you feel that you can 271 00:12:37,520 --> 00:12:41,360 Speaker 4: feed chat, GPT or whatever AI technology with something that 272 00:12:41,480 --> 00:12:43,600 Speaker 4: is gold, right, then you're also going to get something 273 00:12:43,600 --> 00:12:44,679 Speaker 4: that's very valuable out. 274 00:12:45,559 --> 00:12:48,199 Speaker 1: So we had this great headline at Bloomberg this week 275 00:12:48,240 --> 00:12:50,760 Speaker 1: that said hedge funds are deploying chat gipt to handle 276 00:12:50,880 --> 00:12:53,400 Speaker 1: all the gruntwork. So I'm just curious how else you 277 00:12:53,520 --> 00:12:56,320 Speaker 1: see AI being deployed on Wall Street? Like, how are 278 00:12:56,360 --> 00:12:57,320 Speaker 1: companies using it? 279 00:12:57,400 --> 00:12:59,160 Speaker 4: I think a lot of hedge funds, a lot of 280 00:12:59,200 --> 00:13:02,199 Speaker 4: asset managers, and I've spoken too many about this over 281 00:13:02,200 --> 00:13:04,520 Speaker 4: the last couple of weeks, right, they all thinking about 282 00:13:04,520 --> 00:13:06,120 Speaker 4: how to go about doing this. 283 00:13:06,920 --> 00:13:07,560 Speaker 3: I think we're. 284 00:13:07,440 --> 00:13:11,040 Speaker 4: Still pretty early. We're still pretty early they're thinking about it. 285 00:13:11,120 --> 00:13:13,120 Speaker 4: I don't think this is something that has cost job 286 00:13:13,160 --> 00:13:15,920 Speaker 4: losses already. It's almost like in the short term is 287 00:13:15,960 --> 00:13:18,520 Speaker 4: almost the opposite, right, There has to be some investments 288 00:13:18,559 --> 00:13:22,600 Speaker 4: made in this field to harvest the benefit. But it's 289 00:13:22,640 --> 00:13:25,400 Speaker 4: gonna take a little while before we have matured to 290 00:13:25,440 --> 00:13:29,720 Speaker 4: a degree where it's actually gonna like substitute for some people. 291 00:13:29,760 --> 00:13:31,040 Speaker 2: I think, I like, how you. 292 00:13:31,000 --> 00:13:35,120 Speaker 1: Thought garbage was a bad word. Yeah, right, we say 293 00:13:35,160 --> 00:13:35,800 Speaker 1: garbage here? 294 00:13:36,120 --> 00:13:39,640 Speaker 2: Are you gonna name names some people here? So in 295 00:13:39,760 --> 00:13:42,319 Speaker 2: talking about AI in this conversation, I mean, it seems 296 00:13:42,320 --> 00:13:47,080 Speaker 2: like we're talking about language processing. We're talking about generative AI, 297 00:13:47,720 --> 00:13:53,400 Speaker 2: not necessarily like machine learning. Because I'm pretty new to AI, 298 00:13:53,679 --> 00:13:56,280 Speaker 2: obviously interested in it, covered a lot, talk about it 299 00:13:56,320 --> 00:13:59,400 Speaker 2: a lot, But when it comes to investing, it seems 300 00:13:59,400 --> 00:14:02,280 Speaker 2: like there's this dichotomy here that you know, there's sort 301 00:14:02,320 --> 00:14:05,720 Speaker 2: of the text based AI, and then there's like machine learning. 302 00:14:05,720 --> 00:14:07,720 Speaker 2: Maybe you run a model a bunch and bunch a 303 00:14:07,720 --> 00:14:09,439 Speaker 2: bunch of times and it gets a little bit better 304 00:14:09,760 --> 00:14:13,160 Speaker 2: each time. You're firmly situated. It seems like in the 305 00:14:13,240 --> 00:14:14,199 Speaker 2: text based AI. 306 00:14:15,120 --> 00:14:18,240 Speaker 4: In the end, we will have the world kind of 307 00:14:18,840 --> 00:14:23,680 Speaker 4: past in a new way, right because we're gonna have 308 00:14:23,920 --> 00:14:26,640 Speaker 4: costal explanations for everything, and we're going to have a 309 00:14:26,840 --> 00:14:30,320 Speaker 4: very high frequency data set that has those costal explanations 310 00:14:30,360 --> 00:14:31,040 Speaker 4: attached to it. 311 00:14:31,760 --> 00:14:31,960 Speaker 3: Right. 312 00:14:32,080 --> 00:14:36,200 Speaker 4: So with that proprietary data set, you can take the 313 00:14:36,240 --> 00:14:38,720 Speaker 4: next step and say, okay, let's do some machine learning 314 00:14:38,720 --> 00:14:40,920 Speaker 4: on that. Figure out Okay, where are the alpha streams? 315 00:14:41,000 --> 00:14:41,160 Speaker 3: Right? 316 00:14:41,240 --> 00:14:44,160 Speaker 4: So I'm not planning to do that internally. I don't 317 00:14:44,160 --> 00:14:46,720 Speaker 4: have an aspiration to become an asset manager. We want 318 00:14:46,720 --> 00:14:49,280 Speaker 4: to help other asset managers do that, but there will 319 00:14:49,280 --> 00:14:50,600 Speaker 4: be that element to it as well. 320 00:14:51,280 --> 00:14:53,800 Speaker 2: So it's also interesting talking to you because you know, 321 00:14:53,840 --> 00:14:58,520 Speaker 2: you ran through your resume you were in analystic goldmans 322 00:14:58,760 --> 00:15:01,760 Speaker 2: or analysts. I don't know if you get picky about you. 323 00:15:01,760 --> 00:15:04,200 Speaker 4: Know, some people call it economists, but some people don't 324 00:15:04,280 --> 00:15:07,280 Speaker 4: like that, so, uh, strategists, some people like better. 325 00:15:07,760 --> 00:15:08,960 Speaker 3: We can choose whatever you like. 326 00:15:09,040 --> 00:15:10,880 Speaker 2: So you were a strategist at Goldvin, you were a 327 00:15:10,920 --> 00:15:14,640 Speaker 2: strategist at Nomura. Are you basically trying to put past 328 00:15:14,760 --> 00:15:18,640 Speaker 2: versions of yourself out of business? Is what you're you're doing? 329 00:15:18,680 --> 00:15:21,880 Speaker 2: What market reader does does that sort of displace the 330 00:15:21,960 --> 00:15:25,920 Speaker 2: traditional research analyst. 331 00:15:25,680 --> 00:15:29,520 Speaker 4: Within Excante Data, we have a Bloomberg chat right where 332 00:15:29,560 --> 00:15:34,120 Speaker 4: we talk to top institutional investors around the world around. 333 00:15:34,160 --> 00:15:35,400 Speaker 1: You don't talk to us, though. 334 00:15:37,200 --> 00:15:39,680 Speaker 4: If you want to pay, maybe you can get involved 335 00:15:39,680 --> 00:15:42,000 Speaker 4: in the chat, But this is like people pay for 336 00:15:42,080 --> 00:15:45,560 Speaker 4: that service, right, and we have an intense discussion about stuff. 337 00:15:46,000 --> 00:15:49,400 Speaker 4: To answer your question, right, It's totally possible that some 338 00:15:49,480 --> 00:15:53,160 Speaker 4: of the content that is there could be done by 339 00:15:53,520 --> 00:15:56,480 Speaker 4: a robot version of one of my team members or 340 00:15:56,480 --> 00:16:01,280 Speaker 4: a robot version of myself, and we definitely think about that. 341 00:16:01,480 --> 00:16:04,080 Speaker 4: We can also think about using essentially the market reader 342 00:16:04,160 --> 00:16:06,440 Speaker 4: technology to do aspects of what we do for the 343 00:16:06,440 --> 00:16:09,240 Speaker 4: Exante Data clients. This is going to be fun stuff 344 00:16:09,560 --> 00:16:12,960 Speaker 4: and maybe we can do analysis that we otherwise wouldn't do. 345 00:16:13,720 --> 00:16:15,760 Speaker 4: Maybe there's some analysis we don't like to do that 346 00:16:15,800 --> 00:16:17,600 Speaker 4: we find boring, that we give to the role what 347 00:16:17,720 --> 00:16:19,080 Speaker 4: to do, and then we have more time to do 348 00:16:19,160 --> 00:16:19,880 Speaker 4: some fun stuff. 349 00:16:20,480 --> 00:16:22,680 Speaker 1: And we can't let you go because of this Wall 350 00:16:22,720 --> 00:16:25,560 Speaker 1: Street background of yours without you sharing some thoughts on 351 00:16:25,600 --> 00:16:28,640 Speaker 1: what you're actually seeing in terms of markets. I think 352 00:16:28,880 --> 00:16:32,760 Speaker 1: you're looking at global growth slowing down. What evidence are 353 00:16:32,760 --> 00:16:35,160 Speaker 1: you looking at and just what are your thoughts, probably 354 00:16:35,240 --> 00:16:40,280 Speaker 1: speaking on everything that's going on with global economies and markets. 355 00:16:40,600 --> 00:16:44,400 Speaker 4: Yeah, we crunch a lot of data. So for China, 356 00:16:44,960 --> 00:16:47,920 Speaker 4: we essentially have a rule that we try not to 357 00:16:48,120 --> 00:16:51,160 Speaker 4: look at official data because we think it is since 358 00:16:51,160 --> 00:16:54,840 Speaker 4: we're allowed to use the word garbage. So we use 359 00:16:54,880 --> 00:16:57,280 Speaker 4: our own alternative data to have a sense of what's 360 00:16:57,320 --> 00:17:00,800 Speaker 4: going on in the Chinese economy. And we've had pretty 361 00:17:00,800 --> 00:17:03,520 Speaker 4: weak signals since the end of March. Right now it's 362 00:17:03,520 --> 00:17:06,000 Speaker 4: showing up in the official data while least the PMIS 363 00:17:06,040 --> 00:17:09,399 Speaker 4: will a lag. So this is an important thing for 364 00:17:09,440 --> 00:17:13,600 Speaker 4: the global economy, right, because other economies when they've reopened, 365 00:17:14,119 --> 00:17:17,760 Speaker 4: it was a multi quarter process with a booming economy, right, 366 00:17:17,800 --> 00:17:19,600 Speaker 4: And in China's case, it looks like they had one 367 00:17:19,680 --> 00:17:22,840 Speaker 4: quarter of better growth and now they're slowing already, right, 368 00:17:22,880 --> 00:17:24,879 Speaker 4: So that's the negative surprise for a lot of people. 369 00:17:25,600 --> 00:17:27,280 Speaker 4: Do you want to talk about assets as well? 370 00:17:27,720 --> 00:17:29,600 Speaker 1: Yeah, we love assets, So. 371 00:17:31,119 --> 00:17:34,800 Speaker 4: I think this is interesting about psychology in the market. Right, 372 00:17:34,840 --> 00:17:39,040 Speaker 4: We've had this whole wave of let's focus on the 373 00:17:39,160 --> 00:17:43,360 Speaker 4: dollar being doomed, that we're going to dedollarize and so forth, right, 374 00:17:43,920 --> 00:17:46,680 Speaker 4: and then you look at what's actually happening in the market, right, 375 00:17:47,440 --> 00:17:51,040 Speaker 4: and the currency that in the narrative is perceived to 376 00:17:51,080 --> 00:17:54,880 Speaker 4: be the new alternative to the dollar, the Chinese currency see, 377 00:17:54,920 --> 00:17:59,680 Speaker 4: and why it's literally going weaker every day, not every 378 00:17:59,680 --> 00:18:02,280 Speaker 4: single but if you look at the chat since January, 379 00:18:02,920 --> 00:18:05,800 Speaker 4: there are so few strengthening days, right, and we just 380 00:18:05,880 --> 00:18:10,159 Speaker 4: have a long trend now of dollar gains and c 381 00:18:10,280 --> 00:18:13,160 Speaker 4: andhy weakness. It's just so interesting, right that you can 382 00:18:13,200 --> 00:18:15,840 Speaker 4: have a disconnect between a narrative that if you read 383 00:18:15,880 --> 00:18:17,919 Speaker 4: all those stories, it look like, okay, the dollar's going 384 00:18:18,000 --> 00:18:19,960 Speaker 4: to die tomorrow, right, and then you look at what's 385 00:18:19,960 --> 00:18:23,000 Speaker 4: actually going on in the market, and the CNY. 386 00:18:22,800 --> 00:18:23,760 Speaker 3: Is having trouble. 387 00:18:24,000 --> 00:18:26,120 Speaker 4: Literally you can look at a lot of capital flow 388 00:18:26,160 --> 00:18:28,080 Speaker 4: data as well, but they're just in the price actions. 389 00:18:28,119 --> 00:18:46,919 Speaker 2: It's just stunning, well that, I mean deja vu again. 390 00:18:47,400 --> 00:18:50,080 Speaker 2: I first met yen's because I was a currency reporter 391 00:18:50,119 --> 00:18:53,720 Speaker 2: and I was obsessed with that theme in like twenty seventeen, 392 00:18:53,760 --> 00:18:56,400 Speaker 2: twenty eighteen, Oh my gosh, when will do you want 393 00:18:56,520 --> 00:18:59,359 Speaker 2: displace the dollar as the global reserve currency? And I 394 00:18:59,359 --> 00:19:00,919 Speaker 2: think some and said to me, I don't think it 395 00:19:00,920 --> 00:19:03,959 Speaker 2: was you like you will write this story every five years, 396 00:19:04,000 --> 00:19:06,359 Speaker 2: like it becomes a narrative and then it dies, like 397 00:19:06,560 --> 00:19:10,040 Speaker 2: nothing will displace the dollar. And it feels like we're 398 00:19:10,080 --> 00:19:12,879 Speaker 2: in that part of the arc again where people are 399 00:19:12,920 --> 00:19:15,400 Speaker 2: realizing that, Okay, the dollar's not going anywhere. 400 00:19:15,480 --> 00:19:18,600 Speaker 4: I get so many questions on it that I actually wrote. 401 00:19:18,680 --> 00:19:22,679 Speaker 4: I wrote a public substack today that's called a brief 402 00:19:22,840 --> 00:19:26,359 Speaker 4: History of Dollar Hatred. I tweeted it out as well. 403 00:19:26,600 --> 00:19:29,840 Speaker 4: Because we go through these waves, and I've been doing 404 00:19:29,880 --> 00:19:32,439 Speaker 4: this for about a little bit more than twenty years now, right, 405 00:19:32,480 --> 00:19:34,680 Speaker 4: I've been through free waves like there was one before 406 00:19:34,720 --> 00:19:36,840 Speaker 4: the global financial crisis where people say, oh, the US 407 00:19:36,920 --> 00:19:39,400 Speaker 4: current account definit is so big, the dollar's going to die. 408 00:19:39,880 --> 00:19:43,399 Speaker 4: Then there was you know, from twenty ten to twenty thirteen, 409 00:19:43,480 --> 00:19:46,160 Speaker 4: or sellers, ah, the Fed is printing so much money 410 00:19:46,200 --> 00:19:48,720 Speaker 4: que infinity is Dollar's going to die. And now this 411 00:19:48,880 --> 00:19:51,720 Speaker 4: year we've had a China has a much better currency. 412 00:19:51,760 --> 00:19:53,480 Speaker 4: The dollar's going to die. Right, So we go through 413 00:19:53,480 --> 00:19:57,200 Speaker 4: these phases, and then you look at how the dollar's trading. 414 00:19:58,119 --> 00:20:01,359 Speaker 4: Dollars is strong, doesn't matter how you look at it, 415 00:20:01,640 --> 00:20:05,720 Speaker 4: normal exchange rate, real exchange rate, which index it's a 416 00:20:05,760 --> 00:20:08,280 Speaker 4: strong currency. There's no evidence that the dollar is about 417 00:20:08,280 --> 00:20:11,479 Speaker 4: to collapse. Doesn't mean it couldn't collapse in the future, right, 418 00:20:11,520 --> 00:20:14,400 Speaker 4: But it's not. It's not something that is in motion yet. 419 00:20:15,080 --> 00:20:17,320 Speaker 2: What is that a function of rate? 420 00:20:17,760 --> 00:20:18,000 Speaker 3: Now? 421 00:20:18,240 --> 00:20:21,840 Speaker 2: Is it you know, the Federal Reserve having you know, 422 00:20:21,920 --> 00:20:26,320 Speaker 2: still on its hiking path higher terminal rate than everyone 423 00:20:26,359 --> 00:20:27,920 Speaker 2: else it seems like at least at this moment, or 424 00:20:28,000 --> 00:20:31,480 Speaker 2: is this more about other economies, other central banks not 425 00:20:31,520 --> 00:20:32,160 Speaker 2: looking as hot. 426 00:20:32,520 --> 00:20:34,520 Speaker 4: Well, we can keep it very simple, right, So we're 427 00:20:34,520 --> 00:20:37,399 Speaker 4: discussing how much above five percent interest rates should go 428 00:20:37,440 --> 00:20:40,160 Speaker 4: in the US, right, that's the debate we're having every day. 429 00:20:40,880 --> 00:20:43,800 Speaker 4: And then in China right now, the debate is whether 430 00:20:43,840 --> 00:20:45,840 Speaker 4: they have to lower interest rates below two percent? 431 00:20:46,040 --> 00:20:48,480 Speaker 3: Right, we're going lower every day, right. 432 00:20:48,560 --> 00:20:50,720 Speaker 4: So it used to be the case that interest rates 433 00:20:50,760 --> 00:20:53,160 Speaker 4: we're much much higher in China than the United States, right, 434 00:20:53,160 --> 00:20:54,680 Speaker 4: and now it's totally flip. 435 00:20:54,800 --> 00:20:54,960 Speaker 3: Right. 436 00:20:55,040 --> 00:20:57,600 Speaker 4: So I don't do analysis based on one verbal but 437 00:20:57,640 --> 00:21:00,280 Speaker 4: that's a pretty important veribal and it's like showing a 438 00:21:00,400 --> 00:21:03,200 Speaker 4: very dramatic effect. Like the other thing that I think 439 00:21:03,640 --> 00:21:06,919 Speaker 4: it's sort of grotesque about this debate about okay, is 440 00:21:06,960 --> 00:21:14,040 Speaker 4: the Chinese currency gonna take all the reserve currency money 441 00:21:14,080 --> 00:21:17,440 Speaker 4: like from central bank reserves instead of the dollar. Right, 442 00:21:17,760 --> 00:21:20,080 Speaker 4: you look at the capital flow, which is what I'm 443 00:21:20,080 --> 00:21:22,960 Speaker 4: doing every day, and literally since the beginning of twenty 444 00:21:23,160 --> 00:21:26,280 Speaker 4: twenty two, there's not been a single month with any 445 00:21:26,359 --> 00:21:29,760 Speaker 4: meaningful inflows into China. Nobody wants their bonds. How can 446 00:21:29,760 --> 00:21:31,600 Speaker 4: you have a reserve currency when people don't want to 447 00:21:31,600 --> 00:21:35,199 Speaker 4: hold their bonds. So there's a lot of stuff missing. 448 00:21:35,520 --> 00:21:38,480 Speaker 4: It's not because everything is fantastic in the US, right, 449 00:21:39,280 --> 00:21:42,040 Speaker 4: we have political issues. We talk about that ceiling, which 450 00:21:42,080 --> 00:21:44,919 Speaker 4: really is a silly thing, like why do we have 451 00:21:44,960 --> 00:21:48,280 Speaker 4: such a bizarre system? Right, So it's not that everything's 452 00:21:48,320 --> 00:21:51,119 Speaker 4: fantastic in the US, but we need to have an alternative. 453 00:21:51,160 --> 00:21:52,760 Speaker 4: To have a real threat to the dollar, needs to 454 00:21:52,760 --> 00:21:56,040 Speaker 4: be an alternative, and it's so hard to find a 455 00:21:56,080 --> 00:21:59,000 Speaker 4: real alternative, and the Chinese currency is definitely not one. 456 00:21:59,000 --> 00:22:00,960 Speaker 4: If people don't want the bond is not all alternative. 457 00:22:01,560 --> 00:22:03,440 Speaker 1: Katy, I'm so glad you're here because I know nothing 458 00:22:03,480 --> 00:22:04,120 Speaker 1: about currency. 459 00:22:04,240 --> 00:22:06,800 Speaker 2: This is so fun. It's like twenty nineteen again. It's great. 460 00:22:07,080 --> 00:22:11,000 Speaker 1: Well, Yen's Nordvick, thank you so much for joining the podcast. 461 00:22:11,040 --> 00:22:14,120 Speaker 1: This has been a real pleasure, but I'm gonna hold 462 00:22:14,160 --> 00:22:17,520 Speaker 1: both of you hostage until we play. Craziest thing we 463 00:22:17,560 --> 00:22:18,840 Speaker 1: saw in markets this week. 464 00:22:19,080 --> 00:22:21,520 Speaker 2: This is the most ready I've ever been for craziest. 465 00:22:21,160 --> 00:22:23,240 Speaker 1: Are you sure? Okay, you go first? Since wait, you 466 00:22:23,320 --> 00:22:23,680 Speaker 1: tease this? 467 00:22:23,960 --> 00:22:27,359 Speaker 2: So I cover ETFs now? Yes, So I moved on 468 00:22:27,520 --> 00:22:30,840 Speaker 2: from the world of currencies. I love covering bond ETFs. 469 00:22:30,840 --> 00:22:32,320 Speaker 2: Obviously it's been the year of the bond. But the 470 00:22:32,359 --> 00:22:35,199 Speaker 2: craziest thing I saw in markets this week I was 471 00:22:35,240 --> 00:22:41,760 Speaker 2: looking at a triple leveraged long duration treasuries ETF. So 472 00:22:41,840 --> 00:22:46,480 Speaker 2: it's twenty year treasuries and then you triple that exposure. 473 00:22:46,600 --> 00:22:49,600 Speaker 2: Bonds are already so volatile, but apparently people really want 474 00:22:49,600 --> 00:22:53,000 Speaker 2: this because this ETF it's the direction daily twenty plus 475 00:22:53,119 --> 00:22:57,439 Speaker 2: year treasury bowl three times ETF. The ticker is TMF uh. 476 00:22:57,680 --> 00:23:01,479 Speaker 2: It's assets have hit two billion dollars. It's more than 477 00:23:01,560 --> 00:23:06,520 Speaker 2: double this year alone in assets. People just really want 478 00:23:06,880 --> 00:23:07,919 Speaker 2: very volatile bonds. 479 00:23:07,920 --> 00:23:10,560 Speaker 1: I guess, Oh my gosh, I want a calmer market. 480 00:23:10,920 --> 00:23:11,879 Speaker 2: No, not these people. 481 00:23:12,240 --> 00:23:13,679 Speaker 1: I want calm. I won't calm. 482 00:23:13,760 --> 00:23:16,840 Speaker 2: I asked Dave Nadig over at a Verify, what's going on? 483 00:23:17,000 --> 00:23:20,800 Speaker 2: Who's buying this like, who the heck wants TMF right now? 484 00:23:20,800 --> 00:23:24,040 Speaker 2: And he said, it's all headline day traders. Wow, yeah, 485 00:23:24,080 --> 00:23:25,440 Speaker 2: that's cool, that is cool. 486 00:23:25,480 --> 00:23:26,440 Speaker 1: Okay, you were prepared. 487 00:23:26,640 --> 00:23:27,399 Speaker 2: Yeah. Y. 488 00:23:28,640 --> 00:23:33,520 Speaker 4: So we've had a bit of movement in some semiconductor 489 00:23:33,800 --> 00:23:36,880 Speaker 4: stocks over the last I think so two weeks. So, 490 00:23:36,880 --> 00:23:39,520 Speaker 4: so I'll give you a stat from market Rita. So 491 00:23:39,600 --> 00:23:44,320 Speaker 4: we we look at all the volumes and those instruments, 492 00:23:45,080 --> 00:23:49,280 Speaker 4: and some of these semiconductor companies they just have totally 493 00:23:49,480 --> 00:23:52,040 Speaker 4: insane volumes. Right if you look at the volume and 494 00:23:52,080 --> 00:23:55,000 Speaker 4: that you've learned about the normal distribution in school, right, 495 00:23:55,000 --> 00:23:57,760 Speaker 4: if you get like two standard deviations, it's big and free, 496 00:23:57,800 --> 00:24:01,320 Speaker 4: it's very big. I've seen sick, seen standard the aviations. 497 00:24:01,359 --> 00:24:04,080 Speaker 4: Like the volume was above normal here this week. So 498 00:24:04,240 --> 00:24:07,120 Speaker 4: there's definitely a little bit of focus on semiconductors like. 499 00:24:07,080 --> 00:24:09,240 Speaker 1: Con video or which stocks are. 500 00:24:09,080 --> 00:24:12,119 Speaker 4: We talking about some of the like my actual so 501 00:24:12,240 --> 00:24:14,480 Speaker 4: for some of the like second tier semiconductors, like just 502 00:24:14,560 --> 00:24:15,320 Speaker 4: crazy volume. 503 00:24:15,680 --> 00:24:17,800 Speaker 2: Well I would imagine people are trying to find the 504 00:24:17,880 --> 00:24:21,800 Speaker 2: next and video, right, Holy moly, yes, well, Tana. 505 00:24:21,880 --> 00:24:24,840 Speaker 1: I have trouble pronouncing video and video. 506 00:24:25,400 --> 00:24:27,840 Speaker 4: Why I said sector pronunciation? 507 00:24:28,040 --> 00:24:31,040 Speaker 2: I said no video on videos and I got like 508 00:24:31,080 --> 00:24:33,439 Speaker 2: three people paying me and they were like, it's end videos. 509 00:24:33,440 --> 00:24:34,399 Speaker 1: Oh is it end video? 510 00:24:34,440 --> 00:24:35,080 Speaker 4: So I was right. 511 00:24:35,440 --> 00:24:37,320 Speaker 2: I just know it's not no video. I feel like 512 00:24:37,320 --> 00:24:38,960 Speaker 2: I toggle between ND video and. 513 00:24:39,720 --> 00:24:41,760 Speaker 1: I feel superior now because I got it right. 514 00:24:41,840 --> 00:24:44,520 Speaker 2: It's definitely not no no video. 515 00:24:44,920 --> 00:24:46,040 Speaker 1: Let's see who hits us up. 516 00:24:46,200 --> 00:24:47,080 Speaker 2: Yeah, not video. 517 00:24:48,280 --> 00:24:50,680 Speaker 1: Okay, it's my turn, and I'm gonna make you both 518 00:24:50,720 --> 00:24:51,320 Speaker 1: play a game. 519 00:24:51,800 --> 00:24:52,040 Speaker 2: Okay. 520 00:24:52,119 --> 00:24:55,280 Speaker 1: Mike usually does this role, and he's very good at officiating, 521 00:24:55,359 --> 00:24:58,719 Speaker 1: as like the game master. But I'm gonna read it 522 00:24:58,720 --> 00:25:00,879 Speaker 1: you a little description and then I'm going to have 523 00:25:00,920 --> 00:25:05,359 Speaker 1: you play. The price is precise, okay, So it's not 524 00:25:05,440 --> 00:25:07,480 Speaker 1: the price is right, it's the prices precise. 525 00:25:07,560 --> 00:25:08,720 Speaker 2: So what are the rules. 526 00:25:08,760 --> 00:25:11,399 Speaker 1: It's the same rules. Oh okay, we just can't call it, okay, 527 00:25:12,080 --> 00:25:16,120 Speaker 1: a Japanese ice cream. This is good, so far? Right, 528 00:25:16,160 --> 00:25:19,040 Speaker 1: so far just set the world record as the most 529 00:25:19,080 --> 00:25:23,840 Speaker 1: expensive ice cream ever. It's made of truffles from Italy 530 00:25:24,200 --> 00:25:28,199 Speaker 1: and they go for fifteen thousand dollars per kilogram, and 531 00:25:28,240 --> 00:25:32,040 Speaker 1: it also has Parmisano Vergiano in it and Saki Lee's 532 00:25:32,280 --> 00:25:36,600 Speaker 1: which is left over paced from Saki production. And when 533 00:25:36,640 --> 00:25:39,400 Speaker 1: you buy this ice cream, you also get a handcrafted 534 00:25:39,440 --> 00:25:43,440 Speaker 1: metal spoon that's made with materials used to build temples 535 00:25:43,560 --> 00:25:44,480 Speaker 1: and shrines. 536 00:25:44,920 --> 00:25:47,480 Speaker 2: I'm sorry, I thought this was the craziest thing in markets. 537 00:25:47,600 --> 00:25:51,640 Speaker 1: Yeah, it's fine, Okay, I'm giving myself a pass. Okay, 538 00:25:52,200 --> 00:25:55,400 Speaker 1: if you wanted to order this ice cream, how much 539 00:25:55,440 --> 00:25:59,720 Speaker 1: are you paying for it in dollars? And it's the 540 00:25:59,760 --> 00:26:01,960 Speaker 1: most expensive ice cream ever? And you go first? 541 00:26:02,240 --> 00:26:04,440 Speaker 4: Okay, I think it's twenty thousand dollars? 542 00:26:05,080 --> 00:26:08,000 Speaker 2: Katie? Is this for? What unit? Is this for a 543 00:26:08,000 --> 00:26:09,960 Speaker 2: cup of ice cream? A scoop of ice cream? 544 00:26:10,080 --> 00:26:12,040 Speaker 1: I think you get a little bit of ice cream 545 00:26:12,040 --> 00:26:16,960 Speaker 1: because yeah, like a pint maybe because this the story 546 00:26:17,040 --> 00:26:18,920 Speaker 1: said that you should try to eat it as soon 547 00:26:18,920 --> 00:26:22,600 Speaker 1: as possible, like doesn't technically go bad, but you should 548 00:26:22,680 --> 00:26:26,600 Speaker 1: eat it within ten days of getting it, so okay, 549 00:26:27,080 --> 00:26:29,480 Speaker 1: and you're supposed to put truffle oil on top of it. 550 00:26:29,680 --> 00:26:31,880 Speaker 2: I'm gonna say, I feel like you kind of low 551 00:26:31,920 --> 00:26:35,400 Speaker 2: balled it. So I'm going to say, like two hundred 552 00:26:35,400 --> 00:26:36,440 Speaker 2: and fifty thousand. 553 00:26:36,480 --> 00:26:37,399 Speaker 1: For ice cream. 554 00:26:37,680 --> 00:26:39,400 Speaker 2: I have no idea. 555 00:26:41,280 --> 00:26:42,600 Speaker 4: Twenty thousand ice cream is cheap? 556 00:26:43,040 --> 00:26:45,080 Speaker 1: All these people and tell them to up their price. 557 00:26:45,119 --> 00:26:45,520 Speaker 2: How wrong? 558 00:26:45,560 --> 00:26:49,000 Speaker 1: Am I you're both so wrong. What is it six thousand, 559 00:26:49,119 --> 00:26:51,399 Speaker 1: six hundred ninety six dollars. 560 00:26:51,640 --> 00:26:52,480 Speaker 3: Only three times? 561 00:26:52,560 --> 00:26:55,359 Speaker 2: Yeah, we're going to rage. Quit this podcast. 562 00:26:56,000 --> 00:26:59,439 Speaker 1: Slam your cups down, slam your fists down. Anyway, it 563 00:26:59,480 --> 00:27:01,920 Speaker 1: was a great pleasure to have you both on here 564 00:27:01,960 --> 00:27:05,600 Speaker 1: and have you guests so very incorrectly about this ice cream. 565 00:27:05,720 --> 00:27:07,080 Speaker 2: Thank you for the opportunity. 566 00:27:07,480 --> 00:27:10,639 Speaker 1: Thank you, and YenS, thank you so much for joining 567 00:27:10,680 --> 00:27:13,199 Speaker 1: us on the podcast this week. YenS Nordwick, co founder 568 00:27:13,240 --> 00:27:14,080 Speaker 1: and CEO of. 569 00:27:14,080 --> 00:27:16,320 Speaker 4: Market Reader, I hope with come back soon. 570 00:27:16,800 --> 00:27:18,200 Speaker 1: I hope so too. Thank you both. 571 00:27:18,359 --> 00:27:27,880 Speaker 2: That was fun What Goes Up. 572 00:27:27,960 --> 00:27:30,720 Speaker 1: We'll be back next week. Until then, you can find 573 00:27:30,800 --> 00:27:34,320 Speaker 1: us on the Bloomberg Terminal, website and app, or wherever 574 00:27:34,359 --> 00:27:37,120 Speaker 1: you get your podcasts. We'd love it if you took 575 00:27:37,119 --> 00:27:39,320 Speaker 1: the time to rate and review the show so more 576 00:27:39,320 --> 00:27:42,360 Speaker 1: listeners can find us. You can find us on Twitter, 577 00:27:43,119 --> 00:27:47,639 Speaker 1: follow me at Goldana Hirich. Mike Reagan is at Reaganonymous. 578 00:27:48,119 --> 00:27:52,640 Speaker 1: You can also follow Bloomberg Podcasts at podcasts. What Goes 579 00:27:52,720 --> 00:27:55,240 Speaker 1: Up is produced by Stacey Wong, and our head of 580 00:27:55,280 --> 00:27:58,639 Speaker 1: podcasts is Stage Bauman. Thanks for listening. We'll see you 581 00:27:58,640 --> 00:28:02,520 Speaker 1: next week at b