Speaker 1: Welcome to Wako's Up, a weekly markets podcast. Humble Dona
Speaker 1: Hire a market supporter with Bloomberg.
Speaker 2: And I'm Katie Greifeld. Mike Reagan is off this week.
Speaker 1: I'm so happy to have you here.
Speaker 2: Yeah, thank god.
Speaker 1: But first, Katie, it's been a really long time since
Speaker 1: you've been on the podcast. I want to loll youoateral years,
Speaker 1: not literal, No, it actually no literal.
Speaker 2: But I'm thrilled to be here because it feels like
Speaker 2: there's a lot to talk about.
Speaker 1: There is, but I'm gonna put you on the spot first.
Speaker 2: Okay.
Speaker 1: I want you to tell the audience something about you
Speaker 1: that nobody knows, not even your husband.
Speaker 2: Oh no, I mean I don't think there's anything I
Speaker 2: would say. I am extremely afraid of the dark.
Speaker 3: I knew that.
Speaker 1: Yeah, we are in a bit of a dark home.
Speaker 1: It has black walls.
Speaker 2: No, it's just like when you're in maybe a room
Speaker 2: or your house saloon and the lights go out. I
Speaker 2: hate that.
Speaker 1: That's not a secret.
Speaker 2: I'm media trained enough, don't you don't do.
Speaker 1: That secret.
Speaker 2: Anyway.
Speaker 1: Artificial intelligence is all the hype on Wall Street these days,
Speaker 1: and we have some strategists even seeing AI trends driving
Speaker 1: further gains for stocks. Now one company is looking to
Speaker 1: analyze US equity market trends using AI technology to project
Speaker 1: in real time why stocks might be moving, and its
Speaker 1: founder is joining us today. Gets Yen's Nordvic. He's the
Speaker 1: co founder and CEO of Market Reader, and he's also
Speaker 1: the founder and CEO of ex Anti Data. So, YenS,
Speaker 1: thank you so much for joining us. I told so
Speaker 1: many people you were going to be on the podcast,
Speaker 1: and everybody's like, oh my god, I know him.
Speaker 4: It was crazy that there was a line of people
Speaker 4: when I came into the building. What's going on here?
Speaker 1: Yeah, there was literally a lot of people. They were like,
Speaker 1: I would like to meet him, and so that's why
Speaker 1: we were like.
Speaker 2: I was thrilled that Mike Reagan happened to be off
Speaker 2: this week because I haven't talked to you in years.
Speaker 2: But we go back.
Speaker 4: I have been doing surveillance in the morning, probably forty times,
Speaker 4: but I've not been in the office. This office I've
Speaker 4: not been to I think since nineteen So it was weird,
Speaker 4: but it's still the same building at least.
Speaker 1: Oh yeah, it feels like Mike Reagan also hasn't been
Speaker 1: in this office. Okay, so again, just to start, Katie
Speaker 1: knows you. All these people I talk to know you,
Speaker 1: but maybe just tell our audience a little bit about you.
Speaker 4: I started my career being a currency strategist to Goldman
Speaker 4: and then I was a currency strategist at Bridgeward Associates.
Speaker 3: I was head of research.
Speaker 4: At the More Securities, and then in sixteen I founded
Speaker 4: a company called Exante Data where we advise institutional investors
Speaker 4: on what's going on in the world using a lot
Speaker 4: of data. And that's the sort of background. But really
Speaker 4: the reason why I invite me in today is that
Speaker 4: we have a new company called market Reader, where we
Speaker 4: use latest technology to really crunch data in real time
Speaker 4: and explain why the market is moving. So if you
Speaker 4: have a stock that's gapping down, market readers should be
Speaker 4: able to tell you why using all the different techniques
Speaker 4: that like a top hedge fund would also be using,
Speaker 4: but all programmed and a piece of software something that
Speaker 4: everybody reader should be able to use.
Speaker 1: And are you also afraid of the dark?
Speaker 2: Not?
Speaker 4: Not really, but this room is a little bit scary.
Speaker 4: It is one of the blackest rooms I've seen.
Speaker 2: Yeah, it's pretty cool. I don't know, it's just you know,
Speaker 2: when the lights go out, you don't know where everything is. Yeah,
Speaker 2: I like to know in any case, So market reader,
Speaker 2: so you say that this is theoretically similar to what
Speaker 2: a hedge fund would be doing. Who is your client then?
Speaker 2: Is it hedge funds?
Speaker 4: This is my biggest problem with the market reader that
Speaker 4: I think it's literally for everybody, So everybody else wants to, Oh,
Speaker 4: who's your target audience?
Speaker 3: Who is it?
Speaker 4: I think it could be used by a hedge fund
Speaker 4: manager or an asset manager, but it could also be used
Speaker 4: by somebody who has, you know, a savings account and
Speaker 4: investing on their own and they've maybe lost some money
Speaker 4: and made some money. They want to know why so
Speaker 4: they can good decisions. So this could be used by everybody,
Speaker 4: Like I have a company where we cater to like
Speaker 4: a niche audience of institutional investors. Right, the whole goal
Speaker 4: of market reader is to do something for millions of users.
Speaker 3: Really like have a tool that everybody can use.
Speaker 4: And I have to say the AI aspect actually has
Speaker 4: turned out to be very important in that regard, right,
Speaker 4: because we have complex models that generate the results, but
Speaker 4: the AI, the language processing, really allows us to present
Speaker 4: that in a very easy to understand way, right, a
Speaker 4: couple of lines of text saying Okay, this is really
Speaker 4: what you need to know about this stock or this ETF.
Speaker 4: And if it's just like you know, model output, it
Speaker 4: would actually be kind of hard to digest. So the
Speaker 4: AI technology has really allowed us to present stuff in
Speaker 4: an easier to digest way.
Speaker 1: Okay, so talk more about the AI aspect, because that
Speaker 1: part obviously is super interesting to a lot of people
Speaker 1: these days. But how even does market reader work? And
Speaker 1: I know you won't spill the secret sauce I suppose
Speaker 1: I could call it, but just like, give us an
Speaker 1: overview of how it works, what it does, and how
Speaker 1: the AI plays it.
Speaker 4: I will give an example. Right, So, one of the
Speaker 4: inputs into our system is literally everything that's going on
Speaker 4: on social media. We so ingest a lot of data, right,
Speaker 4: but if you look at everything that's going on on Twitter,
Speaker 4: for example, there's a lot of stuff that is not
Speaker 4: that relevant, a lot of stuff that is fairly noisy.
Speaker 3: Right.
Speaker 4: So one of the ways we can apply AI is
Speaker 4: to literally say, is this a high quality tweet that
Speaker 4: we should consider? Is this a low quality maybe even
Speaker 4: inappropriate tweet right, and that process is extremely powerful, extremely powerful.
Speaker 4: So there's sort of analytical aspect, but there's also as
Speaker 4: a set before in presentational aspect when you then have
Speaker 4: all the results from all the different fundamental models, just
Speaker 4: summarizing in a way where the human can actually understand.
Speaker 3: It very quickly.
Speaker 4: It's very hard to get like how should we call
Speaker 4: it mechanical robot to do it? But the possibilities you
Speaker 4: have within different AI tools such as chet GPT to
Speaker 4: really generate text that is what should we call it,
Speaker 4: like digestible but also appetizing is something new and give
Speaker 4: some new opportunities.
Speaker 2: And that's what I wanted to sort of hone in
Speaker 2: on on the new part, because you know, describing you know,
Speaker 2: scraping social media that necessarily isn't new, but it sounds
Speaker 2: like it's the step further turning that into text that
Speaker 2: kind of makes sense and you can read and is
Speaker 2: pleasant to look at. Am I understanding that correctly.
Speaker 4: There's many ways we use AI, but two good examples
Speaker 4: would be the filtering itself, Like we can actually use
Speaker 4: the language models to say, okay, is this the type
Speaker 4: of content we want to look at it all? So
Speaker 4: it can kind of look as a filtering mechanism, right.
Speaker 4: And then there's a further step that is close to
Speaker 4: where the user gets the output, where we essentially, okay,
Speaker 4: present all this model output in a way where it's
Speaker 4: easy to digest in one second. Right, there's those two
Speaker 4: aspects of it.
Speaker 3: Okay.
Speaker 1: And then let's say I ran an analysis on some
Speaker 1: stock and then I got the little paragraph, Right, what
Speaker 1: do I then do with that information? What does the
Speaker 1: typical client do?
Speaker 4: It could be used by financial advisor, right, Like if
Speaker 4: financial advisor has fifty stocks that his clients are following,
Speaker 4: you can very quickly get a gist of Okay, that
Speaker 4: client just lost five percent, and now I know why.
Speaker 4: I can tell him if he calls, right, so it
Speaker 4: can be used for that. Actually was last week nowadays
Speaker 4: of passing fast, we had Madona was popping again. Like
Speaker 4: in the old days vaccine stocks, and I used to
Speaker 4: do a lot of COVID forecasting, but it's not a
Speaker 4: big part of our process anymore.
Speaker 3: It does move marketing.
Speaker 4: Would Madanna moved ten percent very quickly, and it popped
Speaker 4: up in our system, right, And the reason was there
Speaker 4: was actually a new wave of COVID in China that
Speaker 4: I had not known like, so even though I've been
Speaker 4: doing this for three years now, it actually showed me
Speaker 4: something where I said, Okay, go and look at that,
Speaker 4: and then we want to analyze the COVID data as well.
Speaker 3: There was a spike.
Speaker 4: We didn't think it was enough to worry about, but
Speaker 4: it can actually allert you to stuff happen that you're
Speaker 4: otherwise not aware of.
Speaker 1: Audience can't see us. But I went whow because I
Speaker 1: hadn't heard that COVID news either.
Speaker 3: Yeah.
Speaker 4: No, and I like, we're known for literally like tracking
Speaker 4: COVID like that. We have clients that came to us
Speaker 4: in the COVID crisis, So it was kind of interesting
Speaker 4: to see that example. Like obviously the Silicon Valley bank
Speaker 4: situation that everybody knows about is another one where the
Speaker 4: system was very helpful. Say okay on that Wednesday night, Okay,
Speaker 4: there's something happening here. It's because of the announcement they
Speaker 4: made about the bond says. By the way, First Republic
Speaker 4: is also doing something it's not supposed to do on
Speaker 4: Wednesday night. And those are instruments. I'm not used to
Speaker 4: having those instruments on my screen. I'm traditionally currency guy. Right,
Speaker 4: if there's something happening with the Euro, I'll have it
Speaker 4: on my screen. But all those smallest stocks I don't have.
Speaker 3: Right.
Speaker 4: So having a system that can monitor everything that's going
Speaker 4: on in the US equity market eleven thousand instruments and
Speaker 4: pick out what should you focus on now gives you.
Speaker 3: A really anability to absorb more information.
Speaker 2: And so it's the US equity market, right, that's the
Speaker 2: only asset class right now.
Speaker 4: We want to do everything. So at the moment in
Speaker 4: the system, we have US stocks ad rs like foreign
Speaker 4: companies that are listed here ETFs that will cover the macroangle, right,
Speaker 4: but we want to do literally every single financial instruments.
Speaker 3: So give us a few weeks, few months, we will
Speaker 3: have that done.
Speaker 2: You have a few weeks, a few weeks, we'll give
Speaker 2: you two weeks, yeah, two weeks, and then we'll check
Speaker 2: back in. So it creates this text that you can
Speaker 2: actually read. It doesn't necessarily issue like a buy sell recommendation.
Speaker 4: No, So we call it market reader very intentionally, like
Speaker 4: we want it to really generate information that describes what's
Speaker 4: going on. I'm sure there's going to be quant funds
Speaker 4: and so forth. That can use the data set that
Speaker 4: we're generating to create their own signals and outph and
Speaker 4: so forth. But we want this to be for a
Speaker 4: lot of people, and the purpose of our company is
Speaker 4: to explain in a very objective, very precise way what's
Speaker 4: going on.
Speaker 1: Right, Okay, So is it fortuitous or maybe lucky is
Speaker 1: the right word that you're that you started using AI
Speaker 1: to do this and now AI is the big thing?
Speaker 1: Or was it the case that even a couple of
Speaker 1: years ago you thought to yourself, AI is going to
Speaker 1: be the next big thing, so I'm going to incorporate
Speaker 1: it into my strategy.
Speaker 4: I think what's happened this year is that actually applying
Speaker 4: AI has become so much easier than it was six
Speaker 4: months ago or twelve months ago, Right, So the hurdle
Speaker 4: to really embedding it in a system has been lowered
Speaker 4: so much so. Our original plan was more focused on
Speaker 4: structural modeling, traditional fundamental modeling, but we've really seen how
Speaker 4: this actually allows us to do stuff that we just
Speaker 4: can't do with traditional models.
Speaker 3: So it's very it's.
Speaker 4: Very rewarding to actually see that you can take the
Speaker 4: technology step further.
Speaker 2: I haven't really curious. I mean, my question at the
Speaker 2: outset was who is this for? I'm curious about how
Speaker 2: you market something like this because obviously, with AI being
Speaker 2: the hot topic, there's going to be a lot of
Speaker 2: snake oil out there, and obviously you have a track
Speaker 2: record well respected. But when you're you're pitching this, I mean,
Speaker 2: what does that pitch sound like? Especially when I'm sure
Speaker 2: there's a lot of pitches out there right now.
Speaker 4: If I'm gonna describe it very simply, is that we
Speaker 4: don't have any garbage. Like when you look at financial markets,
Speaker 4: they are so complex, like trying to figure out how
Speaker 4: they linked, how processed correlations work, like one thing moves
Speaker 4: the other and so forth, it's very easy to get
Speaker 4: information overload and get like false positives effectively. Right, So
Speaker 4: I think what our system is very good at is
Speaker 4: to really cut down to what really matters and have
Speaker 4: almost no noise, no garbage.
Speaker 3: In the feed.
Speaker 4: And I think that's what we've seen, Like once in
Speaker 4: a while there's an AI tool that pops up with
Speaker 4: the headlines, oh that sounds like market reader, and then
Speaker 4: we see what is surfaced. Right, if you're just letting
Speaker 4: the AI technology lose without you know, strict control of it,
Speaker 4: it's going to find a lot of garbage and it's
Speaker 4: going to become from it. For a financial market participant,
Speaker 4: it's going to become not just useless, but like a
Speaker 4: total waste of time, right, because it gives you information
Speaker 4: that you don't want. So getting rid of the garbage
Speaker 4: is very important. Also, if you think about if you
Speaker 4: want to have alerts coming out from this thing, like
Speaker 4: you don't want to get woken up at night because
Speaker 4: there's something happened that is totally relevant.
Speaker 3: For your portfolios. So getting rid of the garbage is cool.
Speaker 4: So if you feel, if you feel that you can
Speaker 4: feed chat, GPT or whatever AI technology with something that
Speaker 4: is gold, right, then you're also going to get something
Speaker 4: that's very valuable out.
Speaker 1: So we had this great headline at Bloomberg this week
Speaker 1: that said hedge funds are deploying chat gipt to handle
Speaker 1: all the gruntwork. So I'm just curious how else you
Speaker 1: see AI being deployed on Wall Street? Like, how are
Speaker 1: companies using it?
Speaker 4: I think a lot of hedge funds, a lot of
Speaker 4: asset managers, and I've spoken too many about this over
Speaker 4: the last couple of weeks, right, they all thinking about
Speaker 4: how to go about doing this.
Speaker 3: I think we're.
Speaker 4: Still pretty early. We're still pretty early they're thinking about it.
Speaker 4: I don't think this is something that has cost job
Speaker 4: losses already. It's almost like in the short term is
Speaker 4: almost the opposite, right, There has to be some investments
Speaker 4: made in this field to harvest the benefit. But it's
Speaker 4: gonna take a little while before we have matured to
Speaker 4: a degree where it's actually gonna like substitute for some people.
Speaker 2: I think, I like, how you.
Speaker 1: Thought garbage was a bad word. Yeah, right, we say
Speaker 1: garbage here?
Speaker 2: Are you gonna name names some people here? So in
Speaker 2: talking about AI in this conversation, I mean, it seems
Speaker 2: like we're talking about language processing. We're talking about generative AI,
Speaker 2: not necessarily like machine learning. Because I'm pretty new to AI,
Speaker 2: obviously interested in it, covered a lot, talk about it
Speaker 2: a lot, But when it comes to investing, it seems
Speaker 2: like there's this dichotomy here that you know, there's sort
Speaker 2: of the text based AI, and then there's like machine learning.
Speaker 2: Maybe you run a model a bunch and bunch a
Speaker 2: bunch of times and it gets a little bit better
Speaker 2: each time. You're firmly situated. It seems like in the
Speaker 2: text based AI.
Speaker 4: In the end, we will have the world kind of
Speaker 4: past in a new way, right because we're gonna have
Speaker 4: costal explanations for everything, and we're going to have a
Speaker 4: very high frequency data set that has those costal explanations
Speaker 4: attached to it.
Speaker 3: Right.
Speaker 4: So with that proprietary data set, you can take the
Speaker 4: next step and say, okay, let's do some machine learning
Speaker 4: on that. Figure out Okay, where are the alpha streams?
Speaker 3: Right?
Speaker 4: So I'm not planning to do that internally. I don't
Speaker 4: have an aspiration to become an asset manager. We want
Speaker 4: to help other asset managers do that, but there will
Speaker 4: be that element to it as well.
Speaker 2: So it's also interesting talking to you because you know,
Speaker 2: you ran through your resume you were in analystic goldmans
Speaker 2: or analysts. I don't know if you get picky about you.
Speaker 4: Know, some people call it economists, but some people don't
Speaker 4: like that, so, uh, strategists, some people like better.
Speaker 3: We can choose whatever you like.
Speaker 2: So you were a strategist at Goldvin, you were a
Speaker 2: strategist at Nomura. Are you basically trying to put past
Speaker 2: versions of yourself out of business? Is what you're you're doing?
Speaker 2: What market reader does does that sort of displace the
Speaker 2: traditional research analyst.
Speaker 4: Within Excante Data, we have a Bloomberg chat right where
Speaker 4: we talk to top institutional investors around the world around.
Speaker 1: You don't talk to us, though.
Speaker 4: If you want to pay, maybe you can get involved
Speaker 4: in the chat, But this is like people pay for
Speaker 4: that service, right, and we have an intense discussion about stuff.
Speaker 4: To answer your question, right, It's totally possible that some
Speaker 4: of the content that is there could be done by
Speaker 4: a robot version of one of my team members or
Speaker 4: a robot version of myself, and we definitely think about that.
Speaker 4: We can also think about using essentially the market reader
Speaker 4: technology to do aspects of what we do for the
Speaker 4: Exante Data clients. This is going to be fun stuff
Speaker 4: and maybe we can do analysis that we otherwise wouldn't do.
Speaker 4: Maybe there's some analysis we don't like to do that
Speaker 4: we find boring, that we give to the role what
Speaker 4: to do, and then we have more time to do
Speaker 4: some fun stuff.
Speaker 1: And we can't let you go because of this Wall
Speaker 1: Street background of yours without you sharing some thoughts on
Speaker 1: what you're actually seeing in terms of markets. I think
Speaker 1: you're looking at global growth slowing down. What evidence are
Speaker 1: you looking at and just what are your thoughts, probably
Speaker 1: speaking on everything that's going on with global economies and markets.
Speaker 4: Yeah, we crunch a lot of data. So for China,
Speaker 4: we essentially have a rule that we try not to
Speaker 4: look at official data because we think it is since
Speaker 4: we're allowed to use the word garbage. So we use
Speaker 4: our own alternative data to have a sense of what's
Speaker 4: going on in the Chinese economy. And we've had pretty
Speaker 4: weak signals since the end of March. Right now it's
Speaker 4: showing up in the official data while least the PMIS
Speaker 4: will a lag. So this is an important thing for
Speaker 4: the global economy, right, because other economies when they've reopened,
Speaker 4: it was a multi quarter process with a booming economy, right,
Speaker 4: And in China's case, it looks like they had one
Speaker 4: quarter of better growth and now they're slowing already, right,
Speaker 4: So that's the negative surprise for a lot of people.
Speaker 4: Do you want to talk about assets as well?
Speaker 1: Yeah, we love assets, So.
Speaker 4: I think this is interesting about psychology in the market. Right,
Speaker 4: We've had this whole wave of let's focus on the
Speaker 4: dollar being doomed, that we're going to dedollarize and so forth, right,
Speaker 4: and then you look at what's actually happening in the market, right,
Speaker 4: and the currency that in the narrative is perceived to
Speaker 4: be the new alternative to the dollar, the Chinese currency see,
Speaker 4: and why it's literally going weaker every day, not every
Speaker 4: single but if you look at the chat since January,
Speaker 4: there are so few strengthening days, right, and we just
Speaker 4: have a long trend now of dollar gains and c
Speaker 4: andhy weakness. It's just so interesting, right that you can
Speaker 4: have a disconnect between a narrative that if you read
Speaker 4: all those stories, it look like, okay, the dollar's going
Speaker 4: to die tomorrow, right, and then you look at what's
Speaker 4: actually going on in the market, and the CNY.
Speaker 3: Is having trouble.
Speaker 4: Literally you can look at a lot of capital flow
Speaker 4: data as well, but they're just in the price actions.
Speaker 2: It's just stunning, well that, I mean deja vu again.
Speaker 2: I first met yen's because I was a currency reporter
Speaker 2: and I was obsessed with that theme in like twenty seventeen,
Speaker 2: twenty eighteen, Oh my gosh, when will do you want
Speaker 2: displace the dollar as the global reserve currency? And I
Speaker 2: think some and said to me, I don't think it
Speaker 2: was you like you will write this story every five years,
Speaker 2: like it becomes a narrative and then it dies, like
Speaker 2: nothing will displace the dollar. And it feels like we're
Speaker 2: in that part of the arc again where people are
Speaker 2: realizing that, Okay, the dollar's not going anywhere.
Speaker 4: I get so many questions on it that I actually wrote.
Speaker 4: I wrote a public substack today that's called a brief
Speaker 4: History of Dollar Hatred. I tweeted it out as well.
Speaker 4: Because we go through these waves, and I've been doing
Speaker 4: this for about a little bit more than twenty years now, right,
Speaker 4: I've been through free waves like there was one before
Speaker 4: the global financial crisis where people say, oh, the US
Speaker 4: current account definit is so big, the dollar's going to die.
Speaker 4: Then there was you know, from twenty ten to twenty thirteen,
Speaker 4: or sellers, ah, the Fed is printing so much money
Speaker 4: que infinity is Dollar's going to die. And now this
Speaker 4: year we've had a China has a much better currency.
Speaker 4: The dollar's going to die. Right, So we go through
Speaker 4: these phases, and then you look at how the dollar's trading.
Speaker 4: Dollars is strong, doesn't matter how you look at it,
Speaker 4: normal exchange rate, real exchange rate, which index it's a
Speaker 4: strong currency. There's no evidence that the dollar is about
Speaker 4: to collapse. Doesn't mean it couldn't collapse in the future, right,
Speaker 4: But it's not. It's not something that is in motion yet.
Speaker 2: What is that a function of rate?
Speaker 3: Now?
Speaker 2: Is it you know, the Federal Reserve having you know,
Speaker 2: still on its hiking path higher terminal rate than everyone
Speaker 2: else it seems like at least at this moment, or
Speaker 2: is this more about other economies, other central banks not
Speaker 2: looking as hot.
Speaker 4: Well, we can keep it very simple, right, So we're
Speaker 4: discussing how much above five percent interest rates should go
Speaker 4: in the US, right, that's the debate we're having every day.
Speaker 4: And then in China right now, the debate is whether
Speaker 4: they have to lower interest rates below two percent?
Speaker 3: Right, we're going lower every day, right.
Speaker 4: So it used to be the case that interest rates
Speaker 4: we're much much higher in China than the United States, right,
Speaker 4: and now it's totally flip.
Speaker 3: Right.
Speaker 4: So I don't do analysis based on one verbal but
Speaker 4: that's a pretty important veribal and it's like showing a
Speaker 4: very dramatic effect. Like the other thing that I think
Speaker 4: it's sort of grotesque about this debate about okay, is
Speaker 4: the Chinese currency gonna take all the reserve currency money
Speaker 4: like from central bank reserves instead of the dollar. Right,
Speaker 4: you look at the capital flow, which is what I'm
Speaker 4: doing every day, and literally since the beginning of twenty
Speaker 4: twenty two, there's not been a single month with any
Speaker 4: meaningful inflows into China. Nobody wants their bonds. How can
Speaker 4: you have a reserve currency when people don't want to
Speaker 4: hold their bonds. So there's a lot of stuff missing.
Speaker 4: It's not because everything is fantastic in the US, right,
Speaker 4: we have political issues. We talk about that ceiling, which
Speaker 4: really is a silly thing, like why do we have
Speaker 4: such a bizarre system? Right, So it's not that everything's
Speaker 4: fantastic in the US, but we need to have an alternative.
Speaker 4: To have a real threat to the dollar, needs to
Speaker 4: be an alternative, and it's so hard to find a
Speaker 4: real alternative, and the Chinese currency is definitely not one.
Speaker 4: If people don't want the bond is not all alternative.
Speaker 1: Katy, I'm so glad you're here because I know nothing
Speaker 1: about currency.
Speaker 2: This is so fun. It's like twenty nineteen again. It's great.
Speaker 1: Well, Yen's Nordvick, thank you so much for joining the podcast.
Speaker 1: This has been a real pleasure, but I'm gonna hold
Speaker 1: both of you hostage until we play. Craziest thing we
Speaker 1: saw in markets this week.
Speaker 2: This is the most ready I've ever been for craziest.
Speaker 1: Are you sure? Okay, you go first? Since wait, you
Speaker 1: tease this?
Speaker 2: So I cover ETFs now? Yes, So I moved on
Speaker 2: from the world of currencies. I love covering bond ETFs.
Speaker 2: Obviously it's been the year of the bond. But the
Speaker 2: craziest thing I saw in markets this week I was
Speaker 2: looking at a triple leveraged long duration treasuries ETF. So
Speaker 2: it's twenty year treasuries and then you triple that exposure.
Speaker 2: Bonds are already so volatile, but apparently people really want
Speaker 2: this because this ETF it's the direction daily twenty plus
Speaker 2: year treasury bowl three times ETF. The ticker is TMF uh.
Speaker 2: It's assets have hit two billion dollars. It's more than
Speaker 2: double this year alone in assets. People just really want
Speaker 2: very volatile bonds.
Speaker 1: I guess, Oh my gosh, I want a calmer market.
Speaker 2: No, not these people.
Speaker 1: I want calm. I won't calm.
Speaker 2: I asked Dave Nadig over at a Verify, what's going on?
Speaker 2: Who's buying this like, who the heck wants TMF right now?
Speaker 2: And he said, it's all headline day traders. Wow, yeah,
Speaker 2: that's cool, that is cool.
Speaker 1: Okay, you were prepared.
Speaker 2: Yeah. Y.
Speaker 4: So we've had a bit of movement in some semiconductor
Speaker 4: stocks over the last I think so two weeks. So,
Speaker 4: so I'll give you a stat from market Rita. So
Speaker 4: we we look at all the volumes and those instruments,
Speaker 4: and some of these semiconductor companies they just have totally
Speaker 4: insane volumes. Right if you look at the volume and
Speaker 4: that you've learned about the normal distribution in school, right,
Speaker 4: if you get like two standard deviations, it's big and free,
Speaker 4: it's very big. I've seen sick, seen standard the aviations.
Speaker 4: Like the volume was above normal here this week. So
Speaker 4: there's definitely a little bit of focus on semiconductors like.
Speaker 1: Con video or which stocks are.
Speaker 4: We talking about some of the like my actual so
Speaker 4: for some of the like second tier semiconductors, like just
Speaker 4: crazy volume.
Speaker 2: Well I would imagine people are trying to find the
Speaker 2: next and video, right, Holy moly, yes, well, Tana.
Speaker 1: I have trouble pronouncing video and video.
Speaker 4: Why I said sector pronunciation?
Speaker 2: I said no video on videos and I got like
Speaker 2: three people paying me and they were like, it's end videos.
Speaker 1: Oh is it end video?
Speaker 4: So I was right.
Speaker 2: I just know it's not no video. I feel like
Speaker 2: I toggle between ND video and.
Speaker 1: I feel superior now because I got it right.
Speaker 2: It's definitely not no no video.
Speaker 1: Let's see who hits us up.
Speaker 2: Yeah, not video.
Speaker 1: Okay, it's my turn, and I'm gonna make you both
Speaker 1: play a game.
Speaker 2: Okay.
Speaker 1: Mike usually does this role, and he's very good at officiating,
Speaker 1: as like the game master. But I'm gonna read it
Speaker 1: you a little description and then I'm going to have
Speaker 1: you play. The price is precise, okay, So it's not
Speaker 1: the price is right, it's the prices precise.
Speaker 2: So what are the rules.
Speaker 1: It's the same rules. Oh okay, we just can't call it, okay,
Speaker 1: a Japanese ice cream. This is good, so far? Right,
Speaker 1: so far just set the world record as the most
Speaker 1: expensive ice cream ever. It's made of truffles from Italy
Speaker 1: and they go for fifteen thousand dollars per kilogram, and
Speaker 1: it also has Parmisano Vergiano in it and Saki Lee's
Speaker 1: which is left over paced from Saki production. And when
Speaker 1: you buy this ice cream, you also get a handcrafted
Speaker 1: metal spoon that's made with materials used to build temples
Speaker 1: and shrines.
Speaker 2: I'm sorry, I thought this was the craziest thing in markets.
Speaker 1: Yeah, it's fine, Okay, I'm giving myself a pass. Okay,
Speaker 1: if you wanted to order this ice cream, how much
Speaker 1: are you paying for it in dollars? And it's the
Speaker 1: most expensive ice cream ever? And you go first?
Speaker 4: Okay, I think it's twenty thousand dollars?
Speaker 2: Katie? Is this for? What unit? Is this for a
Speaker 2: cup of ice cream? A scoop of ice cream?
Speaker 1: I think you get a little bit of ice cream
Speaker 1: because yeah, like a pint maybe because this the story
Speaker 1: said that you should try to eat it as soon
Speaker 1: as possible, like doesn't technically go bad, but you should
Speaker 1: eat it within ten days of getting it, so okay,
Speaker 1: and you're supposed to put truffle oil on top of it.
Speaker 2: I'm gonna say, I feel like you kind of low
Speaker 2: balled it. So I'm going to say, like two hundred
Speaker 2: and fifty thousand.
Speaker 1: For ice cream.
Speaker 2: I have no idea.
Speaker 4: Twenty thousand ice cream is cheap?
Speaker 1: All these people and tell them to up their price.
Speaker 2: How wrong?
Speaker 1: Am I you're both so wrong. What is it six thousand,
Speaker 1: six hundred ninety six dollars.
Speaker 3: Only three times?
Speaker 2: Yeah, we're going to rage. Quit this podcast.
Speaker 1: Slam your cups down, slam your fists down. Anyway, it
Speaker 1: was a great pleasure to have you both on here
Speaker 1: and have you guests so very incorrectly about this ice cream.
Speaker 2: Thank you for the opportunity.
Speaker 1: Thank you, and YenS, thank you so much for joining
Speaker 1: us on the podcast this week. YenS Nordwick, co founder
Speaker 1: and CEO of.
Speaker 4: Market Reader, I hope with come back soon.
Speaker 1: I hope so too. Thank you both.
Speaker 2: That was fun What Goes Up.
Speaker 1: We'll be back next week. Until then, you can find
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Speaker 1: listeners can find us. You can find us on Twitter,
Speaker 1: follow me at Goldana Hirich. Mike Reagan is at Reaganonymous.
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Speaker 1: Up is produced by Stacey Wong, and our head of
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