The Derivative - Yep, She's Opening Up Option Eyes with NOPE It's Lily

Episode Date: April 15, 2021

*We apologize for potential audio issues with this episode – please see the transcript HERE or go to the episode blog on rcmalts.com to download.* NOPE - we’re not just going to be talking to our typical vol guest. NOPE – we’re not going over the usual trend following or strategy. NOPE we definitely (did) find today’s guest off of VolTwit. @nope_it’s_lily is Head Researcher at Salience Capital and the creator of NOPE (Net Options Pricing Effect), a proprietary metric used to measure the notional impact of options hedging on underlying liquidity. In addition to NOPE, we’re also talking with Lily about The Bay, PHDs, birth of NOPE from the market crash, Robinhood, GEX and squeezemetrics, bioinformatics and COVID-19, gamma, Salience Capital, and the future use cases for NOPE. Chapters: 00:00-02:04=Intro 02:05-11:45=Grad School & the Vaccine Pirate 11:46-27:28=Launching NOPE & The NOPE measurement 27:29-41:09=NOPE Products / Salience Capital 41:10-49:02=Liquidity in the Markets 49:03-53:25=What's next? 53:26-01:02:21=Favorites Follow along with Lily on Twitter and on her blog. And last but not least, don't forget to subscribe to The Derivative, and follow us on Twitter, or LinkedIn, and Facebook, and sign-up for our blog digest. Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit www.rcmalternatives.com/disclaimer

Thanks for listening to The Driven. of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations nor reference past or potential profits, and listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. Welcome to The Derivative by RCM Alternatives, where we dive into what makes alternative investments go, analyze the strategies of unique hedge fund managers, and chat with interesting guests from across the investment world. When you're at certain points of the monthly options expiration cycle, then the maximum amount of note can move, for instance intraday tends to increase so when you're in all effects week which we're actually currently in then it's not necessarily a great idea especially to use

the short signals because the maximum nope that can achieve intraday tends to increase dramatically so perhaps normally we see it reverse around 30 or 40. Now we can see it like at 70, 80 or 90. So we actually, you know, overlay other, I guess, so to say features for determining when one should enter or exit. That tends to improve let's say the win rate because one of the biggest issues with meme reversion strategies is they tend to have what's called a negative skew. So although they have a pretty high win rate by themselves, they tend to lose a lot of money when they lose. So small wins just do not cover it. Hi, everyone. As an intro to our guest today, I'm going to paraphrase from a recent blog post of hers. In this weird niche of the Internet called Fintwit, you see an interesting confluence of people across the spectrum.

Ambitious youngsters, talented researchers, complete charlatans, legendary investors, powerful hedge fund managers, regular day traders, and low information investors. What's fascinating about the internet is it ends up fairly meritocratic. We'll have to ask her how to say that. Meritocratic, up into a point, regardless of origin, if you produce quality, you will get noticed. So that was from her blog post, and that's's you will get noticed is how we ended up noticing one of these ambitious youngsters. Nope, it's Lily or more formally Lily Frankus here on the pod where we've had those powerful hedge fund managers and they're hundreds of millions. So meritocracy indeed. So welcome Lily. Thank you, glad to be here. So let's get into exactly what you're doing out there to be catching so many eyeballs. So you're, but you're currently a PhD student?

So currently I'm doing mathematics at UC San Diego. University of San Diego or I thought you were up in Cupertino or both? I used to be. I used to work in the tech industry for a couple of years. So I was offering to be at LinkedIn and Cupertino. And then I decided to go back to school because I wanted to try something different. And so now you're down in San Diego though? Yeah, right now I'm in Mobile, which is pretty new for us. I love it.

And so software, so you were up in Cobertina, you worked at where? LinkedIn and Stripe? Yeah, I started full-stack development for a couple years once I got out of college. This was like five years ago. I had a stint in tech industry, as we like to call it. And, you know, that was kind of one of my first actual, like, four days into finance, not only through my previous employer, you I didn't really work on the finance side. I worked more on developing the risk tools. But through the equity grants, for instance, from Microsoft and other employers, it was a good way to start paying attention to software. And so working full stack there, what does it tell us what that means for us that aren't coders? So for computer science and software engineers in general, you're really divided into a couple of different spheres. Some people kind of do everything, but in general, you have front-end devs, so those are people who do web development, for instance.

You have back-end devs, so they tend to be more integrated into developing the infrastructure, the back-end tools, the actual API of whatever platform you're building. You also have data scientists, which is kind of what I went back to school for, where they used computer science and programming and all these tools to understand patterns in data. machine learning. And I would say those are, for sure, like, infrastructure developers who, they really specialize in letting the low level and setting up these gigantic systems. Got it. So did you do anything cool at those two, or it was just entry-level kind of stuff? I would say there were some interesting experiences, but in general, my experiences in tech were not, I guess, what I wanted to do. So when I spent all four years in the tech industry, and I ended up deciding to go back to school because I really was passionate. I had actually previously run a company to these startups and

basically I was pretty passionate about the biology side of things. So I was interested in can we use these kind of software tools and big data for instance to develop better therapies to help people actually improve their lives. And so the PhD, tell me what those things are. So bioinformatics and systems biology. Yeah, so PhD, I mean, it's a long process. I actually just started last year which not a super opportune time to also you know pick up an addiction to the market but essentially it's really focused on using software computer science to understand the audible problems so one of my areas of interest for my research in that field was for autism spectrum disorder, where my brother had autism spectrum disorder.

So it was something that I was passionate about, could we develop better tools and better therapies for children, especially with autism. So does that tie in with like, were you, was any of the coursework or any of your talks around the virus and the, all of that? Is that kind of bioinformatics or no? and I don't when I was applying actually for grad school this was late 2019 then you know you get to the point where you're actually doing interviews and COVID started being a thing when I was doing my graduate interviews with different universities you know February March COVID started going to Italy and I remember talking to my mom and I was like, this is going to be really bad. I think a lot of people in the States really underestimated what, you know, a high reproductive number of viruses. And from the reports in China and the reports in

Italy, it was pretty clear by mid-February that you know things were getting bad I remember that I actually my roommate at the time was laughing at me because she was like why are you stockpiling food and I'm like you're gonna see in a couple months why I'm stockpiling food so and of course and of course in the market yeah and to me it was all about like people just a basic lack of understanding of arithmetic growth versus geometric growth right people like what there's only 40 cases in the u.s i'm like well yeah it'll be 80 tomorrow and 160 the day after that yeah i mean it was pretty clear by april i'd say that there was just no way it was going to be under control. I mean, I remember reading some reports that, I don't know if these are still occurring because I haven't picked as much up with the Coralli science papers for people who are not in the biology sphere, epidemiology,

that essentially means that for every person who gets sick, we expect that they're going to infect six people. And this really is a pretty rare and rapid growing virus. The only one that's kind of similar is measles. And measles, we haveles we have vaccine for I mean now we have a COVID vaccine but at the time it was pretty clear that once it started spreading in the U.S. that nothing would have done much. I hear you I got my first shot so I'm scheduled for my second how about you? I got my first shot in February and it was nice because I worked in a lab that was kind of affiliated with COVID so I got the Pfizer shot really wasn't a big deal I tried to share my experience on Twitter a bit but you know I mean hopefully this will be it hopefully we'll go back to normal

I know I don't know though I'm kind of enjoying the home office here. But yeah, hopefully people stop dying. And so what's with the, what's your new Twitter handle? Or not your handle, but your name? Vaccine Pirate? Yeah, it was interesting. We're actually incorporating, you know, business to run research stuff that we're doing, as well as hopefully, you know, other stuff in the future. And one of the questions that they asked me when i was incorporating this was are you a vaccine distributor and i was very confused that a vaccine distributor would use like an end business dot com but i thought it was hilarious because maybe maybe at this point you know we're in the wrong industry maybe we should

get into selling vaccines and giving it to people because i'm sure those pretty good margins yeah definitely vaccine pirate so all this school work all this stuff and then you decide to get uh launched nope how did that all go down like just going to the movies or reading a book or something wasn't enough i mean so the story would know was that i started trading actively right around the market crash my background is in business and computer science so I knew more than how to say about the market but most of my knowledge kind of extended to this is how you price like a basic bond this is what the market should do or I don't know discounted cash flows or something you know completely completely irrelevant for today's market and basically what happened was that I started with

Robinhood just like everybody else you know Robinhood was free everybody knew Robinhood I'd previously tried Robinhood years ago didn't really stick with it I made free options for it and in May and April, everything was going up. So you could just buy polls and make money. And you're like, oh my God, I'm a genius. I remember in May, I went from $6,000, I think I started with, to about $20,000 by the end of the month. And I'm like, this is so easy. And of course, you know, then Judy and the market went volatile again I knew a lot of people who were over leveraged to completely wipe themselves out and basically when the volatility

started my first reaction was like oh shit this is not as easy as I thought it was so my second reaction was I'm a computer science I'm a quantitative person let me start analyzing this because you know the best way to treat would be what if I complete a system and that way when I'm doing my PhD in this fall I won't have to worry about it I'll just come and model that runs for instance and make some money i mean one thing that if you're not familiar with the phd life it's not hiking versus you know working in tax so i was like this could be some money on the side while i do my studies yeah let me let me stop you for a second i'm just always curious so how did how did you find out about robin hood and how did you want to start trading like was it did you in high school do trading contests or were you or is this more like nefarious and they're getting into

every corner of the uh young people and getting into colleges and being like you need to trade I mean so one I guess I had you know previous births of Robin Hood. I also read Wall Street Bets. I was pretty active on that for a couple years. So I knew of the option trading market. I didn't really actively trade. I mostly just enjoyed the memes of people losing money. But then I remember pitching to my friend Monica. I was like, we should just start trading.

I mean, she loves to gamble. There we go. There's my answer. But no, for me, honestly, I'm not a game changer. I do not like, I do not have risk. And when I take risk, I'm very confident in what I'm going to do. Because one of the things that's been interesting is the more I learn about the market, the less I wanted to bet a lot of money on options. Right, the more you know it's a rigged game, so to speak.

But so, when I'm in college, it was like, okay, are we gonna play beer pong at 2 p.m. or 4 p.m.? But so in your undergrad, were people like, okay, what are you doing? Do you have your Robinhood account? Which stocks are you looking at? Where'd you go to undergrad by the way? For undergrad I went to University of Southern California which you might know from old scandals.

Yeah. That I did so undergrad business and see if I graduated in 2015. So marketing is what it was actually nice is when I started in 2015. So the market was hot. It was actually nice because when I started at LinkedIn, which was the private Microsoft, I remember joking, that's when I first actively started investing, but my investments were primarily my 401k and the Microsoft shares, which I still talk a lot on Twitter but it was interesting because it was kind of completely different than what normal you know market was and what you're supposed to diversify and you're supposed to I don't know put it all on you know Vue or Ivy or whatever's the lowest name right now. And with Microsoft, I got it right when it started the master tech bull run for 2016 to 2020.

And I joke with people, I'm like, I had coworkers who are selling it. And I'm just like, there's never a good time to sell. Like it just keeps going up. Yeah. What are you doing? I've got friends who work at Microsoft and they get the stock options right at 10% discount to the current NAV. So that's just a money printing machine as long as it's going up as well. Anyway, I buried the lead there, but was just interested in like, how did all these people get to Robin Hood? Especially young people, like how do they all decide, right?

So you're saying it wasn't the normal path, it wasn't the the normal thing but it seems more and more like it is the normal thing like everyone's trading well it's interesting i mean i would say these two courses that work here one was kind of the crypto in 2018 which definitely made a difference a lot of people joined the markets around that time you know everybody even people who don't trade know of bitcoin for instance even before 2020's massive bull run on bitcoin so i wouldn't say it was like something completely divorced from what people were already thinking also a lot of people had money in the stock market and in 2020 there is some volatility so people not only bought at the bottom and a lot of people made a lot of money by buying at the bottom but

they also were like well now it's like maybe maybe i'm a genius so i could just start trading. And what's interesting is that the market mostly rewarded that. So in 2020, it was in multiple periods of time very easy to make a lot of money. Right, and it seems like it's, yeah, it's still rewarding it to a degree, right? I would say a bit less since the stock bubble in february 2021 but it's definitely i mean it was very difficult to be a very you know methodological investor in 2020 yeah okay so you start um you're investing your friend monica gambling, but you're both doing similar things.

And so you say, hey, I need to get a little more analytical on this and I create NOPE. So explain to us what NOPE is and a quick summary, quick elevator pitch on what NOPE is. I mean, NOPE essentially is a way to measure the notional impact of option hedging on underlying liquidity. And if none of those would make sense, I could definitely go into more like depth what that is. Yeah, yeah, we'll dive in. And our listeners will know more than the average bear, to quote yourself. So let's start with how does it differ from Gecs, from squeeze metrics and the kind of those things that are getting put out there. So it's interesting, Gecs and Yorke actually have a really significant correlation and there's a lot of, I'm actually exploring some stuff right now

with for instance, the monthly operation or operations option cycle and how it impacts you know for instance no in this case for gets it's more a measure of like net gamma exposure they tend to have fairly similar meanings when you apply them end of days. So an elevated end of day note, for instance, tends to imply that tomorrow we'll have lower realizable utility, similar to guess. The difference I would say is your goal. One note is an intraday metric. So in fairly normal times, it tends to be pretty successful at, you know,

targeting intraday tops and bottoms on various indices, especially ones that normally need a verb, for instance. And two, it has some interesting corollaries with the direction of the market. Gex by itself is more a measure of this realized volatility. So when Gex is high, for instance, the market is not going to move. So we've seen a lot this week. The market basically has been in range pretty much most of the day. Even today, the massive explosion was about 0.5% up. For NOPE, what's interesting is that this realized volatility actually can be predicted forward in the same day. So if note is less than 50, that usually implies that realized volatility, let's say 60 minutes forward, is going to increase. Similarly, like when note is negative

50, so similar to, I guess you would say, a negative gamma in Gex, it tends to be related to increased realized volatility. And I guess the number one differential between the two is that NOP seems, I mean, one of our major use cases for it is using it for these intraday reversals on SPY, instance, which gets by definition is open interest. So it's not necessarily something that you can really modify during the day. Yeah, but so it's net option, what does it stand for again? Net option pricing. Yeah, net option pricing. So essentially what it's measuring is you have all the options that are traded today. You're looking at for the options that were traded today, what was their delta?

So you look not only at the time of the transaction, but you have to adjust it forward. So if let's say the transaction was at 9am, then I would say 1pm, then I need not going to say 1 p.m. then I need to reflect what the current LDA is. And what that

basically does for you is you can understand at least naively this potentially

is the amount of shares that a market maker who is going to hedge

those options would need to buy or sell in order to stay built in neutral right and in reality this is what it means i mean you know i'm sure your listeners know crossing the spread they know that market makers are often short um they're often short this long pause for instance on indices, they can hedge using other options. But it kind of gives you this naive metric that you can kind of start doing statistical analysis on.

And so it seems, but if you're using it mainly as a mean reversion tool, so it's kind of like can identify when they're out of powder, so to speak, of like they need to do this hedging, this hedging, then they run out of need to hedge, which is right when the market might revert. Yeah, so at least my hypothesis is what happens

when we see these entry reversals essentially is that when this metric is elevated, the primary method of the market moving, you know, as the day goes long is really related to hedging money so if you think about let's say hedging whole options so on the industry for instance i'm a market maker i let's say to keep it simple let's say i'm short of home so if i'm short of column index assuming that i don't know you can hedge with options or maybe,

you know, let's keep it really simple. You're going to buy shares. And when you buy shares, this pushes the price up. Similarly, when another person then, you know, buys another full option, then that market maker needs to hedge as well and they need to buy more shares. And what happens is that that actually continues to push the price of the index up. And I guess kind of, you know, the major takeaway here is that because of gamma, which for instance, the change of Delta in relation to the spot price, because of that, the initial market maker

needs to also buy more shares. So when the market makers are, for instance, short gamma, they're always chasing the underlying movement. But what's interesting is that once you get super far divorced from, let's say, normal buying and selling patterns, the price of the tendency to reverse. So when, for instance, if you've traded or look at the market much, you can observe that rallies tend to reverse

when volume tends to go down. So what we've noticed similarly is that when there's a substantial elevation of this metric intraday, there is a tendency for the market to reverse direction. And because of that, you can use it intraday there is a tendency for the market to reverse direction and because of that you know you can use it into pretty successfully normal times as a good indicator this is not as you know ideal for i recommend people to use but you can use it either as a risk off so if you let's say have a momentum following strategy you can use it to figure out when to exit a trade or if

you're more adventurous you can use it as a mean reversion tool and figure out and use it how to enter a trade yeah it seems interesting to me it kind of seems counter to to gamma right like that it's running out of steam instead of you'd think gamma's like no it's going to accelerate into it into new prices because they have to hedge ever more ever more well so the thing about let's take the example of short gamma so short gamma doesn't occur very often on the emissaries at least theoretically and observationally i'd say when it does it tends to lead to increased realized volatility so you can see this you know when gex for instance is negative but when market makers are long gamma actually the reverse happens so when you're long gamma let's say you've sold a market maker call option and they're going to

hedge it now by selling shares and when they do that they're actually going to hedge it now by selling shares. And when they do that, they're actually going to depress realized volatility. So if we're in, let's say, this long-gamma regime, it tends to be more mean or visionary because if the index starts rallying, you're going to see that the hedging aspect of it is actually pushing it back down. Yeah, yeah.

That's interesting. and just to reiterate you've learned all this since march of 2020 yeah i just really like to notice that's crazy to me and then so tell me all the you've kind of spun out these products out of here, right? So there's Nope, Chart. There's like, what can people use? Do you charge them for it? What does that all look like? So for Nope, I mean, I have a white paper on it where I kind of go into the analysis.

I release this in, I would say, November. Nope, Chart, we're actually developing signals on Alpevec because we've seen, for instance, I've actually was talking about this recently on Twitter, that when you're at certain points of the monthly options expiration cycle, then the maximum amount of note can move, for instance, in today tends to increase.

So when you're in opex week which we're actually currently in then it's not necessarily a great idea especially to use the short signals because the maximum nope that can be achieved intraday tends to increase dramatically so perhaps normally we see it reverse around 30 or 40. now we can see it like at 70, 80 or 90. So we actually, you know, overlay other, I guess, so to say features for determining when one should enter or exit that tends to impair blood-sated win rate. Because one of the biggest issues when we have reversion strategies is they tend to have what's called a negative skew so although they have a pretty high win rate by themselves they tend to lose a lot of money when they lose

yeah small wins just do not cover it which is my issue with kind of like all these gags and all this it works until it doesn't right so it's like a great indicator it's a great tool until there's a phase shift and you totally blow through those levels and you get into a new regime how do you think about that yeah so it's i mean it's something that when you're dealing with especially intraday let's say market dynamics you have to be very cautious i mean one of the one of the biggest problems with let's say mean reversion by itself is it's very difficult to tell when the trade has gone bad with a mean reversion trade you know there are optimal levels you can use stochastic calculus for for instance to figure out where to put your exits or entrances but in general i mean even in our

data set you perform worse with literally any stop loss so the issue with that is how do you know when it's not going to revert that's why you know in those cases you need to look at other market dynamics because you know no index for instance are just one factor you know i mean we saw this a couple weeks ago when archie goes it was like if i remember it was like a friday and you just saw spy tear down you saw my common discovery tear down there's no model that would have fared for that yeah i mean it was literally just Credit Suisse and Morgan Stanley and Goldman trying to front run each other to sell gigantic blocks of shares. And then we saw a rip basically at the end of the day because they were unwinding shorts. And in those cases,

you cannot model your way out of this. And to that point, do you run it just on index options or it's on individual names as well? So we haven't really looked at it on individual names. My understanding is that the shape of like the distributed liquidity. So when you look at left-face buy, for instance, if you look at the position of limit orders around spot, it tends to be approximately normal. I mean, not really normal, but at least normal enough. So because of that, that's one of the reasons why price tends to reverse when, let's say, all the available liquidity in one direction has just been absorbed.

But when you see it on Tesla, for instance, the shake of liquidity is different. You know, as you see today, there are certain stocks that when you buy, it triggers more limit orders above the swap price. So instead of people saying, okay, it's too expensive, I'm not going to, you know, I'm going to remove my order, I'm not going to put more they're chasing momentum so in those cases my hunch is that nope and other metrics actually have more of a tendency to predict it to continue to go up versus for it to let's say reverse direction right and then have you run it on other like on nasdaq or russell or dax and things of that nature so we currently currently run it on QQQ, which I guess is NASDAQ. And it's worked pretty well.

I mean, it tends to work better, of course, for intraday usage on these indices that have a tendency to be inverted in the first place. That said, you know, we haven't done as much analysis. I mean, QQQ tends to be more volatile, for instance, than SPI. So theoretically, it should work. It's, I guess, less useful. I mean, one of the reasons historically that I ran on SPY, I guess not SPX as well, too,

is because it tends to be more predictive of the whole market. So while, let's say, QQQ may be rallying because tech is on fire potentially the market itself be going down and what's interesting about when you use it on let's say SBX or SPY as I talk about on Twitter very often you can predict multiple things you can actually predict currencies to some degree you can predict stocks that have a pretty high beta just by so it tends to be a lot more useful for actually when you're trying to predict the market direction and when you say have you given any thought to like turning it i'm not sure how to ask this question but like turning it into a machine learning right seems like a lot of the things you're saying

are like that machine learning would do of like, hey, we're running this, we're throwing in all the option data, all the S&P data, and then we're getting outputs of signals on currencies, right? That's the thing that machine learning people are doing in the market. Yeah, so I mean, we looked at it,

I mean, I'm not personally a huge fan of machine learning, it's like a way to solve, I feel that like the noise or signal to noise ratio in general for financial data or machine learning tends to make it unseeable for a lot of problems. That said, we are running a model and we have our energy models using XT Boos, for instance, with multiple different features. So we are looking to productionize this and part of that process is the signals that we show on the. And then you keep saying, we're doing this,

we're doing that, who's we? So we're actually a team of like seven people at this point, which is fairly large. I mean, there's Sean, who, for instance, runs the website with me. He's actually the main engineer architect of the website. I mostly work on, let's say, the quantitative research part of it. Then a couple of others are also working or were currently working, I would say, on productionizing our intraday and our energy models okay and but this is all pro bono like you're doing it for

fun for education as a resume like what's the end goal no i mean we're hoping i mean we kind of bought for instance trading on this our intraday model which got like 54 percent return last month but not as good so far this month just these markets been weird so we're hoping to productionize it i mean we're legitimately incorporated got it um and then will that will you cease your phd or you still got to get that my hope is to continue my phd i mean i'm really hoping to actually like explore analogous phenomenon in biological systems that say i mean five years is a long time yeah maybe there's

little market makers in our body that are like gamma hedging or you know the diseases and stuff and we have to you can figure that out i like it um and so is this that's what salient capital is is this sorry go ahead uh yeah so salience right now is just kind of this umbrella term that you know a lot of these different projects we're mostly working with some others on you know using social data as kind of this interesting feature on mental stocks. I've talked a bit about it on Twitter with basically this analogous version of the normal cap-and-model to understand how people, or rather how influencers, tend to draw audiences on social media.

So I'm hoping maybe that'll be a good thing for crypto because it's very hip driven. So help social media people get more, identify influencers or there's the financial aspect of like how to trade. Yeah, so there's a financial aspect on this. So my hunch is that you know a lot of cases you see changes in activity on twitter for example that tend to be predictive of market moves

got it um there's been a few of those that have tried and failed so i wish you the best of success on that oh i was going to say so to me and've got these, a few headwinds for yourself, which I'm just curious how you view those. So one being you're a millennial, you're young, like, so are you getting any pushback from people you talk to of like, you've only done this a few months, you don't know what you're talking about.

Call me back when you've been at it for three years or when you've worked at Goldman or something like that? I mean, definitely. There's really, I would say, two groups of people that I've encountered. One, you know, I've found some really supportive people on Twitter, primarily also through my writings. I mean, my blog got a lot of traction, especially during the GameStop debacle. I would say the other group, you know, might think I'm overstepping my boundaries and i probably am to some degree with a lot of my research and what i talk about i definitely do not think i know everything i mean there's still a lot a lot that i am still learning and i'm trying to you know fine-tune my models but i'm

also just really passionate about research I'm just passionate about talking to people and I guess like significantly passionate about just educating others so as I you know say on Twitter I'm often wrong but if you're not wrong you're not trying like yeah and i think if you don't i think it is a generational thing of the your willingness to just put it out there even if you're wrong right like i see that with some other younger people and they just ask questions on twitter i'm like why would you ask that everyone knows now that you don't know that but it's just this kind of feeling of like no i want to know the answer i don't care if they know now that i don't know that but it's just this kind of feeling of like no i want to know the answer i

don't care if they know now that i didn't know that yeah you know kind of i actually talk on twitter a little bit about how twitter is new to some pedagogical school because or i think someone corrected me there's an adult version of pedagogy but i don't remember what it's called, but essentially you get, especially if you have a good audience and you ask questions, give a pretty strong feedback loop. So one of the things I did early on was ask a lot of questions. I still do pretty frequently, but maybe not to the same degree. I know more now than I did when I started,

but you get answers quickly you get to talk to people that you never would talk to normally and if you say something and you're wrong someone will correct you right immediately even when you're right someone someone will correct you. And then my second one would be being a female in a male-driven industry, unfortunately still. So how do you feel about that? No worries? Attack it? I mean, I was in touch with one of those, so it's pretty much

the same thing. I think for the most part, I've definitely encountered some people who think I'm disingenuous or people who think that I'm disingenuous for attention or people that maybe lose credit when talking about me more because I'm young or fairly new at this or also just because I'm female. But at the end of the day, my hope is for digital adults because it's awesome to be popular online. It doesn't pay any bills at all. And, you know,

what I'm trying to do is get people to talk to, learn more, and work my models. Work my models. my models so you mentioned the liquidity i saw that in your last newsletters um it seems more and more people are kind of complaining about and talking about the lack of liquidity in the uh s&p and emini futures. So what are you noticing there? Kind of paraphrase what you put in the newsletter if you would. So the interesting thing was I noticed this more in February right before the correction period. It was pretty obvious when we saw those massive teardowns in price that let's say we fell

1% in like 15 minutes. I guess you could argue, and obviously we're kind of seeing that, like for instance today, we saw that the price went up pretty dramatically 20% even without seeing the volume. So this tends to be a more unstable state. You know, people have talked about the decrease in let's say the liquidity index on Bloomberg, the widening of bid-off spreads on many futures, especially as you open size. I wouldn't say, you know, we're in the worst scenario that I've seen personally with terms of liquidity. One of the sterling indicators, I guess, in this case for diminished liquidity is the nope, because when we're in this fairly illiquid state,

it tends to move more dramatically based on pretty small price moves. So we're kind of seeing that again i wouldn't tell everybody to rush into shorts yet um but it is something that concerns a lot of people because we've seen this massive march higher literally what like 10 percent we're up since i would say like March. And there's just no volume. People are just not trading. A lot of this seems to be driven primarily by this hedging of options.

And speak to it. And you were saying in the paper, you'll often see on more, right? So if we're saying it's illiquid, usually means more volatile. But we're seeing illiquid equals less volatile. How do you square that? We just haven't seen the volatility yet. I guess I wouldn't say, for instance, volume is a good proxy of liquidity in general.

An example is that if you actually look at heavy volume data on the S&P, there's some TV more volatile. So you kind of have this paradox where if you have, let's say, a very thinly traded ticker like apple or like spy for instance in general days with low volume can be very you know calm yeah so i think that a lot of the diminishing liquidity is still pretty well under the surface. You know, we did see, like I said, in early February, these teardowns of price because the order book likely actually didn't check. But the order book was very thin order book with GameStop when it was rallying for the second high and within literally 30 minutes fell from $340 to like $190. So I'll be more concerned when I see that

in the case. I think this general turn toward illiquidity is a much more gradual process and it's really difficult to say that like it's gotten worse for instance in the last two weeks yeah um yeah and our algo execution group keeps saying well you got to be careful there because even the order book doesn't necessarily mean liquidity right because there can groups hiding their size and and whatnot So I keep hounding them. I'm going to tell them to listen to this. I keep hounding them to come up with a metric, their own NOP, so we can see the true liquidity based on several measures

instead of just most people are using the order book. I mean, that's an interesting thing. A lot of people consider NOP indexes and other option metrics to be a more true measure of the actual workbook than the workbook itself. Because when you're trying to reconstruct it, especially in real time, it's very difficult except looking at,

let's say how much you actually price, to really determine what is the true liquidity under the service. Right, right, and you see that in futures, especially, right, if a market's limit down, and what is the true liquidity under the surface. Right. And you see that in futures especially, right? If a market's limit down, the options are still trading. So that's the ultimate definition of liquidity, right? Like I literally can't transact in the futures,

but I can go create a synthetic long or short in the options. Yeah. Sorry, go ahead. And I forgot to ask back on salient capital B, is it salient capital? I feel like I'm saying it incorrectly. Salience. Salience or salient with a T?

It's salience, but I mean, it actually comes from these posts that I wrote in January called Trading Salience, which kind of talks about the role of the internet in basically these high levels and why it's a bit different than let's say, I don't know, we'll compare it to let's say the chat groups in the 90s. And I'm just like there is a completely different level of penetrance here. And who is it? David Nadig?

I don't know how to say his name, who had a good post on, to your point, this is way different because GameStop, for example, you're getting the more people that click on and look at GameStop, the more the social networks are going to serve up that content. So it's like a self-fulfilling. It's not just 10 people are talking about it on chat room. It's like, it's a cycle where the more people that click on it,

the more it's going to get served up, which leads to more people clicking on it. You kind of, is that the premise you were going with? Something along those lines? Yeah. At this point, it's more that, you know, you see these influencers like Deep Value, and Keep Girls, you knowalas, RealName.

They have massive impacts on the market. Maybe for many years you could get away with not paying attention to social media on the market. I think it probably did not have a significant influence until Robin and showed up in 2017. But we saw just this last year so many different social media driven squeezes. significant influence until Robin and showed up in 2017. But we saw just this last year, so many different social media driven squeezes that famously Melvin Capital reported like a 50% loss because in Q1 in January,

all the shorts were completely destroyed. Yeah, which that's kind of the perfect example of like new school versus old school right um and so i keep forgetting my question on salience and so that's going to be managing money you're going to be a like a hedge fund or investment advisor or what's the goal then that's kind of too but i can't really talk publicly about that okay I like it come when you decide let me know um and but it's not currently managing any people's money it's just the signals currently right yeah the same as a research company is right now just

signals got it so what's next for salience after the no after all this what you what else do you have on the you mentioned the crypto do you want to dive into that a little more yeah it's a really interesting market i mean one of the things that draws me into a lot is just there's not much exploration, at least publicly on papers, about the market microstructure of crypto. So I recently had a question on Twitter where I was like, how do you even hedge a Bitcoin option? And surprisingly, it's just not very well known. I mean, theoretically, it's done pretty similar to, let's say, you know, an option on equities. But nobody really has a good understanding, at least publicly, about the behavior of various parties in the crypto, I guess, marketplace. And there's a lot of

persistent arbitrage opportunities, which kind of are related to the fact that this market is still pretty underdeveloped. And, you know, there's a lot of, I would say there's a lot of alpha out there. I mean, there's a lot of people that are pretty mortified of what's going on with the bubble. But it seems like Bitcoin is going to be the future. Wait, with the bubble? What's the bubble with Bitcoin? It seems like Bitcoin is going to be the future. Yeah. So… Wait, with the bubble? What's the bubble with Bitcoin? Yeah, I mean, Bitcoin has gone up, what, 1,200, 1,100 percent since March 2020, which probably

is unsustainable. But at the same time, you know, there's a lot of money sitting off this which similarly i know a lot of people have made a lot of money yeah and we talked to some groups that do actually do options on that right and they imply it as like 80 percent 800 percent some insane numbers like how do you even model uh normal option stuff when the vol is that high. Exactly, I mean, I assume that senior black shows break down, you know, you see this asset that could rock it up 10% one day, and we're seeing today, I think, dope when it went up, like, 30%. And this just popped in my head, so I ask you, like, have you entertained at all, like, going to work for a prop firm uh so like in chicago there's tons of groups i'm sure would hire you and say

hey here's right here's some money work your model you keep whatever half what you make i definitely this is so pretty i would say you know maybe until like late January I was more just I'm going to talk about stuff on Twitter it wasn't like you know any material aspirations of what I was doing or what I talk about I definitely consider it you know I mean I'm still you know trying to I guess weigh my options and also just develop more because one of the things I like most about the market is just it's literally the greatest game I mean at the end of the day it is this continuous game where everybody is you know in the competitive field and it keeps changing and if you do not adapt continuously you're just eventually you're just being a great stop so because of that you know i've really just been

focusing lately on making sure that the models i'm working on not only are pretty sound causally but also start actually make money right you you belong back in the 80s in chicago because it was people from nowhere just ambitious they'd end up on the trading floor right and whether they came up with a model or whether they just had tons of bravery and some stupidity but right they could come from nowhere and make a lot of money um versus i kind of say chicago versus new york like new york to get in you had to have more of a pedigree and have gone to the right schools and get the right internships to eventually make it to the trading rooms. Yeah I guess it goes back to your quote of my blog post about you know I would say

if you're into it and even market itself at the end of the day it's very product. If you're not good at what you're doing you're just not gonna last in the industry. So I need to make sure I'm just like everybody else and I'm on top of my game. I love it. Cole, we're gonna go to a favorite. So my first favorite would favorite san diego restaurant it's kind of a shame i'm actually here now

six months and i still have not really gone to a restaurant yeah yeah i mean i love i love Yeah, I mean, I love takeout, but if I was to say my favorite restaurant in the area, it's probably the Hot Case Pizzeria, which is a really awesome, super popular place. All right, I'll take both of those. Favorite Gamma type Twitter follow? That is a good question. I mean, there's lots of people on Twitter, you know, Sam Carson, Jim Brisson, Chris Cedillo. Probably my favorite, of course, Ben Eifert, but he doesn't really talk about the market too much. Probably my favorite overall, just because he's so nice, is Chris Abdel-Messier. He's great. We're trying So he actually writes blogs for London and New York. He's great. We're trying to get him on the pod eventually, but he craves his privacy as well. No, those are all great ones. Favorite thing you were going to call NOPE before you

called it NOPE? Did you have some other choices? So the first version that we called skew because essentially What was it? Just skew. Just like everything is skew anyway. So then we spoke at option and then someone who was just in the chat when I was talking about it was like, why don't you just call it the net option price and the factor of eight?

And I'm just like, I love it. Let's do it. Done. No. You think that's part of the success of it all? I feel like it is catchy. It was like, all right. If it was called something else, 826264 model i might have skipped over the blog post but nope made me go to the next line so it's been a double-edged sword because you know i've had a lot of detractors and working on it we just make jokes like oh it doesn't work no I've never written for congratulations and I guess you know I'm a very comedy person you know I post a lot of jokes on twitter but I think it's more because I think it's funny than actually everyone else is as funny yeah and you know that's why whenever i make a model i always find it's a funny name

you know it isn't necessarily because i'm trying to like get the attention it's more just like of course it's not be serious here we were just looking at there's some group in brazil i think they call all their uh model their training models like little robots, like George and Adam and all these little, and they have little images, cartoon images for all of them. So I'm going to bury my section now in like my 15 bad dad jokes about note. I don't want to offend you. And favorite biotech something. I'll take favorite biotech something. I'll take favorite biotech company.

I'm trying to think what one would be. I mean that's the good things about Oxford and Oxford-Van Boren. Those are kind of names that have trickled down to academia. So even as like a first year student for sequencing, like we know a lot of Nanopore, we know a lot of PacBio. And it's just like, I always think that as a good sign for market penetration, it reaches the point where you have people actually learning how to use it. Yeah. Cause people tend, I would say maybe less in bioinformatics

just because it's such a cutting edge field as is. But in general, my experience with the wind, people are constantly in school. They tend to keep doing it for a while. And is the bioinformatics like CRISPR technology and that kind of thing? Okay. Yeah, I was just, I was talking with the private equity manager who was like i he's like i think in 30 years like students are going to be instead of learning how to code they're like learning right they're going into bioinformatics

like much more uh much more than today just good learning how to code and do what like let's learn how to code the body and biological stuff versus the computerized coding is going to basically start to take care of itself. I mean it's one of the worst things of like working in research and stuff is you know learning about this stuff is you kind of get a lot of your aspirations attached because once you realize what we're actually at with this research, for instance, or 3D printing, or organs, or any kind of science fiction you've done in genealogies, you're not like, okay, it's going to be here in five years. Oh, it's like way out?

I mean, I wouldn't say like, let's say life turns away. I would not expect you're going to get a kidney printed for you in the next decade. All right. What do we have first, autonomous trucking in the United States or 3D printed kidney? I mean, I have to talk about this because right now there's another EV company going to IPL and it's just like a lot of them are going to fade away because as lovely as like autonomous driving sounds I'm not for instance in that industry I kind of understand it just from you know service level but it really does not seem there oh it's coming i'm i'm a big believer there that will happen

um probably in the next 10 years or so but i mean i just do not see it by like 2023 for instance right and one truck will kill like one little girl and it'll get put on hold for 15 years um not that we want that to happen and then you can break my heart now because i have a feeling you're not a star wars fan but favorite Wars character, which we ask all our guests. It's been a while. I guess maybe Princess Leia. Is who? Princess Leia? All right. You ever do like do the do for Halloween or anything? So my mom actually adopted us by training. And as it came to like I think we went for tutoring for maybe like the first six or seven years. But she would always think we were a teen.

She just literally like we'd go to the sugar and the sugar replace it with like a diet chocolate or something so just like once you get that feedback and you feel like why are we doing this work in person i love it good one um well thanks lily this has been fun uh tell everyone where they can find you on all the your links are all too confusing for me it's like nope.lily.medium.whatever but give you have any we'll put it in the show notes as well but give us the audio version of where they can find all your good stuff sure so i guess the best and like most up-to-date place always, it's underscore Lily at Twitter.

Then you can also look at which is where I'm writing my weekly forecasts. Well, it's my long-form content there. And finally, like, is like the official-ish version of like the new model. So if you want to check it out, feel free to look. All right, will do. Thanks so much.

We'll talk to you soon. Best of luck with everything. You've got a bright future ahead. You've been listening to The Derivative. Links from this episode will be in the episode description of this channel. Follow us on Twitter at RCM Alts and visit our website to read our blog or subscribe to our newsletter at rcmalts.com. If you liked our show, introduce a friend and show them how to subscribe. And be sure to leave comments. We'd love to hear from you.