The Crowdsourced ML Challenge Behind a Stat Arb Vault

Transcript

I'm incredibly excited to be launching something here in a couple of minutes. But in order to set the stage for that, I'm going to have our partner at Crowdsourced, the founder, Jason Rosenfeld, come up and explain what they've been working on for the last couple of years. >> Awesome. Uh thanks, Lee. I'll talk for a few minutes and then uh Lee will come back up as well. Uh we're very excited to tell you about Crowdsourced, which is the crowdsourced machine learning challenge that's soon going to be powering a Stat Arb market neutral vault on Hyperliquid. Uh

so in sum, we have data scientists come to our platform every day. They're ranking every Hyperliquid perpetual based on how they think future performance is going to do. Then we aggregate all of those predictions into a single meta model and then turn that meta model into a portfolio that can be traded. Um I'm just curious to get a sense of the audience. You're watching a talk that has Stat Arb in the title. Uh how many people know what Stat Arb is or you're familiar with it? Um okay, so a few people.

Good news is it's not too complicated. We use machine learning, we use statistics to try to identify inefficiencies in the market and then exploit that. And so because we are in the finance hedge fund industry, I have a nice uh wall of text of disclaimers here for you. I'm not going to read the whole thing. Uh mainly this is not a securities offering. Uh you can go to our website for more on the disclaimers and disclosures. And so without that out of the way, um tell you a bit about the premise of Crowdsourced. And the premise is to take the traditional hedge fund industry model and then flip it on its head. So what is the traditional fund model? It

is basically hiring a few portfolio managers or researchers, hiding them in a room and having them do their work in secret. Usually, if you're not moving to New York City, London, or Singapore, you're not going to be one of these few quote-unquote smart people in the room making these investment decisions. And so at Crowd Cent, we think there is an alternative better model. Basically, talent is globally distributed, but we've seen that opportunity is not. So let's give an opportunity to everyone.

If we have an open platform where anyone can download the data, they can bring their own data, and then submit a model, you know, we we see that we can clearly aggregate all of those. And then the models are only scored on live data, so we know who is doing well and who isn't. And so the question is with a properly aligning incentives, can you build this alternative hedge fund model that can compete with the likes of Citadel and Millennium? Obviously, we think the answer is yes, and there's some precedent for this.

Crowdsourced machine learning already works. It's been out there for 10, 15 years. Maybe another poll of the audience. I'm curious if people have heard of the data science competition called Kaggle. Okay, a few people. And then more crypto-friendly, how many people have heard of Numerai? Okay. Um so Numerai runs a hedge fund. They run an equity hedge fund. It's a stock market prediction, similar to what Crowd Cent is doing, but Crowd Cent is doing it with hyper liquid perpetuals. Um but Numerai works, and Crowd Cent actually participates as machine learning engineers to Numerai. And so we've competed, we've staked for a number of years now, and we

know what it takes to build a tournament, and we know what it takes to build good machine learning models. So here's a nice visual of kind of what the process looks like. So on the left, we have a bunch of model rankings. Each column that you see is somebody's prediction. So you'll see all on the far left, someone thinks Athena is going to do the best over the next 30 days, and at the bottom of the list is Doge. So Doge may do the worst. And then each each new column is somebody else's prediction.

And then you see the meta model, which is aggregating all of those signals every day. Then we take the meta model column, and we turn that into a long-short book. So maybe you want to go long the top 40 of the meta model, maybe you go short the bottom 40 of the meta model at the same time to create that portfolio. Uh and there are a lot of ways to do this. You can do different portfolio optimization, you can do different neutralization. Um and we'll see more about that with the lead. Um So what does it look like to actually participate? It's very easy. It's also becoming a lot more fun. You may have seen the little clip of our game.

Uh it really is just kind of five lines of Python. But if you've been following AI agents, nobody's writing code or Python anymore. So it's really just asking an AI agent. Um we give out free data, so it you can just come with nothing, or you can bring your own data. Orthogonal signal is always good. We have a leaderboard that's scoring everybody out of sample, so it's pretty competitive. Everyone wants to do uh pretty well. And we recently launched Crowd Cent Cloud, which is external compute for your AI agents to go build your machine learning models.

And so why is this kind of the inflection point? It's because over the last 6 months to 12 months the best forecaster is now a machine. The best machine learning model builder is also now a machine. So, like I said, it has never been easier, it has never been more fun to kind of build these machine learning models, even if you don't know machine learning. I encourage you to go to Crowd Cent. You can start talking to our agent, and you may accidentally become a machine learning expert. Uh we saw that bots took first and second in the Metaculus Cup. So, this is really really happening.

Our agent is named Centaur, and again, yeah, I I just encourage everybody to check it out. So, I've talked a lot about the challenge side of things, and what we've built to build the signal, but there's a lot of work that goes in moving the signal to a live portfolio. So, that's why we partnered with Bleeker Rogan and Star Killer Capital. And uh you want to come on up and tell me about it? >> Thank you, Jason. So, what is Star Killer Capital?

>> [clears throat] >> Star Killer is a classic institutional hedge fund that I founded back in 2021. We run two different strategies today, two different books. One is a quant-driven liquid token fund, really based off of trend following and momentum models with a bunch of quantitative fundamental overlay. The other one is a DeFi market neutral yield fund that also does some arbitrage-y type stuff. Both funds are in the top decile relative to their peers, according to Crypto Insights Group. So, we're really proud of what we've built.

My background >> begins in the equity stat arb community, which is actually where I met Jason over a decade ago when he was a researcher at Millennium using data built by company that I founded called Estimize back in 20 11. Now, Estimize was a big crowdsourcing competition for not machine learning models, but equity fundamental estimates. So, earnings per share, revenue, EBITDA margins.

Estimize eventually ended up becoming the largest crowdsourcing platform for equity fundamentals in the world to rival Thomson Reuters I/B/E/S or First Call or Bloomberg estimates. Uh but instead of from the sell-side, the estimates were from the buy-side. But one thing that we were always a little annoyed about was that we were not really able to give a ton of monetary value back to our contributors uh because there was no mechanism back in 2011.

Now, we have decentralized blockchains and the ability to imbue tokens with the value from certain businesses. Which brings us to 6 months ago when Starkiller started to look at the Crowd Sentiment data. And what we found was incredible. The meta model associated with the Crowd Sentiment data happens to be incredibly accurate. 84% of days it's more accurate than the median model.

the average daily percentile in the 69th percentile. So, the very basics of is this competition actually producing quality data was very obvious right away. So, what we did was we looked at a very naive model actually turning that into a portfolio. So, this is a equally weighted book long and short. Jason alluded to this. And even that equally weighted book not neutralized for any factors already produces very consistent high sharp returns especially relative to Bitcoin.

So, what we're excited about here is there is a process that we believe is repeatable across potentially many different asset classes and assets to take a challenge turn it into a meta model and then turn it into an actual risk managed portfolio that can be allocated to by both individuals and institutions. And so what we're here to do today is actually launch the process of building a vault which will be available soon and we'll get to that in a second.

Uh that actually trades the model that StarKicker has helped build here. So, when we look at actually building a portfolio off of that meta model, there are a whole bunch of things that need to be done. One is we need to neutralize all of the different factors. [snorts] Now, in crypto we don't have a lot of classic factors like we have in the equity market. There isn't really a good agreed upon sector classification. And so, these factors that we use to neutralize the portfolio are purely statistical and they add a ton of value in producing a book that can be

allocated to uh institutionally. Um we also conviction weight the portfolio. So, obviously um the competitors in the tournament uh produce models that uh have more conviction in the top decile of the universe than the, you know, the third decile. Um and when we do this in different ways, it obviously improves the portfolio. And what we eventually get is a portfolio that produces high 20% returns >> with a, you know, almost two sharp uh nine ball and a max 7% drawdown. Those are really

high-quality statistics associated with a stat arb strategy. And the thing that we're excited the most about here is that this is a proprietary data set that nobody else has that should not be uh arbitrage, the value of which should not be arbitraged away uh anytime soon. So, what are we doing here? Uh we're launching the sign-up for the vault. This is a capacity-constrained strategy. you can go to crowdsourced.com/challenge today and get on that list.

The vault will be launching in a number of weeks here. Um we're incredibly excited for it. The last thing I wanted to talk about is over the long term what's going on with the advent of these AI models and the fact that our belief in getting involved in this project, you know, part of it is that >> as AI models really eat away at the advantage that large hedge funds have in executing on these systematic strategies, they are definitely democratizing the ability to build models.

Uh our belief is that the future of alpha in almost all markets is going to come down to two things. Diligence in hard to quantify strategies. And do you have a unique data set that nobody else has? It is going to be a treadmill for everything else besides those two things. And as individuals get their hands on these AI models and point them at competitions like CrowdSent, our general belief is that this can be a sync for value capture.

Uh where an individual or an institution produces models, they participate in tournaments like this, and they receive value back through that competition in different ways. And so, we're incredibly excited. Please get in touch with Jason or I if you'd like to discuss those at more length. Thank you.