Numerai

“That's, in some ways, the opposite of machine learning, which is a path of saying, we have no idea what patterns are Real or we don't have any theory. We don't have any hypothesis, but we do have a large data set. What can we learn in that data set that will g…”

“Turns out I spent that 250,000 dollars in about three months and ran out of money pretty much the day of first round seeding the company as with a venture investment from Howard Morgan and Bill Trenchard.”

“Data science is not quant. None of these people were quants. None of them had any background in quant. They're just the types of people who can solve abstract problems to do with data.”

“They just model the data, and that's what, what's happening at Numeri, and just like Google Translate, they can be a lot better than a professional Translator on certain languages, and Numeri can be better than a professional investor.”

“What they're doing is downloading a dataset, and the dataset is about, I think it's over a million rows long. So it's just millions of numbers between zero and one. You can download it, by the way, anyone can. You can just go to a website and download it. But …”

“Numeri has created the highest paying data science tournament on the internet. We pay out more than Kaggle does, and we've paid out over thirty million dollars since we started.”

“And that mechanism is something we pioneered back in 2017, long before most people had heard about staking.”

“In fact, our cryptocurrency is more liquid than some stocks, and so people will earn some of it, maybe they'll earn 10,000 dollars by staking on Numurai, and then they'll go to Coinbase and sell it into dollars, or they'll keep staking it on their model.”

“I talk about Numerize being a kind of AI version of Millennium. Millennium, they do not have all the same people they had 20 years ago working at Millennium. They have a totally dynamic system of PMs coming in and out, and that's how year over year they've pro…”

“Well, there was a time in 2018 when NMR, our cryptocurrency, was down maybe, yeah, more than 80%, maybe 90%. And in that year, our user base doubled.”

“We had a big conference in San Francisco called Numicon. Where we had probably the largest gathering of Kaggle grandmasters in real life ever.”

“There are now almost 2000. Three years ago, it was 40. We only had 40 features, and now it's 2000, and over that same period, the number of data scientists submitting models has grown from a hundred to five and a half thousand.”

“If we stuck a seven day momentum feature into Numerai's dataset, the Numerai users would like it and their models would pick up on it. But coming to trade execution, we wouldn't want to trade that fast. And then we wouldn't actually make any money from that.”

“We get the predictions sent to us now every day, but we only trade quite slowly. So we'll only turn over the portfolio quite slowly, minimizing our market impact. And we end up holding positions for three or four months.”

“They're giving us a signal, which is just a long vector of predictions, 5000 predictions on every stock in the universe. And we take those predictions. We compute the stake weighted average.”

“And Numeri is factor neutral. So we will have the optimizer take out country risk, sector risk, factor risk.”

“If you make a mediocre model that's very uncorrelated, you can do particularly well. Because of its lack of correlation is actually helping Numeri more than a good model that's correlated with the one we already have.”

“It's about 500 stocks long and 500 stocks short, and a lot of the position sizes are kind of equal weight. Some that are higher than others, but it's basically, we never have more than three percent of the fund in one name.”

“The biggest one was cracking staking. I mean, we had a period where, yes, we were getting a lot of people submitting models, but we couldn't trust them. We couldn't trust that they would keep working out of sample. We didn't even know who the people were, but …”

“We don't even build the models. Our data scientists do, and we don't even know what they built. Like, they might have used a neural network, or they might have done something else. We don't know.”

“With a single model, you can get very high accuracy with that type of image detection. That's sort of like a solved problem. And therefore, why would you need to do crowdsourcing? That's a solved problem. But stock market is never solved. It's a permanent race…”

“To go from 52 to 52 and a half is massive in terms of your Sharpe ratio returns, volatility. And so the fact is, it's just one of these industries where a tiny bit helps.”

“Large language models, which is probably the most hyped thing in the world, is actually something I want to have Numeri become the best at. And I think we have a shot at it because Machine learning is so in our DNA. So I think that's something I'm excited abou…”

“And so we have the best modeling talent because we have much more More, many more data scientists working on Numeri than any other hedge fund in the world.”

“And you know, for example, in March, 2020, when the market fell 30% in about 20 days we were down one and a half percent.”

“We probably spend more than a million dollars a year on data.”

“We have one really good user who works at NASA Jet Propulsion Lab and who's really strong at data science. And he's actually working on a mission to one of Jupiter's moons.”

“Numeri's mission is to kind of monopolize intelligence and monopolize data, and then monopolize money, and then decentralize the monopoly.”

“So everyone's providing signals on all 5000 stocks in global equities, and we take the stake weighted average of all those signals.”

“And if your model performs well on Nimri, we'll give you more NMR tokens. And if your model performs badly, we can burn your stake.”

“We've even, I think even just last year we paid ten million dollars. So it's so much higher than the rewards are so much higher than the other data science competitions on online by orders of magnitude.”

“And there's some users that have made more than a million dollars.”

“That's why I think we have, yeah, about right now over fifteen million dollars at stake.”

“A big investor could come and give us a hundred million dollars and we would charge two and 20 on the investment.”

“They never give us their code. They never assigning us IP around their models. They can train them in any way they want to keep them on any server they want. The only thing they have to do is provide us the predictions every week.”

“If we do say a linear model just on our data, it sort of performs okay. If we do our own internal machine learning model on the data, it performs better than that. And if we use everyone's model that's being submitted to Numeri, it's way better than even that.”

“At the end of this process, we have a hedge fund that is basically closed to everybody except for like seven investors in the world.”

“The whole point of staking is to get an assessment of how much they believe their model will Generalize.”

“So we trade about A thousand stocks at a time. 500 long, 500 short.”

“We have made investments in other sectors where we believe the answer to that question may be yes. We've done that in consumer products with CircleUp. We've done it in quantitative hedge type financing with Numeri.”