Richard Craib
Founder & CEO, Numerai · 1 appearance on the record.
computed by AI from the episodes · how this works → · full disclaimer →
founderexecutiveinvestor@richardcraib ↗LinkedIn ↗numer.ai ↗
Richard Craib founded Numerai in 2015, an AI-run quantitative hedge fund that aggregates encrypted stock-market predictions built by a global network of data scientists. He also created Numeraire, an Ethereum-based token used by tournament participants to stake value on the performance of their models.
29statements → 8claims → 5claims resolved → 60%fully supported → 3.97/5average certainty → 2/5average debate potential → ≈4.5/5argument clarity, estimated →
3 supported 2 partly supported 0 contradicted 3 not checkable as stated how the 8 claims stand · each chip opens the sources
8 assertions · 3 opinions · 9 insights · 8 disclosures · 1 what if · every statement was checked. The predictions and assertions are the 8 claims: statements the public record can support or contradict. 5 are resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Richard argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
How they sound: not measured why? →
We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Richard Craib on measured tape to publish a rate. This says nothing about how they speak.
Everything Richard Craib said on Capital Allocators that made the record, most notable first. Filter by type, assessment or year in the ledger →
Craib: Uncorrelated mediocre models help Numerai more than correlated good models
“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.”
Craib: Jim Simons would make no money without leverage
“Jim Simons would make absolutely no money if it weren't for leverage. That's just how, how it works.”
Craib: Numerai does not build or know its models' underlying architectures
“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.”
Craib: Machine learning quant investing relies entirely on theory-free models
“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…”
Craib: Numerai contributors lack traditional quantitative finance backgrounds
“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.”
Craib: Numerai outperforms pros similarly to Google Translate outperforming human linguists
“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.”
Craib: Numerai has paid contributors over $30 million, surpassing Kaggle payouts
“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.”
Craib: Numerai accesses unhireable global talent through crowdsourcing
“So I think it's very clear to me that for right now, Numeri has a very big edge on talent. We have people working on our data that aren't even hireable.”
Craib: Markets require continuous crowdsourcing because they are never fully solved
“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…”
Craib: High PE returns require less skill than market-neutral quant investing
“If a PE fund makes 40% in a single year because they were actually holding positions that were two times market beta and the market went up, That doesn't have anything close to the investment skill of a quant fund that's making money kind of out of nothing by …”
Craib: Short-term trading strategies lack the capacity to scale
“If you do short-term trading, your capacity is very low. So if you trade very frequently in very small stocks, you're moving the market a lot, and it's very hard to have a strategy that scales.”
Craib: Numerai pioneered cryptocurrency staking back in 2017
“And that mechanism is something we pioneered back in 2017, long before most people had heard about staking.”
Craib: Numerai's native cryptocurrency is more liquid than some stocks
“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.”
Craib: Numerai operates as an AI-driven version of Millennium Management
“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…”
Craib: Numerai user base doubled in 2018 despite token crashing 90%
“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.”
Craib: Numerai avoids high-turnover trading signals due to execution limits
“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.”
Craib: Numerai holds portfolio positions for three to four months
“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.”
Craib: Numerai was unaffected by the January momentum crash
“For example, you know, in January, momentum, the factor crashed, and a lot of quants did badly. Numeri is neutral to momentum, so we basically didn't even notice this crash because we're hedged to that risk, and we do that with as many things as possible to ma…”
Craib: Requiring financial stakes forced contributors to submit their best models
“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 …”
Craib: Numerai plans to build dataset features using LLMs
“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…”
Craib: Numerai closed seed round the day personal savings ran out
“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.”
Craib: Numerai's public dataset contains over one million rows
“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 …”
Craib: Numerai hosted the largest in-person gathering of Kaggle Grandmasters
“We had a big conference in San Francisco called Numicon. Where we had probably the largest gathering of Kaggle grandmasters in real life ever.”
Craib: Numerai scaled to 2,000 data features and 5,500 active modelers
“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.”
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| Richard Craib – Crowdsourcing Data Science for Returns at Numerai (Capital Allocators, EP. | May 11, 2023 | 35m |