Craib: Numerai was unaffected by the January momentum crash
Richard Craib · Richard Craib – Crowdsourcing Data Science for Returns at Numerai (Capital Allocators, EP.314) · May 11, 2023 · at 32:23
Richard Craib, founder of Numerai, discusses how factor neutrality protected the fund during a major momentum factor drawdown.
0:00 / 0:23exact quote · 23.5s
720p mp4 · rendered on demand · StarZero watermark
“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 make our track record more and more idiosyncratic and focused on alpha.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Richard Craib
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.”