
Sign up to save your podcasts
Or


While many companies like to use buzzwords like crypto, blockchain, AI and encryption, Numerai actually combines all these technologies in a novel way. Founder Richard Craib discusses how his hedge fund started off crowdsourcing good financial models from data scientists to optimize its trading strategies in global equities. He then explains how it airdropped a token, the Numeraire, to those data scientists, giving them the option to stake some Numeraire for models they were especially confident in, and how staked models have higher returns. He also describes Numerai's new Erasure protocol, which helps build a marketplace for predictions for sellers who could profit more from selling their predictions than trading on them, and for buyers who can now use the signal of staking to filter between good and bad data. We dive into the protocol's "griefing" process, in which buyers can punish sellers, as well as the thorny issue of whether selling predictions based on proprietary information could constitute insider trading. Plus, we explore whether or not other hedge funds would really want to buy a token created by another hedge fund, and what Numerai's plans are to become more decentralized.
Thank you to our sponsor!
Onramp: http://www.thinkonramp.com
Episode links:
Numerai: https://numer.ai
Richard Craib: https://twitter.com/richardcraib
My Forbes article on the Numeraire: https://www.forbes.com/sites/laurashin/2017/02/21/this-is-the-worlds-first-cryptocurrency-issued-by-a-hedge-fund/#3cda45f360b6
Erasure: https://erasure.xxx
Announcement about Erasure: https://medium.com/numerai/numerai-reveals-erasure-unstoppable-peer-to-peer-data-feeds-4fbb8d92820a
Unchained episode with Olaf Carlson-Wee of Polychain Capital: http://unchainedpodcast.co/why-the-first-employee-of-coinbase-launched-a-hedge-fund
Unchained episode with Joey Krug of Augur: http://unchainedpodcast.co/joey-krug-on-how-augur-is-like-any-other-tool-ep79
Learn more about your ad choices. Visit megaphone.fm/adchoices
By Laura Shin4.6
11841,184 ratings
While many companies like to use buzzwords like crypto, blockchain, AI and encryption, Numerai actually combines all these technologies in a novel way. Founder Richard Craib discusses how his hedge fund started off crowdsourcing good financial models from data scientists to optimize its trading strategies in global equities. He then explains how it airdropped a token, the Numeraire, to those data scientists, giving them the option to stake some Numeraire for models they were especially confident in, and how staked models have higher returns. He also describes Numerai's new Erasure protocol, which helps build a marketplace for predictions for sellers who could profit more from selling their predictions than trading on them, and for buyers who can now use the signal of staking to filter between good and bad data. We dive into the protocol's "griefing" process, in which buyers can punish sellers, as well as the thorny issue of whether selling predictions based on proprietary information could constitute insider trading. Plus, we explore whether or not other hedge funds would really want to buy a token created by another hedge fund, and what Numerai's plans are to become more decentralized.
Thank you to our sponsor!
Onramp: http://www.thinkonramp.com
Episode links:
Numerai: https://numer.ai
Richard Craib: https://twitter.com/richardcraib
My Forbes article on the Numeraire: https://www.forbes.com/sites/laurashin/2017/02/21/this-is-the-worlds-first-cryptocurrency-issued-by-a-hedge-fund/#3cda45f360b6
Erasure: https://erasure.xxx
Announcement about Erasure: https://medium.com/numerai/numerai-reveals-erasure-unstoppable-peer-to-peer-data-feeds-4fbb8d92820a
Unchained episode with Olaf Carlson-Wee of Polychain Capital: http://unchainedpodcast.co/why-the-first-employee-of-coinbase-launched-a-hedge-fund
Unchained episode with Joey Krug of Augur: http://unchainedpodcast.co/joey-krug-on-how-augur-is-like-any-other-tool-ep79
Learn more about your ad choices. Visit megaphone.fm/adchoices

899 Listeners

644 Listeners

742 Listeners

1,833 Listeners

293 Listeners

276 Listeners

137 Listeners

250 Listeners

166 Listeners

450 Listeners

135 Listeners

277 Listeners

51 Listeners

61 Listeners

76 Listeners