Numerai is an Ethereum-based platform allowing developers and data scientists to experiment and create machine learning models with improved reliability. The platform’s main goal is to bring decentralization to the data science field and allow developers to compete in creating effective machine learning prediction models.
Founded in late 2015 in San Francisco, Numerai claims to be the first hedge fund to launch a cryptocurrency on the market. Unlike traditional hedge funds, however, Numerai relies on the data and predictions produced by tournament participants to participate in the stock market. They claim to be the first hedge fund to use machine learning so heavily in its investment strategy.
Who Are the Founders of Numeraire?
Richard Craib is the founder and CEO of Numerai. He graduated from the University of Cape Town in 2008 and later acquired one more bachelor’s degree from Cornell University, and spent a year in UC Berkeley. His professional career started with founding Numerai and has taken him to a Forbes 30 under 30 designee position.
Richard Craib is often cited for his innovative approach to hedge fund management and the revolutionary idea to rely so heavily on artificial intelligence for stock price prediction. Not only that, Numerai has created one of the largest global data science tournaments.
What Makes Numeraire Unique?
Numerai and the Numeraire token are unique in terms of the idea behind their creation. This is reportedly the first cryptocurrency to be created and released by a hedge fund. One of the main benefits of the NMR token is that it is awarded to data scientists whose models perform well in the Numerai tournament. This means that the token becomes more valuable as more people enter the tournament and start competing.
Not only that, but the models ventured for the tournament allow Numerai to actively participate in stock market trading based on the results revealed by participating projects. This innovative approach to stock trading makes Numerai one of the few hedge funds to rely significantly on AI-generated data predictions.
