The stock market is inefficient with respect to new developments in machine learning because only a small fraction of the world's data scientists have access to its data.
Numerai data scientists aren't traders or quants, and they don't want to be. They are experts in statistics, machine learning and artificial intelligence, working as geneticists, physicists, students and professors.
They have specialized in building predictive models on data—any data. So we give them stock market data in its purest, most abstract form and let their machine learning algorithms discover its predictive structure.
Learn more about our thesis in Encrypted Data For Efficient Markets
Numerai is not a search for the best model; it is a platform to synthesize many different, uncorrelated models with many different characteristics.
Data scientists compete on a leaderboard but models are ranked and rewarded based on their contribution to a meta model.
Learn more in Super Intelligence for the Stock Market
Our data scientists don't need capital, data, or finance domain knowledge to compete on Numerai. Numerai investors provide the capital, and Numerai embeds our finance domain knowledge into the design of our datasets.
Because Numerai data scientists do not know what our data represents, human biases and overfitting are overcome. Numerai does not know what algorithms our data scientists are using because data scientists only upload predictions. Data scientists do not need to tell us who they are, and receive payments in Bitcoin.