Crowdsourcing a Machine Learning Hedge Fund

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

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Episode Summary: Crowdsourcing is a relatively common term in technical vernacular today. Even if you’re not a self-identified “techie”, you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off is in finance and hedge funds. In this episode, we speak with Numerai Founder Richard Craib, whose company is crowdsourcing a machine learning hedge fund. Their model is based on pooling data science talent from all over the world and using “anonymous” models to train financial data. These models compete against one another, and the winning models’ creators are rewarded in bitcoin – a process based entirely on encryption and anonymity. Craib speaks about his overarching vision for the company, and also delves into his thoughts on the past, present, and future of AI applications in finance.


Expertise: Quantitative finance and machine learning

Brief Recognition: Prior to building Numerai – “a global artificial intelligence tournament to solve the stock market” – Richard Craib studied pure mathematics with a focus on abstract algebra, and worked in quantitative finance with a focus on machine learning. He holds a BA in Mathematics from Cornell University and also studied machine learning at Stanford.

Current Affiliations: Founder of Numerai

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Interview Highlights:

(1:30) You’re allowing teams or individuals to submit predictions based on their algorithms that are helping to yield a financial return in the markets – what sorts of predictions are they sending to you and what types of models are they building?

(4:27) The kinds of models these folks are building…what does it look like to build a predictive model for equities or other financial markets…what are the basic strategies at play?

(7:00) Who is the talent that’s working with you folks…are there some self-educated folks, are there some from ivy league schools? Who composes these people on Numerai?

(11:17) There’s machines making trades every day today, how does that change in the future, how do you see the financial market shifting…how might machine learning play a bigger role in the grand sweep of finance at large?

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Big Ideas:

1 – Financial data has historically been secret, expensive and prohibitive, and financial institutions have not tapped the data science pool available to leverage the potentials of machine learning and artificial intelligence

2 – Numerai is working to free stock market data and remove limitations by turning the data into abstract learning problems and giving access to anonymous data scientists, who build veiled predictive algorithmic models. These diverse models are synthesized to yield stronger models that move the company closer to its far-reaching goal – to achieve “perfect capital allocation without the human capital cost”

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