The hedge fund Numerai is unusually large in staff compared to its rivals, boasting over 7,500 developers. But what’s most unusual is that those employees can be entirely anonymous.
As TechCrunch reports, the San Francisco startup sends out encrypted trading data to coders that have signed up to work for it. They each develop different machine-learning techniques to make forecasts based on the data, then send predictions back to Numerai. If they’re useful, the data scientist gets paid in Bitcoin.
It sounds like a buzzword-fueled fever dream of a venture capitalist. But after a year of trading, its founder, Richard Craib, says that it’s making money. And the company recently closed a round of funding, securing $6 million, which at least suggests that the dream is one that investors think has legs.
Algorithms certainly aren’t a new idea for hedge funds, and Numerai isn’t alone in using artificial intelligence to approach its business. Machine-learning approaches can crunch through extra data that humans would never have the time to process, and regular algorithms couldn’t make sense of. They can be used to mine insights from news coverage and social media, for instance, as well as spotting trends in unstructured data.
Indeed, many hedge funds are investing heavily in AI. And perhaps for good reason: the industry has come under fire this year from numerous parties for being overpriced while also underperforming. But while it sounds like a good idea to have a machine learn to spot trends, it remains to be seen how well such systems will work in the longer term. AI systems can be sensitive to uncertainty and noise, for instance—something that’s all too common in financial markets.
Numerai’s situation raises some of its own unique questions, too. The anonymity of its employees, for instance, means that it’s impossible to tell if the data scientists working for Numerai might also be involved with other companies and institutions that present a conflict of interest. And, as Wired notes, the company’s use of encryption is a delicate balancing act between speed and security that it will need to handle with care.
Still, it’s an interesting approach in an industry in need of a shot in the arm. Perhaps Wall Street may soon have its own army of anonymous data scientists, too.
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