As artificial intelligence (AI) and machine learning (ML) experts displace traditional fundamental investment professionals as the most sought-after people in hedge funds, a new group holds the keys to the industry’s future. AI and related technologies are being used to find the fastest and best way to execute trades, to place bets on market momentum and to scan online documents and financial reports for trading signals, among a wide range of other applications.
We’ve assembled a list of some of the current top AI and ML professionals working in hedge funds. Let us know if you think we’ve left anyone out via the comments box at the bottom of the page. The list below is not in ranked order.
A former equity derivatives strategist at J.P. Morgan with a Ph.D. in mathematical logic, Granger joined Man AHL in 2008. Now a senior portfolio manager, he was the one who decided to take the firm’s AI trading algorithms out of testing and into production. He kept giving the AI system more and more money from a portfolio he was managing, and the program was profitable. Granger built up the firm’s confidence in the technology.
Bloomberg reported that by 2015, AI was contributing roughly half the profits in one of Man’s biggest funds, the AHL Dimension Programme that now manages $5.1bn, even though AI had control over only a small proportion of overall assets.
John Alberg, co-founder of hedge fund firm Euclidean Technologies, collaborated with Zachary Lipton, a computer scientist at Amazon’s AI lab, to discover a new way to use AI to pick stocks over longer periods than the typical machine-driven approaches favored by Wall Street. Their technique generated 17.1% annualized returns compared with 14.4% using a standard statistical model, according to a paper they presented at the 2017 Neural Information Processing Systems (NIPS) conference.
Babak Hodjat, co-founder of Sentient Technologies, an AI startup with a hedge-fund arm, says that machine-learning techniques are prone to “overfit” – that is, finding peculiar patterns in the specific data they are trained on that do not hold up in the chaotic markets.
Even so, the firm’s Sentient Investment Management division develops and applies quantitative trading and investment strategies using AI, constantly aiming to evolve and optimize its investment strategies visa deep learning and large-scale distributed computing.
Andre earned B.S. and B.A. degrees from Stanford University and a Ph.D. in AI from the University of California, Berkeley, where he specialized in statistical machine learning, robotics, reinforcement learning, evolutionary computation and parallel processing.
He is the CEO and director of Cerebellum Capital, a hedge fund firm with a software system based on techniques from statistical machine learning that continuously designs, executes and improves its investment programs.
The firm’s website explains: “The system is responsible for constantly creating its own new models for how the markets will move, testing those models, refining them, and learning trading strategies that take advantage of these predictive models.”
Yoshinori Nomura joined Japan-based Simplex Asset Management a director after working at Accenture and Citigroup Global Markets. He has a master’s degree in physics from Waseda University in Tokyo. His strategy blends elements of quantitative analysis and AI.
The Simplex fund analyzes huge amounts of data to make buy-sell decisions using ML software that focuses on indicators of momentum and trend deviation, focusing on futures on the Topix index. If Nomura’s program works as designed, its predictive power should improve over time.
Granade is a managing director and the chief market intelligence office Point72 Asset Management who is in charge of Point72 Ventures, which funds and helps to develop early-stage fintech and AI start-ups, and the firm’s data science unit, Aperio. Previously he worked at McKinsey and was the co-head of research at Bridgewater Associates before co-founding Domino Data Lab.
The director of the Ph.D. program in data science at New York University and a professor of information systems at NYU's Stern School of Business, Dhar is also an entrepreneur in the field of finance. In 1998, he founded SCT Capital Management, a hedge fund that uses machine learning to make investment decisions without human intervention, and co-founded Deep Blue Analytics, a consulting company that applies data analysis to commercial problems in 2012.
Narang is co-founder, CEO and chief investment strategist of MANA Partners, a New York hedge fund firm that raised $1bn prior to its January 2017 launch. The fund combines traditional quantitative investing – statistical arbitrage – with AI-powered high-frequency trading.
Joel Nathaniel Bloch is a co-founder of New York-based Trinnacle Capital Management, which analyzes big data via AI. Trinnacle returned +23.5% net of fees in its flagship share class from its inception in November 2016 through the end of its first full year, according to Bloomberg.
Botlo, the co-founder and CEO of Quantbot Technologies, which is backed by the Schonfeld Group, is a nuclear physicist who helped establish electronic-trading platforms as a managing director at Morgan Stanley and Merrill Lynch. He tries to lure disgruntled portfolio managers and algorithmic traders from biggest firms via its Quantbot Multi-Manager (QMM) program, which “caters to those with experience trading and generating PnL based on fully-automatic systematic trading.”
Richard Craib is the founder of Numerai, a hedge fund built by an open-source network of data scientists. He describes his firm as “a global artificial intelligence tournament to solve the stock market.”
Known as Fawce, the founder and CEO of Quantopian wrote a manifesto proclaiming that the crowdsourced hedge fund firm’s “mission is to attract the world's algorithmic and financial talent [who] share code, know-how, and data. Quantopian sets the tone by providing open-sourced code, discussing our techniques, and supplying the historical data needed for algorithmic investing.”
Hedge fund billionaire Steve Cohen has bought in to the tune of $252m.
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