Enticed by the prospect of better outcomes from deeper analysis of vast amounts of newly available data, hedge fund managers are turning en masse to artificial intelligence and its subset, machine learning, to fine-tune their investment processes.
Long the tools of the largest, most sophisticated and well-heeled systematic quantitative hedge fund managers, AI/ML processes are making their way down to smaller quantitative and fundamental firms at a rapid rate, sources said.
Data-driven approaches “are still are in their infancy in many industries, not just investment management,” said Daniel Connell, managing director and head of market structure and technology at manager consultant Greenwich Associates, Stamford, Conn.
But based on recent advances in the investment arena, such as robo-advisers that provide asset allocation advice to individual investors based on machine-learning processes, Mr. Connell predicted “the growth in the adoption of AI and machine learning by money managers will be faster than we've ever seen before.”
A very small cadre of well-established managers dominate the ranks of quantitative hedge funds because of their strong record of harnessing big data with the use of machine learning. Observers said even quick implementation now of AI/ML will not help later-adopting hedge fund firms catch up.
Members of this rarified group include PDT Partners LLC, Renaissance Technologies LLC, D.E. Shaw & Co. and Two Sigma Investments LLC, sources said.
“Neither the technology nor the math behind artificial intelligence and machine learning is new. What's new is the vast amount of data that's available now,” said Anthony K. Caruso, vice president and head of quantitative and macro strategies, Mesirow Advanced Strategies Inc., Chicago.
By way of example, researchers at BlackRock Inc. noted that “where the methodology for incorporating analysts' views into the investment process once involved portfolio managers reading a small number of reports from their favored analysts that corresponded with their areas of investment responsibility, today's technology uses multilingual text mining to search the entire universe of analyst reports for new and potentially market-moving information,” in its report, “Finding Big Alpha in Big Data.”
Jeffrey Shen, the San Francisco-based managing director, co-CIO of active equity and co-head of BlackRock's scientific active equity strategy, was a co-author of the report and said in an interview that “an explosion of data sources and advances in computational power and speed allows for far deeper analysis for investment managers than was available one or two years ago.”
Mr. Shen noted, for example, that valuation signals can be collected for every single stock in the world, the analysis of which would be “impossible for any human, no matter how intelligent, to perform. But machines are really good at this.”
The granularity of the resulting analysis allows hedge fund managers to get both macro and fundamental insights about the data, Mr. Shen said, stressing AI/ML “allows you to be both broad on a macro level and deep on a fundamental level. You used to be able to be only macro or only quant.”
BlackRock's SAE unit manages $7 billion in hedge funds and liquid hedge fund strategy mutual funds using AI/ML techniques.