Citadel is expanding its quantitative investment business and widening its talent search to artificial intelligence laboratories. As AI accelerates its penetration into quantitative trading, hedge funds are competing with Silicon Valley for researchers.
Navneet Arora, Citadel's global head of quantitative strategies, said the team of roughly 180 people includes financial researchers and data engineers, and the firm plans to expand headcount at a steady double-digit pace over the next year. According to the company's website, a new team led by Alexey Poyarkov has just been established, focusing on systematic trading in global equities. Poyarkov recently joined from TGS Management.
Beyond traditional university graduate recruitment channels, the hedge fund founded by billionaire Ken Griffin is also setting its sights on AI labs such as Google DeepMind, hoping to recruit researchers who can propose investment ideas and use increasingly powerful AI tools to put them into practice. Arora said that although AI is accelerating its adoption in quantitative investing, with tasks from data collection to analysis becoming increasingly automated, AI has not replaced human researchers. On the contrary, AI has reduced the importance of programming ability while elevating the value of researchers who can generate investment ideas and guide machines to translate them into profitable trades.
"Now one person can effectively manage two, four, or in some cases even more processes. Humans are also becoming 'managers of machines,'" Arora said. "And these people are not as easily replaced as machines."
According to a person familiar with the matter, Citadel's Tactical Trading fund, which combines fundamental equity investing with quantitative strategies, gained 24.7% this year through August. The fund has delivered annualized returns of about 20% since its inception in 2008. Navneet Arora Citadel manages approximately $76 billion in assets, spanning equities, fixed income, and commodities, and currently allocates "several billion dollars" to quantitative strategies. Arora expects that expanding the team will help strengthen the firm's core equities business and further develop strategies in areas such as futures and volatility.
AI has also pushed the talent war beyond the traditional boundaries of banks, hedge funds, and proprietary trading firms. Citadel now competes with Silicon Valley in some recruiting efforts, rivaling AI companies such as OpenAI and Anthropic. Gerald Beeson, Citadel's chief operating officer, said: "This opens up a new frontier for our recruiting. Solving complex, real-world business problems is very appealing to the talent we want to recruit."
The spread of AI also poses another question for quantitative investment firms: as more investors gain access to similar tools, will profitable trading ideas be discovered more easily and become obsolete more quickly? A recent paper by researchers at New York University shows that the excess returns of a profitable trading signal may halve in about 18 months, whereas before the widespread adoption of AI, this process took five to seven years. Arora believes that the penetration of machine learning varies greatly across different trading areas. In short-horizon trading, machines can learn from large amounts of volume and price movement data. The advantages of such trades are relatively limited and more easily replicated by competitors, so excess returns may disappear quickly. By contrast, longer-horizon strategies rely on scarcer data and require more complex models to generate trading signals. Arora said this may make the relevant advantages harder for machines and competitors to discover, and they may also last longer.