Profile: Quant Shop Two Sigma

A recent article in Institutional Investor offers an overview of the quantitative investing powerhouse Two Sigma as well as insights on its early history from Paul Tudor Jones, an early backer of the firm.

Of the firm’s co-founders David Siegel and John Overdeck, Jones recalls, “It was pretty obvious the combination was going to be a world beater…Together this mix gave us high confidence.” Today, Two Sigma manages roughly $60 billion and it one of the world’s largest hedge fund firms, employing 1,600 people. Of that number, the article reports, two-thirds are in research and development, primarily with science and math backgrounds.

The firm has been “wildly” successful, the article says, with the first fund having compounded at a double-digit rate. Overall, it adds, the firm’s funds have been profitable every year and, in 2018, generated $3.2 billion in net gains for its investors.

Siegel, an artificial intelligence specialist, earned a Ph.D. in computer science from MIT and launched his Wall Street career at D.E. Shaw. Overdeck also began his career at Shaw, “eventually directing the firm’s Japanese equity and equity-linked investments and oversaw its investment management affiliate in London.” Siegel left Shaw to take a position as technical assistant to founder Jeff Bezos before starting Two Sigma.

According to the article, “When Siegel and Overdeck launched Two Sigma, quantitative investing was sort of a niche neighborhood in the hedge fund world, populated by few firms other than Shaw and Jim Simons’ Renaissance, which at the time managed $4 billion and less than $6 billion, respectively.” Like Shaw and Renaissance, it says, Two Sigma was created more like a tech company than a financial services firm, adding that the founders believe that “innovative technology and data science can help discover value in the world’s data.”

Two Sigma has what the article describes as seven absolute-return groups of funds using models in the following four broad categories:

  1. Fundamentals: Models that emphasize a company’s fundamentals, such as financial statements.
  2. Technical: This category looks at exchange-related data such as pricing, volume and momentum.
  3. Events: These models examine earnings announcements as well as non-scheduled events such as mergers and acquisitions.
  4. Alpha Capture: This category solicits information from Wall Street’s sell-side salespeople in a systematic way.