Introducing the AngelList Early Stage Quant Fund

In a world increasingly driven by big data and AI, many elements of early-stage venture investing remain woefully antiquated. Often, investment decisions are determined by some combination of network, founder respectability, market valuation, and investor “intuition.” 

The new AngelList Early Stage Quant Fund introduces what we believe to be a competitive advantage over these methods by leveraging proprietary data to make systematic investment decisions on early-stage startups. The fund recently held its final close on an oversubscribed $25M fundraise to begin testing its unique quantitative investment thesis.

The Quant Fund is anchored by WorldQuant Ventures, an early-stage investment firm focused on disruptive companies in technology, data, and finance. WorldQuant Ventures was founded by Igor Tulchinsky, who’s also the founder, chairman, and CEO of WorldQuant, a global quantitative asset management firm. 

The partnership between AngelList and WorldQuant Ventures represents a significant step forward for AngelList as an institutional capital manager and also advances WorldQuant Ventures’ data-driven activity in early-stage investing.

“The opportunity to utilize data and AI in the venture investing process is immense,” said Tulchinsky. “We’re optimistic that the power of predictive methods can provide significant insight into early stage startups, especially as technology and the broader external environment continue to evolve faster than ever. We’re thrilled to partner with AngelList on its proprietary Quant Fund and advance data-driven venture investing.”

Additional LPs in the fund include Two Sigma Ventures, KAMCO Invest, Plexo Capital, Tom Tunguz of Redpoint, and AngelList founder Naval Ravikant.

How Does It Work?

Quantitative analysis for early-stage startups has historically been challenging due to a lack of performance-related signals. AngelList is uniquely positioned to accrue large amounts of high-quality, trustworthy data by virtue of the volume of early-stage investment and hiring activity that takes place on AngelList platforms. In 2021 alone, funds and SPVs on the AngelList Venture platform invested in 56% of all top-tier early-stage U.S. deals. Further, nearly 2M users apply to startups on AngelList Talent each quarter. 

Using this data, the Quant Fund has endeavored to create a predictive model for startup success by quantifying a variety of business inputs—such as the strength of the team and investor demand. This approach automates the screening and selection process while mitigating human bias.

Research and development of the Quant Fund was principally performed by Abe Othman, PhD, who directs AngelList’s data science efforts. Othman is also directing the Quant Fund’s investment activities.

“AngelList’s proprietary data on startup success is any data scientist’s dream,” said Othman. “I’m so excited to have the opportunity to leverage that data to invest in startups and see them grow.”

Following Othman’s research that early-stage venture returns follow a power-law distribution, the Quant Fund will apply a strategy of broadly indexing the market via exposure to thousands of deals that take place on the AngelList platform annually. The majority of the capital in the fund is intended to be invested into high-signal startups as identified by AngelList’s data. 

“Working with the Quant Fund is a great experience,” said Lily Liu, founder and CEO of Piñata, a service that helps renters build credit. “It was hands down the smoothest and fastest funding process I've ever been through. They did their research upfront, committed to a clear timeline, and the team was wonderful to work with.”

Big data has revolutionized financial markets over the past decade. With the launch of the Quant Fund, AngelList and WorldQuant Ventures look forward to bringing the principles of predictive analytics to the early-stage venture ecosystem.