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Data-driven VC #28: What VCs can learn from hedge funds
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Data-driven VC #28: What VCs can learn from hedge funds

Where venture capital and data intersect. Every week.

Andre Retterath's avatar
Andre Retterath
Mar 23, 2023
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Data-Driven VC
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Data-driven VC #28: What VCs can learn from hedge funds
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👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.

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As a few days ago this newsletter turned 6 months, I sat down to reflect on your valuable feedback and diverse perspectives. One of the most frequently asked questions is about my perspective on the future of VC and “where this whole data-driven stuff will eventually lead to”. Therefore, I dedicate today’s post to sharing a brief update of my “The Future of VC: Augmenting Humans with AI” post from Dec 2020.

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The hedge fund industry’s inflection point

Up until the 1980s, hedge funds were largely run by Wall Street veterans who relied on their instincts and connections to make investment decisions. However, as more data became available, a new breed of hedge funds emerged that used quantitative models to make data-driven investment decisions and eliminate human cognitive bias in the chase for returns.

When Stephen Cohen, founder of S.A.C. Capital, one of the top hedge funds of the last century, started aggressively hiring quantitative specialists while at the same time cutting his stock-picking team of fund managers by nearly two-thirds, it became clear that “quant” was here to stay. 

With pioneers like Cohen leading the algorithmic trading revolution of the hedge fund industry, we saw a range of manifestations on the spectrum of pure quantitative strategies, where mathematical algorithms make all of the asset allocation or stock-picking decisions, to “quantamental” strategies, which combine the traditional stock-picking skills of fund managers with data and computing power.

Today, quant hedge funds (in whatever manifestation) are the most successful and profitable firms in the industry and manual trading went extinct. According to recent figures, quant strategies account for 75%+ of total trading and more than $ 1 trillion in assets under management in the public markets.

What does that mean for VC?

Similarly, I’m convinced that the venture capital industry is now at an inflection point, just like the hedge fund industry was in the 1980s. Remembering that first VCs started to employ data-driven investment approaches about a decade ago, you might wonder why most of them came to nothing and why now might be a better time, right?

Well, before the rise of big data and large-scale web scraping in the early 2010s, private company data was difficult to collect at scale. Just ten years ago, public registers were not digitized, startup databases such as Crunchbase, Pitchbook and others in their early days and professional networks like LinkedIn had less than a 10th of today’s active users. As a result, comprehensive sourcing coverage was impossible to achieve and even the most data-driven investors needed to rely on their manual networks. Given the lack of proper tools and sufficient compute, it was also difficult to process these vast amounts of unstructured data and make sense of non-quantitative information.

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