In algorithmic investing, investors use a company’s metrics to decide whether to participate in a deal. But when the art of choice is eliminated, it becomes more difficult to perform in-depth due diligence on founders who could be on the cusp of receiving millions of dollars via a wire transfer.
In practice, attempts to remove bias can create newer and harder-to-identify blind spots.
In theory, algorithmic investing guards against investors’ preconceived notions and pushes emotions to the side. Fintech unicorn Clearco and venture firm SignalFire have spent years implementing data-focused investment processes, most recently joined by AngelList and Hum Capital. While this approach isn’t new, the movement against emotion-only decisions seems louder due to the proliferation of dollars out there.
Metrics, even in the early stages, are becoming more common.
AngelList’s recently closed early-stage venture fund is basing all of its investments on a key metric that AngelList has tracked for years: a startup’s hiring ability.
When I spoke to Abraham Othman, head of the investment and data science committee at AngelList Venture, he told me that they win business because they are less adversarial to portfolio companies than other companies. “Our approach? This is our dataset – let’s see if we can invest in it,” he said.
No additional due diligence? No problem.
It’s not a small set. About 2 million people use AngelList Talent to apply to startups each quarter. About 35,000 companies a quarter are candidates for AngelList talent, but only half of those companies are early-stage businesses for investment.