In algorithmic investing, investors use a company’s metrics to decide whether to participate in a deal. But when the art of choice is factored out, it becomes more difficult to perform deep due diligence on founders who may be about to receive millions of dollars via a wire transfer.
In practice, attempts to remove bias can create newer, blind spots that are harder to identify.
In theory, algorithmic investing hedges 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, joined more recently by AngelList and Hum Capital. While this approach isn’t new, the movement against solely emotion-based decisions feels louder given the proliferation of dollars out there.
Metrics, even in the earliest stages, are becoming more mainstream.
AngelList’s recently closed early-stage venture fund is basing all of its investments off of one key metric that AngelList has been tracking for years: a startup’s ability to hire.
When I spoke to Abraham Othman, head of the investment committee and data science at AngelList Venture, he told me they win deals because they are less adversarial to portfolio companies than other firms.“Our approach? This is our data set — let’s see if we can put money into them,” he said.
No further due diligence? No problem.
It’s no small set. About 2 million individuals use AngelList Talent to apply to startups each quarter. About 35,000 companies per quarter are contenders for AngelList talent, but only half of those companies are investable early-stage businesses.
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