How Startup Investors Could Back More Winners

On TV shows such as Shark Tank and in private boardrooms across the world, startup founders passionately make the case for why their fledgling companies are the next Ubers or WhatsApps. While the business cases they make are critical, so is the ability to pitch with charisma, clarity, and a personal story.

But venture capitalists tend to rely too heavily on founders’ backgrounds when investing in startups, suggests Princeton postdoctoral researcher Diag Davenport (a recent graduate of Chicago Booth’s PhD Program). And they could make more money—10 percent more, according to Davenport—by relying less on their impressions and more on data to make investment decisions, he argues.

Comparing real investor choices with an algorithm’s predictions, Davenport finds that up to half of VC investments were “predictably bad,” he writes.

“A purely intuitive, qualitative approach to investing is more costly than they likely realize,” Davenport says. “Relying on data is a way to make their decisions more objective and less vulnerable to mistakes.”

Davenport identified every company that participated in any of the top 100 startup incubator and accelerator programs between 2009 and 2016. Some eventually became household names—Instacart and Uber, for instance—whereas others never went anywhere. He created a data set of all the equity deals venture capitalists made backing the 16,054 companies within the first five years of their completion of an incubator or accelerator program. Then he tracked their valuations over time as well as whether they made a successful exit via an initial public offering, a merger or acquisition, or any Series C or later funding round.

He trained an algorithm to make predictions about the companies by analyzing a wealth of data, including the financial information available at the time of the incubator or accelerator program, the founders’ backgrounds, the companies’ missions, and the ways they made money.