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.
An algorithm trained to assess whether venture capital–funded companies would achieve an exit was able to correctly predict their success rate.
When Davenport contrasted the algorithm’s predictions with the investors’ decisions, he finds that the venture capitalists spent millions of dollars to back startups they should’ve known would inevitably fail. For his data set covering $9 billion of investments, the lost returns came to $900 million, he calculates, reaching that conclusion after modeling alternate portfolios that dropped low-performing startups and replaced them with bonds or stocks.
There was a strong link between decisions to invest and startup founders’ backgrounds—details such as where the founders went to school or how many board seats they occupied, the study indicates. This was especially the case when investors were considering low-quality startups. A founder’s background and experience do matter, but “the algorithm tells us venture capitalists rely on these things too much,” Davenport says.
The good news is that they could easily improve their decisions by incorporating more machine-learning techniques, which would benefit not only themselves but their limited partners (including their clients, university endowments, and retirement pensions), who rely on them to make savvy financial moves.
At least for now, however, most venture capitalists are reluctant to use artificial-intelligence tools. For one thing, they worry about the reliability of algorithms, which use past patterns to make projections. Data on startups are also notoriously difficult to obtain. But perhaps the biggest reason is pride.
“They’ll say, ‘Look, you have to be in the room and see the passion in the eyes of the founder—no algorithm could ever detect that,’” Davenport says. “That highlights the overconfidence a lot of investors have, particularly because they already have good returns. But they underperform what they could be getting. It creates this situation where, if you win the game, it’s hard to understand how many more points you could’ve scored.”
Diag Davenport, “Predictably Bad Investments: Evidence from Venture Capitalists,” Working paper, June 2022.
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