Data-Driven Investors

Abstract How does the increased use of data technologies, like machine learning, by financial intermediaries affect the allocation of capital towards innovation? I study this question in the context of startup financing by venture capitalists (VCs). While VCs adopting data technologies become better at screening startups similar to those in historical data, they tilt their investments towards this pool and become concurrently less likely to finance innovative startups that achieve rare major success. Plausibly exogenous variations in VCs’ screening automation suggest that these effects are causal. These findings highlight how investors’ adoption of data technologies can have real effects through innovation financing.