Can AI tools predict successful exits of startups? PitchBook has an answer
1 min read 31 Mar 2023, 10:12 PM ISTPitchBook launched a new tool called VC Exit Predictor, which uses machine learning algorithm to predict the growth prospects of a startup
PremiumChatGPT like AI tools have been revolutionising the way we work, but can it predict the future of the companies? May or may not be possible.
PitchBook, a venture capital and private equity database, has launched a new tool called VC Exit Predictor, which uses machine learning algorithm to predict the growth prospects of a startup. The tool generates a score indicating the likelihood of a venture-backed company being acquired, going public, becoming self-sustaining, or experiencing an event, such as bankruptcy, that prevents an exit.
The algorithm was developed by PitchBook's quantitative research team and trained on data available within the PitchBook platform, including deal activity, active investors, and company details. “To ensure accuracy, predictions are made for venture-backed companies that have received at least two rounds of venture financing deals," McKinley McGinn, product manager of market intelligence at PitchBook, told TechCrunch in an email interview.
PitchBook is not the first to develop an algorithmic tool to inform investment decisions. Investors have been seeking an AI-driven competitive advantage for years, and Gartner predicts that more than 75% of venture capital and early-stage investor executive reviews will be informed by AI and data analytics by 2025. VC firms such as SignalFire, EQT Ventures, and Nauta Capital are using AI-powered platforms to identify potential top firms.
Does it work?
PitchBook claims to have back-tested VC Exit Predictor on a historical set of companies with known exits, including firms such as Blockchain.com, Revolut, and Bitso. Averaged across the set, the tool was 74% accurate in predicting a successful exit.
“The VC Exit Predictor can be leveraged by venture capitalists looking for a data-driven approach for their initial evaluation of a venture-backed company," he added. “However, we anticipate a long tail of use cases for industry players searching for upcoming IPO candidates, monitoring competitors in the market or seeking validation for an investment in their next round."
However, the tool's resilience to black swan events such as pandemics, global conflicts, and natural disasters that cannot be anticipated is still in question. Algorithms have struggled historically with these events due to their limited training data.
