Professor of Finance at the Stanford Graduate School of Business (GSB) Ilya A. Strebulaev recently published a paper reporting that venture-backed companies worth more than $1 billion — colloquially known as “unicorns” — are overvalued by about 51 percent. The paper studied the fair value of unicorns and the securities that these unicorns will issue.
Will Gornall, Assistant Professor of Finance at the Saunder School of Business, was the co-author of the paper.
According to the researchers’ financial model, almost one half of the unicorns they studied lost their unicorn status after value recalculations, and 13 of the 116 unicorns were overvalued by more than 100 percent.
This overvaluation is a result of the generalization that all of the company’s shares are of equal value. Currently, professionals assign rough values to companies by multiplying the stock prices of the latest round of funding and the outstanding shares together for an overall estimation of worth. However, the researchers argue that the most recently issued shares have a different value than older shares, so equating them inflates valuations.
For example, Square, a credit card processing startup, was valued at $6 billion after its last financing round and was ultimately priced at $15.46 per share. The $6 billion calculation was obtained after multiplying $15.46 Series E shares, which belong to a specific round of funding, by outstanding shares and unissued options.
However, using the researchers’ financial model, Square was more accurately valued at $2.2 billion. This number was reflected in its valuation after its IPO in 2015, which was closer to $3 billion.
Strebulaev and Gornall’s financial model accounted for companies’ most recent financing round price and the terms of that contract to calculate the value of the company’s shares. Though the model can be applied to any startup, the team focused specifically on 116 unicorns founded after 1994 that had raised at least one round of funding after 2004. The researchers used the available corporate legal filings and Venture Capital data sets to construct their model.
“Our model is designed to produce fair value estimates that are better proxies for expected value at exit,” the researchers told the GSB.