Elon Musk’s acquisition of Twitter was chaotic and unconventional to say the least, but investors did have several months advance warning that this transaction—and the shakeups at Twitter itself—could happen. Twitter’s management acknowledged this in their last earnings call, admitting that uncertainty about the platform’s future had already caused a materially negative impact to revenues as advertisers pulled back and went into a “wait and see” approach about the platform. Now that it is a private company, news reports about mass firings have hit the wires—but again this is not too surprising, as Twitter workers were already voicing caution about their own job security if Musk bought the company.
Such known variables are supposed to be priced into capital markets, meaning that the stocks not only of Twitter but its competitors—firms like Meta (META), Alphabet (GOOG, GOOGL), and Snap (SNAP)—should have also priced in this dynamic at Twitter in some way or not. But how exactly does this chaos get priced in—and how can we say for sure whether this is good or bad for competitors?
The most direct line from Twitter’s internal turmoil to a competitor’s future would in theory revolve around the recent layoffs. Again, speaking theoretically, high profile Twitter workers who have been laid off could easily be seen as assets at competing firms, and hiring these recently laid off workers could be a boon to a competitor. But how can an analyst price this potential in?
This is a problem that growth investors in particular struggle with: the need to quantify the unquantifiable. Let us take a hypothetical, which is becoming increasingly less hypothetical as news comes out of Twitter trying to re-hire employees that it has fired and afterwards realized are essential to its operations. In theory let’s assume one of these workers is crucial for keeping advertisers on the platform, and that worker can bring with her that client base to a competing firm. The chaos at Twitter makes it a risky partner for an advertiser, while that worker’s ability to move to, say, Alphabet, would be a boon to the latter. But how much—how exactly could we quantify this dynamic, especially given the extremely hypothetical and unquantifiable nature of the situation?
One way to approach the issue would not be to quantify it in a fixed model that tries to derive the revenue potential of a transplanted ex-Twitter employee, but to invoke Keynes’s Beauty Contest. The idea here would be to price in not the potential but how much that potential is being underpriced in a stock.
A risky but potentially lucrative bet would be around Meta. That company has also reported a massive firing spree and the first downsizing in the company’s history. At first glance, such a company would not be on the market for a new hire, so the stock has both priced in the negative impact of losing so many workers as well as not pricing in the potential of acquiring a Twitter employee.
A savvy and daring analyst, however, might see the potential for Meta if it hires the right ex-Twitter employee into the right place. That analyst would also have to remember that companies who downsize don’t necessarily stop hiring, and it is possible that Meta could pick up a handful of key executives while shedding thousands of other workers. And if the analyst were daring enough, they would do all that they could to try to find any evidence at all that Meta is actively looking for Twitter recruits.
Such an activity is not easy, of course, and some approaches to find this information out would be illegal (the analyst, for instance, can’t call Meta up and ask, as this would be material non-public information). However, if the analyst could find out from fired ex-Twitter employees that they’re in talks with Meta, that would not only be a legal acquisition of non-MNPI (i.e., legal public information), but it would also be a significant early indicator that this market dynamic is not being sufficiently priced into the stock.
An analysis could suggest that, in actual fact, this move might not be enough to move the needle—but an analysis would be required first to find out. And this is the kind of quest for information that the financial analyst, typically associated with spreadsheets and complex math, must do to earn her keep—and those who do it well can earn quite a bit indeed.