Can Prospect Theory Explain High Start-up Valuations?

Human beings are not particularly good at thinking about probabilities. The last several decades of research in psychology and behavioral economics have unearthed an array of cognitive biases in how we reason about uncertain events. For example, we are prone to misinterpret the results of diagnostic tests, by failing to account for the base rate of a disease in the population. This has enormous implications in the medical field, and may be leading us to over-diagnose and over-prescribe treatments for a variety of illnesses.

One of the foundational theories in behavioral economics is prospect theory, formulated by Daniel Kahneman and Amos Tversky. This theory is most well known for the observation that humans interpret losses as more consequential than gains of the same magnitude. Thus, simply changing the framing of a decision from a ‘gain frame’ to a ‘loss frame’ can make people much more averse to taking risks. In this blog post I want to focus on another prong of prospect theory: the over-weighting of rare events.

Prospect theory suggests—and experimental evidence supports—the idea that people ‘filter’ probabilities: we act as though very low probability events are more likely than they really are (and as though high probability events are less likely than they really are). This helps explain why people fret so much over low probability dangers such as shark attacks and ignore more mundane risks such as accidental falls. It also helps explain why people gamble money on lotteries even when the odds of winning are very slim, and the expected return is negative.

What has this got to do with valuing a start-up? The conventional way to value a company is to make a forecast of its future cash flows, then discount these back to find the ‘net present value’ of its future income. Alternatively, as a heuristic we can apply a multiplier to its earnings based on accepted valuations of other companies. Neither of these works for an emerging venture with a novel business proposition (i.e. your typical Silicon Valley start-up). The future prospects of such a company are shrouded in uncertainty.

Instead of trying to establish the likely path of a given venture’s future cash flows, investors—usually venture capitalists—take a portfolio approach. They pick companies they think will have a chance at becoming massively successful, but realize that many will fail to do so. Each investment is a bit like a lottery ticket. In the classical VC portfolio model, roughly one investment in ten would need to exit at a blockbuster valuation for the overall fund to make a decent return on investment.

In the present wave of technology venture activity, three key things are being done differently to the past. First, the definition of a ‘massively successful exit’ has inflated: ventures now aspire to be ‘unicorns’ with a billion-dollar valuation. Second, investors are spreading their money out, investing in a larger number of ventures. This is most visibly true in accelerator programs, which provide large numbers of nascent ventures with seed funding and mentoring in return for a small equity stake: they explicitly rely on a scattershot approach. Instead of a VC picking ten investments and hoping for two or three large exits, the accelerator approach is to invest in a hundred startup teams and hope for one unicorn. Third, more ventures are staying private for longer, rather than go public through an IPO. As described in this FT article, this allows them to effectively manage their headline valuation figure by giving new investors guaranteed financial returns (risking, in the process, the equity of preceding investors). This prevents negative opinions of the venture’s prospects from being incorporated in its valuation.

And so we have a perfect storm in which valuations are based on someone’s estimate that a given venture will become a unicorn, and—according to prospect theory—they are biased to overestimate how likely this is. For every thousand startups, maybe one of them will be hugely successful, but all of them might be valued as if they have a one-in-a-hundred chance of this success. This is a problem. More fundamentally, we are dealing with such small probabilities that we can easily get them very wrong.

Earlier in the year I considered a few possible mechanisms by which a hypothetical technology bubble might burst. Here, I’ve described one psychological factor that might be behind high startup valuations in the first place. It’s also worth noting that prospect theory can explain rapid changes in investor sentiment. If prices start falling—for example if a bubble shows signs of bursting—investors can switch from a gain mindset to a loss mindset, and immediately become much more risk averse. I hope this doesn’t happen, because the present wave of entrepreneurial activity is generating a lot of innovation. But a wise investor or entrepreneur should be aware that the tide might turn in the near future, and plan accordingly—or risk getting swept away when it does.