The episode centers on research by Robyn Greenwood that challenges Eugene Fama's efficient market hypothesis by attempting to empirically identify bubbles before they pop. The research identified a "constellation" of four indicators—high valuations, volatility, stock issuance, and price acceleration—that, when present, increase the probability of a subsequent crash.
The core question of the episode is whether the current AI boom is a bubble. The hosts apply the four-factor model to the current market, noting that while valuations for companies like NVIDIA are high, other key indicators like widespread new stock issuance are absent, leading to an ambiguous but cautious conclusion.
The discussion moves beyond investor losses to the broader economic impact of bubbles, referencing the dot-com crash and the 2008 financial crisis. It covers the two main ways bubbles hurt the economy: the crash itself (especially when fueled by debt) and the misallocation of capital and labor during the boom phase.
A counter-intuitive theory is presented suggesting that not all bubbles are entirely destructive. Using the example of excess fiber optic cable laid during the dot-com bubble, the episode explores how irrational exuberance can sometimes fix market failures by funding the creation of valuable infrastructure that has positive, long-term externalities.
Keep pulling the thread on S&P 500.