Complex Capital Markets

Abstract

We propose the Complex Market Hypothesis: many individually weak relationships jointly generate substantial return predictability. We develop an equilibrium model of the computational arms race in which investors learn from limited data and choose how many signals to acquire. We solve the model using random matrix theory. In the unique equilibrium, prices transmit information from larger models to smaller ones, allowing both to coexist. In our calibration, cheaper computation can substantially increase aggregate spending and widen fund performance gaps. Yet observing signals is not the same as knowing how they predict payoffs. Model uncertainty therefore sustains a substantial equity premium even when everyone observes all signals that determine payoffs.

BibTeX

@unpublished{GlebkinKellyMalamudPalmunenPourmohammadi2026,
  author = {Glebkin, Sergei and Kelly, Bryan and Malamud, Semyon and Palmunen, Matias and Pourmohammadi, Mohammad},
  title = {Complex Capital Markets},
  year = {2026},
  note = {Working paper},
  url = {https://sglebkin.com/CCM.pdf}
}