Complex Capital Markets

Sergei Glebkin, Bryan Kelly, Semyon Malamud, Matias Palmunen and Mo Pourmohammadi

No 22144, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We propose the Complex Market Hypothesis: substantial return predictability arises from many individually weak signals. We model a computational arms race in which investors choose model scale and learn from limited data. Random matrix theory yields an essentially unique linear equilibrium and characterizes the distribution of model sizes. Prices transmit information from larger models to smaller ones, allowing both to coexist. Cheaper computation can raise aggregate spending and widen performance gaps across funds. Even when every investor observes all signals determining payoffs, uncertainty about their predictive coefficients sustains a sizable equity premium.

Keywords: Asset pricing; Rational expectations; Machine learning; Information acquisition; Market efficiency (search for similar items in EconPapers)
JEL-codes: D82 D83 G12 G14 (search for similar items in EconPapers)
Date: 2026-11
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP22144 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:22144

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP22144

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().