The dominance of large factor models in finance

 [[{“value”:”This is less news to the private sector traders on the frontier, but the idea now has reached academia and the NBER working paper series: We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT’s foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by
The post The dominance of large factor models in finance appeared first on Marginal REVOLUTION.”}]] 

This is less news to the private sector traders on the frontier, but the idea now has reached academia and the NBER working paper series:

We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT’s foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that nonlinear models with an exorbitant number of factors (many more than the number of training observations or base assets) are far more successful in describing the out-of-sample behavior of asset returns than simpler standard models. We then theoretically characterize the behavior of large factor pricing models, from which we show that the AIPT’s “many factors” conjecture faithfully explains our empirical findings, while the APT’s “few factors” conjecture is contradicted by the data.

That is from a new paper by  Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly & Semyon Malamud.

The post The dominance of large factor models in finance appeared first on Marginal REVOLUTION.

 Economics, Uncategorized, Web/Tech