APT or “AIPT”? The Surprising Dominance of Large Factor Models

Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly and Semyon Malamud

No 33012, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C1 C40 C45 C55 G1 G11 G12 G14 G17 (search for similar items in EconPapers)
Date: 2024-09
New Economics Papers: this item is included in nep-ain
Note: AP
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