Semyon Malamud
Swiss Finance Institute
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We introduce artificial intelligence pricing theory (AIPT). In contrast with the APTs 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...
We introduce a novel shrinkage methodology for building optimal portfolios in environments of high complexity, where the number of assets is comparable to or larger than the number of observations. Our universal portfolio shrinkage approximator (UPSA) is given in closed form, is easy to implement,...
We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performancein terms of SDF Sharpe ratio and test asset pricing errorsis improving in model parameterization (or complexity). Our empirical findings verify the theoretically...
Much of the extant literature predicts market returns with simple models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to complex models in which the number of parameters exceeds the...
We propose a new asset-pricing framework in which all securities signals are used to predict each individual return. While the literature focuses on each securitys own-signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross...
We calculate equilibria of dynamic double-auction markets in which agents are distinguished by their preferences and information. Over time, agents are privately informed by bids and offers. Investors are segmented into groups that differ with respect to characteristics determining information...
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