Swiss Finance Institute @ EPFL
Quartier UNIL-Dorigny, Extranef 213
CH - 1015 Lausanne
Institutional Affiliation: Swiss Finance Institute
Information about this author at RePEc
NBER Working Papers and Publications
|June 2020||Principal Portfolios|
with Bryan T. Kelly, Lasse H. Pedersen: w27388
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 security’s own-signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross-predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out-of-sample alphas to standard factors in several data sets.
|August 2011||Information Percolation in Segmented Markets|
with Darrell Duffie, Gustavo Manso: w17295
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 quality, including initial information precision as well as market "connectivity," the expected frequency of their trading opportunities. Investors with superior information sources attain strictly higher expected profits, provided their counterparties are unable to observe the quality of those sources. If, however, the quality of bidders' information sources are commonly observable, then, under conditions, investors with superior information sources have strictly lower expected profits.
"Information Percolation in Segmented Markets" (with Semyon Malamud and Gustavo Manso), Graduate School of Business, Stanford University, forthcoming, Journal of Economic Theory, 2014, Technical Appendices (published online only). citation courtesy of