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...

Despite its presumed role as an engine of economic growth, we know surprisingly little about the drivers of scientific creativity. In this paper, we exploit key differences across funding streams within the academic life sciences to estimate the impact of incentives on the rate and direction of...

We develop a theoretical model to study the effects of libertarian paternalism on knowledge acquisition and social learning. Individuals in our model are permitted to appreciate and use the information content in the default options set by the government. We show that in some settings...

Investor sophistication has lagged behind the growing complexity of retail financial markets. To explore this, we develop a dynamic model to study the interaction between obfuscation and investor sophistication. Taking into account different learning mechanisms within the investor population, we...

We examine innovative contexts like scientific research or technical R&D where agents must search across many potential projects of varying and uncertain returns. Is it better to possess incomplete but accurate data on the value of some projects, or might there be cases where it is better to...

The National Institute of Health (NIH), through its extramural grant program, is the primary public funder of health-related research in the United States. Peer review at NIH is organized around the twin principles of investigator initiation and rigorous peer review, and this combination has...

Gains and losses would be measured in terms of market value and also in terms of cash flow, allowing regulators to assess risk magnitudes in terms of stresses to both economic values and also liquidity. Exposures would be measured before and after collateralization. One of the scenarios would be...

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...

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 theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...