We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

We use a standard consumption-based asset pricing model incorporating conditioning information to explain the risk-return profile of currency carry trade portfolios. We use a scaled stochastic discount factor instead of scaled or managed portfolio returns as in previous work. Our conditioning...

We use a standard consumption-based asset pricing model incorporating conditioning information to explain the risk-return profile of currency carry trade portfolios. We use a scaled stochastic discount factor instead of scaled or managed portfolio returns as in previous work. Our conditioning...

When the pricing kernel is U-shaped, then expected returns of claims with payout on the upside are negative for strikes beyond a threshold, determined by the slope of the U-shaped kernel in its increasing region, and have negative partial derivative with respect to strike in the increasing...

This paper proposes a risk measure, based on first-passage probability, which reflects intra-horizon risk in jump models with finite or infinite jump activity. Our empirical investigation shows, first, that the proposed risk measure consistently exceeds the benchmark Value-at-Risk (VaR). Second,...

Using a two-period model of a commodity market with a large number of atomistic consumers and two strategic sellers, we show that a speculator with access to storage can lower the market price while buying and raise the price while selling by clever use of limit, stop-loss, and market orders....

In over-the-counter (OTC) markets, customers search for counterparties. Little is known about this process, however, because existing data is comprised of transaction records, which are only informative about the end of a successful search. Leveraging data from the leading trading platform for...

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...

Market participants often invest in order to acquire information that pertains to the market itself (e.g. order flow) rather than to fundamentals. This enables them to infer more information from past trades. I show that agents trading on such information, typically high-frequency traders,...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...