This paper provides a comprehensive analysis of portfolios of active mutual funds, ETFs and hedge funds through the lens of risk (anomaly) factors. We show that that these funds do not systematically tilt their portfolios towards profitable factors, such as high book-to-market (BM) ratios, high...

We provide novel evidence on the driving forces behind the sharp increase in equity values over the post-war era. From the beginning of 1989 to the end of 2017, 23 trillion dollars of real equity wealth was created by the nonfinancial corporate sector. We estimate that 54% of this increase was...

We study the role of information in asset pricing models with long-run cash flow risk. When investors can distinguish short- from long-run consumption risks (full information), the model generates a sizable equity risk premium only if the equity term structure slopes up, contrary to the data. In...

Two potential driving forces of house price fluctuations are commonly cited: credit conditions and beliefs. We posit some simple empirical calculations using direct measures of credit conditions and beliefs to consider their potentially distinct roles in house price fluctuations at the aggregate...

What explains stock market behavior in the early weeks of the coronavirus pandemic? Estimates from a dynamic asset pricing model point to wild fluctuations in the pricing of stock market risk, driven by shifts in risk aversion or sentiment. We find further evidence that the Federal Reserve...

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large on average even for professional forecasters, with all...

The outbreak of COVID-19 has significantly disrupted the economy. This paper attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of COVID-19. A costly disaster series is...

We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events using a new methodology that synthesizes estimation of a structural asset pricing model with algorithmic machine learning to quantify bias. We estimate that investors...

We integrate a high-frequency monetary event study into a mixed-frequency macro-finance model and structural estimation. The model and estimation allow for jumps at Fed announcements in investor beliefs, providing granular detail on why markets react to central bank communications. We find that...

This paper proposes a new approach to the "factor zoo" conundrum. Instead of applying dimension-reduction methods to a large set of portfolios obtained from sorts on characteristics, I construct factors that summarize the information in characteristics across assets and then sort assets into...