The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in...

Many market participants invest resources to acquire information about liquidity rather than fundamentals. I show that agents using such information can reduce the magnitude of short-lived pricing errors by trading against liquidity shocks. However, the short-run stabilizing effect of this...

We explore a subtle but important mechanism through which firms manipulate their information environments. We show that firms control information flow to the market through their specific organization and choreographing of earnings conference calls. Firms that "cast" their conference calls by...

We provide an empirical evaluation of the forward-looking long-run risks (LRR) model and highlight model differences with the backward-looking habit based asset pricing model. We feature three key results: (i) Consistent with the LRR model, there is considerable evidence in the data of...

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...

We model the demand-pressure effect on prices when options cannot be perfectly hedged. The model shows that demand pressure in one option contract increases its price by an amount proportional to the variance of the unhedgeable part of the option. Similarly, the demand pressure increases the...

We model the demand-pressure effect on prices when options cannot be perfectly hedged. The model shows that demand pressure in one option contract increases its price by an amount proportional to the variance of the unhedgeable part of the option. Similarly, the demand pressure increases the...

This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting where the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale...

We combine self-collected historical data from 1867 to 1907 with CRSP data from 1926 to 2012, to examine the risk and return over the past 140 years of one of the most popular mechanical trading strategies — momentum. We find that momentum has earned abnormally high risk-adjusted returns — a...

Long memory in variance or volatility refers to a slow hyperbolic decay in auto-correlation functions of the squared or log-squared returns. GARCH models extensively used in empirical analysis do not account for long memory in volatility. The present paper examines the issue of long memory in...