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We classify a unique and comprehensive data set of corporate press releases into topics and study the market reaction to various types of news. While confirming prior findings regarding strong stock price responses to financial news, we also document significant reactions to news about corporate strategy, customers and partners, products and services, management changes, and legal developments. Consistent with regulators' expectations, the level of informational asymmetry in the market declines following most types of press releases. At the same time, return volatility frequently increases in the post-announcement period, which we show can be attributed to higher levels of valuation uncertainty.

We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don’t provide incremental information for expected returns, and nonlinearities are important. We study our method’s properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods.

We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U. S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

In Chapter 1 (jointly with Andrew Thompson), we study the returns to a simple trend following strategy in commodity markets and the returns' potential drivers. The returns are positively correlated to a lack of available arbitrage capital providing direct evidence of limits of arbitrage in commodity futures markets. The strategy delivers low annualized excess returns in the period from 1990 to 2004 of -0.2% that show a significant increase from 2005 to 2013 to 4.2% and yield a Sharpe ratio of 1.1. This rise in returns coincides with increased participation in commodity markets by financial investors. Financial investors who use futures to maintain constant commodity market exposure must periodically rebalance the maturity risk of their portfolios so as to avoid physical settlement when futures contracts expire. This rebalancing creates predictable steepening and flattening of commodity term structures, which our trend following strategy can use to time commodity funds' demand for liquidity. In Chapter 2 we test the model of Diamond and Verrecchia, which predicts that prices adjust more slowly to new information if short selling is costly or banned. We study the speed of price adjustment following corporate press releases during the 2008 short sale ban. In this context we find that, consistent with the model, stocks which are affected by the ban have significantly slower price adjustment following the release of bad information. We find no significant difference in the speed of price discovery between the two groups in a period shortly before and after the short sale ban.

Value-at-Risk (VaR) and conditional value-at-risk (CVaR) are important risk measures. Especially VaR is very popular and widespread in risk management and banking supervision. However, VaR has some unwelcome properties which are not shared by CVaR. Therefore CVaR is preferable from a theoretical point of view. Both VaR and CVaR are discussed for long and short positions. It is pointed out that short positions and heavy tails are incompatible with a finite CVaR.

This paper studies linear overparameterized models in economic forecasting and highlights that including noise variables (regressors with no predictive power) regularizes the estimator. We consider a setting where both the outcome variable and the high-dimensional predictors are driven by a small number of latent factors, and show that the linear forecast model is dense rather than sparse. It turns out that a ridgeless regression augmented with noise predictors attains the same asymptotic forecast accuracy as an oracle with known true factors, without estimating the factors or assuming them to be strong. The gain comes from shrinkage of the eigenvalues of the design matrix, which reduces the out-of-sample variance. In contrast, perfect variable selection that removes noise variables can worsen forecasts when the number of retained predictors is comparable to the sample size. Empirically, we apply this approach to forecasting U.S. inflation, international GDP growth, and the U.S. equity risk premium, finding that noise regularization improves and stabilizes predictive performance.

Machine learning in asset pricing typically predicts expected returns as point estimates, ignoring uncertainty. We develop new methods to construct forecast confidence intervals for expected returns obtained from neural networks. We show that neural network forecasts of expected returns share the same asymptotic distribution as classic nonparametric methods, enabling a closed-form expression for their standard errors. We also propose a computationally feasible bootstrap to obtain the asymptotic distribution. We incorporate these forecast confidence intervals into an uncertainty-averse investment framework. This provides an economic rationale for shrinkage implementations of portfolio selection. Empirically, our methods improve out-of-sample performance.

We document a large return drift around monetary policy announcements by the Federal Open Market Committee (FOMC). Stock returns start drifting up 25 days before expansionary monetary policy surprises, whereas they decrease before contractionary surprises. The cumulative return difference across expansionary and contractionary policy decisions amounts to 2.5% until the day of the policy decision and continues to increase to more than 4.5% 15 days after the meeting. The drift is more pronounced during periods of high uncertainty, it is a market-wide phenomenon, and it is present in all industries and many international equity markets. Standard returns factors and time-series momentum do not span the return drift around FOMC policy decisions. A simple trading strategy exploiting the drift around FOMC meetings increases Sharpe ratios relative to a buy-and-hold investment by a factor of 4. The cumulative returns before FOMC meetings significantly predict the subsequent policy surprise.

Working Paper No. 24748 We document a large return drift around monetary policy announcements by the Federal Open Market Committee (FOMC). Stock returns start drifting up 25 days before expansionary monetary policy surprises, whereas they decrease before contractionary surprises. The cumulative return difference across expansionary and contractionary policy decisions amounts to 2.5% until the day of the policy decision and continues to increase to more than 4.5% 15 days after the meeting. Standard returns factors and time-series momentum do not span the return drift around FOMC policy decisions. The return drift is a market-wide phenomenon and holds for all industries and many international equity markets. A simple trading strategy exploiting the drift around FOMC meetings increases Sharpe ratios relative to a buy-and-hold investment by a factor of 4.

We document a large return drift around monetary policy announcements by the Federal Open Market Committee (FOMC). Stock returns start drifting up 25 days before expansionary monetary policy surprises, whereas they decrease before contractionary surprises. The cumulative return difference across expansionary and contractionary policy decisions amounts to 2.5% until the day of the policy decision and continues to increase to more than 4.5% 15 days after the meeting. Standard returns factors and time-series momentum do not span the return drift around FOMC policy decisions. The return drift is a market-wide phenomenon and holds for all industries and many international equity markets. A simple trading strategy exploiting the drift around FOMC meetings increases Sharpe ratios relative to a buy-and-hold investment by a factor of 4.