We investigate whether stock betas vary with the release of firm-specific news. Using daily firm-level betas estimated from intraday prices, we find that betas increase on earnings announcement days and revert to their average levels two to five days later. The increase in betas is greater for earnings announcements that have larger positive or negative surprises, convey more information about other firms in the market, and resolve greater ex ante uncertainty. Our results are consistent with a learning model in which investors use information on announcing firms to revise their expectations about the profitability of the aggregate economy. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: [email protected]., Oxford University Press.

Is there a gap between the profitability of a trading strategy on paper and that which is achieved in practice? We answer this question by developing a general technique to measure the real-world implementation costs of financial market anomalies. Our method extends Fama-MacBeth regressions to compare the on-paper returns to factor exposures with those achieved by mutual funds. Unlike existing approaches, ours delivers estimates of all-in implementation costs without relying on parametric microstructure models or explicitly specified factor trading strategies. After accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum.

Equal compensation across assets for the same risk exposures is a bedrock of asset pricing theory and empirics. Yet real-world frictions can violate this equality and create apparently high Sharpe ratio opportunities. We develop new methods for asset pricing with cross-sectional heterogeneity in compensation for risk. We extend k-means clustering to group assets by risk prices and introduce a formal test for whether differences in risk premiums across market segments are too large to occur by chance. We find significant evidence of cross-sectional variation in risk prices for almost all combinations of test assets, factor models, and time periods considered.

We construct a simple measure of the aggregate illiquidity of hedge fund portfolios, based on the cross-sectional average first-order autocorrelation coefficient of hedge fund returns, and show that it has strong and robust in- and out-of-sample forecasting power for 72 portfolios of international equities, U.S. corporate bonds, and currencies over the 1994 to 2013 period. The forecasting ability of hedge fund illiquidity for asset returns is in most cases greater than, and provides independent information relative to, well-known predictive variables. We rationalize these findings using a simple equilibrium model, in which hedge funds provide liquidity in asset markets.

Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model (CAPM) implies higher expected returns for stocks with higher betas; and standard asset pricing models imply that the pricing kernel is declining in market returns. The full set of implications of monotonicity is generally not exploited in empirical work, however. This paper proposes new and simple ways to test for monotonicity in financial variables and compares the proposed tests with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.

Using estimators of the variation of positive and negative returns (realized semivariances) and high-frequency data for the S&P 500 Index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility.We showthat future volatility is more strongly related to the volatility of past negative returns than to that of positive returns and that the impact of a price jump on volatility depends on the sign of the jump, with negative (positive) jumps leading to higher (lower) future volatility. We show that models exploiting these findings lead to significantly better out-of-sample forecast performance.

We propose a new method to model hedge fund risk exposures using relatively high‐frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within‐month variation is more important for hedge funds than for mutual funds. We consider different within‐month functional forms, and uncover patterns such as day‐of‐the‐month variation in risk exposures. We also find that changes in portfolio allocations, rather than in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation.

We analyze the reliability of voluntary disclosures of financial information, focusing on widely‐employed publicly‐available hedge fund databases. Tracking changes to statements of historical performance recorded between 2007 and 2011, we find that historical returns are routinely revised. These revisions are not merely random or corrections of earlier mistakes; they are partly forecastable by fund characteristics. Funds that revise their performance histories significantly and predictably underperform those that have never revised, suggesting that unreliable disclosures constitute a valuable source of information for investors. These results speak to current debates about mandatory disclosures by financial institutions to market regulators.