We apply a new methodology for identifying pervasive and discrete changes (“breaks”) in cross-sectional risk premia. Size, value, and investment risk premia have fallen off to the point where they are insignificantly different from zero at the end of the sample period. The market risk premium has also declined systematically over time but remains significant and positive as do the momentum and profitability risk premium. We construct a new instability risk factor from cross-sectional differences in individual stocks’ exposure to time-varying risk premia and show that this factor earns a premium comparable to that of commonly used risk factors.

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.

We propose a new approach to imposing economic constraints on time series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates time-varying volatility in the predictive regression framework. Empirically, we find that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance.

This paper implements strategies that use macroeconomic variables to select European equity mutual funds, including Pan-European, country, and sector funds. We find that several macro-variables are useful in locating funds with future outperformance and that country-specific mutual funds provide the best opportunities for fund rotation strategies using macroeconomic information. Specifically, our baseline long-only strategies that exploit time-varying predictability provide four-factor alphas of 12–13% per year over the 1993–2008 period. Our study provides new evidence on the skills of local versus Pan-European asset managers, as well as how macroeconomic information can be used to locate and time these local fund manager skills.

We present a new approach to selecting actively managed mutual funds that uses both portfolio holdings and fund return information to eliminate funds with predicted inferior performance through a sequence of pairwise fund comparisons. Our methodology determines both the number of skilled funds and their identities, and locates funds with substantially higher risk-adjusted returns than those identified by conventional alpha-ranking methods. We find strong evidence of time-series variation in both the number of funds identified as superior using our approach, as well as in their performance across different economic states.

Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this implication is difficult in practice, as it requires noisy high-frequency data from after-hours markets, where most earnings announcements are released. Using a unique dataset and a new microstructure noise-robust jump test, we show that earnings announcements almost always induce jumps in the stock price of announcing firms. They also significantly raise the probability of price co-jumps in non-announcing firms and the market. We find that returns from a post-announcement trading strategy are consistent with efficient price formation after 2016.

We show a positive relation between network centrality and risk-adjusted performance in a delegated investment management setting. More connected managers take more portfolio risk and receive higher investor flows, consistent with these managers improving their ability to exploit investment opportunities through their network connections. Greater network connections are shown to be particularly important in reducing the diseconomies of scale for large managers who are well connected. We also use the exogenous merger of two investment consultants, which creates a sudden change in the network connections of the managers they oversee, to provide evidence that a greater number of connections translates into better portfolio performance.