Three concepts: stochastic discount factors, multi-beta pricing and mean-variance efficiency, are at the core of modern empirical asset pricing. This chapter reviews these paradigms and the relations among them, concentrating on conditional asset-pricing models where lagged variables serve as...

We propose and test a methodological framework to examine the relation between mutual fund fees and return predictability. Gil-Bazo and Ruiz-Verdu (2009) drew attention to the puzzling fact that funds with worse before-fee performance charge higher fees. We make another contribution to the...

Final working paper version. "" Published version: The Review of Financial Studies, Volume 31, Issue 7, July 2018, pp. 2499–2552. Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling...

We study how 9 different market participants trade with respect to 130 different stock return anomalies and how each participant's trades predict returns. Retail investors trade against anomalies, while firms' and short sellers' trades agree with anomalies. Institutional portfolios are weighted...

Using a novel equity lending dataset, this paper is the first to show that expected returns strongly and negatively predict future equity lending fees. In comparing two expected return measures, I find that a rational expected return has stronger predictive power of future short selling activity...

We compare the performance of time-series (TS) and cross-sectional (CS) strategies based on past returns. While CS strategies are zero-net investment long/short strategies, TS strategies take on a time-varying net-long investment in risky assets. For individual stocks, the difference between the...

This paper analyzes whether realized higher moments are able to predict out-of-sample sovereign bond returns using high-frequency data from the European bond market. We study bond return predictability over tranquil and crisis periods and across core and periphery markets at the index and...

The importance of solar energy has been growing in recent years. This raises the need for efficient modelling and forecasting methods. The existing methods are predominantly based on weather predictions or forecast solar radiation, which is not easy to convert into production forecast. Instead...

This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances....

We develop in this paper a novel portfolio selection framework with a feature of dual robustness in both return distribution modeling and portfolio optimization. While predicting the return distributions of the future market always represents the most compelling challenge in investment, any...