We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

For the popular mean-variance portfolio choice problem in the case without a risk-free asset, we develop a new portfolio strategy to mitigate estimation risk. We show that in both calibrations and real datasets, optimally combining the sample global minimum variance portfolio with a sample...

Discount rates affect stock prices directly via the discount-rate channel or indirectly via the cash-flow channel because expected future cash-flow growth varies with the discount rates. The traditional Macaulay duration captures the effect from the discount-rate channel. I propose a novel...

Asset pricing models can reinforce asset allocation decisions and promote risk management gains. We compare the out-of-sample performance of mean-variance strategies when mean and covariance are sample estimators of (1) unfiltered excess returns; and (2) filtered excess returns through an asset...

It is well known that estimated mean-variance portfolios deliver, on average, poor out-of-sample performance. A lesser-known fact that we characterize in this paper is that their out-of-sample performance is also very volatile. Using our analytical characterization of out-of-sample performance...

When a benchmark model is inefficient, including additional assets to the benchmark portfolios can improve its performance. In reality, however, the efficiency of a benchmark model relative to a given set of test assets is ex ante unknown, and the optimal portfolio is constructed based on...

Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock's expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which...

We study the optimal combination of different commodity signals in a dynamic portfolio theoretic framework. Following Brandt et al. (2006, 2009) we parameterize the portfolio weights of a risk-averse mean-variance investor to integrate information from time series predictors and cross-sectional...

We evaluate the use of Generalized Empirical Likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. Estimators from GEL family present some optimal statistical properties, such as robustness to misspecification and better...

We develop a new family of estimators of the covariance matrix that relies solely on forwardlooking information. It uses only current prices of plain-vanilla options. In an out-of-sample study we show that a minimum-variance strategy based on these fully-implied estimators outperforms several...