Mutual funds often disappear following poor performance. When this poor performance is partly attributable to negative idiosyncratic shocks, funds' estimated alphas understate their true alphas. This paper estimates a structural model to correct for this bias. Although most funds still have...

In this paper, we document that an application of a moving average strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that often outperform the buy-and-hold strategy substantially. For high volatility portfolios, the abnormal returns,...

We propose a new approach to imposing economic constraints on 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...

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls below the variable's past 12-month high....

You're probably familiar, at least in passing, with the 'convexity' of long-term bonds - i.e. that yields dropping 1% produce a bigger price move than yields rising 1%. A significant amount of brainpower has gone into understanding all the ramifications of this convexity in the fixed income...

The intuitiveness and practicability of mean-variance portfolios largely depends on the accuracy of moment estimates, which are subject to large estimation errors and conditional on time. We propose a model accounting for factor dynamics in a Bayesian setting, in which the impact of estimation...

Long-run risk models, a cornerstone in the macro-finance literature for their ability to capture key asset price phenomena, are known to entail implausibly high levels of timing and risk premia. Our paper resolves this puzzle by considering consumption of durable goods in addition to that of...

Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and variance-covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions...

We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions,...

The proliferation of anomalies and the resulting `factor zoo' has challenged finance researchers to identify firm characteristics that are genuinely related to the cross-sectional variation in expected stock returns. We address this challenge using a Bayesian ensemble of trees approach, namely,...