We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...

We provide a short and selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with major predictors and are primarily interested in the ideas, methods, and their applications. To understand...

This paper suggests a novel approach for predicting aggregate stock returns at quarterly and annual frequencies. Weak return predictability is consistent with the view that a stationary component of stock prices is highly persistent. In such cases, expected returns are time-varying but also...

We propose direct multiple time series models for predicting high dimensional vectors of observable realized global minimum variance portfolio (GMVP) weights computed based on high-frequency intraday returns. We apply Lasso regression techniques, develop a class of multiple AR(FI)MA models for...

Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

The relationship between excess returns and the dividend price ratio is known to be unstable. However, there is no consensus on the type of instability, i.e. few or many breaks. Differences in parameter instability affect the long-term investor in particular, as misspecification errors are...

Financial data often contain information that is helpful for macroeconomic forecasting, while multistep forecast accuracy also benefits by incorporating good nowcasts of macroeconomic variables. This paper considers the role of nowcasts of financial variables in making conditional forecasts of...

The industry consensus on the implementation of the International Financial and Reporting Standard 9 - Financial Instruments (IFRS9) in the field of credit risk is that the estimation of credit risk parameters should be conditioned in the baseline, upside and downside macroeconomic scenarios...