Journal ArticleDOI
International Stock Return Predictability: What Is the Role of the United States?
TLDR
In this article, the authors investigate lead-lag relationships among monthly country stock returns and identify a leading role for the United States: lagged U.S. returns significantly predict returns in numerous non-U.S., industrialized countries, while lagged non.Abstract:
We investigate lead-lag relationships among monthly country stock returns and identify a leading role for the United States: lagged U.S. returns significantly predict returns in numerous non-U.S. industrialized countries, while lagged non-U.S. returns display limited predictive ability with respect to U.S. returns. We estimate a news-diffusion model, and the results indicate that return shocks arising in the United States are only fully reflected in equity prices outside of the United States with a lag, consistent with a gradual information diffusion explanation of the predictive power of lagged U.S. returns.read more
Citations
More filters
ReportDOI
Empirical Asset Pricing via Machine Learning
TL;DR: Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.
Journal ArticleDOI
Empirical Asset Pricing via Machine Learning
TL;DR: The authors performed a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia, and demonstrated large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature.
Journal ArticleDOI
Shrinking the cross-section
TL;DR: In this paper, a robust stochastic discount factor (SDF) summarizing the joint explanatory power of a large number of cross-sectional stock return predictors is proposed.
Journal ArticleDOI
Stock Return Predictability and Variance Risk Premia : Statistical Inference and International Evidence
TL;DR: In this article, the authors show that the variance risk premium predicts aggregate stock market returns and demonstrate that statistical finite sample biases cannot explain the apparent predictability of stock market return in the U.S. They also show that country specific regressions for France, Germany, Japan, Switzerland, and U.K result in quite similar patterns.
Posted Content
Dissecting Characteristics Nonparametrically
TL;DR: The authors proposed a nonparametric method to test which characteristics provide independent information for the cross-section of expected returns, and used the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically.
References
More filters
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
Journal ArticleDOI
Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Posted Content
Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
ReportDOI
Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
Related Papers (5)
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction
Ivo Welch,Amit Goyal +1 more
Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more