.png){kind=icon}
Return
Robust Econometric Inference for Stock Return Predictability
.png){kind=icon}
OA
AI
DOI：10.1093/rfs/hhu139.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study examines stock return predictability via lagged financial variables with unknown stochastic properties. We propose a novel testing procedure that (1) robustifies inference to regressors' degree of persistence, (2) accommodates testing the joint predictive ability of financial variables in multiple regression, (3) is easy to implement as it is based on a linear estimation procedure, and (4) can be used for long-horizon predictability tests. We provide some evidence in favor of short-horizon predictability during the 1927-2012 period. Nevertheless, this evidence almost entirely disappears in the post-1952 period. Moreover, predictability becomes weaker, not stronger, as the predictive horizon increases.
Keywords:
EXPECTED RETURNS
TIME-SERIES
DIVIDEND YIELDS
EFFICIENT TESTS
LIMIT THEORY
REGRESSIONS
MODELS
HETEROSKEDASTICITY
PERFORMANCE
HYPOTHESIS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.4
Papers:
2.8K
Citations:
3.0W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveIssues in training set selection and refinement for classification by a feedforward neural network
PREAI
ShareSaveShareSaveShareSaveShareSave
View more