Add to ORKGWe propose using the statistical method of Bagging to forecast the equity premium out-of-sample for multivariate regression models. Bagging allows for the flexible and efficient extraction of valuable informational content from a large set of predictors, leading to statistically and economically significant gains relative to not only the historical mean, but also other soft-threshold methods such as forecast combinations and shrinkage estimators in our empirical results. Furthermore, we find that the source of economic gains for Bagging primarily comes from the fact that it encourages the investor to actively manage portfolio by flexibly utilizing short selling or leveraging to better time the market following correctly prognosticated trend...
This study comprehensively investigates the uncertainty on parameter instability and model selection...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
The literature on excess return prediction has considered a wide array of estimation schemes, among ...
Abstract The literature on excess return prediction has considered a wide array of estimation scheme...
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both...
This article shows that bagging can improve the forecast accuracy of time series models for realized...
Bootstrap aggregating or Bagging, introduced by Breiman (1996a), has been proved to be effective to ...
We introduce a flexible utility-based empirical approach to directly determine asset allocation deci...
Neely et al. (2014) have recently demonstrated how to efficiently combine information from a set of ...
Abstract: This paper uses a predictive regression framework to examine the out-of-sample predictabil...
This article explores the usefulness of bagging methods in forecasting economic time series from lin...
Purpose This paper aims to study whether the industry indexes predict the evolution of the broad st...
Many of the sophisticated models for stock return forecasting and portfolio optimisation cannot beat...
The portfolio selection problem is one of the most discussed topics in financial literature. Harry ...
This study comprehensively investigates the uncertainty on parameter instability and model selection...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
The literature on excess return prediction has considered a wide array of estimation schemes, among ...
Abstract The literature on excess return prediction has considered a wide array of estimation scheme...
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both...
This article shows that bagging can improve the forecast accuracy of time series models for realized...
Bootstrap aggregating or Bagging, introduced by Breiman (1996a), has been proved to be effective to ...
We introduce a flexible utility-based empirical approach to directly determine asset allocation deci...
Neely et al. (2014) have recently demonstrated how to efficiently combine information from a set of ...
Abstract: This paper uses a predictive regression framework to examine the out-of-sample predictabil...
This article explores the usefulness of bagging methods in forecasting economic time series from lin...
Purpose This paper aims to study whether the industry indexes predict the evolution of the broad st...
Many of the sophisticated models for stock return forecasting and portfolio optimisation cannot beat...
The portfolio selection problem is one of the most discussed topics in financial literature. Harry ...
This study comprehensively investigates the uncertainty on parameter instability and model selection...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...