Understanding and Forecasting Stock Price Changes
Pedro N. Rodríguez, and
Simon Sosvilla-Rivero
No 2006-03, Working Papers from FEDEA
Abstract:
Previous empirical studies have shown that predictive regressions in which model uncertainty is assessed and propagated generate desirable properties when predicting out-of-sample. However, it is still not clear (a) what the important conditioning variables for predicting stock returns out-of-sample are, and (b) how composite weighted ensembles outperform model selection criteria. By comparing the unconditional accuracy of prediction regressions to the conditional accuracy conditioned on specific explanatory variables masked), we find that cross-sectional premium and term spread are robust predictors of future stock returns. Additionally, using the bias-variance decomposition for the 0/1 loss function, the analysis shows that lower bias, and not lower variance, is the fundamental difference between composite weighted ensembles and model selection criteria. This difference, nevertheless, does not necessarily imply that model averaging techniques improve our ability to describe monthly up-and-down movements' behavior in stock markets.
Date: 2006-03
New Economics Papers: this item is included in nep-ecm, nep-fmk, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://documentos.fedea.net/pubs/dt/2006/dt-2006-03.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:fda:fdaddt:2006-03
Access Statistics for this paper
More papers in Working Papers from FEDEA
Bibliographic data for series maintained by Carmen Arias ().