EconPapers    
Economics at your fingertips  
 

Long horizon predictability: An asset allocation perspective

Abraham Lioui and Patrice Poncet

European Journal of Operational Research, 2019, vol. 278, issue 3, 961-975

Abstract: Consider investors with a 10-year investment horizon who rebalance their portfolio at the monthly frequency. Should they use information from monthly returns, 10-year returns or intermediate returns to build their optimal portfolios? When stock and bond returns are i.i.d., the frequency of returns is not relevant. However, when they are predictable, it is. Using a new estimation approach and before correcting for overlapping observations, we show that the positive impact of predictability on investors’ welfare is stronger for longer prediction horizons and the more so as the investment horizon enlarges. This welfare improvement is achieved by adopting realistic portfolio positions. When we correct for the persistence in the predictive regression residuals due to overlapping observations, our results are preserved for short to medium investment horizons although the added value of long horizon predictability is reduced. Our results are robust to various checks and also hold out-of-sample. Overall, short to medium term investors should exploit long horizon predictability even though they rebalance their portfolios at high frequency.

Keywords: Finance; Dynamic portfolio decision; Predictive regression; Long horizon predictability; Inter-temporal hedging (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221719303777
Full text for ScienceDirect subscribers only

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:eee:ejores:v:278:y:2019:i:3:p:961-975

DOI: 10.1016/j.ejor.2019.04.040

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:ejores:v:278:y:2019:i:3:p:961-975