Backward/forward optimal combination of performance measures for equity screening
Monica Billio,
Massimiliano Caporin and
Michele Costola
The North American Journal of Economics and Finance, 2015, vol. 34, issue C, 63-83
Abstract:
We introduce a novel criterion for performance measure combination designed to be used as an equity screening algorithm. The proposed approach follows the general idea of linearly combining selected performance measures with positive weights and combination weights are determined by means of an optimisation step. The underlying criterion function takes into account the risk-return trade-off potentially associated with the equity screens, evaluated on a historical and rolling basis. By construction, performance combination weights can vary over time, allowing for changes in preferences across performance measures. An empirical example shows the benefits of our approach compared to naive screening rules based on the Sharpe ratio.
Keywords: Performance measures; Combining performance measures; Portfolio allocation; Equity screening; Differential evolution (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1062940815000571
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Backward/forward optimal combination of performance measures for equity screening (2012) 
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:ecofin:v:34:y:2015:i:c:p:63-83
DOI: 10.1016/j.najef.2015.08.002
Access Statistics for this article
The North American Journal of Economics and Finance is currently edited by Hamid Beladi
More articles in The North American Journal of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().