A moment-free nonparametric quantity-of-quality approach to optimal portfolio selection: A role for endogenous shortfall and windfall boundaries?

M. Ryan Haley

Journal of the Operational Research Society, 2018, vol. 69, issue 10, 1678-1687

Abstract: This article proposes a Quantity-of-Quality (QoQ) approach to optimal portfolio selection, which builds on the intuition of the widely applied h-index and e-index from the bibliometric literature. While moment-free and nonparametric, the method embraces quantity-of-high-quality returns and upside potential while simultaneously avoiding quantity-of-low-quality returns and downside risk. A non-standard measure of central tendency is also present, which functions in a way similar to a portfolio mean or median. The method delivers attractive and intuitively appealing results, and appears to be less susceptible to overfitting issues than the stylized Sharpe Ratio portfolio. The method is demonstrated with an established data set, and out-of-sample performance is gauged using training-holdout analysis in two distinct data sets. Because the proposed method uses a fundamentally different portfolio selection objective function than standard moment-based methods, the QoQ approach extracts information about the data-generating process that is perhaps overlooked or deemphasized by traditional moment-based methods, and as such may serve as a capable complement to standard moment-based portfolio selection criteria.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2018.1489356 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjorxx:v:69:y:2018:i:10:p:1678-1687

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2018.1489356

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().