EconPapers    
Economics at your fingertips  
 

Genetic algorithm versus classical methods in sparse index tracking

Margherita Giuzio ()
Additional contact information
Margherita Giuzio: EBS Universität für Wirtschaft und Recht

Decisions in Economics and Finance, 2017, vol. 40, issue 1, No 13, 243-256

Abstract: Abstract The main objective in index tracking is to replicate the performance of a target index by using a small subset of its constituents. Non-convex regularization techniques, such as the $$\ell _q$$ ℓ q and the log penalization, which are able to enhance portfolio sparsity by selecting a low number of active weights, recently proved to perform remarkably well in index tracking problems. The resulting non-convex optimization is NP-hard and deterministic optimization methods, such as interior point and gradient projection algorithms, may not efficiently reach the optimal solution due to the presence of multiple local optima and discontinuities in the search space. Therefore, heuristic approaches can be more helpful and easy to implement, thanks to recent hardware development. In this paper, we compare three state-of-the-art estimation techniques, i.e., the interior point, the gradient projection and the coordinate descent algorithms, to a popular heuristic method, the genetic algorithm, in index tracking optimization. We show and evaluate the performance of the four methods in a penalized framework on different simulated settings and on real-world financial data.

Keywords: Portfolio optimization; Sparsity; Heuristics; Index tracking (search for similar items in EconPapers)
JEL-codes: C15 C61 G11 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10203-017-0191-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0191-y

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10203/PS2

DOI: 10.1007/s10203-017-0191-y

Access Statistics for this article

Decisions in Economics and Finance is currently edited by Paolo Ghirardato

More articles in Decisions in Economics and Finance from Springer, Associazione per la Matematica
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2020-04-23
Handle: RePEc:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0191-y