The Portfolio Heuristic Optimisation System (PHOS)

Nikolaos Loukeris, I. Eleftheriadis () and E. Livanis ()
Additional contact information
I. Eleftheriadis: University of Macedonia
E. Livanis: University of Macedonia

Computational Economics, 2016, vol. 48, issue 4, No 5, 627-648

Abstract: Abstract The efficient representation of the accurate corporate value on the stock price is vital to investors and fund managers that desire to optimise the net worth of the overall stock portfolio. Although the efficient market hypothesis sets limits, the practice of markets is an ideal place of manipulation, and corruption on prices. The accounting statements, evaluated by support vector machines and the SVM hybrids under genetic algorithms provide superiority in portfolio selection, on condition. A specific genetic hybrid SVM outperformed all examined SVM models being a powerful tool in financial analysis. We also offer the integrated model of portfolio selection, PHOS.

Keywords: Support vector machines; Genetic algorithms; Portfolio selection; Bankruptcy (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-015-9552-1 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:kap:compec:v:48:y:2016:i:4:d:10.1007_s10614-015-9552-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-015-9552-1

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().