SciELO - Scientific Electronic Library Online

 
vol.28 issue1Postharvest Physiological study and evaluation of the quality of plums (Prunus domestica L.) Cv. Horvin under three conditions of cold storageThe performance of mortar containing added metakaolin regarding sulfate action author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Ingeniería e Investigación

Print version ISSN 0120-5609

Abstract

LINARES VASQUEZ, Mario; HERNANDEZ LOSADA, Diego Fernando  and  GONZALEZ OSORIO, Fabio. Exploiting stock data: a survey of state of the art computational techniques aimed at producing beliefs regarding investment portfolios. Ing. Investig. [online]. 2008, vol.28, n.1, pp.105-116. ISSN 0120-5609.

Selecting an investment portfolio has inspired several models aimed at optimising the set of securities which an investtor may select according to a number of specific decision criteria such as risk, expected return and planning horizon. The classical approach has been developed for supporting the two stages of portfolio selection and is supported by disciplines such as econometrics, technical analysis and corporative finance. However, with the emerging field of computational finance, new and interesting techniques have arisen in line with the need for the automatic processing of vast volumes of information. This paper surveys such new techniques which belong to the body of knowledge concerning computing and systems engineering, focusing on techniques particularly aimed at producing beliefs regarding investment portfolios.

Keywords : portfolio; optimisation; stock; securities; return; risk; profile; belief; rules set.

        · abstract in Spanish     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License