Stock Risk Assessment via Multi-Objective Genetic Programming

Tahmassebi, A; Gandomi, AH; Meyer-Baese, A

Stock Risk Assessment via Multi-Objective Genetic Programming

Tahmassebi, A Gandomi, AH Meyer-Baese, A

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Publisher:: Postdoc Journal
Publication Type:: Journal Article
Citation:: Journal of Postdoctoral Research, 2018, 6, (3), pp. 33-41
Issue Date:: 2018-03-20

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Field	Value	Language
dc.contributor.author	Tahmassebi, A
dc.contributor.author	Gandomi, AH
dc.contributor.author	Meyer-Baese, A
dc.date.accessioned	2022-08-30T07:14:05Z
dc.date.available	2022-08-30T07:14:05Z
dc.date.issued	2018-03-20
dc.identifier.citation	Journal of Postdoctoral Research, 2018, 6, (3), pp. 33-41
dc.identifier.issn	2328-9791
dc.identifier.issn	2328-9791
dc.identifier.uri	http://hdl.handle.net/10453/161116
dc.description.abstract	Recent exponential growth of investors in stock markets brings the idea to develop a predictive model to forecast the total risk of investment in stock markets. In this paper, an evolutionary approach was proposed to predict the total risk in stock investment based on an S&P 500 database in a time period of 1991-2010 employing a multi-objective genetic programming along with an adaptive regression by mixing algorithm. The reasonable results suggest that the proposed model can be applied to various stock databases to assess the total risk of investment. The proposed model along with stock selection decision support systems can overcome the disadvantages of weighted scoring stock selection.
dc.language	en
dc.publisher	Postdoc Journal
dc.relation.ispartof	Journal of Postdoctoral Research
dc.relation.isbasedon	10.14304/surya.jpr.v6n3.6
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Stock Risk Assessment via Multi-Objective Genetic Programming
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-30T07:14:04Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	3

Abstract:

Recent exponential growth of investors in stock markets brings the idea to develop a predictive model to forecast the total risk of investment in stock markets. In this paper, an evolutionary approach was proposed to predict the total risk in stock investment based on an S&P 500 database in a time period of 1991-2010 employing a multi-objective genetic programming along with an adaptive regression by mixing algorithm. The reasonable results suggest that the proposed model can be applied to various stock databases to assess the total risk of investment. The proposed model along with stock selection decision support systems can overcome the disadvantages of weighted scoring stock selection.

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http://hdl.handle.net/10453/161116