EconPapers    
Economics at your fingertips  
 

Stock market prediction and Portfolio selection models: a survey

Akhter Mohiuddin Rather (), V. N. Sastry () and Arun Agarwal ()
Additional contact information
Akhter Mohiuddin Rather: University of Hyderabad
V. N. Sastry: Institute for Development and Research in Banking Technology
Arun Agarwal: University of Hyderabad

OPSEARCH, 2017, vol. 54, issue 3, No 6, 558-579

Abstract: Abstract Stock data is known to be chaotic in nature and it is a challenging task to predict the non-linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging task and limitations do exist in every portfolio model in some form or the other. In order to resolve such problems, many artificial intelligence models have appeared in literature which are also known as intelligent models. Prediction of stocks as well as investing in appropriate stocks has remained in focus among investors, industrialists as well as among academicians. This paper surveys important published articles in the related area available in literature. This survey highlights traditional mathematical models available in articles which have appeared decades back till artificial intelligence based models available in recent articles.

Keywords: Stock returns; Portfolio selection models; Artificial intelligence based models; Time series prediction (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/s12597-016-0289-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:opsear:v:54:y:2017:i:3:d:10.1007_s12597-016-0289-y

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-016-0289-y

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2020-04-23
Handle: RePEc:spr:opsear:v:54:y:2017:i:3:d:10.1007_s12597-016-0289-y