Cargando…

Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA

This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two d...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Silva, Antonio Daniel (Autor), Neves, Rui Ferreira (Autor), Horta, Nuno (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:SpringerBriefs in Computational Intelligence,
Temas:
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-319-29392-9
003 DE-He213
005 20220116171419.0
007 cr nn 008mamaa
008 160211s2016 sz | s |||| 0|eng d
020 |a 9783319293929  |9 978-3-319-29392-9 
024 7 |a 10.1007/978-3-319-29392-9  |2 doi 
050 4 |a Q342 
072 7 |a UYQ  |2 bicssc 
072 7 |a TEC009000  |2 bisacsh 
072 7 |a UYQ  |2 thema 
082 0 4 |a 006.3  |2 23 
100 1 |a Silva, Antonio Daniel.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA  |h [electronic resource] /  |c by Antonio Daniel Silva, Rui Ferreira Neves, Nuno Horta. 
250 |a 1st ed. 2016. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a XVII, 95 p. 46 illus., 18 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a SpringerBriefs in Computational Intelligence,  |x 2625-3712 
505 0 |a Introduction -- Literature Review -- System Architecture -- Multi-Objective optimization -- Simulations in single and multi-objective optimization -- Outlook. 
520 |a This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage. 
650 0 |a Computational intelligence. 
650 0 |a Algorithms. 
650 0 |a Social sciences-Mathematics. 
650 0 |a Finance. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Algorithms. 
650 2 4 |a Mathematics in Business, Economics and Finance. 
650 2 4 |a Financial Economics. 
700 1 |a Neves, Rui Ferreira.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Horta, Nuno.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319293905 
776 0 8 |i Printed edition:  |z 9783319293912 
830 0 |a SpringerBriefs in Computational Intelligence,  |x 2625-3712 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-29392-9  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712)