大葉大學圖書館 |

Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	332.6
書名/作者:	Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA/ by Antonio Daniel Silva, Rui Ferreira Neves, Nuno Horta.
作者:	Silva, Antonio Daniel.
其他作者:	Neves, Rui Ferreira.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvii, 95 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Portfolio management - Mathematical models.
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Algorithm Analysis and Problem Complexity.
標題:	Quantitative Finance.
標題:	Finance, general.
ISBN:	9783319293929
ISBN:	9783319293905
內容註:	Introduction -- Literature Review -- System Architecture -- Multi-Objective optimization -- Simulations in single and multi-objective optimization -- Outlook.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-29392-9

Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA[electronic resource] /
Silva, Antonio Daniel.

Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA[electronic resource] /by Antonio Daniel Silva, Rui Ferreira Neves, Nuno Horta. - Cham :Springer International Publishing :2016. - xvii, 95 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Introduction -- Literature Review -- System Architecture -- Multi-Objective optimization -- Simulations in single and multi-objective optimization -- Outlook.

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.

ISBN: 9783319293929

Standard No.: 10.1007/978-3-319-29392-9doiSubjects--Topical Terms:

445010
Portfolio management
--Mathematical models.

LC Class. No.: HG4529.5

Dewey Class. No.: 332.6

Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA[electronic resource] /
LDR:02425nam a2200325 a 4500 001 456689
003 DE-He213
005 20160824131847.0
006 m d
007 cr nn 008maaau
008 161227s2016 gw s 0 eng d
020 $a 9783319293929 $q (electronic bk.)
020 $a 9783319293905 $q (paper)
024 7 $a 10.1007/978-3-319-29392-9 $2 doi
035 $a 978-3-319-29392-9
040 $a GP $c GP
041 0 $a eng
050 4 $a HG4529.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 332.6 $2 23
090 $a HG4529.5 $b .S586 2016
100 1 $a Silva, Antonio Daniel. $3 656271
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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvii, 95 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
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 Portfolio management $x Mathematical models. $3 445010
650 1 4 $a Engineering. $3 372756
650 2 4 $a Computational Intelligence. $3 463962
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 463701
650 2 4 $a Quantitative Finance. $3 464461
650 2 4 $a Finance, general. $3 634722
700 1 $a Neves, Rui Ferreira. $3 656272
700 1 $a Horta, Nuno. $3 607065
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in applied sciences and technology. $3 463858
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-29392-9
950 $a Engineering (Springer-11647)