An Optimization Model Based on Principal Component Theory and Genetic Algorithm
2015 ◽
Vol 742
◽
pp. 384-389
Keyword(s):
Stock investment is risky and beyond fixed rules to forecast precisely. In order to realize proper stocks selection from specified mathematical function model, principal component factor analysis is proposed to rebuild various stocks via its contribution rates so as to extract the principal component factors from the elimination of weak ones. According to the synthesis scoring and ranking, optimized stocks has been selected as valuable targets. Test from Genetic Algorithm to the ranking aforementioned indicates that the rationality and validity of the results.
1997 ◽
Vol 3
(5)
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pp. 420-427
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1988 ◽
Vol 44
(9)
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pp. 857-861
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2007 ◽
Vol 7
(3-4)
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pp. 161-175
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1997 ◽
Vol 31
(2)
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pp. 337-345
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2017 ◽
Vol 6
(6)
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pp. 36-40
1982 ◽
Vol 32
(6)
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pp. 637-642
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1982 ◽
Vol 23
(41)
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pp. 4241-4244
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1995 ◽
Vol 697
(1-2)
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pp. 429-440
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2009 ◽
Vol 8
(11)
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pp. 1100-1103
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