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Portfolio Selection Using Evolutionary Computational Techniques

Authors: Ishu Bhansal; Dr. S. Srinivasan;

Portfolio Selection Using Evolutionary Computational Techniques

Abstract

We present in this paper a new approach to portfolio selection using imprecise model, Fuzzy goal programming and Genetic algorithm. The results of this approach is compared with traditional portfolio selection and presented here. In conventional methods, the skewness of portfolio returns is not considered. In this new approach, we prefer the stocks in the portfolio which has demonstrated positive skewness in the past patterns. By careful analysis of past patterns we give preference to positively skewed stocks and strengthen the portfolio. The paper discusses a brief literature survey and elaborates on our model and finally gives the results and conclusions.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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