Portfolio optimization based on self-organizing maps clustering and genetics algorithm

In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in...

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Main Authors: Farid, Fajri, Rosadi, Dedi
Format: Other NonPeerReviewed
Language:English
Published: International Journal of Advances in Intelligent Informatics 2022
Subjects:
Online Access:https://repository.ugm.ac.id/283965/1/113.Portfolio-optimization-based-on-selforganizing-maps-clustering-and-genetics-algorithmInternational-Journal-of-Advances-in-Intelligent-Informatics.pdf
https://repository.ugm.ac.id/283965/
https://ijain.org/index.php/IJAIN/article/view/587
https://doi.org/10.26555/ijain.v8i1.587
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Institution: Universitas Gadjah Mada
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spelling id-ugm-repo.2839652023-11-24T08:18:15Z https://repository.ugm.ac.id/283965/ Portfolio optimization based on self-organizing maps clustering and genetics algorithm Farid, Fajri Rosadi, Dedi Statistics In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory. International Journal of Advances in Intelligent Informatics 2022 Other NonPeerReviewed application/pdf en https://repository.ugm.ac.id/283965/1/113.Portfolio-optimization-based-on-selforganizing-maps-clustering-and-genetics-algorithmInternational-Journal-of-Advances-in-Intelligent-Informatics.pdf Farid, Fajri and Rosadi, Dedi (2022) Portfolio optimization based on self-organizing maps clustering and genetics algorithm. International Journal of Advances in Intelligent Informatics. https://ijain.org/index.php/IJAIN/article/view/587 https://doi.org/10.26555/ijain.v8i1.587
institution Universitas Gadjah Mada
building UGM Library
continent Asia
country Indonesia
Indonesia
content_provider UGM Library
collection Repository Civitas UGM
language English
topic Statistics
spellingShingle Statistics
Farid, Fajri
Rosadi, Dedi
Portfolio optimization based on self-organizing maps clustering and genetics algorithm
description In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory.
format Other
NonPeerReviewed
author Farid, Fajri
Rosadi, Dedi
author_facet Farid, Fajri
Rosadi, Dedi
author_sort Farid, Fajri
title Portfolio optimization based on self-organizing maps clustering and genetics algorithm
title_short Portfolio optimization based on self-organizing maps clustering and genetics algorithm
title_full Portfolio optimization based on self-organizing maps clustering and genetics algorithm
title_fullStr Portfolio optimization based on self-organizing maps clustering and genetics algorithm
title_full_unstemmed Portfolio optimization based on self-organizing maps clustering and genetics algorithm
title_sort portfolio optimization based on self-organizing maps clustering and genetics algorithm
publisher International Journal of Advances in Intelligent Informatics
publishDate 2022
url https://repository.ugm.ac.id/283965/1/113.Portfolio-optimization-based-on-selforganizing-maps-clustering-and-genetics-algorithmInternational-Journal-of-Advances-in-Intelligent-Informatics.pdf
https://repository.ugm.ac.id/283965/
https://ijain.org/index.php/IJAIN/article/view/587
https://doi.org/10.26555/ijain.v8i1.587
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