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An adaptive metaheuristic approach for risk-budgeted portfolio optimization

An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns comple...

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Main Authors: Gandikota, N.S.K., Hasan, M.H., Jaafar, J.
Format: Article
Published: Institute of Advanced Engineering and Science 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34122/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140461793&doi=10.11591%2fijai.v12.i1.pp305-314&partnerID=40&md5=7a8e4feae49fc89941a2feebab80435b
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spelling oai:scholars.utp.edu.my:341222023-01-04T02:51:56Z http://scholars.utp.edu.my/id/eprint/34122/ An adaptive metaheuristic approach for risk-budgeted portfolio optimization Gandikota, N.S.K. Hasan, M.H. Jaafar, J. An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame (HF) via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study. © 2023, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 2023 Article NonPeerReviewed Gandikota, N.S.K. and Hasan, M.H. and Jaafar, J. (2023) An adaptive metaheuristic approach for risk-budgeted portfolio optimization. IAES International Journal of Artificial Intelligence, 12 (1). pp. 305-314. ISSN 20894872 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140461793&doi=10.11591%2fijai.v12.i1.pp305-314&partnerID=40&md5=7a8e4feae49fc89941a2feebab80435b 10.11591/ijai.v12.i1.pp305-314 10.11591/ijai.v12.i1.pp305-314 10.11591/ijai.v12.i1.pp305-314
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame (HF) via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Gandikota, N.S.K.
Hasan, M.H.
Jaafar, J.
spellingShingle Gandikota, N.S.K.
Hasan, M.H.
Jaafar, J.
An adaptive metaheuristic approach for risk-budgeted portfolio optimization
author_facet Gandikota, N.S.K.
Hasan, M.H.
Jaafar, J.
author_sort Gandikota, N.S.K.
title An adaptive metaheuristic approach for risk-budgeted portfolio optimization
title_short An adaptive metaheuristic approach for risk-budgeted portfolio optimization
title_full An adaptive metaheuristic approach for risk-budgeted portfolio optimization
title_fullStr An adaptive metaheuristic approach for risk-budgeted portfolio optimization
title_full_unstemmed An adaptive metaheuristic approach for risk-budgeted portfolio optimization
title_sort adaptive metaheuristic approach for risk-budgeted portfolio optimization
publisher Institute of Advanced Engineering and Science
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34122/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140461793&doi=10.11591%2fijai.v12.i1.pp305-314&partnerID=40&md5=7a8e4feae49fc89941a2feebab80435b
_version_ 1754532130110570496
score 13.222552

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