Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10260361" target="_blank" >RIV/61989100:27240/24:10260361 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s12065-024-00929-4" target="_blank" >https://link.springer.com/article/10.1007/s12065-024-00929-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12065-024-00929-4" target="_blank" >10.1007/s12065-024-00929-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

Popis výsledku v původním jazyce

Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts.

Název v anglickém jazyce

Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

Popis výsledku anglicky

Portfolio optimization has long been a challenging proposition and a widely studied topic in finance and management. It involves selecting and allocating the right assets according to the desired objectives. It has been found that this nonlinear constraint problem cannot be effectively solved using a traditional approach. This paper covers and compares quantum-inspired versions of four popular evolutionary techniques with three benchmark datasets. Genetic algorithm, differential evolution, particle swarm optimization, ant colony optimization, and their quantum-inspired incarnations are implemented, and the results are compared. Experiments have been carried out with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. This work proposes several enhancements to allocate funds efficiently, such as improved crossover techniques and dynamic and adaptive selection of parameters. Furthermore, it is observed that the quantum-inspired techniques outperform the classical counterparts.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Evolutionary Intelligence

ISSN

1864-5909

e-ISSN

1864-5917

Svazek periodika

17

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

40

Strana od-do

nestránkováno

Kód UT WoS článku

001190462000003

EID výsledku v databázi Scopus

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