Cardinality Constrained Portfolio Optimization on an Ising Machine

Matthieu Parizy, Przemyslaw Sadowski, Nozomu Togawa

研究成果: Conference contribution

抄録

In this paper, we propose an Ising-machine based method for solving the cardinality constrained mean-variance portfolio optimization problem (CCMVPOP), which is an NP-hard problem and often solved using metaheuristics. Firstly, we formulate this problem as a binary quadratic program (BQP) to be solved by an Ising machine-software system. Namely, we propose formulations for each objective and constraint using binary variables exclusively. Furthermore, we evaluate and compare well known integer to binary variable encoding as well as propose a new encoding for the CCMVPOP. The evaluation is done by studying which encoding converges the fastest to the highest return over risk collection of assets for a given data set which represent stocks involved in a capital market index. Used data range from capital market index composed of 31 assets for the smallest and up to 225 for the largest. The experimental results confirm that the proposed formulations to the CCMVPOP for an Ising machine-software system are effective.

本文言語English
ホスト出版物のタイトルProceedings - 2022 IEEE 35th International System-on-Chip Conference, SOCC 2022
編集者Sakir Sezer, Thomas Buchner, Jurgen Becker, Andrew Marshall, Fahad Siddiqui, Tanja Harbaum, Kieran McLaughlin
出版社IEEE Computer Society
ISBN(電子版)9781665459853
DOI
出版ステータスPublished - 2022
イベント35th IEEE International System-on-Chip Conference, SOCC 2022 - Belfast, Northern Ireland, United Kingdom
継続期間: 2022 9月 52022 9月 8

出版物シリーズ

名前International System on Chip Conference
2022-September
ISSN(印刷版)2164-1676
ISSN(電子版)2164-1706

Conference

Conference35th IEEE International System-on-Chip Conference, SOCC 2022
国/地域United Kingdom
CityBelfast, Northern Ireland
Period22/9/522/9/8

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ
  • 制御およびシステム工学
  • 電子工学および電気工学

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