Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks
In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem is central to quantitative finance....
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2022-01-01
|
| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.013006 |
| _version_ | 1797210891409686528 |
|---|---|
| author | Samuel Mugel Carlos Kuchkovsky Escolástico Sánchez Samuel Fernández-Lorenzo Jorge Luis-Hita Enrique Lizaso Román Orús |
| author_facet | Samuel Mugel Carlos Kuchkovsky Escolástico Sánchez Samuel Fernández-Lorenzo Jorge Luis-Hita Enrique Lizaso Román Orús |
| author_sort | Samuel Mugel |
| collection | DOAJ |
| description | In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem is central to quantitative finance. After a detailed introduction to the problem, we implement a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete formulation using real data from daily prices over 8 years of 52 assets, and do a detailed comparison of the obtained Sharpe ratios, profits, and computing times. In particular, we implement classical solvers (Gekko, exhaustive), D-wave hybrid quantum annealing, two different approaches based on variational quantum eigensolvers on IBM-Q (one of them brand-new and tailored to the problem), and for the first time in this context also a quantum-inspired optimizer based on tensor networks. In order to fit the data into each specific hardware platform, we also consider doing a preprocessing based on clustering of assets. From our comparison, we conclude that D-wave hybrid and tensor networks are able to handle the largest systems, where we do calculations up to 1272 fully-connected qubits for demonstrative purposes. Finally, we also discuss how to mathematically implement other possible real-life constraints, as well as several ideas to further improve the performance of the studied methods. |
| first_indexed | 2024-04-24T10:17:48Z |
| format | Article |
| id | doaj.art-dcc0d10b15734f83beb4681c4879f631 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:17:48Z |
| publishDate | 2022-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-dcc0d10b15734f83beb4681c4879f6312024-04-12T17:16:53ZengAmerican Physical SocietyPhysical Review Research2643-15642022-01-014101300610.1103/PhysRevResearch.4.013006Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networksSamuel MugelCarlos KuchkovskyEscolástico SánchezSamuel Fernández-LorenzoJorge Luis-HitaEnrique LizasoRomán OrúsIn this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem is central to quantitative finance. After a detailed introduction to the problem, we implement a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete formulation using real data from daily prices over 8 years of 52 assets, and do a detailed comparison of the obtained Sharpe ratios, profits, and computing times. In particular, we implement classical solvers (Gekko, exhaustive), D-wave hybrid quantum annealing, two different approaches based on variational quantum eigensolvers on IBM-Q (one of them brand-new and tailored to the problem), and for the first time in this context also a quantum-inspired optimizer based on tensor networks. In order to fit the data into each specific hardware platform, we also consider doing a preprocessing based on clustering of assets. From our comparison, we conclude that D-wave hybrid and tensor networks are able to handle the largest systems, where we do calculations up to 1272 fully-connected qubits for demonstrative purposes. Finally, we also discuss how to mathematically implement other possible real-life constraints, as well as several ideas to further improve the performance of the studied methods.http://doi.org/10.1103/PhysRevResearch.4.013006 |
| spellingShingle | Samuel Mugel Carlos Kuchkovsky Escolástico Sánchez Samuel Fernández-Lorenzo Jorge Luis-Hita Enrique Lizaso Román Orús Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks Physical Review Research |
| title | Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks |
| title_full | Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks |
| title_fullStr | Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks |
| title_full_unstemmed | Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks |
| title_short | Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks |
| title_sort | dynamic portfolio optimization with real datasets using quantum processors and quantum inspired tensor networks |
| url | http://doi.org/10.1103/PhysRevResearch.4.013006 |
| work_keys_str_mv | AT samuelmugel dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT carloskuchkovsky dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT escolasticosanchez dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT samuelfernandezlorenzo dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT jorgeluishita dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT enriquelizaso dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks AT romanorus dynamicportfoliooptimizationwithrealdatasetsusingquantumprocessorsandquantuminspiredtensornetworks |