Hybrid quantum-classical optimization for financial index tracking

Samuel Fern\'andez-Lorenzo, Diego Porras and Juan Jos\'e Garc\'ia-Ripoll

Papers from arXiv.org

Abstract: Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a weighted subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging NP-hard problem even for moderately large indices consisting of dozens or hundreds of assets, thereby requiring heuristic methods to find approximate solutions. Hybrid quantum-classical optimization with variational gate-based quantum circuits arises as a plausible method to improve performance of current schemes. In this work we introduce a heuristic pruning algorithm to find weighted combinations of assets subject to cardinality constraints. We further consider different strategies to respect such constraints and compare the performance of relevant quantum ans\"{a}tze and classical optimizers through numerical simulations.

Date: 2020-08, Revised 2021-10
New Economics Papers: this item is included in nep-cmp and nep-ore
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Published in Quantum Sci. Technol. 6 034010 (2021)

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