Hybrid quantum-classical optimization for financial index tracking

• URL: http://arxiv.org/abs/2008.12050v2
• Date: Thu, 21 Oct 2021 15:23:37 GMT
• Title: Hybrid quantum-classical optimization for financial index tracking
• Authors: Samuel Fern\'andez-Lorenzo, Diego Porras, Juan Jos\'e Garc\'ia-Ripoll
• Abstract summary: Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging weighted-hard problem even for moderately large indices consisting of dozens or hundreds of assets. We introduce a pruning algorithm to find weighted combinations of assets subject to cardinality constraints.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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