Hybrid Quantum Investment Optimization with Minimal Holding Period

TL;DR

This paper introduces a hybrid quantum-classical algorithm for dynamic portfolio optimization that efficiently finds near-optimal investment trajectories with minimal holding periods, demonstrating improved results on real financial data.

Contribution

It presents a novel hybrid quantum-classical method for portfolio optimization with minimal holding periods, leveraging quantum sampling and post-selection techniques.

Findings

Achieved optimal investment trajectories on a dataset of 50 assets.

Produced portfolios closer to the efficient frontier than traditional methods.

Demonstrated adaptability to different risk profiles.

Abstract

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a one year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.