Quantum-Inspired Hierarchical Risk Parity

By Elham Alipour, Clemens Adolphs, Arman Zaribafiyan, & Maxwell Rounds

We present a quantum-inspired approach to portfolio optimization that is based on an optimization problem that can be solved using a quantum annealer. The proposed algorithm utilizes a hierarchical clustering tree that is based on the covariance matrix of the asset returns. We use real market data to benchmark our approach against other common portfolio optimization methods and demonstrate its strong performance in terms of a variety of risk measures and lower susceptibility to inaccuracies in the input data.

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