Quantum-Inspired Weighting Approach to Correlation Diversified Passive Portfolio Strategy

13 Pages Posted: 3 Dec 2019 Last revised: 26 Jan 2020

See all articles by Yutaka Sakurai

Yutaka Sakurai

AI Finance Application Research Institute

Yusuke Yuki

NTT Data

Ryota Katsuki

NTT Data

Takashi Yazane

NTT Data

Fumio Ishizaki

AI Finance Application Research Institute; Modal Stage

Date Written: November 15, 2019

Abstract

In this paper, we present a quantum-inspired new approach for passive portfolio strategy improving index investing. The proposed method adjusts weight vector of original index based on the permutation of assets composing the original index. We seek the permutation of assets such that assets with strong correlation to many other assets should be placed in the central part of permutation. Since the number of permutations can be prohibitively large, it is difficult to find the optimal permutation. To overcome the computational difficulty, we introduce a quantum-inspired new technology. By reducing the weights of assets placed in the central area of permutation, we can construct portfolios which are more diversified and have better risk-return characteristics than original index. To examine the usefulness of the proposed method, we apply it to 30 DJIA assets and 33 TOPIX sector indices, and we provide numerical experiments. The numerical experiments show that portfolios constructed by the proposed method can achieve higher return with lower volatility than the original indices, while their behaviors are still similar to those of the original indices.

Keywords: Index Investing, Inverse Volatility Weighting, Correlation Diversified Portfolio (CDP), Inverse Volatility Correlation Diversified Portfolio (IVCDP), Quantum Annealer, Permutation

JEL Classification: G11

Suggested Citation

Sakurai, Yutaka and Yuki, Yusuke and Katsuki, Ryota and Yazane, Takashi and Ishizaki, Fumio, Quantum-Inspired Weighting Approach to Correlation Diversified Passive Portfolio Strategy (November 15, 2019). Available at SSRN: https://ssrn.com/abstract=3487195 or http://dx.doi.org/10.2139/ssrn.3487195

Yutaka Sakurai

AI Finance Application Research Institute ( email )

2-2-8 Minamiaoyama
Minato, Tokyo 107-0062
Japan
+81-3-6434-0482 (Phone)

Yusuke Yuki

NTT Data ( email )

Japan

Ryota Katsuki

NTT Data ( email )

Japan

Takashi Yazane

NTT Data ( email )

Japan

Fumio Ishizaki (Contact Author)

AI Finance Application Research Institute ( email )

2-2-8 Minamiaoyama
Minato, Tokyo 107-0062
Japan

Modal Stage ( email )

2-29-7 Shiba
Minato, Tokyo 105-0014
Japan

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