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• W3119854094 abstract "We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers." @default.
• W3119854094 created "2021-01-18" @default.
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• W3119854094 date "2021-01-12" @default.
• W3119854094 modified "2023-09-26" @default.
• W3119854094 title "Market Graph Clustering via QUBO and Digital Annealing" @default.
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• W3119854094 doi "https://doi.org/10.3390/jrfm14010034" @default.
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