Penalty-free QUBO sampling on quantum annealers followed by classical cardinality post-processing yields feasible low-energy portfolios with chain-break rates below 0.04 percent up to N=49.
Journal of Finance 7, 77–91
4 Pith papers cite this work. Polarity classification is still indexing.
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A motif-based decomposition of quantile risk networks shows that local triadic topology and orbit-position diversity carry portfolio-relevant information missed by aggregate connectedness, with motif-based portfolios outperforming benchmarks and positional diversity marking tail transmitters.
D-Wave's constraint-native hybrid service for mean-variance-turnover portfolio optimization with cardinality constraints is 99.3 percent classical, with mean QPU access of 0.034 seconds out of a 5-second budget, and returns identical solutions deterministically.
Characterizes Nash equilibria for MMV portfolio problems via FBSDEs and extended HJBs, with MMV equilibria investing more than MV ones and gap narrowing over time.
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A Penalty-Free Pipeline for Direct Quantum-Annealer Portfolio Optimization
Penalty-free QUBO sampling on quantum annealers followed by classical cardinality post-processing yields feasible low-energy portfolios with chain-break rates below 0.04 percent up to N=49.
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A Motif-Based Framework for Decomposing Risk Spillovers
A motif-based decomposition of quantile risk networks shows that local triadic topology and orbit-position diversity carry portfolio-relevant information missed by aggregate connectedness, with motif-based portfolios outperforming benchmarks and positional diversity marking tail transmitters.
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Where the Quantum Lives in D-Wave Hybrid Portfolio Optimization
D-Wave's constraint-native hybrid service for mean-variance-turnover portfolio optimization with cardinality constraints is 99.3 percent classical, with mean QPU access of 0.034 seconds out of a 5-second budget, and returns identical solutions deterministically.
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Time-consistent portfolio selection with monotone mean-variance preferences
Characterizes Nash equilibria for MMV portfolio problems via FBSDEs and extended HJBs, with MMV equilibria investing more than MV ones and gap narrowing over time.