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From Risk-Sensitive Foraging to Scalable Portfolio Rebalancing: State-Conditioned Artificial Bee Colony Optimization
Haoran Nie1, Yuanbo Lu2, Hua Xie3
1School of Banking and Finance, Business School, University of New South Wales, Sydney, NSW 2052, Australia.
Abstract:
This study examines whether candidate-level gain-loss information improves search for cost-aware portfolio rebalancing. We propose Risk-Sensitive Foraging Artificial Bee Colony (RSF-ABC) for long-only portfolios with exact cardinality and active-weight bounds. A bounded state derived from the Omega objective controls the probability of heavy-tailed exploration versus conservative best-guided search. A state-independent mixture of the same operators isolates the value of state conditioning. Monthly evaluation on U.S. industry portfolios uses 504-day formation windows and proportional transaction costs. Mean net Omega is 1.1635 for RSF-ABC, 1.1284 for Standard ABC, 1.1342 for Fixed-Mix-ABC, 1.1410 for particle swarm optimization, and 1.1450 for differential evolution. Monthly turnover is 0.4780 versus 0.5570 for Standard ABC. The proposed method leads in five of seven boundary conditions; deterministic alternatives lead in two. At 49 assets, its reported median runtime is 1.11 s versus 1.08 s for Standard ABC. These results support the evaluated state-operator coupling.
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