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CBO algorithm with average drift and applications to portfolio optimization
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AI
DOI：10.1016/j.cam.2026.117535.png)
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Abstract
En 中文
We propose a consensus based optimization algorithm with average drift (in short Ad-CBO) and provide a theoretical framework for it. In the theoretical analysis, we show that particle solutions to Ad-CBO converge to a global minimizer. In numerical simulations, we examine Ad-CBO's performance in optimizing static and dynamic objective functions. As a real-time application, we test the efficiency of Ad-CBO to find the optimal portfolio given stochastically evolving multi-asset prices in a financial market. The proposed Ad-CBO exhibits higher searching speed, lower tracking errors and regret bound than the CBO without stochastic diffusion.
Keywords:
Consensus based optimization
Adaptive momentum
Average drift
Portfolio selection
Regret bound
Journal
J
IF:
2.6
Papers:
336
Citations:
0