With the advent of the era of big data and the increasing demand for privacy protection, decentralized portfolio optimization has garnered significant attention in practical implementations. This paper addresses the problem of decentralized portfolio optimization within the mean-variance portfolio framework. A decentralized multi-period portfolio optimization model is established using the alternating direction method of multipliers (ADMM). The methodology incorporates a distributed event-triggered approach to imitate the professional investment manager for each sub-portfolio, where each investment manager independently triggers the rebalancing moment of the respective sub-portfolio. Empirical analysis is conducted on four well-known stock trading markets to demonstrate the performance of the decentralized multi-period portfolio optimization model.
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