An asset allocation dynamic optimization system and method for smart robo-advisory

By using a dynamic optimization system for asset allocation in intelligent investment advisory systems, which employs time-varying parameter models and dynamic Bayesian hybrid optimization algorithms, the problem of poor adaptability of intelligent investment advisory asset allocation solutions to market dynamics has been solved. This enables personalized and highly interpretable asset allocation in complex market environments, thereby improving the stability of returns and the controllability of risks.

CN122798539APending Publication Date: 2026-09-22UNIV OF SCI & TECH OF CHINA
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Patent Information

Application Number
CN202610979230.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-07-02
Publication Date
2026-09-22

AI Technical Summary

Technical Problem

Existing robo-advisory asset allocation solutions suffer from poor adaptability to market dynamics, weak multi-dimensional information integration capabilities, insufficient personalized allocation, and low decision interpretability. They struggle to balance returns, risks, liquidity, and transaction costs, and cannot meet the diverse investment needs of users in complex market environments.

Method used

The system employs a dynamic asset allocation optimization system for robo-advisors, comprising a data acquisition and preprocessing layer, an environmental perception and feature extraction layer, a hierarchical dynamic optimization layer, a strategy generation and interpretation layer, and a feedback and closed-loop adjustment layer. It utilizes time-varying parameter hidden Markov models, dynamic conditional correlation models, skewed t-distributions, and conditional value at risk technologies, combined with dynamic Bayesian hybrid optimization algorithms and multi-objective adaptive trade-off mechanisms, to achieve market state identification, user preference modeling, and personalized allocation.

Benefits of technology

It enables rapid switching of adaptive strategies under different market environments, enhances the risk resistance and environmental adaptability of asset allocation, accurately calculates the posterior return distribution, and balances returns, risks, liquidity and transaction costs to meet users' differentiated investment needs and ensure the compliance and controllability of the allocation plan.

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Abstract

The present application relates to the field of financial technology and artificial intelligence technology, and provides an asset allocation dynamic optimization system for intelligent investment consultants, comprising: a data acquisition and preprocessing layer for acquiring and preprocessing multi-source heterogeneous data, wherein the multi-source heterogeneous data comprises real-time market data, user behavior data, and policy and constraint data; an environment perception and feature extraction layer comprising a market state perceiver, an asset correlation modeling module, and a user dynamic portrait module; a strategy generation and interpretation layer comprising an allocation strategy generator, an interpretability module, and a visualization output module; a feedback and closed-loop adjustment layer comprising a performance attribution analysis module, a parameter self-adaptive updating module, and a user feedback acquisition module. By dynamically perceiving market changes, capturing asset time-varying correlation, and preventing and controlling tail risk, the system optimizes the balance of yield and risk by fusing multi-source information, takes into account liquidity and transaction costs, supports personalized allocation and decision-making interpretability, and improves the adaptability, accuracy, and robustness of the investment consultant.
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