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  • Publications
    • A Two-Layer Attribution Framework for Portfolios Built from Mixed-Asset Products
    • Drift Minimization under Lot-Size Constraints: A Practical Integer-Share Rebalancing Problem in Robo-Advisory
    • Defining and Measuring Portfolio Health: A Drift-Based Metric Relative to Model Portfolios
    • Optimal Initiation of Guaranteed Lifelong Withdrawal Benefit with Dynamic Withdrawals
    • Weak Convergence of Path-Dependent SDEs and Functionals in Pricing Basket CDS with Counterparty Risk and Contagion Risk
    • Regression-Based Monte Carlo Methods for Stochastic Control Models: Variable Annuities with Lifelong Guarantees
    • Analysis of Optimal Dynamic Withdrawal Policies in Withdrawal Guarantee Products
  • Research
  • Talks
    • Introduction of AI Quant Investment
    • AI Application in FinTech
    • Regression-Based Monte Carlo Methods for Stochastic Control Models
    • Analysis of Optimal Dynamic Withdrawal Policies in Withdrawal Guarantee Products
    • Analysis of Optimal Dynamic Withdrawal Policies in Withdrawal Guarantee Products
    • Analysis of Optimal Dynamic Withdrawal Policies in Withdrawal Guarantee Products
  • News
    • Live Lecture: Introduction of AI Quant Investment
    • Guest Lecture: AI Application in FinTech at HKUST
  • Projects
    • AI-Driven Automatic Wealth Management Solution
    • AI-Driven MPF Retirement Advisory System
    • ESG Quantitative Scoring System and ML-Driven Investment Research
    • Portfolio Management via Reinforcement Learning
  • Experience

A Two-Layer Attribution Framework for Portfolios Built from Mixed-Asset Products

Jan 1, 2026·
Yao Tung Huang (Don HUANG)
Yao Tung Huang (Don HUANG)
· 0 min read
Type
Preprint
Publication
Working paper, AQUMON Research
Last updated on Jan 1, 2026
Working-Paper Portfolio Attribution Wealth Management
Yao Tung Huang (Don HUANG)
Authors
Yao Tung Huang (Don HUANG)
Co-Founder & Chief Scientist, AQUMON
Drift Minimization under Lot-Size Constraints: A Practical Integer-Share Rebalancing Problem in Robo-Advisory Jan 1, 2025 →

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