基金研究系列（35）：从股债二元到多元配置：多资产基金投顾的三维画像与业绩归因

Quantitative Models and Construction Methods 1. Model Name: "Risk Preference-Concentration-Turnover" Three-Dimensional Label Classification System - Model Construction Idea: The model aims to classify multi-asset fund advisory products based on three dimensions: risk preference, concentration, and turnover rate, to better understand their risk-return characteristics and performance differentiation[3][32] - Model Construction Process: - Risk Preference: Classified based on the proportion of income-generating assets and growth assets in the portfolio. If income-generating assets exceed 70%, it is classified as debt-oriented; if growth assets exceed 70%, it is equity-oriented; otherwise, it is balanced[34] - Concentration: Measured using the Herfindahl-Hirschman Index (HHI), calculated as $ \sum_{i} w_{i}^{2} $, where $w_{i}$ represents the weight of each asset class. Thresholds are set as follows: HHI > 0.5 is high concentration, HHI < 0.25 is low concentration, and values in between are medium concentration[34] - Turnover Rate: Measures the timing adjustment ability of multi-asset fund advisory products at the asset class level. Annualized one-sided turnover rate is used, with thresholds defined as follows: turnover rate > 2 is high turnover, < 1 is low turnover, and values in between are medium turnover[34] - Model Evaluation: The model effectively captures the heterogeneity in multi-asset fund advisory products and provides insights into their risk-return characteristics and strategic differences[3][34] --- Model Backtesting Results 1. "Risk Preference-Concentration-Turnover" Three-Dimensional Label Classification System - Risk Preference: - Equity-oriented products: 2025 annualized return 18.5%, 2024 annualized return 10.5%, 2023 annualized return -1.0%[37][39] - Debt-oriented products: 2025 annualized return 7.4%, 2024 annualized return 5.9%, 2023 annualized return 3.9%[37][39] - Balanced products: 2025 annualized return 15.7%, 2024 annualized return 8.8%, 2023 annualized return -4.7%[37][39] - Concentration: - Low concentration (HHI < 0.25): 2025 annualized return 17.7%, 2024 annualized return 8.2%, 2023 annualized return 0.4%[37][39] - Medium concentration (0.25 ≤ HHI ≤ 0.5): 2025 annualized return 13.0%, 2024 annualized return 6.9%, 2023 annualized return -4.0%[37][39] - High concentration (HHI > 0.5): 2025 annualized return 7.8%, 2024 annualized return 6.9%, 2023 annualized return 3.9%[37][39] - Turnover Rate: - Low turnover (< 1): 2025 annualized return 15.6%, 2024 annualized return 8.8%, 2023 annualized return 1.7%[37][39] - Medium turnover (1 ≤ turnover ≤ 2): 2025 annualized return 10.6%, 2024 annualized return 7.3%, 2023 annualized return 0.5%[37][39] - High turnover (> 2): 2025 annualized return 11.2%, 2024 annualized return 7.6%, 2023 annualized return -5.4%[37][39] --- Quantitative Factors and Construction Methods 1. Factor Name: Brinson Attribution Model - Factor Construction Idea: The model decomposes the excess return of multi-asset fund advisory products into two components: allocation return and selection return, to evaluate the sources of excess returns[42][46] - Factor Construction Process: - Allocation Effect: Measures the timing and allocation ability of fund managers across major asset classes. The formula is: $R_{allocation} = \sum_{i} (w_{i}^{actual} - w_{i}^{benchmark}) \times r_{i}^{asset}$ where $w_{i}^{actual}$ is the actual weight of asset $i$, $w_{i}^{benchmark}$ is the benchmark weight, and $r_{i}^{asset}$ is the return of asset $i$[42][46] - Selection Effect: Reflects the ability to select superior funds within each asset class. The formula is: $R_{selection} = R_{excess} - R_{allocation}$ where $R_{excess}$ is the total excess return relative to the benchmark[42][46] - Factor Evaluation: The model provides a clear decomposition of excess returns, helping to identify whether returns are driven by strategic asset allocation or fund selection[42][46] --- Factor Backtesting Results 1. Brinson Attribution Model - Equity-Oriented Products: - Example: "Guotai Global Allocation" achieved 2025 allocation return of 10.5% and selection return of 6.3%[48][49] - Example: "招商海外掘金" achieved 2025 allocation return of -0.8% and selection return of 14.5%[48][49] - Debt-Oriented Products: - Example: "嘉实百灵全天候策略" achieved 2025 allocation return of 3.8% and selection return of 0.5%[56][58] - Example: "全球固收+" achieved 2025 allocation return of 2.6% and selection return of 1.3%[56][58] - Balanced Products: - Example: "时光旅行者" achieved 2025 allocation return of 15.6% and selection return of -10.3%[65][66] - Example: "绘盈长投计划" achieved 2023 allocation return of 10.1%, providing a strong safety net during a bear market[65][66]