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沪指突破3400点！中证A500ETF（159338）涨近1%，行业均衡、龙头荟萃，攻守兼备

Mei Ri Jing Ji Xin Wen· 2025-06-11 05:32

Core Viewpoint - The A-shares market experienced a rebound, with the Shanghai Composite Index surpassing 3400 points, and the CSI A500 ETF (159338) rising nearly 1% during active trading [1] Group 1: Index Overview - The CSI A500 Index is constructed using an internationally recognized "industry balanced" approach and is referred to as the "top class" of the A-share market, akin to a domestic "S&P 500" [1] - It selects 500 securities with large market capitalization and good liquidity from various industries, representing the core assets of the A-share market [1] - The index includes nearly all leading companies across three levels of industries, achieving a true "gathering of leaders" [1] Group 2: Composition and Growth Potential - The CSI A500 Index comprises approximately 50% traditional value industries (finance, materials, consumer, energy, public utilities) and about 50% emerging growth industries (industrial, information technology, communication services, healthcare) [1] - Although both the CSI A500 and CSI 300 are value indices, the CSI A500 is more growth-oriented, representing growth within value [1] - During market rebounds, the CSI A500 is expected to capture the upward momentum of value stocks while also benefiting from the rebound elasticity of growth stocks [1] Group 3: Investment Opportunity - Investors interested in this segment may consider the CSI A500 ETF (159338), which ranks first in scale among similar products [1]

HTSC· 2025-06-10 06:40

Quantitative Models and Construction Methods 1. Model Name: Brinson Attribution Model - **Model Construction Idea**: The model is used to decompose the excess returns of active equity funds into stock selection and sector allocation contributions, providing insights into the sources of fund performance [16][19][22] - **Model Construction Process**: The Brinson model calculates excess returns as follows: $ R_{excess} = \sum_{i=1}^{n} (W_{i,f} - W_{i,b}) \cdot R_{i,b} + \sum_{i=1}^{n} W_{i,f} \cdot (R_{i,f} - R_{i,b}) $ - $ W_{i,f} $: Fund weight in sector $ i $ - $ W_{i,b} $: Benchmark weight in sector $ i $ - $ R_{i,f} $: Fund return in sector $ i $ - $ R_{i,b} $: Benchmark return in sector $ i $ The first term represents the allocation effect, and the second term represents the selection effect [16][19] - **Model Evaluation**: The model highlights that stock selection contributes more significantly to excess returns than sector allocation, with stock selection accounting for 83.17% of the total contribution on average [16][22] --- Model Backtesting Results 1. Brinson Attribution Model - Average stock selection contribution: 5.38% per half-year [22] - Probability of positive stock selection returns: 69.12% [23] - Probability of positive sector allocation returns: 53.66% [23] --- Quantitative Factors and Construction Methods 1. Factor Name: Fund Stability Factor - **Factor Construction Idea**: This factor measures the stability of a fund's sector allocation and its impact on outperforming benchmarks [10][12] - **Factor Construction Process**: Funds are categorized into 16 groups based on static and dynamic sector allocation characteristics: - Static categories: Highly diversified, diversified, concentrated, highly concentrated - Dynamic categories: Highly stable, stable, rotational, highly rotational The average probability of outperforming benchmarks is calculated for each group [10][12] - **Factor Evaluation**: Funds with highly stable and diversified sector allocations have the highest probability of outperforming benchmarks, exceeding 73% on average [12][14] 2. Factor Name: Style Consistency Factor - **Factor Construction Idea**: This factor evaluates the consistency of a fund's style (e.g., large-cap value) and its correlation with performance [27][30] - **Factor Construction Process**: Funds are classified based on their style consistency over time: - Long-term stable allocation - Majority-time allocation - Partial-time allocation - Rare-time allocation The probability of outperforming benchmarks is calculated for each group [27][28] - **Factor Evaluation**: Funds with long-term stable large-cap value styles have the highest probability of outperforming benchmarks, reaching 79.77% [28][30] --- Factor Backtesting Results 1. Fund Stability Factor - Highly diversified-highly stable funds: - Probability of outperforming benchmark: 73.12% - Probability of outperforming benchmark +10%: 57.29% [12] 2. Style Consistency Factor - Long-term stable large-cap value funds: - Probability of outperforming benchmark: 79.77% - Probability of outperforming benchmark +10%: 69.05% [28]

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