Core Insights - The performance of various funds managed by Invesco Great Wall has been highlighted, showcasing their strong returns in the equity market as of June 30, 2025 [1][2] Group 1: Fund Performance - Invesco Great Wall's equity funds have shown exceptional performance, ranking 1st out of 13 and 2nd out of 13 in excess returns over the last three and ten years respectively [1] - Six of their actively managed equity funds ranked in the top 10 of their category over the past year, with 19 in the top 20% and 28 in the top third [1] - The growth style funds have particularly excelled, with several funds managed by veteran manager Yang Ruiwen ranking in the top 7 of their category over the past year [1] Group 2: Manager Highlights - Yang Ruiwen's funds, including Invesco Great Wall Preferred and Corporate Governance, ranked 15th out of 144 and 5th out of 552 respectively over the past three years [1] - Other notable managers include Nong Bingli, whose fund ranked 2nd out of 1595 over the past two years, and Zhang Zhongwei, whose fund ranked 6th out of 324 [1] - The performance of the funds managed by Jiang Shan and Dong Han also stood out, with Jiang's fund ranking 2nd out of 108 and Dong's fund in the top 13% [1] Group 3: Diverse Strategies - In addition to growth funds, Invesco Great Wall's funds in other styles have also performed well, with manager Zou Lihua's fund ranking 5th in its category over the past two and three years [2] - The quant strategies have gained traction in the current structural market, with several quant funds showing strong performance, including Li Haiwei's fund ranking 32nd out of 344 over the past year [2] - The company emphasizes its commitment to active management and aims to optimize investment strategies for better investor experiences [2]

Group 1 - Public funds have distributed over 110 billion yuan in dividends this year, with more than 2,100 funds collectively distributing 113.546 billion yuan as of June 21, marking a 38.46% increase compared to 820.05 billion yuan last year [1][2] - Stock funds have seen a significant increase in dividends, totaling 21.922 billion yuan, which is nearly four times the amount from the same period last year, while mixed funds' dividends reached 4.476 billion yuan, approximately 2.2 times last year's figures [2][3] - ETFs have dominated the dividend distribution, with the top three funds being the CSI 300 ETFs, which collectively distributed 134.12 billion yuan, and six out of nine funds with over 1 billion yuan in dividends being ETFs [1][2] Group 2 - The number of dividend distributions has also increased, with the top fund, Ganhu Zhiyuan Jiayue Rate Bond A, distributing dividends eight times this year, while six funds have exceeded 100 distributions [2][3] - The increase in ETF dividends is attributed to their growth in scale and the better market performance this year compared to last year, along with a high proportion of institutional funds that demand dividends [3][4] - Public REITs have been active in dividend distribution, with 55 out of 69 REITs distributing a total of 4.459 billion yuan this year, highlighting the appeal of alternative assets in the current market [4]

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]