Quantitative Models and Construction Methods 1. Model Name: 博道远航 (Bodao Yuanhang) - **Model Construction Idea**: The product employs an enhanced model based on the average performance index of偏股混合型基金 (partial equity hybrid funds). The fund manager uses quantitative methods to replicate the underlying stock holdings of the 885001 index and then enhances it further [43][43]. - **Model Construction Process**: - The model first replicates the stock holdings of the 885001 index using quantitative techniques. - Enhancements are applied to the replicated portfolio to achieve superior performance while maintaining a controlled deviation from the benchmark [43][43]. - **Model Evaluation**: The model demonstrates significant and stable excess returns over the 885001 index, with a high information ratio and low relative drawdowns [43][43]. 2. Model Name: 兴业聚利 (Xingye Juli) - **Model Construction Idea**: The product adopts a "process-to-result management" approach, setting偏股基准 (partial equity benchmark) and enhancing it while keeping deviations under control [57][57]. - **Model Construction Process**: - The benchmark is first established based on the偏股基准. - Enhancements are applied to the benchmark portfolio, with a focus on maintaining a relatively low deviation and incorporating moderate timing strategies [57][57]. - **Model Evaluation**: The model achieves stable excess returns over the 885001 index, though its lower average position results in occasional underperformance during strong market uptrends [57][57]. 3. Model Name: 信澳宁隽智选 (Xinao Ningjun Zhixuan) - **Model Construction Idea**: This product belongs to the "Index Plus" series, aiming to achieve higher excess returns with relatively loose deviation constraints. It targets the 885001 index as its benchmark [70][70]. - **Model Construction Process**: - The model allows for a certain degree of deviation from the benchmark to capture higher excess returns. - It incorporates港股 (Hong Kong stocks) allocation, which is particularly relevant in the current environment where active equity funds are increasing their exposure to Hong Kong stocks [70][70]. - **Model Evaluation**: The model demonstrates high and stable excess returns over the 885001 index, though it experiences relatively higher maximum relative drawdowns during sharp market uptrends [70][70]. --- Model Backtesting Results 1. 博道远航 (Bodao Yuanhang) - **Excess Return**: 12.65% over the 885001 index [43][43] - **Information Ratio (IR)**: 1.46 [43][43] - **Maximum Relative Drawdown**: -3.41% [43][43] 2. 兴业聚利 (Xingye Juli) - **Excess Return**: 9.24% over the 885001 index [57][57] - **Information Ratio (IR)**: 0.95 [57][57] - **Maximum Relative Drawdown**: -4.84% [57][57] 3. 信澳宁隽智选 (Xinao Ningjun Zhixuan) - **Excess Return**: 11.26% over the 885001 index [70][70] - **Information Ratio (IR)**: 1.07 [70][70] - **Maximum Relative Drawdown**: -7.29% [70][70]

Core Viewpoint - A significant number of active equity funds are experiencing a performance turnaround, with over 180 funds reaching new historical net asset value highs as of June 25, driven by market uptrends and favorable external factors [1][2]. Group 1: Performance of Active Equity Funds - Over 180 active equity funds have achieved historical net asset value highs, with more than half of these funds established for over a year, and some for nearly 14 years [1][2]. - The fund with the highest increase is Jin Yuan Shun An Yuan Qi, which has risen over 450% since its inception in November 2017, primarily investing in small-cap stocks [2][3]. - Other notable funds include Guangfa Multi-Factor and Dacheng Jingheng, with increases of over 340% and nearly 300% respectively, focusing on quantitative investment strategies [2][3]. Group 2: Market Trends and Investment Strategies - Approximately 80% of active equity funds have seen positive performance this year, with around 1,100 funds increasing by over 10%, particularly those focused on Hong Kong stocks, pharmaceuticals, and technology [3][4]. - The highest-performing fund this year is Huatai-PB Hong Kong Advantage Selection, which has increased by over 90%, primarily investing in the Hong Kong pharmaceutical sector [4]. - Three main investment directions have gained consensus among institutions: innovative pharmaceuticals, technology, and dividend stocks, with a preference for a "barbell" strategy that balances aggressive and defensive investments [4][5]. Group 3: Future Outlook and Recommendations - Fund managers suggest focusing on high-potential international pharmaceutical companies and stable dividend assets, especially in a declining interest rate environment [5][6]. - The AI sector is highlighted as a key area for investment, with significant growth in AI applications and user engagement noted [6][7]. - Overall, there is optimism for the A-share market, with recommendations to prioritize stable dividend returns and sectors with strong industrial and policy catalysts [7].

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]

Group 1 - The core viewpoint of the news is the introduction of a new policy by the China Securities Regulatory Commission (CSRC) aimed at enhancing the long-term performance of public fund managers by linking their compensation to the performance of their funds relative to benchmarks [2][3] - The policy targets fund managers whose products have underperformed their benchmarks by more than 10 percentage points over three years, leading to a significant reduction in their performance-based compensation [2][3] - The initiative is expected to align the interests of fund managers with those of investors, encouraging a shift away from short-term speculation towards a focus on long-term investment capabilities [2][3] Group 2 - As of May 21, 2023, there are 5,898 public funds managed by fund managers with over three years of experience, with 1,341 funds underperforming their benchmarks by over 10 percentage points [3][4] - Among these, 31 funds have underperformed their benchmarks by more than 50 percentage points, including notable funds managed by well-known managers such as Zheng Chengran from GF Fund and Yao Zhipeng from Harvest Fund [3][4][5] - The worst-performing fund, Morgan Small Cap A, managed by Guo Chen, has a cumulative return of -23.03% over three years, underperforming its benchmark by 127.69 percentage points [4][5] Group 3 - Conversely, there are 543 funds that have outperformed their benchmarks by over 10 percentage points, with 33 funds exceeding their benchmarks by more than 50 percentage points [7][9] - The top-performing fund, Huaxia North Exchange Innovation Small and Medium Enterprises Selected Fund, managed by Gu Xin Feng, achieved a cumulative return of 194.13%, surpassing its benchmark by 175.89 percentage points [9][10] - The North Exchange theme funds have emerged as a significant area for excess returns, with several funds exceeding their benchmarks by over 60 percentage points [10] Group 4 - In response to the new policy, many fund companies are adjusting their performance benchmarks to better reflect the risk-return characteristics of their funds [11][12] - Recent adjustments include changes to benchmarks for various funds, such as the adjustment of the performance benchmark for the浦银安盛稳健增利债券 from "CSI All Bond Index" to a more complex composite benchmark [11][12] - The trend of benchmark adjustments is expected to continue as fund companies seek to align their performance metrics with regulatory expectations and improve their competitive positioning [13][14]

以下文章来源于望京博格投基 ，作者喜胖不胖 望京博格投基 . 记录望京博格投资基金的故事 长按即可免费加入 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者：望京博格 来源：雪球 之前写过一篇 ， 《 今年业绩最牛的30位基金经理 ， 闫思倩排不进前10~ 》 不少朋友说 ： 但看今年短期没啥用 ， 时间要拉长点 ！ 这不 ？ 今天给大家看看 【 最近3年 】 业绩排行榜 全市场目前有三四千名基金经理 ， 所以能排进前35的算是前1% ， 也算是非常优秀的基金经理 。 | 序 | 名称 | 3年涨跌幅 | 年初至今 | 管理 | 管理规模 | 现任基金公司 | | --- | --- | --- | --- | --- | --- | --- | | 号 | | | | 基金数 | (亿元) | | | 1 | 冷文鹏综合指数 | 69.27% | 26.61% | 1 | 1.57 | 中信建投基金 | | 2 | 缪玮彬综合指数 | 68.99% | 0.92% | 1 | 11.73 | 金元顺安基金 | | 3 | 白冰洋综合指数 | 63.38% | 19. ...