中国经济网北京1月22日讯 昨日，中新经纬发布《中庚基金陈涛在管产品规模走低 2025年收益跑输业 绩基准》一文。文中称，陈涛管理的产品有中庚价值先锋、中庚小盘价值。尽管2只产品2025年的收益率 均为正，但没有跑赢各自业绩比较基准收益率。2025年，中庚价值先锋取得了10.33%的收益率，而同期业 绩比较基准收益率约为27.33%，基金收益跑输业绩比较基准收益率17个百分点；中庚小盘价值股票取得 20.54%的收益率，同期业绩比较基准收益率约为24.83%。 根据中国经济网记者了解，陈涛从2013年7月起从事证券研究、投资管理相关工作，历任泰康资产研 究员、华创证券高级分析师、浙商基金高级研究员、汇丰晋信基金投资经理等，2018年7月加入中庚基金 管理有限公司。 从中庚价值先锋股票的基金经理变更情况看，陈涛从2022年9月1日起独自管理该基金，但如果与前任 曹庆所管理时的业绩对比来看，陈涛的任职回报并没有太大亮点。 资料显示，中庚价值先锋股票成立于2021年8月20日，从完整年度的业绩表现看，在与曹庆共同管理 三个季度的2022年里，该基金的收益率无论是与同类产品均值相比，还是同期业绩基准相比，都表现亮 眼 ...

Core Viewpoint - The departure of renowned fund managers from small and medium-sized fund companies has significant impacts, but it also presents opportunities for these firms to rethink their strategies and diversify their product lines [1][5][7]. Group 1: Impact of Departures - Since 2024, several well-known fund managers have left their positions, leading to noticeable declines in the managed equity scale of small and medium-sized fund companies [1]. - The exit of a "star" manager often results in substantial changes in fund performance, with some successor managers maintaining or even improving the investment strategies [2][3][4]. Group 2: Performance of Successor Managers - After the departure of Qiu Dongrong, Liu Sheng took over the management of Zhonggeng Value Navigation, achieving a return of 15.90% year-to-date and 18.83% since the departure date, outperforming the CSI 300 Index [3]. - Other funds managed by successors also showed varied performance, with Zhonggeng Value Quality achieving an 11.31% return year-to-date, while Zhonggeng Small Cap Value had a return of 16.53% since the departure but underperformed year-to-date [3]. Group 3: Industry Trends and Responses - The frequent turnover of fund managers is attributed to various factors, including performance pressure, industry competition, and personal career plans [6]. - The China Securities Regulatory Commission's recent action plan aims to shift the focus of fund companies from "scale" to "returns," providing new guidance for the development of small and medium-sized fund companies [8][9]. Group 4: Strategic Shifts in Fund Companies - The departure of key talent is prompting fund companies to reflect on their governance mechanisms and long-term incentives to retain core personnel [7]. - Companies are encouraged to adopt a platform-based survival strategy, focusing on building brand value and investment capabilities independent of individual managers [7][9].

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]