同时，基金业绩比较基准并非一成不变的标尺。当基金投资范围调整时，基金公司可能会公告调整基 准。这一调整反映了基金未来的投资方向将会更聚焦于港股，投资者需及时关注公告，评估基金的投资 方向是否仍符合自身需求。 晨星中国表示，第一步，通过基准判断基金是否匹配自身偏好。在选择基金前，先查看其基准构成：若 基准以宽基指数为主，如沪深300、中证500，基金适合追求分散投资的投资者；若基准包含细分赛道指 数，如半导体指数、新能源指数，需确认自己对该赛道的了解程度与风险承受能力；若基准为合成指 数，需关注股债比例、跨市场配置比例是否符合自身资产配置需求。 第二步，通过基准评价基金真实管理能力。观察现任基金经理管理期、近3年、5年这些期间的基金收益 率与业绩比较基准收益率，对比两者的差距：若基金长期跑赢基准，超额收益稳定，而且基金背后的投 研团队具备较强的能力、基金经理拥有成熟稳定的投资策略，基金费用也不高，则说明这只基金有比较 好的投资价值。此外，投资者需关注基金业绩基准的适配性。 随着《推动公募基金高质量发展行动方案》在行业内逐步落地，基金业绩比较基准的约束性与指导性被 提升至新高度。监管层明确要求，基金的业绩比较基准 ...

Core Viewpoint - Understanding the performance benchmark of mutual funds is crucial for investors to establish a rational investment perspective, as it conveys important information about the fund's investment direction, style, and strategy [1][2]. Group 1: Importance of Performance Benchmarks - The regulatory framework has elevated the significance of performance benchmarks in mutual funds, guiding product positioning, investment strategies, and performance evaluation [1]. - Performance benchmarks help investors identify the investment focus and style of funds, such as large-cap blue-chip stocks or specific sectors like semiconductors [2][3]. - Composite benchmarks, such as a mix of equity and bond indices, indicate a balanced investment strategy, while more complex benchmarks reflect specific asset allocation strategies [3]. Group 2: Evaluating Fund Performance - Performance benchmarks serve as a tool to filter market styles and assess the true management capabilities of fund managers, allowing for a more accurate evaluation of their performance [3][4]. - For actively managed funds, deviations from benchmarks can indicate attempts to achieve excess returns, while passive index funds rely heavily on accurately tracking their benchmarks [4]. - The introduction of floating fee structures linked to performance benchmarks ensures that investors pay for value, with fund managers receiving higher compensation only when they outperform the benchmark [4]. Group 3: Analyzing Fund Selection - Investors should analyze benchmarks to determine if a fund aligns with their preferences, considering the composition of the benchmark and its implications for risk and return [5][6]. - The choice of benchmark is critical; funds using price indices rather than total return indices may mislead investors regarding their true performance and management effectiveness [6]. - Adjustments to performance benchmarks may occur as funds change their investment focus, necessitating investor awareness of such changes to ensure alignment with their investment goals [7].

Core Viewpoint - The article analyzes the performance and strategies of the "Guangfa Juxin" fund, highlighting its long-term success and the stable management by fund manager Zhang Qian since 2015, which allows for a comprehensive understanding of its investment approach [2][29]. Group 1: Fund Performance - "Guangfa Juxin" has been established for over twelve years and has achieved an annualized return of over 9%, making it a standout in the 10-year performance category [2]. - The fund has significantly outperformed representative "fixed income +" fund indices, with an annualized excess return exceeding 4% compared to the Wind Mixed Bond Secondary Index [10][7]. - The fund demonstrates resilience, quickly recovering from downturns and consistently generating excess returns [10]. Group 2: Investment Strategy - The fund employs a dual strategy of equity and bond investments, effectively utilizing a "stock-bond seesaw" approach to mitigate volatility [13]. - The bond investment strategy focuses on leveraging, duration management, and credit risk assessment, maintaining a leverage ratio around 120% for stability [18][20]. - The equity investment strategy emphasizes growth stocks, with a concentrated portfolio that avoids mainstream sectors, instead focusing on underappreciated industries like military and Hong Kong stocks [27][31]. Group 3: Risk Management - The fund manager exhibits a cautious approach to credit risk, having shifted away from low-rated bonds post-2020, demonstrating strong risk sensitivity [24][25]. - The duration of the bond portfolio is managed to remain within a safe range, avoiding excessive risk from interest rate fluctuations [20]. Group 4: Conclusion - The fund manager is characterized as a dynamic alpha hunter, adept at navigating both equity and bond markets, with a focus on growth-oriented strategies [30][31]. - The analysis concludes that the fund's success can be attributed to its balanced approach in managing systemic risks while capitalizing on market opportunities [32].

（2）在风险调整后的表现上，也就是投资圈常用的夏普比率（Sharpe ratio），有47个因子组合在波动率管理后提升了，其中15个 提升显著。 （3）这些超额收益主要集中在价值（value）、盈利能力（profitability）、交易摩擦（trading friction）三类因子上。换句话说，波 动率管理让这些经典的选股逻辑更"抗揍"。 举个形象的例子：原始的价值因子，就像是一辆"快车"，能跑得快，但一旦遇到急转弯就容易翻车；加上波动率管理，就好比给这 辆车装了防抱死制动系统（ABS），速度可能稍慢，但在急弯处不容易被甩出去，跑完全程的胜率反而更高。 如果你是个A股老股民，特别是如果经历过2015年的股市大跌，大概会记得那种"手抖"的感觉：早晨还在犹豫要不要加仓，午后就 被跌停板封死在里面；好不容易熬到反弹，却又在高点追进去，第二天再被套牢。当时短短三个月的时间里，沪深总市值急剧蒸发 了24.28万亿，这是中国股市发展史上最严重的一次股灾。 围绕"3000点魔咒"，这种"追涨杀跌"的故事，过去数年几乎每个中国股民都耳熟能详。问题在于：面对剧烈的市场波动，能不能有 一种机制，既能保住底线，又能抓住机会？ ...

Core Viewpoint - The article emphasizes the importance of excess return (Alpha) in quantitative investment, highlighting the need for thorough analysis and attribution of performance to understand the sources of excess returns and evaluate the effectiveness of quantitative strategies [2][3]. Group 1: Excess Return and Its Calculation - Excess return (Alpha) is defined as the return of an investment portfolio relative to a benchmark, reflecting the ability to outperform passive benchmarks through active management [3]. - The calculation of excess return varies based on the chosen strategy and benchmark, with a core formula being: Excess Return = Portfolio Return - Benchmark Return [3]. - An example illustrates that if a quantitative strategy has a return of 25% while the benchmark (e.g., CSI 300) returns 10%, the simple excess return is 15% [3]. Group 2: Sources of Excess Return - Excess return can be categorized into three components: Pure Alpha, Smart Beta, and Beta, each with different characteristics and risk profiles [3]. - The performance of excess return is influenced by external market factors and the comprehensive investment capabilities of the institution, which are critical for assessing a fund's sustainability of returns [3]. Group 3: Brinson Attribution Model - The Brinson attribution model is a widely used method for performance attribution, breaking down excess return into allocation effect, selection effect, and interaction effect [4]. - The model requires detailed portfolio holding data to accurately assess the contributions of asset allocation and stock selection to excess returns [4]. Group 4: Performance Attribution Example - An example using the Brinson model shows a fund outperforming the CSI 300 by 4.2%, with contributions from asset allocation and stock selection analyzed to determine the sources of excess return [9]. - The analysis reveals that stock selection contributes significantly to excess return, indicating a strong capability in identifying high-performing stocks [9]. Group 5: Barra Risk Model - The Barra risk model is utilized for post-performance analysis, helping to identify risk exposures and optimize investment strategies [10][11]. - The model decomposes risk into various factors, allowing for a detailed understanding of how different risk factors contribute to overall portfolio volatility [13]. Group 6: Risk Management and Optimization - The article discusses the importance of managing risk while maintaining return potential, with specific strategies for adjusting factor exposures to enhance performance [15][16]. - It highlights the need for continuous strategy iteration and adaptation to market conditions to mitigate risks associated with excess returns [17].

Group 1 - The A-share market has entered a bull market since September 24, 2024, with major indices significantly rising, such as the North Exchange 50 Index increasing by 158.01% [1] - The average daily trading volume in the market surged from less than 500 billion to over 2 trillion [1] - 13 mutual funds have seen a net value growth rate exceeding 200%, while 774 funds have surpassed 100% [1][2] Group 2 - The performance of equity mixed funds has rebounded, with the index rising by 57.88% since September 24, 2024 [2] - Notable funds include Debon Xinxing Value Mixed Fund, which achieved a net value growth of 280.31% [2] - The strong performance is attributed to the robust market rally and the significant returns from technology stocks [2] Group 3 - Key factors driving the market's rise include ongoing stock market reforms, improved policy expectations, and breakthroughs in various sectors such as innovative drugs and robotics [3] - The market's risk appetite has notably increased, with more retail investors entering the market since June [6][7] Group 4 - The A-share market has shown significant improvement in valuation, liquidity, and investor structure, with the overall valuation rising from 15.63 times to 22.16 times [6] - The market is expected to maintain a "slow bull" trend, supported by continuous policy backing and structural upgrades in industries [7] Group 5 - Investment opportunities are seen in sectors like AI, innovative drugs, and electric new energy, driven by supportive industrial policies and technological breakthroughs [8][9] - The focus on sectors such as AI computing, electric new energy, and innovative pharmaceuticals is expected to yield significant returns [9][10]

Core Viewpoint - The active equity funds have shown a remarkable recovery since September 24, 2023, with many products achieving significant returns, reflecting the resilience of the public fund industry [1][2]. Performance Recovery - Over 90% of active equity funds have recorded positive returns this year, with a batch of "doubling funds" emerging [2]. - As of September 18, 2024, 429 mixed equity funds and 112 ordinary stock funds have achieved over 100% returns since September 24, 2023, primarily driven by strong-performing leading public funds [2][5]. Sector Performance - The technology sector, represented by AI, and the pharmaceutical sector, represented by innovative drugs, have shown strong growth, with the CSI Artificial Intelligence Theme Index and the CSI Innovative Drug Industry Index rising by 134.77% and 62.88% respectively since September 24, 2023 [5]. - Many top-performing active equity funds have capitalized on opportunities in the pharmaceutical or technology sectors, with a significant portion of funds focusing on healthcare and technology growth [5]. Fund Manager Performance - A significant number of active equity fund managers have turned around their performance, with over 800 active equity funds reaching historical net asset value highs in the past month [5][6]. - Despite many active equity funds still in the process of recovering from previous losses, their short-term performance has significantly improved, contributing to long-term growth [5]. Comparison of Active and Passive Funds - The average performance of ordinary stock funds, mixed equity funds, and passive index funds has become closely aligned, with ordinary stock funds averaging 59.55%, mixed equity funds at 58.57%, and passive index funds at 60.21% from September 24, 2023, to September 18, 2024 [7][8]. - The challenge for active fund managers to outperform passive funds has intensified, with only 13.4% of active funds beating the average return of passive funds in 2024, a significant drop from the previous year [8]. Long-term Investment Perspective - While active equity funds are influenced by industry cycles, there are still long-term standout products in the market, indicating that active funds are not inherently inferior to passive index funds [9]. - Investors are encouraged to focus on long-term performance rather than short-term results when selecting funds that align with their investment needs [9].

Group 1 - The article emphasizes the importance of financial education in protecting financial rights and enhancing quality of life, particularly in the context of the fund industry taking action during the 2025 Financial Education Promotion Week [1] - It discusses the composition of fund returns, highlighting that fund returns consist of benchmark returns and excess returns (α), with a benchmark return of 10% and an excess return of 8% leading to a total fund return of 18% [3][4] - The article explains that known declines are not risks; rather, uncertainty (volatility) is what constitutes risk in investments [3] Group 2 - It introduces the concept of "Sharpe Ratio" as a key metric for evaluating the risk-return profile of funds, defined as (Fund Return - Risk-Free Return) / Volatility, indicating a higher investment "cost-performance" ratio with a higher Sharpe Ratio [5] - The article also presents the "Information Ratio" as a measure of a fund manager's ability to generate excess returns relative to the benchmark, calculated as Excess Return (α) / Tracking Error, with a higher Information Ratio indicating better sustainable enhancement effects [6][7] - Understanding these five key metrics helps investors comprehend what they are earning, where risks originate, and the investment cost-performance ratio, facilitating rational decision-making in investments [7]

Core Viewpoint - The article presents a metaphorical exploration of various investment strategies in the private equity space, likening them to legendary weapons, each with unique strengths and weaknesses, suitable for different market conditions and investor preferences [2][6]. Group 1: Investment Strategies - The first strategy, "Qinglong Yanyue Dao" (Subjective Long), relies heavily on the fund manager's ability to select stocks and time the market, performing well in bullish markets with clear themes [9][10][15]. - The second strategy, "Xuedizi" (Quantitative Long), utilizes complex algorithms to identify stocks based on specific metrics, excelling in active markets with high trading volumes [18][20][23]. - The third strategy, "Zhuge Lian" (Macro Hedging), involves top-down asset allocation across stocks, bonds, and commodities, generally effective in diverse market conditions but can fail during extreme events [26][30][31]. - The fourth strategy, "Fang Tian Hua Ji" (CTA Strategy), focuses on futures markets, capturing trends regardless of price direction, suitable for markets with significant price movements [33][35][39]. - The fifth strategy, "Taiji Shuang Jian" (Market Neutral), aims to generate absolute returns by hedging market risks, effective in bear and volatile markets but may underperform in bull markets [41][45][48]. - The sixth strategy, "Ruan Wei Jia" (Fixed Income +), combines high-quality bonds with a small allocation to riskier assets, providing stability but vulnerable to rising interest rates [50][53][56]. - The seventh strategy, "Xiu Hua Zhen" (Arbitrage), exploits price discrepancies across markets, generating small but cumulative profits, effective in volatile conditions but reliant on market efficiency [58][61][63]. Group 2: Strategy Suitability - Each strategy is designed for specific market conditions, with subjective long strategies thriving in bullish environments, while quantitative strategies excel in active trading scenarios [15][23]. - Macro hedging strategies are versatile but can falter during extreme market events, while CTA strategies benefit from significant price trends [31][39]. - Market neutral strategies provide a buffer against market downturns, whereas fixed income plus strategies are contingent on interest rate movements [48][56]. - Arbitrage strategies are most effective in volatile markets but depend on the quick correction of price discrepancies [63]. Group 3: Conclusion - The article concludes by encouraging investors to choose strategies that align with their risk preferences, highlighting the importance of understanding each strategy's unique attributes and market applicability [67][69].

Core Viewpoint - Market style rotation is a typical characteristic of A-shares, where no single style can consistently outperform the market. This rotation significantly impacts quantitative long strategies, influencing their excess returns directly [2][3]. Group 1: Impact of Style Rotation - Style rotation serves as a double-edged sword for quantitative long strategies, affecting performance and sustainability. When market style aligns with historical preferences of quantitative models (e.g., small-cap style), strategies can capture significant stock selection alpha, leading to outstanding performance [3]. - In the first half of 2023 and the small-cap market in 2024, many quantitative products achieved considerable returns. However, when market styles reverse sharply (e.g., collective pullback of small-cap stocks in early 2024), quantitative strategies face significant challenges, often resulting in noticeable drawdowns [3]. - Quantitative models rely on historical data to identify patterns. If a particular style (like small-cap) remains dominant, models will increase exposure to that style. A sudden style reversal can lead to the short-term failure of factors based on historical data, causing stock selection alpha to vanish or even turn negative [3]. Group 2: Performance Disparity Among Strategies - Style rotation exacerbates performance disparities among different quantitative products. Funds focusing on different tracks (e.g., 300 index enhancement vs. 1000 index enhancement) or employing varying style constraints or risk control capabilities will exhibit significant performance differences during style shifts [3]. - The average excess return of over 200 quantitative long strategy products under billion-yuan private equity was approximately -1.69%, with only 22.67% showing positive excess returns, indicating a high exposure to small-cap and growth styles [7]. Group 3: Market Conditions and Future Outlook - The market exhibited significant style switching from August to September 2025, driven by macroeconomic changes, capital flows, and policy expectations. The relative performance of broad-based indices reflects the rotation between large-cap and small-cap styles [7]. - The small-cap factor's return volatility has increased, and the average excess drawdown during rapid style transitions typically ranges from 1-4%, with the potential for a higher average excess drawdown of 8-9% in February 2024. However, subsequent recovery trends are generally smooth [11].