Core Viewpoint - The article emphasizes the importance of creating a fund manager ranking list that not only reflects past performance but also provides guidance for future investment opportunities, aiming to identify managers who can outperform the market [1][11]. Group 1: Purpose of the Ranking List - The increasing number of funds (over 20,000) has made it difficult for investors to select the right ones, and many existing rankings do not provide future performance guidance [2][3]. - Many funds have managers whose performance may not be sustainable due to factors like increased fund size, managerial distractions, or even departures [3][7]. - The article argues that simply selecting past winners does not guarantee future success, and a more systematic approach is needed to identify potential outperformers [9][11]. Group 2: Methodology for Identifying Future Alpha - The methodology consists of four steps: scientific classification, establishing a fund pool, quantitative validation, and qualitative assessment [12][13]. - The first step involves categorizing funds into distinct styles to allow for meaningful comparisons, using a proprietary classification system that includes eight styles and four industries [14][15]. - The second step involves populating these categories with researched fund managers, ensuring a comprehensive pool for analysis [19][20]. - The third step focuses on quantitative metrics to filter out underperformers, while the fourth step involves qualitative assessments to finalize the rankings [23][24]. Group 3: Performance Verification - The performance of the funds listed in the rankings is evaluated against market indices, with the rankings showing a consistent ability to outperform benchmarks over time [26][28]. - From 2022 to 2024, the funds in the equity ranking outperformed the Wind Mixed Equity Index by 9.31% cumulatively [28]. - The rankings also demonstrated that selected funds maintained their performance over multiple years, indicating their sustainability [31]. Group 4: Recognition of Potential Managers - The rankings have successfully identified lesser-known but capable fund managers, leading to significant growth in their fund sizes post-inclusion in the rankings [35][38]. - For instance, a manager from Huazhong Fund saw a size increase of 56.9 billion after being included in the rankings, highlighting the recognition and validation from the market [36][39]. Group 5: Continuous Recognition - The rankings have received consistent support from investors and industry peers over the past five years, reinforcing the value of the lists [42]. - A list of fund managers who have been recognized for five consecutive years is provided, showcasing their sustained performance [44].

证券研究报告 关注自由现金流指数 ——3月基金配置展望 证券分析师 郭子睿 投资咨询资格编号：S1060520070003 任书康 投资咨询资格编号：S1060525050001 陈 瑶 投资咨询资格编号：S1060524120003 高 越 一般从业资格编号：S1060124070014 胡心怡 一般从业资格编号：S1060124030069 研究助理 2026年3月3日 请务必阅读正文后免责条款 基金配置建议：关注自由现金流指数 2 2月回顾：A股、美股市场涨跌分化；美债、国债利率下行；商品价格先下后上，原油价格上涨，黄金上涨；美元指数上行，人民币升值。海外 政策和地缘环境扰动、AI替代恐慌等因素共同作用下，权益市场涨跌分化。全球避险情绪升温，美债、国债利率下行。 3月展望：资产配置的逻辑。股债轮动模型显示，2026年1月私人部门融资增速、通胀因子下行，基本面复苏信号信号有所波动。2月市场动量因 子收正，综合基本面与动量信号的择时模型继续看多权益资产。本月基于7个情绪指标构建的A股情绪指数显示，看多未来一个月权益市场表现 的情绪指标有所回落，市场情绪指数跌出乐观区间。基于宏观综合指标的港股择时策略显示看多 ...

在确定性中寻找超额收益。 作者 | 破浪 编辑 | 小白 天相投顾统计显示，截至2024年底，被动指数基金规模首次超越主动权益类基金；进入2025年，以E TF为代表的指数化投资工具迎来爆发式增长，进一步拉大了两者之间的规模差距。 在这一趋势下，部分基民的投资逻辑正在悄然生变：一方面，他们对部分主动型基金"风格漂移"、业 绩变脸等现象感到厌倦；另一方面又不甘心仅仅获取指数的平均收益。 于是，一种折中且更具性价比的投资品种——指数增强基金，顺势成为各路资金的新宠。 那么，去年四季度，资金重点涌向了哪些指增基金？作为普通投资者，这一轮大规模增持又是否具 备"抄作业"的参考价值？ 二是α收益的可能性，由于A股市场并非完全有效，机构投资者可以通过选股、打新增强、基差套利 等手段，在长期内积攒出超过指数的回报。 当然，增强基金也并非稳赚不赔。如果增强策略失效，或者基金经理在风格上过于偏移，就会演变 成"负增强"。 因此，看待这类基金，不仅要看规模，更要看它在关键阶段的真实表现。 简单来说，指数增强基金就像是一个"混血儿"：既保留了被动指数基金的基因（大部分仓位跟踪指 数，获取β收益），又融入了主动基金的灵魂（利用量化模 ...

Group 1: Fund Market Tracking - As of February 28, 2026, there are 13,817 existing funds in the market, an increase of 95 from the previous month, with total net asset value reaching 37.23 trillion yuan, up by 9.7 billion yuan [3][8] - In February 2026, the returns of value, balanced, and growth fund indices were 1.00%, 1.40%, and 0.72% respectively, indicating a performance difference among various fund styles [3][13] - The proportion of mixed, bond, and stock funds is the highest in terms of quantity, while money market, bond, and stock funds dominate in terms of asset scale [8][10] Group 2: ETF Market Tracking - As of February 28, 2026, there are 1,446 ETFs in the Shanghai and Shenzhen markets, an increase of 16 from the previous period, with total assets under management at 5.39 trillion yuan, a decrease of 73.79 billion yuan [4][18] - The median return for stock ETFs in February was 0.70%, while cross-border ETFs had the lowest median return at -3.30% [4][23] - The internal deviation of cross-border ETFs was the highest at 3.18%, while bond ETFs had the lowest internal deviation at 0.11% [4][23] Group 3: ETF Strategy Tracking - The industry ETF rotation strategy based on main capital focused on steel, coal, and non-ferrous metals in February 2026, achieving a cumulative return of 6.17%, significantly outperforming the CSI 300 index, which had a return of 0.09% [5][40] - The PB-ROE framework strategy focused on non-ferrous metals, transportation, and utilities in February 2026, with a cumulative return of 4.25%, also outperforming the CSI 300 index [5][47] - Since the beginning of 2023, the main capital industry ETF rotation strategy has achieved a cumulative return of 71.82%, compared to 21.67% for the CSI 300 index, resulting in an excess return of 50.15% [5][43]

金融工程 证 券 研 究 报 告 【金工周报】（20260224-20260227） 短期模型以中性为主，后市或维持中性震荡 本周回顾 本周市场普遍上涨，上证指数单周上涨 1.98%，创业板指单周上涨 1.05%。 A 股模型： 短期：成交量模型中性。特征龙虎榜机构模型中性。特征成交量模型看空。智 能算法沪深 300 模型中性，智能算法中证 500 模型中性。 中期：涨跌停模型中性。上下行收益差模型绝大部分宽基指数看多。月历效应 模型中性。 长期：长期动量模型中性。 综合：A 股综合兵器 V3 模型看空。A 股综合国证 2000 模型看空。 港股模型： 中期：成交额倒波幅模型看空。上下行收益差模型中性，上下行收益差相似模 型看多。 本周行业指数普遍上涨，涨幅前五的行业为：钢铁、有色金属、基础化工、煤 炭、电力及公用事业，跌幅前五的行业为：传媒、消费者服务、食品饮料、非 银行金融、银行。从资金流向角度来说，有色金属、石油石化、银行、农林牧 渔主力资金净流入居前，传媒、电力设备及新能源、计算机、机械、基础化工 主力资金净流出居前。 本周股票型基金总仓位为 95.75%，相较于上周增加了 24 个 bps，混合型基 ...

同时，大量资金借道基金抢筹布局油气板块。Wind数据显示，开年以来超80亿元资金净流入油气ETF， 国泰石油ETF、鹏华石油ETF分别获得29.91亿元、15.42亿元净流入。 招商证券认为，抛开短期地缘催化，市场对全球能源需求的持续增长与产能瓶颈存在的明显认知差是油 服行业更为关键的长期成长逻辑。东莞证券进一步分析表示，2025年原油市场因供给阶段性偏松呈累库 状态，2026年随OPEC+强化减产、供给端显著缩量，叠加需求持续复苏，市场进入去库阶段，供需格 局从宽松转向紧平衡。原油需求复苏的持续性与库存去化的确定性形成双重基本面支撑，进一步夯实国 际油价中期偏强运行的基础，为油气企业资本开支落地、油服设备需求释放筑牢核心逻辑。（闻辉） 【环球网财经综合报道】开年以来，受全球宏观周期转向、地缘风险溢价等因素影响，油气板块持续走 强，成为市场关注焦点。年初至今，通源石油涨超170%，洲际油气、招商轮船等多只个股涨幅超 50%，中证油气资源指数整体上涨超33%。 2月27日，油气板块再度上扬，和顺石油涨停，杰瑞股份、胜通能源等个股涨幅超5%。从更长时间维度 看，全球油气板块集体走高，ICE布油价格从去年末的58. ...

Group 1 - Over 30 funds have entered the liquidation process since 2026, primarily due to insufficient scale, with many initiated funds triggering termination clauses after failing to reach a net asset value of 200 million RMB within three years [1][2] - As of February 25, 2026, 31 funds have completed liquidation, with most facing scale challenges that led to the activation of termination clauses [2][3] - Mixed funds account for the majority of liquidated funds, totaling 15, followed by 9 bond funds, 2 equity funds, and 5 fund of funds (FOF) [3] Group 2 - Initiated funds face a "three-year test," where if their scale is below 200 million RMB after three years, they automatically enter liquidation without the need for a shareholder meeting [4][5] - Among the 31 liquidated funds, 24 are initiated funds, representing over 70% of the total, including several pension-targeted FOF products [4] - As of February 25, 2026, there are 4,560 initiated funds in the market, with 2,260 (nearly half) having a scale below 200 million RMB [4] Group 3 - The A-share market has seen increased style rotation in 2026, with significant disparities between industries; some sectors have risen over 30%, while others, like banking, have declined nearly 7% [6][7] - Certain thematic funds, such as those focused on automotive and dividends, are struggling, with some triggering liquidation clauses due to low asset values [6][7] - The market sentiment for 2026 is expected to remain positive, with a potential for more diverse opportunities, despite the pronounced differentiation among sectors [7]

Market Overview - The A-share market showed mixed performance with the Shanghai Composite Index rising by 0.39%, while the Shenzhen Component Index fell by 0.06%, and the ChiNext Index decreased by 1.04% [1] - The total trading volume in the Shanghai, Shenzhen, and Beijing markets reached 2.51 trillion yuan, slightly down from the previous day [1] Sector Performance - The rare metals and rare earth sectors led the gains, with several ETFs in these categories rising over 4%, including the Rare Metals ETF from ICBC, which increased by 4.96% [2] - Other notable performers included the Rare Metals ETFs from Jiashi and Guangfa, both up by 4.68%, and the Rare Metals ETF Fund from Huafu, which rose by 4.55% [2] - The non-ferrous metals sector also saw significant increases, with the Non-Ferrous Metals ETF from Tianhong up by 4.28% and the Non-Ferrous ETF from Huashan rising by 4.17% [3] Declining Sectors - The semiconductor equipment sector experienced a notable pullback, with several ETFs in this category showing declines, including the Semiconductor Equipment ETF from Guotai, which fell by 2.16% [4] - The small-cap growth style, particularly in the ChiNext and National 2000 indices, faced pressure as funds rotated out of previously popular themes into cyclical resource sectors for safety [4] Trading Activity - The Short-term Bond ETF from Haifutong led trading activity with a turnover of 576.06 billion yuan, followed by the National Debt ETF from Huaxia at 168.25 billion yuan and the Sci-Tech Bond ETF from Nanfang at 137.24 billion yuan [5] - The turnover rates were high for bond ETFs, with the Sci-Tech Bond ETF from Nanfang achieving a turnover rate of 156.87%, indicating strong liquidity in this segment [6] ETF Issuance - Six new ETFs are set to launch, focusing on Hong Kong Stock Connect and Sci-Tech sectors, with fundraising starting on March 2, 2026 [8] - The Cash Flow ETF from Great Wall will track the National Index of Free Cash Flow, while the Engineering Machinery ETF from Penghua will follow the China Securities Engineering Machinery Theme Index [9] - Cross-border products include the Internet ETFs from ICBC and Xingye, which will track the China Securities Hong Kong Stock Connect Internet Index [9][10]

流动性方面，科创100ETF鹏华盘中换手4.05%，成交4.31亿元。拉长时间看，截至2月26日，科创 100ETF鹏华近1周日均成交6.03亿元，昨日单日净流入达4.44亿元。 科创100ETF鹏华紧密跟踪上证科创板100指数，上证科创板100指数从上海证券交易所科创板中选取市 值中等且流动性较好的100只证券作为样本。上证科创板100指数与上证科创板50成份指数共同构成上证 科创板规模指数系列，反映科创板市场不同市值规模上市公司证券的整体表现。 数据显示，截至2026年1月30日，上证科创板100指数(000698)前十大权重股分别为华虹公司、源杰科 技、东芯股份、睿创微纳、中科飞测、国盾量子、芯源微、百济神州、安集科技、复旦微电，前十大权 重股合计占比27.42%。 科创100ETF鹏华(588220)，场外联接(A：019861；C：019862；I：022845)。 消息面上，全球算力需求高涨，单纯依赖地面数据中心逐步显现瓶颈，太空算力已成为卫星新的应用场 景。据Starcloud白皮书，在太空部署40MW数据中心（10年运行）总成本约820万美元，相比地面数据 中心的1.67亿美元，降低95%。S ...

Group 1 - The core highlight of the 2026 Spring Festival Gala is the integration of AI and humanoid robots, showcasing the fusion of technology and art, which draws national attention to the development of new productive forces [1] - Penghua Fund has established a comprehensive ETF matrix for the sci-tech sector, with 13 sci-tech themed ETFs totaling over 36 billion yuan, covering broad-based, industry, thematic, and bond types, making it a preferred tool for investors [1] - The AI-focused ETFs from Penghua Fund have shown significant performance, with the Penghua Sci-Tech AI ETF (589090) tracking the AI index rising by 35.52% in the past year, while the Penghua Sci-Tech Entrepreneurship AI ETF (588410) surged by 87.23% [2] Group 2 - Institutions have reached a consensus on the value of allocating to technology assets, with strong demand for computing power driven by advancements in AI models, indicating a shift towards monetization in the AI industry [3] - The market is expected to maintain a volatile upward trend post-holiday, with cyclical price increases and the expansion of AI themes as the main market drivers [3] - Penghua Fund continues to focus on the sci-tech investment ecosystem, offering standardized, low-cost ETF products that help investors capture sector benefits while mitigating individual stock selection risks [3]