| 表1：科技50策略指数中选股因子的定义 | | | --- | --- | | 因子名称 | 细分指标 | | 成长 | 修正利润增长、修正营收增长、利润超预期增长、营收超预期增长 | | 研发创新 | 研发支出占比、研发支出增长 | | 一致预期 | 一致预期净利润上调、一致预期评级上调 | | 动量 | 过去11个月动量、过去11个月Alpha | | 价值 | 市净率、市盈率、市销率、企业价值 | | 低波 | 残差波动率、过去12个月每月最高日收益率平均 | | 盈利 | 毛利润率、主营业务利润率 | | 偿债能力 | 短期债务覆盖率、速动比率 | | 运营效率 | 净营运资产收益率、净营运资产周转率、净营运资产周转率增长 | | 财务稳健 | 应计盈余、净营运资产占比 | | 投资稳健 | 利润分配、股息分配 | 资料来源：Wind，申万宏源研究 计算待选样本的自由流通市值占比，作为基础得分，并将基础得分与上述所有因子倾斜得分相加得到待选样本的调整后得分；将待选样本按照调整后得分由高到低排名，选取排名前 50 的 证券作为指数样本。 科技50策略指数采用调整后得分加权，且单个样本权重不超过10% ...

2026 年 01 月 09 日 融合多因子策略的科技指数 ——科技 50 策略指数投资价值分析 相关研究 证券分析师 杨俊文 A0230522070001 yangjw@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 研究支持 杨俊文 A0230522070001 yangjw@swsresearch.com 联系人 杨俊文 A0230522070001 yangjw@swsresearch.com 本研究报告仅通过邮件提供给 中庚基金 使用。1 权 益 量 化 研 究 证 券 研 究 报 告 请务必仔细阅读正文之后的各项信息披露与声明 指 数 研 究 - ⚫ 中证科技优势成长 50 策略指数（931696.CSI，简称科技 50 策略）从科技行业上市公 司证券中，采用成长、创新、价值、低波、质量等因子进行选样和加权，旨在为投资者提 供基于科技行业的多因子策略投资标的。 ⚫ 截取 2025/12/31 科技 50 策略指数的前十大成分股信息，可以看到，权重股基本上都 是科技各个细分领域的头部公司，比如通信设备行业的中际旭创、新易盛，半导体行业 ...

2025年马上就要过去了~~ 今年，从年初到年底，有一个词语一直伴随着大家，成为大家茶前饭后的谈资，这个词语，就是AI。 AI的爆发式发展，不仅带火了指数基金，更是让一类产品火出了圈，它就是主动量化基金。 最主要的原因在于：主动量化基金依靠着人机结合，做出了1+1>2的效果，尤其是在今年这种行业轮动+个股分化的条件下，这类产品不仅能穿越市场和 周期还能在取得超额收益的同时降低波动风险。 这也是为什么，机构目前正在大手笔布局主动量化基金。Wind数据显示，截至到三季度末，全市场292只主动量化基金（份额合并计算）合计份额为805 亿份，较去年年末增长了27%（170亿份），二季度单季就增长了144亿份。 而且，根据今年中报披露的数据，所有主动量化基金份额是661亿份，其中机构投资者的份额是465亿份，占比超过70%。 老虎君为了让大家更直观地看到，机构投资者更喜欢哪些主动量化基金，特意统计了一下截至今年中报，机构投资者持有份额前十名的主动量化基金（份 额合并计算，只展示A类份额），这其中有几个基金我认为十分值得拿出来跟大家说一说。 | 证券代码 | 证券简称 | 基金规模 | 机构投资者持有份额 | 基金经理 ...

Group 1 - The report defines "Quantitative Fixed Income+" as a type of fund that focuses on fixed income assets as the core, using quantitative models to enhance asset allocation in equities and convertible bonds while controlling volatility and maximum drawdown to pursue "fixed income + excess returns" [7][8] - Key features of Quantitative Fixed Income+ include a stable core of pure bond assets, quantitative-driven strategies for stock selection, and various operational methods such as collaboration between fixed income and quantitative fund managers [8][10] Group 2 - Common strategies in Quantitative Fixed Income+ include focusing on single clear return-driving factors, using broad-based indices as benchmarks, and diversifying across multiple independent risk factors to achieve more stable excess returns [10][12] - The report highlights the performance of the "Dividend Low Volatility" strategy, which aims to invest in companies with stable cash flows and low stock price volatility to achieve favorable risk-adjusted returns over the long term [13][22] Group 3 - The report provides a comparative analysis of the performance of various indices, showing that the "Dividend Low Volatility Index" outperformed both the "CSI 300" and "CSI Dividend" indices over the past 20 years [15][22] - The report details the characteristics of representative funds employing the Dividend Low Volatility strategy, including their investment types, benchmarks, and total assets under management [25][26] Group 4 - The report discusses the asset allocation strategies of the funds, emphasizing the importance of adjusting positions based on market trends and maintaining a balance between equities and bonds to achieve stable long-term growth [27][49] - It also highlights the management of duration in bond investments, indicating that the funds adjust duration based on market conditions to optimize returns [49][104] Group 5 - The report outlines the performance of funds using the Index Enhancement strategy, which aims to increase equity returns through quantitative models that optimize asset allocation based on various factors [73][85] - It emphasizes the importance of flexible asset allocation and proactive position adjustments in response to market conditions to enhance overall fund performance [86][127] Group 6 - The report describes the Multi-Factor strategy as a core approach for equity asset selection, utilizing a multi-dimensional factor model to identify high-quality stocks and optimize overall portfolio performance [129][183] - It highlights the importance of dynamic adjustment of industry weightings based on factor performance, allowing for a diversified approach to asset allocation [167][182]

Group 1 - The report focuses on factor allocation research based on ETFs in the A-share market, providing effective strategies for investors to utilize ETFs for style allocation [2][4] - Style factors significantly influence the returns of A-share strategies, with notable style trends observed over the past decade, such as small-cap value and large-cap growth, leading to substantial excess returns when aligned with main style trends [2][10] - There are currently 107 factor strategy ETFs in China, with a total net asset value of approximately 127.06 billion, representing about 4.09% of the total net asset value of equity ETFs, but these products face challenges in style coverage and liquidity [2][14][17] Group 2 - The report proposes a stock-based ETF factor allocation scheme starting from holding styles, exemplified by the construction of a dividend low-volatility ETF combination that aligns closely with the CSI Dividend Low Volatility Total Return Index [2][26] - The use of ETF style scoring for factor allocation offers significant advantages, allowing for broader coverage of style factors and providing more liquid solutions when the scale of related factor strategy ETFs is small [2][36] - A multi-factor strategy is constructed based on the "anti-involution" policy, focusing on high-quality growth and high-margin safety combinations, with backtesting showing strong performance for both strategies [2][38][51] Group 3 - The report highlights the importance of using a comprehensive ETF selection process to address the limitations of existing factor strategy ETFs, particularly in terms of style coverage and liquidity [2][18][36] - The methodology for constructing the dividend low-volatility ETF combination involves detailed indicator breakdowns and ETF product sorting based on style characteristics [2][26][30] - The performance analysis of the constructed multi-factor strategies indicates a strong correlation with benchmark indices, showcasing the effectiveness of the proposed ETF combinations [2][32][51]

Core Viewpoint - The article emphasizes the importance of scientific thinking and critical analysis in quantitative investment, highlighting the influence of philosopher Karl Popper on investment strategies and the development of models that seek to identify and exploit market inefficiencies. Group 1: Investment Philosophy - The essence of quantitative investment lies in modeling the securities market using scientific methods to identify reproducible patterns that can influence market behavior [16][6] - The investment approach is heavily influenced by Popper's philosophy of "conjecture and refutation," which encourages the search for rules in an uncertain world [7][56] - The focus on objective analysis helps avoid the pitfalls of linear thinking and cognitive biases that can obscure judgment [2][61] Group 2: Performance Metrics - The performance of the multi-strategy fund, specifically the Nuon Multi-Strategy Mixed Fund, achieved a return of 100.74% over the past year, while the Nuon CSI 300 Index Enhanced Fund outperformed the CSI 300 Index by 2.06% with a return of 15.42% [3][29] - The significant outperformance of the Nuon Multi-Strategy Fund compared to small-cap indices like the CSI 2000 indicates that the excess returns are not merely a result of small-cap exposure but rather from sophisticated modeling techniques [3][34] Group 3: Investment Strategies - The concept of "attention value" in the A-share market suggests that investors frequently shift their focus due to the inability of many companies to meet return expectations, which can be strategically exploited for excess returns in micro-cap stocks [26][4] - The investment strategy emphasizes the importance of understanding the underlying statistical patterns and market behaviors rather than relying solely on historical performance [20][22] Group 4: Machine Learning and Model Development - The transition from multi-factor strategies to machine learning models allows for the capture of non-linear patterns, leading to superior returns that exceed human cognitive limitations [3][30] - The use of machine learning in investment models is seen as a way to enhance predictive capabilities and adapt to rapidly changing market conditions [30][40] Group 5: Market Dynamics and Future Outlook - The article argues that the excess returns from micro-cap stocks in the Chinese market are unlikely to converge due to the unique market dynamics and investor behavior [34][35] - The focus on scientific and systematic approaches in investment is expected to reveal opportunities that are not crowded, as many competitors rely on outdated inductive reasoning [45][46]

Report Industry Investment Rating No relevant content provided. Core Viewpoints of the Report - The Industrial and Commercial Bank of China (ICBC) Credit Suisse Active Quantitative Team has an adequate number of personnel and diverse research directions, with the "ARC" investment navigation system at its core, enabling it to actively seize certain investment opportunities [1][8][14]. - The team's investment framework features a forward - looking perspective and diverse strategies, including multi - factor and SmartBeta strategies, which are characterized by "forward - looking perspectives" and "diverse methods" [1][19]. - The team manages a wide range of products across different quantitative tracks, each with distinct features, aiming to provide investors with specialized solutions and generate excess returns in different tracks [38]. Summary According to the Table of Contents 1. ICBC Active Quantitative Team - Forward - looking Perspective + Diverse Coverage, Actively Seize Certain Investment Opportunities 1.1 Team Overview: Adequate Personnel, Diverse Research Directions, Centered on the "ARC" Concept - The ICBC Credit Suisse Fund Index and Quantitative Investment Department has 15 research and investment personnel, including 8 investment and 7 research staff, led by Mr. Jiao Wenlong. The team members are clearly divided in their responsibilities, covering multiple areas in passive and active quantitative fields [8]. - The core members of the team, such as Jiao Wenlong, He Shun, Zhang Letao, and Liu Zihao, have rich experience in securities and investment management, with different research focuses [9]. - The team's investment philosophy is based on the "ARC" investment navigation system, where A stands for Active, R for Reversion, and C for Certainty, which can maximize its effectiveness given the sufficient personnel and diverse research directions [14]. 1.2 Active Quantitative Investment Framework: Forward - looking Perspective, Diverse Strategy Methods - The team's fund managers adopt various investment methods, including multi - factor and SmartBeta strategies, with "forward - looking perspectives" and "diverse methods" as prominent features [19]. - In the multi - factor investment framework, factors are constructed using both manual and algorithmic mining methods, which are then combined to enhance efficiency. A domain - learning model system is also used to improve factor combination efficiency [20][23][25]. - In SmartBeta product investment, there are four decision - making steps: strategy definition, multi - factor stock selection, fundamental confirmation, and deep - learning - assisted trading [30]. 1.3 Active Quantitative Product Line: Comprehensive Categories, Diverse Product Types - The team manages 11 active products across multiple quantitative tracks, such as SmartBeta enhancement, fixed - income plus, broad - based index enhancement, and long - short strategies, each with distinct features [38]. - The diverse product positioning can meet different investment needs of investors and generate excess returns in different tracks by integrating various quantitative strategies [40]. 2. Analysis of Investment Characteristics of Representative Products of ICBC Active Quantitative 2.1 ICBC Juxiang: Quantitative Strategy Fixed - income Plus Product - Since February 2024, ICBC Juxiang has significantly outperformed its performance benchmark, achieving a return of over 23.4% from 2024 to May 31, 2025 [43]. - The product is positioned as a high - position fixed - income plus product, mainly investing in small - cap stocks in the equity segment, with a moderate turnover rate and low concentration [45][50]. - The product's industry allocation has remained stable and diversified since H2 2023, with no significant industry rotation [54]. 2.2 ICBC Credit Suisse CSI 1000 Index Enhancement: Trading Turnover Contributes Significant Excess Returns - Since He Shun took over the product on May 15, 2024, it has achieved significant excess returns over the CSI 1000 index, with an excess return of over 12.6% as of May 31, 2025 [56]. - The product's excess returns mainly come from stock turnover, with a high turnover rate of over 8 times in H2 2024. It moderately invests in micro - cap stocks [60][63]. - The product has moderate industry deviations and makes small adjustments in industry allocation between periods, with relatively mild style factor exposures that also have small adjustments [67][68]. 2.3 ICBC New Value: Quality Dividend SmartBeta Enhancement - Since 2024, ICBC New Value has outperformed its performance benchmark, with strong performance stability [72]. - The product adopts a low - turnover and moderately diversified investment style, with a preference for large - cap stocks and moderate industry adjustments [73][76]. - The product's excess returns mainly come from stock selection, with diverse sources of absolute returns and strong relative return - capturing ability in the cycle and advanced manufacturing sectors [81][85].

Core Insights - The report introduces a quarterly rebalancing industry rotation strategy based on traditional quantitative multi-factor scoring, focusing on "valuation," "quality," "liquidity," and "momentum" [1][11] - The composite strategy achieved an annualized return of 19.64% during the backtesting period (April 1, 2014 - June 6, 2025), significantly outperforming the industry equal-weight benchmark which returned 7.55%, resulting in an annualized excess return of 12.09% [1][68] - The strategy prioritizes low valuation, low crowding, improving economic conditions, upward price momentum over the past year, and industries that have been at low price levels for the past three years [1][11] Industry Factor Backtesting Framework - The backtesting period spans from January 2010 to September 2024, with a quarterly rebalancing approach using data from the last trading day of each quarter [12] - The strategy excludes industries with a weight of less than 2% in the CSI 800 index for risk control, retaining approximately 15-16 major industries for rotation calculations [12][3] Industry Rotation Strategy Overview Valuation Factors - Valuation factors include PE_TTM, PB_LF, PCF_TTM, PEG, and dividend yield, evaluated through various methods such as historical percentiles and marginal changes [15] - Notable factors include: - Dividend yield ranking over three years (4.0% annualized excess for TOP-5) [16] - PE_TTM marginal change over two months (5.8% annualized excess for TOP-5) [16] Quality Factors - Quality factors are based on ROE and ROA, focusing on profitability and financial stability [19] - Key factors include: - ROA_TTM marginal change over one quarter (4.3% annualized excess for TOP-5) [20] - ROE_FY2 (4.7% annualized excess for TOP-5) [20] Liquidity Factors - Liquidity factors are derived from turnover rates of freely circulating shares, assessed through various time frames [21] - Effective factors include: - 21-day average turnover rate (4.3% annualized excess for TOP-5) [22] - Margin of turnover rates over two months (4.6% annualized excess for TOP-5) [22] Momentum Factors - Momentum factors are calculated based on recent returns over different periods, showing varying characteristics [24] - Significant factors include: - One-month momentum (7.7% annualized excess for TOP-5) [26] - Three-month momentum (1.9% annualized excess for TOP-5) [26] Factor Combination - The report explores both z-score and rank equal-weight combinations of selected factors to enhance model performance [27] - The top-performing combinations include: - z-score combination with PE_TTM marginal change, ROE marginal change, and one-year momentum [32] - rank combination with PE_TTM three-year ranking, ROE marginal change, and 21-day momentum [37] Recommended Factors - The report recommends specific factors for the composite strategy: - Momentum: 252_momentum (one-year) and 756_momentum (three-year) [68] - Liquidity: TURNOVER_FREE_m (21-day average) and TURNOVER_FREE_Q_margin (quarterly margin) [68] - Valuation: 股息率_3Y_rank (three-year dividend yield ranking) and PB_LF_d2m (two-month marginal change) [68] - Quality: ROE_TTM_d1q (one-quarter marginal change) and ROE_FY2 (next year's expected ROE) [68]