兴业基金多元业务部、指数与量化业务部总经理助理徐成城认为，2026年权益市场的投资主线依然将围 绕科技展开，后续科技风格的演绎更多体现在产业成长趋势与业绩实际兑现的相互印证，业绩的确定性 有望成为2026年科技主题投资的核心线索。 本报讯(记者昌校宇)2月27日，兴业基金旗下兴业中证科技优势成长50策略ETF正式在上海证券交易所上 市交易。 从产品设计来看，兴业中证科技优势成长50策略ETF可以助力投资者跳出传统的特定行业选择逻辑，在 A股中直接配置计算机、半导体、电子、通信设备、数字媒体、生物科技、航空航天与国防等"硬科 技"含量较高的行业；还能在上述行业中通过复杂精细的指数量化手段，为投资者遴选出兼具成长性和 一定安全边际的优质资产。 兴业中证科技优势成长50策略ETF较为契合当下科技投资主线，其跟踪的中证科技优势成长50策略指数 在A股科技板块中采用成长、创新、质量等因子进行量化选股和加权，并创新性地引入了分析师一致预 期因子，来增强对上市公司未来营收、业绩变化的预测能力，为投资者提供了多因子策略的投资工具。 ...

Report Industry Investment Rating - Not provided in the document Core Viewpoints - The report recommends mid - term unilateral long positions in cotton and PX futures. For cotton, on January 30th, it triggered the RCMS model's negative - correlation long - opening signal, and the technical analysis shows potential upward movement. For PX, it also triggered the same signal on January 30th, with favorable technical indicators [4][5][6][7] Summary by Directory 01 Multi - factor Strategy Tracking - **Strategy Introduction**: It is a multi - factor strategy constructed based on multi - dimensional correlations. It uses the Pearson correlation coefficient formula to calculate the correlation coefficients and volatilities between the net positions of multiple - dimension seats and the prices of varieties, and builds a multi - factor strategy model. The average investment cycle for a single variety is medium - to long - term, with most varieties having a holding period of 1 to 12 months. The investment scope covers all futures market varieties [14] - **Open Positions - Cotton**: Multiple key seats and the top ten and top twenty total net positions show a high negative correlation with the price trend. According to the RCMS multi - factor model's opening signal, a long - opening trading strategy is implemented. Technically, the monthly K - line chart of cotton weighted has continuously broken through and stood above the 5 - and 13 - period SMMA moving averages, and the current price is at a medium - low historical level with greater upward potential [15][19][21] - **Open Positions - PX**: Key seat A and the top ten and top twenty total net positions show a high negative correlation with the price trend. According to the RCMS multi - factor model's opening signal, a long - opening trading strategy is implemented. Technically, the weekly K - line chart of PX weighted has continuously broken through and stood above the 5, 13, and 34 - period SMMA moving averages, and the current trend is in a continued oscillating uptrend of the moving - average long arrangement [22][26][28] - **Strategy Performance**: Since the strategy is a new simulated account established on the last trading day of January, there is no data on net value, return rate, volatility, maximum drawdown, and Sharpe ratio yet. The current strategy holding position is 10% [29] 02 Main Varieties and Seat Correlations - **Financial Sector - Stock Index**: It shows the net position changes and closing prices of IC top ten total, IM seat A, IF seat A, and IC seat A over time [32][33][35][37] - **Financial Sector - Precious Metals**: It shows the net position changes and closing prices of Shanghai Silver seat A and PT seat A over time [41][42] - **Non - ferrous Metals Sector - Shanghai Copper**: It shows the net position changes and closing prices of Shanghai Copper's top twenty total, top ten total, top five total, and seat A over time [44][45][46][48] - **Non - ferrous Metals Sector - Shanghai Aluminum**: It shows the net position changes and closing prices of Shanghai Aluminum's top twenty total, seat A, seat B, and seat C over time [52][53][54][58] - **Non - ferrous Metals Sector - Lithium Carbonate**: It shows the net position changes and closing prices of Lithium Carbonate's seat A, seat B, and seat C over time [61][62][63][64] - **Non - ferrous Metals Sector - Shanghai Nickel**: It shows the net position changes and closing prices of Shanghai Nickel's seat A, seat B, seat C, and seat D over time [66][67][69][70] - **Non - ferrous Metals Sector - Shanghai Tin**: It shows the net position changes and closing prices of Shanghai Tin's seat A, seat B, and seat C over time [74][75][76][77] - **Non - ferrous Metals Sector - Shanghai Zinc and Shanghai Lead**: It shows the net position changes and closing prices of Shanghai Zinc's seat A and seat B, and Shanghai Lead's seat A over time [79][80][83] - **Black Metals Sector - Iron Ore, Stainless Steel, and Manganese Silicon**: It shows the net position changes and closing prices of Iron Ore seat A, Stainless Steel seat A, and Manganese Silicon seat A over time [87][88][90][91] - **Energy and Chemicals Sector - Methanol**: It shows the net position changes and closing prices of Methanol's top five total, seat A, and top ten total over time [93][94][96][99] - **Agricultural Products - Corn, Starch, Live Pigs, and Sugar**: It shows the net position changes and closing prices of Corn's top five total, Starch's top twenty total, Live Pigs seat A, and Sugar seat A over time [100][101][104][105] - **Agricultural Products - Rapeseed Oil, Rapeseed Meal, and Soybean No.1**: It shows the net position changes and closing prices of Rapeseed Oil seat A and seat B, Rapeseed Meal's top twenty total, and Soybean No.1 seat A over time [106][107][108][112]

Group 1 - The core viewpoint of the article is to introduce the CSI Technology Advantage Growth 50 Strategy Index, which aims to provide investors with multi-factor strategy investment targets based on the technology sector [1] - The index selects stocks from the technology industry based on factors such as growth, innovation, value, low volatility, and quality, focusing on companies with high trading volumes [1] - The index is rebalanced quarterly, with a maximum weight of 10% and a minimum of 0.1% for individual stocks [2] Group 2 - The top ten constituent stocks of the index include leading companies in various technology sectors, with a combined weight of 30.93% for the top ten stocks [2][3] - The index is heavily weighted towards large-cap stocks, with 31 constituents having a market capitalization exceeding 100 billion yuan [3][4] - The index shows a significant focus on the electronics industry, while also maintaining weights in telecommunications, computing, and pharmaceuticals [3] Group 3 - The article presents the performance of various factors within the sample space, indicating that growth, consensus expectations, and low volatility factors exhibit strong stock selection effects [20] - The analysis includes a comparison of the Technology 50 Strategy Index with other technology and innovation indices, highlighting its historical performance and annual returns [20][22] - The Technology 50 Strategy Index has a cumulative return of 97.26% over the backtesting period, ranking third among six technology indices [23] Group 4 - The expected revenue growth for the Technology 50 Strategy Index in 2026 is projected at 33.65%, outperforming other technology indices [25][29] - The index employs 11 factors for stock selection, providing a comprehensive evaluation compared to other indices that focus on fewer factors [31][32] - The article emphasizes the importance of analyst consensus expectations as a forward-looking indicator for company performance [31]

Group 1 - The core viewpoint of the report is that the CSI Technology Advantage Growth 50 Strategy Index (referred to as Technology 50 Strategy) utilizes multiple factors such as growth, innovation, value, low volatility, and quality to select and weight stocks from the technology sector, aiming to provide investors with a multi-factor strategy investment target based on the technology industry [1][7][11] - As of December 31, 2025, the top ten constituent stocks of the Technology 50 Strategy Index are primarily leading companies across various technology sub-sectors, with the top five stocks accounting for 17.63% and the top ten stocks accounting for 30.93% of the index's total weight [1][10][11] - The index is biased towards large-cap stocks, with 31 constituents having a market capitalization exceeding 100 billion yuan, while only 2 constituents have a market capitalization below 10 billion yuan [1][11] Group 2 - The report compares the Technology 50 Strategy Index with other representative technology and innovation indices, noting that the Technology 50 Strategy Index achieved an annualized return of 11.96% from January 1, 2020, to December 31, 2025, ranking third among six technology and innovation indices [1][57][60] - The Technology 50 Strategy Index shows lower volatility in revenue growth compared to other indices, with a projected revenue growth rate of 33.65% for 2026, which is higher than that of the other five technology and innovation indices [1][61][62] - The selection of constituent stocks for the Technology 50 Strategy Index incorporates 11 factors, providing a more comprehensive evaluation compared to other indices that focus on fewer factors [1][67][68]

Core Viewpoint - The explosive growth of AI has significantly boosted the popularity of actively managed quantitative funds, which have shown the ability to outperform the market while reducing volatility risk [2][3]. Group 1: Market Trends - The total share of actively managed quantitative funds reached 80.5 billion units by the end of Q3 2025, marking a 27% increase from the previous year [2]. - Institutional investors hold 46.5 billion units of these funds, accounting for over 70% of the total shares [3]. Group 2: Fund Performance - The "Huaan Event-Driven Quantitative Strategy A" fund has outperformed the CSI 300 index for six consecutive years, with a significant lead in 2025 [6]. - In 2025, the fund achieved a return of 35.77%, compared to 14.04% for its benchmark and 17.20% for the CSI 300 [8]. - The fund's risk-return profile is strong, with annualized returns of 33.02% and a maximum drawdown of -9.96%, outperforming peers in all six key metrics [10]. Group 3: Fund Management - The success of the "Huaan Event-Driven" fund is attributed to its manager, Zhang Xu, who employs a multi-faceted strategy that includes industry rotation and event-driven factors [13][18]. - The "Guojin Quantitative Multi-Factor A" fund, managed by Ma Fang, has also shown resilience, achieving positive returns in 2022 and 2023 despite market downturns [26][29]. Group 4: Investment Strategies - Actively managed quantitative funds are increasingly favored for their ability to adapt to market conditions, utilizing diverse strategies to capture excess returns [18][29]. - The focus on risk-adjusted returns and the ability to navigate different market environments are key factors driving institutional interest in these funds [22][23].

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]