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]