Market Performance - As of February 6, 2026, the Shanghai Composite Index fell by 1.27%, the Shenzhen Component Index decreased by 2.11%, and the CSI 300 Index dropped by 1.33%[8] - The market saw a rise in micro-cap stocks despite the overall market adjustment[8] - Year-to-date, the CSI 300 Index has increased by 0.3%, while the CSI 300 enhanced portfolio has risen by 0.5%, yielding an excess return of 0.2%[20] Enhanced Fund Performance - For the CSI 300 enhanced fund, the minimum excess return was -1.39%, the median was 0.24%, and the maximum was 1.33% for the week ending February 6, 2026[12] - The CSI 500 enhanced fund had a minimum excess return of -0.67%, a median of 0.38%, and a maximum of 1.40%[12] - The CSI 1000 enhanced fund reported a minimum excess return of -0.78%, a median of 0.34%, and a maximum of 1.66%[12] Sector Performance - The food and beverage, beauty care, and electric equipment sectors performed well this week with returns of 4.31%, 3.69%, and 2.20% respectively[9] - Conversely, the non-ferrous metals, telecommunications, and electronics sectors underperformed with returns of -8.51%, -6.95%, and -5.23% respectively[9] Risk Considerations - There are risks associated with factor failure, model failure, and market style changes that could impact the effectiveness of the investment strategies employed[4]

Core Viewpoint - The article highlights the outstanding performance of quantitative stock selection strategies in 2025, with an average return of 43.82% and a median return of 46.11%, indicating a strong market for these strategies [2]. Group 1: Performance of Quantitative Stock Selection Strategies - By the end of 2025, quantitative stock selection products showed a remarkable average return of 43.82%, with 97.97% of products achieving positive returns [2]. - The average return for quantitative stock selection products from private equity firms with over 10 billion in assets reached 56.16%, showcasing strong performance [2]. - The top-performing quantitative stock selection products had a minimum return threshold of ***%, with Dragon Flag Technology leading the rankings [3]. Group 2: Leading Firms and Their Strategies - Dragon Flag Technology's "Dragon Flag Innovation Select No. 1 C Class" achieved a return of ***% in 2025, focusing on technology innovation and leveraging the dual innovation strategy of the Sci-Tech Innovation Board and the Growth Enterprise Market [3]. - Stable Investment's "Stable Small Cap Aggressive Timing Index Increase No. 1" also performed well, with a return of ***%, utilizing a multi-factor model for stock selection [4]. - The average return for quantitative stock selection products from firms with 50-100 billion in assets was 53.45%, with Hanrong Investment and Yunqi Quantitative leading the pack [5]. Group 3: Performance in Different Asset Classes - For firms managing 20-50 billion, the average return was 48.27%, with Gaia Qingke Private Equity and Jiuming Investment among the top performers [8]. - In the 10-20 billion category, the average return was 38.85%, with Longyin Tiger Roar and Shanghai Zijie Private Equity achieving notable results [11]. - The average return for firms with less than 5 billion in assets was 37.25%, with Shuizhuquan Asset leading this segment [17].

Core Insights - The report emphasizes the construction of an AI-driven low-frequency index enhancement strategy using deep learning frameworks to build alpha and risk models [3][15] - The market indices showed mixed performance, with the Shanghai Composite Index declining by 0.44% and the Shenzhen Component Index dropping by 1.62% as of January 30, 2026 [6][9] - The report provides detailed performance metrics for various index enhancement funds, highlighting their excess returns compared to their respective benchmarks [12][13] Market Index Performance - As of January 30, 2026, the Shanghai Composite Index was at 4117.9 points, down 0.44% for the week, while the Shenzhen Component Index was at 14205.9 points, down 1.62% [10] - The CSI 300 Index increased by 0.08% to 4706.3 points, while the CSI 500 Index decreased by 2.56% to 8370.5 points [10] - The report notes that the oil and petrochemical, telecommunications, and coal industries performed well, with weekly returns of 7.95%, 5.83%, and 3.68% respectively [10][11] Index Enhancement Fund Performance - The CSI 300 index enhancement fund had an excess return range from -1.05% to 1.08%, with a median of -0.04% for the week ending January 30, 2026 [12][13] - The CSI 500 index enhancement fund showed a median excess return of 0.42%, with a maximum of 1.85% [12][13] - Year-to-date, the CSI 300 index enhancement fund has an excess return of -0.4%, while the CSI 500 index enhancement fund has an excess return of -2.6% [19][25] Tracking Portfolio Performance - The report outlines the use of deep learning frameworks to create tracking portfolios for the CSI 300, CSI 500, and CSI 1000 indices, with a weekly rebalancing strategy [15][19][23] - The CSI 300 index enhancement portfolio has a year-to-date return of 1.2%, while the CSI 500 index enhancement portfolio has a return of 9.5% [19][25] - The report indicates that the tracking error for the CSI 300 index enhancement strategy is 1.2% as of January 30, 2026 [20]

- The short-term trading volume model indicates a bullish outlook for some broad-based indices [1][10] - The characteristic institutional model from the Dragon and Tiger list is neutral [1][10] - The characteristic trading volume model is neutral [1][10] - The intelligent algorithm model for the CSI 300 index is bullish, while the intelligent algorithm model for the CSI 500 index is bearish [1][10] - The mid-term limit-up and limit-down model is neutral [1][11] - The up-and-down return difference model is bullish for all broad-based indices [1][11] - The calendar effect model is neutral [1][11] - The long-term momentum model is neutral [1][12] - The comprehensive A-share V3 model is bullish [1][13] - The comprehensive A-share Guozheng 2000 model is neutral [1][13] - The mid-term trading volume to volatility model for Hong Kong stocks is bullish [1][14] - The up-and-down return difference model for the Hang Seng Index is neutral, while the similar up-and-down return difference model is bullish [1][14]

Market Performance - As of January 23, 2026, the Shanghai Composite Index rose by 0.84%, while the Shenzhen Component Index increased by 1.11%[11] - The CSI 300 Index decreased by 0.62%, with notable performance from small-cap indices[11] - Year-to-date, the CSI 300 Index is up 1.6%, while the CSI 300 enhanced portfolio has increased by 1.8%, yielding an excess return of 0.2%[23] Enhanced Fund Performance - For the CSI 300 enhanced fund, the minimum excess return was -0.48%, the median was 0.42%, and the maximum was 2.47% for the week[15] - The CSI 500 enhanced fund had a minimum excess return of -1.42%, a median of -0.12%, and a maximum of 1.56%[15] - The CSI 1000 enhanced fund reported a minimum excess return of -0.15%, a median of 0.72%, and a maximum of 3.15%[15] Sector Performance - The construction materials, petroleum and petrochemicals, and steel sectors performed well this week, with weekly returns of 9.23%, 7.71%, and 7.31% respectively[12] - Conversely, the banking, telecommunications, and non-bank financial sectors underperformed, with weekly returns of -2.70%, -2.12%, and -1.45% respectively[12] Risk Considerations - There are risks associated with factor failure, model failure, and market style changes that could impact the effectiveness of the investment strategies employed[6][45]

Core Viewpoint - The "Fixed Income +" funds are gaining traction as a viable asset allocation option amid a rising market and increasing transaction volumes, with Longcheng Fund launching the Changcheng Xingyi Bond Fund to cater to this demand [1][5]. Group 1: Market Trends - The market for "Fixed Income +" funds has seen significant growth, with the total market size increasing from 1.6 trillion yuan in Q4 2024 to 2.5 trillion yuan in Q3 2025, returning to historical peak levels from 2021-2022 [1][5]. - The adaptability of "Fixed Income +" funds is highlighted by their performance, with the secondary bond fund index rising by 5.59% in 2025, outperforming the pure bond fund index which only increased by 0.95% [1][5]. Group 2: Fund Strategy - The Changcheng Xingyi Bond Fund aims to maintain a bond asset allocation of no less than 80%, focusing on high-grade credit bonds rated AA+ and above, while also engaging in flexible trading of medium to long-term interest rate bonds to enhance returns [1][6]. - The fund will allocate 5% to 20% of its assets to equity, convertible bonds, and exchangeable bonds, with a focus on high-quality dividend assets and opportunities in sectors like technology and non-ferrous metals [6]. Group 3: Management Expertise - The fund manager, Wei Jian, has over 17 years of experience in the securities industry and emphasizes a quantitative stock selection strategy to control volatility and seek excess returns [6]. - Longcheng Fund's research-driven approach and strong performance in fixed income categories position it well in the market, with its fixed income funds ranking in the top 20% for returns over the past three and five years [6].

Core Insights - The core viewpoint of the article highlights the performance of various investment strategies in 2025, particularly the growth-oriented selected portfolio which achieved a cumulative return of 84.1%, significantly outperforming the 885001 index by 50.9% [1]. Group 1: Performance Analysis - In 2025, the growth-oriented selected portfolio showed the best performance among active quantitative portfolios, with a net cumulative return of 84.1%, surpassing the cumulative excess returns of the CSI 800 and 885001 indices by 63.2% and 50.9% respectively [1]. - The small-cap style portfolio also performed well, significantly outperforming the CSI 2000 index, while the high-dividend style portfolio had a weaker performance with a cumulative return of 15.0%, underperforming the CSI 800 index but exceeding the corresponding CSI dividend index [1]. Group 2: Strategy Enhancement - The monthly rebalancing index-enhanced portfolio constructed based on a linear multi-factor model showed that the ICIR weighted method significantly outperformed the IC mean weighted method in 2025 [2]. - Under the IC mean weighted method, the excess returns for the CSI 300, CSI 500, CSI 1000, and CSI A500 index enhancement strategies relative to their benchmark indices were 6.8%, 3.1%, 5.1%, and 4.8% respectively, while the ICIR weighted method yielded excess returns of 10.7%, 9.5%, 10.2%, and 13.2% [2]. - To improve the performance of index-enhanced portfolios in a challenging excess return environment, a multi-strategy approach was proposed, consisting of a basic index enhancement strategy (60% weight), an intra-domain satellite strategy focusing on momentum and fundamental factors (30% weight), and an extra-domain satellite strategy targeting small-cap high-growth stocks (10% weight), resulting in an annualized return improvement of 3.6% compared to the basic strategy [2].

Quantitative Models and Construction Methods National Enterprise Fundamental Factor Stock Selection Strategy - **Model Name**: National Enterprise Fundamental Factor Stock Selection Strategy [3] - **Model Construction Idea**: The strategy is based on fundamental factors tailored to national enterprises, considering both general and industry-specific factors [5][6] - **Model Construction Process**: - Define the sample pool using the CSI National Enterprise Index (000955.CSI) and stocks listed on the Beijing Stock Exchange for over six months with central or local state-owned enterprise attributes [3] - Classify industries into dividend-oriented and growth-oriented categories based on ZX third-level industry logic [3][4] - Select general factors such as ROE (TTM), operating cash ratio, labor productivity, asset-liability ratio, and dividend yield [5][6] - Incorporate industry-specific factors like ROIC, prepayment growth rate, inventory turnover rate, and capital expenditure/depreciation ratio for different industries [6][8] - Adjust factor weights based on industry characteristics, emphasizing dividend yield for dividend-oriented industries and reducing the weight of asset-liability ratio for growth-oriented industries [9] - Calculate scores using weighted averages of general and industry-specific factors, normalize the scores, and assign weights to stocks based on their scores [11] - Formula for stock weight: $$w_{i}={\frac{s c o r e_{i}^{3}}{\sum_{i=1}^{N}s c o r e_{i}^{3}}}$$ [11] - **Model Evaluation**: The strategy effectively captures the characteristics of national enterprises, balancing dividend stability and growth potential [5][6] Technology Theme Fundamental Factor Stock Selection Strategy - **Model Name**: Technology Theme Fundamental Factor Stock Selection Strategy [19] - **Model Construction Idea**: Focus on technology stocks with high R&D investment and strong growth potential, using fundamental factors to identify stocks in their growth and mature stages [20][23] - **Model Construction Process**: - Define the sample pool based on SW third-level industries and R&D investment criteria (R&D expenses > 5% of revenue or R&D personnel > 10% of total employees) [19][20] - Exclude stocks in the shock and decline stages based on cash flow lifecycle analysis [22][23] - Select general factors such as profitability, growth ability, technical level, supply chain concentration, and alpha factors [24][28] - Incorporate specific factors for growth and mature stages, such as management expense ratio, R&D expense ratio, accounts receivable turnover rate, and PB-ROE [24][28] - Adjust scores using R&D expense multipliers to emphasize high R&D industries [28][29] - Formula for stock weight: $$w e i g h t_{i}={\frac{s c o r e_{i}}{\sum_{i=1}^{50}s c o r e_{i}}}$$ [30] - **Model Evaluation**: The strategy highlights technology stocks with strong R&D capabilities and growth potential, effectively capturing industry-specific dynamics [24][28] Consumer Theme Fundamental Factor Stock Selection Strategy - **Model Name**: Consumer Theme Fundamental Factor Stock Selection Strategy [38] - **Model Construction Idea**: Focus on consumer stocks with direct-to-consumer business models, using fundamental factors to identify stocks with strong growth, profitability, and governance [38][39] - **Model Construction Process**: - Define the sample pool based on SW third-level industries, categorizing stocks into daily manufacturing, optional manufacturing, daily services, and optional services [38][39] - Select general factors such as growth-profitability-cash flow composite factor, operating cash flow ratio, ESG management score, and economic sensitivity [40][41] - Incorporate specific factors like market share, R&D expense ratio, accounts receivable turnover rate, and marketing expense ratio [40][41] - Adjust scores using PS (TTM) multipliers to emphasize stocks with lower price-to-sales ratios [46][47] - Formula for stock weight: $$w e l g h t_{i}={\frac{S c o r e_{i}^{a d j}}{\sum_{i=1}^{50}S c o r e_{i}^{a d j}}}$$ [48] - **Model Evaluation**: The strategy effectively identifies consumer stocks with strong fundamentals and growth potential, balancing profitability and governance [40][41] --- Model Backtesting Results National Enterprise Fundamental Factor Stock Selection Strategy - **Annualized Return**: 22.93% [12][15] - **Annualized Volatility**: 20.85% [15] - **Sharpe Ratio**: 1.0961 [15] - **Calmar Ratio**: 0.9963 [15] - **Maximum Drawdown**: -23.01% [15] Technology Theme Fundamental Factor Stock Selection Strategy - **Annualized Return**: 30.61% [31][34] - **Annualized Volatility**: 27.61% [34] - **Sharpe Ratio**: 1.1070 [34] - **Calmar Ratio**: 0.8962 [34] - **Maximum Drawdown**: -34.16% [34] Consumer Theme Fundamental Factor Stock Selection Strategy - **Annualized Return**: 24.86% [49][52] - **Annualized Volatility**: 22.99% [52] - **Sharpe Ratio**: 1.0825 [52] - **Calmar Ratio**: 1.0197 [52] - **Maximum Drawdown**: -24.38% [52]

- Short-term volume models for some broad-based indices turned bullish[1][3][11] - Feature-based institutional model turned bullish[1][3][11] - Feature-based volume model remained neutral[1][3][11] - Intelligent algorithm model for CSI 300 remained neutral, while for CSI 500 turned bullish[1][3][11] - Mid-term limit-up and limit-down model turned bullish[1][3][12] - Up-down return difference model turned bullish for all broad-based indices[1][3][12] - Calendar effect model remained neutral[1][3][12] - Long-term momentum model turned bullish for some broad-based indices[1][3][13] - Comprehensive A-share V3 model turned bullish[1][3][13] - Comprehensive A-share Guozheng 2000 model turned bullish[1][3][13] - Mid-term turnover amplitude model for Hong Kong stocks turned bullish[1][3][14] - Hang Seng Index up-down return difference model remained neutral[1][3][14]

Report Summary 1. Report Industry Investment Rating No industry investment rating is provided in the report. 2. Core Viewpoints of the Report - In the US, multiple quantitative enhancement products were issued last week, with an increasing issuance speed at the end of the year. Various asset classes in US ETFs maintained inflows, and alternative strategies such as long - short equity performed well. US domestic stock - type mutual funds still faced significant redemption pressure, while bond funds had a slight inflow [2]. 3. Summary by Directory 3.1 US ETF Innovation Products: Multiple Quantitative Enhancement Products Issued - Last week, 43 new products were issued in the US, including 6 individual stock leveraged products and 3 digital currency - related products. One product combined crude oil and Bitcoin with 2x leverage, and Simplify's US stocks + futures strategy also had a 1:1 investment ratio. Motley Fool issued 3 single - factor ETFs, each holding about 150 stocks [5][6]. - BlackRock's quantitative team issued an alternative product, and NEOS issued a long - short equity product. Hedgeye's 130/30 product also adopted a long - short strategy. Global X issued a gold miners ETF, Franklin Templeton issued a small - cap enhanced ETF, and Sterling Capital's stock option product used a quantitative stock - selection strategy [7]. - Columbia issued 6 ETFs, 3 bonds and 3 stocks. The stock products mainly used a quantitative enhancement strategy with semi - annual rebalancing [8]. 3.2 US ETF Dynamics 3.2.1 US ETF Fund Flows: All Asset Classes Maintained Inflows - In the past week, US ETF inflows remained above $40 billion, and domestic stock products had inflows of over $30 billion. There was a significant difference in fund flows between BlackRock's S&P 500 ETF (outflow) and Vanguard's products (inflow). Russell 2000 and high - yield bond ETFs had inflows, indicating a relatively high risk appetite [2][9]. - S&P 500 ETFs had significant recent fund fluctuations, Russell 2000 ETFs had continuous inflows, and gold also returned to an inflow state [13]. 3.2.2 US ETF Performance: Alternative Strategies such as Long - Short Equity Performed Well - Many long - short equity products were issued last week. In the past two years, products replicating futures and combining multiple hedge fund strategies have been increasing. Among the top ten alternative strategy products in the US, State Street's multi - strategy product and Convergence's long - short equity product performed best [14]. 3.3 Recent Fund Flows of US Ordinary Public Offering Funds - In October 2025, the total amount of non - money public offering funds in the US was $23.7 trillion, an increase of $0.22 trillion from September. The S&P 500 rose 2.27% in October, and the scale of domestic stock - type products increased by 0.9%, but the redemption pressure was still high. - From November 25th to December 3rd, domestic stock funds in the US had outflows of over $15 billion. Hybrid products had continuous outflows, while bond funds had a slight inflow [15].