Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - **Model Construction Idea**: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - **Model Construction Process**: 1. **Revenue Prediction**: Predict stock returns using multiple factors. 2. **Risk Control**: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. **Portfolio Optimization**: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is as follows: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( w \) is the stock weight vector, and \( f^{T}w \) is the weighted exposure to the factor. - **Constraints**: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark component, and \( b_l, b_h \) are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensure non-negative weights and limit individual stock weights. - **Full Investment**: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. **Backtesting**: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - **Model Construction Idea**: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - **Model Construction Process**: 1. **Sample Selection**: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. **Data Collection**: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. **Weight Calculation**: Average the stock weights across eligible funds. 4. **Index Construction**: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - **CSI 300 Enhanced Portfolio**: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - **CSI 500 Enhanced Portfolio**: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - **CSI 1000 Enhanced Portfolio**: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - **CSI A500 Enhanced Portfolio**: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - **CSI 300 Index Enhanced Products**: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - **CSI 500 Index Enhanced Products**: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - **CSI 1000 Index Enhanced Products**: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - **CSI A500 Index Enhanced Products**: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - **Factor Construction Idea**: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - **Factor Construction Process**: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - **Factor Construction Idea**: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - **Factor Construction Process**: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - **CSI 300 Sample Space**: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - **CSI 500 Sample Space**: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - **CSI 1000 Sample Space**: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - **CSI A500 Sample Space**: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - **Public Fund Heavyweight Index**: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]

- The report introduces a quantitative model named "250-day new high distance" to track market trends and identify hot spots. The model is based on momentum and trend-following strategies, emphasizing stocks that consistently hit new highs. The calculation formula is: $ 250\text{-day new high distance} = 1 - \frac{Close_{t}}{ts\_max(Close, 250)} $ where $ Close_{t} $ represents the latest closing price, and $ ts\_max(Close, 250) $ is the maximum closing price over the past 250 trading days. If the latest closing price reaches a new high, the distance equals 0; otherwise, it is a positive value indicating the degree of fallback [11][12][13] - The report evaluates the model positively, citing its ability to capture market leaders and trends effectively. It references studies by George (2004), William O'Neil, and Mark Minervini, which highlight the importance of tracking stocks near their 52-week highs for superior returns [11][18] - The report provides backtesting results for the "250-day new high distance" model across major indices as of November 21, 2025. The distances are: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45% [12][13][32] - The report introduces a factor named "Stable New High Stocks" to identify stocks with smooth price paths and sustained momentum. The factor construction involves: - Analyst attention: At least five buy or overweight ratings in the past three months - Relative strength: Top 20% in 250-day price change - Price stability: Ranking top 50% based on metrics like price displacement ratio and smoothness of 250-day new high distance over the past 120 days - Trend continuation: Ranking top 50 stocks based on the average 250-day new high distance over the past five days [24][27][28] - The report evaluates the "Stable New High Stocks" factor positively, citing research by Turan G Bali et al. (2011) and Da et al. (2012), which demonstrate the superior returns of stocks with smooth momentum paths compared to those with jumpy price movements [24][27] - Backtesting results for the "Stable New High Stocks" factor show 15 selected stocks, including Heertai, Sray New Materials, and Zangge Mining. These stocks are distributed across manufacturing and cyclical sectors, with manufacturing focusing on construction and cyclical sectors on non-ferrous metals [28][31][33]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: This model tracks the distance of stock prices or indices from their 250-day high to monitor market trends and identify potential market leaders. It is based on the momentum and trend-following strategy, which has been proven effective in various studies[11][18]. - **Model Construction Process**: The 250-day new high distance is calculated as follows: $ 250 \text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ Where: - $\text{Close}_{t}$ represents the latest closing price - $\text{ts\_max(Close, 250)}$ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value, indicating the degree of decline[11]. - **Model Evaluation**: The model effectively identifies market trends and highlights stocks or indices that are leading the market, aligning with the principles of momentum and trend-following strategies[11][18]. 2. Model Name: Stable New High Stock Selection Model - **Model Construction Idea**: This model focuses on selecting stocks that exhibit stable price paths and consistent momentum, as smoother price trajectories are associated with stronger momentum effects[24][27]. - **Model Construction Process**: The selection process involves the following criteria: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: 250-day price change in the top 20% of the market - **Price Stability**: Stocks are ranked based on: - **Price Path Smoothness**: Ratio of price displacement to the total price path - **Sustainability of New Highs**: Average 250-day new high distance over the past 120 days - **Trend Continuity**: Average 250-day new high distance over the past 5 days The top 50 stocks based on these criteria are selected[24][27]. - **Model Evaluation**: The model emphasizes the importance of smooth price paths and consistent momentum, which are less likely to attract excessive attention and thus yield stronger returns[24][27]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45%[12][13][32] 2. Stable New High Stock Selection Model - **Selected Stocks**: 15 stocks were identified, including Heertai, Sray New Materials, and Zangge Mining. - **Sector Distribution**: - Manufacturing: 5 stocks (e.g., construction industry) - Cyclical: 5 stocks (e.g., non-ferrous metals industry)[28][33] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: This factor measures the relative distance of a stock's price from its 250-day high, serving as an indicator of momentum and trend strength[11]. - **Factor Construction Process**: The formula is: $ 250 \text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ Where: - $\text{Close}_{t}$ is the latest closing price - $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: The factor effectively captures momentum and trend-following characteristics, making it a reliable indicator for identifying market leaders[11]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: This factor evaluates the smoothness of a stock's price trajectory, as smoother paths are associated with stronger momentum effects[24]. - **Factor Construction Process**: - Calculate the ratio of price displacement to the total price path over a specified period - Rank stocks based on this ratio and select the top performers[24]. - **Factor Evaluation**: The factor highlights stocks with stable momentum, which are less likely to attract excessive attention and thus yield stronger returns[24]. --- Factor Backtesting Results 1. 250-Day New High Distance Factor - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45%[12][13][32] 2. Price Path Smoothness Factor - **Selected Stocks**: 15 stocks were identified, including Heertai, Sray New Materials, and Zangge Mining. - **Sector Distribution**: - Manufacturing: 5 stocks (e.g., construction industry) - Cyclical: 5 stocks (e.g., non-ferrous metals industry)[28][33]

Core Insights - BNY Mellon's core strategy for expanding its investment research capabilities is centered on precise acquisitions to achieve capability integration and leapfrogging, rather than relying solely on internal development [3] - The group adopts a management philosophy of independent empowerment and collaborative complementarity, forming a distinctive diversified product matrix [3] - BNY Mellon's model provides key insights for Chinese asset management institutions, emphasizing that the extension of capability circles can be achieved through acquisitions while maintaining a balance between independence and integration [3] Group 1: Company Overview - BNY Mellon, formed from the merger of the first U.S. bank and Mellon Financial Corporation in 2007, is the world's largest custodian bank, with over 70% of its revenue derived from fees and commissions from 2020 to 2024 [5][6] - The company's strategic transformation has involved shedding traditional banking operations and focusing on core strengths, marking a significant shift towards a fee-driven financial services model [5] Group 2: Acquisition Strategy - BNY Mellon's asset management capabilities have been built through a series of strategic acquisitions rather than organic growth, leveraging its vast institutional client base to drive demand for asset management products [12] - Key acquisitions include the purchase of Dreyfus in 1994 for cash management expertise, Insight Investment in 2009 for fixed income, and ARX in 2008 to expand into the South American market [12][14] Group 3: Product Matrix and Strategies - BNY Mellon has established a vast product matrix through its asset management subsidiaries, focusing on niche markets and specialized strategies, such as Insight Investment's leadership in fixed income and Newton's tailored equity investment strategies [19][20] - The company offers a comprehensive range of products from active to passive investments, covering traditional equities and bonds to alternative investments [19] Group 4: Research and Investment Capability Development - BNY Mellon's investment research capability development can be divided into two acquisition waves, with the first focusing on foundational asset management capabilities and the second on global expansion and specialization [38] - The company emphasizes differentiated positioning among its subsidiaries, ensuring collaboration rather than overlap in multi-asset strategies [41][42] Group 5: Management Philosophy and Core Competencies - BNY Mellon respects the independence of its acquired asset management subsidiaries while empowering them with resources and distribution channels, optimizing asset management scale based on each subsidiary's expertise [44] - The BNY Investment Institute serves as a central hub for macroeconomic insights and investment strategy support, enhancing the overall research capabilities of the group [44]

Core Insights - The report highlights strong performance in the textile and apparel industry, particularly for Amer Sports, which reported a 26% year-on-year revenue increase for the first three quarters of 2025, reaching $4.465 billion, and a 153% increase in adjusted net profit to $369 million [5][6] - The report also notes the positive outlook for the media and internet sector, with Marble 3D's world model public beta launch and a focus on AI applications [7][8] - In the pharmaceutical sector, Eli Lilly's revenue surged by 52% in Q3 2025, driven by GLP-1 drugs, with Tirzepatide exceeding $10 billion in quarterly revenue [10][11] Textile and Apparel Industry - Amer Sports' Q3 2025 performance showed a 30% revenue increase, with adjusted net profit rising by 161% to $185 million [5][6] - The management has raised its full-year guidance for revenue growth to 23-24%, with an expected EPS of $0.88-$0.92 [6] - Key growth drivers include the Salomon brand, direct-to-consumer (DTC) channels, and strong performance in the Greater China and Asia-Pacific regions [6] Media and Internet Sector - The media industry experienced a decline of 2.31%, underperforming compared to the broader market indices [7] - Marble 3D's public beta launch is expected to enhance opportunities in the sector, with significant advancements in AI technology [8] - The report emphasizes the potential for growth in gaming and IP trends, recommending companies like Giant Network and Kuaishou [8] Pharmaceutical Industry - Eli Lilly's Q3 2025 revenue growth was significantly driven by its GLP-1 drug portfolio, with a notable increase in market coverage due to pricing agreements with the U.S. government [10][11] - Novo Nordisk faced challenges in the competitive landscape for weight loss drugs, leading to multiple downward revisions of its performance guidance [11][12] - The report indicates that 11 out of 16 multinational pharmaceutical companies raised their revenue and profit forecasts for the year, reflecting better-than-expected sales from new products [12]

- The report does not contain any quantitative models or factors for analysis[1][2][3]

Investment Rating - The report maintains an "Outperform" rating for the pharmaceutical industry [2] Core Insights - Eli Lilly's revenue increased by 52% year-on-year in Q3 2025, driven by GLP-1 drugs, with Tirzepatide's quarterly revenue exceeding $10 billion for the first time, showing a 131% year-on-year growth [4] - Novo Nordisk faced intensified competition in the weight loss drug market, leading to a modest revenue growth of 1% for Ozempic and 6% for Wegovy in Q3 2025, prompting a downward revision of its annual performance guidance [4] - A total of 11 out of 16 companies in the report raised their revenue and/or net profit/EPS forecasts for the year, primarily due to better-than-expected sales of new products [4] Summary by Sections 1. Q3 2025 Performance Review - Eli Lilly's Q3 revenue reached $17.6 billion, with a 62% increase in sales volume, while net prices decreased by 10% [12] - Novo Nordisk's sales revenue for Q3 was 75 billion Danish Kroner, with a net profit decline of 27% [13] - JNJ's pharmaceutical segment achieved revenue of $15.6 billion, with significant contributions from oncology and neurology products [14] - AbbVie reported global sales of $15.8 billion, with notable growth in immunology and neurology sectors [15] - Gilead's revenue for Q3 was $7.3 billion, with a 4% increase in HIV product sales [16] 2. MNC Product Performance in the U.S. Market - The median time for MNC products to reach peak sales in the U.S. market is approximately 8 years, with first-in-class (FIC) products achieving this in about 7 years [4] 3. Revenue and Guidance Adjustments - Eli Lilly raised its full-year revenue guidance from $60-62 billion to $63-63.5 billion [12] - Novo Nordisk revised its revenue growth forecast down from 8%-14% to 8%-11% [13] - JNJ increased its full-year revenue guidance from $93.2-93.6 billion to $93.5-93.9 billion [14]

Investment Rating - The report maintains an "Outperform" rating for the media industry, indicating expectations for better performance compared to the market index [5][40]. Core Insights - The media industry experienced a decline of 2.31% during the week of November 10 to November 16, underperforming against the CSI 300 index (-0.66%) and the ChiNext index (-1.68%) [1][12]. - Key companies showing positive performance included Xiangyuan Cultural Tourism, Sanwei Communication, Xinhua Du, and Tianxia Show, while companies like Yue Media, Kunlun Wanwei, Rongxin Culture, and Kaiying Network faced significant declines [1][12]. - The report highlights the launch of Marble 3D by World Labs, which allows the creation of navigable 3D virtual worlds from various input formats, marking a significant advancement in AI-generated content [2][16]. - The report emphasizes the importance of AI applications and policy shifts in the media sector, recommending companies such as Mango TV, Bilibili, Light Media, and Huace Film & TV for potential investment opportunities [4][35]. Summary by Sections Industry Performance - The media sector's performance ranked 23rd among all sectors, with a notable decline of 2.31% [1][12][14]. - The top three films for the week generated a total box office of 5.81 billion yuan, with "The Life of Langlang" leading at 3.38 billion yuan, accounting for 58.2% of the total [3][19]. Key Company Recommendations - The report recommends focusing on companies within the gaming and IP sectors, particularly Giant Network, Kaiying Network, and Jibite, as well as IP brands like Pop Mart [4][35]. - For media companies, it suggests monitoring potential improvements in advertising spending as the economy stabilizes, with a focus on companies like Focus Media and Bilibili [4][35]. AI and Content Development - The report discusses the launch of the Kosong AI framework, which enhances the development of intelligent applications, and the improvements in the 2.5 Turbo model for video generation [2][17]. - It highlights the potential for AI applications in various sectors, including animation, marketing, education, e-commerce, and social media [4][35].

Investment Rating - The report maintains an "Outperform the Market" rating for the media industry [5][35][40]. Core Views - The media industry experienced a decline of 2.31%, underperforming both the CSI 300 (-0.66%) and the ChiNext Index (-1.68%) during the week of November 10 to November 16 [1][12]. - Key companies showing positive performance include Xiangyuan Cultural Tourism, Sanwei Communication, Xinhua Du, and Tianxia Show, while companies like Yue Media, Kunlun Wanwei, Rongxin Culture, and Kaiying Network faced significant declines [1][12]. - The report emphasizes the importance of monitoring the economic bottoming out and potential policy shifts, particularly in AI applications and content production [4][35]. Summary by Sections Industry Performance - The media sector ranked 23rd in terms of weekly performance among all sectors, with a decline of 2.31% [1][12][14]. - The top three films for the week generated a total box office of 5.81 billion yuan, with "Lifelong Life" leading at 3.38 billion yuan (58.2% market share) [3][19]. Key Developments - The launch of Marble 3D by World Labs allows for the creation of navigable 3D virtual worlds from various input formats, marking a significant advancement in AI-generated content [2][16]. - The introduction of the new "head and tail frame" feature in the Keling 2.5 Turbo model enhances the generation quality of AI videos, improving controllability and consistency [2][17]. - The open-source AI framework Kosong by Moonlight provides developers with efficient tools for building intelligent applications [2][17]. Investment Recommendations - The report suggests focusing on the gaming sector and IP trends, recommending companies like Giant Network, Kaiying Network, and Jibite for their strong product cycles and performance [4][35]. - It also highlights the potential for growth in media companies like Mango Super Media and Bilibili, particularly in light of improving economic conditions and policy shifts [4][35]. - AI applications are identified as a key area for investment, with recommendations for companies involved in AI animation, marketing, and education [4][35].

Investment Rating - The report maintains an "Outperform the Market" rating for the media industry [5][35][40]. Core Views - The media industry experienced a decline of 2.31%, underperforming both the CSI 300 (-0.66%) and the ChiNext Index (-1.68%) during the week of November 10 to November 16 [1][12]. - Key companies showing positive performance include Xiangyuan Cultural Tourism, Sanwei Communication, Xinhua Dou, and Tianxia Show, while companies like Yue Media, Kunlun Wanwei, Rongxin Culture, and Kaiying Network faced significant declines [1][12]. - The report emphasizes the importance of monitoring the economic bottom and potential policy shifts, particularly in AI applications and content creation [4][35]. Summary by Sections Industry Performance - The media sector ranked 23rd in terms of weekly performance among all sectors, with a notable decline of 2.31% [1][12][14]. - The top three films for the week generated a total box office of 581 million yuan, with "The Life of Langlang" leading at 338 million yuan, accounting for 58.2% of the total [3][19]. Key Developments - The launch of Marble 3D by World Labs allows for the creation of navigable 3D virtual worlds from various input formats, marking a significant advancement in AI-generated content [2][16]. - The introduction of the new "head and tail frame" feature in the Keling 2.5 Turbo model enhances the generation quality of AI videos, improving controllability and consistency [2][17]. - The open-source AI framework Kosong by Moonlight provides developers with efficient tools for building intelligent applications [2][17]. Investment Recommendations - The report suggests focusing on the gaming sector and IP trends, recommending companies like Giant Network, Kaiying Network, and Jibite for their potential in the current market cycle [4][35]. - It highlights the importance of content policy shifts and AI application opportunities, recommending platforms like Mango Super Media and Bilibili, as well as content producers like Light Media and Huace Film [4][35].