Quantitative Models and Construction Methods - **Model Name**: "Kaiyuan Quantitative Engineering Preferred Gold Stock Portfolio" **Model Construction Idea**: The model is based on the observation that newly introduced stocks outperform repeated stocks. It incorporates the "SUE factor" (Surprise Earnings Factor), which demonstrates superior stock selection ability within newly introduced stocks[25] **Model Construction Process**: 1. Select the top 30 newly introduced stocks with the highest earnings surprise (SUE factor) 2. Weight the portfolio based on the number of recommendations by brokers 3. Construct the portfolio using these weighted stocks[25] **Model Evaluation**: The model demonstrates better performance compared to the overall gold stock portfolio, with higher annualized returns and lower maximum drawdowns[25][27] Model Backtesting Results - **Kaiyuan Quantitative Engineering Preferred Gold Stock Portfolio**: - November return: -0.2% - 2025 YTD return: 45.1% - Annualized return: 22.5% - Annualized volatility: 25.3% - Return-to-volatility ratio: 0.89 - Maximum drawdown: 24.6%[27][28] - **Overall Gold Stock Portfolio**: - November return: -3.4% - 2025 YTD return: 32.2% - Annualized return: 13.2% - Annualized volatility: 23.5% - Return-to-volatility ratio: 0.56 - Maximum drawdown: 42.6%[23][27] - **CSI 300 Index**: - November return: -2.5% - 2025 YTD return: 15.0% - Annualized return: 3.3% - Annualized volatility: 21.1% - Return-to-volatility ratio: 0.16 - Maximum drawdown: 40.6%[23][27] - **CSI 500 Index**: - November return: -4.1% - 2025 YTD return: 22.8% - Annualized return: 1.2% - Annualized volatility: 23.8% - Return-to-volatility ratio: 0.05 - Maximum drawdown: 37.5%[23][27] Quantitative Factors and Construction Methods - **Factor Name**: Surprise Earnings Factor (SUE Factor) **Factor Construction Idea**: The factor captures stocks with earnings that significantly exceed market expectations, which are likely to outperform in the short term[25] **Factor Construction Process**: 1. Calculate the earnings surprise for each stock as the difference between reported earnings and consensus estimates 2. Rank stocks based on their earnings surprise values 3. Select the top-ranked stocks for portfolio inclusion[25] **Factor Evaluation**: The SUE factor demonstrates strong stock selection ability, particularly within newly introduced stocks, and contributes to the superior performance of the preferred gold stock portfolio[25] Factor Backtesting Results - **SUE Factor**: - Incorporated into the preferred gold stock portfolio, contributing to its annualized return of 22.5% and return-to-volatility ratio of 0.89[25][27]

- The report highlights that in November 2025, the top-performing factors in the broker's gold stock pool were total market capitalization, single-quarter revenue surprise, and SUR, while factors like intraday return, analyst net upgrade magnitude, and analyst net upgrade ratio performed poorly[3][26] - For the year 2025, the best-performing factors were total market capitalization, single-quarter revenue growth, and SUR, whereas EPTTM, expected dividend yield, and BP underperformed[3][26] - The broker's gold stock performance enhancement portfolio achieved an absolute return of -1.06% for the month (20251103-20251128) and an excess return of 1.39% relative to the mixed equity fund index[5][41] - For the year (20250102-20251128), the portfolio achieved an absolute return of 33.65% and an excess return of 4.42% relative to the mixed equity fund index, ranking in the 35.37% percentile among active equity funds[5][41] - The broker's gold stock performance enhancement portfolio has consistently outperformed the mixed equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][37][43]

Core Viewpoint - The performance of listed companies in the A-share market has stabilized as the third-quarter reports are disclosed, leading to a focus on certain sectors and stocks by brokerages for November's "golden stocks" list [1] Group 1: Sector Recommendations - Over 180 stocks have been included in brokerages' November "golden stocks" list, with notable recommendations in the automotive and parts sector, as well as in the new energy and home appliance sectors [1] - Top Group has been recommended by five brokerages in the automotive and parts sector, while CATL has also received five recommendations in the new energy sector [1] - Haier Smart Home stands out in the home appliance sector with four recommendations, making it the most recommended company in its industry [1] Group 2: Home Appliance Sector Insights - The home appliance sector is benefiting from policies like trade-in programs and increasing export demand, which favor leading companies with stable performance and global presence [1] - Haier Smart Home is highlighted for its improving operational performance in the European and American markets and its ability to capture market share in emerging markets, supported by strong product innovation capabilities [1] - Despite challenges from real estate and related factors, leading companies in the home appliance sector are expected to experience structural growth driven by consumer recovery and industrial upgrades [1]

Industry Overview - In November, the electronic sector remains the most favored industry among brokerages, accounting for 15.1% of the recommended stocks [2][3] - Coal is the industry with the most significant increase in allocation, with a rise of 2.2% compared to October [3] - The media and machinery sectors have seen a notable decrease in weight, with reductions of 2.0% and 1.7% respectively [4] Stock Recommendations - Kingsoft Office (688111) is the most recommended stock, appearing in the top recommendations six times [6][7] - Other highly recommended stocks include Zijin Mining (601899), Zhongji Xuchuang (300308), and Top Group (601689), each recommended five times [6][7] - New entries in the recommended stocks include Haier Smart Home (600690) and Ping An Insurance (601318), both recommended four times [9] Sector-Specific Insights - The top recommended stocks in the electronic sector include Industrial Fulian, recommended five times [11] - In the power equipment sector, both Ningde Times and Tianci Materials were recommended three times [11] - The automotive sector's leading stock is Top Group, also recommended five times [10][11] Market Trends - The overall market trend indicates a shift towards value style, as evidenced by the declining market capitalization and valuation levels of the recommended stocks [4] - The increase in coal, home appliances, and banking sectors suggests a potential pivot in investment strategies [3][4]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection from the broker gold stock pool to outperform the benchmark index of equity-biased hybrid funds[12][39] - **Model Construction Process**: - The model uses the broker gold stock pool as the stock selection space and constraint benchmark - It employs portfolio optimization to control deviations in individual stocks and styles from the broker gold stock pool - The industry allocation is based on the industry distribution of all public funds - The portfolio is adjusted at the closing price on the first day of each month[12][39][42] - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the equity-biased hybrid fund index annually from 2018 to 2022[12][39][42] Model Backtest Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: -0.77% (20251009-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Monthly)**: 1.37% (20251009-20251031)[41] - **Absolute Return (Year-to-date)**: 35.08% (20250102-20251031)[41] - **Excess Return Relative to Equity-biased Hybrid Fund Index (Year-to-date)**: 2.61% (20250102-20251031)[41] - **Ranking in Active Equity Funds (Year-to-date)**: 40.13% percentile (412/3469)[41] Quantitative Factors and Construction Methods 1. Factor Name: Total Market Value - **Factor Construction Idea**: This factor measures the total market capitalization of a stock, which is often used to capture the size effect in stock returns[3][28] - **Factor Construction Process**: - The total market value is calculated as the product of the stock's current price and the total number of outstanding shares[3][28] - **Factor Evaluation**: The total market value factor has shown good performance in the recent month and year-to-date periods[3][28] 2. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of a company's revenue in a single quarter, indicating its short-term growth potential[3][28] - **Factor Construction Process**: - The single quarter revenue growth rate is calculated as the percentage change in revenue from the previous quarter to the current quarter[3][28] - **Factor Evaluation**: The single quarter revenue growth rate factor has shown good performance year-to-date[3][28] 3. Factor Name: Analyst Net Upward Revision - **Factor Construction Idea**: This factor measures the net number of upward revisions by analysts, reflecting positive changes in analyst sentiment[3][28] - **Factor Construction Process**: - The analyst net upward revision is calculated as the difference between the number of upward revisions and the number of downward revisions over a specific period[3][28] - **Factor Evaluation**: The analyst net upward revision factor has shown good performance year-to-date[3][28] Factor Backtest Results Total Market Value Factor - **Recent Month Performance**: Good[3][28] - **Year-to-date Performance**: Good[3][28] Single Quarter Revenue Growth Rate Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28] Analyst Net Upward Revision Factor - **Recent Month Performance**: Not specified - **Year-to-date Performance**: Good[3][28]

Group 1 - The core viewpoint of the article emphasizes the performance of the "brokerage golden stocks" and their ability to track the performance of mixed equity funds, showcasing the analytical capabilities of brokerage firms [2][10][31] - In October 2025, the top-performing stocks in the brokerage golden stock pool included GuoDun Quantum, Rongxin Culture, and JiangBolong, with significant monthly increases [1][3][4] - The top three brokerages in terms of monthly returns were Western Securities, Great Wall Securities, and Guoyuan Securities, with returns of 5.84%, 5.43%, and 4.03% respectively, while the mixed equity fund index returned -2.14% [6][8] Group 2 - As of November 3, 2025, a total of 42 brokerages released their golden stocks for the month, resulting in 275 unique A-shares after deduplication [21][27] - The sectors with the highest allocation in the current golden stock pool were electronics (15.26%), non-ferrous metals (8.68%), and basic chemicals (6.84%) [27] - The brokerage golden stock performance enhancement portfolio had an absolute return of -0.77% for the month and a relative excess return of 1.37% compared to the mixed equity fund index [35] Group 3 - The article highlights the performance of various selection factors within the brokerage golden stock pool, noting that total market capitalization and quarterly revenue growth rates performed well recently [18][16] - The article also discusses the stocks that received multiple recommendations from analysts, indicating higher market attention, with stocks like Industrial Fulian and Kingsoft receiving recommendations from five or more analysts [22][23] - The brokerage golden stock index showed a year-to-date return of 28.59%, compared to the mixed equity fund index's return of 32.47% [14][35]

- The report highlights the performance of selection factors in the "broker gold stock pool," including factors such as total market capitalization, single-quarter revenue growth, and analyst net upward revisions, which performed well this year. Conversely, factors like EPTTM, expected dividend yield, and volatility showed weaker performance this year[3][28][29] - The "broker gold stock performance enhancement portfolio" is constructed using a multi-factor approach to optimize stock selection within the broker gold stock pool. The portfolio aims to outperform the benchmark, the actively managed equity fund index, by controlling deviations in individual stocks and styles while aligning industry allocation with the overall distribution of public funds[12][39][42] - The historical performance of the "broker gold stock performance enhancement portfolio" demonstrates robust results, with annualized returns of 19.34% and an annualized excess return of 14.38% relative to the actively managed equity fund index during the period from 2018 to 2025. The portfolio consistently ranked in the top 30% of actively managed equity funds each year[43][46] - The monthly performance of the "broker gold stock performance enhancement portfolio" (20251009-20251031) showed an absolute return of -0.77% and an excess return of 1.37% relative to the actively managed equity fund index. Year-to-date (20250102-20251031), the portfolio achieved an absolute return of 35.08% and an excess return of 2.61%, ranking in the 40.13% percentile among actively managed equity funds[5][41][46]

- The report analyzes the performance of broker-recommended "gold stocks" for November, highlighting top recommendations such as Kingsoft Office, Zijin Mining, and Haier Smart Home, among others [2][14][15] - November's "gold stocks" are categorized into new entries and repeated recommendations, with repeated stocks like Kingsoft Office and Zijin Mining being recommended multiple times, while new entries include Haier Smart Home and China Ping An [2][14][15] - Industry-wise, November's "gold stocks" are concentrated in sectors like electronics (15.1%), power equipment (10.8%), non-ferrous metals (7.8%), and automobiles (6.2%) [2][15][18] - The weighted market capitalization and valuation levels of November's "gold stocks" have decreased, indicating a shift towards value-oriented stocks [3][19][20] - October's "gold stocks" portfolio had an overall return of -2.5%, with new entries outperforming repeated recommendations. The annualized return for all "gold stocks" was 13.8%, higher than the CSI 300 and CSI 500 indices [4][23][20] - The "Open Source Quantitative Preferred Gold Stock Portfolio" for October achieved a return of 2.2%, with an annualized return of 22.9%, outperforming the overall "gold stocks" portfolio and benchmark indices [5][26][28] - The preferred portfolio for November includes stocks like Salt Lake Co., China Life Insurance, and Shanghai Lingang, with a focus on industries such as non-bank finance, machinery, and pharmaceuticals [5][29][30]

Group 1: Technology Sector - The technology sector is highlighted by 11 out of 13 brokerages, with SMIC being the top pick due to its leading position in domestic wafer foundry and a backlog of orders extending to 2026, ensuring stable capacity [3] - SMIC has recently added a 28nm mature process production line, with a gross margin expected to remain above 25%, providing visible performance support [3] - The focus on long-term investment in SMIC is emphasized, as the overall trend of domestic substitution remains intact, allowing for continuous performance realization [3] Group 2: Pharmaceutical Sector - WuXi AppTec is included in the "golden stock" list by 8 brokerages, driven by a recovery in global pharmaceutical R&D demand and an increase in outsourcing orders from multinational pharmaceutical companies [3] - The company is expanding its innovative drug service business in China, collaborating with several biotech firms to develop new cancer drugs, with a growth rate projected around 30% [3] - The pharmaceutical sector is deemed suitable for long-term investment, with WuXi AppTec's global business support enhancing its risk resilience compared to smaller firms [3] Group 3: New Energy Sector - CATL is favored by 7 brokerages, with two main reasons: the recent implementation of export license management for lithium batteries, which helps eliminate disordered competition, stabilizing overseas orders, particularly from European automakers, which now account for 45% of its orders [4] - The mass production of CATL's Kirin battery technology, which has a 20% higher energy density than traditional batteries, is expected to convert technological advantages into profits as it supplies to companies like Li Auto and NIO [4] - Investors are advised to focus on technological iterations and order volumes in the new energy sector rather than short-term fluctuations [4] Group 4: Consumer Sector - Kweichow Moutai remains a "cornerstone" choice for 5 brokerages, supported by strong sales data during the National Day holiday and stable batch prices around 1900 yuan, indicating steady demand [4] - The company is expanding its direct sales channels, with a 20% increase in advertising on e-commerce platforms, which is expected to directly enhance profit margins [4] - Moutai is considered a suitable long-term investment for its strong cash flow and brand power, providing a stable foundation for investors amid market fluctuations [4] Group 5: Overall Investment Strategy - The investment logic of the 13 brokerages aligns with a long-term investment approach, focusing on companies with performance support and industry prospects rather than chasing hot stocks [4] - The highlighted companies possess core advantages in their respective fields, and any market corrections should be viewed as potential opportunities for increasing positions, provided the fundamentals remain unchanged [4]

Quantitative Models and Construction Methods 1. **Model Name**: Kaiyuan Quantitative Engineering Preferred Golden Stock Portfolio **Model Construction Idea**: The model is based on the observation that newly introduced golden stocks outperform repeated golden stocks. It incorporates the earnings surprise factor (SUE factor) to select stocks with superior performance potential[23] **Model Construction Process**: - The sample consists of newly introduced golden stocks - Select the top 30 golden stocks with the highest earnings surprise (SUE factor) - Weight the selected stocks based on the number of recommendations by securities firms[23] **Model Evaluation**: The model demonstrates superior performance compared to the overall golden stock portfolio, with higher annualized returns and a better return-to-volatility ratio[23] --- Model Backtesting Results 1. **Kaiyuan Quantitative Engineering Preferred Golden Stock Portfolio** - September return: 3.4% - 2025 YTD return: 42.3% - Annualized return: 22.6% - Annualized volatility: 25.5% - Return-to-volatility ratio: 0.89 - Maximum drawdown: 24.6%[26][27] 2. **Overall Golden Stock Portfolio** - September return: 3.1% - 2025 YTD return: 39.2% - Annualized return: 14.1% - Annualized volatility: 23.6% - Return-to-volatility ratio: 0.60 - Maximum drawdown: 42.6%[21][26] 3. **Newly Introduced Golden Stock Portfolio** - September return: 2.2% - 2025 YTD return: 41.6% - Annualized return: 16.7% - Annualized volatility: 24.2% - Return-to-volatility ratio: 0.69 - Maximum drawdown: 38.5%[21][18] 4. **Repeated Golden Stock Portfolio** - September return: 3.9% - 2025 YTD return: 38.0% - Annualized return: 11.9% - Annualized volatility: 23.8% - Return-to-volatility ratio: 0.50 - Maximum drawdown: 45.0%[21][18] 5. **Benchmark Indices** - CSI 300 Index: - September return: 3.2% - 2025 YTD return: 17.9% - Annualized return: 3.7% - Annualized volatility: 21.3% - Return-to-volatility ratio: 0.17 - Maximum drawdown: 40.6%[21][26] - CSI 500 Index: - September return: 5.2% - 2025 YTD return: 29.5% - Annualized return: 2.0% - Annualized volatility: 23.9% - Return-to-volatility ratio: 0.08 - Maximum drawdown: 37.5%[21][26] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Earnings Surprise Factor (SUE Factor) **Factor Construction Idea**: The factor identifies stocks with earnings that significantly exceed market expectations, which are likely to outperform in the short term[23] **Factor Construction Process**: - Calculate the earnings surprise for each stock as the difference between actual earnings and consensus estimates - Rank stocks based on their earnings surprise values - Select the top-performing stocks with the highest earnings surprise values for inclusion in the portfolio[23] **Factor Evaluation**: The SUE factor demonstrates strong stock selection ability, particularly within the newly introduced golden stock portfolio[23] --- Factor Backtesting Results 1. **SUE Factor** - Demonstrated superior stock selection ability in the newly introduced golden stock portfolio, contributing to its outperformance over repeated golden stocks[23]