- 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]

Quantitative Models and Construction Methods 1. **Model Name**: Securities Firms' Golden Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection of stocks from the securities firms' golden stock pool to outperform the benchmark, which is the median of equity-biased hybrid fund indices. The model leverages a multi-factor approach to select stocks with high alpha potential while controlling for deviations in individual stocks and style factors from the golden stock pool [39][43]. - **Model Construction Process**: - The securities firms' golden stock pool is used as the stock selection universe and constraint benchmark. - A multi-factor model is applied to further optimize the selection of stocks from the pool. - The portfolio is constructed by controlling the deviation of individual stocks and style factors from the golden stock pool. - The industry allocation is based on the distribution of all public funds [43]. - **Model Evaluation**: The model demonstrates strong alpha generation potential and consistently outperforms the equity-biased hybrid fund index. It reflects the research strength of securities firms and their ability to capture market trends effectively [43]. --- Model Backtesting Results 1. **Securities Firms' Golden Stock Performance Enhancement Portfolio** - **Absolute Return (Monthly)**: -0.55% (2025/09/01 - 2025/09/30) [42] - **Excess Return (Monthly)**: -3.50% relative to equity-biased hybrid fund index (2025/09/01 - 2025/09/30) [42] - **Absolute Return (Year-to-Date)**: 33.26% (2025/01/02 - 2025/09/30) [42] - **Excess Return (Year-to-Date)**: 1.19% relative to equity-biased hybrid fund index (2025/01/02 - 2025/09/30) [42] - **Ranking in Active Equity Funds (Year-to-Date)**: 43.07% percentile (1494/3469) [42] - **Historical Performance (2018-2025)**: - Annualized Return: 19.34% - Annualized Excess Return: 14.38% relative to equity-biased hybrid fund index - Consistently ranked in the top 30% of active equity funds each year [44][47] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Intraday Return - **Factor Construction Idea**: Measures the return generated within a single trading day to capture short-term price movements [27][28]. - **Factor Evaluation**: Demonstrated strong performance in the most recent month [27][28]. 2. **Factor Name**: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Reflects the valuation of a stock by comparing its book value to its market price [27][28]. - **Factor Evaluation**: Performed well in the most recent month but underperformed year-to-date [27][28]. 3. **Factor Name**: Volatility - **Factor Construction Idea**: Measures the degree of variation in a stock's price over a specific period, capturing risk and uncertainty [27][28]. - **Factor Evaluation**: Showed strong performance in the most recent month but underperformed year-to-date [27][28]. 4. **Factor Name**: Total Market Capitalization - **Factor Construction Idea**: Represents the total market value of a company's outstanding shares, often used to gauge company size [27][28]. - **Factor Evaluation**: Underperformed in the most recent month but performed well year-to-date [27][28]. 5. **Factor Name**: SUE (Standardized Unexpected Earnings) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of past earnings surprises [27][28]. - **Factor Evaluation**: Underperformed in the most recent month [27][28]. 6. **Factor Name**: Single-Quarter Earnings Surprise - **Factor Construction Idea**: Captures the magnitude of earnings surprises in a single quarter [27][28]. - **Factor Evaluation**: Underperformed in the most recent month but performed well year-to-date [27][28]. 7. **Factor Name**: Single-Quarter Revenue Growth - **Factor Construction Idea**: Measures the growth in revenue over a single quarter, reflecting a company's sales performance [27][28]. - **Factor Evaluation**: Performed well year-to-date [27][28]. 8. **Factor Name**: Analyst Net Upward Revision - **Factor Construction Idea**: Tracks the net number of upward revisions in analysts' earnings estimates for a stock [27][28]. - **Factor Evaluation**: Performed well year-to-date [27][28]. 9. **Factor Name**: Expected Dividend Yield - **Factor Construction Idea**: Represents the expected annual dividend payments as a percentage of the stock price [27][28]. - **Factor Evaluation**: Underperformed year-to-date [27][28]. --- Factors' Backtesting Results 1. **Intraday Return Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Not specified [27][28] 2. **BP Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Weak [27][28] 3. **Volatility Factor** - **Recent Month Performance**: Strong [27][28] - **Year-to-Date Performance**: Weak [27][28] 4. **Total Market Capitalization Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Strong [27][28] 5. **SUE Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Not specified [27][28] 6. **Single-Quarter Earnings Surprise Factor** - **Recent Month Performance**: Weak [27][28] - **Year-to-Date Performance**: Strong [27][28] 7. **Single-Quarter Revenue Growth Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Strong [27][28] 8. **Analyst Net Upward Revision Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Strong [27][28] 9. **Expected Dividend Yield Factor** - **Recent Month Performance**: Not specified [27][28] - **Year-to-Date Performance**: Weak [27][28]

Group 1 - The core viewpoint of the article emphasizes the performance and characteristics of the "brokerage golden stocks" pool, highlighting significant monthly and annual returns compared to benchmarks like the mixed equity fund index and the CSI 300 index [1][8][35] - In September 2025, the top-performing stocks included Jiangbolong, Xiechuang Data, and Jingzhida, with monthly increases of 86.50%, 81.70%, and 71.73% respectively [4][5] - The top three brokerages by monthly returns were Huazheng Securities, Hualong Securities, and Fangzheng Securities, with returns of 17.45%, 15.17%, and 14.38% respectively, outperforming the mixed equity fund index and the CSI 300 index [7][9] Group 2 - The "brokerage golden stocks" pool is characterized by a strong alpha extraction ability, reflecting both top-down industry allocation and bottom-up stock selection capabilities [3][32] - As of October 9, 2025, 42 brokerages had released their golden stocks for the month, resulting in a total of 304 unique A-shares after deduplication [22] - The sectors with the highest allocation in the golden stocks pool were Electronics (16.11%), Machinery (9.13%), and Non-ferrous Metals (8.17%) [29] Group 3 - The performance of the brokerage golden stock performance enhancement portfolio showed an absolute return of -0.55% for the month ending September 30, 2025, and a year-to-date return of 33.26% [35] - The portfolio's relative performance against the mixed equity fund index was -3.50% for the month and +1.19% year-to-date [35] - The portfolio ranked in the 43.07 percentile among active equity funds this year, indicating a competitive performance [35] Group 4 - The article discusses the factors influencing stock selection within the golden stocks pool, noting that recent performance indicators such as daily return rates and volatility have been strong, while total market capitalization and quarterly surprise performance have lagged [21][19] - The article also highlights the importance of analyst recommendations, noting that stocks with fewer prior recommendations tend to gain more market attention once included in the golden stocks pool [25][30]

========= - The "券商金股业绩增强组合" (Broker Gold Stock Performance Enhanced Portfolio) aims to outperform the median of public funds by optimizing the broker gold stock pool[39] - The portfolio uses the偏股混合型基金指数 (Equity-biased Hybrid Fund Index) as its benchmark, with a 90% position last month[39] - The absolute return of the portfolio for the month (2050901-20250930) was -0.55%, with an excess return of -3.50% relative to the偏股混合型基金指数[42] - The absolute return for the year (20250102-20250930) was 33.26%, with an excess return of 1.19% relative to the偏股混合型基金指数[42] - The portfolio ranked in the 43.07% percentile among active equity funds this year (1494/3469)[42] - The annualized return of the portfolio from 2018.1.2 to 2025.6.30 was 19.34%, with an annualized excess return of 14.38% relative to the偏股混合型基金指数[44] - The portfolio consistently ranked in the top 30% of active equity funds each year from 2018 to 2025[44] - The portfolio's performance statistics for each year from 2018 to 2025 are detailed in Table 6[47] - The portfolio's historical performance is illustrated in Figure 12[46] =========

Group 1 - In August, the A-share market strengthened, leading to positive returns for most of the recommended stocks by brokerages, with 244 out of 287 stocks recommended showing price increases [1][3] - The average gain of the recommended stocks in August was positive, with notable performances from brokerages such as Kaiyuan Securities and AVIC Securities [1][5] - The top three performing stocks in August were Huasheng Tiancai (600410.SH) with a gain of 115.11%, followed by Hanwujing (688256.SH) with a gain of 110.36%, and Siquan New Materials (301489.SZ) with a gain of 100.66% [3][4] Group 2 - As of September 4, over 40 brokerages had recommended 285 stocks for September, with Kaiying Network (002517.SZ) and Deepin Technology (300454.SZ) being the most frequently recommended [2][8] - Kaiying Network was recommended by multiple brokerages due to its upcoming product cycle and stable mid-year performance, reporting a revenue of 2.578 billion yuan, a year-on-year increase of 0.89%, and a net profit of 950 million yuan, a year-on-year increase of 17.41% [9][10] Group 3 - The technology sector performed exceptionally well in August, with 7 out of the top 10 recommended stocks belonging to this sector [5][6] - Conversely, the healthcare sector underperformed, with half of the stocks in the top 10 largest declines being from this industry, including Yifang Biotechnology (688382.SH) which fell by 19.62% [6][7] Group 4 - Among the stocks recommended for September, Deepin Technology had a high price-to-earnings ratio of 240.05, indicating a significant valuation compared to other recommended stocks [12] - Deepin Technology reported a revenue of 3.009 billion yuan for the first half of 2025, a year-on-year increase of 11.16%, but also reported a net loss of 228 million yuan, which was a 61.54% increase in loss compared to the previous year [12]

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 fund indices effectively, showcasing the analytical capabilities of brokerage firms [2][37]. - In August 2025, the top-performing stocks in the brokerage golden stock pool included Huasheng Tiancai, Hanwujing-U, and Siquan New Materials, with significant monthly increases [1][3]. - The top three brokerages by monthly returns were Kaiyuan Securities, AVIC Securities, and China Merchants Securities, with returns of 26.42%, 25.08%, and 24.07% respectively, outperforming the mixed equity fund index and the CSI 300 index [6][10]. Group 2 - The brokerage golden stock pool showed strong performance in factors such as quarterly net profit growth, quarterly ROE, and quarterly revenue growth, while factors like BP, intraday return, and expected dividend yield performed poorly [21][23]. - As of September 1, 2025, there were 39 brokerages publishing golden stocks, resulting in a total of 301 unique A-shares after deduplication, with significant allocations in the electronics, machinery, and basic chemical industries [23][27]. - The brokerage golden stock performance enhancement portfolio achieved an absolute return of 15.49% for the month and 34.01% year-to-date, outperforming the mixed equity fund index by 3.59% and 5.72% respectively [33][38]. Group 3 - The article highlights the interaction between brokerage analysts and public fund managers, indicating that the recommendations from brokerage analysts can significantly influence market attention towards certain stocks [13][27]. - The article also discusses the identification of stocks with relatively low market attention that have been recommended as golden stocks, suggesting potential investment opportunities [29][30]. - The performance of the brokerage golden stock industry portfolio showed excess returns in the electronics, electric new energy, and basic chemical industries for the month, while the year-to-date performance highlighted excess returns in electronics, automotive, and agriculture sectors [17][23].

Quantitative Models and Construction Methods Model Name: Broker Gold Stock Performance Enhancement Portfolio - **Model Construction Idea**: The model aims to optimize the selection of stocks from the broker gold stock pool to outperform the median of active equity funds[39][43]. - **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 distribution of all public funds is used as the industry allocation benchmark. - The model's detailed construction method can be found in the report "Broker Gold Stock Full Analysis - Data, Modeling, and Practice" published on February 18, 2022[39][43]. - **Model Evaluation**: The model has shown stable performance historically, consistently outperforming the active equity fund index from 2018 to 2022, ranking in the top 30% of active equity funds each year[12][39]. Model Backtesting Results Broker Gold Stock Performance Enhancement Portfolio - **Absolute Return (Monthly)**: 15.49%[5][42] - **Excess Return Relative to Active Equity Fund Index (Monthly)**: 3.59%[5][42] - **Absolute Return (Year-to-Date)**: 34.01%[5][42] - **Excess Return Relative to Active Equity Fund Index (Year-to-Date)**: 5.72%[5][42] - **Ranking in Active Equity Funds (Year-to-Date)**: 30.38% percentile (1054/3469)[5][42] Quantitative Factors and Construction Methods Factor Name: Single Quarter Net Profit Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of net profit in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net profit for the current quarter. - Compare it to the net profit of the same quarter in the previous year. - The formula is: $ \text{Net Profit Growth Rate} = \frac{\text{Current Quarter Net Profit} - \text{Previous Year Same Quarter Net Profit}}{\text{Previous Year Same Quarter Net Profit}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter ROE - **Factor Construction Idea**: This factor measures the return on equity for a single quarter[3][28]. - **Factor Construction Process**: - Calculate the net income for the quarter. - Divide it by the average shareholders' equity for the quarter. - The formula is: $ \text{ROE} = \frac{\text{Net Income}}{\text{Average Shareholders' Equity}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Name: Single Quarter Revenue Growth Rate - **Factor Construction Idea**: This factor measures the growth rate of revenue in a single quarter[3][28]. - **Factor Construction Process**: - Calculate the revenue for the current quarter. - Compare it to the revenue of the same quarter in the previous year. - The formula is: $ \text{Revenue Growth Rate} = \frac{\text{Current Quarter Revenue} - \text{Previous Year Same Quarter Revenue}}{\text{Previous Year Same Quarter Revenue}} \times 100\% $ - **Factor Evaluation**: This factor has performed well recently[3][28]. Factor Backtesting Results Single Quarter Net Profit Growth Rate - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter ROE - **Performance**: This factor has shown good performance recently[3][28]. Single Quarter Revenue Growth Rate - **Performance**: This factor has shown good performance recently[3][28].

Quantitative Models and Construction Methods 1. Model Name: "All Stocks Portfolio" - **Model Construction Idea**: This model aggregates all broker-recommended stocks ("golden stocks") and evaluates their performance as a portfolio[18][21] - **Model Construction Process**: 1. Collect all broker-recommended stocks for the month 2. Weight the stocks within the portfolio based on the number of recommendations by brokers 3. Exclude non-A-share stocks and Hong Kong-listed stocks to focus solely on A-shares[18] - **Model Evaluation**: The model demonstrates strong performance, significantly outperforming benchmark indices such as CSI 300 and CSI 500[18][21] 2. Model Name: "Newly Added Stocks Portfolio" - **Model Construction Idea**: Focuses on stocks newly added to the broker-recommended list, as they tend to exhibit better performance compared to repeated recommendations[18][23] - **Model Construction Process**: 1. Identify stocks newly added to the broker-recommended list for the month 2. Construct a portfolio weighted by the number of broker recommendations 3. Exclude non-A-share stocks and Hong Kong-listed stocks[18] - **Model Evaluation**: Newly added stocks outperform repeated recommendations, showcasing their superior return potential[18][23] 3. Model Name: "Repeated Stocks Portfolio" - **Model Construction Idea**: Focuses on stocks that have been repeatedly recommended by brokers across multiple months[18] - **Model Construction Process**: 1. Identify stocks that were recommended in the previous month and continue to be recommended in the current month 2. Construct a portfolio weighted by the number of broker recommendations 3. Exclude non-A-share stocks and Hong Kong-listed stocks[18] - **Model Evaluation**: While the performance is positive, it is generally weaker compared to newly added stocks[18] 4. Model Name: "Optimized Golden Stocks Portfolio" - **Model Construction Idea**: Selects the top 30 newly added stocks with the highest earnings surprise factor (SUE factor) to construct an optimized portfolio[23] - **Model Construction Process**: 1. Filter newly added stocks based on their earnings surprise factor (SUE factor) 2. Select the top 30 stocks with the highest SUE factor 3. Weight the portfolio based on the number of broker recommendations[23] - **Model Evaluation**: This optimized portfolio demonstrates superior performance compared to the "All Stocks Portfolio" and benchmark indices[23][25] --- Model Backtesting Results 1. All Stocks Portfolio - **August Return**: 13.6%[21] - **2025 YTD Return**: 33.5%[21] - **Annualized Return**: 13.7%[21] - **Annualized Volatility**: 23.6%[21] - **Sharpe Ratio**: 0.58[21] - **Maximum Drawdown**: 42.6%[21] 2. Newly Added Stocks Portfolio - **August Return**: 11.8%[21] - **2025 YTD Return**: 37.9%[21] - **Annualized Return**: 16.5%[21] - **Annualized Volatility**: 24.3%[21] - **Sharpe Ratio**: 0.68[21] - **Maximum Drawdown**: 38.5%[21] 3. Repeated Stocks Portfolio - **August Return**: 15.6%[21] - **2025 YTD Return**: 30.2%[21] - **Annualized Return**: 11.3%[21] - **Annualized Volatility**: 23.7%[21] - **Sharpe Ratio**: 0.48[21] - **Maximum Drawdown**: 45.0%[21] 4. Optimized Golden Stocks Portfolio - **August Return**: 19.6%[25] - **2025 YTD Return**: 37.6%[25] - **Annualized Return**: 22.3%[25] - **Annualized Volatility**: 25.5%[25] - **Sharpe Ratio**: 0.88[25] - **Maximum Drawdown**: 24.6%[25] --- Quantitative Factors and Construction Methods 1. Factor Name: Earnings Surprise Factor (SUE Factor) - **Factor Construction Idea**: Measures the degree to which a company's earnings exceed or fall short of market expectations, serving as a key indicator for stock selection[23] - **Factor Construction Process**: 1. Calculate the earnings surprise for each stock as the difference between reported earnings and consensus estimates 2. Normalize the earnings surprise to account for variations across stocks and industries 3. Rank stocks based on their normalized earnings surprise values[23] - **Factor Evaluation**: The SUE factor demonstrates strong predictive power, particularly in identifying high-performing newly added stocks[23] --- Factor Backtesting Results 1. SUE Factor - **Performance**: The SUE factor is highly effective in selecting top-performing stocks within the newly added category, contributing to the superior returns of the Optimized Golden Stocks Portfolio[23]