Quantitative Factors and Construction Methods Factor Name: Turnover Distribution Uniformity (UTD) - **Construction Idea**: The UTD factor is constructed based on minute-level trading volume data to improve the traditional turnover factor by reducing the misjudgment rate of stock samples and enhancing stock selection performance[1][7]. - **Construction Process**: 1. Collect minute-level trading volume data for individual stocks. 2. Calculate the turnover rate for each minute and aggregate it to form a distribution. 3. Measure the uniformity of this distribution to construct the UTD factor. 4. The formula for the UTD factor is not explicitly provided in the report, but it involves statistical measures of distribution uniformity[1][7]. - **Evaluation**: The UTD factor significantly reduces the misjudgment rate of stock samples and performs better than traditional factors in stock selection[1][7]. Factor Backtesting Results UTD Factor - **Annualized Return**: 20.06%[1][7][11] - **Annualized Volatility**: 7.40%[1][7][11] - **Information Ratio (IR)**: 2.71[1][7][11] - **Monthly Win Rate**: 77.30%[1][7][11] - **Maximum Monthly Drawdown**: 5.51%[1][7][11] Monthly Performance (October 2025) UTD Factor - **Long Portfolio Return**: 3.37%[1][10] - **Short Portfolio Return**: 0.59%[1][10] - **Long-Short Portfolio Return**: 2.78%[1][10]

- The "Information Distribution Uniformity" (UID) factor is introduced as part of the "volatility stock selection factor" series, aiming to improve traditional volatility factors by leveraging high-frequency minute-level data to construct a more effective stock selection factor[6][7] - The construction process of the UID factor involves calculating daily high-frequency volatility using minute-level stock data and then deriving the UID factor based on the uniformity of information distribution. This factor is designed to outperform traditional volatility factors in stock selection[6][10] - The UID factor demonstrates strong performance metrics in the A-share market from January 2014 to October 2025, with an annualized return of 26.68%, annualized volatility of 9.89%, an IR of 2.70, a monthly win rate of 78.72%, and a maximum monthly drawdown of 6.05%[7][12] - In October 2025, the UID factor's 10-group long portfolio achieved a return of 4.49%, the short portfolio achieved a return of 0.76%, and the long-short hedged portfolio achieved a return of 3.72%[10] - The UID factor is evaluated as having significant stock selection capabilities, even after removing common market style and industry interferences. Its annualized ICIR remains at -3.17, showcasing its robustness and effectiveness in capturing unique information[6][7]

- Factor Name: EPD (Valuation Deviation Factor) - Construction Idea: Combining the Bollinger Bands mean reversion strategy commonly used in the CTA field with the logic of fundamental valuation repair, utilizing the mean reversion characteristic of the PE valuation indicator[7] - Construction Process: The EPD factor is constructed by using the mean reversion characteristic of the PE valuation indicator[7] - Evaluation: The EPD factor aims to capture valuation deviations and mean reversion in stock prices[7] - Factor Name: EPDS (Slow Deviation Factor) - Construction Idea: To eliminate the impact of changes in individual stock valuation logic, the EPD factor is used to remove the probability of individual stock valuation logic being altered (represented by the individual stock information ratio)[7] - Construction Process: The EPDS factor is constructed by using the EPD factor to remove the probability of individual stock valuation logic being altered[7] - Evaluation: The EPDS factor aims to provide a more stable measure of valuation deviations by accounting for changes in individual stock valuation logic[7] - Factor Name: EPA (Valuation Anomaly Factor) - Construction Idea: Removing the influence of Beta, growth, and value styles that affect the "valuation anomaly" logic[7] - Construction Process: The EPA factor is constructed by removing the influence of Beta, growth, and value styles from the EPD factor[7] - Evaluation: The EPA factor aims to capture valuation anomalies by eliminating the influence of common market factors[7] Factor Backtesting Results - EPD Factor - Annualized Return: 17.46%[2][8][12] - Annualized Volatility: 9.92%[2][8][12] - Information Ratio (IR): 1.76[2][8][12] - Monthly Win Rate: 70.37%[2][8][12] - Maximum Drawdown: 8.93%[2][8][12] - EPDS Factor - Annualized Return: 16.03%[2][8][12] - Annualized Volatility: 5.74%[2][8][12] - Information Ratio (IR): 2.79[2][8][12] - Monthly Win Rate: 78.31%[2][8][12] - Maximum Drawdown: 3.10%[2][8][12] - EPA Factor - Annualized Return: 17.15%[2][8][12] - Annualized Volatility: 5.16%[2][8][12] - Information Ratio (IR): 3.33[2][8][12] - Monthly Win Rate: 80.42%[2][8][12] - Maximum Drawdown: 3.12%[2][8][12]

Investment Rating - The report maintains an "Accumulate" rating for the liquor industry [1] Core Viewpoints - The liquor industry is currently in a phase of pressure relief and clearing, with expectations for performance recovery in the future. The focus should be on companies that show early signs of a turning point and have leading growth elasticity [3] - The overall revenue of the liquor sector has declined, with a 5.5% year-on-year drop in total revenue for the first three quarters of 2025, and an 18.3% decline in Q3 alone. Net profit also saw a significant decrease of 21.9% in Q3 [12][24] - The high-end liquor segment is under pressure, with a need for macroeconomic recovery to achieve a balance in volume and price. Companies with strong brand positioning and national expansion potential are recommended for investment [3][12] Summary by Sections 1. Q3 Performance and Market Conditions - The Q3 performance of the liquor sector shows a slow recovery in consumption scenarios, with overall sales continuing to face pressure. The high-end and next-high-end liquor demand remains under pressure, particularly in business and personal dining scenarios [12][13] - The overall revenue for the liquor sector in Q3 dropped by 18.3% year-on-year, with net profit down by 21.9%, indicating a significant acceleration in the decline compared to previous quarters [12][24] 2. Revenue Trends - The liquor sector's revenue has been on a downward trend, with a 5.5% year-on-year decline in the first three quarters of 2025. The Q3 revenue decline is particularly sharp at 18.3% [12][24] - High-end liquor companies are experiencing a shift in their financial reports, with revenue declines driven by pressure on major brands like Moutai and Wuliangye [30][41] 3. Profitability Analysis - The gross profit margin for the liquor sector has decreased, with Q3 margins at 81.7%, down 0.7 percentage points year-on-year. The decline in profitability is attributed to structural issues and increased costs [2][3] - The report highlights that the majority of liquor companies have seen an increase in sales expenses, while management expenses have also risen slightly due to weaker revenue realization [2][3] 4. Investment Recommendations - The report suggests prioritizing investments in companies that are likely to recover first, such as Luzhou Laojiao and Shanxi Fenjiu, which have strong governance and dividend yields. Other companies to watch include Zhenjiu Lidu and Shede Liquor [3][12] - The focus should be on companies that can maintain channel stability and show early signs of marginal recovery, as the market is expected to support valuations for these firms [12][13]

Quantitative Models and Construction Methods - **Model Name**: "Day and Night Convergence" New Momentum Factor **Model Construction Idea**: The model is based on the price-volume relationship during intraday and overnight trading sessions. It improves traditional momentum factors by incorporating transaction volume information and separating the trading periods into day and night to explore their respective characteristics and logic[6][7] **Model Construction Process**: 1. The trading period is divided into intraday and overnight sessions 2. The price-volume relationship is analyzed separately for each session to identify distinct features 3. The improved intraday and overnight factors are synthesized into a new momentum factor 4. The factor is tested on the entire A-share market (excluding Beijing Stock Exchange stocks) from February 2014 to October 2025, using a 10-group long-short hedging strategy[7] **Model Evaluation**: The model demonstrates significant stock selection ability, outperforming traditional momentum factors in terms of stability and performance[6][7] Model Backtesting Results - **"Day and Night Convergence" New Momentum Factor**: - Annualized Return: 18.15% - Annualized Volatility: 8.68% - Information Ratio (IR): 2.09 - Monthly Win Rate: 78.01% - Maximum Drawdown: 9.07%[1][7][14] Quantitative Factors and Construction Methods - **Factor Name**: "Day and Night Convergence" New Momentum Factor **Factor Construction Idea**: The factor leverages the distinct characteristics of price-volume relationships during intraday and overnight trading sessions to enhance the signal strength of momentum factors[7] **Factor Construction Process**: 1. Separate the trading period into intraday and overnight sessions 2. Analyze the price-volume relationship for each session to identify unique features 3. Combine the improved intraday and overnight factors into a single momentum factor 4. Test the factor on the entire A-share market (excluding Beijing Stock Exchange stocks) from February 2014 to October 2025, using a 10-group long-short hedging strategy[7] **Factor Evaluation**: The factor significantly outperforms traditional momentum factors, with higher stability and better stock selection ability[6][7] Factor Backtesting Results - **"Day and Night Convergence" New Momentum Factor**: - Annualized Return: 18.15% - Annualized Volatility: 8.68% - Information Ratio (IR): 2.09 - Monthly Win Rate: 78.01% - Maximum Drawdown: 9.07%[1][7][14] - **Traditional Momentum Factor**: - Information Ratio (IR): 1.09 - Monthly Win Rate: 62.75% - Maximum Drawdown: 20.35%[6] October 2025 Performance - **"Day and Night Convergence" New Momentum Factor**: - Long Portfolio Return: 0.85% - Short Portfolio Return: -2.35% - Long-Short Hedging Return: 3.20%[1][10]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods Model Name: TPS (Turnover Price Stability) - **Model Construction Idea**: The TPS factor is constructed from the perspective of examining the stability of daily turnover rates, aiming to improve the traditional turnover rate factor by incorporating price factors[1][9]. - **Model Construction Process**: - Calculate the average turnover rate over the past 20 trading days (Turn20). - Neutralize the turnover rate by market capitalization. - Use the shadow difference as the price factor to pair with the Turn20 factor. - Construct the TPS factor by combining the turnover rate and the price factor[6][9]. - **Model Evaluation**: The TPS factor significantly outperforms traditional turnover rate factors and maintains good stock-picking ability even after removing market style and industry interference[1][9]. Model Name: SPS (Stable Price Stability) - **Model Construction Idea**: Similar to the TPS factor, the SPS factor is constructed from the perspective of examining the stability of daily turnover rates, aiming to improve the traditional turnover rate factor by incorporating price factors[1][9]. - **Model Construction Process**: - Calculate the average turnover rate over the past 20 trading days (Turn20). - Neutralize the turnover rate by market capitalization. - Use the shadow difference as the price factor to pair with the Turn20 factor. - Construct the SPS factor by combining the turnover rate and the price factor[6][9]. - **Model Evaluation**: The SPS factor significantly outperforms traditional turnover rate factors and maintains strong stock-picking ability even after removing market style and industry interference[1][9]. Model Backtesting Results TPS Model - **Annualized Return**: 39.34%[1][11] - **Annualized Volatility**: 15.74%[1][11] - **Information Ratio (IR)**: 2.50[1][11] - **Monthly Win Rate**: 77.54%[1][11] - **Maximum Drawdown**: 18.19%[1][11] SPS Model - **Annualized Return**: 43.18%[1][12] - **Annualized Volatility**: 13.16%[1][12] - **Information Ratio (IR)**: 3.28[1][12] - **Monthly Win Rate**: 83.47%[1][12] - **Maximum Drawdown**: 11.58%[1][12] Factor Backtesting Results TPS Factor - **10-Group Long Portfolio Return**: 4.09%[1][12] - **10-Group Short Portfolio Return**: -1.73%[1][12] - **10-Group Long-Short Portfolio Return**: 5.82%[1][12] SPS Factor - **10-Group Long Portfolio Return**: 4.22%[1][14] - **10-Group Short Portfolio Return**: -0.78%[1][14] - **10-Group Long-Short Portfolio Return**: 5.00%[1][14]

金工定期报告 20251106 新价量相关性因子绩效月报 20251031 2025 年 11 月 06 日 [Table_Tag] [Table_Summary] 报告要点 证券研究报告·金融工程·金工定期报告 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 庞格致 执业证书：S0600524090003 panggz@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《 新 价 量 相 关 性 因 子 绩 效 月 报 20250930》 2025-10-14 《"技术分析拥抱选股因子"系列研究 （十四）：RPV 聪明版——聪明换手率 是更好的配料》 2023-09-27 东吴证券研究所 1 / 7 请务必阅读正文之后的免责声明部分 ◼ 新价量相关性 RPV 因子多空对冲绩效（全市场）：2014 年 1 月至 2025 年 10 月，新价量相关性 RPV 因子在全体 A 股（剔除北交所股票）中， 10 分组多空对冲的年化收益为 14.38% ...

- The "Rediscover Confidence 2.0" RCP factor is constructed based on behavioral finance principles, specifically addressing the common expectation bias of overconfidence. The CP factor is initially created using the time gap between rapid price increases and decreases as a proxy variable. Subsequently, the RCP factor is derived by orthogonalizing the CP factor with intraday returns, using the residuals to represent the second-generation factor[6][7] - The RCP factor is further refined by replacing ranking values with standardized factor values to preserve factor information, resulting in improved performance. This adjustment enhances the purity and effectiveness of the RCP factor[7] - The RCP factor demonstrates strong performance metrics during the backtesting period from February 2014 to October 2025. The annualized return is 17.55%, annualized volatility is 7.85%, IR is 2.24, monthly win rate is 77.30%, and maximum monthly drawdown is 7.46%[7][12] - During October 2025, the RCP factor's 10-group long portfolio achieved a return of 2.40%, the short portfolio achieved a return of 1.97%, and the long-short hedged portfolio achieved a return of 0.43%[1][10] - The RCP factor's backtesting results from January 2014 to August 2022 show an IC mean of 0.04, annualized ICIR of 3.27, annualized return of 20.69%, IR of 2.91, and a monthly win rate of 81.55%[1][6]

Investment Rating - The report indicates an "Overweight" rating for the industry, suggesting a positive outlook for the sector in the next six months [32]. Core Insights - The report highlights a significant increase in merger and acquisition (M&A) activities, with a total of 269 M&A events recorded, including 58 major transactions during the period from October 13 to November 2, 2025 [10]. - The report emphasizes the importance of policy updates aimed at enhancing the inclusivity and adaptability of capital market regulations, particularly in supporting technology innovation and meeting diverse investor needs [8]. - The restructuring index outperformed the Wind All A index by 1.61% during the reporting period, indicating a strong performance relative to the broader market [24]. Summary by Sections 1. M&A Dynamics Review - A total of 13 failed M&A events were noted, while 44 M&A transactions were completed, including 2 major ones [10][18]. 2. Policy Updates - On October 31, the Chairman of the CSRC emphasized the need for more inclusive policies for M&A and capital market operations [8]. - The Shenzhen government aims to enhance the quality of listed companies, targeting a total market value exceeding 20 trillion yuan by the end of 2027 [8]. 3. Major M&A Updates - The report lists several significant M&A transactions, including the acquisition of 100% equity in various companies across different sectors, with transaction values reaching hundreds of millions to billions of yuan [14][16]. 4. Control Changes - Six companies reported changes in actual control, indicating shifts in ownership that may impact their strategic direction and market performance [21]. 5. Market Performance - The restructuring index showed a positive trend, with a rolling 20-day return turning from negative to positive, reflecting improved investor sentiment in the sector [24].

Macro Strategy - The core view indicates that actual interest rates remain the key anchor for gold prices, with fluctuations driven by macroeconomic policies and geopolitical factors [1][11] - In November, gold prices are expected to be influenced by geopolitical situations, trade negotiations, and macro policies, with a potential for continued high-level fluctuations [1][11] - The CME interest rate futures suggest a widespread expectation of a 25 basis point rate cut by the Federal Reserve in December, which may support gold prices [1][11] Fixed Income Strategy - The report discusses a trading strategy of "long old bonds and short new bonds" based on the behavior of active bond spreads, which typically exhibit a jump during the switching process [2][12] - The active bond spread trading strategy remains profitable, with the maximum spread observed at 9.8 basis points since 2023, indicating a favorable trading environment [2][12] Food and Beverage Industry - The beer sector is currently viewed as being at a bottoming phase, with expectations for demand recovery driven by macro policy changes and improved fundamentals in 2024 [4][14] - The report highlights that the beer sector's revenue for the first three quarters of 2025 reached 617.26 billion yuan, with a year-on-year growth of 1.99% [4][15] - Key players such as Qingdao Beer and Yanjing Beer are expected to perform well, with a focus on high-growth segments and defensive strategies [4][15] Healthcare Products Industry - The healthcare products sector showed a year-on-year revenue growth of 18% and a net profit increase of 122% in Q3 2025, indicating a positive trend despite individual stock variations [16][17] - Companies like Tongrentang and Minsheng Health are highlighted for their strong performance and growth potential in the healthcare market [16][17] Nonferrous Metals Industry - The report notes that industrial metals are experiencing high-level fluctuations, with copper prices expected to strengthen after a period of consolidation due to supply disruptions and improved macro sentiment [5][19] - Aluminum prices have shown an upward trend, supported by supply stability and increased demand, particularly in the context of geopolitical developments [5][19] Media Industry - The media sector reported a revenue of 1,279 billion yuan in Q3 2025, reflecting a 7% year-on-year increase, with the gaming sector showing particularly strong performance [6][20] - The gaming segment's net profit grew by 76% year-on-year, driven by successful product launches and a stable revenue growth trajectory [6][20]