- The Stability of Turnover Rate (STR) factor was constructed to evaluate the stability of daily turnover rates, aiming to improve stock selection efficiency by focusing on turnover rate stability rather than absolute turnover rate values [1][8][9] - STR factor construction involves calculating the stability of daily turnover rates using simple daily frequency data, referencing the methodology of the Uniformity of Turnover Rate Distribution (UTD) factor, which analyzes turnover rate distribution uniformity based on minute-level data [8] - STR factor performance metrics include annualized return of 40.88%, annualized volatility of 14.45%, IR of 2.83, monthly win rate of 76.79%, and maximum monthly drawdown of 9.96% during the period from January 2006 to October 2025 [9][12] - STR factor monthly performance in October 2025 showed a long portfolio return of 4.62%, a short portfolio return of -1.43%, and a long-short portfolio return of 6.06% [10] - Traditional turnover rate factor (Turn20), calculated as the average daily turnover rate over the past 20 trading days with market capitalization neutralization, demonstrated a monthly IC mean of -0.072, annualized ICIR of -2.10, annualized return of 33.41%, IR of 1.90, and monthly win rate of 71.58% from January 2006 to April 2021 [6] - Turnover rate factor logic indicates stocks with lower turnover rates in the past month are more likely to rise in the following month, while stocks with higher turnover rates are more likely to decline, though this logic has limitations due to significant intra-group return variance in high-turnover portfolios [7] - STR factor evaluation highlights its simplicity and effectiveness in stock selection, even after removing common style and industry biases, showcasing robust performance [1][8]

Export Data Overview - In October, China's exports (in USD) recorded a year-on-year decline of -1.1%, down from +8.3% in September, marking the first negative growth since March 2025[3] - Exports to the US saw a significant drop of -25.2%, slightly improving from September's -27.0%[3] - Exports to ASEAN maintained resilience with a growth rate of +11.0%, down from +15.6% in September[3] Regional Export Performance - Exports to the EU grew by only +0.9%, a sharp decline from +14.2% in September[3] - Exports to Africa and Latin America still showed positive growth but decreased significantly, from +56.4% and +15.2% in September to +10.5% and +2.1% respectively[3] Product Category Insights - Labor-intensive products like clothing, bags, and footwear experienced substantial declines, with growth rates of -16.0%, -25.7%, and -21.0% respectively[3] - High-tech manufacturing exports remained strong, with mobile phone exports dropping from -1.7% in September to -16.6% in October, while integrated circuits, automobiles, and ships recorded growth rates of +26.9%, +34.0%, and +68.4% respectively[3] Seasonal and Trade Relationship Factors - October's export data reflects seasonal trends, with a historical average month-on-month decline of -3.8% due to the National Day holiday[3] - The easing of US-EU trade tensions has contributed to the decline in exports to the EU, with a month-on-month decrease of -8.6% in October[3] - The phenomenon of "export rush" appears to be waning, impacting growth rates to ASEAN and other emerging markets[3] Future Outlook and Risks - There is a potential risk of further decline in export growth rates in Q4, with the possibility of turning negative due to higher base effects in November and December[3] - Ongoing uncertainties in US-China trade relations and a potential slowdown in global economic growth pose additional risks to export performance[3]

Quantitative Factors and Construction Methods - **Factor Name**: UTR2.0 (Upgraded Turnover Rate 2.0) **Factor Construction Idea**: The UTR2.0 factor is an upgraded version of the original UTR factor. It combines the "volume stability factor" (STR) and the "small volume factor" (Turn20) using a new methodology. The key improvement involves transitioning from ordinal scale to ratio scale for factor values, which retains more information and adjusts the impact of the small volume factor based on the stability of the volume[6][7]. **Factor Construction Process**: 1. At the end of each month, calculate the small volume factor (Turn20) and the volume stability factor (STR) for all stocks[6]. 2. Sort all samples by STR in ascending order and assign scores (1, 2, ..., N), where N is the total number of samples. This is recorded as "Score 1"[6]. 3. For the top 50% of samples ranked by STR, sort them by Turn20 in descending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 2". The final score for these stocks is "Score 1 + Score 2"[6]. 4. For the bottom 50% of samples ranked by STR, sort them by Turn20 in ascending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 3". The final score for these stocks is "Score 1 + Score 3"[6]. 5. Transition from ordinal scale to ratio scale by introducing a coefficient for Turn20, which is a function of STR. The coefficient reflects the impact of Turn20 on returns: the more stable the volume, the stronger the positive impact; the less stable the volume, the stronger the negative impact. The formula for UTR2.0 is: $$ \mathrm{UTR2.0} = \mathrm{STR} + \text{softsign}(\mathrm{STR}) \cdot \mathrm{Turn20} $$ where $\text{softsign}(x) = \frac{x}{1 + |x|}$[7]. **Factor Evaluation**: The UTR2.0 factor improves upon the original UTR factor by achieving better performance in terms of volatility, information ratio (IR), and monthly win rate, although its returns are slightly lower[6][7]. --- Factor Backtesting Results - **UTR2.0 Factor**: - Annualized Return: 40.48% - Annualized Volatility: 14.98% - Information Ratio (IR): 2.70 - Monthly Win Rate: 75.53% - Maximum Drawdown: 11.03%[8][12] - **October 2025 Performance**: - Long Portfolio Return: 4.64% - Short Portfolio Return: -1.50% - Long-Short Portfolio Return: 6.14%[10]

Exchange Rate Outlook - The report predicts that the RMB may enter a medium to long-term appreciation cycle, with expectations for the USD/CNY exchange rate to break below 7.0 in 2026, potentially reaching 6.70-6.80 by the end of that year[1] - The RMB has ended a three-year depreciation cycle, with a significant appreciation expected to begin from April 2025, when the USD/CNY was at 7.42[6] Trade and Current Account - The current account surplus is expected to stabilize, driven by a recovery in merchandise trade, with a monthly surplus reaching $63.9 billion in September 2025, the highest since 2020[18] - The merchandise trade surplus has been expanding, with a single-month surplus of $72.4 billion recorded in September 2025[18] Investment Dynamics - Foreign investment in RMB-denominated assets is increasing, with a net inflow of $10.57 billion in securities investments by September 2025, reversing previous outflows[34] - Foreign investors have increased their holdings in A-shares by 622.9 billion CNY, indicating a strong interest in the Chinese equity market[42] Risk Factors - Potential risks include uncertainties in U.S. fiscal and tariff policies, unclear paths for Federal Reserve interest rate cuts, and political risks in non-U.S. regions that could lead to currency depreciation[1] - The report highlights the importance of monitoring the evolving dynamics of the U.S.-China interest rate differential, which significantly influences foreign investment behavior in Chinese bonds[51]

Quantitative Models and Construction Methods 1. Model Name: Stability of the Growth Rate of Turnover Rate (GTR) - **Model Construction Idea**: The model identifies stocks with high turnover rate volatility but stable growth or decline in turnover rate, which may indicate potential future price increases. The model introduces a new metric, "stability of the growth rate of turnover rate," to capture this trend[6][15]. - **Model Construction Process**: The GTR factor is constructed based on the acceleration property of turnover rate changes, combined with the stability of the new factor. The specific formula or mathematical representation of the GTR factor is not provided in the report[6][15]. - **Model Evaluation**: The GTR factor demonstrates low correlation (less than 0.1) with other turnover rate factors in the same series, indicating its uniqueness and potential to enhance the performance of related factors[6]. 2. Model Name: Purely Enhanced TPS_Turbo Factor - **Model Construction Idea**: The TPS_Turbo factor is derived by applying a "purely enhanced" method to combine the GTR factor with Turn20 and STR factors, aiming to improve stock selection capabilities[6]. - **Model Construction Process**: The TPS_Turbo factor is constructed by integrating the GTR factor with Turn20 and STR factors using a purely enhanced method. The exact mathematical formulation of the combination is not provided in the report[6]. - **Model Evaluation**: The TPS_Turbo factor demonstrates superior stock selection ability compared to its components, as evidenced by its performance metrics[6]. 3. Model Name: Purely Enhanced SPS_Turbo Factor - **Model Construction Idea**: Similar to TPS_Turbo, the SPS_Turbo factor is created by applying the "purely enhanced" method to combine the GTR factor with other turnover rate factors, aiming to achieve better performance in stock selection[6]. - **Model Construction Process**: The SPS_Turbo factor is constructed by combining the GTR factor with other turnover rate factors using the purely enhanced method. The specific formula or mathematical representation is not provided in the report[6]. - **Model Evaluation**: The SPS_Turbo factor exhibits excellent stock selection performance, surpassing the other factors in the series[6]. --- Model Backtesting Results 1. GTR Factor - **Annualized Return**: 13.11% - **Annualized Volatility**: 10.22% - **IR**: 1.28 - **Monthly Win Rate**: 66.53% - **Maximum Drawdown**: 10.81%[7][11] 2. TPS_Turbo Factor - **Annualized Return**: 36.11% - **Annualized Volatility**: 13.22% - **IR**: 2.73 - **Monthly Win Rate**: 78.39% - **Maximum Drawdown**: 9.86%[7][11] 3. SPS_Turbo Factor - **Annualized Return**: 37.21% - **Annualized Volatility**: 10.87% - **IR**: 3.42 - **Monthly Win Rate**: 81.36% - **Maximum Drawdown**: 7.22%[7][11] --- Factor Backtesting Results (October 2025) 1. GTR Factor - **Long Portfolio Return**: 2.27% - **Short Portfolio Return**: 0.96% - **Long-Short Portfolio Return**: 1.31%[15] 2. TPS_Turbo Factor - **Long Portfolio Return**: 3.19% - **Short Portfolio Return**: -0.33% - **Long-Short Portfolio Return**: 3.52%[16] 3. SPS_Turbo Factor - **Long Portfolio Return**: 3.65% - **Short Portfolio Return**: 0.09% - **Long-Short Portfolio Return**: 3.56%[19]

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]