Group 1: Report Industry Investment Rating - No information provided on the industry investment rating in the report Group 2: Core Viewpoints of the Report - In the primary market, 17 new green bonds were issued in the inter - bank and exchange markets from November 3, 2025, to November 7, 2025, with a total issuance scale of about 41.219 billion yuan, an increase of 31.983 billion yuan from the previous week. The issuance terms are mostly 3 years, and the issuers have diverse natures, ratings, regions, and bond types [1] - In the secondary market, the weekly trading volume of green bonds from November 3, 2025, to November 7, 2025, totaled 71.3 billion yuan, an increase of 4.4 billion yuan from the previous week. Non - financial corporate credit bonds, financial institution bonds, and interest - rate bonds had the highest trading volumes. Green bonds with a term of less than 3 years had the highest trading volume. The financial, public utilities, and transportation equipment industries had the highest trading volumes, and Beijing, Guangdong, and Hubei were the top three regions in terms of trading volume [2] - From November 3, 2025, to November 7, 2025, the overall deviation of the trading average price valuation of green bonds was not large. The discount trading amplitude was greater than the premium trading, and the discount trading ratio was less than the premium trading. The report lists the top three discount and premium individual bonds and their characteristics [3] Group 3: Summary by Relevant Catalogs Primary Market Issuance - In the week of November 3 - 7, 2025, 17 new green bonds were issued in the inter - bank and exchange markets, with a total scale of about 41.219 billion yuan, up 31.983 billion yuan from the previous week. Issuance terms are mostly 3 years, issuers include local state - owned enterprises, central enterprise subsidiaries, central state - owned enterprises, large private enterprises, etc., with most having AAA or AA+ ratings. Issuers are from various regions, and the bond types include medium - term notes, private corporate bonds, enterprise ABS, and ABN [1] Secondary Market Trading - Weekly trading volume from November 3 - 7, 2025, was 71.3 billion yuan, up 4.4 billion yuan from the previous week. By bond type, non - financial corporate credit bonds, financial institution bonds, and interest - rate bonds had the highest trading volumes. By term, bonds under 3Y had the highest trading volume, accounting for about 84.63%. By industry, the financial, public utilities, and transportation equipment industries had the highest trading volumes. By region, Beijing, Guangdong, and Hubei had the highest trading volumes [2] Valuation Deviation of Top 30 Individual Bonds - The overall deviation of the trading average price valuation of green bonds was not large. The discount trading amplitude was greater than the premium trading, and the discount trading ratio was less than the premium trading. For discount bonds, the top three discount rates were for 20 Changding Green Bond 02 (-1.0176%), 25 Puzhi G1 (-0.7639%), and 25 Guangxi Jintou PPN002 (Green) (-0.4849%). For premium bonds, the top three premium rates were for 20 Guangdong Bond 19 (0.3269%), 20 Fujian Bond 14 (0.3112%), and 25 Anyang Iron and Steel MTN004 (Science and Technology Innovation Bond) (0.2649%) [3]

Macro Strategy - The core view indicates that actual interest rates remain the key anchor for gold prices, with fluctuations in monetary policy impacting market sentiment and gold's value [1][6] - In October, gold prices experienced a "rise then fall" pattern, influenced by geopolitical tensions and economic data, leading to a 5.27% increase in the Shanghai gold futures by the end of the month [1][6] - The outlook for November suggests that gold prices will be driven by geopolitical situations, trade negotiations, and macroeconomic policies, with expectations of continued high volatility [1][6] Fixed Income - The report on Qizhong Convertible Bonds anticipates a listing price between 126.64 and 140.59 CNY, with a subscription rate of 0.0028% [2][7] - The bond has a total issuance scale of 850 million CNY, with proceeds allocated for advanced packaging and testing projects [7][8] Industry Analysis - The food and beverage industry report highlights a 5.5% decline in total revenue and a 6.7% drop in net profit for the liquor sector in the first three quarters of 2025, with a more pronounced 18.3% revenue decline in Q3 [3][10] - The report notes that the recovery of consumption scenarios is slow, particularly in business and personal dining contexts, leading to sustained pressure on demand for high-end and mid-range liquor [3][10] - Investment recommendations suggest focusing on companies that are likely to see early signs of recovery and have strong growth potential, such as Luzhou Laojiao and Shanxi Fenjiu, while also considering companies with solid governance and dividend yields [4][11] Company Recommendations - Tiangong International is highlighted for its potential growth in titanium alloy production, with projected revenues of 5.2 billion, 6.1 billion, and 7 billion CNY from 2025 to 2027, reflecting growth rates of 8%, 16%, and 14% respectively [5][11] - The company is positioned well in the consumer electronics sector and is expanding into new materials for robotics and nuclear fusion applications, which are expected to drive future growth [5][11]

Group 1 - The report highlights that Zhuomei Convertible Bond (123260.SZ) has a total issuance scale of 450 million yuan, with net proceeds used for construction and equipment procurement [4][10] - The current bond floor valuation is 83.70 yuan, with a yield to maturity (YTM) of 2.97%, indicating general bond floor protection [12][16] - The initial conversion price is set at 52.3 yuan per share, with the conversion parity at 107.11 yuan, reflecting a negative premium rate of -6.64% [13][14] Group 2 - The company, Xingyuan Zhuomei, is located in Ningbo, known as the hometown of die-casting molds, and specializes in designing and manufacturing large and medium-sized aluminum and magnesium alloy die-casting molds [18][32] - Since 2019, the company's revenue has shown steady growth, with a compound annual growth rate (CAGR) of 22.69% from 2019 to 2024, achieving a revenue of 409 million yuan in 2024, a year-on-year increase of 16.01% [19][20] - The net profit attributable to the parent company has also fluctuated, with a CAGR of 10.31% from 2019 to 2024, reaching 80 million yuan in 2024, a slight increase of 0.31% year-on-year [19][24] Group 3 - The revenue of Xingyuan Zhuomei primarily comes from precision die-casting parts made of aluminum and magnesium alloys, with the proportion of project operation business revenue increasing from 56.09% in 2021 to 69.40% in 2024 [20][23] - The company has experienced a decline in net profit and gross profit margins, with the sales net profit margin ranging from 33.46% to 19.66% from 2019 to 2024 [24][29] - The company has a strong focus on research and development, continuously innovating and increasing R&D expenses, which has contributed to its competitive edge in the magnesium alloy precision die-casting sector [32][24]

- 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]