1. Report Industry Investment Rating No information provided in the content. 2. Core Viewpoints of the Report - The bond market adjusted until late September, and the cost - effectiveness of coupon assets continued to increase. A repair market started around the National Day holiday, but there was a significant term differentiation, with short - duration assets being more favored [5][10][34]. - The current proportion of ordinary credit bonds with valuations in the 2.2% - 2.6% range is relatively high, offering a wide selection [5][34]. - The strategy should prioritize liquidity. There are some opportunities to participate in 3 - 5 - year bank secondary capital bonds after adjustment. Also, considering the curve steepness, it is advisable to continue participating in the sinking of weak - quality urban investment bonds with a 1 - 3 - year term. For ultra - long - term bonds, although the yield cost - effectiveness has increased after adjustment, the recent market is highly uncertain, and the ultra - long - duration strategy may only be suitable for some allocation investors [5][34]. 3. Summary According to Related Catalogs 3.1 Bond Market Performance - **Overall Repair and Term Differentiation**: The bond market experienced continuous adjustment in September, and the repair market started around the National Day holiday. The short - end of credit bonds had a stronger repair, while ultra - long - term credit bonds had a weaker repair, with some varieties still adjusting and performing worse than the same - term interest - rate bonds [10][12][34]. - **Yield Changes**: From September 28 to October 11, 2025, the yields of 1Y, 2Y, 3Y, 4Y, and 5Y national bonds decreased by 1.3BP, 3.0BP, 3.7BP, 4.0BP, and 4.4BP respectively. The yields of the same - term AAA medium - term notes decreased by 7.7BP, 5.7BP, 4.2BP, 3.0BP, and 4.4BP respectively, and the yields of AA + medium - term notes decreased by 5.7BP, 2.7BP, 2.2BP, 1.0BP, and 1.4BP respectively [10][11]. - **Curve Shape**: The steepness of the 1 - 2 - year and 2 - 3 - year periods for all ratings was the highest, and the steepness of the 3 - 5 - year period for low - rated bonds was also relatively high, showing a certain bear - steepening characteristic [14]. - **Historical Quantiles**: Currently, 2 - 3Y, especially around 3Y, coupon assets have certain cost - effectiveness after adjustment. The valuation yields to maturity of 1Y - AAA, 3Y - AAA, 5Y - AAA, 1Y - AA +, 3Y - AA +, 5Y - AA +, 1Y - AA, and 3Y - AA ChinaBond medium - short - term notes from September 28 to October 11, 2025, were at the 23.87%, 40.54%, 49.54%, 25.22%, 39.63%, 46.84%, 28.15%, and 38.73% levels since 2024. The historical quantiles of the 1Y - AAA, 3Y - AAA, 5Y - AAA, 1Y - AA +, 3Y - AA +, 5Y - AA +, 1Y - AA, and 3Y - AA credit spreads were 1.12%, 34.98%, 74.04%, 2.25%, 31.37%, 56.43%, 13.54%, and 29.79% respectively, indicating that the cost - effectiveness around 3Y was relatively high [16]. 3.2 Perpetual and Subordinated Bonds (Er Yong Bonds) - **Market Characteristics**: The market of Er Yong bonds was strongly synchronized, with an obvious "volatility amplifier" characteristic. The repair degree of 1Y - 5Y was higher than that of ordinary credit bonds, but the market for ultra - long - term bonds was poor and continued to weaken [3][18]. - **Yield Changes**: The yields of 1 - 5 - year, 7 - year, and 10 - year AAA - bank secondary capital bonds decreased by 7.89BP, 10.18BP, 10.93BP, 11.20BP, 7.25BP, 3.84BP, and increased by 6.69BP respectively. Currently, the part of the curve above 3 years was still 30BP - 62BP away from the lowest yield point since 2025. Compared with the sharp decline at the end of July, the yield points above 3 years had broken through new highs, and the adjustment amplitude was higher than that of the sharp decline at the end of July [18]. 3.3 Institutional Behavior - **Trading and Allocation**: Public funds and other trading desks continued to sell credit bonds, while wealth management, insurance, and other allocation desks moderately bought on dips, but the incremental purchases were limited, and the overall demand was weak [4][26]. - **Public Funds**: Since mid - August, public funds have sold 3 - 5 - year secondary capital bonds worth 47 billion yuan, with a much higher selling intensity than in previous years [4][27]. - **Wealth Management**: Since August, the weekly net purchase scale of ordinary credit bonds by bank wealth management has remained stable, and the weekly change in the stock scale of wealth management has also been small, indicating that the liability side of wealth management has been relatively stable during this adjustment, but the incremental allocation demand is also weak [4][27]. - **Insurance Funds**: Since August, insurance funds have continued to buy on dips, with a relatively high increase in ordinary credit bonds, reaching 70.8 billion yuan from August to the present. However, since it is not the peak allocation period, and the strengthening of equity assets has also suppressed the preference of insurance funds for fixed - income assets to some extent, the overall allocation demand is not strong [4][27]. - **Credit Bond ETFs**: The performance of credit bond ETFs has been below expectations. The scale and net value of credit market - making ETFs have declined significantly. For science - innovation ETFs, the listing of the second batch of products in late September provided a short - term boost to the overall market, but the sustainability was weak [4][28].

Group 1: Macroeconomic Overview - The U.S. government shutdown continues, leading to a slight increase in unemployment claims, with initial claims rising from 224,000 to 235,000 as of the week ending October 4[2] - The number of individuals receiving ongoing unemployment benefits increased from 1.919 million to 1.927 million[2] - Historical context shows that previous shutdowns, like in October 2013, resulted in a significant rise in unemployment claims, indicating potential future increases if the shutdown persists[3] Group 2: Market Reactions - U.S. stock markets experienced a sharp decline, with the Dow Jones dropping 878.82 points (1.9%) to close at 45,479.60 points, and the S&P 500 and Nasdaq falling by 2.71% and 3.56%, respectively[9] - High valuation tech stocks led the market downturn, with Nvidia and AMD dropping by 5% and nearly 8%, respectively[9] - Despite current high valuations, tech stocks are driven by strong fundamentals, suggesting potential for future growth once policy uncertainties are resolved[3] Group 3: Consumer Confidence and Inflation Expectations - The University of Michigan's consumer confidence index slightly decreased from 55.1 in September to 55 in October[10] - One-year inflation expectations fell from 4.7% to 4.6%, while five-year expectations remained stable at 3.7%[10] Group 4: Risks and Future Outlook - Ongoing trade tensions could lead to decreased market risk appetite, putting pressure on tech stock valuations[4] - If corporate earnings fall short of expectations or major companies report disappointing results, the market's high valuation logic may be re-evaluated, triggering potential corrections[23]

Industry Investment Rating - The investment rating for the real estate industry is "Outperform the Market" and is maintained [1] Core Viewpoints - The report indicates that the sales revenue of the top 100 real estate companies in the first nine months of 2025 was CNY 26,065.9 billion, a year-on-year decrease of 12.2%. However, the decline has narrowed by 1.1 percentage points compared to the first eight months. In September alone, sales increased by 11.9% month-on-month. The market is stabilizing, but high inventory levels in key cities remain a concern, particularly in third and fourth-tier cities where sales continue to lag [4][5] - The report highlights that the policy toolbox is continuously being enriched, but no nationwide strong stimulus policies have been introduced yet. The market is looking forward to special policies for October [4] Summary by Relevant Sections 1. Industry Fundamentals Tracking 1.1 New Housing Transactions and Inventory - In the last week, the new housing transaction area in 30 major cities was 121.78 million square meters, with a cumulative total of 68,646.7 million square meters for the year, reflecting a year-on-year decrease of 4.2%. The average transaction area over the past four weeks was 154.64 million square meters, down 8% year-on-year and 2.6% month-on-month [5][13] - The average transaction area for first-tier cities was 46.79 million square meters, up 0.8% year-on-year but down 5.7% month-on-month. For second-tier cities, it was 74.99 million square meters, up 0.5% year-on-year and down 0.2% month-on-month. Third-tier cities saw an average transaction area of 32.86 million square meters, down 30.2% year-on-year and down 3.3% month-on-month [5][13] 1.2 Second-Hand Housing Transactions and Listings - In the last week, the transaction area for second-hand housing in 20 cities was 134.71 million square meters, with a cumulative total of 86,440.6 million square meters for the year, reflecting a year-on-year increase of 14.4%. The average transaction area over the past four weeks was 165.98 million square meters, up 7% year-on-year but down 10.2% month-on-month [6][19] - As of September 29, 2025, the national second-hand housing listing index was 7.51, down 29.1% month-on-month, and the listing price index was 150.87, down 0.15% month-on-month [23] 1.3 Land Market Transactions - In the last week, 61 residential land plots were newly supplied in 100 major cities, with 39 plots sold. The average transaction price for residential land was CNY 5,466 per square meter, with a premium rate of 2.58%, down 0.28 percentage points month-on-month [28][29] 2. Market Review - Last week, the A-share real estate index fell by 0.82%, while the CSI 300 index decreased by 0.51%, indicating that the real estate index underperformed the CSI 300 by 0.3 percentage points. In contrast, the Hong Kong Hang Seng Property Services and Management Index rose by 0.65% [33][34]

Group 1: Report General Information - Report Type: Fixed Income Report [1] - Release Time: October 14, 2025 [1] - Analysts: Liang Weichao, Xie Peng [2] Group 2: Core Views - Core view of the weekly report: Track the liquidity score of individual bonds in different bond sectors based on the bond asset liquidity score of qb. For urban investment bonds, the number of high - grade and high - liquidity bond items with medium - short duration and low rating has increased. For industrial bonds, the number of high - grade and high - liquidity bond items with medium - long duration and medium - high rating has generally remained stable [2]. Group 3: Urban Investment Bonds Quantity and Distribution - By region: The number of high - grade and high - liquidity bond items in Sichuan has increased, while that in Shandong, Tianjin, and Chongqing has generally remained stable, and that in Jiangsu has decreased [2][9]. - By duration: The number of high - grade and high - liquidity bond items within 1 year and 1 - 2 years has increased, while that in 2 - 3 years, 3 - 5 years, and over 5 years has decreased [2][9]. - By implied rating: The number of high - grade and high - liquidity bond items of AA+ and AA - has increased, especially AA -; the number of AAA has generally remained stable, and the number of AA and AA(2) has decreased [2][9]. Yield - By region: The yields of high - grade and high - liquidity bond items in Jiangsu, Shandong, Sichuan, Tianjin, and Chongqing have generally declined, with the decline ranging from 1 - 7bp [11]. - By duration: The yields of high - grade and high - liquidity bond items within 1 year, 1 - 2 years, 2 - 3 years, 3 - 5 years, and over 5 years have generally declined, with the decline ranging from 1 - 9bp [11]. - By implied level: The yields of high - grade and high - liquidity bond items of AAA, AA+, AA, AA(2), and AA - have generally declined, with the decline ranging from 1 - 10bp [11]. Score Changes - Ascending top twenty: The main body levels are mainly AA, concentrated in Jiangsu, Zhejiang, and Shandong, and mainly involve industries such as building decoration and comprehensive [12]. - Descending top twenty: The main body levels are mainly AA, distributed in regions such as Jiangsu and Chongqing, and mainly involve industries such as building decoration, comprehensive, and retail [12]. Group 4: Industrial Bonds Quantity and Distribution - By issuer's industry: The overall situation of transportation, coal, and steel industries has remained stable, while that of real estate and public utilities industries has decreased [3][18]. - By duration: The number of high - grade and high - liquidity bond items within 1 year and over 5 years has decreased slightly; the number of those in 1 - 2 years, 2 - 3 years, and 3 - 5 years has generally remained stable [3][18]. - By implied rating: The number of high - grade and high - liquidity bond items with an implied rating of AA+ has increased, while the number of AAA, AAA -, and AA has decreased, and the number of AAA+ has generally remained stable [3][18]. Yield - By industry: The yields of high - grade and high - liquidity bond items in real estate, public utilities, transportation, coal, and steel industries have generally declined, with the decline ranging from 2 - 9bp [20]. - By duration: The yields of high - grade and high - liquidity bond items within 1 year, 1 - 2 years, 2 - 3 years, 3 - 5 years, and over 5 years have generally declined, with the decline ranging from 2 - 8bp [20]. - By implied level: The yields of high - grade and high - liquidity bond items of AAA+, AAA, AAA -, AA+, and AA have generally declined, with the decline ranging from 3 - 11bp [20]. Score Changes - Ascending top twenty: The industries of the main bodies are mainly building decoration, real estate, and non - ferrous metals, with main body levels of AAA and AA+; the industries of the top twenty bonds are mainly transportation and building decoration [21][22]. - Descending top twenty: The main bodies mainly involve industries such as building decoration, real estate, transportation, and public utilities, with main body levels of AAA and AA+; the industries of the top twenty bonds are mainly transportation and public utilities [22].

Economic Overview - Fengdu County's GDP structure shows that the primary industry accounts for less than 15%, while the secondary industry decreased from 45.57% in 2019 to 38.87% in 2023, and the tertiary industry is approaching 50% and increasing annually[20] - In 2023, Fengdu County's total industrial output value was 136.42 billion yuan, a year-on-year decrease of 13.06%[27] - The county's permanent population was 544,200 at the end of 2023, a decrease of 8,800 from the previous year, with an urbanization rate of 51.89%, up by 0.86 percentage points[29] Fiscal Situation - Fengdu County achieved a general budget revenue of 2.765 billion yuan in 2023, with tax revenue of 1.05 billion yuan, showing a gradual increase in fiscal self-sufficiency[31] - The fiscal self-sufficiency rate reached 39.37% in 2023, placing Fengdu County among the top in Chongqing[32] - Government fund income surged by 95.25% in 2023, totaling 1.535 billion yuan, alleviating previous revenue pressures[37] Debt Pressure - As of the end of 2023, Fengdu County's government debt balance was 16.3 billion yuan, with a limited refinancing space of 163.7 billion yuan[40] - The debt-to-GDP ratio was 40.14%, indicating moderate debt pressure compared to other counties in Chongqing[45] - The county's comprehensive debt ratio was 255%, showing a decrease of 28 percentage points from the previous year, indicating manageable overall debt risk[46] Industry Analysis - Fengdu County has over 20 identified mineral resources, with significant reserves of bauxite, shale gas, and limestone, supporting industrial development[49] - The county has established a clean energy industry centered on wind, hydro, and solar power, with a total installed capacity of 710,000 kilowatts and an annual power generation of approximately 1.8 billion kilowatt-hours[52] - Fengdu's agricultural sector is robust, with a focus on six leading industries, including grain, beef cattle, and citrus, ensuring food security and economic stability[51]

证券研究报告：农林牧渔|行业周报 发布时间：2025-10-14 行业投资评级 强于大市|维持 | 行业基本情况 | | --- | | 收盘点位 | | 2992.07 | | --- | --- | --- | | 52 | 周最高 | 3158.8 | | 52 | 周最低 | 2367.56 | 行业相对指数表现 -9% -6% -3% 0% 3% 6% 9% 12% 15% 18% 21% 24% 2024-10 2024-12 2025-03 2025-05 2025-07 2025-10 农林牧渔 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:王琦 SAC 登记编号:S1340522100001 Email:wangqi2022@cnpsec.com 近期研究报告 《"反内卷"下，8 月能繁存栏下降》 - 2025.09.30 农林牧渔行业报告 （2025.9.30-2025.10.12） 节后猪价宽幅下跌 ⚫ 行情回顾：节后市场调整，农业跑赢大盘 节后仅 2 个交易日，市场整体有所调整。由于前期涨幅较小，且 相对抗跌，农林牧渔（申万）指数累计上涨 1.03%，在申万 31 个一级 ...

证券研究报告：食品饮料|行业周报 发布时间：2025-10-13 行业投资评级 强于大市 |维持 | 行业基本情况 | | | --- | --- | | 收盘点位 | 17022.92 | | 52 | 周最高 | 19550.64 | | --- | --- | --- | | 52 | 周最低 | 16379.5 | 行业相对指数表现 2024-10 2024-12 2025-03 2025-05 2025-07 2025-10 -10% -7% -4% -1% 2% 5% 8% 11% 14% 17% 食品饮料 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:蔡雪昱 SAC 登记编号:S1340522070001 Email:caixueyu@cnpsec.com 分析师:张子健 SAC 登记编号:S1340524050001 Email:zhangzijian@cnpsec.com 分析师:杨逸文 SAC 登记编号:S1340522120002 Email:yangyiwen@cnpsec.com 近期研究报告 《IFBH（6603.HK）：椰子水空间广 阔，公司产品市场品牌端持续加码、 ...

- The report introduces the **Diffusion Index Model** for industry rotation, which has been tracked for four years. The model is based on momentum strategies to capture industry trends. It showed strong performance in 2021 with excess returns exceeding 25% before experiencing a significant drawdown due to cyclical stock adjustments. In 2022, the strategy delivered stable returns with an annual excess return of 6.12%. However, in 2023 and 2024, the model faced challenges, with annual excess returns of -4.58% and -5.82%, respectively. For October 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and automobiles[26][30] - The **Diffusion Index Model** is constructed by ranking industries based on their diffusion index values, which reflect upward trends. The top six industries as of October 10, 2025, are non-ferrous metals (0.98), banking (0.951), communication (0.909), steel (0.877), electronics (0.823), and automobiles (0.813). The bottom six industries are food and beverage (0.137), consumer services (0.297), real estate (0.407), coal (0.445), transportation (0.457), and construction (0.489)[27][28][29] - The **Diffusion Index Model** achieved an average weekly return of 2.59%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 0.70%. For October, the model's excess return is -0.37%, while the year-to-date excess return is 4.60%[30] - The report also discusses the **GRU Factor Model** for industry rotation, which utilizes minute-level price and volume data processed through a GRU deep learning network. The model has shown strong adaptability in short cycles but struggles in long cycles and extreme market conditions. Since February 2025, the model has focused on growth industries but has faced difficulties in capturing excess returns due to concentrated market themes[32][38] - The **GRU Factor Model** ranks industries based on GRU factor values. As of October 10, 2025, the top six industries are comprehensive (6.64), building materials (5.21), construction (3.55), textile and apparel (3.31), transportation (2.99), and steel (2.88). The bottom six industries are computing (-41.87), food and beverage (-35.34), electronics (-34.87), non-ferrous metals (-28.25), power equipment and new energy (-26.61), and communication (-22.71)[33][36] - The **GRU Factor Model** achieved an average weekly return of 2.88%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 1.01%. For October, the model's excess return is 1.67%, while the year-to-date excess return is -6.55%[36]

- **Barra style factors**: The report tracks various style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is constructed using specific financial metrics and formulas. For example, the Profitability factor combines analyst forecast earnings price ratio, inverse price-to-cash flow ratio, and inverse price-to-earnings ratio (TTM), among others. The Growth factor incorporates earnings growth rate and revenue growth rate. These factors are used to evaluate stocks based on their historical and financial characteristics [13][14][15]. - **GRU factors**: GRU factors are derived from different training objectives, such as predicting future one-day close-to-close or open-to-open returns. Examples include `close1d`, `open1d`, `barra1d`, and `barra5d`. These factors are constructed using GRU models trained on historical data to forecast short-term stock movements. GRU factors showed strong performance, with most models achieving positive multi-period returns, except for `barra1d`, which experienced some drawdowns [20][28][32]. - **Factor testing methodology**: Factors are tested using a long-short portfolio approach. At the end of each month, stocks are ranked based on the latest factor values, with the top 10% being long positions and the bottom 10% being short positions. The portfolios are equally weighted, and factors are industry-neutralized before testing. This methodology ensures robust evaluation of factor performance across different market conditions [15][16][31]. - **Factor performance results**: - **Style factors**: Valuation, Profitability, and Leverage factors showed strong long performance, while Beta, Liquidity, and Momentum factors performed well on the short side [15][16]. - **Technical factors**: Across various time windows, low momentum and low volatility stocks generally outperformed, while high volatility and high momentum stocks underperformed. For example, the 60-day momentum factor showed a negative return of -3.11% in the last month but a positive return of 2.12% over the last six months [19][26][30]. - **GRU factors**: GRU models like `barra1d` achieved a year-to-date excess return of 5.22%, while `barra5d` and `open1d` also delivered strong multi-period returns. However, `barra1d` experienced a weekly drawdown of -1.65% [20][32][33]. - **Multi-factor portfolio performance**: The multi-factor portfolio outperformed the benchmark (CSI 1000 Index) by 1.35% over the past week. GRU-based models also showed strong excess returns, ranging from 0.68% to 1.60% over the same period. Year-to-date, the `barra1d` model achieved an excess return of 5.22% [32][33][34].

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Model Construction Idea**: The model is designed to monitor the future critical points of diffusion index changes and provide trading signals based on different methods such as threshold methods and moving average methods [6][37][38] **Model Construction Process**: - The diffusion index is calculated based on the relative price changes of micro-cap stock index constituent stocks over a specific time window. - The horizontal axis represents the percentage change in stock prices from +10% to -10% over the next N days, while the vertical axis represents the length of the retrospective window T days or the future N days. - For example, at a horizontal axis value of 0.95 and a vertical axis value of 15 days, the diffusion index value is 0.04, indicating that if all micro-cap stock index constituent stocks drop by 5% after N=5 days, the diffusion index value will be 0.04. - The model uses three methods to generate trading signals: - **First Threshold Method (Left-side Trading)**: Triggered a buy signal on September 23, 2025, with a closing value of 0.0575 [40] - **Delayed Threshold Method (Right-side Trading)**: Gave a buy signal on September 25, 2025, with a closing value of 0.1825 [44] - **Double Moving Average Method (Adaptive Trading)**: Provided a sell signal on August 4, 2025 [45] **Model Evaluation**: The diffusion index is currently at a medium level, indicating a short-term downward trend but not expected to trigger the 0.1 threshold in the next 10 trading days [37][38] Model Backtesting Results - **Diffusion Index Model**: - Current diffusion index value: 0.50 [37] - First Threshold Method: Buy signal triggered at 0.0575 on September 23, 2025 [40] - Delayed Threshold Method: Buy signal triggered at 0.1825 on September 25, 2025 [44] - Double Moving Average Method: Sell signal triggered on August 4, 2025 [45] --- Quantitative Factors and Construction Methods - **Factor Name**: Leverage Factor **Factor Construction Idea**: Measures the financial leverage of companies to assess their risk and return potential [5][31] **Factor Construction Process**: - The leverage factor is calculated as the ratio of total debt to total equity. - The rank IC for this factor is calculated weekly to evaluate its predictive power for stock returns [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Free Float Ratio Factor **Factor Construction Idea**: Evaluates the proportion of shares available for trading in the market [5][31] **Factor Construction Process**: - The free float ratio factor is calculated as the ratio of free float shares to total shares outstanding. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Dividend Yield Factor **Factor Construction Idea**: Measures the dividend yield of stocks to identify value opportunities [5][31] **Factor Construction Process**: - The dividend yield factor is calculated as the ratio of annual dividend per share to the current stock price. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] - **Factor Name**: Single-quarter ROE Factor **Factor Construction Idea**: Measures the return on equity for a single quarter to evaluate profitability [5][31] **Factor Construction Process**: - The single-quarter ROE factor is calculated as the ratio of net income to shareholders' equity for a single quarter. - Weekly rank IC is used to measure its predictive ability [31] **Factor Evaluation**: This factor demonstrated a positive rank IC this week, suggesting its utility in forecasting stock performance [5][31] - **Factor Name**: Growth Factor **Factor Construction Idea**: Measures the growth potential of companies based on financial metrics [5][31] **Factor Construction Process**: - The growth factor is calculated using metrics such as revenue growth, earnings growth, and other growth indicators. - Weekly rank IC is computed to assess its predictive power [31] **Factor Evaluation**: This factor showed a positive rank IC this week, indicating its effectiveness in predicting stock returns [5][31] --- Factor Backtesting Results - **Leverage Factor**: Rank IC this week: 0.176, historical average: -0.006 [5][31] - **Free Float Ratio Factor**: Rank IC this week: 0.156, historical average: -0.013 [5][31] - **Dividend Yield Factor**: Rank IC this week: 0.109, historical average: 0.021 [5][31] - **Single-quarter ROE Factor**: Rank IC this week: 0.091, historical average: 0.022 [5][31] - **Growth Factor**: Rank IC this week: 0.091, historical average: -0.003 [5][31]