Quantitative Factors and Construction Methods 1. Factor Name: UTR (U-Turnover Rate) - **Construction Idea**: The UTR factor combines two sub-factors, "Turn20" (volume-small factor) and "STR" (volume-stable factor), using a scoring method to address the issue of "1+1<2" in factor integration. The key idea is to prioritize stocks with stable volumes while favoring higher turnover within this stable group [6] - **Construction Process**: 1. At the end of each month, calculate the "Turn20" and "STR" values for all stocks [6] 2. Rank all stocks by the "STR" factor in ascending order and assign scores from 1 to N (N is the total number of stocks), referred to as "Score 1" [6] 3. For the top 50% of stocks ranked by "STR", rank them by "Turn20" in descending order and assign scores from 1 to N/2, referred to as "Score 2". The final score for these stocks is "Score 1 + Score 2" [6] 4. For the bottom 50% of stocks ranked by "STR", rank them by "Turn20" in ascending order and assign scores from 1 to N/2, referred to as "Score 3". The final score for these stocks is "Score 1 + Score 3" [6] 5. The resulting factor is named "UTR" [6] - **Evaluation**: The UTR factor effectively integrates the two sub-factors, achieving the goal of favoring higher turnover within stable-volume stocks [6] 2. Factor Name: UTR2.0 (U-Turnover Rate 2.0) - **Construction Idea**: UTR2.0 improves upon the original UTR factor by transitioning from ordinal scale to ratio scale for factor values. Additionally, it introduces a coefficient for the "Turn20" factor, which is a function of "STR", to better capture the varying impact of "Turn20" across different stability levels. The softsign activation function from neural networks is used to model this relationship [7] - **Construction Process**: 1. Transition from ordinal scale to ratio scale for factor values to retain more information [7] 2. Define the coefficient for "Turn20" as a function of "STR", where the coefficient increases with stability (positive impact) and decreases with instability (negative impact) [7] 3. Use the softsign activation function to model the relationship: $$ \mathrm{UTR2.0} = \mathrm{STR} + \text{softsign}(\mathrm{STR}) \cdot \mathrm{Turn20} $$ $$ \text{softsign}(x) = \frac{x}{1 + |x|} $$ [7] - **Evaluation**: Compared to the original UTR factor, UTR2.0 achieves lower returns but demonstrates superior volatility, information ratio (IR), and monthly win rate, making it a more robust factor [7][9] --- Factor Backtesting Results 1. UTR2.0 Factor - **Annualized Return**: 40.36% [9] - **Annualized Volatility**: 14.97% [9] - **Information Ratio (IR)**: 2.70 [9] - **Monthly Win Rate**: 75.74% [9] - **Maximum Drawdown**: 11.03% [9] 2. July 2025 Performance (UTR2.0 Factor) - **Long Portfolio Return**: 1.29% [9] - **Short Portfolio Return**: -0.06% [9] - **Long-Short Portfolio Return**: 1.35% [9]

证券研究报告·金融工程·金工定期报告 金工定期报告 20250806 换手率变化率的稳定 GTR 选股因子绩效月 报 20250731 [Table_Tag] [Table_Summary] 报告要点 2025 年 08 月 06 日 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《换手率变化率的稳定 GTR 因子— —助推换手率的所有家族成员》 2023-05-16 《换手率变化率的稳定 GTR 选股因 子绩效月报 20250430》 2025-05-07 东吴证券研究所 1 / 8 请务必阅读正文之后的免责声明部分 ◼ 换手率变化率的稳定 GTR 因子多空对冲绩效（全市场）：2006 年 1 月 至 2025 年 7 月，换手率变化率的稳定 GTR 因子在全体 A 股中，10 分 组多空对冲的年化收益率为 13.29%，年化波动为 10.24%，信息比率为 1.30，月度胜率为 67.09%，月度最大回撤为 10.81%； ...

证券研究报告·金融工程·金工定期报告 金工定期报告 20250806 TPS 与 SPS 选股因子绩效月报 20250731 2025 年 08 月 06 日 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 凌志杰 2022-08-16 《 TPS 与 SPS 选股因子绩效月报 20250430》 2025-05-07 东吴证券研究所 1 / 9 请务必阅读正文之后的免责声明部分 [Table_Tag] [Table_Summary] ◼ TPS 因子多空对冲绩效（全市场）： 2006 年 1 月至 2025 年 7 月，TPS 因子在全体 A 股中，10 分组多空对冲的年化收益率为 39.56%，年化波 动为 15.70%，信息比率为 2.52，月度胜率为 77.78%，月度最大回撤为 18.19%。 ◼ SPS 因子多空对冲绩效（全市场）： 2006 年 1 月至 2025 年 7 月，SPS 因子在全体 A 股中，10 分组多空对冲的年化收益率为 43.26%，年化波 动为 13.15%，信息比率为 3.29，月度胜率 ...

Investment Rating - The report maintains a "Buy" rating for the company [1] Core Insights - The ASC30 pipeline has successfully completed patient enrollment for the Phase IIa trial, with top-line data expected in Q4 2025. This compound has patent protection in the U.S. and globally until 2044 [7] - ASC30 has shown superior efficacy in previous trials, achieving a maximum weight loss of 6.5% over four weeks, outperforming a competitor's product [7] - The company has multiple promising pipelines, including ASC47 and ASC50, which are expected to have strong business development potential [7] - Revenue forecasts for 2025-2027 are projected at 0, 0.4, and 1 billion RMB respectively, with an updated target price of 14.02 RMB [7] Financial Summary - Total revenue for 2023 is projected at 566.9 million RMB, with a significant decline expected in 2024 [1] - The company is expected to incur net losses, with a projected net profit of -300.94 million RMB in 2024 and -454.44 million RMB in 2025 [1] - The price-to-earnings ratio is forecasted to improve from -49.48 in 2023 to -15.76 in 2025 [1][8]

Macro Strategy - The report analyzes three historical cases of capacity adjustment over a century, highlighting the transition from imbalance to rebalancing in supply and demand [1][13] - It concludes that capacity imbalance can lead to a negative feedback loop lasting 20-30 years if not controlled, emphasizing the need for government intervention rather than relying solely on market forces [1][13] - Effective rebalancing requires simultaneous efforts in controlling capacity, restoring credit, and stabilizing employment, rather than relying on supply or demand policies alone [1][13] Fixed Income - The report discusses the current state of urban investment bonds in Shaanxi Province, noting that the bond market is experiencing a downward trend due to macroeconomic uncertainties, but urban investment bonds still hold strong allocation value [2][14] - Shaanxi's GDP is projected to reach approximately 3.55 trillion yuan in 2024, with a growth rate of 5.30%, indicating a robust economic environment [2][14] - The report suggests a cautious approach to investing in lower-rated bonds due to compressed credit spreads, recommending a focus on higher-rated bonds with good liquidity [2][16] Industry Analysis - The report highlights Scale AI as a leading company in the AI data labeling sector, with significant revenue growth driven by demand from large enterprises and government [4][16] - Scale AI's revenue is projected to reach $20 billion by 2025, with a gross margin of 49%, indicating strong market potential despite current EBITDA losses [4][16] - The report emphasizes the importance of data quality and neutrality in the AI training data market, recommending investment in leading companies in high-quality data sets [4][16] Stock Recommendations - Pony.ai is identified as a leader in the Robotaxi sector, with significant cost reductions and safety improvements expected to drive commercialization [5][18] - Revenue forecasts for Pony.ai are projected at $0.78 billion, $1.05 billion, and $3.42 billion for 2025-2027, with a "buy" rating based on strong growth potential [5][18] - Yutong Bus is expected to maintain revenue growth of 15%-16% from 2025 to 2027, with a "buy" rating supported by a strong market position and recent contract wins [6][19]

证券研究报告·公司点评报告·饲料 中宠股份（002891） 2025 年中报业绩点评：25H1 归母净利 +42.6%，自有品牌快速成长 买入（维持） | [Table_EPS] 盈利预测与估值 | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业总收入（百万元） | 3,747 | 4,465 | 5,252 | 6,161 | 7,152 | | 同比（%） | 15.37 | 19.15 | 17.64 | 17.29 | 16.10 | | 归母净利润（百万元） | 233.16 | 393.80 | 450.68 | 553.17 | 652.15 | | 同比（%） | 120.12 | 68.89 | 14.44 | 22.74 | 17.89 | | EPS-最新摊薄（元/股） | 0.77 | 1.29 | 1.48 | 1.82 | 2.14 | | P/E（现价&最新摊薄） | 76.65 | 45.39 | 39.66 | 32.31 | 27.41 | [Table_T ...

Investment Rating - The report assigns a "Buy" rating for the company, marking its first coverage [1]. Core Views - The company is positioned to leverage its foundation in water treatment specialty chemicals to create a second growth curve in the new energy sector, with a focus on advanced materials and solutions for data center cooling systems [8][13]. - The company has demonstrated strong growth potential, with significant increases in net profit projected for 2025-2027, driven by new business lines in silicon-carbon and solid-state battery materials [8][10]. Summary by Sections 1. Water Treatment Specialty Chemicals - The company is a leading provider in the domestic water treatment sector, serving over 3,500 clients, including more than 150 Fortune 500 companies [13][16]. - The business model encompasses three core areas: specialty chemicals and customized equipment for water treatment, cooling systems for data centers, and advanced materials for solid-state batteries [13][16]. 2. Silicon-Carbon Materials - The company collaborates with top research teams to develop leading silicon-carbon anode materials, achieving stable mass production and validation from major battery manufacturers [8][10]. - The silicon-carbon materials are expected to open new market opportunities due to their energy density advantages and technological breakthroughs [8][10]. 3. Solid-State Batteries - The company has made significant advancements in solid-state battery materials, including the industrial-scale production of oxide and halide electrolytes [8][10]. - Partnerships with research institutions have strengthened the company's competitive edge in solid-state battery technology [8][10]. 4. Financial Projections - The company forecasts net profits of 1.42 billion, 2.03 billion, and 6.29 billion yuan for 2025, 2026, and 2027 respectively, reflecting year-on-year growth rates of 229.5%, 43.6%, and 209.3% [1][8]. - The projected price-to-earnings ratios for the same years are 85.24, 59.36, and 19.19, indicating a favorable valuation outlook as the company expands its new energy business [1][8]. 5. Market Data - The company's closing price is reported at 72.40 yuan, with a market capitalization of approximately 12.7 billion yuan [5][6]. - The company maintains a stable financial structure with a debt-to-asset ratio of 34.49% and a net asset value per share of 5.53 yuan [6][5].

Investment Rating - The report assigns a "Buy" rating for the company, marking its first coverage [1]. Core Insights - The company is positioned as a leader in the Robotaxi sector, expected to benefit from improved policy frameworks, breakthroughs in autonomous driving technology, and cost reductions across the industry. The unit economic model is anticipated to turn positive, enabling rapid scaling and profitability [9][14]. - The company has a strong technical foundation and is actively expanding its market presence both domestically and internationally, with significant partnerships and operational licenses in key cities [9][14]. Summary by Sections 1. Company Overview - The company was established in December 2016 and focuses on providing safe and advanced autonomous driving technology. Its core businesses include autonomous ride-hailing services, autonomous truck logistics, and intelligent driving solutions [14]. - The company launched the first Robotaxi service in China in 2018 and has since achieved significant milestones, including being the first to receive a taxi operating license for autonomous vehicles [14][18]. 2. Financial Projections - Revenue projections for the company are as follows: - 2023: $71.90 million - 2024: $75.03 million - 2025: $77.58 million - 2026: $104.91 million - 2027: $342.42 million - The company is expected to experience a revenue growth rate of 226.39% from 2026 to 2027 [1]. 3. Cost Reduction and Safety Improvements - The company has achieved significant cost reductions in its Robotaxi operations, with the BOM cost decreasing to around 300,000 yuan. This is attributed to mass production and advancements in technology [9][57]. - The safety of the autonomous driving system has been enhanced through a multi-sensor fusion approach, which significantly reduces accident rates compared to human drivers [44][52]. 4. Market Expansion and Partnerships - The company is focusing on expanding its operations in major cities like Beijing, Shanghai, Guangzhou, and Shenzhen, while also pursuing international opportunities in markets such as the United States and Singapore [9][14]. - Strategic partnerships with major players like Uber and local transportation companies are being leveraged to enhance market penetration and operational efficiency [9][14]. 5. Technical Advancements - The company has developed a robust technical framework, including the PonyWorld system, which has generated over 10 billion kilometers of testing data, contributing to the safety and reliability of its autonomous driving solutions [9][14]. - The seventh-generation autonomous driving system is set to enter mass production, further solidifying the company's position in the market [9][14].

Group 1: Historical Capacity Adjustment Cases - The report analyzes three historical cases of capacity adjustment: the Long Depression (1873-1896), the Great Depression (1929), and Japan's capacity reductions in the 1970s and 1990s, highlighting their implications for supply-demand rebalancing[4] - During the Long Depression, nominal wage growth in the U.S. was only 5.4%, while industrial output increased over 300%, leading to significant supply-demand imbalances[16] - The Great Depression saw a shift from non-intervention to government intervention, with policies like the Agricultural Adjustment Act (AAA) and the National Industrial Recovery Act (NIRA) aimed at stabilizing production and prices[36] Group 2: Economic Impacts and Policy Responses - The Long Depression resulted in a cumulative CPI decline of 29.9% in the U.S., with real GDP growth averaging 3.5% annually, indicating severe deflationary pressures[19] - The AAA reduced agricultural output significantly, with oat production dropping by 57% from 1932 to 1934, leading to a price increase of 207%[37] - NIRA aimed to stabilize industrial production by setting production quotas and minimum prices, although it faced legal challenges and was eventually deemed unconstitutional[41] Group 3: Lessons for Emerging Industries - The report suggests that capacity reduction and anti-monopoly measures may alternate in emerging industries, necessitating a regulatory framework to ensure fair competition[4] - Historical cases indicate that government intervention is generally more effective than market self-correction in addressing capacity imbalances, as seen in the U.S. response to the Great Depression[4] - The transition from a production-oriented to a consumption-oriented society can be facilitated by policies that improve labor rights and wages, as evidenced by labor movements during the Long Depression[4]

Group 1: Historical Capacity Adjustment Cases - The report analyzes three historical cases of capacity adjustment: the Long Depression (1873-1896), the Great Depression (1929), and Japan's capacity reductions in the 1970s and 1990s, highlighting lessons for supply-demand rebalancing[6] - During the Long Depression, nominal wage growth was only 5.4% in the U.S., while industrial output increased over 300%, leading to a significant supply-demand imbalance[10] - The Great Depression saw a shift from non-intervention to government intervention, with policies like the Agricultural Adjustment Act (AAA) and the National Industrial Recovery Act (NIRA) implemented to stabilize production and demand[30][34] Group 2: Mechanisms of Supply-Demand Rebalancing - Capacity imbalances can create a negative feedback loop, potentially lasting 20-30 years if not controlled, as seen in the Long Depression and Japan's lost decades[1] - Government intervention is more effective than non-intervention in addressing capacity imbalances, as demonstrated by the U.S. response to the Great Depression compared to Japan's approach in the 1990s[2] - Successful rebalancing requires simultaneous efforts in controlling capacity, restoring credit, and stabilizing employment, rather than relying solely on supply or demand policies[3] Group 3: Economic and Social Implications - Large-scale supply-demand imbalances can present opportunities for improving labor wages and boosting domestic demand, facilitating a shift from production-oriented to consumption-oriented economies[4] - In the U.S., labor movements during the Long Depression led to wage increases, with wage growth eventually reaching 49% of nominal GDP growth by the late 19th century[26] - Japan's capacity adjustments in the 1970s relied on government-led initiatives, while the 1990s saw a shift towards market-driven solutions, resulting in slower recovery from imbalances[5]