Core Insights - The report highlights the performance tracking of Guosen Securities' active quantitative strategies, indicating that the excellent fund performance enhancement portfolio achieved an absolute return of 3.70% this week and 17.22% year-to-date, ranking in the 49.35th percentile among active equity funds [1][12][23] - The report emphasizes the strong performance of the expected selection portfolio, which recorded an absolute return of 3.78% this week and 34.72% year-to-date, ranking in the 12.68th percentile among active equity funds [1][12][33] - The brokerage gold stock performance enhancement portfolio achieved an absolute return of 4.00% this week and 23.05% year-to-date, ranking in the 33.58th percentile among active equity funds [1][12][39] - The growth and stability portfolio reported an absolute return of 3.65% this week and 40.87% year-to-date, ranking in the 8.30th percentile among active equity funds [1][12][46] Excellent Fund Performance Enhancement Portfolio - The strategy aims to benchmark against the median return of active equity funds, utilizing a quantitative approach to enhance performance based on the holdings of top-performing funds [3][17][52] - The portfolio's year-to-date performance shows a relative underperformance of -3.26% compared to the mixed equity fund index [16][23] Expected Selection Portfolio - This strategy selects stocks based on expected performance and analyst profit upgrades, focusing on both fundamental and technical criteria to build a robust portfolio [4][24][58] - The portfolio has outperformed the mixed equity fund index by 14.24% year-to-date [16][33] Brokerage Gold Stock Performance Enhancement Portfolio - The strategy utilizes a selection from the brokerage gold stock pool, optimizing the portfolio to minimize deviations from the benchmark [5][34][63] - Year-to-date, this portfolio has outperformed the mixed equity fund index by 2.57% [16][39] Growth and Stability Portfolio - The strategy employs a two-dimensional evaluation system for growth stocks, prioritizing those with upcoming earnings announcements to capture potential excess returns [6][40][68] - The portfolio has achieved a year-to-date return of 40.87%, significantly outperforming the mixed equity fund index by 20.39% [16][46]

Core Insights - The report highlights the significant performance of the A-share market driven by passive investment and leveraged funds, with the total margin financing and securities lending balance exceeding 1.99 trillion as of August 4, 2025, marking a historical high since 2024 [5][8][6] - The automotive sector, particularly the company North Car Blue Valley (600733.SH), has launched a "Three-Year Leap Plan" aimed at enhancing profitability through sales growth, structural optimization, cost control, and expanding its profit ecosystem [4][16] - The company reported a 151% year-on-year increase in revenue for Q1 2025, with a gross margin improvement of 4.1 percentage points, and a reduction in net loss by 60 million [4][16] Industry Overview - The automotive industry is focusing on high-end market penetration, with North Car Blue Valley collaborating with Huawei to enhance its brand image and product offerings, particularly in the high-end vehicle segment [18][17] - The report indicates a notable increase in sales for the "Extreme Fox" brand due to comprehensive adjustments in product positioning, marketing strategies, and channel expansion [17] - The "Enjoy" brand, under the Huawei partnership, aims to redefine high-end sedans with innovative features and improved range, which is expected to boost sales significantly [18] Market Dynamics - The report discusses the microstructure of the market, emphasizing the importance of early trading concentration and the dynamics between institutional and retail investors [9][10][12] - It notes that the market's profitability effect has increased retail participation, contrasting with the trend of rising institutional ownership since 2017 [6][8] - The report tracks high-frequency factors, indicating strong performance in various trading strategies, with notable returns from specific factors such as the high-dimensional memory factor yielding 29.3% since 2023 [14]

2025 年 08 月 06 日 证券分析师 高子剑 金工定期报告 20250806 优 加 换 手 率 UTR2.0 选 股 因 子 绩 效 月 报 20250731 证券研究报告·金融工程·金工定期报告 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 《"技术分析拥抱选股因子"系列研究 （十二）：优加换手率 UTR 选股因子 2.0》 2023-05-04 《优加换手率 UTR2.0 选股因子绩效 月报 20250430》 2025-05-07 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 [Table_Tag] 金工定期报告 图表目录 | 图 | 1： | 优加换手率 | UTR2.0 | 因子 10 | 分组及多空对冲净值走势（2006/01-2025/07） 5 | | --- | --- | --- | --- | --- | --- | | 图 | 2： | 优加换手率 | UTR2.0 | 因子多头净值 | 7 月走 ...

- The report aims to track large purchases and net active purchases using transaction detail data[1] - The indicators used are the proportion of large order transaction amounts and the proportion of net active purchase amounts[7] - The proportion of large order transaction amounts reflects the buying behavior of large funds[7] - The proportion of net active purchase amounts reflects the active buying behavior of investors[7] - The top 5 stocks with the highest average proportion of large order transaction amounts over the past 5 days are: Sobute, China Railway Industry, Tibet Tianlu, Poly United, and China Power Construction[4][9] - The top 5 stocks with the highest average proportion of net active purchase amounts over the past 5 days are: Weixing Co., HNA Holdings, Kaili Medical, Liaogang Co., and Hengyi Petrochemical[4][10] - The top 5 industries with the highest average proportion of large order transaction amounts over the past 5 days are: Banking, Real Estate, Petroleum and Petrochemical, Transportation, and Coal[4] - The top 5 industries with the highest average proportion of net active purchase amounts over the past 5 days are: Media, Textile and Apparel, Computers, Electronics, and Light Manufacturing[4] - The top 5 ETFs with the highest average proportion of large order transaction amounts over the past 5 days are: China Agricultural Theme ETF, E Fund CSI 300 Medical and Health ETF, Huabao CSI Medical ETF, Bosera SSE STAR 100 ETF, and Guotai CSI Livestock Breeding ETF[4][15] - The top 5 ETFs with the highest average proportion of net active purchase amounts over the past 5 days are: Penghua CSI Subdivision Chemical Industry Theme ETF, GF SSE STAR 50 ETF, Harvest CSI Rare Metals Theme ETF, E Fund Guozheng Robotics Industry ETF, and Harvest CSI Software Services ETF[4][16]

Report Summary 1. Report Industry Investment Rating - There is no information provided regarding the industry investment rating in the report. 2. Core View of the Report - As of the week ending July 25, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond Index, and Nanhua Commodity Index were 2.11%, -0.48%, and 2.73% respectively. In the public - fund market, the returns of stock - bond strategies were differentiated in the past week. Among equity strategies, passive index - type products led in returns, and market - neutral strategy products mostly rose. In bond strategies, the pure - bond fund index showed a significant decline. In the commodity market, energy - chemical ETFs were strong with a weekly increase of 6.00%, non - ferrous metal ETFs rebounded, and precious - metal ETFs continued the upward trend of net value [3]. - Among the CITIC five - style indices, all style indices closed up last Friday. The cycle and growth styles led in returns. The style rotation chart showed that the relative strength of the cycle and stable styles increased significantly, while the momentum of the consumption style decreased slightly. In the public - fund pool, the average returns of financial and consumption - style funds significantly outperformed the index in the past week, with excess returns of 1.14% and 0.23% respectively. The excess returns of cycle and growth - style funds continued to shrink. The stable style strengthened slightly, and the cycle style declined. In terms of crowding, the growth and cycle styles rebounded marginally, while the consumption and financial styles remained in the historically high - crowding range [3]. - Among Barra factors, the residual volatility factor performed well in the past week, with an excess return of 0.60%. The returns of momentum and valuation factors weakened marginally, and the excess return of the profitability factor continued to shrink. In terms of winning rate, the growth factor declined, and the capital - flow factor strengthened slightly. This week, the cross - sectional rotation speed of factors rose from the historically low - quantile range to the middle range. According to the latest scoring results of the style timing model, the financial style weakened marginally this week, and the consumption style recovered. The current signal favors the consumption style. The return of the style timing strategy last week was 0.36%, and the excess return compared to the benchmark balanced allocation was - 1.59% [3]. 3. Summary by Relevant Catalogs 3.1 Market Index Performance - Tonglian All A (Shanghai, Shenzhen, Beijing) had a weekly return of 2.11%, the ChinaBond Composite Bond Index had a return of - 0.48%, and the Nanhua Commodity Index had a return of 2.73% as of July 25, 2025 [3]. 3.2 Public - Fund Market Performance - **Equity Strategies**: Passive index - type products led in returns, and market - neutral strategy products mostly rose [3]. - **Bond Strategies**: The pure - bond fund index showed a significant decline [3]. - **Commodity Market**: Energy - chemical ETFs had a weekly increase of 6.00%, non - ferrous metal ETFs rebounded, and precious - metal ETFs continued the upward trend of net value [3]. 3.3 CITIC Five - Style Index Performance - **Return Performance**: All style indices closed up last Friday. The cycle and growth styles led in returns [3]. - **Relative Strength and Momentum**: The relative strength of the cycle and stable styles increased significantly, while the momentum of the consumption style decreased slightly [3]. - **Fund Excess Return**: The average returns of financial and consumption - style funds significantly outperformed the index in the past week, with excess returns of 1.14% and 0.23% respectively. The excess returns of cycle and growth - style funds continued to shrink [3]. - **Style Trend**: The stable style strengthened slightly, and the cycle style declined [3]. - **Crowding**: The growth and cycle styles rebounded marginally, while the consumption and financial styles remained in the historically high - crowding range [3]. 3.4 Barra Factor Performance - **Factor Return**: The residual volatility factor had an excess return of 0.60%. The returns of momentum and valuation factors weakened marginally, and the excess return of the profitability factor continued to shrink [3]. - **Winning Rate and Momentum**: The growth factor declined in terms of winning rate, and the capital - flow factor strengthened slightly [3]. - **Factor Rotation Speed**: The cross - sectional rotation speed of factors rose from the historically low - quantile range to the middle range [3]. 3.5 Style Timing Strategy - According to the latest scoring results of the style timing model, the financial style weakened marginally this week, and the consumption style recovered. The current signal favors the consumption style. The return of the style timing strategy last week was 0.36%, and the excess return compared to the benchmark balanced allocation was - 1.59% [3].

- The report mentions the "Four-Wheel Drive Model" as a key quantitative model for sector selection and investment strategy recommendations[2][8][15] - The "Four-Wheel Drive Model" suggests focusing on sectors such as pharmaceuticals, petrochemicals, computers, electronics, automobiles, basic chemicals, and machinery equipment, identifying potential opportunities based on specific signals like "Bullish Retracement" and "Abnormal Momentum Effects"[8][15] - The model's sector recommendations are supported by recent signal dates and Sharpe ratio rankings, with examples including pharmaceuticals (signal date: 2025-07-25, Sharpe rank: 16) and petrochemicals (signal date: 2025-07-24, Sharpe rank: 28)[15]

Market Overview - Global markets showed a trend of "growth assets leading, risk aversion cooling," with the Hang Seng Tech Index rising by 5.53% due to easing US-China tariff negotiations and improved internet profit expectations[4] - The Nasdaq continued to reach new highs, increasing by 1.51%, driven by better-than-expected retail data and the recovery of AI chip supplies[4] - A-share market favored growth style, with total A-share trading volume increasing by 3.4% week-on-week, but valuation pressures are rising as the CSI 800 PE ratio is at the 33rd percentile of the past three years[4] Fixed Income Market - Short-term bonds outperformed long-term bonds, with 2-year treasury futures up by 0.02% while 30-year bonds fell by 0.04%, indicating a flattening yield curve[4] - The AAA credit spread has compressed to the 9th percentile of the past three years, suggesting a preference for high-grade credit bonds[4] Commodity Market - Agricultural products surged due to weather-related supply concerns, with US corn up by 3.82% and soybean meal up by 2.86%[4] - However, European shipping rates plummeted by 20.01% due to weak demand, reflecting significant divergence in the commodity market[4] Derivatives Strategy - Implied volatility for stock indices hit a three-month low, with the Shanghai 50 ETF IV dropping by 8.04%, indicating a shift from risk-averse to risk-seeking assets[4] - The current low volatility environment poses risks for option sellers, necessitating caution against potential Gamma risks[4] Asset Allocation Recommendations - Focus on short-duration high-grade credit bonds to mitigate long-term interest rate risks in the bond market[5] - Consider US equity opportunities as economic data shows marginal improvement and tariff disruptions ease[5] - Maintain a cautious stance on A-shares due to potential volatility from lowered profit expectations and policy catalysts[5]

- The report introduces a "Four-Wheel Drive Model" to identify potential opportunities in specific sectors such as automobiles, computers, machinery, electronics, pharmaceuticals, and communications[8][14] - The "Four-Wheel Drive Model" is constructed based on sectoral signals, including metrics like "profitability effect anomalies" and "holding effect anomalies," which are used to detect potential opportunities or risks in various industries[14] - The model's evaluation suggests it is effective in identifying sectoral opportunities during periods of market rotation, particularly under high financing balance conditions, which indicate elevated risk appetite and short holding periods[8][14] - Backtesting results for the "Four-Wheel Drive Model" highlight specific sector signals, such as: - Automobile sector: Signal date 2025-06-24, latest signal 2025-07-16, categorized as "profitability effect anomaly," with no exit signal yet[14] - Computer sector: Signal date 2025-06-25, latest signal 2025-07-11, categorized as "holding effect anomaly," exited on 2025-07-17[14] - Machinery sector: Signal date 2025-06-24, latest signal 2025-06-24, categorized as "profitability effect anomaly," exited on 2025-07-01[14] - Electronics sector: Signal date 2025-07-03, latest signal 2025-07-03, categorized as "profitability effect anomaly," exited on 2025-07-04[14] - Pharmaceutical sector: Signal date 2025-06-24, latest signal 2025-06-24, categorized as "profitability effect anomaly," exited on 2025-06-30[14] - Communication sector: Signal date 2025-06-16, latest signal 2025-06-16, categorized as "profitability effect anomaly," exited on 2025-06-18[14]

Quantitative Factors and Construction Methods 1. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Measures the valuation of a stock by comparing its book value to its market value [13] - **Factor Construction Process**: - Formula: $ BP = \frac{\text{Current Net Asset}}{\text{Current Total Market Value}} $ [13] 2. Factor Name: BP Three-Year Percentile - **Factor Construction Idea**: Evaluates the relative valuation of a stock over the past three years [13] - **Factor Construction Process**: - Formula: $ \text{BP Three-Year Percentile} = \text{Percentile of Current BP in the Last Three Years} $ [13] 3. Factor Name: Quarterly EP (Earnings-to-Price Ratio) - **Factor Construction Idea**: Measures profitability relative to market value on a quarterly basis [13] - **Factor Construction Process**: - Formula: $ \text{Quarterly EP} = \frac{\text{Quarterly Net Profit}}{\text{Net Asset}} $ [13] 4. Factor Name: Quarterly EP One-Year Percentile - **Factor Construction Idea**: Tracks the relative profitability of a stock over the past year [13] - **Factor Construction Process**: - Formula: $ \text{Quarterly EP One-Year Percentile} = \text{Percentile of Current Quarterly EP in the Last Year} $ [13] 5. Factor Name: Quarterly SP (Sales-to-Price Ratio) - **Factor Construction Idea**: Measures revenue generation relative to market value on a quarterly basis [13] - **Factor Construction Process**: - Formula: $ \text{Quarterly SP} = \frac{\text{Quarterly Operating Revenue}}{\text{Net Asset}} $ [13] 6. Factor Name: Quarterly SP One-Year Percentile - **Factor Construction Idea**: Tracks the relative revenue generation of a stock over the past year [13] - **Factor Construction Process**: - Formula: $ \text{Quarterly SP One-Year Percentile} = \text{Percentile of Current Quarterly SP in the Last Year} $ [13] 7. Factor Name: Quarterly Gross Margin - **Factor Construction Idea**: Measures profitability efficiency by comparing gross profit to sales revenue [13] - **Factor Construction Process**: - Formula: $ \text{Quarterly Gross Margin} = \frac{\text{Quarterly Gross Profit}}{\text{Quarterly Sales Revenue}} $ [13] 8. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Quantifies earnings surprises relative to historical growth trends [13] - **Factor Construction Process**: - Formula: $ \text{SUE} = \frac{\text{Current Quarterly Net Profit} - (\text{Last Year Same Quarter Net Profit} + \text{Average Growth of Last 8 Quarters})}{\text{Standard Deviation of Growth in Last 8 Quarters}} $ [13] 9. Factor Name: Standardized Unexpected Revenue (SUR) - **Factor Construction Idea**: Quantifies revenue surprises relative to historical growth trends [13] - **Factor Construction Process**: - Formula: $ \text{SUR} = \frac{\text{Current Quarterly Revenue} - (\text{Last Year Same Quarter Revenue} + \text{Average Growth of Last 8 Quarters})}{\text{Standard Deviation of Growth in Last 8 Quarters}} $ [13] 10. Factor Name: 1-Month Turnover Rate and Price Correlation - **Factor Construction Idea**: Measures the relationship between turnover rate and price over the past month [13] - **Factor Construction Process**: - Formula: $ \text{Correlation} = \text{Correlation Coefficient of Turnover Rate and Price over the Last 20 Trading Days} $ [13] --- Factor Backtesting Results IC Performance - **BP**: Weekly IC: -7.07%, Monthly IC: 0.84%, Yearly IC: 1.37%, Historical IC: 2.35% [9] - **BP Three-Year Percentile**: Weekly IC: -4.35%, Monthly IC: -0.95%, Yearly IC: 2.26%, Historical IC: 1.73% [9] - **Quarterly EP**: Weekly IC: 4.35%, Monthly IC: 0.50%, Yearly IC: -0.03%, Historical IC: 1.07% [9] - **Quarterly EP One-Year Percentile**: Weekly IC: -0.91%, Monthly IC: 2.98%, Yearly IC: 1.21%, Historical IC: 1.72% [9] - **Quarterly SP**: Weekly IC: -1.33%, Monthly IC: 0.50%, Yearly IC: 0.45%, Historical IC: 0.70% [9] - **Quarterly SP One-Year Percentile**: Weekly IC: 1.57%, Monthly IC: 1.02%, Yearly IC: 2.99%, Historical IC: 1.87% [9] - **Quarterly Gross Margin**: Weekly IC: 7.04%, Monthly IC: 0.06%, Yearly IC: 0.49%, Historical IC: 0.50% [9] - **SUE**: Weekly IC: 4.59%, Monthly IC: 4.44%, Yearly IC: 0.87%, Historical IC: 0.97% [9] - **SUR**: Weekly IC: 3.53%, Monthly IC: 2.05%, Yearly IC: 0.98%, Historical IC: 0.86% [9] - **1-Month Turnover Rate and Price Correlation**: Weekly IC: 10.17%, Monthly IC: 2.65%, Yearly IC: 2.75%, Historical IC: 1.69% [9] Excess Return of Long-Only Portfolios - **BP**: Weekly: -0.90%, Monthly: 0.06%, Yearly: -0.30%, Historical: 33.52% [11] - **BP Three-Year Percentile**: Weekly: -0.60%, Monthly: -2.29%, Yearly: -0.76%, Historical: -1.67% [11] - **Quarterly EP**: Weekly: -0.16%, Monthly: 0.58%, Yearly: 2.84%, Historical: 29.38% [11] - **Quarterly EP One-Year Percentile**: Weekly: -0.61%, Monthly: 0.55%, Yearly: 3.45%, Historical: 31.87% [11] - **Quarterly SP**: Weekly: -0.39%, Monthly: 0.15%, Yearly: 0.93%, Historical: -3.80% [11] - **Quarterly SP One-Year Percentile**: Weekly: -0.30%, Monthly: -0.19%, Yearly: 10.46%, Historical: -0.86% [11] - **Quarterly Gross Margin**: Weekly: -0.09%, Monthly: -0.15%, Yearly: 5.26%, Historical: 15.26% [11] - **SUE**: Weekly: 0.92%, Monthly: 2.09%, Yearly: -0.37%, Historical: 6.59% [11] - **SUR**: Weekly: 0.83%, Monthly: 1.29%, Yearly: 1.23%, Historical: 13.76% [11] - **1-Month Turnover Rate and Price Correlation**: Weekly: 0.13%, Monthly: 1.11%, Yearly: 7.31%, Historical: 20.14% [11]

Core Insights - Tang Liuchian, known as the "Chinese Wall Street Wolf," has made significant impacts in capital, art, and charity, representing a 16-generation family legacy [1][28] - The Tang family has a rich history, with roots tracing back to Tang Jingchuan, a notable Ming dynasty official, and has evolved from cultural pursuits to industrial ventures [1][3] Group 1: Family Background and Early Life - Tang Liuchian was born into an industrial family, with his father, Tang Bingyuan, dominating the wool textile industry in Shanghai [3] - The family relocated to Hong Kong in 1948 due to political turmoil, where Tang Liuchian began his education in English and Cantonese [3][4] - At age 11, he was sent to the U.S. for elite education, attending Phillips Academy, a prestigious institution [4] Group 2: Education and Early Career - Tang excelled academically, leading in math competitions and participating in English debate, eventually gaining admission to Yale University [4][6] - He later attended Harvard Business School, where he developed a passion for investment theories and financial engineering [6] - In 1970, he founded an investment company in Manhattan, raising $1.2 billion in just two years, becoming the first publicly traded private equity fund in the U.S. [6][12] Group 3: Business Ventures and Innovations - In 1978, he acquired KOA, the largest camping chain in the U.S., which had over 500 campsites across 50 states [8][10] - He transformed the camping business by implementing digital innovations, catering to middle-class American families' needs [12] - Tang co-founded the "Hundred People’s Association" in 1990, a platform for cultural dialogue between China and the U.S. [12][14] Group 4: Philanthropy and Cultural Contributions - After the death of his first wife, he sold his company for $350 million and shifted focus to rebuilding social trust and engaging in philanthropy [16] - He made significant donations to the Metropolitan Museum of Art, including $125 million for a new modern art wing, which was named after him [16][19] - Tang has donated numerous Chinese artworks and artifacts, contributing to the reestablishment of cultural heritage [19][20] Group 5: Personal Life and Legacy - Tang Liuchian remarried to Xu Xinmei, an archaeologist, and they established a scholarship fund for Chinese-American youth in arts and archaeology [20][25] - The couple's foundation emphasizes cross-cultural exchange and the importance of heritage [25][27] - In 2024, the Metropolitan Museum announced the establishment of the "Tang Liuchian China Research Award" to promote the international circulation and academic study of Chinese art [27]