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]

- 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]

Quantitative Models and Construction Methods - **Model Name**: A-Share Industry Rotation Model **Model Construction Idea**: The model uses six factors—capital, valuation, sentiment, momentum, overbought/oversold, and profitability—to build a scoring system for industry evaluation[17] **Model Construction Process**: - **Capital Factor**: Based on industry net inflow rate of major funds - **Valuation Factor**: Uses the valuation percentile of the industry over the past year - **Sentiment Factor**: Derived from the proportion of rising constituent stocks - **Momentum Factor**: Based on MACD indicator - **Overbought/Oversold Factor**: Uses RSI indicator - **Profitability Factor**: Based on the consensus forecast EPS percentile of the industry over the past year[17] **Model Evaluation**: The model provides a comprehensive scoring system to assess industry rotation trends[17] - **Model Name**: Consensus Stock Selection Model **Model Construction Idea**: The model identifies high-growth industries and selects stocks with high similarity between high-frequency capital flow trends and stock price trends[20] **Model Construction Process**: - Filters high-growth industries at the Shenwan secondary industry level based on the past 30-day performance - Calculates momentum, valuation, and frequency of price increases for stocks within these industries - Uses high-frequency minute-level capital flow data to compute changes in inflow/outflow for each stock - Selects stocks with the highest similarity between capital flow trends and price trends within the top-performing secondary industries[20] **Model Evaluation**: The model effectively identifies stocks with strong capital flow and price trend alignment[20] --- Model Backtesting Results - **A-Share Industry Rotation Model**: - **Top Scoring Industries**: Comprehensive (+10), Non-ferrous Metals (+10), Electronics (+7)[18][19] - **Low Scoring Industries**: Banking (-15), Petrochemicals (-9), Transportation (-8)[19] - **Consensus Stock Selection Model**: - **Selected Industries**: Communication Equipment, Ground Armament II, Components[21] - **Selected Stocks**: - Communication Equipment: New Yisheng, Move Communication, Feiling Kesi, Hengtong Optoelectronics, Meixin Technology - Ground Armament II: Great Wall Military Industry, Optical Shares, Inner Mongolia First Machine, Sweet Qin Equipment, Ganfa Technology - Components: Jingwang Electronics, Deep South Circuit, Fangbang Shares, Zhongjing Electronics, Shenghong Technology[21] --- Quantitative Factors and Construction Methods - **Factor Name**: Capital Factor **Construction Idea**: Measures industry net inflow rate of major funds[17] **Construction Process**: Aggregates daily net inflow data for transactions exceeding 10,000 shares or 200,000 yuan[12] **Evaluation**: Reflects the strength of capital flow within industries[17] - **Factor Name**: Valuation Factor **Construction Idea**: Uses industry valuation percentile over the past year[17] **Construction Process**: Calculates the relative valuation position of the industry within a one-year window[17] **Evaluation**: Indicates whether an industry is undervalued or overvalued[17] - **Factor Name**: Sentiment Factor **Construction Idea**: Based on the proportion of rising constituent stocks[17] **Construction Process**: Computes the percentage of stocks within the industry that have increased in price[17] **Evaluation**: Captures market sentiment towards the industry[17] - **Factor Name**: Momentum Factor **Construction Idea**: Uses MACD indicator to measure price trends[17] **Construction Process**: Applies MACD calculations to industry-level data[17] **Evaluation**: Identifies industries with strong upward or downward trends[17] - **Factor Name**: Overbought/Oversold Factor **Construction Idea**: Uses RSI indicator to assess market conditions[17] **Construction Process**: Calculates RSI values for industries to determine overbought or oversold conditions[17] **Evaluation**: Helps identify potential reversals in industry trends[17] - **Factor Name**: Profitability Factor **Construction Idea**: Based on consensus forecast EPS percentile over the past year[17] **Construction Process**: Aggregates EPS forecasts and calculates relative percentile rankings[17] **Evaluation**: Reflects the earnings potential of industries[17] --- Factor Backtesting Results - **Capital Factor**: Comprehensive (++), Non-ferrous Metals (++), Electronics (++), Banking (---), Petrochemicals (---), Transportation (---)[19] - **Valuation Factor**: Comprehensive (+++), Non-ferrous Metals (++), Electronics (+), Banking (-), Petrochemicals (---), Transportation (---)[19] - **Sentiment Factor**: Comprehensive (-), Non-ferrous Metals (+++), Electronics (+++), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Momentum Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Overbought/Oversold Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+), Banking (--), Petrochemicals (---), Transportation (---)[19] - **Profitability Factor**: Comprehensive (+++), Non-ferrous Metals (+++), Electronics (+++), Banking (---), Petrochemicals (---), Transportation (---)[19]

Group 1: Report Investment Rating - The operation rating for CITIC Five-Style - Growth is ★☆☆ [3][4] Group 2: Core Viewpoints - In the public fund market, the enhanced index strategy led the gains in the past week, while the ordinary stock strategy index in the equity strategy was relatively weak. The net value of non-ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent. The style timing signal currently favors the growth style, and the style timing strategy had an excess return compared to the benchmark [4] Group 3: Summary by Related Catalogs Fund Market Review - As of the week ending on June 27, 2025, the weekly returns of Tonglian All A (Shanghai, Shenzhen, Beijing), ChinaBond Composite Bond Index, and Nanhua Commodity Index were 3.35%, -0.10%, and -2.00% respectively [4] - In the public fund market, the enhanced index strategy had a weekly return of 3.18%. Among equity strategies, the ordinary stock strategy index was relatively weak, and neutral strategy products had more losses than gains. In the bond market, medium - and long - term pure bonds had a small pullback, and convertible bonds outperformed pure bonds. In the commodity market, the returns of energy - chemical and soybean meal ETFs pulled back, the net value of non - ferrous metal ETFs rebounded, and the performance of precious metal ETFs was divergent, with silver ETFs rising slightly and gold ETFs continuing to weaken [4] Equity Market Style - In the CITIC Five - Style, all style indices closed up last Friday, with the growth and financial styles leading. In terms of relative strength, consumption and stability were at a relatively low level, and in terms of indicator momentum, all five styles strengthened compared to the previous week, with consumption and stability having a large increase [4] - In the public fund pool, the average returns of cycle and consumption style funds outperformed the index in the past week, with excess returns of 0.60% and 0.06% respectively. Some growth - style funds shifted towards cycle and consumption styles [4] - In terms of crowding, consumption fell from a high - crowding range to a neutral range, the cycle style increased significantly, and the growth style was in a historically low - crowding range [4] Barra Factors - In the past week, the growth, liquidity, and momentum factors had better returns, the excess return of the profitability factor was compressed, the return of the volatility factor continued to decline, the dividend factor continued to weaken in terms of winning rate, and the momentum and residual volatility factors rebounded [4] - The cross - sectional rotation speed of factors decreased compared to the previous week and was currently in a historically low - quantile range [4] Style Timing - According to the latest scoring results of the style timing model, the financial style weakened slightly this week, while consumption and growth recovered, and the current signal favored the growth style [4] - The return of the style timing strategy last week was 3.41%, with an excess return of 0.63% compared to the benchmark balanced allocation [4]