Investment Rating - The industry investment rating is "Positive" (maintained) [1] Core Insights - The report emphasizes the significant growth potential in the AI application sector, particularly in the design field, as evidenced by Figma's successful IPO and strong revenue growth [4][5][8] - Figma's revenue growth remains robust, with 2024 and Q1 2025 revenues of $749 million and $228 million respectively, reflecting year-on-year growth rates of 48% and 46% [5] - The report highlights the importance of cloud-based collaborative design as Figma's core philosophy, showcasing a strong customer base and high customer retention rates [6] Summary by Sections Industry Overview - The report indicates a positive outlook for the AI design sector, driven by Figma's market performance and the increasing adoption of AI technologies [8] Company Performance - Figma's stock price surged to $115.5 per share, marking a 250% increase and a market capitalization of approximately $56.3 billion, making it the largest IPO in the U.S. for 2025 [4] - The company maintains a high gross margin of around 90% since 2023, with non-GAAP operating profit margins reaching 17-18% in 2024 and Q1 2025 [5] Customer Metrics - Figma's Dollar-Based Net Retention rate was 134% in 2024, up from 122% in 2023, indicating strong product stickiness and expansion strategies [6] - The number of customers contributing over $100,000 in annual recurring revenue (ARR) increased by nearly 47% from 701 in 2024 to 1,031 in Q1 2025 [6] Product Development - Figma is expanding its product matrix with the introduction of AI-driven tools such as Figma Make, Figma Draw, Figma Sites, and Figma Buzz in 2025 [7] Investment Recommendations - The report suggests a continued positive outlook for AI applications, recommending various companies in the AI design and computing sectors as potential beneficiaries [8]

Quantitative Models and Construction - **Model Name**: "Kaiyuan JinGong Optimal Stock Portfolio" **Model Construction Idea**: This model is based on the observation that newly recommended stocks ("newly entered stocks") outperform repeatedly recommended stocks ("repeated stocks"). It incorporates the "Surprise in Earnings" (SUE) factor to select stocks with strong earnings surprises, aiming to enhance portfolio returns[23][25]. **Model Construction Process**: 1. Use newly recommended stocks as the sample universe. 2. Select the top 30 stocks with the highest SUE factor values. 3. Weight the portfolio based on the number of recommendations by brokers. **Model Evaluation**: The model demonstrates superior performance compared to the overall stock portfolio and benchmark indices, indicating its effectiveness in capturing excess returns[23][25]. Model Backtesting Results - **Kaiyuan JinGong Optimal Stock Portfolio**: - **July Return**: 5.8%[25] - **2025 YTD Return**: 18.0%[25] - **Annualized Return**: 20.8%[25] - **Annualized Volatility**: 25.2%[25] - **Sharpe Ratio (Return-to-Volatility)**: 0.82[25] - **Maximum Drawdown**: 24.6%[25] - **Overall Stock Portfolio**: - **July Return**: 7.7%[21] - **2025 YTD Return**: 18.5%[21] - **Annualized Return**: 12.2%[21] - **Annualized Volatility**: 23.4%[21] - **Sharpe Ratio (Return-to-Volatility)**: 0.52[21] - **Maximum Drawdown**: 42.6%[21] - **Benchmark Indices**: - **CSI 300**: - **July Return**: 3.5%[21] - **2025 YTD Return**: 3.6%[21] - **Annualized Return**: 2.4%[21] - **Annualized Volatility**: 21.2%[21] - **Sharpe Ratio (Return-to-Volatility)**: 0.11[21] - **Maximum Drawdown**: 40.6%[21] - **CSI 500**: - **July Return**: 5.3%[21] - **2025 YTD Return**: 8.7%[21] - **Annualized Return**: 0.1%[21] - **Annualized Volatility**: 23.7%[21] - **Sharpe Ratio (Return-to-Volatility)**: 0.01[21] - **Maximum Drawdown**: 37.5%[21] Quantitative Factors and Construction - **Factor Name**: Surprise in Earnings (SUE) Factor **Factor Construction Idea**: The SUE factor measures the degree of earnings surprise, which is a key indicator of stock performance. Stocks with higher earnings surprises are expected to outperform[23]. **Factor Construction Process**: 1. Calculate the earnings surprise for each stock based on the difference between actual and expected earnings. 2. Rank stocks by their SUE values. 3. Select the top stocks with the highest SUE values for portfolio inclusion[23]. **Factor Evaluation**: The SUE factor demonstrates strong stock selection ability, particularly within the newly recommended stock universe, contributing to the superior performance of the optimal stock portfolio[23]. Factor Backtesting Results - **SUE Factor**: - Integrated into the "Kaiyuan JinGong Optimal Stock Portfolio," contributing to its superior performance metrics as detailed above[23][25].

Investment Rating - The industry investment rating is "Positive" (maintained) [1] Core Insights - The report highlights a positive outlook for the basic chemical industry, driven by supply optimization and recovery in profitability [3][4] - The demand for Sustainable Aviation Fuel (SAF) is expected to grow steadily due to continuous support from multiple countries [4] - The price of Used Cooking Oil (UCO) is anticipated to rise further due to increasing demand and carbon tax prices [5] - The European Union has confirmed that there is no evidence of fraud in the import of biodiesel from China, which may boost demand [6] Summary by Sections Industry Trends - The basic chemical industry is projected to outperform the overall market, with a positive investment rating [1] - The industry has shown a significant price increase for SAF, with EU and China prices rising by 10% and 3% respectively since the beginning of 2025 [4][10] Demand Drivers - The demand for UCO is increasing, driven by the growth in SAF and Hydrotreated Vegetable Oil (HVO) requirements [5] - The HVO demand in Germany is expected to increase by 1.5 million tons by 2026, nearly quadrupling the 2025 levels [5] Beneficiary Companies - Companies such as Shandong Hi-Speed Energy and Jiaao Environmental Protection are positioned to benefit from the growing SAF market and UCO production [7] - Shandong Hi-Speed Energy plans to increase its waste processing capacity, which will double its UCO output [7] - Jiaao Environmental Protection is set to gain market share in the domestic SAF sector with new capital investments [7]

Group 1: National Fertility Subsidy - The national fertility subsidy covers a wider range, with a total subsidy of 10,800 CNY per newborn over three years, compared to a median of 6,600 CNY and an average of 8,700 CNY for local subsidies[3][16] - The first-year budget for the national fertility subsidy is approximately 100 billion CNY, expected to promote the birth of about 330,000 newborns[4][16] - The short-term leverage effect of the subsidy is estimated at 0.9 times, potentially increasing to about 1.4 times in the medium to long term, with a GDP increase of 926 billion CNY in 2025[4][19] Group 2: Manufacturing Sector - The manufacturing PMI for July is reported at 49.3%, down 0.4 percentage points from the previous month, indicating a decline in manufacturing activity[5][13] - The production PMI decreased by 0.5 percentage points to 50.5%, while new orders, new export orders, and imports fell to 49.4%, 47.1%, and 44.7% respectively[5][22] - The "anti-involution" trend is expected to boost commodity prices, with July PPI projected to improve slightly to -3.0% year-on-year[5][29] Group 3: Non-Manufacturing Sector - The construction PMI fell by 2.2 percentage points to 50.6%, indicating a potential continuation of the slowdown in infrastructure investment[6][35] - The service sector remains relatively stable, with a service PMI of 50.0%, down 0.1 percentage points, and new orders declining to 46.3%[6][42] - Infrastructure investment may be affected by high base effects in Q3 and Q4, requiring policy measures to mitigate the impact[6][35] Group 4: Risks and Economic Outlook - Risks include unexpected policy changes and a potential recession in the U.S. economy[7][45] - The overall economic impact of the fertility subsidy includes direct boosts to consumer spending and indirect effects on child-rearing and housing demand[4][18]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The Barra style factors are designed to capture the performance of different market styles, such as size, value, growth, and profitability, through specific factor definitions[4][14] - **Construction Process**: - **Size Factor**: Measures the market capitalization of stocks - **Value Factor**: Captures the book-to-market ratio of stocks - **Growth Factor**: Reflects the growth potential of stocks - **Profitability Factor**: Based on earnings expectations[4][14] - **Evaluation**: These factors are widely used in the industry to analyze market trends and style rotations[4][14] --- Open-source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies the strongest reversal days by analyzing the average transaction size of large trades[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the average transaction size per day (transaction amount/number of transactions) 3. Identify the 10 days with the highest transaction sizes and sum their returns (M_high) 4. Identify the 10 days with the lowest transaction sizes and sum their returns (M_low) 5. Compute the factor as $M = M_{high} - M_{low}$[43] - **Evaluation**: Captures the microstructure of reversal forces in the A-share market[5][15] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity by analyzing minute-level price and volume data[5][15] - **Construction Process**: 1. Retrieve the past 10 days' minute-level data for a stock 2. Construct the indicator $S_t = |R_t| / V_t^{0.25}$, where $R_t$ is the return at minute $t$, and $V_t$ is the trading volume at minute $t$ 3. Sort minute-level data by $S_t$ in descending order and select the top 20% of minutes by cumulative trading volume 4. Calculate the volume-weighted average price (VWAP) for smart money trades ($VWAP_{smart}$) and all trades ($VWAP_{all}$) 5. Compute the factor as $Q = VWAP_{smart} / VWAP_{all}$[42][44] - **Evaluation**: Effectively identifies institutional trading patterns[5][15] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in trading behavior between morning (or overnight) and afternoon sessions[5][15] - **Construction Process**: 1. Retrieve the past 20 days' data for a stock 2. Calculate daily overnight and afternoon returns for both the stock and the index 3. Perform a regression of stock returns on index returns to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic $\mathrm{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t) / \sqrt{N}}$, where $\mu$ is the mean, $\sigma$ is the standard deviation, and $N$ is the sample size 6. Regress the statistic on momentum factors and use the residual as the APM factor[43][45][46] - **Evaluation**: Captures intraday trading behavior differences[5][15] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the daily amplitude as $(\text{High Price}/\text{Low Price}) - 1$ 3. Compute the average amplitude for the top 25% of days with the highest closing prices ($V_{high}$) 4. Compute the average amplitude for the bottom 25% of days with the lowest closing prices ($V_{low}$) 5. Compute the factor as $V = V_{high} - V_{low}$[48] - **Evaluation**: Highlights amplitude differences across price states[5][15] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above trading behavior factors using ICIR-based weights to enhance predictive power[31] - **Construction Process**: 1. Standardize and winsorize the individual factors within industries 2. Use the past 12 periods' ICIR values as weights to compute the composite factor[31] - **Evaluation**: Demonstrates superior performance in small-cap stock pools[32] --- Backtesting Results of Models and Factors Barra Style Factors - **Size Factor**: Return of 0.64% in July 2025[4][14] - **Value Factor**: Return of 0.59% in July 2025[4][14] - **Growth Factor**: Return of 0.16% in July 2025[4][14] - **Profitability Factor**: Return of -0.32% in July 2025[4][14] Open-source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.050 - RankIC: -0.061 - IR: 2.52 - Long-short monthly win rate: 78.3% (historical), 66.7% (last 12 months) - July 2025 long-short return: 0.47%[6][16] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.061 - IR: 2.76 - Long-short monthly win rate: 82.2% (historical), 91.7% (last 12 months) - July 2025 long-short return: 1.78%[6][19] - **APM Factor**: - IC: 0.029 - RankIC: 0.034 - IR: 2.30 - Long-short monthly win rate: 77.4% (historical), 58.3% (last 12 months) - July 2025 long-short return: 1.42%[6][23] - **Ideal Amplitude Factor**: - IC: -0.054 - RankIC: -0.073 - IR: 3.03 - Long-short monthly win rate: 83.6% (historical), 75.0% (last 12 months) - July 2025 long-short return: 3.86%[6][28] - **Composite Trading Behavior Factor**: - IC: 0.067 - RankIC: 0.092 - IR: 3.30 - Long-short monthly win rate: 82.6% (historical), 83.3% (last 12 months) - July 2025 long-short return: 2.13%[6][31]

Group 1: Macro Economic Insights - The Federal Reserve maintained the interest rate at 4.25%-4.5% during the July FOMC meeting, indicating high economic uncertainty and internal divisions within the Fed regarding potential rate cuts [4][5][6] - The U.S. GDP for Q2 recorded a growth of 3.0% quarter-on-quarter, showing resilience despite signs of economic softening, which reduces the urgency for rate cuts [5][6] - The political bureau meeting in China emphasized the need to enhance awareness of potential risks while focusing on expanding domestic demand and maintaining strategic determination during the 14th Five-Year Plan [9][10][11] Group 2: Industry Insights - Communication - Meta's Q2 revenue reached $47.52 billion, exceeding expectations, and the company raised its full-year capital expenditure guidance to between $66 billion and $72 billion, reflecting significant investments in AI and smart glasses [27] - Microsoft reported a strong performance in its cloud business, with Q4 revenue of $76.44 billion, a year-on-year increase of 18%, driven by a 26% growth in its intelligent cloud segment [28] - The AI computing industry is expected to enter a valuation uplift phase, with significant investment opportunities identified in various segments such as optical modules and liquid cooling technologies [29] Group 3: Industry Insights - Banking - Insurance capital is increasingly allocated to bank stocks, driven by high dividend yields and favorable tax conditions in the Hong Kong market, indicating a shift towards long-term equity investments [36][37] - The current environment of declining asset yields and regulatory changes is prompting insurance companies to seek higher dividend investments, particularly in the banking sector [37] - The evolving PB-ROE curve suggests a shift in valuation logic from fundamental factors to dividend-based assessments, highlighting the importance of maintaining reasonable PB differentials among banks [39][40] Group 4: Industry Insights - Pharmaceuticals - Guobang Pharmaceutical reported a 4.63% year-on-year increase in revenue for H1 2025, with a significant 70.37% growth in its animal health segment, indicating strong market demand [42][43] - WuXi AppTec's H1 revenue grew by 20.64% year-on-year, driven by robust performance in its TIDES business, with a significant increase in orders and revenue projections for the upcoming years [46][48] - Enhua Pharmaceutical's revenue for H1 2025 reached 3.01 billion yuan, with a notable 107.33% growth in its neurology segment, reflecting successful product differentiation and innovation [52][54]

Quantitative Models and Construction Methods Model: Duration Timing Model - **Construction Idea**: Predict the yield curve and map the expected returns of bonds with different durations[20] - **Construction Process**: - Use the improved Diebold2006 model to predict the instantaneous yield curve - Predict level, slope, and curvature factors - Level factor prediction based on macro variables and policy rate following - Slope and curvature factors prediction based on AR(1) model[20] - **Evaluation**: The model effectively predicts the yield curve and provides actionable insights for bond duration management[20] - **Test Results**: - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] Model: Gold Timing Model - **Construction Idea**: Relate the forward real returns of gold and US TIPS to construct the expected return model for gold[32] - **Construction Process**: - Use the formula: $E[Real\_Return^{gold}]=k\times E[Real\_Return^{Tips}]$ - Estimate parameter k using OLS with an extended window - Use the Fed's long-term inflation target of 2% as a proxy[32] - **Evaluation**: The model provides a robust framework for predicting gold returns based on TIPS yields[32] - **Test Results**: - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] Model: Active Risk Budget Model - **Construction Idea**: Combine the risk parity model with active signals to construct an active risk budget model for optimal stock and bond allocation[37] - **Construction Process**: - Use the Fed model to define equity risk premium (ERP): $ERP={\frac{1}{PE_{ttm}}}-YTM_{TB}^{10Y}$ - Adjust asset weights dynamically based on ERP, stock valuation percentiles, and market liquidity (M2-M1 spread) - Convert equity asset signal scores into risk budget weights using the softmax function: $softmax(x)={\frac{\exp(\lambda x)}{\exp(\lambda x)+\exp(-\lambda x)}}$[39][47] - **Evaluation**: The model dynamically adjusts asset weights based on multiple dimensions, providing a balanced risk-return profile[37] - **Test Results**: - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Model Backtest Results 1. **Duration Timing Model** - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] 2. **Gold Timing Model** - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] 3. **Active Risk Budget Model** - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Quantitative Factors and Construction Methods Factor: High-Frequency Macroeconomic Factors - **Construction Idea**: Use asset portfolio simulation to construct a high-frequency macro factor system to observe market macro expectations[12] - **Construction Process**: - Combine real macro indicators to form low-frequency macro factors - Select assets leading low-frequency macro factors - Use rolling multiple leading regression to determine asset weights and simulate macro factor trends[12] - **Evaluation**: High-frequency macro factors provide leading indicators for market expectations, offering valuable insights for asset allocation[12] Factor: Convertible Bond Valuation Factors - **Construction Idea**: Compare the relative valuation of convertible bonds and stocks, and between convertible bonds and credit bonds[25] - **Construction Process**: - Construct the "100-yuan conversion premium rate" to compare the valuation of convertible bonds and stocks - Use the "modified YTM - credit bond YTM" median to compare the valuation of debt-biased convertible bonds and credit bonds - Construct style rotation portfolios based on market sentiment indicators like 20-day momentum and volatility deviation[25][27] - **Evaluation**: The factors effectively capture the relative valuation and style characteristics of convertible bonds, aiding in portfolio construction[25][27] - **Test Results**: - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29] Factor Backtest Results 1. **High-Frequency Macroeconomic Factors** - High-frequency economic growth: Upward trend - High-frequency consumer inflation: Downward trend - High-frequency producer inflation: Upward trend[17] 2. **Convertible Bond Valuation Factors** - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29]

Investment Rating - The investment rating for Guobang Pharmaceutical is "Buy" (maintained) [1] Core Views - In Q2 2025, the company's performance exceeded expectations, with significant growth in the animal health sector. The company achieved a revenue of 30.26 billion yuan in the first half of 2025, representing a year-on-year increase of 4.63%. The net profit attributable to the parent company was 4.56 billion yuan, up by 12.6% [4][5] - The animal health segment saw a remarkable revenue increase of 70.37% in the first half of 2025, with the sales volume of Florfenicol surpassing 2000 tons, indicating a continuous rise in market share [5] - The company maintains its profit forecast, expecting net profits of 9.95 billion yuan, 12.35 billion yuan, and 13.91 billion yuan for 2025, 2026, and 2027 respectively, with a current price-to-earnings ratio of 13.3, 10.7, and 9.5 times for the respective years, indicating high valuation attractiveness [4][5] Financial Summary - In the first half of 2025, the company reported a gross margin of 26.85% and a net margin of 15.00%, with improvements in both metrics compared to the previous year [4] - The company’s revenue for the pharmaceutical segment was 17.35 billion yuan, down by 9.87%, while the animal health segment generated 12.59 billion yuan [5] - Research and development expenses increased to 0.97 billion yuan, representing a growth of 4.80%, with a research expense ratio of 3.22% [6] Financial Forecast - The company forecasts revenues of 67.86 billion yuan, 76.15 billion yuan, and 82.65 billion yuan for 2025, 2026, and 2027 respectively, with year-on-year growth rates of 15.2%, 12.2%, and 8.5% [7] - The net profit attributable to the parent company is projected to be 9.95 billion yuan, 12.35 billion yuan, and 13.91 billion yuan for the same years, with corresponding year-on-year growth rates of 27.4%, 24.1%, and 12.7% [7][10]

Investment Rating - The industry investment rating is "Overweight" (maintained) [1] Core Viewpoints - The report highlights a positive outlook for the telecommunications industry, driven by significant growth in AI-related investments and advancements in optical communication technologies [4][5] - Major companies such as Meta and Microsoft are increasing their capital expenditures, indicating a robust investment environment in AI and cloud services [4][5] - The report emphasizes the importance of the AI computing power supply chain and suggests various investment opportunities across different segments, including optical modules, liquid cooling, and optical chips [5] Summary by Sections Industry Trends - The telecommunications sector is expected to outperform the overall market, with a focus on AI and optical communication growth [1][4] - Recent financial results from companies like Celestica and Corning show significant year-over-year revenue growth, indicating a strong market demand [4] Company Performance - Meta reported Q2 2025 revenue of $47.52 billion, exceeding expectations, and raised its full-year capital expenditure guidance to between $66 billion and $72 billion [4] - Microsoft’s cloud business showed impressive growth, with a 26% year-over-year increase in Azure revenue [4] Investment Opportunities - The report identifies key investment targets within the AI computing power supply chain, recommending specific companies in various segments such as optical modules and liquid cooling [5] - Notable recommended stocks include Zhongji Xuchuang and New Yisheng in the optical module sector, and Yingwei in the liquid cooling segment [5]

Investment Rating - The industry investment rating is "Overweight" [1][11] Core Views - The report highlights the strong performance of companies in the liquid cooling sector, driven by increasing demand due to the rise of AI technologies [4][5][6] - The report emphasizes that liquid cooling is transitioning from an optional solution to a necessary one in the AI era, due to factors such as increasing power consumption of mainstream computing chips and the need for high-density data centers [6] Summary by Sections Company Performance - Vertiv reported Q2 2025 revenue of $2.638 billion, exceeding the previous guidance of $2.35 billion, with a year-on-year growth of 35% and a quarter-on-quarter growth of 30% [4] - Celestica achieved Q2 2025 revenue of $2.89 billion, a 21% year-on-year increase, and raised its full-year revenue guidance to $11.55 billion from $10.85 billion [5] Market Trends - The report notes that the demand for liquid cooling solutions is expected to rise significantly, driven by the AI boom and the increasing power density of AI clusters [6] - The report recommends several companies as key players in the liquid cooling market, including Yingwei, Shunling Environment, and Yinhong Shares, among others [6]