Quantitative Models and Construction 1. Model Name: Financial Report Fraud Detection Score - **Model Construction Idea**: This model identifies and quantifies the risk of financial fraud in corporate reports by detecting anomalies such as fabricated revenue, inflated profits, hidden liabilities, and other manipulative practices[20][39]. - **Model Construction Process**: - The model captures six types of anomalies: fabricated/inflated revenue, inflated profits, inflated assets, hidden liabilities, fund misappropriation, and benefit transfers[20]. - Scores are assigned based on the severity of detected anomalies, with higher scores indicating higher fraud risk[20]. - The model updates its scores in sync with financial report disclosures[20]. - **Model Evaluation**: The model demonstrates strong predictive power for ST events, with an AUC of 0.806, significantly outperforming the benchmark factor of net profit (AUC = 0.656)[5][34][38]. 2. Model Name: Corporate Behavior Profiling Score - **Model Construction Idea**: This model evaluates the operational health of companies and quantifies their default risk based on comprehensive behavioral profiling[20]. - **Model Construction Process**: - The score is derived from operational data, with higher scores indicating better operational health and lower risk[20]. - The model is updated in alignment with financial report disclosure schedules[20]. - **Model Evaluation**: This model outperforms the Financial Report Fraud Detection Score, with an AUC of 0.865, and demonstrates stronger predictive power for ST events[5][34][39]. 3. Model Name: Cox Proportional Hazards Model (Survival Analysis) - **Model Construction Idea**: This model predicts the probability of ST events by analyzing the impact of risk events (e.g., regulatory actions, shareholder actions) on the survival time of stocks[44][61]. - **Model Construction Process**: - The survival function \( S(t) \) is defined as the probability of a stock surviving beyond time \( t \), with \( S(0) = 1 \) and \( S(t) \) decreasing over time[41][44]. - The Cox model formula is: \[ h(t, X) = h_0(t) \cdot \exp(\beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n) \] where \( h_0(t) \) is the baseline hazard, \( X \) represents risk events, and \( \beta \) are coefficients[61]. - Risk ratios (HR) are calculated as \( HR = e^\beta \), with \( HR > 1 \) indicating risk factors and \( HR < 1 \) indicating protective factors[61]. - Risk events include regulatory letters (e.g., inquiry letters), shareholder actions (e.g., equity freezes), and regulatory measures (e.g., public censure)[45][61]. - **Model Evaluation**: The model achieves an AUC of 0.810 and demonstrates flexibility in incorporating new risk events, making it suitable for high-frequency monitoring[6][73][75]. --- Model Backtesting Results Financial Report Fraud Detection Score - **AUC**: 0.806[5][34] - **Recall Rate**: Average recall rate exceeds 90% for ST predictions, with a minimum recall rate above 80%[5][30]. Corporate Behavior Profiling Score - **AUC**: 0.865[5][34] - **Recall Rate**: Average recall rate exceeds 90%, with a minimum recall rate above 80%[5][31]. Cox Proportional Hazards Model - **AUC**: 0.810[6][73] - **Recall Rate**: Recall rates improved significantly after 2019, averaging around 85%[6][71]. --- Quantitative Factors and Construction 1. Factor Name: Financial Report Fraud Detection Score - **Factor Construction Idea**: Quantifies the risk of financial fraud based on anomalies in financial reports[20]. - **Factor Construction Process**: - Scores are assigned based on six types of anomalies, with higher scores indicating higher fraud risk[20]. - **Factor Evaluation**: Demonstrates strong predictive power for ST events, with an AUC of 0.806[5][34]. 2. Factor Name: Corporate Behavior Profiling Score - **Factor Construction Idea**: Measures operational health and default risk based on corporate behavior[20]. - **Factor Construction Process**: - Scores are derived from operational data, with higher scores indicating better operational health[20]. - **Factor Evaluation**: Outperforms the Financial Report Fraud Detection Score, with an AUC of 0.865[5][34]. 3. Factor Name: Risk Events (Cox Model) - **Factor Construction Idea**: Analyzes the impact of risk events on stock survival time[44][61]. - **Factor Construction Process**: - Risk events include regulatory letters, shareholder actions, and regulatory measures[45][61]. - Risk ratios are calculated to determine the significance of each event[61]. - **Factor Evaluation**: Provides high-frequency tracking and flexibility, with an AUC of 0.810[6][73]. --- Factor Backtesting Results Financial Report Fraud Detection Score - **AUC**: 0.806[5][34] - **Recall Rate**: Average recall rate exceeds 90% for ST predictions, with a minimum recall rate above 80%[5][30]. Corporate Behavior Profiling Score - **AUC**: 0.865[5][34] - **Recall Rate**: Average recall rate exceeds 90%, with a minimum recall rate above 80%[5][31]. Risk Events (Cox Model) - **AUC**: 0.810[6][73] - **Recall Rate**: Recall rates improved significantly after 2019, averaging around 85%[6][71]

- The report tracks the performance of style factors, including Beta, liquidity, leverage, profitability, and market capitalization, with Beta and liquidity showing strong long positions, while leverage, profitability, and market capitalization exhibit strong short positions [2][16] - Style factors are constructed using various metrics, such as historical Beta, logarithm of total market capitalization, historical excess return averages for momentum, and a weighted combination of volatility measures for the volatility factor. For example, the volatility factor is calculated as $ 0.74 * historical excess return volatility + 0.16 * cumulative excess return deviation + 0.1 * historical residual return volatility $ [15] - Fundamental factors, including growth-related financial metrics and static financial metrics, are tested across different stock pools (e.g., CSI 300, CSI 500, CSI 1000). Growth-related financial factors generally show mixed or negative performance, while static financial factors exhibit varied results depending on the stock pool [3][4][5][6][18][20][23][25] - Technical factors, such as momentum and volatility, generally show positive performance across stock pools, with high-volatility and high-momentum stocks being dominant. For example, the 120-day momentum factor and 20-day volatility factor are highlighted for their significant contributions [3][4][5][6][18][20][23][26] - GRU factors are tested using different models (e.g., barra1d, barra5d, close1d), with performance varying across stock pools. For instance, barra1d shows strong positive performance in CSI 500 and CSI 1000 pools, while close1d experiences significant drawdowns in CSI 1000 [3][4][5][6][18][20][23][26] - Multi-factor strategies and GRU-based long portfolios are evaluated against the CSI 1000 index. GRU long portfolios show weak performance this week, with relative drawdowns of 0.11%-0.25%, while the barra5d model demonstrates strong year-to-date performance, achieving an excess return of 8.36% [7][30][31]

Industry Investment Rating - The investment rating for the construction materials industry is "Outperform the Market" and is maintained [1] Core Insights - The report emphasizes the ongoing theme of "anti-involution" in the construction materials sector, with a focus on the marginal changes in the fundamentals. The recent Politburo meeting highlighted the importance of high-quality urban renewal and the need to regulate chaotic competition among enterprises, which is expected to influence capacity management in key industries [4] - In the cement sector, a policy document released by the Cement Association on July 1 is anticipated to enhance the enforcement of production limits, leading to a potential decrease in capacity and an increase in utilization rates. The report predicts a gradual price recovery in August as demand improves [4] - The glass industry is experiencing a downward trend in demand due to the real estate sector's impact, with supply-demand imbalances persisting. However, the report notes that most companies in the float glass sector meet environmental standards, which may prevent drastic capacity cuts but could raise costs and accelerate maintenance schedules [5] - The fiberglass segment is expected to benefit from the AI industry, with demand for low-dielectric products projected to rise significantly. The report highlights a clear upgrade in product structure, indicating a potential explosive growth in demand [5] - The consumer building materials sector has reached a profitability bottom, with no further downward price pressure. The report notes a strong push for price increases across various categories, suggesting a potential improvement in profitability [5] Summary by Sections Cement - Cement prices are currently declining due to seasonal factors, with a 2.13% decrease in the price of ordinary cement (P.O 42.5) reported this week. The monthly production in June 2025 saw a year-on-year decline of 5.3% [8] Glass - The glass market is facing challenges, with a 0.76% increase in prices this week, but overall demand remains weak. The report indicates that the industry is still grappling with supply-demand contradictions [13] Fiberglass - The fiberglass industry is experiencing a positive trend driven by AI-related demand, with expectations for both volume and price increases [5] Consumer Building Materials - The consumer building materials sector is showing signs of recovery, with companies actively raising prices after years of competitive pressure. This sector includes waterproofing materials, coatings, and gypsum boards [5] Recent Company Announcements - Oriental Yuhong reported a revenue of 13.569 billion yuan for the first half of 2025, a year-on-year decrease of 10.84%, with a net profit of 564 million yuan, down 40.16% [17] - Rabbit Baby's associated company, Hanhai Group, was listed on the Shenzhen Stock Exchange, with Rabbit Baby holding a 1.85% stake post-IPO [17]

Investment Rating - The industry investment rating is "Strongly Outperforming the Market" and is maintained [1] Core Viewpoints - This week, commodity prices have retreated, leading to a weakening in the petrochemical sector. Continuous attention is required on the progress of phasing out old facilities and upgrading within the petrochemical industry [2] - The oil and petrochemical index closed at 2262.71 points, down 2.94% from last week, indicating a weaker performance compared to other sectors [3][7] - In the upstream sector, geopolitical factors may provide a premium for oil, benefiting upstream stocks. In the refining sector, a recovery in demand and progress in eliminating outdated capacity would be favorable for midstream refining [2] Summary by Sections Oil - Energy prices have fluctuated, with Brent crude oil futures and TTF natural gas futures closing at $69.54 per barrel and €33.81 per megawatt-hour, respectively, reflecting increases of 1.1% and 4.2% from last week [10] - U.S. crude oil inventories have risen, with total crude and petroleum product inventories (excluding strategic reserves) at 1,257,771 thousand barrels, an increase of 7,087 thousand barrels [15] Polyester - The price of polyester filament remains stable, with POY, DTY, and FDY prices at 6,680, 7,930, and 6,930 yuan per ton, respectively. The price differentials have decreased by 101 yuan per ton [18] - The inventory days for polyester filament in Jiangsu and Zhejiang have increased, with operating rates for filament and downstream looms at 91.5% and 55.5%, respectively, both showing slight declines [22] Olefins - Sample prices for polyethylene (PE) and polypropylene (PP) are 7,710 and 8,050 yuan per ton, with changes of 0.13% and -1.11% from last week. The petrochemical inventory for olefins has increased by 70,000 tons, totaling 800,000 tons [26]

1. Report Industry Investment Rating There is no information provided regarding the report industry investment rating in the given content. 2. Core Viewpoints of the Report - The policy tone has been revealed, and expectations have been revised. The bond yield's阶段性 top is clear, with the 10 - year Treasury bond's mid - term top forming around 1.75% [3][10][12]. - Tax policy changes have a "one - time" impact on the nominal interest rate center. The expected tax burden spread is around 5BP, and it may affect the selection of the cheapest to deliver bond in far - month Treasury bond futures contracts [4][14]. - It is necessary to re - define the interest rate's fluctuation center. The 1.75% mid - term top of the 10 - year Treasury bond may be challenged but remains relatively reliable, and the 1.65% fluctuation center is still valid. There is a possibility of opening up downward interest rate space in the second half of the year [5][15][16]. 3. Summary According to the Directory 3.1 How to Redefine the Interest Rate Center? - **Policy Expectations and Bond Yield Top** - The prediction of policy deployment is mostly fulfilled. The demand - side pulling policy pattern remains unchanged, and there is no unexpected urban renewal policy. The "anti - involution" policy exists but with lower - than - expected progress and attention [3][10][11]. - The "anti - involution" policy has long - term impacts on price and interest rate pricing, but the results are not linearly the same as historical trends [11]. - The demand - side pulling policy maintains its pattern, and the pricing difference between commodities and bonds regarding demand - pulling policies should end with commodity pricing correction [11]. - The monetary policy's task of "lowering social comprehensive financing costs" persists. Liquidity is expected to remain stable and loose in Q3, and a new round of policy interest rate cuts and liquidity easing is in the making [11]. - From the perspective of policy expectations, the mid - term top of the 10 - year Treasury bond around 1.75% has formed [3][12][16]. - **Impact of Tax Policy Changes** - Starting from August 8, 2025, the interest income of newly issued Treasury bonds, local government bonds, and financial bonds will be subject to value - added tax. The actual tax burden for self - operated financial institutions is 6.34%, and for asset management institutions is 3.26% [4][13]. - The theoretical tax burden spread for long - duration bonds is 5 - 12BP, but it is expected to be around 5BP considering previous factors [4][13][14]. - Near - month Treasury bond futures contracts are less affected, while far - month contracts may see an impact on the selection of the cheapest to deliver bond, and tax burden differences can be considered in determining conversion factors [4][14]. - **Redefining the Interest Rate Fluctuation Center** - The interest rate increase since early July is driven by expectations of "anti - involution" and demand - side policies, with risk preference playing a role in asset re - pricing [15]. - Given the "high - first - then - low" trend of the fundamentals throughout the year, the 1.75% mid - term top of the 10 - year Treasury bond may be challenged but is still relatively reliable. The 1.65% fluctuation center is still valid. There is potential for interest rates to decline in the second half of the year [5][15][16].

Market Performance Review - The A-share market experienced a rise followed by a decline, with all major indices closing down after the Politburo meeting, where the CSI 1000 had the smallest drop of 0.54%, while the CSI A50 and CSI 300 fell by 2.48% and 1.75% respectively [3][12] - There was a noticeable divergence in market styles, with growth and consumption sectors experiencing smaller declines, while financial, stability, and cyclical styles saw significant pullbacks [3][12] - Mid-cap and small-cap indices outperformed large-cap indices during the week, with the NING and MAO indices, representing core assets and growth leaders, also declining, with the NING combination down 1.74% and the MAO index down 0.84% [3][12] Investor Sentiment and Market Outlook - The personal investor sentiment index has continued to decline, entering a negative zone, with the 7-day moving average reported at -0.42% as of August 2, down from 4.35% on July 26 [4][19] - The recent Politburo meeting did not indicate any large-scale stimulus plans, suggesting that the focus will shift back to demand recovery rather than potential supply-side reductions [4][32] - The current market dynamics indicate a potential vacuum in buying momentum, necessitating a cautious approach to investment strategies [4][32] Sector Analysis - The healthcare and communication sectors saw gains of over 2%, driven by significant partnerships and strong performance in the CPO sector, respectively, indicating a return to prosperity trading [15] - Conversely, sectors such as coal, non-ferrous metals, construction materials, and steel experienced substantial declines due to the withdrawal of "anti-involution" trading sentiment following policy announcements [15] Investment Strategy and Recommendations - The report emphasizes a return to prosperity trading, highlighting opportunities for valuation recovery in technology growth sectors, particularly in AI applications, computing power chains, and optical modules [5][33] - The fundamentals of innovative pharmaceuticals and CROs are showing signs of transformation, with continued trading logic for Chinese pharmaceuticals going overseas [5][33]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of trend changes in the micro-cap stock index by analyzing the distribution of stock price movements over a specific time window [5][39] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a retrospective or forward-looking window. For example, if the horizontal axis is 0.95 and the vertical axis is 15 days, the value of 0.31 indicates that if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.31. The model uses thresholds to signal trading actions: - **First Threshold Method (Left-Side Trading)**: Triggered a sell signal on May 8, 2025, when the index reached 0.9850 [43] - **Delayed Threshold Method (Right-Side Trading)**: Triggered a sell signal on May 15, 2025, at 0.8975 [47] - **Double Moving Average Method (Adaptive Trading)**: Triggered a buy signal on July 3, 2025 [48] - **Model Evaluation**: The model effectively identifies trend changes but may be influenced by the distribution of constituent stocks and their updates [39][40] 2. Model Name: Small-Cap Low-Volatility 50 Strategy - **Model Construction Idea**: This strategy selects 50 stocks with small market capitalization and low volatility from the micro-cap stock index, rebalancing every two weeks [7][36] - **Model Construction Process**: - Select stocks with the smallest market capitalization and lowest volatility from the micro-cap index - Rebalance the portfolio bi-weekly - Benchmark: Wind Micro-Cap Stock Index (8841431.WI) - Transaction cost: 0.3% on both sides [7][36] - **Model Evaluation**: The strategy demonstrates strong performance in 2025 but underperformed in 2024, indicating sensitivity to market conditions [7][36] --- Model Backtesting Results 1. Diffusion Index Model - **First Threshold Method**: Triggered sell signal at 0.9850 on May 8, 2025 [43] - **Delayed Threshold Method**: Triggered sell signal at 0.8975 on May 15, 2025 [47] - **Double Moving Average Method**: Triggered buy signal on July 3, 2025 [48] 2. Small-Cap Low-Volatility 50 Strategy - **2024 Return**: 7.07%, underperformed by -2.93% relative to the benchmark [7][36] - **2025 YTD Return**: 69.79%, underperformed by -1.88% relative to the benchmark [7][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Unadjusted Stock Price Factor - **Factor Construction Idea**: Measures the rank IC of unadjusted stock prices within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the rank IC of unadjusted stock prices weekly - Compare with historical averages for evaluation [4][17] - **Factor Evaluation**: Demonstrated strong performance this week with a rank IC of 0.177, significantly above the historical average of -0.015 [4][17] 2. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the systematic risk of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the beta of each stock relative to the market - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Performed well this week with a rank IC of 0.15, above the historical average of 0.006 [4][17] 3. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Measure illiquidity based on trading volume and price impact - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Strong performance this week with a rank IC of 0.143, above the historical average of 0.04 [4][17] 4. Factor Name: 10-Day Return Factor - **Factor Construction Idea**: Tracks the short-term momentum of stocks within the micro-cap stock index [4][17] - **Factor Construction Process**: - Calculate the 10-day return for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Positive performance this week with a rank IC of 0.105, above the historical average of -0.061 [4][17] 5. Factor Name: PE_TTM Reciprocal Factor - **Factor Construction Idea**: Measures valuation based on the reciprocal of the trailing twelve-month price-to-earnings ratio [4][17] - **Factor Construction Process**: - Calculate the reciprocal of PE_TTM for each stock - Evaluate weekly rank IC and compare with historical averages [4][17] - **Factor Evaluation**: Moderate performance this week with a rank IC of 0.041, above the historical average of 0.017 [4][17] --- Factor Backtesting Results Top 5 Factors by Weekly Rank IC 1. **Unadjusted Stock Price Factor**: Weekly rank IC = 0.177, Historical Average = -0.015 [4][17] 2. **Beta Factor**: Weekly rank IC = 0.15, Historical Average = 0.006 [4][17] 3. **Illiquidity Factor**: Weekly rank IC = 0.143, Historical Average = 0.04 [4][17] 4. **10-Day Return Factor**: Weekly rank IC = 0.105, Historical Average = -0.061 [4][17] 5. **PE_TTM Reciprocal Factor**: Weekly rank IC = 0.041, Historical Average = 0.017 [4][17] Bottom 5 Factors by Weekly Rank IC 1. **Turnover Factor**: Weekly rank IC = -0.189, Historical Average = -0.082 [4][17] 2. **Momentum Factor**: Weekly rank IC = -0.132, Historical Average = -0.005 [4][17] 3. **Residual Volatility Factor**: Weekly rank IC = -0.13, Historical Average = -0.04 [4][17] 4. **10-Day Free Float Turnover Factor**: Weekly rank IC = -0.12, Historical Average = -0.062 [4][17] 5. **Liquidity Factor**: Weekly rank IC = -0.118, Historical Average = -0.041 [4][17]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[26][39] - **Model Construction Process**: The diffusion index is calculated for each industry, reflecting the proportion of stocks within the industry that exhibit positive momentum. The index ranges from 0 to 1, where higher values indicate stronger momentum. The model selects industries with the highest diffusion indices for allocation. For example, as of August 1, 2025, the top-ranked industries included Steel (1.0), Comprehensive Finance (1.0), and Non-Banking Finance (0.999)[27][28] - **Model Evaluation**: The model has shown mixed performance over the years. While it achieved significant excess returns in 2021 (up to 25% before September), it experienced notable drawdowns in 2023 (-4.58%) and 2024 (-5.82%) due to its inability to adjust to market reversals[26] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[40] - **Model Construction Process**: The GRU network is trained on historical minute-level data to predict industry factor rankings. The model then allocates to industries with the highest predicted rankings. As of August 1, 2025, the top-ranked industries included Non-Banking Finance (-1.15), Steel (0.7), and Base Metals (0.5)[34][38] - **Model Evaluation**: The model has demonstrated strong adaptability in short-term scenarios but struggles in long-term or extreme market conditions. Its performance in 2025 has been hindered by concentrated market themes, resulting in difficulty capturing inter-industry excess returns[33][40] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -1.67%[30] - **Excess Return (August)**: -0.44%[30] - **Excess Return (2025 YTD)**: -0.40%[25][30] 2. GRU Factor Model - **Weekly Average Return**: 0.00%[38] - **Excess Return (August)**: 0.16%[38] - **Excess Return (2025 YTD)**: -2.35%[33][38] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of positive momentum within an industry[27] - **Factor Construction Process**: The diffusion index is calculated as the proportion of stocks in an industry with positive momentum. For example, as of August 1, 2025, the diffusion index for Steel was 1.0, while for Coal it was 0.23[27][28] - **Factor Evaluation**: The factor effectively identifies industries with strong upward trends but may underperform during market reversals[26] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: Utilizes GRU deep learning to rank industries based on high-frequency trading data[40] - **Factor Construction Process**: The GRU network processes minute-level volume and price data to generate factor rankings. For instance, as of August 1, 2025, the GRU factor for Non-Banking Finance was -1.15, while for Steel it was 0.7[34][38] - **Factor Evaluation**: The factor is effective in capturing short-term trends but struggles in long-term or highly volatile markets[33][40] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Top Industries (August 1, 2025)**: Steel (1.0), Comprehensive Finance (1.0), Non-Banking Finance (0.999)[27][28] - **Weekly Average Return**: -1.67%[30] - **Excess Return (August)**: -0.44%[30] - **Excess Return (2025 YTD)**: -0.40%[25][30] 2. GRU Industry Factor - **Top Industries (August 1, 2025)**: Non-Banking Finance (-1.15), Steel (0.7), Base Metals (0.5)[34][38] - **Weekly Average Return**: 0.00%[38] - **Excess Return (August)**: 0.16%[38] - **Excess Return (2025 YTD)**: -2.35%[33][38]

Industry Investment Rating - The industry investment rating is "Outperform" [2] Core Views - The report emphasizes the focus on leading chemical companies such as Wanhua Chemical, Yangnong Chemical, and Hualu Hengsheng, while also highlighting the need to prevent excessive competition in sectors like silicon materials and pesticides [5][6] - The basic chemical sector has shown a decline of 1.46% this week, outperforming the CSI 300 index, which declined by 1.75% [6][19] Summary by Sections Industry Overview - The closing index for the basic chemical sector is at 3727.14, with a weekly high of 3806.19 and a low of 2687.54 [2] Weekly Market Performance - The basic chemical sector has a year-to-date performance of 7.87%, underperforming the CSI 300 index, which has a gain of 19.74% [19] - This week, the top gainers in the basic chemical sector include Siquan New Materials (up 50.75%), Shangwei New Materials (up 39.37%), and Tiancheng Technology (up 28.83%) [19][20] Price Movements - Key products that saw price increases include chicken seedlings (up 31.86%), oxalic acid (up 14.29%), and liquid chlorine (up 12.72%) [9][25] - Conversely, PVDF powder saw a significant price drop of 15.38%, along with other products like TMA and industrial-grade lithium carbonate [10][27] Investment Recommendations - The report suggests a focus on leading companies in the chemical sector, particularly in the silicon material and pesticide markets, to mitigate risks associated with excessive competition [5][6] - Specific investment ratings for key companies include: - Wanhua Chemical: Buy, closing price 60.9, market cap 190.71 billion [12] - Yangnong Chemical: Buy, closing price 68.0, market cap 27.55 billion [12] - Hualu Hengsheng: Buy, closing price 23.8, market cap 50.62 billion [12]

证券研究报告：固定收益报告 研究所 分析师:梁伟超 SAC 登记编号:S1340523070001 Email:liangweichao@cnpsec.com 研究助理:谢鹏 SAC 登记编号:S1340124010004 Email:xiepeng@cnpsec.com 近期研究报告 《下半年政府债供给怎么看？》 - 2025.07.10 固收专题 科创债 ETF 做市怎么看？ ——科创债专题之四 20250801 为支持科创债发行，交易所支持的一个重点方向是降低做市门 槛，如上交所将科创债纳入基准做市的发行规模门槛由 20 亿元下 调至 15 亿元，为科创债 etf 提供更大流动性。债券及 ETF 做市模 式如何?做市券流动性和利差机会如何？本文对此进行详细分析。 ⚫ 债券做市制度：两大做市品种，三类做市模式 债券主要分为个券做市和债券 ETF 做市。个券方面，做市商对做 市品种双边买卖报价的单笔申报数量不低于 100 万元面额。基金方 面，上交所要求债券 ETF 做市商最小申报金额为 30 万元，最大买卖 价 0.40%，最小平均每笔申报金额 5 万元，最低集合竞价参与率 80%， 最低连续竞价参与率 ...