Investment Rating - The report assigns an "Accumulate" rating for the company, marking its first coverage [1]. Core Insights - The company focuses on the new materials industry with a diversified layout, including four core product lines: ePTFE micro-permeable products, CMD and gas management products, SiO2 aerogel products, and high-performance wiring products, primarily serving the automotive, military, and aerospace sectors [4]. - The company operates in a high-barrier technology sector with excellent product profitability, maintaining a gross margin above 45% and a net margin around 20% over the past two years [4]. - The company's revenue growth remains robust, with a 25% year-on-year increase in 2024, reaching 515 million yuan, and a 43% year-on-year increase in net profit for Q1 2025 [5]. Company Overview - Latest closing price: 50.50 yuan - Total shares: 0.91 billion, Market capitalization: 4.6 billion yuan - Debt-to-asset ratio: 28.9%, PE ratio: 35.56 [3]. Business Segment Performance - **ePTFE Micro-permeable Materials**: Generated 162 million yuan in revenue in 2024, a 34% increase, accounting for approximately 32% of total revenue [6]. - **CMD and Gas Management Products**: Achieved 142 million yuan in revenue, a 45% increase, representing about 28% of total revenue [7]. - **SiO2 Aerogel Products**: Revenue reached 65 million yuan, growing by 47%, making up about 13% of total revenue [8]. - **High-performance Wiring Products**: The company expanded into high-performance cables and connectors, establishing an automotive wiring division in 2024 [9]. Financial Forecast and Valuation - Revenue projections for 2025-2027 are 678 million, 890 million, and 1.102 billion yuan, with year-on-year growth rates of 31.72%, 31.28%, and 23.78% respectively [10]. - Expected net profits for the same period are 145 million, 192 million, and 241 million yuan, with growth rates of 46.42%, 32.13%, and 25.60% respectively [10]. - Corresponding PE ratios are projected to be 31.65, 23.95, and 19.07 [10].

Investment Rating - The report assigns an "Accumulate" rating for the company, marking its first coverage [1] Core Views - The company, Jinchuan Group, is experiencing a period of rapid growth due to strong demand in the railway equipment sector, with a comprehensive product system and one-stop supply capabilities [4][5] - The railway fixed asset investment in China is in a high prosperity state, with significant increases in new lines and maintenance demands, projected to reach 165,000 kilometers of operational railway by 2025 [5] - The company reported a 244% year-on-year increase in profit for Q1 2025, indicating a strong performance driven by the booming rail transit industry [6] Company Overview - Latest closing price: 11.85 yuan - Total shares: 784 million, with a total market value of 9.3 billion yuan - Debt-to-asset ratio: 43.5% - Price-to-earnings ratio: 30.38 - Major shareholder: Yu Jinkun [3] Financial Performance - In 2024, the company achieved revenue of 4.499 billion yuan, a 22% increase year-on-year, and a net profit of 302 million yuan, up 9% year-on-year [6] - For Q1 2025, revenue was 1.086 billion yuan, a 22% increase, with net profit soaring to 149 million yuan, a 244% increase [6] - Revenue projections for 2025-2027 are 5.601 billion, 6.379 billion, and 7.069 billion yuan, with corresponding net profits of 611 million, 685 million, and 782 million yuan [7][9] Valuation Metrics - The estimated price-to-earnings ratios for 2025, 2026, and 2027 are 15.21, 13.56, and 11.87 respectively, indicating a favorable valuation trend [7][9] - The company is expected to maintain a gross margin of approximately 25.4% and a net profit margin of around 10.9% by 2025 [10]

Investment Rating - The industry investment rating is "Strongly Outperform the Market" and is maintained [1] Core Viewpoints - The current focus in the petrochemical sector is on crude oil prices, which are primarily influenced by geopolitical factors, including developments in Iran, U.S. military actions, and negotiations regarding the Strait of Hormuz [2] - This week, the petrochemical index performed relatively well, closing at 2248.68 points, with a decrease of 1.03% compared to last week, while the oilfield services sector saw the best performance with a rise of 4.53% [3][2] - Crude oil prices have increased this week, with a decline in U.S. crude oil inventories and an increase in refined oil inventories [5][10] - Polyester prices, particularly for polyester filament yarn, have risen, with inventory days decreasing and operating rates declining in Jiangsu and Zhejiang [12][18] - Polyolefin prices remained stable, with a decrease in inventory levels during the week [20][24] Summary by Sections Crude Oil - Crude oil prices have risen, with U.S. crude oil inventories decreasing and refined oil inventories increasing [5][10] - As of June 20, Brent crude futures and TTF natural gas futures closed at $76.52 per barrel and €40.94 per MWh, respectively, marking increases of 4.1% and 9.0% compared to the previous week [8] Polyester - Polyester filament yarn prices have increased, with POY, DTY, and FDY prices at 7150, 8350, and 7430 yuan per ton, respectively, showing price differentials that have expanded [14][12] - Inventory days for polyester filament yarn in Jiangsu and Zhejiang have decreased, with operating rates at 90.6% and 60.7% for filament yarn and downstream weaving machines, respectively [18][12] Polyolefins - Sample prices for polyethylene and polypropylene were 7700 and 8076 yuan per ton, with slight changes of 0% and -0.20% respectively [24] - Polyolefin inventory stood at 740,000 tons, down by 70,000 tons from the previous week [24]

Economic Overview - The economic climate in June remains stable, with industrial demand slightly declining while consumption and exports show resilience, and real estate sales maintain high activity levels[1] - The expected economic growth rate for the second quarter is around 5.2%[1] Real Estate Market - Real estate sales in 30 major cities have turned positive year-on-year, with a daily average transaction area of 24.38 million square meters, a 10.69% increase compared to May[12] - First-tier cities are expected to stabilize housing prices by the end of the year, while second-tier cities may stabilize by June next year[2] Consumer Activity - The "618" shopping festival, combined with the "trade-in" policy, has significantly boosted consumption, with online retail sales reaching nearly 2 trillion yuan, a year-on-year increase of approximately 9.8%[28] - However, some regions are experiencing a temporary decline in government subsidies, which may lead to fluctuations in retail sales growth[2] Industrial Demand - Industrial demand is showing signs of recovery, with the rebar production rate increasing to 42.19% in June, although prices remain low due to ample supply[19] - The average price of rebar in June is 3,385 yuan per ton, a decrease of 2.28% compared to May[19] Price Index Trends - The Producer Price Index (PPI) is expected to continue its decline, with a projected year-on-year decrease of around 3.4% in June, marking 32 months of negative growth[39] - The PPI's ongoing decline is a significant factor affecting corporate profitability[48] Government Policy and Market Sentiment - The central bank is expected to maintain a moderately loose monetary policy, while geopolitical risks are suppressing global risk appetite[4] - The issuance of new special bonds in June has slowed, with a total of 568.53 billion yuan, a decrease of 77.65% from May[23]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model integrates fundamental and technical features to predict stock performance[3][19] - **Model Construction Process**: The GRU model is a recurrent neural network (RNN) variant designed to handle sequential data. It uses gating mechanisms to control the flow of information, allowing it to capture temporal dependencies in financial data. Specific details on the input features or training process are not provided in the report[3][19] - **Model Evaluation**: The GRU model shows mixed performance, with significant drawdowns in certain market segments[3][19] 2. Model Name: Barra1d - **Model Construction Idea**: A short-term factor model based on the Barra framework, focusing on daily data[3][19] - **Model Evaluation**: Barra1d exhibits significant drawdowns in multiple market segments, indicating weaker performance[3][19] 3. Model Name: Barra5d - **Model Construction Idea**: A medium-term factor model based on the Barra framework, focusing on 5-day data[3][19] - **Model Evaluation**: Barra5d demonstrates strong performance, achieving positive returns in various market segments[3][19] 4. Model Name: Close1d - **Model Construction Idea**: A short-term model focusing on daily closing prices[3][19] - **Model Evaluation**: Close1d performs well in certain market segments, achieving positive returns[3][19] 5. Model Name: Open1d - **Model Construction Idea**: A short-term model focusing on daily opening prices[3][19] - **Model Evaluation**: Open1d shows weaker performance, with significant drawdowns in certain market segments[3][19] --- Model Backtesting Results 1. GRU Model - **Weekly Excess Return**: -0.08% to -0.54% relative to the CSI 1000 Index[7][30] 2. Barra1d - **Weekly Excess Return**: -0.54%[31] - **Year-to-Date Excess Return**: 3.75%[31] 3. Barra5d - **Weekly Excess Return**: -0.31%[31] - **Year-to-Date Excess Return**: 7.42%[31] 4. Close1d - **Weekly Excess Return**: -0.40%[31] - **Year-to-Date Excess Return**: 5.73%[31] 5. Open1d - **Weekly Excess Return**: -0.08%[31] - **Year-to-Date Excess Return**: 6.68%[31] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical beta to capture market sensitivity[15] 2. Factor Name: Market Capitalization - **Factor Construction Idea**: Logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Average historical excess returns[15] 4. Factor Name: Volatility - **Factor Construction Process**: $ Volatility = 0.74 * \text{Historical Excess Return Volatility} + 0.16 * \text{Cumulative Excess Return Deviation} + 0.1 * \text{Residual Return Volatility} $ - **Parameters**: - Historical Excess Return Volatility: Measures the standard deviation of excess returns - Cumulative Excess Return Deviation: Captures deviations in cumulative returns - Residual Return Volatility: Measures the volatility of residual returns[15] 5. Factor Name: Nonlinear Market Capitalization - **Factor Construction Idea**: Cubic transformation of market capitalization[15] 6. Factor Name: Valuation - **Factor Construction Idea**: Inverse of price-to-book ratio[15] 7. Factor Name: Liquidity - **Factor Construction Process**: $ Liquidity = 0.35 * \text{Monthly Turnover} + 0.35 * \text{Quarterly Turnover} + 0.3 * \text{Annual Turnover} $ - **Parameters**: - Monthly Turnover: Measures trading activity over a month - Quarterly Turnover: Measures trading activity over a quarter - Annual Turnover: Measures trading activity over a year[15] 8. Factor Name: Profitability - **Factor Construction Process**: $ Profitability = 0.68 * \text{Analyst Forecast Earnings Yield} + 0.21 * \text{Inverse Price-to-Cash Flow} + 0.11 * \text{Inverse Price-to-Earnings (TTM)} $ $ + 0.18 * \text{Analyst Long-Term Growth Forecast} + 0.11 * \text{Analyst Short-Term Growth Forecast} $ - **Parameters**: - Analyst Forecast Earnings Yield: Measures expected earnings relative to price - Inverse Price-to-Cash Flow: Captures cash flow efficiency - Analyst Growth Forecasts: Reflects expected growth rates[15] 9. Factor Name: Growth - **Factor Construction Process**: $ Growth = 0.24 * \text{Earnings Growth Rate} + 0.47 * \text{Revenue Growth Rate} $ - **Parameters**: - Earnings Growth Rate: Measures growth in earnings - Revenue Growth Rate: Measures growth in revenue[15] 10. Factor Name: Leverage - **Factor Construction Process**: $ Leverage = 0.38 * \text{Market Leverage} + 0.35 * \text{Book Leverage} + 0.27 * \text{Debt-to-Asset Ratio} $ - **Parameters**: - Market Leverage: Measures leverage based on market value - Book Leverage: Measures leverage based on book value - Debt-to-Asset Ratio: Captures the proportion of debt in total assets[15] --- Factor Backtesting Results 1. Momentum Factors - **120-Day Momentum**: Weekly return -2.37%[28] - **60-Day Momentum**: Weekly return -2.17%[28] - **20-Day Momentum**: Weekly return -1.69%[28] 2. Volatility Factors - **60-Day Volatility**: Weekly return -1.53%[28] - **20-Day Volatility**: Weekly return -0.96%[28] - **120-Day Volatility**: Weekly return 0.78%[28] 3. Median Deviation - **Weekly Return**: -0.40%[28]

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[27][28] - **Model Construction Process**: The diffusion index is calculated for each industry, ranking them based on their momentum. Industries with higher diffusion index values are considered to have stronger upward trends. The model selects industries with the highest diffusion index values for allocation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown mixed performance over the years. It performed well in 2021 and 2022 but faced significant drawdowns in 2023 and 2024 due to market reversals and failure to adjust to cyclical changes[27] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry trends and generate excess returns[34][39] - **Model Construction Process**: The GRU network is trained on historical minute-level price and volume data to predict industry rankings. The model then allocates to industries with the highest GRU factor scores. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown strong adaptability in short-term cycles but struggles in long-term trends and extreme market conditions. It has faced challenges in capturing excess returns in 2025 due to concentrated market themes[34][39] --- Model Backtesting Results 1. Diffusion Index Model - **2025 YTD Excess Return**: 0.37%[26][31] - **June 2025 Excess Return**: 1.99%[31] - **Weekly Average Return (June 2025)**: -0.65%[31] - **Weekly Excess Return (June 2025)**: 0.79%[31] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.83%[34][37] - **June 2025 Excess Return**: 0.25%[37] - **Weekly Average Return (June 2025)**: -1.18%[37] - **Weekly Excess Return (June 2025)**: 0.25%[37] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the momentum of industries by ranking them based on their upward trends[28] - **Factor Construction Process**: The diffusion index is calculated for each industry weekly. Industries are ranked based on their index values, with higher values indicating stronger momentum. - Example Values (as of June 20, 2025): - Top Industries: Comprehensive Finance (1.0), Non-Bank Finance (0.973), Banking (0.97)[28] - Bottom Industries: Coal (0.174), Food & Beverage (0.313), Oil & Gas (0.387)[28] - **Factor Evaluation**: The factor effectively captures upward trends but may underperform during market reversals[27][28] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and rank industries based on predicted performance[34][39] - **Factor Construction Process**: The GRU network processes minute-level price and volume data to generate factor scores for each industry. Industries are ranked based on these scores. - Example Values (as of June 20, 2025): - Top Industries: Coal (3.48), Non-Bank Finance (3.15), Utilities (2.65)[35] - Bottom Industries: Communication (-17.95), Media (-15.45), Defense (-11.87)[35] - **Factor Evaluation**: The factor is effective in short-term trend identification but struggles with long-term stability and extreme market conditions[34][39] --- Factor Backtesting Results 1. Diffusion Index Factor - **Top Weekly Changes (June 20, 2025)**: - Oil & Gas: +0.09 - Textiles: +0.044 - Metals: +0.036[30] - **Bottom Weekly Changes (June 20, 2025)**: - Agriculture: -0.229 - Defense: -0.086 - Building Materials: -0.078[30] 2. GRU Factor - **Top Weekly Changes (June 20, 2025)**: - Non-Bank Finance: Significant increase - Consumer Services: Significant increase - Comprehensive: Significant increase[35] - **Bottom Weekly Changes (June 20, 2025)**: - Communication: Significant decrease - Electronics: Significant decrease - New Energy Equipment: Significant decrease[35]

Market Performance Review - A-shares faced pressure due to ongoing geopolitical conflicts, with all major indices declining this week. The Shanghai Composite Index, which has a high proportion of large-cap dividend stocks, fell by only 0.10%, while other indices like the ChiNext and CSI 1000 saw larger declines [3][12][13] - The market sentiment has turned cautious, with only the banking, communication, and electronics sectors showing positive returns amid the intensifying Israel-Iran conflict. Investors shifted towards defensive sectors, particularly high-dividend stocks represented by banks [3][13] A-share High-frequency Data Tracking - The personal investor sentiment index fell into negative territory, with a 7-day moving average of -0.05% as of June 21, down from 4.6% on June 14. This indicates a shift from persistent pessimism to a more neutral stance among investors [4][15] - The financing capital showed slight net inflows, suggesting a potential recovery in investor sentiment, as the financing transaction volume as a percentage of total A-share trading has not decreased after a rebound [18] Future Market Outlook and Investment Views - The A-share market is expected to face continued pressure from geopolitical conflicts, particularly the Israel-Iran situation and potential escalations in U.S. tariffs. The upcoming internal policy window in July may reignite expectations for stimulus policies, particularly in real estate [4][28][29] - Investment strategy should focus on high dividend stocks with good value, such as banks, railways, and utilities. The timing for traditional domestic demand trades remains uncertain and will depend on the introduction of clear stimulus policies [5][29] Industry Rotation and Dividend Value Tracking - The current market is characterized by high rotation speed but low intensity, indicating a sideways trend in indices. The industry rotation began at the end of April and is expected to maintain a fast pace into June [19][21] - The dividend yield of high-dividend stocks, particularly in the banking sector, remains attractive in the context of potential interest rate cuts, enhancing their value proposition [25][21]

Quantitative Models and Construction Methods Diffusion Index Model - Model Name: Diffusion Index Model - Model Construction Idea: The model monitors the critical point of future diffusion index changes to predict market trends. - Model Construction Process: - The horizontal axis represents the relative price change of stocks in the future, ranging from 1.1 to 0.9, indicating a 10% rise to a 10% fall. - The vertical axis represents the length of the review period or future days, ranging from 20 to 10 days. - Example: A value of 0.07 at the horizontal axis 0.95 and vertical axis 15 days indicates that if all stocks in the micro-cap index fall by 5% after 5 days, the diffusion index value is 0.07. - Formula: $ \text{Diffusion Index} = \frac{\text{Number of stocks rising}}{\text{Total number of stocks}} $ - Model Evaluation: The model is useful for monitoring the critical point of future diffusion index changes and predicting market trends.[6][17][40] First Threshold Method (Left-side Trading) - Model Name: First Threshold Method - Model Construction Idea: The model triggers a signal based on the first threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.9850 on May 8, 2025. - Formula: $ \text{Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides early signals for trading actions based on threshold values.[6][43][44] Delayed Threshold Method (Right-side Trading) - Model Name: Delayed Threshold Method - Model Construction Idea: The model triggers a signal based on the delayed threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.8975 on May 15, 2025. - Formula: $ \text{Delayed Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides delayed signals for trading actions based on threshold values.[6][45][47] Dual Moving Average Method (Adaptive Trading) - Model Name: Dual Moving Average Method - Model Construction Idea: The model uses dual moving averages to trigger trading signals. - Model Construction Process: - The model triggered a no-position signal at the closing value on June 11, 2025. - Formula: $ \text{Signal} = \text{Short-term Moving Average} - \text{Long-term Moving Average} $ - Model Evaluation: The model adapts to market changes using dual moving averages to provide trading signals.[6][48][49] Model Backtesting Results Diffusion Index Model - Diffusion Index Model, Current Value: 0.34[40] First Threshold Method (Left-side Trading) - First Threshold Method, Closing Value: 0.9850[43] Delayed Threshold Method (Right-side Trading) - Delayed Threshold Method, Closing Value: 0.8975[47] Dual Moving Average Method (Adaptive Trading) - Dual Moving Average Method, Closing Value: Not specified[48] Quantitative Factors and Construction Methods Past Year Volatility Factor - Factor Name: Past Year Volatility Factor - Factor Construction Idea: The factor measures the volatility of stocks over the past year. - Factor Construction Process: - Formula: $ \text{Volatility} = \sqrt{\frac{\sum (R_i - \bar{R})^2}{N}} $ - This week's rank IC: 0.171, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the volatility of stocks over the past year.[5][16][33] Beta Factor - Factor Name: Beta Factor - Factor Construction Idea: The factor measures the sensitivity of stocks to market movements. - Factor Construction Process: - Formula: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ - This week's rank IC: 0.145, Historical average: 0.004 - Factor Evaluation: The factor is effective in capturing the sensitivity of stocks to market movements.[5][16][33] Logarithmic Market Value Factor - Factor Name: Logarithmic Market Value Factor - Factor Construction Idea: The factor measures the logarithmic market value of stocks. - Factor Construction Process: - Formula: $ \text{Log Market Value} = \log(\text{Market Value}) $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the logarithmic market value of stocks.[5][16][33] Nonlinear Market Value Factor - Factor Name: Nonlinear Market Value Factor - Factor Construction Idea: The factor measures the nonlinear market value of stocks. - Factor Construction Process: - Formula: $ \text{Nonlinear Market Value} = (\text{Market Value})^2 $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the nonlinear market value of stocks.[5][16][33] Non-liquidity Factor - Factor Name: Non-liquidity Factor - Factor Construction Idea: The factor measures the non-liquidity of stocks. - Factor Construction Process: - Formula: $ \text{Non-liquidity} = \frac{\text{Number of non-trading days}}{\text{Total number of days}} $ - This week's rank IC: 0.125, Historical average: 0.038 - Factor Evaluation: The factor is effective in capturing the non-liquidity of stocks.[5][16][33] Factor Backtesting Results Past Year Volatility Factor - Past Year Volatility Factor, This week's rank IC: 0.171, Historical average: -0.033[5][16][33] Beta Factor - Beta Factor, This week's rank IC: 0.145, Historical average: 0.004[5][16][33] Logarithmic Market Value Factor - Logarithmic Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Nonlinear Market Value Factor - Nonlinear Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Non-liquidity Factor - Non-liquidity Factor, This week's rank IC: 0.125, Historical average: 0.038[5][16][33]

Report Summary 1. Report Industry Investment Rating No information about the report industry investment rating is provided in the given content. 2. Core Viewpoints - The market's "consensus expectation" of the bond market in the third quarter is strong, but it may lead to low volatility after "front - running." The probability of low - volatility due to "consensus expectation" is higher in the current market. When expectations are fulfilled, it may be the time for profit - taking. Asset - side interest rates will experience oscillations after "front - running" [4][20]. - The central bank's restart of treasury bond purchases needs to form a large short - end buying increment to create the "steep illusion" of the treasury bond yield curve, which is crucial for long - end interest rate trading [3][4]. - The long - end interest rate may break through the previous low by relying on the "steep illusion" of the treasury bond yield curve. However, the 1 - year treasury bond has limited further downward space, and if it continues to decline in the same way as funds and short - end coupon products, the space it brings to the 10 - year treasury bond is also limited [3][17]. 3. Summary by Related Catalogs "Consensus Expectation" as a Trading Bottleneck - **Funds**: The view on funds has been mostly realized. The 7D central rate still has some downward space. The rapid relaxation of the funds in June was driven by the increase in large - bank lending scale, which reached around 5 trillion. The 7 - month early period may see the funds price reach the bottom of its decline. Although there may be fluctuations during the tax period in late July, the stable and loose state is likely to continue, and the loose window can be measured in quarters [11][13]. - **Inter - bank Certificates of Deposit (NCDs)**: The view on NCDs has also been mostly realized. The subsequent trading center of NCDs may be 1.6%. Although the NCD interest rate may be lower than 1.6% at certain points in early July, the significance of 1.6% as the pricing center can be maintained throughout the third quarter [14]. - **Long - end Interest Rates**: The long - end interest rate may break through the previous low through the "steep illusion" of the treasury bond yield curve. The 1 - year treasury bond has limited further downward space, and the market hopes to see an "excess" downward space for the 1 - year treasury bond to bring more downward space for the 10 - year treasury bond [17]. - **Market Expectation of Central Bank's Treasury Bond Purchase**: The market's bet on the central bank's restart of treasury bond purchases is overly consistent. The "consensus expectation" of the bond market in the third quarter may lead to low volatility after "front - running" [4][20].

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [2] Core Views - Precious metals are expected to perform well in the long term despite a recent pullback, with a recommendation to overweight this sector [5] - Copper prices are expected to remain strong, with a support level around 9,350 USD per ton, influenced by macroeconomic factors and trade dynamics [6] - Aluminum prices are anticipated to trend upward, supported by easing trade tensions and a decrease in inventory levels [6] - Rare earth prices are projected to rise following a significant drop in export volumes, with expectations of increased demand due to recent diplomatic agreements [7] - Uranium prices have seen a significant increase, with expectations for a new upward trend in the second half of the year [8] Summary by Sections Industry Overview - The closing index for the industry is at 4,846.95, with a weekly high of 5,047.03 and a low of 3,700.9 [2] Price Movements - Basic metals saw price changes: Copper up 0.13%, Aluminum up 2.34%, Zinc up 0.86%, Lead up 0.13%, and Tin down 0.27% [20] - Precious metals experienced declines: Gold down 1.98%, Silver down 1.15%, while Platinum and Palladium saw increases of 4.08% and 1.69% respectively [20] Inventory Changes - Global visible inventories showed a decrease in Copper by 12,511 tons, Aluminum by 5,439 tons, and Zinc by 5,004 tons, while Lead saw an increase of 18,731 tons [34]