Investment Rating - The report maintains a "Buy" rating for the company [2][9] Core Insights - The company reported a strong performance in the first half of 2025, achieving operating revenue of 360.76 billion yuan, a year-on-year increase of 35.58%, and a net profit attributable to shareholders of 12.11 billion yuan, up 38.61% year-on-year [5][6] - The demand for high-end AI servers is surging, driven by significant capital expenditure growth from North American cloud service providers, with AI cloud infrastructure investments expected to increase [6][7] - The GB200 series is seeing continuous improvement in yield rates, and the GB300 series is expected to enter substantial shipment phases in the second half of the year, which will support the company's AI server business profitability [7][9] Financial Performance - The company forecasts operating revenues of 882.92 billion yuan, 1,235.84 billion yuan, and 1,668.09 billion yuan for 2025, 2026, and 2027 respectively, with net profits of 33.43 billion yuan, 47.52 billion yuan, and 62.39 billion yuan for the same years [9][11] - The projected growth rates for operating revenue are 44.95% in 2025, 39.97% in 2026, and 34.98% in 2027 [11] - The company’s earnings per share (EPS) is expected to rise from 1.68 yuan in 2025 to 3.14 yuan in 2027, reflecting strong profitability growth [11]

Group 1: General Information - The report is a fixed - income report released on August 26, 2025 [1] - The analysts are Liang Weichao and research assistant Xie Peng [2] Group 2: Core Viewpoints - For urban investment bonds, the liquidity of medium - to long - term and high - rating bond items has declined, with the number of high - grade and high - liquidity bond items decreasing. For industrial bonds, the liquidity of long - term and medium - to low - rating bond items has declined, and the number of high - grade and high - liquidity bond items has also decreased [2][3][9][18] Group 3: Urban Investment Bonds Distribution of Bond Items - Regionally, the number of high - grade liquidity bond items in Jiangsu increased, while that in Shandong decreased, and Sichuan, Tianjin, and Chongqing remained stable. In terms of maturity, the number of high - grade liquidity bond items within 1 year and 2 - 3 years increased, while those in 1 - 2 years remained stable, and those in 3 - 5 years and over 5 years decreased. In terms of implicit ratings, the number of high - grade liquidity bond items with an implicit rating of AA(2) increased, those with AA+ remained stable, and those with AAA, AA, and AA - decreased [2][9] Yield - The yields of high - grade liquidity urban investment bonds mainly increased, with the increase ranging from 2 - 8bp [11] Score Changes - Among the top twenty in terms of score increase, the main body levels are AA+ and AA, concentrated in regions such as Jiangsu, Zhejiang, Sichuan, and Shandong, and mainly involve industries such as building decoration and comprehensive. Among the top twenty in terms of score decrease, the main body level is mainly AA, and the regional distribution is mainly in Zhejiang, Jiangsu, Guizhou, etc., and the main industries are building decoration and comprehensive [12] Group 4: Industrial Bonds Distribution of Bond Items - By industry, the number of high - grade liquidity bond items in public utilities and transportation increased, while that in real estate decreased, and coal and steel remained stable. In terms of maturity, the number of high - grade liquidity bond items within 1 year increased, those in 1 - 2 years, 2 - 3 years, and 3 - 5 years remained stable, and those over 5 years decreased. In terms of implicit ratings, the number of high - grade liquidity bond items with an implicit rating of AAA increased, those with AAA+ and AAA - remained stable, and those with AA+ and AA decreased [3][18] Yield - The yields of high - grade liquidity bond items mainly increased, with the increase ranging from 2 - 12bp. Some sub - items decreased significantly, and the B - grade liquidity bond items with an implicit rating of AAA+ increased by 12bp [21] Score Changes - Among the top twenty in terms of score increase, the main industries of the entities are transportation and real estate, and the main body levels are AAA and AA+. The industries of the top twenty bonds are mainly transportation, real estate, and building decoration. Among the top twenty in terms of score decrease, the main industries of the entities are building decoration, real estate, and public utilities, and the main body levels are AAA and AA+. The industries of the top twenty bonds are mainly transportation and building decoration [22]

Investment Rating - The report maintains a "Buy" rating for the company, indicating an expected relative price increase of over 20% compared to the benchmark index within the next six months [6][11]. Core Insights - The company reported a significant revenue increase of 34.46% year-on-year, reaching 764.63 billion yuan, with a remarkable net profit growth of 1169.77% to 105.30 billion yuan, driven by declining costs [3][4]. - The company has a strong cash flow position, with net cash flow from operating activities amounting to 173.51 billion yuan, a 12.13% increase year-on-year, and a reduced debt ratio of 56.06% as of the end of Q2 2025 [3][4]. - The company is committed to returning value to shareholders, proposing a cash dividend of 9.32 yuan per 10 shares, totaling 50.02 billion yuan, which represents 47.50% of the net profit for the first half of 2025 [5]. Summary by Sections Company Overview - Latest closing price is 51.38 yuan, with a total market capitalization of 280.7 billion yuan and a P/E ratio of 15.57 [2]. Financial Performance - The company achieved a net profit of 105.30 billion yuan in the first half of 2025, with a significant increase in sales volume of live pigs by 44.84% year-on-year [3][4]. - The average cost of pig farming decreased from 13.1 yuan/kg in January to 11.8 yuan/kg in July, positioning the company as a leader in cost efficiency within the industry [4]. Future Outlook - The earnings per share (EPS) forecasts for 2025, 2026, and 2027 are projected at 3.57 yuan, 5.26 yuan, and 5.58 yuan respectively, reflecting a positive growth trajectory [6][10].

Investment Rating - The investment rating for the company is "Buy" [13] Core Views - The company reported a revenue of 10.55 billion yuan for the first half of 2025, a year-on-year increase of 2.17%, but the net profit attributable to shareholders decreased by 32.69% to 288 million yuan [5][6] - The decline in net profit was primarily due to a reduction in government subsidies, which decreased by 144 million yuan year-on-year [6] - New orders remained stable, with a total of 14.38 billion yuan in new orders signed in the first half of 2025, a slight increase of 0.2% year-on-year [6] - The company has made progress in robotics, having developed a welding robot control system and begun external sales [7] Financial Summary - The company’s total market capitalization is 12.7 billion yuan, with a total share capital of 690 million shares [4] - The company’s debt-to-asset ratio stands at 61.9% [4] - The projected revenue for 2025 and 2026 is expected to be 22.2 billion yuan and 23 billion yuan, respectively, with growth rates of 3.4% and 3.5% [7][9] - The estimated net profit for 2025 is 680 million yuan, reflecting a decrease of 12.1%, while the profit for 2026 is projected to increase by 62% to 1.1 billion yuan [7][9]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is used to predict stock returns based on historical data and incorporates various factors to optimize portfolio performance [3][4][5] - **Model Construction Process**: - The GRU model is trained on historical data to capture temporal dependencies in stock returns - It uses multiple input features, including technical and fundamental factors, to predict future returns - The model is applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate its performance [5][6][7] - **Model Evaluation**: The GRU model demonstrates strong performance in most stock pools, with positive long-short returns across various factors. However, certain sub-models (e.g., `barra5d`) show occasional underperformance [5][6][7] 2. Model Name: Open1d and Close1d Models - **Model Construction Idea**: These models focus on short-term price movements and are designed to capture daily return patterns [8][31] - **Model Construction Process**: - Open1d and Close1d models are trained on daily open and close price data, respectively - They are evaluated based on their ability to generate excess returns relative to the CSI 1000 index [8][31] - **Model Evaluation**: These models show mixed performance, with occasional drawdowns relative to the benchmark index [8][31] 3. Model Name: Barra1d and Barra5d Models - **Model Construction Idea**: These models are based on the Barra factor framework and aim to capture short-term and medium-term return patterns [8][31] - **Model Construction Process**: - Barra1d focuses on daily factor returns, while Barra5d aggregates returns over a 5-day horizon - Both models are tested for their ability to generate excess returns relative to the CSI 1000 index [8][31] - **Model Evaluation**: Barra5d demonstrates strong year-to-date performance, significantly outperforming the benchmark, while Barra1d shows consistent but less pronounced gains [8][31] --- Model Backtest Results 1. GRU Model - **Excess Return**: Positive across most stock pools, with occasional underperformance in specific sub-models like `barra5d` [5][6][7] 2. Open1d Model - **Weekly Excess Return**: -0.01% - **Year-to-Date Excess Return**: 5.23% [32] 3. Close1d Model - **Weekly Excess Return**: -0.38% - **Year-to-Date Excess Return**: 3.64% [32] 4. Barra1d Model - **Weekly Excess Return**: 0.65% - **Year-to-Date Excess Return**: 3.80% [32] 5. Barra5d Model - **Weekly Excess Return**: 0.02% - **Year-to-Date Excess Return**: 6.44% [32] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical beta to capture market sensitivity [15] - **Factor Construction Process**: Historical beta is calculated based on the covariance of stock returns with market returns [15] 2. Factor Name: Momentum - **Factor Construction Idea**: Captures historical excess return trends [15] - **Factor Construction Process**: - Momentum = 0.74 * Historical Excess Return Volatility + 0.16 * Cumulative Excess Return Deviation + 0.1 * Historical Residual Return Volatility [15] 3. Factor Name: Volatility - **Factor Construction Idea**: Measures stock price fluctuations to identify high-volatility stocks [15] - **Factor Construction Process**: - Volatility = Weighted combination of historical residual return volatility and other metrics [15] 4. Factor Name: Growth - **Factor Construction Idea**: Focuses on earnings and revenue growth rates [15] - **Factor Construction Process**: - Growth = 0.24 * Earnings Growth Rate + 0.47 * Revenue Growth Rate [15] 5. Factor Name: Liquidity - **Factor Construction Idea**: Measures stock turnover to identify liquid stocks [15] - **Factor Construction Process**: - Liquidity = 0.35 * Monthly Turnover + 0.35 * Quarterly Turnover + 0.3 * Annual Turnover [15] --- Factor Backtest Results 1. Beta Factor - **Weekly Long-Short Return**: Positive [16][18] 2. Momentum Factor - **Weekly Long-Short Return**: Negative [16][18] 3. Volatility Factor - **Weekly Long-Short Return**: Positive [16][18] 4. Growth Factor - **Weekly Long-Short Return**: Positive [16][18] 5. Liquidity Factor - **Weekly Long-Short Return**: Positive [16][18]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is used to monitor the critical points of future diffusion index changes, providing insights into potential market turning points[34][35] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. For example, if all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.33. The current diffusion index value is 0.82, indicating a relatively uniform distribution[34][35] - **Model Evaluation**: The model provides a systematic way to observe market heat and potential upward space, though it is sensitive to the dynamic updates of constituent stocks[34][35] 2. Model Name: First Threshold Method (Left-Side Trading) - **Model Construction Idea**: This method triggers a sell signal when the diffusion index reaches a predefined threshold[39] - **Model Construction Process**: The first threshold method triggered a sell signal on May 8, 2025, when the diffusion index closed at 0.9850[39] 3. Model Name: Delayed Threshold Method (Right-Side Trading) - **Model Construction Idea**: Similar to the first threshold method but with a delayed signal to confirm the trend[41][43] - **Model Construction Process**: The delayed threshold method triggered a sell signal on May 15, 2025, when the diffusion index closed at 0.8975[43] 4. Model Name: Dual Moving Average Method (Adaptive Trading) - **Model Construction Idea**: This method uses two moving averages to adaptively identify trading signals[44] - **Model Construction Process**: The dual moving average method issued a sell signal again on August 4, 2025[44] --- Model Backtesting Results 1. Diffusion Index Model - Current diffusion index value: 0.82[34][35] 2. First Threshold Method - Triggered sell signal at diffusion index value: 0.9850[39] 3. Delayed Threshold Method - Triggered sell signal at diffusion index value: 0.8975[43] 4. Dual Moving Average Method - Triggered sell signal on August 4, 2025[44] --- Quantitative Factors and Construction Methods 1. Factor Name: One-Year Volatility Factor - **Factor Construction Idea**: Measures the stock's price volatility over the past year[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.135, with a historical average of -0.032[3][29] 2. Factor Name: Residual Volatility Factor - **Factor Construction Idea**: Captures the residual volatility of stock returns after accounting for market movements[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.057, with a historical average of -0.039[3][29] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Reflects the growth potential of stocks based on financial metrics[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.053, with a historical average of -0.004[3][29] 4. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.042, with a historical average of -0.006[3][29] 5. Factor Name: Illiquidity Factor - **Factor Construction Idea**: Captures the illiquidity of stocks based on trading volume and price impact[3][29] - **Factor Construction Process**: Rank IC for this factor this week is 0.041, with a historical average of 0.04[3][29] 6. Factor Name: 10-Day Return Factor - **Factor Construction Idea**: Measures the stock's return over the past 10 days[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.131, with a historical average of -0.061[3][29] 7. Factor Name: Nonlinear Market Cap Factor - **Factor Construction Idea**: Captures the nonlinear relationship between market capitalization and stock returns[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.033[3][29] 8. Factor Name: Logarithmic Market Cap Factor - **Factor Construction Idea**: Uses the logarithm of market capitalization to explain stock returns[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.033[3][29] 9. Factor Name: 10-Day Total Market Cap Turnover Factor - **Factor Construction Idea**: Measures the turnover of total market capitalization over the past 10 days[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.13, with a historical average of -0.06[3][29] 10. Factor Name: PE_TTM Reciprocal Factor - **Factor Construction Idea**: Uses the reciprocal of the price-to-earnings ratio (trailing twelve months) as a valuation metric[3][29] - **Factor Construction Process**: Rank IC for this factor this week is -0.129, with a historical average of 0.017[3][29] --- Factor Backtesting Results Top 5 Factors by Rank IC This Week 1. One-Year Volatility Factor: 0.135[3][29] 2. Residual Volatility Factor: 0.057[3][29] 3. Growth Factor: 0.053[3][29] 4. Leverage Factor: 0.042[3][29] 5. Illiquidity Factor: 0.041[3][29] Bottom 5 Factors by Rank IC This Week 1. 10-Day Return Factor: -0.131[3][29] 2. Nonlinear Market Cap Factor: -0.13[3][29] 3. Logarithmic Market Cap Factor: -0.13[3][29] 4. 10-Day Total Market Cap Turnover Factor: -0.13[3][29] 5. PE_TTM Reciprocal Factor: -0.129[3][29]

Quantitative Models and Construction 1. Model Name: Diffusion Index Industry Rotation Model - **Model Construction Idea**: This model is based on the principle of price momentum, aiming to capture upward trends in industries through a diffusion index[24][25]. - **Model Construction Process**: The diffusion index is calculated for each industry, reflecting the proportion of stocks within the industry that exhibit upward momentum. The index ranges from 0 to 1, where higher values indicate stronger upward trends. The model selects industries with the highest diffusion index values for rotation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown mixed performance over the years. It performed well in capturing trends during certain periods (e.g., pre-September 2021) but struggled during market reversals or when trends shifted to mean-reversion patterns[24]. 2. Model Name: GRU Factor Industry Rotation 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[37]. - **Model Construction Process**: The GRU network is trained on historical minute-level data to predict industry factor rankings. The model then selects industries with the highest predicted factor scores for rotation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The GRU model has demonstrated strong adaptability in short-term scenarios but has underperformed in long-term or extreme market conditions. Its reliance on high-frequency data makes it sensitive to market noise[37]. --- Model Backtesting Results 1. Diffusion Index Industry Rotation Model - **Annualized Excess Returns**: - 2021: +25% (pre-September), followed by significant drawdowns later in the year - 2022: +6.12% - 2023: -4.58% - 2024: -5.82% - 2025 (YTD as of August): +2.71%[24][28] - **Monthly Performance (August 2025)**: - Average Return: +4.18% - Excess Return (vs. Equal-Weighted Industry Index): +0.78%[28] 2. GRU Factor Industry Rotation Model - **Annualized Excess Returns**: - 2025 (YTD as of August): -8.59%[31][34] - **Monthly Performance (August 2025)**: - Average Return: +1.80% - Excess Return (vs. Equal-Weighted Industry Index): -1.58%[34] --- Quantitative Factors and Construction 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the proportion of stocks within an industry exhibiting upward momentum, serving as a proxy for industry-level price trends[25]. - **Factor Construction Process**: - The diffusion index is calculated weekly for each industry. - Industries are ranked based on their diffusion index values, with higher values indicating stronger momentum. - Example Rankings (as of August 22, 2025): - Top Industries: Comprehensive Finance (1.0), Comprehensive (1.0), Steel (1.0) - Bottom Industries: Coal (0.262), Electric Utilities (0.587), Real Estate (0.694)[25][26]. 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: Derived from GRU deep learning models, this factor captures industry-level signals based on high-frequency trading data[37]. - **Factor Construction Process**: - The GRU model processes minute-level volume and price data to generate factor scores for each industry. - Industries are ranked based on their GRU factor scores. - Example Rankings (as of August 22, 2025): - Top Industries: Building Materials (3.32), Electronics (2.36), Non-Banking Finance (1.97) - Bottom Industries: Electric Utilities (-25.33), Banking (-24.29), Pharmaceuticals (-20.97)[32]. --- Factor Backtesting Results 1. Diffusion Index - **Weekly Rankings (August 22, 2025)**: - Top Industries: Comprehensive Finance (1.0), Comprehensive (1.0), Steel (1.0) - Bottom Industries: Coal (0.262), Electric Utilities (0.587), Real Estate (0.694)[25][26]. 2. GRU Industry Factor - **Weekly Rankings (August 22, 2025)**: - Top Industries: Building Materials (3.32), Electronics (2.36), Non-Banking Finance (1.97) - Bottom Industries: Electric Utilities (-25.33), Banking (-24.29), Pharmaceuticals (-20.97)[32].

- The Genie Envisioner platform introduces a video-centric world modeling paradigm, directly modeling robot-environment interactions in the visual space, which retains spatial structure and temporal evolution information. This approach enhances cross-domain generalization and long-sequence task execution capabilities, achieving a 76% success rate in long-step tasks like folding cardboard boxes, outperforming the π0 model's 48%[12][13][16] - The Genie Envisioner platform comprises three core components: GE-Base, a multi-view video world foundation model trained on 3000 hours of real robot data; GE-Act, a lightweight 160M parameter action decoder enabling real-time control; and GE-Sim, a hierarchical action-conditioned simulator for closed-loop strategy evaluation and large-scale data generation[16][17][19] - The GLM-4.5V visual reasoning model, with 106B total parameters and 120B activation parameters, achieves state-of-the-art (SOTA) performance across 41 multimodal benchmarks, including image, video, document understanding, and GUI agent tasks. It incorporates 3D-RoPE and bicubic interpolation mechanisms to enhance 3D spatial relationship perception and high-resolution adaptability[20][21][22] - GLM-4.5V employs a three-stage training strategy: pretraining on large-scale multimodal corpora, supervised fine-tuning with "chain of thought" samples, and reinforcement learning with RLVR and RLHF techniques. This layered training enables superior document processing capabilities and emergent abilities like generating structured HTML/CSS/JavaScript code from screenshots or videos[23][24][26] - VeOmni, a fully modular multimodal training framework, decouples model definition from distributed parallel logic, enabling flexible parallel strategies like FSDP, HSDP+SP, and EP. It achieves 43.98% MFU for 64K sequence training and supports up to 192K sequence lengths, reducing engineering complexity and improving efficiency by over 90%[27][28][31] - VeOmni introduces asynchronous sequence parallelism (Async-Ulysses) and COMET technology for MoE models, achieving linear scalability in training throughput for 30B parameter models under 160K sequence lengths. It also integrates dynamic batch processing and FlashAttention to minimize memory waste and optimize operator-level recomputation[31][32][34] - Skywork UniPic 2.0, a unified multimodal framework, integrates image understanding, text-to-image (T2I) generation, and image-to-image (I2I) editing within a single model. It employs a progressive dual-task reinforcement strategy (Flow-GRPO) to optimize image editing and T2I tasks sequentially, achieving superior performance in benchmarks like GenEval and GEdit-EN[35][38][39] - UniPic 2.0 leverages Skywork-EditReward, an image-editing-specific reward model, to provide pixel-level quality scores. This design enables precise recognition of image elements and generation of corresponding textual descriptions, achieving 83.5 points in MMBench, comparable to 19B parameter models[38][42][43] - FlowReasoner, a query-level meta-agent framework, dynamically generates personalized multi-agent systems for individual queries. It employs GRPO reinforcement learning with multi-objective reward mechanisms, achieving 92.15% accuracy on the MBPP dataset and outperforming baseline models like Aflow and LLM-Blender[63][64][68] - FlowReasoner utilizes a three-stage training process: supervised fine-tuning with synthetic data, SFT fine-tuning for workflow generation, and RL with external feedback for capability enhancement. It demonstrates robust generalization, maintaining high accuracy even when the base worker model is replaced[66][68][69]

Investment Rating - The report assigns an "Accumulate" rating to the company [2]. Core Views - The company has shown stable revenue growth, achieving an operating income of 6.336 billion yuan in the first half of 2025, a year-on-year increase of 20.14%, and a net profit attributable to shareholders of 265 million yuan, up 1162.42% year-on-year [5][6]. - The company is expanding into high-barrier markets, including large home appliances, automotive, new energy, industrial, communications, and computing, which has contributed to its revenue growth [6]. - The company has increased production capacity across various subsidiaries, maintaining full-load production in its chip manufacturing lines, which has further improved profitability [6]. - The integrated circuit business has seen a year-on-year growth of approximately 26%, with significant increases in the sales of power management chips and MCU products [7]. - The fourth generation of SiC power modules is expected to ramp up production, with the company achieving a revenue of 3.008 billion yuan from power semiconductors and discrete devices, a year-on-year increase of about 25% [8]. - The company is optimizing its LED business structure, with expectations of reduced operational losses as production capacity utilization improves [9][10]. Financial Projections - The company is projected to achieve operating revenues of 13.48 billion yuan, 16.00 billion yuan, and 18.81 billion yuan for the years 2025, 2026, and 2027, respectively, with net profits attributable to shareholders of 616.64 million yuan, 869.91 million yuan, and 1.183 billion yuan for the same years [11][13]. - The report anticipates a steady growth rate in operating income of approximately 20.14% for 2025, 20.17% for 2026, and 18.63% for 2027 [13]. - The earnings per share (EPS) are expected to be 0.37 yuan, 0.52 yuan, and 0.71 yuan for the years 2025, 2026, and 2027, respectively [13]. Market Performance - The stock has shown a significant upward trend, with performance increasing from 4% in August 2024 to 76% by August 2025 [3]. - The company's market capitalization is currently 48.9 billion yuan, with a total share capital of 1.664 billion shares [4]. - The price-to-earnings (P/E) ratio is reported at 226.00, indicating high market expectations for future growth [4].

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [2] Core Views - The report highlights that the recent dovish stance from Federal Reserve Chairman Powell has led to an increase in metal prices, with expectations of potential interest rate cuts strengthening [5] - Precious metals are expected to perform well due to increased ETF inflows and a long-term view on de-dollarization [5] - Copper prices are supported by weak supply and the end of the consumption off-season, with a recommendation to wait for price adjustments before going long [6] - Aluminum prices are expected to rise due to inventory depletion during the peak demand season, despite limited impact from U.S. tariffs [6] - Tungsten prices are on the rise, driven by increased demand from military and infrastructure sectors, with exports showing significant growth [7] - Cobalt prices are expected to increase due to U.S. Department of Defense's strategic stockpiling plans and improved demand from the battery sector [8] Summary by Sections Industry Overview - The closing index for the industry is at 5984.59, with a weekly high of 5984.59 and a low of 3700.9 [2] Price Movements - Basic metals saw price increases: Copper up 0.50%, Aluminum up 0.73%, Zinc up 0.32%, Lead up 0.56%, and Tin up 0.70% [21] - Precious metals also increased: Gold up 1.05%, Silver up 2.26%, Palladium up 2.06%, and Platinum up 1.39% [21] Inventory Changes - Global visible inventory changes: Copper increased by 2179 tons, Aluminum decreased by 8872 tons, Zinc increased by 4521 tons, Lead increased by 9112 tons, Tin decreased by 243 tons, and Nickel decreased by 1503 tons [33]