Employment Data Overview - December non-farm payrolls increased by 50,000, below the expected 60,000, while the unemployment rate fell to 4.4%[2] - The labor force participation rate declined to 62.4%, which statistically suppresses the unemployment rate, diluting its actual significance[4] Employment Sector Insights - Private sector added 37,000 jobs, with leisure and hospitality, and education and healthcare contributing 88,000 jobs combined, significantly boosting overall non-farm employment[14] - Job losses were evident in the goods-producing sectors, with construction, manufacturing, and mining losing 11,000, 8,000, and 2,000 jobs respectively, indicating weakening demand in the real economy[14] Wage Trends - Average hourly earnings in the service sector rose by 3.7% year-on-year, while goods-producing sectors saw a 4.1% increase, driven more by structural factors than by demand[20] - The increase in average wages reflects a structural effect where low-wage positions are being eliminated, raising the average wage of remaining employees[20] Monetary Policy Outlook - The probability of a rate cut in January is near zero, with the Federal Reserve likely to maintain a cautious stance due to the current employment and inflation dynamics[24] - The Fed's policy decisions will be influenced by upcoming inflation data and potential changes in the Federal Open Market Committee (FOMC) membership, which could reshape market expectations[24]

Investment Rating - The report maintains an investment rating of "Outperform the Market" for the oil and petrochemical industry [2][10]. Core Insights - The restructuring of Sinopec and China National Aviation Fuel (CNAF) is expected to enhance the resilience of China's aviation fuel supply chain [4][11]. - The merger will allow for complementary advantages in the production, sales, and refueling of aviation fuel, thereby increasing the international competitiveness of China's aviation fuel industry [4][12]. - The restructuring is also anticipated to promote the application of Sustainable Aviation Fuel (SAF) domestically [4][13]. Summary by Sections Restructuring Impact - The merger between Sinopec and CNAF, approved by the State Council, is set to take place on January 8, 2026 [3]. - Sinopec is a leading producer of aviation kerosene in China, with a projected consumption of approximately 38 million tons for the year, and a forecasted increase to 75 million tons by 2040, representing over 100% growth [5]. Competitive Landscape - CNAF is the largest aviation fuel supplier in Asia, providing services to 258 transport airports and 454 general airports in China, and supporting 585 global airline customers [11]. - The merger will streamline operations, reduce supply costs, and enhance energy security for China's aviation sector [11]. Sustainable Aviation Fuel (SAF) - Sinopec has been a pioneer in SAF production, with significant advancements in technology and production capacity since 2011 [13]. - The National Development and Reform Commission and the Civil Aviation Administration of China have initiated pilot applications for SAF, with plans for regular use starting in March 2025 [13][14]. Investment Recommendations - The report recommends investing in companies with advantages in aviation kerosene production, specifically China National Petroleum Corporation (CNPC) and companies involved in biodiesel and SAF, such as Zhenhua Energy [4][16].

Group 1: AIDC Power Equipment Industry - The AIDC power equipment sector is expected to benefit from the surge in data center construction driven by major tech companies, with significant investments planned by firms like Samsung SDS, Tesla, Amazon, and Oracle [1][28] - The demand for power equipment in the AI era is projected to experience explosive growth, with key companies to watch including Jinpan Technology, Xinte Electric, Igor, Hewei Electric, Shenghong Co., and Zhongheng Electric [1][28] Group 2: Lithium Battery Industry - The lithium battery industry is undergoing a transformation with the ongoing efforts to eliminate excess capacity and improve competitive dynamics, which is expected to enhance profitability for companies in the sector [2][68] - Solid-state battery technology is accelerating towards industrialization, with expectations for expanded production lines and increased testing in 2026, laying the groundwork for mass application from 2027 to 2030 [2][69] - Key companies to focus on in the lithium battery sector include CATL, EVE Energy, Zhongchuang航, Zhuhai Guanyu, Enjie, Dingsheng Technology, and Xiamen Tungsten [2][69] Group 3: Wind Power Industry - The domestic wind power sector is anticipated to see a 10%-20% increase in new installations in 2026, supported by saturated orders and stable pricing [3][53] - The profitability of wind turbine manufacturers is expected to recover gradually, with exports contributing positively to performance [3][54] - Key companies in the wind power sector include Goldwind Technology, Tiansheng Wind Energy, Sany Renewable Energy, and Dongfang Cable [3][54] Group 4: Energy Storage Industry - Global energy storage demand is projected to reach 404 GWh in 2026, representing a 38% year-on-year increase, driven by strong domestic market demand and supportive government policies in emerging markets [2][94][96] - The U.S. is expected to see significant growth in large-scale storage installations due to rising energy demands from data centers and ongoing power supply shortages [2][94][96] - Companies to watch in the energy storage space include CATL, EVE Energy, Deye Technology, Hewei Electric, Shenghong Co., and Kelu Electronics [2][94] Group 5: Power Grid Equipment Industry - The power grid equipment sector is expected to experience increased demand due to accelerated approvals and tenders for ultra-high voltage projects, with significant opportunities for companies involved in converter stations and related equipment [3][36] - The implementation of the 2025 version of the State Grid's smart meter standards is anticipated to lead to a price recovery in 2026, with head companies expected to see high growth in overseas revenues and orders [3][36] - Key companies in the power grid equipment sector include Sifang Co., Jinpan Technology, Siyuan Electric, and Huaming Equipment [3][36] Group 6: Photovoltaic Industry - The photovoltaic industry is expected to see improvements in supply-demand dynamics due to policies aimed at reducing excess capacity, with a focus on leading companies in the silicon material segment [83][84] - Innovations such as silver-free materials and perovskite solar cells are anticipated to drive cost reductions and technological advancements in the industry [83][84] - Companies to monitor in the photovoltaic sector include GCL-Poly Energy, Tongwei Co., and Juhua Materials [83][84]

Group 1 - The report emphasizes that in a declining economic growth environment, dividend strategies remain effective as investors seek more certain assets, benefiting dividend strategies which have bond-like attributes during low interest periods [1][9][44] - Policy support is enhancing the attractiveness of dividend assets, with increasing dividend payouts from listed companies, particularly notable since 2024, indicating a trend towards greater dividend distributions [1][17][20] - The S&P Hong Kong Low Volatility High Dividend Index (SPAHLVCP.SPI) offers better investment value compared to A-shares, with a 12-month dividend yield of 5.6% and a PE ratio of 5.7, showcasing its comparative advantage in valuation and yield [1][23][39] Group 2 - The S&P Hong Kong Low Volatility High Dividend Index was launched on February 20, 2017, and includes stocks from the Hang Seng Composite Index that meet specific dividend yield and volatility criteria [2][27][45] - The index is primarily composed of large-cap stocks, with a balanced distribution across sectors such as finance, real estate, and energy, ensuring diversification and stability [2][29][31] - As of December 31, 2025, the index's historical performance is strong, with a cumulative increase of 99.41% since 2021 and an annualized return of approximately 14.8%, outperforming the Hang Seng Index and other dividend indices [2][41][45]

Group 1 - The report highlights that all four active quantitative strategies outperformed the equity mixed fund index this week, with absolute returns of 4.86% for the Excellent Fund Performance Enhancement Portfolio, 5.13% for the Exceeding Expectations Selected Portfolio, 5.39% for the Brokerage Golden Stock Performance Enhancement Portfolio, and 5.98% for the Growth and Stability Portfolio [1][2][17] - Year-to-date, the Excellent Fund Performance Enhancement Portfolio ranks in the 42.03 percentile among active equity funds, while the Exceeding Expectations Selected Portfolio ranks in the 38.48 percentile, the Brokerage Golden Stock Performance Enhancement Portfolio ranks in the 35.18 percentile, and the Growth and Stability Portfolio ranks in the 28.46 percentile [1][2][17] Group 2 - The Excellent Fund Performance Enhancement Portfolio is constructed by benchmarking against active equity funds rather than broad indices, utilizing quantitative methods to enhance performance based on the holdings of top-performing funds [3][18] - The Exceeding Expectations Selected Portfolio is built by screening stocks based on exceeding expectations events and analyst profit upgrades, focusing on both fundamental and technical criteria to select stocks that show strong support [4][25] - The Brokerage Golden Stock Performance Enhancement Portfolio uses a stock pool from brokerage recommendations, optimizing the combination to minimize deviation from the stock pool while aiming to outperform the equity mixed fund index [5][33] - The Growth and Stability Portfolio employs a two-dimensional evaluation system for growth stocks, prioritizing stocks closer to their earnings report dates and using multi-factor scoring to select high-quality stocks [6][40] Group 3 - The report provides performance statistics for each strategy, indicating that the Excellent Fund Performance Enhancement Portfolio achieved an annualized return of 21.40% from 2012 to 2025, outperforming the equity mixed fund index by 9.85% [55] - The Exceeding Expectations Selected Portfolio recorded an annualized return of 35.09% from 2010 to 2025, exceeding the equity mixed fund index by 23.98% [60] - The Brokerage Golden Stock Performance Enhancement Portfolio achieved an annualized return of 21.71% from 2018 to 2025, outperforming the equity mixed fund index by 14.18% [65] - The Growth and Stability Portfolio achieved an annualized return of 40.56% from 2012 to 2025, exceeding the equity mixed fund index by 26.33% [70]

Quantitative Models and Construction Methods 1. Model Name: Hong Kong Stock Selection Portfolio - **Model Construction Idea**: The model aims to select stocks with both fundamental support and technical resonance from an analyst-recommended stock pool[14][15] - **Model Construction Process**: - **Step 1**: Construct an analyst-recommended stock pool based on three types of analyst recommendation events: upward earnings forecast revisions, initial analyst coverage, and analyst report titles exceeding expectations[15] - **Step 2**: Perform dual-layer selection on the analyst-recommended stock pool using fundamental and technical dimensions to select stocks with both fundamental support and technical resonance[15] - **Step 3**: The backtest period for the Hong Kong Stock Selection Portfolio is from January 1, 2010, to December 31, 2025. Considering transaction costs in a fully invested state, the portfolio's annualized return is 19.08%, with an excess return of 18.06% relative to the Hang Seng Index[15] - **Model Evaluation**: The model demonstrates a strong performance with significant excess returns over the Hang Seng Index, indicating its effectiveness in stock selection[15] Model Backtest Results - **Hong Kong Stock Selection Portfolio**: - **Annualized Return**: 19.08%[15] - **Excess Return**: 18.06% relative to the Hang Seng Index[15] - **Information Ratio (IR)**: 1.19[20] - **Tracking Error**: 14.60%[20] - **Maximum Drawdown**: 23.73%[20] - **Return-to-Drawdown Ratio**: 0.76[20] Quantitative Factors and Construction Methods 1. Factor Name: Stable New High Stocks - **Factor Construction Idea**: The factor aims to identify stocks that have recently reached new highs and exhibit stable price paths, leveraging the momentum and trend-following strategies that are particularly effective in the Hong Kong market[21] - **Factor Construction Process**: - **Step 1**: Calculate the 250-day new high distance using the formula: $$ 250 \text{ day new high distance} = 1 - \frac{Close_t}{\text{ts\_max(Close, 250)}} $$ where $Close_t$ is the latest closing price, and $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days[23] - **Step 2**: Screen stocks that have reached a 250-day new high in the past 20 trading days based on analyst attention, relative stock strength, price path stability, and new high continuity[23] - **Step 3**: Select stocks with the following criteria: - Analyst attention: At least 5 buy or hold ratings in the past 6 months - Relative stock strength: Top 20% in terms of price change over the past 250 days - Price path stability: Top 50% based on price displacement ratio and 250-day new high distance over the past 120 days - Trend continuity: Top 50 stocks based on the 250-day new high distance over the past 5 days[24] - **Factor Evaluation**: The factor effectively captures stocks with strong momentum and stable price paths, which are likely to continue their upward trends[21][23] Factor Backtest Results - **Stable New High Stocks**: - **Example Stocks**: J&T Express-W, China Eastern Airlines, Youran Dairy, Hansoh Pharmaceutical, China XLX Fertilizer, etc.[23][29] - **Sector Distribution**: Most new high stocks are in the cyclical sector, followed by finance, technology, consumer, manufacturing, and healthcare sectors[23][29]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12] - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to risk constraints[12] - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11][12] Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.44%, annual excess return 0.44%[5][14] - **CSI 500 Index Enhanced Portfolio**: Weekly excess return -1.80%, annual excess return -1.80%[5][14] - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return -2.20%, annual excess return -2.20%[5][14] - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.61%, annual excess return 0.61%[5][14] Quantitative Factors and Construction Methods Factor Name: Single Factor MFE (Maximized Factor Exposure) Portfolio - **Factor Construction Idea**: The factor aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and stock weight deviations[40][41] - **Factor Construction Process**: 1. **Optimization Model**: The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T} w \\ \text{s.t.} & s_{l} \leq X(w - w_{b}) \leq s_{h} \\ & h_{l} \leq H(w - w_{b}) \leq h_{h} \\ & w_{l} \leq w - w_{b} \leq w_{h} \\ & b_{l} \leq B_{b} w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ where \( f \) represents the factor values, \( w \) is the stock weight vector, and the constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[40][41] 2. **Constraints**: The constraints include: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_{b} \) is the benchmark weight vector, \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style exposure[41] - **Industry Exposure**: \( H \) is the industry exposure matrix, \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry exposure[41] - **Stock Weight Deviations**: \( w_{l} \) and \( w_{h} \) are the lower and upper bounds for stock weight deviations[41] - **Component Stock Weight Limits**: \( B_{b} \) is the 0-1 vector indicating whether a stock is a benchmark component, \( b_{l} \) and \( b_{h} \) are the lower and upper bounds for component stock weights[41] - **No Short Selling**: The weights are non-negative and sum to 1[41] 3. **Portfolio Construction**: The MFE portfolio is constructed by maximizing the factor exposure while adhering to the constraints[42][44] - **Factor Evaluation**: The MFE portfolio is used to test the effectiveness of single factors under realistic constraints, making it more likely to reflect the true predictive power of the factors in the final portfolio[40][41] Factor Backtesting Results - **CSI 300 Index**: - **Best Performing Factors (Weekly)**: Three-month institutional coverage (0.86%), DELTAROA (0.61%), DELTAROE (0.52%)[19] - **Worst Performing Factors (Weekly)**: Expected net profit QoQ (-0.78%), one-year momentum (-0.45%), idiosyncratic volatility (-0.42%)[19] - **CSI 500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (0.06%), expected net profit QoQ (0.33%), idiosyncratic volatility (0.22%)[21] - **Worst Performing Factors (Weekly)**: One-month volatility (-2.47%), EPTTM (-3.56%), single-quarter ROE (-0.67%)[21] - **CSI 1000 Index**: - **Best Performing Factors (Weekly)**: One-year momentum (1.94%), single-quarter revenue YoY growth (1.31%), standardized unexpected income (0.92%)[23] - **Worst Performing Factors (Weekly)**: EPTTM (-3.56%), dividend yield (-3.27%), expected EPTTM (-3.22%)[23] - **CSI A500 Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), DELTAROE (0.88%), single-quarter operating profit YoY growth (0.70%)[25] - **Worst Performing Factors (Weekly)**: EPTTM (-1.29%), one-month volatility (-1.22%), three-month volatility (-1.09%)[25] - **Public Fund Heavy Index**: - **Best Performing Factors (Weekly)**: Single-quarter net profit YoY growth (1.14%), expected net profit QoQ (0.88%), three-month reversal (0.29%)[27] - **Worst Performing Factors (Weekly)**: Expected EPTTM (-0.74%), EPTTM (-1.29%), one-month volatility (-1.22%)[27]

- The report introduces a quantitative model named "250-day new high distance" to track market trends and identify investment hotspots. The model is based on momentum and trend-following strategies, emphasizing the effectiveness of monitoring stocks near their 52-week high prices[11][19][20] - The construction process of the "250-day new high distance" model is as follows: Formula: $ 250\text{-day new high distance} = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Explanation: - $ Close_t $ represents the latest closing price - $ ts\_max(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance equals 0; otherwise, it is a positive value indicating the degree of price fallback[11] - The report evaluates the model positively, highlighting its ability to capture market trends and identify leading stocks in various sectors. It references studies by George (2004), William O'Neil, and Mark Minervini, which support the effectiveness of tracking stocks near their high prices[11][19] - The model's testing results show that as of January 9, 2026, major indices such as the Shanghai Composite Index, Shenzhen Component Index, and CSI 500 have a "250-day new high distance" of 0.00%, indicating they are at their peak levels. Other indices like CSI 300 and ChiNext have distances of 0.66% and 0.06%, respectively[12][13][33] - A quantitative factor named "Stable New High Stocks" is constructed to identify stocks with smooth price paths and consistent momentum. The factor incorporates analyst attention, relative price strength, price path smoothness, and sustained new high performance[26][28] - The construction process of the "Stable New High Stocks" factor includes: - Analyst attention: At least five buy or overweight ratings in the past three months - Relative price strength: Top 20% in 250-day price change - Price path smoothness: Evaluated using metrics like price displacement ratio - Sustained new high performance: Average "250-day new high distance" over the past 120 days and the last five days[26][28] - The factor is positively evaluated for its ability to capture stocks with strong and consistent momentum, supported by studies on smooth price paths and investor underreaction to gradual price changes[26][28] - Testing results for the "Stable New High Stocks" factor show that 50 stocks were selected, with the highest representation in cyclical and technology sectors. Notable stocks include Yuanjie Technology, Yaxiang Integration, and Xinwei Communication[29][34]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: The model tracks the distance of stock prices or indices from their 250-day high to identify market trends and hotspots. It is based on the momentum and trend-following strategy, which has been proven effective in various studies[11][19]. - **Model Construction Process**: The formula for calculating the 250-day new high distance is: $ 250\text{-day new high distance} = 1 - \frac{Close_t}{\text{ts\_max}(Close, 250)} $ Where: - $ Close_t $ represents the latest closing price - $ \text{ts\_max}(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price falls from the high, the distance is a positive value, indicating the degree of decline[11]. - **Model Evaluation**: The model effectively captures market momentum and highlights leading stocks or indices that are driving market trends[11][19]. 2. Model Name: Stable New High Stock Selection Model - **Model Construction Idea**: This model focuses on selecting stocks with stable price paths and consistent momentum, as smoother price trajectories tend to yield stronger momentum effects[26]. - **Model Construction Process**: The selection criteria include: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: 250-day return in the top 20% of the market - **Price Stability**: Stocks are ranked based on two indicators: - Absolute value of price changes over the past 120 days - Sum of absolute daily price changes over the past 120 days - **New High Continuity**: Average 250-day new high distance over the past 120 days - **Trend Continuity**: Average 250-day new high distance over the past 5 days Stocks meeting these criteria are ranked, and the top 50 are selected[26][28]. - **Model Evaluation**: The model emphasizes smooth price paths and consistent trends, which are less likely to attract excessive attention, thereby enhancing momentum effects[26]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance (as of January 9, 2026)**: - Shanghai Composite Index: 0.00% - Shenzhen Component Index: 0.00% - CSI 300: 0.66% - CSI 500: 0.00% - CSI 1000: 0.00% - CSI 2000: 0.00% - ChiNext Index: 0.06% - STAR 50 Index: 4.10%[12][13][33] 2. Stable New High Stock Selection Model - **Selected Stocks**: 50 stocks were identified as stable new high stocks, including Yuanjie Technology, Asia Integration, and Sunway Communication. - **Sector Distribution**: - Cyclical sector: 22 stocks (e.g., non-ferrous metals) - Technology sector: 14 stocks (e.g., electronics)[29][34] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: Measures the relative position of a stock's price to its 250-day high, capturing momentum and trend-following characteristics[11]. - **Factor Construction Process**: The formula is: $ 250\text{-day new high distance} = 1 - \frac{Close_t}{\text{ts\_max}(Close, 250)} $ Where: - $ Close_t $ is the latest closing price - $ \text{ts\_max}(Close, 250) $ is the maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: The factor effectively identifies stocks with strong momentum and highlights market leaders[11][19]. 2. Factor Name: Price Path Stability - **Factor Construction Idea**: Focuses on the smoothness of price trajectories, as smoother paths are associated with stronger momentum effects[26]. - **Factor Construction Process**: - **Indicator 1**: Absolute value of price changes over the past 120 days - **Indicator 2**: Sum of absolute daily price changes over the past 120 days Stocks are ranked based on these indicators, and the top performers are selected[26]. - **Factor Evaluation**: The factor captures the underreaction of investors to smooth price paths, enhancing the momentum effect[26]. --- Factor Backtesting Results 1. 250-Day New High Distance Factor - **Indices' 250-Day New High Distance (as of January 9, 2026)**: - Shanghai Composite Index: 0.00% - Shenzhen Component Index: 0.00% - CSI 300: 0.66% - CSI 500: 0.00% - CSI 1000: 0.00% - CSI 2000: 0.00% - ChiNext Index: 0.06% - STAR 50 Index: 4.10%[12][13][33] 2. Price Path Stability Factor - **Selected Stocks**: 50 stocks were identified as stable new high stocks, including Yuanjie Technology, Asia Integration, and Sunway Communication. - **Sector Distribution**: - Cyclical sector: 22 stocks (e.g., non-ferrous metals) - Technology sector: 14 stocks (e.g., electronics)[29][34]

Group 1: Commodity Market Trends - The global commodity market has experienced a structural uptrend since late 2025, led by industrial and precious metals, while traditional cyclical products have shown lackluster performance[1] - LME copper prices surged from below $8,000/ton to over $13,000/ton, marking a cumulative increase of over 60%, despite the US manufacturing PMI remaining in a contraction zone of 48.2%-48.3%[2] - The divergence between commodity prices and manufacturing demand indicates a decoupling from traditional manufacturing cycles, driven by rising geopolitical uncertainties and trade protectionism[2][14] Group 2: Demand Dynamics and Economic Shifts - The current market is characterized by extreme differentiation among commodities, with indicators like the copper-oil ratio exceeding two standard deviations, reflecting a fundamental shift in global economic growth models[3][27] - The transition from a traditional growth model centered on real estate and infrastructure to a digital economy model focused on "computing power + electricity" is reshaping demand for commodities[3][31] - Major tech companies are expected to maintain over 20% capital expenditure growth in AI infrastructure, significantly impacting demand for conductive materials like copper and silver[31][33] Group 3: Future Outlook and Risks - The commodity market is entering a new phase driven by "computing power + security," where geopolitical risks create a safety premium, enhancing the financial attributes of commodities[4][34] - Short-term risks include potential price corrections for certain commodities that have surged too quickly, possibly overshooting future demand expectations[4][37] - Ongoing volatility in overseas markets and declining economic growth rates pose additional risks to the commodity landscape[5][38]