"铝代铜"叙事全面升温，巨量资金顺势疯狂涌入，铝价在短短数周内突破三年新高，上演了一场由市场共识切换主导的暴涨行情。 2025年10月末，冰岛的一场"蝴蝶效应"悄然引发全球铝市震动——世纪铝业冰岛冶炼厂因设备故障突发减产三分之二，直接造成全球电解铝供应减少约20万 吨。彼时，多数市场参与者仍持观望态度，直至铜价创下历史新高，推动铜铝比攀升至4.21倍，相较2005年的1.7倍左右，该比值已处于近20年高位区 间。"铝代铜"叙事全面升温，巨量资金顺势疯狂涌入，铝价在短短数周内突破三年新高，上演了一场由市场共识切换主导的暴涨行情。 "这场暴涨看似缺乏复杂的逻辑推演，核心是巨量资金在'信'与'不信'之间的瞬间转向。但当市场共识形成的那一刻，基本面的刚性约束与资金的投机热情形 成强烈共振，直接推动铝价脱离短期波动区间，驶入上行通道。"一位长期跟踪铝行业的大宗商品投资人在接受第一财经采访时表示。而这一共识的核心， 正是本轮铝期货价格上涨的关键推手并非实体生产经营需求，而是资本的投机力量。 "期货市场热、实业市场冷" 作为"中国铝材之都"，佛山成为这场铝价风暴的最直接感知者。记者在采访中发现，这里不仅聚集着兴发铝业、凤铝 ...

Group 1 - The spring market rally has entered its second half, characterized by reduced trading volume and rising prices, as regulatory signals promote a return to rationality in the market [4][6][10] - The average maximum increase of the Shanghai Composite Index during the past 16 spring rallies was 15.8%, while the current rally has seen a maximum increase of 9.8%, indicating potential for further price increases [10][12] - Economic data is showing positive trends, with expectations for a continued rally supported by improving PPI figures and favorable policies from the government [10][20] Group 2 - The focus of the market is shifting from risk appetite to earnings growth, with a notable increase in the proportion of companies reporting positive earnings forecasts, reaching 37.8% as of January 17 [13][19] - The reduction in competitive pressure (internal competition) is leading to a significant increase in the proportion of companies with improved earnings, particularly in industries such as steel, construction materials, and media [20][22] - The overall improvement in earnings among non-financial companies in the A-share market is evident, with a 5.8% increase in the proportion of companies reporting positive net profit growth [20][22] Group 3 - Investment recommendations focus on sectors with strong earnings growth expectations, including non-bank financials, cyclical industries, and technology innovation [23][24] - Non-bank financials have shown the highest proportion of earnings revisions, with a 400% increase in companies adjusting their profit forecasts positively [23][24] - Cyclical sectors such as materials and energy are expected to benefit from fiscal stimulus and demand-side support, with significant upward revisions in profit forecasts [23][24]

投资要点： | | | | | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 曹君豪(分析师) | | | 021-23185657 | | | caojunhao@gtht.com | | 登记编号 | S0880525040094 | [Table_Report] 相关报告 量化择时和拥挤度预警周报（20260116） [Table_Authors] 郑雅斌(分析师) 市场下周有望震荡上行 本报告导读： 从技术面来看，均线强弱指数有所降低，后续指数上行空间充足；情绪模型显示市 场情绪有所减弱，但依旧处于做多趋势中；高频资金流模型显示各大宽基指数依旧 处于做多周期。因此，我们认为，市场下周有望震荡上行。 请务必阅读正文之后的免责条款部分 金 融 工 程 金 融 低频选股因子周报（2026.01.09-2026.01.16） 2026.01.17 绝对收益产品及策略周报（260105-260109） 2026.01.14 量化择时和拥挤度预警周报（20260109） 2026.01.12 ...

Market Review - The A-share market experienced a significant increase followed by a period of volatility, with a notable rise in trading volume driven by a strong profit-making effect, particularly in small-cap and growth styles. On January 14, the total trading volume reached a historical high of 3.99 trillion yuan, with margin financing balances hitting new records. However, following regulatory adjustments to margin requirements, market activity showed signs of cooling, and the previously strong technology index began to stabilize [1][2]. Market Outlook - Regulatory measures aimed at "counter-cyclical adjustment" are expected to support a "slow bull" market for A-shares. The recent surge in trading activity has prompted regulators to signal a need for cooling, leading to a shift from a one-sided increase to high-level fluctuations in the Shanghai Composite Index. Despite this, the overall valuation of A-shares remains reasonable, supported by macro policies, medium to long-term capital inflows, and a mild recovery in corporate earnings. The upcoming earnings announcements in late January are likely to refocus investor attention on performance-driven sectors, particularly in technology and industries benefiting from price increases [2][3]. Counter-Cyclical Adjustment Policies - The recent increase in the minimum margin requirement for financing from 80% to 100% is part of a broader strategy to prevent systemic risks in the market. The regulatory emphasis on maintaining market stability and preventing extreme fluctuations is evident, as seen in the significant net outflow of 142.3 billion yuan from equity ETFs in January, marking the largest monthly outflow since 2021. This counter-cyclical adjustment is viewed as a necessary measure to sustain the bull market trend while mitigating overheating risks [3][4]. Risk Premium and Sector Focus - As of January 16, the equity risk premium (ERP) for the CSI 300 index stands at 5.2%, which is near the median level for the past decade. Compared to previous peaks in January 2018 and February 2021, the current risk premium indicates that A-share valuations are relatively reasonable, although some sectors may experience capital withdrawal due to overheating. Key sectors attracting financing include electronics, power equipment, computers, military, and communications, with a need to monitor the impact of reduced financing on high-volatility stocks in these areas [4][5]. Investment Strategy - The slow bull trend in A-shares is expected to continue, with a focus on sectors showing high growth or improving conditions as companies prepare to announce their 2025 earnings. Key factors supporting this outlook include proactive macro policies, the influx of medium to long-term capital, and a narrowing decline in the Producer Price Index (PPI), which suggests a mild recovery in corporate earnings. Investors should pay attention to sectors such as technology (AI applications, robotics), commodities benefiting from price increases, and industries with anticipated high earnings growth [5].

Quantitative Models and Construction Methods Moving Average Trend Model - **Model Name**: Moving Average Trend Model - **Model Construction Idea**: The model evaluates industry trends using four moving averages and combines three moving average indicators to derive a trend score[2][24] - **Model Construction Process**: 1. **Moving Average Arrangement**: When a shorter-term moving average is above a longer-term moving average, it is considered a bullish arrangement and scores 1 point; conversely, a bearish arrangement scores -1 point[2][24] 2. **Moving Average Spread**: Calculate the price difference between adjacent moving averages and take the average of all differences[2][24] 3. **Moving Average Temporal Change**: When the price of a moving average increases compared to the previous day, it scores 1 point; when it decreases, it scores -1 point[2][24] 4. Combine the three indicators and take the absolute value to get the moving average score. Rank the scores from high to low to identify industries with clear upward or downward trends[2][24] - **Model Evaluation**: This model effectively identifies industries with clear trends by combining multiple moving average indicators[2][24] Capital Flow Model - **Model Name**: Capital Flow Model - **Model Construction Idea**: The model measures changes in industry capital flow using the capital inflow rate[3][26] - **Model Construction Process**: 1. Calculate the capital inflow rate as the ratio of active net capital inflow to transaction amount[3][26] 2. For each industry, calculate the change in recent capital inflow rate relative to historical capital inflow rates[3][26] 3. Rank the changes in capital inflow rates from high to low to identify industries with the most significant capital inflow increases[3][26] - **Model Evaluation**: This model effectively identifies industries with significant capital inflow changes by comparing recent and historical inflow rates[3][26] Combined Moving Average Trend and Capital Flow Model - **Model Name**: Combined Moving Average Trend and Capital Flow Model - **Model Construction Idea**: The model combines moving average trend scores and capital flow scores to select industries with clear trends and high capital inflow rankings[4][27] - **Model Construction Process**: 1. Calculate the moving average trend score for each industry[2][24] 2. Calculate the capital flow score for each industry[3][26] 3. Combine the moving average trend scores and capital flow scores to rank industries[4][27] - **Model Evaluation**: This model effectively identifies industries with clear trends and significant capital inflows by combining two different indicators[4][27] Model Backtest Results - **Moving Average Trend Model**: - Top-ranked industries: Non-ferrous metals, communication, electronics[9] - **Capital Flow Model**: - Top-ranked industries: Media, computer, electronics[9] - **Combined Moving Average Trend and Capital Flow Model**: - Top-ranked industries: Electronics, media, non-ferrous metals, machinery equipment, computer[10]

Global and A-share Market Overview - The A-share market showed mixed performance last week, with the Sci-Tech 50 Index recording the highest increase of 2.58%. The Hong Kong market also saw an uptick, with the Hang Seng Index rising by 2.34%. The top-performing ETFs primarily tracked TMT sector indices [1][11][14]. ETF New Issuance Statistics - Last week, 10 stock ETFs were reported in the A-share market, including 2 focused on non-ferrous metals. A total of 8 new stock ETFs were established. In the US market, 8 equity ETFs were newly established [1][16][18]. Fund Flows in A-share Market - The top 10 ETFs with net inflows were predominantly from the TMT sector, while the top 10 with net outflows were mainly from the CSI 300 Index ETFs. The ETF tracking the Sci-Tech 100 Index had the highest net inflow, while the CSI 300 Index ETF had the highest net outflow [2][25][27]. - In the A-share market, the net inflow for the top 10 broad-based indices included the Sci-Tech 100 with 9.59 billion yuan, while the CSI 300 saw a net outflow of 1,034.75 billion yuan [28][32]. Industry ETF Fund Flows - The TMT sector led the A-share market with a net inflow of 465.84 billion yuan, followed by upstream and materials with 216.32 billion yuan. Other sectors like new energy and consumption also saw positive inflows, while sectors such as low-carbon environmental and agriculture experienced outflows [33][35].

Core Insights - The report indicates that the recent acceleration in net financing inflow has provided incremental capital to the market, driving individual stock performance while significantly increasing overall market leverage and potential volatility risks [5][30]. - To mitigate the rapid rise in leverage, regulatory measures have been intensified, including raising the margin requirement for financing from 80% to 100%, which aims to control new leverage without impacting existing contracts [7][17]. - The report anticipates that the A-share market is likely to shift to a volatile trend after reaching previous highs, with a focus on performance disclosures expected to intensify as the earnings forecast disclosure peak approaches on January 15 [2][30]. Market Analysis - The report highlights that the A-share market experienced a high trading volume, with total market turnover exceeding 3.9 trillion yuan in the first half of the week, followed by a drop below 3 trillion yuan after the margin policy announcement [32]. - The technology sector, particularly AI computing and semiconductor equipment, is identified as a key battleground for January, alongside resource products represented by industrial metals [5][30]. - The report notes that the net outflow from ETFs, amounting to 129.6 billion yuan, has contributed to cooling market enthusiasm, with significant withdrawals from major ETFs such as the CSI 300 ETF [12][15]. Sector Performance - The report indicates that sectors such as computing, electronics, and non-ferrous metals have seen positive valuation trends, while sectors like defense, real estate, and steel have experienced declines [30][33]. - The report emphasizes the importance of cyclical and technology sectors for investment strategies, recommending a focus on industries such as electric equipment, machinery, non-bank financials, electronics, and basic chemicals [6][31]. - The report also highlights the improvement in the semiconductor industry, with December exports of integrated circuits showing a year-on-year increase of 47.72%, indicating a positive trend in the tech sector [38][41]. Investment Strategy - The report suggests a preference for large-cap growth stocks in the current market environment, recommending index combinations including CSI 300, STAR Market 50, and quality indices [6][31]. - It advises that industry allocation should focus on spring market dynamics and forward-looking clues from annual reports, particularly in cyclical and technology sectors [6][31]. - The report underscores the significance of monitoring performance disclosures, especially for small-cap and thematic stocks, as they may face pressure from earnings forecasts [5][30].

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the vague concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of timing signals from 10 selected indicators[9][14][15] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions (e.g., 20-day Bollinger Bands, 20-day price deviation rate, 60-day turnover rate volatility, etc.)[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score[9][14] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Dividend Style Timing Model - **Model Construction Idea**: The model times the dividend style by analyzing the relative performance of the CSI Dividend Index against the CSI All Share Index, using three indicators: relative momentum, 10Y-1Y term spread, and interbank pledged repo trading volume[16][19] - **Model Construction Process**: 1. Generate daily signals (0, +1, -1) for each indicator, representing neutral, bullish, and bearish views, respectively 2. Aggregate the scores to determine the overall long/short view on the dividend style 3. When bullish, fully allocate to the CSI Dividend Index; when bearish, fully allocate to the CSI All Share Index[16][19] - **Model Evaluation**: The model has consistently maintained a bearish view on the dividend style this year, favoring growth style instead[16] 3. Model Name: Large-Cap vs. Small-Cap Style Timing Model - **Model Construction Idea**: The model evaluates the crowding level of large-cap and small-cap styles based on momentum and trading volume differences, adjusting the strategy based on whether the market is in a high or low crowding state[20][22][24] - **Model Construction Process**: 1. Calculate momentum differences and trading volume ratios between the Wind Micro-Cap Index and the CSI 300 Index over multiple time windows 2. Derive crowding scores for both large-cap and small-cap styles based on percentile rankings of the calculated metrics 3. Use a dual moving average model with smaller parameters in high crowding states and larger parameters in low crowding states to determine trends[20][22][24] - **Model Evaluation**: The model effectively captures the medium- to long-term trends in low crowding states and reacts to potential reversals in high crowding states[22] 4. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model employs genetic programming to directly extract factors from industry index data (e.g., price, volume, valuation) without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[27][30][31] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| and NDCG@5 2. Combine multiple factors with weak collinearity into industry scores using greedy strategies and variance inflation factors 3. Select the top five industries with the highest composite scores for equal-weighted allocation[30][33][37] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks[30][33] 5. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing diversification across underlying macro risk sources (growth and inflation surprises) rather than asset classes[38][41] - **Model Construction Process**: 1. Divide macroeconomic scenarios into four quadrants based on growth and inflation surprises 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, actively overweighting favorable quadrants[41][42] - **Model Evaluation**: The strategy achieves enhanced performance by actively allocating based on macroeconomic expectations[38][41] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.67% - Annualized Volatility: 17.33% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.87[15] 2. Dividend Style Timing Model - Annualized Return: 16.65% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.91 - Calmar Ratio: 0.65 - YTD Return: 5.78%[17] 3. Large-Cap vs. Small-Cap Style Timing Model - Annualized Return: 27.79% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.16 - Calmar Ratio: 0.87 - YTD Return: 6.27%[25] 4. Industry Rotation Model (Genetic Programming) - Annualized Return: 31.95% - Annualized Volatility: 17.44% - Maximum Drawdown: -19.62% - Sharpe Ratio: 1.83 - Calmar Ratio: 1.63 - YTD Return: 3.31%[30] 5. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.82% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.91 - Calmar Ratio: 1.88 - YTD Return: 2.02%[42]

Group 1: Metal New Materials - Copper prices are expected to experience high-level fluctuations in the short term due to inventory accumulation and delayed tariff expectations, with LME and COMEX arbitrage space narrowing [8][9] - Aluminum prices are also anticipated to face high-level fluctuations, driven by inventory accumulation and the impact of delayed tariff expectations [9] - Lithium demand remains strong despite seasonal trends, with carbonate lithium prices entering an upward cycle, while cobalt prices are expected to continue rising due to tight raw material supply [10][11] Group 2: Precious Metals - Gold and silver prices have been rising, attributed to weak U.S. employment data and changes in margin requirements for precious metal contracts, which may increase market volatility [13][14] - The geopolitical situation, including U.S. military actions in Venezuela, has heightened demand for safe-haven assets, contributing to the upward momentum in precious metals [14][15] - China's gold reserves increased to 74.15 million ounces by the end of December 2025, reflecting ongoing central bank purchases that support gold prices [18] Group 3: North Exchange Market - The North Exchange has begun disclosing 2025 earnings, with Lin Tai New Materials and Hai Neng Technology forecasting significant profit increases, indicating a positive market outlook [19][20] - The market is expected to maintain liquidity, with structural investment opportunities arising from the technology sector and a focus on companies with strong fundamentals and reasonable valuations [20] - Key investment directions include companies with expected earnings growth, those in the service consumption sector, and firms in the new energy vehicle export chain benefiting from reduced tariffs [20] Group 4: New Consumption - Chao Hong Ji anticipates a substantial increase in net profit for 2025, driven by store expansion and improved brand strength, with a projected profit range of 436 to 533 million yuan [22][23] - The approval of a new medical device by Juzi Biotechnology marks a significant milestone, indicating growth potential in the healthcare sector [23] - The report emphasizes the importance of understanding new consumer trends and suggests focusing on high-quality domestic brands in various sectors [24][25]

2026年01月18日 国泰君安期货研究周报-绿色金融与新能源 观点与策略 | 镍：印尼言论反复扰动，镍价宽幅震荡运行 | 2 | | --- | --- | | 不锈钢：盘面锚定矿端矛盾，镍铁跟涨支撑重心 | 2 | | 工业硅：下游减产，反弹逢高布空 | 12 | | 多晶硅：下周二市场情绪或有提振 | 12 | | 碳酸锂：基本面偏强叠加现货采买意愿升温，短期下方空间有限 | 21 | 国 泰 君 安 期 货 研 究 所 请务必阅读正文之后的免责条款部分 1 期货研究 商 品 研 究 2026 年 1 月 18 日 镍：印尼言论反复扰动，镍价宽幅震荡运行 不锈钢：盘面锚定矿端矛盾，镍铁跟涨支撑重心 张再宇 投资咨询从业资格号：Z0021479 zhangzaiyu@gtht.com 国 泰 君 安 期 货 研 究 期货研究 本轮资金面对镍与不锈钢的关注度提高，本质在于消息面的变化，主要包括：印尼镍矿配额的 2.5 亿 吨目标，以及考虑将伴生矿物，如钴，纳入计价和征税体系，以及违规开采镍矿罚款，具体来看： 1）配额事件：1 月 8 日印尼能矿部表示配额将根据行业需求进行调整，1 月 14 日接受采访时表示 ...