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]

证券研究报告 | 2026年01月09日 热点追踪周报 由创新高个股看市场投资热点（第 226 期） 乘势而起：市场新高趋势追踪：截至 2026 年 1 月 9 日，上证指数、深证 成指、沪深 300、中证 500、中证 1000、中证 2000、创业板指、科创 50 指数 250 日新高距离分别为 0.00%、0.00%、0.66%、0.00%、0.00%、0.00%、 0.06%、4.10%。中信一级行业指数中家电、国防军工、有色金属、传媒、 电子行业指数距离 250 日新高较近，食品饮料、银行、医药、房地产、 电力及公用事业行业指数距离 250 日新高较远。概念指数中，新能源汽 车、华为平台、互联网、金属非金属、电子设备和仪器、半导体、工程 机械等概念指数距离 250 日新高较近。 见微知著：利用创新高个股进行市场监测：截至 2026 年 1 月 9 日，共 911 只股票在过去 20 个交易日间创出 250 日新高。其中创新高个股数量最多的 是机械、电子、基础化工行业，创新高个股数量占比最高的是国防军工、有 色金属、石油石化行业。按照板块分布来看，本周制造、科技板块创新高股 票数量最多；按照指数分布来 ...

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]

Group 1: Market Trends - The global commodity market has entered 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 under $8,000/ton to over $13,000/ton, 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 geopolitical uncertainties and supply chain security concerns[16] Group 2: Demand Dynamics - The current market features 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] - The transition from a traditional growth model centered on real estate and infrastructure to a digital economy model focused on "computing power + electricity" is creating new demand chains for commodities[3] - 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] Group 3: Future Outlook and Risks - The commodity market is entering a new paradigm driven by "computing power + security," where geopolitical risks create a safety premium, enhancing the financial attributes of commodities[4] - Short-term risks include potential price corrections for certain commodities that have surged too quickly, possibly overextending future demand expectations[4] - Economic indicators show a decline in fixed asset investment at -2.6% year-on-year, while retail sales and exports have shown modest growth of 1.3% and 5.9% respectively[7]

Group 1: Macro and Strategy - The core conclusion indicates that the incremental capital entering the A-share market in 2025 is characterized by active funds such as leveraged and private equity funds, with a significant inflow from insurance capital, while public equity funds are experiencing net redemptions [7][10] - It is expected that in 2026, the total incremental capital will reach 2 trillion yuan, driven by a recovery in risk appetite among residents, particularly from high-net-worth individuals [10][9] - The market environment in 2025 shows similarities to 2020, but the structure of incremental capital differs, suggesting a gradual increase in resident participation in the market [10][9] Group 2: Agricultural Industry - The agricultural sector is witnessing a potential upward trend in beef prices due to the implementation of import guarantee measures, indicating a reversal in the livestock cycle [15] - As of December 31, 2025, the price of live pigs was 12.67 yuan/kg, reflecting a week-on-week increase of 10.37%, while beef prices reached 60.91 yuan/kg, up 20.61% year-on-year [16][15] - The report highlights the importance of supply-demand dynamics in the agricultural sector, with a focus on the recovery of pork prices and the potential for sustained growth in beef prices [15][16] Group 3: Chemical Industry - The potassium fertilizer market is experiencing a tight supply-demand balance, with domestic production expected to decrease while imports are projected to rise, leading to a historical high in import volumes [24][25] - The price of potassium chloride as of December 31, 2025, was 3,282 yuan/ton, showing a year-on-year increase of 30.45%, driven by the need for food security [24][25] - The report anticipates a long-term price stability for phosphate rock due to increasing demand from new energy materials, with the market price for 30% grade phosphate rock remaining high [25][26] Group 4: Automotive Industry - The general aviation market is poised for steady development, driven by policy support, technological advancements, and market expansion [18][19] - The report emphasizes the potential for growth in low-altitude operations, with a focus on high-value applications such as logistics and maritime transport [20][21] - The global general aviation market is projected to grow, with an expected compound annual growth rate of 4.72% by 2029, indicating significant opportunities for domestic players [19][20] Group 5: Media and Internet Industry - The media sector has shown resilience, with a 2.27% increase in the industry index, outperforming major indices [22] - Upcoming IPOs for companies like Minimax and Zhiyu are anticipated to attract attention, particularly in the AI application sector [22][23] - The report highlights the strong performance of films during the New Year period, indicating a recovery in consumer spending in the entertainment sector [23][24]

市场资金流向：截至 20260107 两融余额为 26047 亿元，其中融资余额 25872 亿元，融券余额 175 亿元。两融余额占流通市值比重为 2.6%，两融 交易占市场成交额比重为 11.5%。 折溢价：20260107 当日 ETF 溢价较多的是中证 500 价值 ETF，ETF 折价 较多的是线上消费 ETF 基金。近半年以来大宗交易日均成交金额达到 22 亿 元，20260107 当日大宗交易成交金额为 21 亿元，近半年以来平均折价率 6.71%，当日折价率为 6.75%。近一年以来上证 50、沪深 300、中证 500、 中证 1000 股指期货主力合约的年化贴水率中位数分别为 0.85%、3.79%、 11.15%、13.61%，当日上证 50 股指期货主力合约年化升水率为 0.06%， 处于近一年来 67%分位点，当日沪深 300 股指期货主力合约年化贴水率为 2.26%，处于近一年来 71%分位点；当日中证 500 股指期货主力合约年化贴 水率为 5.64%，处于近一年来 83%分位点；当日中证 1000 股指期货主力合 约年化贴水率为 10.52%，处于近一年来 73%分位点。 机构 ...

Group 1 - The core conclusion indicates that in 2025, the A-share market will see significant inflows of incremental funds, primarily from leveraged funds and private equity, while public funds are experiencing net redemptions [1][4] - The current inflow of funds is expected to be mainly from high-net-worth individuals, with ordinary residents likely becoming the main source of market funds by 2026 as their risk appetite recovers [1][3] - The macroeconomic and microeconomic context of 2025 shows similarities to 2020, but the structure of incremental funds differs, leading to an estimated total inflow of 2 trillion yuan in 2026 [1][4] Group 2 - In 2025, the main source of incremental funds in the A-share market will be active funds, with a notable inflow of 4.2 billion yuan from insurance funds and 7 billion yuan from leveraged funds since July [2][19] - The first half of 2025 saw a diverse inflow of funds, with retail investors contributing 240 billion yuan and foreign capital returning with approximately 100 billion yuan [2][13] - The third quarter of 2025 experienced a significant increase in market activity, with private equity funds estimated to have injected around 400 billion yuan into the stock market [2][19] Group 3 - The process of resident funds entering the market is still in its early stages, primarily driven by high-net-worth individuals, as the overall risk appetite among residents remains low [3][36] - Evidence suggests that while some resident funds are entering the market, the majority are still cautious, with a significant portion of funds remaining in low-risk products [3][37] - The willingness of residents to invest in high-risk assets has been gradually increasing, but overall expectations regarding income and housing prices remain low, limiting broader market participation [3][41] Group 4 - The expected net inflow of funds in 2026 is projected to reach 2 trillion yuan, with significant contributions from retail investors and insurance funds, alongside improvements in public and foreign capital [4][55] - The structure of incremental funds in 2025 shows a shift compared to 2020, with a greater reliance on leveraged and private equity funds rather than resident funds [4][50] - The anticipated inflow from insurance funds is estimated at 700 billion yuan, while public and foreign funds are expected to improve, contributing around 700 billion yuan each [4][57]