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 - The 2026 market opening shows a clear characteristic of increased risk appetite driven by inflow of incremental funds, with commercial aerospace and AI application industries trending upwards [1][16] - The current market is experiencing strong momentum and excessive trading, which may lead to a short-term consolidation phase [1][16] - The average holding period for defense and military stocks is significantly lower than historical lows, indicating a decrease in stability for short-term momentum trades [1][19] Group 2 - The opening market is viewed as an extension of the strong structural technology market from 2025, which is now entering a high valuation adjustment phase [2][17] - Since September 2025, several high-momentum industries have entered a high-level consolidation phase, with notable examples including Nvidia's computing chain and Google's computing chain [2][17] - The market is expected to shift towards a consolidation phase after rapid valuation increases in new technology directions [2][17] Group 3 - The policy direction emphasizes a stable and long-term approach to avoid past investment pitfalls, such as the "deposit migration" of 2007 and the excessive trading of 2015 [10][26] - The A-share market has a mid-term upward basis, suggesting that a stable approach can balance short-term volatility with long-term goals [10][26] - The current market dynamics indicate a potential reduction in overall profit effects, with a need to wait for further economic and policy catalysts [10][28] Group 4 - The mid-term outlook for the A-share market suggests two phases of upward movement, with the first phase being driven by strong structural technology trends and the second phase potentially benefiting from cyclical improvements and increased asset allocation towards equities [12][28] - The characteristics of the two upward phases are interconnected, with the first phase led by cyclical alpha and AI computing, while the second phase may see a transition towards application-level AI trends [12][28]

周度报告——股指期货 单边行情纠偏，股指行稳致远 [★Ta一bl周e_复Su盘mm：aAry股] 放量高开低走 股 指 期 货 本周（01/12-01/16）以美元计价的全球股市收涨。MSCI 全球指 数涨 0.33%，其中新兴市场（+2.25%）>前沿市场（+0.89%）> 发达市场（+0.09%）。韩国股市涨 4.29%跑赢全球，法国股市跌 1.47%全球表现最差。中国权益资产涨跌分化，中国权益多数上 涨，分市场看，港股>A 股>中概股。A 股沪深京三市日均成交 额 34653 亿元，环比上周（28521 亿元)放量 6131 亿元。A 股宽 基指数分化，其中科创 50 指数涨 2.58%表现最好，上证 50 指数 跌跌 1.74%表现较弱。本周 A 股中信一级行业中共 10 个上涨（上 周 28 个），20 个下跌（上周 2 个）。涨幅最大的行业为计算机 （+4.31%），跌幅最大的行业为国防军工（-5.66%）。利率方面， 本周 10Y 国债收益率下行，1Y 下行，利差扩大。ETF 资金流向 方面，跟踪沪深 300 指数的 ETF 份额本周减少 214 亿份，跟踪 中证 500 的 ETF 份额减少 ...

- The report introduces a **market timing system** that uses the distance between the 20-day moving average and the 120-day moving average of the WIND All A Index to determine market trends. The system identifies an uptrend when the short-term moving average is above the long-term moving average, with a significant distance threshold of 3%[2][6][11] - The **industry trend allocation model** is highlighted, which signals opportunities in specific sectors. For the medium term, the "distressed reversal expectation model" suggests focusing on innovative healthcare. The "TWO BETA model" continues to recommend the technology sector, particularly AI applications and commercial aerospace after adjustments. In the short term, the "earnings trend model" points to opportunities in computing power (e.g., Sci-Tech Chip ETF, code 588200) and energy storage batteries (e.g., Energy Storage Battery ETF, code 159566)[2][5][7] - The **position management model** is used to determine stock allocation levels. Based on the WIND All A Index's valuation and trend, the model recommends an 80% stock allocation for absolute return products[5][7] - The **valuation indicators** for the WIND All A Index are also discussed. The PE ratio is at the 90th percentile, indicating a relatively high valuation, while the PB ratio is at the 50th percentile, representing a medium level[5][7][11]

Core Insights - The report analyzes the historical patterns of A-share market performance following significant trading volume increases, indicating that market sentiment often shifts after peak trading volumes, with only a few sectors maintaining strong momentum [3][4][5] - It highlights that sectors with robust fundamental expectations tend to sustain their strength post-volume spikes, such as construction during the Belt and Road Initiative in 2014 and AI-related sectors in 2025 [3][4][5] Historical Volume Analysis - A total of six significant volume spikes in A-shares have been identified, characterized by a trading volume increase of 1.5 times or more, with the most recent occurring on January 12, 2026, when the trading volume reached 3.6 trillion yuan [6][7] - Historical data shows that after these volume spikes, the market generally experiences a one-month period of limited risk, with an average return of 1.8% and a median return of 2.7% [26][27] - Over the subsequent three months, the market tends to enter a consolidation phase, with an average decline of 5.05% [26][27] Sector Performance Post-Volume - The report indicates that sectors leading in performance before a volume spike often do not maintain their positions in the following months, suggesting a shift in market focus [37][41] - For instance, sectors like construction and technology have shown varying degrees of performance continuity after volume spikes, with some sectors like food and beverage maintaining strength due to external factors such as foreign investment [41][42] Small vs. Large Cap Stocks - Historically, small-cap stocks tend to outperform large-cap stocks in the month following a volume spike, although this trend does not hold consistently over a three-month period [47][48] - The report emphasizes the importance of monitoring market sentiment and sector fundamentals to gauge future performance [55] Future Market Outlook - The report projects that the A-share market will likely experience a strong upward trend from late January to mid-March 2026, driven by seasonal effects and positive earnings forecasts [4][52] - Key sectors to watch include copper, energy storage, and semiconductor industries, which are expected to perform well in the upcoming months [4][55]

Group 1: Market Index and Style Performance Review - Major broad market indices mostly increased this week, with the CSI 500 rising by 2.18%, the Northbound 50 by 1.58%, and the CSI 1000 by 1.27%. However, the Shanghai Composite Index fell by 0.45% and the CSI 300 by 0.57% [2][10]. - Over the past month, all major broad market indices have risen, with the CSI 500 up by 17.59% and the CSI 1000 by 14.64% [10][11]. - In terms of industry performance, sectors such as computer, electronics, media, non-ferrous metals, and machinery performed well, while defense, agriculture, coal, real estate, and non-bank financials lagged behind [14][16]. Group 2: Factor Performance Tracking - In the CSI 300 stock pool, factors such as the 20-day volume variation coefficient, standardized unexpected earnings, and overnight momentum before earnings announcements performed well this week [3][24]. - In the CSI 500 stock pool, the 60-day specificity, 20-day specificity, and 60-day momentum factors showed strong performance [3][26]. - The overall market stock pool saw strong performance from quarterly ROA, quarterly ROE, and quarterly net profit margin factors [3][22]. Group 3: Quantitative Fund Performance - The average excess return for CSI 300 index-enhanced products was 0.58%, while the CSI 500 index-enhanced products had an average excess return of -0.26% [4][12]. - The best-performing active quantitative fund this week was Huian Quantitative Preferred A [4][12]. Group 4: Quantitative Index Enhancement Portfolio Tracking - The CSI 300 index enhancement portfolio achieved an excess return of 0.24% over the past week, while the CSI 500 index enhancement portfolio had an excess return of 0.27% [5][12]. - The CSI 800 index enhancement portfolio recorded an excess return of 0.59% [5].

Report Industry Investment Rating No relevant content provided. Core Views - PB-ROE framework's ETF rotation strategy recommends next week to focus on the communication, agriculture, forestry, animal husbandry, and transportation industries, corresponding to their industry ETFs; the ETF redemption sentiment indicator model suggests focusing on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF [9][40] - Combining PB and ROE for industry configuration may be a better choice; the third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas; combining the third and fifth quadrants to construct a comprehensive PB-ROE strategy has an annualized return of 11.93% and an annualized excess return of 13.22% [32][33] Summary by Directory 1. Recent Market Overview (January 12 - January 16, 2026) - Index performance: Shanghai Composite Index closed at 4101.91, down 0.45% for the week; Shenzhen Component Index closed at 14281.08, up 1.14%; ChiNext Index closed at 3361.02, up 1.00%; Beijing Stock Exchange 50 closed at 1548.33, up 1.58%; Hang Seng Index closed at 26844.96, up 2.34%. The average daily trading volume of the Shanghai and Shenzhen stock markets was 34250.96 billion yuan, and the total trading volume for the week was 17.13 trillion yuan [12] - Industry performance: Among 31 Shenwan primary industries, 13 industries rose and 18 fell. The top three gainers were computer (up 3.82%), electronics (up 3.77%), and non-ferrous metals (up 3.03%); the top three losers were national defense and military industry (down 4.92%), real estate (down 3.52%), and agriculture, forestry, animal husbandry, and fishery (down 3.27%) [5][12] - Main funds: Main funds had net outflows for 5 trading days and no net inflows, with a total net outflow of 2752.39 billion yuan for the week. The industries with more net inflows were banks, public utilities, and coal; the industries with more net outflows were national defense and military industry, power equipment, and computer [5][13] 2. Recent ETF Market Performance (January 12 - January 16, 2026) - Overall situation: As of January 16, 2026, there were 1411 ETFs in the Shanghai and Shenzhen stock markets, with a total asset management scale of 60766.01 billion yuan. There were 1101 equity ETFs (38892.41 billion yuan), 53 bond ETFs (7479.66 billion yuan), 27 money market ETFs (1529.88 billion yuan), 17 commodity ETFs (2751.84 billion yuan), 207 cross-border ETFs (10070.46 billion yuan), and 6 unlisted ETFs (41.76 billion yuan) [20] - Newly listed and established ETFs: 8 ETFs were newly listed, all equity ETFs; 7 ETFs were newly established, with a total issuance scale of 51.24 billion yuan [21] - Equity ETFs: The median weekly increase or decrease was 0.59%. Science and technology semiconductor ETFs and semiconductor equipment ETFs performed well, with the Science and Technology Semiconductor ETF Peng Hua rising the most at 12.46%; aerospace and high-end equipment ETFs performed poorly, with the Aerospace ETF falling the most at 6.88%. The average weekly share change was a decrease of 19.4716 million shares. Software ETFs and media ETFs had more share increases, while the Science and Technology Innovation 50 ETF and CSI 300 ETF had more share decreases [24] - Bond ETFs: The median weekly increase or decrease of 53 bond ETFs was 0.12%. The convertible bond ETF had the highest increase of 0.91%, while the science and technology innovation bond ETF had the highest decrease of 0.00%. As of January 16, 2026, the Haifutong CSI Short-term Financing ETF had the largest scale of 631.50 billion yuan [27] - Cross-border ETFs: The median weekly increase or decrease was 1.18%. The China-South Korea Semiconductor ETF and Hong Kong Stock Connect Internet ETF had the highest increases, with the China-South Korea Semiconductor ETF rising 6.11%; the Hong Kong Securities ETF and Nasdaq Biotechnology ETF had the highest decreases, with the Hong Kong Securities ETF falling 2.28%. Since the beginning of the year, the median increase or decrease was 3.82%, with the China-South Korea Semiconductor ETF and Hong Kong Medical ETF having higher increases, and the Nasdaq ETF and Nasdaq Technology ETF having higher decreases [29] 3. PB-ROE Framework's ETF Rotation Strategy Tracking - Factor effectiveness: PB factor and PB quantile factor show certain stratification ability, and PB quantile factor is more effective; ROE factor's effectiveness declined after 2018; using ROE factor is better than ROE quantile factor; expected ROE factor is better than expected ROE year-on-year factor. Combining PB and ROE for industry configuration may be a better choice [32] - Key quadrants: The third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas. From 2017 to February 2024, the compound annualized excess returns of the third and fifth quadrant portfolios were 4.27% and 1.55% respectively [32] - Strategy improvement: After supplementing the PB-ROE framework with four dimensions, the annualized excess returns of the third and fifth quadrant strategies were 4.78% and 3.94% respectively. Combining the two strategies, the annualized return was 11.93% and the annualized excess return was 13.22% [33] - Recent performance: This week, the strategy focused on the communication, agriculture, forestry, animal husbandry, and transportation industries, with a cumulative return of -0.86%, and an excess return of -0.29% compared to the CSI 300 Index [8][34] - Performance since 2023: The cumulative return was 26.03%, with an excess return of 3.81% compared to the CSI 300 Index [8][36] - Performance since 2022: The cumulative return was 7.77%, with an excess return of 11.99% compared to the CSI 300 Index [39] 4. Investment Recommendations - PB-ROE framework: Focus on the communication, agriculture, forestry, animal husbandry, and transportation industries next week, corresponding to their industry ETFs [9][40] - ETF redemption sentiment indicator model: Focus on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF next week [9][40]

Core Insights - The market experienced a surge followed by a pullback, with a general trend of "small strength and large weakness" observed. The major indices began to correct their upward slope, indicating a potential short-term consolidation after the spring rally initiated in mid-December last year. However, this correction does not alter the overall "systematic slow bull" nature of the market [1][4][55] - The report suggests that the technology growth sector is expected to outperform, and recommends two strategies for market participation: one is to balance mid-term positions in sectors with high prosperity and reasonable price levels, specifically in the "two electric and non-mechanical" sectors (electronics, new energy, chemicals, non-bank financials, machinery) to adopt an "offensive instead of defensive" approach; the second is to consider the relatively lower positions in the market, such as the CSI 1000 and National CSI 2000, to capture relative returns [1][5][56] Weekly Market Overview - The market saw a significant increase in trading volume followed by a decline, with the major indices showing a "small strength and large weakness" pattern. The Shanghai Composite, SSE 50, and CSI 300 indices fell by 0.45%, 1.74%, and 0.57% respectively, while growth indices like CSI 500, CSI 1000, and National CSI 2000 rose by 2.18%, 1.27%, and 1.31% respectively [2][12][54] - The technology sector is gaining momentum, with TMT sectors (Technology, Media, Telecommunications) showing strong performance, while other styles are generally weakening. The computer, electronics, media, and communication sectors rose by 3.82%, 3.77%, 2.04%, and 1.42% respectively [2][14][54] Market Sentiment and Fund Flows - The average daily trading volume in the Shanghai and Shenzhen markets increased to 3.43 trillion yuan, indicating heightened market activity. However, the financing buy-in ratio decreased to 10.85% [20][26] - The total margin financing balance rose significantly to 2.71 trillion yuan, with a notable inflow of funds into the margin financing sector, while stock ETFs experienced a net outflow of 675 million yuan [26][31] Market Attribution - Key events influencing the market included the increase in financing margin ratios by the Shanghai and Shenzhen stock exchanges, announcements from multiple listed companies urging rational decision-making, and a meeting by the China Securities Regulatory Commission emphasizing market stability [3][50][54]

Market Performance - Since the beginning of 2026, the A-share market has shown a "good start" with a cumulative increase of 5.2% in the Wind All A Index, and the average daily trading volume has exceeded 30 trillion yuan[5] - The leading sectors include media, computer, non-ferrous metals, and military industry, with year-to-date gains of 16.0%, 14.0%, 14.0%, and 9.0% respectively, reflecting the current market focus on AI and geopolitical factors[5] Economic Indicators - In December 2025, China's PPI increased by 0.2% month-on-month, marking the highest monthly increase since 2024, driven by improvements in non-ferrous and technology sector prices[12] - Prices in the non-ferrous metal mining and smelting industries rose by 3.7% and 2.8% respectively, influenced by AI-driven demand for electricity[12] Strategic Trends - The two main strategic lines for A-share pricing in 2026 are AI, reflecting the U.S. focus on technology for growth, and "anti-involution," which corresponds to China's push for reform and high-quality development[3] - The "anti-involution" strategy is entering a new phase, emphasizing "quality over price" and a shift in local government performance perspectives[15] Policy Changes - The cancellation of export tax rebates for photovoltaic products is a national-level manifestation of the "anti-involution" strategy, aimed at promoting price increases among leading companies while eliminating those relying on low prices[18] - Recent regulatory actions against monopolistic practices in the photovoltaic industry signal a commitment to fair competition and the acceleration of "anti-involution" efforts[19] Risks - Potential risks include unexpected geopolitical tensions and slower-than-expected progress in "anti-involution" reforms, which could disrupt market dynamics[4][21]

智通财经APP获悉，美国卫星通信公司AST SpaceMobile(ASTS.US)周五盘前交易中大涨超7%，此前该 公司确认获得美国导弹防御局(MDA)"SHIELD"项目的主承包商资格。该项目全称为"可扩展国土创新企 业分层防御系统"，隶属于更广泛的"金穹顶"战略，旨在构建跨空域、导弹、太空、网络及混合作战领 域的弹性分层防御体系。 此项授标源于美国政府于2026年1月15日公开发布的该项目合格竞标企业名单。AST SpaceMobile首席商 务官兼政府业务负责人克里斯·艾沃里表示："被选为MDA SHIELD项目的主承包商，是对我们独特的在 轨双用途技术及日益增长的国防领域能力的重大认可。" 截至盘前，ASTS股价上涨7.5%，报108.64美元。此次合作标志着该公司在国防领域的业务拓展获得关 键突破，其天地一体通信技术有望融入国家级导弹防御体系，为未来政府订单增长打开空间。 ...