- The report introduces the **Diffusion Index Model** for industry rotation, which has been tracked for four years. The model is based on momentum strategies to capture industry trends. It showed strong performance in 2021 with excess returns exceeding 25% before experiencing a significant drawdown due to cyclical stock adjustments. In 2022, the strategy delivered stable returns with an annual excess return of 6.12%. However, in 2023 and 2024, the model faced challenges, with annual excess returns of -4.58% and -5.82%, respectively. For October 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and automobiles[26][30] - The **Diffusion Index Model** is constructed by ranking industries based on their diffusion index values, which reflect upward trends. The top six industries as of October 10, 2025, are non-ferrous metals (0.98), banking (0.951), communication (0.909), steel (0.877), electronics (0.823), and automobiles (0.813). The bottom six industries are food and beverage (0.137), consumer services (0.297), real estate (0.407), coal (0.445), transportation (0.457), and construction (0.489)[27][28][29] - The **Diffusion Index Model** achieved an average weekly return of 2.59%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 0.70%. For October, the model's excess return is -0.37%, while the year-to-date excess return is 4.60%[30] - The report also discusses the **GRU Factor Model** for industry rotation, which utilizes minute-level price and volume data processed through a GRU deep learning network. The model has shown strong adaptability in short cycles but struggles in long cycles and extreme market conditions. Since February 2025, the model has focused on growth industries but has faced difficulties in capturing excess returns due to concentrated market themes[32][38] - The **GRU Factor Model** ranks industries based on GRU factor values. As of October 10, 2025, the top six industries are comprehensive (6.64), building materials (5.21), construction (3.55), textile and apparel (3.31), transportation (2.99), and steel (2.88). The bottom six industries are computing (-41.87), food and beverage (-35.34), electronics (-34.87), non-ferrous metals (-28.25), power equipment and new energy (-26.61), and communication (-22.71)[33][36] - The **GRU Factor Model** achieved an average weekly return of 2.88%, exceeding the equal-weighted return of the CSI First-Level Industry Index by 1.01%. For October, the model's excess return is 1.67%, while the year-to-date excess return is -6.55%[36]

- The report highlights the "industry rotation model" which suggests focusing on sectors such as dividend low volatility, building materials, Hong Kong Stock Connect consumer, medical, non-ferrous metals, brokerage, and media[2][8][15] - The industry rotation model is constructed based on signals derived from sector performance, crowding metrics, and market trends. It identifies sectors with potential trading opportunities by analyzing ETF benchmark indices and their performance in terms of volume, price movement, and technical indicators[15] - Specific signals from the industry rotation model include opportunities in sectors like CSI Red Dividend Low Volatility 100, CSI Red Dividend, Shanghai Composite Index, and others. These signals are based on factors such as strong oscillation trends, volume increase, and crossing multiple moving averages[15]

Group 1 - The core viewpoint of the report indicates that all three major indices have experienced five consecutive months of gains, with the ChiNext 50 index rising by 59.45% and the Shanghai Composite Index increasing by 12.73% in Q3 2025 [1][9][10] - In terms of industry performance, only a few sectors reported negative returns, with the telecommunications sector up by 50.20% and the electronics sector up by 44.49% [1][11] - The report highlights that the timing models for Q3 2025 generally achieved absolute positive returns, although it was challenging to outperform the benchmark itself [1][5] Group 2 - The report suggests a positive outlook for Q4 2025, particularly favoring sectors such as electric equipment and new energy, telecommunications, and comprehensive sectors [2] - The report emphasizes the development of various effective strategies based on historical timing, industry rotation, and stock selection models [5][6] - The report outlines the performance of different types of funds, noting that equity mixed funds had the best average return of 25.83% during a period of rising market indices [13]

Core Insights - The report emphasizes the potential for low-position industries to rebound in the current market environment, which is characterized by structural fluctuations and a focus on risk aversion and value investing [1][6]. Industry Recommendations - The report identifies four key low-position industries with marginal improvement potential: Environmental Protection, Non-Metallic Materials, Biological Products, and Automotive [5][6]. - The storage sector is highlighted as a critical area for investment, with rising prices for storage chips indicating the start of a new upward cycle, supported by demand from the Sora2 release [6]. - Precious metals continue to hold strong investment value, driven by short-term interest rate expectations and long-term geopolitical risks, which are expected to support gold prices [6]. - The innovative pharmaceutical sector is poised for valuation recovery as previous negative factors have diminished, making it a focus for investors [6]. - The environmental protection industry benefits from favorable policies and a rebound in related sectors [6]. - Non-metallic materials are supported by supply-side policies and demand-side initiatives, such as the revitalization of Xinjiang [6]. - The biological products sector is expected to gain from new productivity policies and the recovery of the innovative pharmaceutical sector [6]. - The automotive industry is benefiting from consumer incentives and synergies within the robotics supply chain [6]. Performance Indicators - The report provides detailed performance indicators for the identified low-position industries, showing positive trends in various metrics such as PPI and production volumes [7][10]. - For example, the waste resource utilization industry shows a 5.74% increase in PPI, while the automotive sector has seen a 3.10% increase in cumulative sales [7][10]. Market Outlook - The market is expected to continue its oscillating upward trend with structural differentiation, highlighting the importance of identifying and investing in undervalued sectors [1][6].

Economic Indicators - The US services PMI was slightly lower than forecasted at 50 instead of 51, but still indicated positive development [1] - Despite the absence of official US labor market data, private sector indicators show a consolidation of hiring and new payrolls, maintaining a mildly positive sentiment among investors [2] Market Performance - The S&P 500 closed the week in green, indicating sustained market momentum [2] - Bitcoin reached a new all-time high at approximately $125,000, while crude oil prices fell to nearly $60 [2] Crude Oil Market - OPEC+ decided to modestly increase production, which is viewed as a bearish factor for crude oil prices [3] - Crude oil futures are trending downward, with potential support around the $59-60 area, where a breakout could trigger short selling [7] - The bearish sentiment in crude oil persists despite geopolitical concerns, as indicated by market reactions to US President Trump's warnings to Hamas [4] S&P 500 Analysis - The S&P 500 index is positioned above the upper line of the Bollinger Bands, showing signs of weakening momentum [8] - The tech sector faced pressure, which may indicate a normal sector rotation or a precursor to a broader market correction [8] Upcoming Events - Traders are anticipating the end of the government shutdown and developments from Israeli-Hamas talks [5] - Key economic publications to watch include the FOMC minutes and the Michigan Consumer Sentiment Index [5]

Core Insights - The article discusses the impact of negative news announcements during the holiday period on stock performance, particularly focusing on companies that faced significant challenges upon market reopening [1][3]. Group 1: Negative Announcements and Their Types - Companies often release sensitive announcements during market closures, leading to concentrated selling pressure when trading resumes [3]. - Four main types of negative announcements were identified: 1. Industry policy shocks, such as a 50% tariff increase on cabinets and sinks affecting a building materials company, which could either compress profits or lead to customer loss [3]. 2. Earnings disappointments, with a technology company reporting a nearly 500 million loss and acknowledging a decline in product competitiveness [3]. 3. Insider selling, where executives of two companies planned to sell nearly 3% of their shares, raising concerns among investors [3]. 4. Regulatory inquiries, with two companies facing scrutiny over discrepancies in financial reporting and unfulfilled project promises [3]. Group 2: Market Reactions and Strategies - Historical data shows a 70% probability of market gains on the first trading day after the National Day holiday, but individual stocks can react differently based on specific negative news [5]. - Investors are advised to assess their holdings for potential risks related to industry policies, earnings changes, or insider selling before the market opens [5]. - The article emphasizes the importance of monitoring external market conditions, such as U.S. Federal Reserve policies and geopolitical events, which can influence A-share market performance [5][7]. Group 3: Sector Rotation and Investment Strategies - Post-holiday sector rotation is evident, with growth sectors like technology performing well while consumer sectors may decline [7]. - Investors should consider using broad-based ETFs to mitigate individual stock risks, especially during periods of rapid sector rotation [7]. - The article highlights that solid fundamentals can present buying opportunities during market volatility, as seen in the recovery of certain consumer stocks after initial declines [7][9]. Group 4: Long-term Perspectives - Long-term negative impacts tend to fade, but companies with strong fundamentals are more likely to recover from downturns compared to smaller firms [9][10]. - The article concludes that understanding the nature of negative news—whether it is a systemic risk or a company-specific issue—is crucial for making informed investment decisions [10].

Core Insights - The report emphasizes the construction of strategy portfolios based on industry and thematic ETFs, focusing on industry style continuation and switching perspectives through quantitative analysis [2][3]. - The strategy update indicates a recommendation to hold or adjust positions in various ETFs, reflecting a tactical approach to industry rotation [2][3]. ETF Performance Summary - The report lists several ETFs with their market values, holding status, and dominant sectors, indicating a focus on aerospace, military electronics, semiconductors, and traditional media sectors [3][12]. - The cumulative net return for the strategy from September 22 to September 26, 2025, was approximately -0.12%, with an excess return of -1.14% compared to the CSI 300 ETF [3][12]. - Since October 14, 2024, the cumulative return for the strategy sample outside the main portfolio was about 24.76%, outperforming the CSI 300 ETF by approximately 4.92% [3][4]. Recommended ETF Adjustments - The report suggests adding positions in the Satellite ETF, Central Enterprise Technology ETF, and Central Enterprise Innovation ETF while maintaining positions in the Aerospace ETF, Film and Television ETF, and Steel ETF [3][12]. - The weekly model recommends focusing on sectors such as aerospace equipment, military electronics, and semiconductors for the upcoming week [12].

Group 1 - The core viewpoint of the article highlights the rapid switching of main lines in the stock market in 2025, with technology leading the charge, particularly in the AI industry and related sectors [2] - The fund manager, Jin Zicai, has effectively captured the explosive opportunities in the overseas computing power sector by strategically investing in sub-sectors like optical modules and PCBs, aligning with the surge in overseas computing power demand [2][28] - Jin Zicai's investment framework prioritizes "Beta first," allowing for dynamic adjustments in portfolio structure while maintaining a focus on core themes [2][4] Group 2 - Jin Zicai has a decade of experience in industry rotation and has developed a unique three-tier analysis system that evaluates macroeconomic cycles, industry trends, and individual stocks [4] - The performance of the fund "Caitong Value Momentum Mixed A" is highlighted, with a return of 833.15% since inception and a year-to-date return of 53.78% [5][6] - The fund's asset allocation strategy combines both strategic long-term assessments and tactical short-term adjustments based on market momentum [7] Group 3 - The article details specific stock purchases and their performance during Jin Zicai's management, including significant gains in stocks like Xinyisheng and Shijia Photon [9][14] - The fund has shown a pattern of buying stocks at low points and benefiting from subsequent price increases, demonstrating Jin Zicai's ability to time the market effectively [12][21] - The fund's performance is attributed to precise industry allocation and stock selection strategies, with a focus on sectors poised for growth, particularly in technology manufacturing [15][16] Group 4 - The article notes that the fund has made strategic adjustments in response to market conditions, such as increasing exposure to computing power and technology manufacturing while reducing holdings in other sectors [15][28] - Jin Zicai's approach includes a flexible strategy that allows for quick shifts in investment focus based on industry trends and economic conditions, which has been a key factor in achieving excess returns [14][28] - The overall sentiment is that the AI computing power sector is experiencing a significant boom, with expectations for continued growth in demand and investment in the coming quarters [28]

Quantitative Models and Construction Methods - **Model Name**: A-Share Multi-Dimensional Timing Model **Model Construction Idea**: The model evaluates the directional judgment of the A-share market using four dimensions: valuation, sentiment, capital, and technical indicators. Valuation and sentiment dimensions adopt a mean-reversion logic, while capital and technical dimensions use trend-following logic. The model combines these dimensions to provide a comprehensive view of market trends [2][9][15]. **Model Construction Process**: 1. The model uses the Wind All A Index as a proxy for the A-share market. 2. Each dimension generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. 3. Valuation indicators include equity risk premium (ERP). 4. Sentiment indicators include option put-call ratio, implied volatility, and futures member position ratio. 5. Capital indicators include financing purchase amount. 6. Technical indicators include Bollinger Bands and the difference in the proportion of individual stock trading volume [11][15]. 7. The final multi-dimensional score is calculated as the sum of the scores from the four dimensions, determining the overall market view [9][15]. **Model Evaluation**: The model effectively captures market trends and provides actionable insights for timing decisions [9]. - **Model Name**: Style Timing Model **Model Construction Idea**: The model evaluates timing for dividend and size styles using trend-based indicators and crowding metrics [3][17][22]. **Model Construction Process**: 1. **Dividend Style Timing**: - The model uses three indicators: relative momentum of the CSI Dividend Index vs. CSI All Index, 10Y-1Y term spread, and interbank pledged repo transaction volume. - Each indicator generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. - The final score is the sum of the three indicators, determining the overall view on dividend style [17][21]. 2. **Size Style Timing**: - The model uses the crowding degree of small-cap and large-cap styles, calculated based on momentum difference and trading volume ratio between the Wind Micro-Cap Index and CSI 300 Index. - Crowding degree is determined by averaging the top three results of six different window lengths for small-cap and large-cap styles. - High crowding is triggered when small-cap crowding exceeds 90% or large-cap crowding falls below 10%. - In high crowding zones, a small parameter double moving average model is used to capture short-term reversals. In low crowding zones, a large parameter double moving average model is used to follow medium- to long-term trends [22][24][26]. **Model Evaluation**: The model provides effective timing signals for style rotation, especially in different market conditions [22][24]. - **Model Name**: Industry Rotation Model **Model Construction Idea**: The model uses genetic programming to directly extract factors from industry index data, focusing on price-volume and valuation characteristics. It employs a dual-objective genetic programming approach to enhance factor diversity and reduce overfitting [4][29][32]. **Model Construction Process**: 1. The model uses 32 CITIC industry indices as underlying assets. 2. Factors are updated quarterly, and the model rebalances weekly. 3. The dual-objective genetic programming approach evaluates factors using |IC| and NDCG@5 metrics to assess monotonicity and performance of long positions. 4. Factors are combined using a greedy strategy and variance inflation factor to reduce collinearity. 5. The highest-weight factor is constructed as follows: - Perform cross-sectional regression of standardized monthly trading volume against the rolling 4-year percentile of price-to-book ratio (P/B). Take residuals as variable A. - Sum the smallest 9 values of variable A over the past 15 trading days to obtain variable B. - Standardize variable B using z-score, reverse values greater than 2.5, and sum the standardized values over the past 15 trading days [29][33][37]. **Model Evaluation**: The model effectively identifies industry rotation factors with strong monotonicity and performance, while reducing overfitting risks [29][33]. - **Model Name**: China Domestic All-Weather Enhanced Portfolio **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively allocates based on macro expectation momentum [5][38][41]. **Model Construction Process**: 1. **Macro Quadrant Division and Asset Selection**: Divide growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations. Determine suitable assets for each quadrant using quantitative and qualitative methods. 2. **Quadrant Portfolio Construction and Risk Measurement**: Construct sub-portfolios with equal weights for assets within each quadrant, focusing on downside risk. 3. **Risk Budgeting Model for Quadrant Weights**: Adjust quadrant risk budgets monthly based on "quadrant views" derived from macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41]. **Model Evaluation**: The model effectively balances macro risks and enhances portfolio performance through active allocation [38][41]. --- Model Backtesting Results - **A-Share Multi-Dimensional Timing Model**: - Annualized Return: 25.23% - Maximum Drawdown: -28.46% - Sharpe Ratio: 1.17 - Calmar Ratio: 0.89 - Year-to-Date (YTD): 40.98% - Last Week's Return: 0.15% [14] - **Style Timing Model**: - **Dividend Style Timing**: - Annualized Return: 16.04% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.87 - Calmar Ratio: 0.63 - YTD: 21.75% - Last Week's Return: 0.23% [20] - **Size Style Timing**: - Annualized Return: 26.25% - Maximum Drawdown: -30.86% - Sharpe Ratio: 1.09 - Calmar Ratio: 0.85 - YTD: 65.89% - Last Week's Return: 1.07% [27] - **Industry Rotation Model**: - Annualized Return: 32.60% - Annualized Volatility: 17.95% - Sharpe Ratio: 1.82 - Maximum Drawdown: -19.63% - Calmar Ratio: 1.66 - Last Week's Return: 0.27% - YTD: 36.44% [32] - **China Domestic All-Weather Enhanced Portfolio**: - Annualized Return: 11.53% - Annualized Volatility: 6.16% - Sharpe Ratio: 1.87 - Maximum Drawdown: -6.30% - Calmar Ratio: 1.83 - Last Week's Return: 0.66% - YTD: 9.02% [42]

- The report introduces the **Diffusion Index Industry Rotation Model**, which tracks industry trends based on momentum strategies. The model has been monitored for four years, with notable performance in 2021 when it captured industry trends effectively, achieving an excess return of over 25% before experiencing a significant drawdown due to cyclical stock adjustments. In 2025, the model suggested allocating to industries such as comprehensive, non-ferrous metals, communication, banking, media, and retail trade[24][28] - The **Diffusion Index Industry Rotation Model** ranks industries weekly based on diffusion index values. As of September 26, 2025, the top six industries were communication (0.949), non-ferrous metals (0.927), banking (0.897), electronics (0.864), automotive (0.859), and comprehensive (0.811). The bottom six industries were food and beverage (0.153), non-bank finance (0.212), coal (0.342), construction (0.348), real estate (0.362), and consumer services (0.415)[25][26][27] - The **GRU Factor Industry Rotation Model** utilizes GRU deep learning networks to analyze minute-level price and volume data. It has shown strong adaptability in short cycles but performs less effectively in long cycles. The model has been operational since 2021, achieving significant excess returns initially. However, in 2025, the model faced challenges in capturing excess returns due to concentrated market themes and speculative trading[31][37] - The **GRU Factor Industry Rotation Model** ranks industries weekly based on GRU factor values. As of September 26, 2025, the top six industries were steel (3.15), real estate (2.6), building materials (2.08), petroleum and petrochemicals (1.85), transportation (0.81), and electric power and utilities (0.01). The bottom six industries were computing (-32.91), media (-29.46), communication (-17.57), food and beverage (-13.4), pharmaceuticals (-13.36), and non-ferrous metals (-12.73)[6][13][32] - The **Diffusion Index Industry Rotation Model** achieved an average weekly return of -0.00%, with an excess return of 0.78% compared to the equal-weighted return of CICC primary industries. Since September, the model has recorded an excess return of -1.10%, and a year-to-date excess return of 3.68%[23][28] - The **GRU Factor Industry Rotation Model** recorded an average weekly return of -0.61%, with an excess return of 0.17% compared to the equal-weighted return of CICC primary industries. Since September, the model has achieved an excess return of 0.07%, and a year-to-date excess return of -7.53%[31][34]