- The report discusses the impact of year-end liquidity disturbances on the market, suggesting that the recent adjustments are temporary and do not alter the upward trend[1][10] - The main investment theme is shifting from technology to cyclical sectors, with recommendations for chemical ETFs, dividend ETFs, and brokerage ETFs[1][10] - The report highlights the importance of the dollar depreciation as a key factor supporting the A-share market's slow bull trend[1][10] - The report mentions the use of a fund position monitoring model to track the allocation of funds, noting increased allocations in sectors like non-ferrous metals, chemicals, and transportation[1][11] - The report indicates that the technology sector's internal growth rate is slowing down, and the market is transitioning to cyclical sectors[1][11] - The report suggests that the recent market adjustments are due to year-end liquidity disturbances, with quantitative private equity products reducing their risk exposure significantly[1][12] - The report notes that the dollar depreciation trend, supported by lower-than-expected US CPI data, will continue to provide effective support for the A-share market's upward movement[1][13] - The report includes a section on timing strategies, mentioning the use of price segmentation systems and insider trading activity indicators[14][15] - The report provides industry monitoring data, including analysts' industry sentiment expectations and financing and securities lending trends[19][21] - The report discusses the performance of BARRA style factors, noting changes in market preferences and the performance of various factors such as turnover, financial leverage, and profitability volatility[24][25]

Core Insights - The real estate sector experienced the highest net inflow of capital today, amounting to 1.965 billion, with a price change of 2.53% and a turnover rate of 3.10% [1] - The electronics sector faced the largest net outflow of capital, totaling -12.574 billion, with a price change of -0.39% and a turnover rate of 3.52% [2] Industry Summary - **Real Estate**: - Trading volume: 6.805 billion - Change in trading volume: +43.48% - Turnover rate: 3.10% - Price change: +2.53% - Net capital inflow: 1.965 billion [1] - **Retail**: - Trading volume: 4.712 billion - Change in trading volume: +5.43% - Turnover rate: 3.91% - Price change: +1.97% - Net capital inflow: 1.307 billion [1] - **Automobile**: - Trading volume: 5.022 billion - Change in trading volume: +12.22% - Turnover rate: 2.25% - Price change: +0.90% - Net capital inflow: 0.949 billion [1] - **Agriculture, Forestry, Animal Husbandry, and Fishery**: - Trading volume: 3.110 billion - Change in trading volume: +11.51% - Turnover rate: 3.24% - Price change: +0.90% - Net capital inflow: 0.757 billion [1] - **Building Materials**: - Trading volume: 1.471 billion - Change in trading volume: -16.21% - Turnover rate: 1.96% - Price change: +0.67% - Net capital inflow: 0.432 billion [1] - **Steel**: - Trading volume: 1.760 billion - Change in trading volume: -16.98% - Turnover rate: 0.89% - Price change: +0.52% - Net capital inflow: 0.143 billion [1] - **Electronics**: - Trading volume: 9.838 billion - Change in trading volume: -11.74% - Turnover rate: 3.52% - Price change: -0.39% - Net capital outflow: -12.574 billion [2] - **Banking**: - Trading volume: 3.832 billion - Change in trading volume: +21.53% - Turnover rate: 0.29% - Price change: -1.58% - Net capital outflow: -3.390 billion [2] - **Telecommunications**: - Trading volume: 3.703 billion - Change in trading volume: -12.59% - Turnover rate: 2.12% - Price change: +1.21% - Net capital outflow: -13.100 billion [2]

Group 1 - On December 1, a total of 17 companies were investigated by institutions, with 13 companies being surveyed by funds, indicating strong interest in these firms [1] - Tianhua New Energy was the most popular, with 40 funds participating in its survey, followed by Yian Technology and Huadian Technology with 14 and 4 funds respectively [1] - Among the surveyed companies, there were 3 from the Shenzhen Main Board, 9 from the ChiNext Board, and 1 from the Shanghai Main Board [1] Group 2 - The total market capitalization of the surveyed A-shares included 1 company with a market cap over 50 billion yuan and 7 companies with a market cap below 10 billion yuan, such as Huawu Co., Weili Transmission, and Yuehai Feed [1] - In terms of market performance, 11 out of the surveyed stocks increased in the last 5 days, with Tongyu Communication, Henghui Security, and Jiayuan Technology showing the highest gains of 43.97%, 25.35%, and 22.20% respectively [1] - Among the surveyed stocks, 6 experienced net capital inflows in the last 5 days, with Hunan Yuneng receiving the most at 449 million yuan, followed by Tongyu Communication and Tianhua New Energy with net inflows of 437 million yuan and 341 million yuan respectively [1]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries and sectors[22][23] - **Model Construction Process**: The diffusion index is calculated for each industry based on its price momentum. The model ranks industries by their diffusion index values and selects the top-performing industries for portfolio allocation. The model has been tracking out-of-sample performance since 2021, with adjustments made monthly or weekly based on updated diffusion index rankings[22][23] - **Model Evaluation**: The model has shown strong performance in capturing industry trends during momentum-driven markets but struggles during market reversals[22][36] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages minute-level price and volume data processed through a GRU (Gated Recurrent Unit) deep learning network to generate industry factors for rotation strategies[37] - **Model Construction Process**: The GRU model uses historical price and volume data as input to train a deep learning network. The network identifies patterns and generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation. The model is updated weekly to reflect changes in the rankings[30][31][37] - **Model Evaluation**: The GRU model performs well in short-term trading environments but has shown limited effectiveness in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Models 1. Diffusion Index Model - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Factor Model - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: The diffusion index measures the momentum of industries by analyzing price trends and ranks industries based on their momentum[22][23] - **Factor Construction Process**: The diffusion index is calculated for each industry using price momentum data. Industries are ranked based on their diffusion index values, and the top-ranked industries are selected for portfolio allocation. The index is updated weekly or monthly to reflect changes in industry momentum[22][23] - **Factor Evaluation**: The factor effectively captures upward trends in industries but may underperform during market reversals[22][36] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: The GRU industry factor is derived from minute-level price and volume data processed through a GRU deep learning network to identify patterns and rank industries[37] - **Factor Construction Process**: The GRU model processes historical price and volume data through a deep learning network. The network generates factors that are used to rank industries. The top-ranked industries are selected for portfolio allocation, with updates made weekly[30][31][37] - **Factor Evaluation**: The factor is effective in short-term trading environments but less so in long-term scenarios. It is also sensitive to extreme market conditions[37] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Weekly Average Return**: -5.50% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: -0.42% - **November-to-Date Excess Return**: -1.13% - **Year-to-Date Excess Return**: 1.22%[26][22][23] 2. GRU Industry Factor - **Weekly Average Return**: -4.71% - **Excess Return over Equal-Weighted CSI First-Level Industry Index**: 0.35% - **November-to-Date Excess Return**: 2.92% - **Year-to-Date Excess Return**: -2.74%[35][30][31]

- The diffusion index model has been tracking out-of-sample performance for four years, with notable results in 2021 when momentum strategies captured industry trends, achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2022, the strategy maintained stable returns with an annual excess return of 6.12%. However, in 2023, excess returns declined to -4.58%, and in 2024, a major drawdown occurred after September due to the model's focus on upward trends, missing rebound industries, resulting in an annual excess return of -5.82%[24][28] - The diffusion index model suggests allocating to industries such as non-bank finance, construction, and defense military, which showed significant week-on-week improvement in rankings. The top six industries based on diffusion index rankings as of October 24, 2025, are non-bank finance (0.988), banking (0.967), steel (0.952), communication (0.946), comprehensive (0.913), and non-bank finance (0.9)[25][26][27] - The GRU factor model, based on minute-level volume and price data processed through GRU deep learning networks, has shown strong performance in short cycles but weaker performance in long cycles. The model has been effective in capturing trading information since 2021, achieving significant excess returns. However, since February 2025, the model has faced challenges in generating excess returns due to market focus on thematic trading[31][37] - The GRU factor model ranks industries based on their GRU factor scores. As of October 24, 2025, the top six industries are non-bank finance (1.13), banking (1), electric power and utilities (0.54), textile and apparel (0.03), automotive (-0.58), and machinery (-0.73). Industries with the lowest GRU factor scores include food and beverage (-17.79), non-ferrous metals (-10.81), basic chemicals (-8.82), agriculture (-8.76), coal (-6.57), and building materials (-6.48)[6][13][32] - The GRU factor model's weekly industry rotation suggests allocating to non-bank finance, electric power and utilities, textile and apparel, transportation, steel, and petrochemicals. For the week ending October 24, 2025, the model achieved an average return of 1.89%, underperforming the equal-weighted return of the CSI first-tier industries by -0.77%. For October, the model's excess return is 1.80%, while the year-to-date excess return stands at -6.41%[6][34][39]

Market Overview - The risk scoring for the stock market by Zhongtai Asset Management is 45.79, a significant drop from 62.77 last month, primarily due to a notable decline in market sentiment [2] - The valuation of the CSI 300 index has increased to 64.74 from 61.90 last month, marking a continuous rise in the overall valuation center for six months [2] - There is a clear differentiation in valuations across sectors, with industries like steel, electronics, real estate, and others remaining above the historical 60th percentile, while the agriculture sector remains below the 10th percentile [2] Economic Indicators - Market expectation scores have slightly improved to 55.00 from 50.00 last month, driven by better-than-expected import and export growth in September [3] - Economic growth has slowed since Q3, but there is no acceleration in the downturn compared to the same period last year [3] - The global liquidity environment is becoming more accommodative due to the Federal Reserve's preventive rate cuts, but geopolitical conflicts and uneven recovery among major economies add uncertainty to the domestic economic environment [3] Market Sentiment - Market sentiment has experienced a drastic decline to 22.24 from 70.03 last month, indicating a shift from a significantly positive to a low sentiment range [5] - Various sentiment indicators have shown a cooling trend, with margin financing scores dropping significantly and retail fund inflows into the equity market slowing down [5] - The current market presents a mixed pattern of rising valuation centers, stable expectations, and sharply declining sentiment, suggesting a need for investors to approach market indicators with rationality [5] Bond Market Analysis - The risk scoring for the bond market is 61.7, reflecting a continuation of weak economic data, particularly in consumption [7] - Fixed asset investment growth has turned negative for the first time since the pandemic, with a cumulative year-on-year decline of 0.5% [8] - The overall liquidity in the market has shown signs of marginal weakening, with a decline in social financing growth since July [9] Key Economic Data - In Q3 2025, the actual GDP growth rate is 4.8%, with nominal GDP growth at 3.7% [8] - The industrial value-added growth in September is reported at 6.5%, while retail sales growth is at 3.0% [8] - The total social financing in September is 3.53 trillion yuan, with new RMB loans amounting to 1.61 trillion yuan [9]

Market Overview - The A-share market showed mixed performance today, with the Shanghai Composite Index rising by 0.10% to close at 3916.23 points, while the Shenzhen Component Index fell by 0.25% to 13086.41 points. The ChiNext Index increased by 0.38% to 3037.44 points, and the Sci-Tech 50 Index decreased by 0.94% to 1416.58 points. Overall, there were 1172 stocks that rose and 4168 stocks that fell, with a total trading volume of 193.11 billion yuan, down by 14.17 billion yuan from the previous trading day [1][12]. Industry Performance - Among the Shenwan first-level industries, coal, banking, food and beverage, telecommunications, and pharmaceutical sectors led the gains, with increases of 2.35%, 1.35%, 0.97%, 0.74%, and 0.20% respectively. Conversely, the steel, non-ferrous metals, building materials, basic chemicals, and agriculture, forestry, animal husbandry, and fishery sectors experienced declines, with decreases of 2.14%, 2.06%, 1.86%, 1.76%, and 1.56% respectively [1][12]. Sector Highlights - The top-performing concept sectors today included continuous limit-up stocks, insurance, coal mining, Hainan Free Trade Port, memory storage, banking, semiconductor packaging, first boards, liquor, beverage manufacturing, ST stocks, near-term new shares, anti-cancer stocks, and brand leaders [2][11].

Core Viewpoint - The A-share market shows a positive trend with significant gains in the non-ferrous metals sector, which has the highest increase among various industries [1] Industry Performance Summary - The Shanghai Composite Index rose by 0.82% with a trading volume of 730.69 million shares and a transaction value of 1,258.105 billion yuan, marking a 20.21% increase compared to the previous trading day [2] - Among 2,752 stocks, 68 reached the daily limit up, while 2,525 stocks declined, with 18 hitting the daily limit down [2] - The non-ferrous metals sector led the gains with an increase of 5.36%, followed by the electronics sector at 4.10% and the power equipment sector at 2.55% [2] - The real estate sector experienced the largest decline at 2.48%, followed by media at 1.98% and agriculture, forestry, animal husbandry, and fishery at 1.37% [2] Detailed Industry Data - Non-ferrous metals: - Increase: 5.36% - Transaction value: 112.587 billion yuan - Leading stock: Zhongzhou Special Materials, up 19.99% [2] - Electronics: - Increase: 4.10% - Transaction value: 276.665 billion yuan - Leading stock: C Yung Han, up 23.18% [2] - Power equipment: - Increase: 2.55% - Transaction value: 161.904 billion yuan - Leading stock: Haike Xinyuan, up 15.43% [2] - Real estate: - Decrease: 2.48% - Transaction value: 210.28 billion yuan - Leading stock: Huangting International, down 9.92% [2] - Media: - Decrease: 1.98% - Transaction value: 317.93 billion yuan - Leading stock: Guomai Culture, down 20.00% [2]

Market Overview - The A-share market experienced a day of volatility with the ChiNext index dropping over 2.5% during the session [2] - Major indices closed in the red, with the Shanghai Composite Index at 3828.11 (-0.65%), Shenzhen Component at 13209.00 (-1.76%), and the ChiNext at 3151.53 (-2.60%) [1][2] Sector Performance - The top-performing sectors included Oil & Petrochemicals (+1.17%), Environmental Protection (+0.38%), and Public Utilities (+0.35%) [1] - Conversely, the weakest sectors were Computer (-3.26%), Electronics (-2.75%), and Media (-2.65%) [1] Investment Insights - The report highlights a robust performance of the basic pension insurance fund, which has reached an investment operation scale of 2.6 trillion, doubling since the end of the 13th Five-Year Plan [3] - The average annual investment return of the pension fund stands at 5.15%, indicating effective value preservation and growth [3] Future Market Outlook - The market is expected to show a trend of oscillating upward rather than a one-sided increase, with a focus on whether growth policies can effectively translate into improved corporate earnings [4] - Key sectors to watch include TMT (Technology, Media, and Telecommunications), Public Utilities, Non-ferrous Metals, and Financials [4]

Core Insights - The report highlights the current industry allocation of the Bank of China’s multi-strategy system, with significant positions in non-bank financials (11.7%), steel (11.0%), and comprehensive sectors (10.1%) [1] - The average weekly return for the CITIC primary industries was -0.4%, while the average return over the past month was 2.3% [3][10] - The report identifies the top-performing industries for the week as automotive (4.4%), electronics (4.4%), and electric equipment and new energy (4.1%), while the worst performers were banking (-5.6%), non-bank financials (-4.4%), and food and beverage (-3.6%) [3][10] Industry Performance Review - The report provides a detailed performance review of CITIC primary industries, indicating that the automotive sector has a year-to-date return of 34.4%, while electronics and electric equipment and new energy have returns of 48.0% and 36.0%, respectively [11] - The report notes that the composite strategy has achieved a cumulative return of 24.5% year-to-date, outperforming the CITIC primary industry equal-weight benchmark return of 22.2% by 2.2% [3] Valuation Risk Warning - The report employs a valuation warning system based on the PB ratio over the past six years, identifying industries with a PB ratio above the 95th percentile as overvalued [12][13] - Currently, the industries triggering high valuation warnings include retail, media, computing, and automotive, with their PB ratios exceeding the 95th percentile [13] Single Strategy Rankings and Recent Performance - The report outlines the top three industries based on the high profitability tracking strategy as non-bank financials, agriculture, and steel [15][16] - The report also details the performance of various strategies, with the S2 strategy (implied sentiment momentum tracking) highlighting mechanical, electric equipment and new energy, and comprehensive sectors as the top three industries [20] Macro Style Rotation Strategy - The macro style rotation strategy identifies the top six industries based on current macro indicators as comprehensive finance, computing, communication, national defense, electronics, and media [24] - The report emphasizes the importance of macroeconomic indicators in predicting industry performance, utilizing a multi-factor approach to assess industry exposure to various macroeconomic styles [22][23]