- The diffusion index model tracks industry rotation based on momentum principles, focusing on upward trends in industry performance. It has been monitored for four years, with notable performance in 2021 achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and power equipment & new energy[22][23][26] - The GRU factor model utilizes minute-level volume and price data processed through GRU deep learning networks. It has shown strong adaptability in short cycles but performs less effectively in long cycles. In 2025, the model's industry rotation includes sectors like agriculture, power & utilities, basic chemicals, transportation, steel, and petrochemicals. Weekly average returns were 2.56%, with excess returns of 1.65% against equal-weighted industry benchmarks. Year-to-date excess returns stand at -4.49%[29][30][32] - Diffusion index weekly tracking shows top-ranked industries as non-ferrous metals (0.991), banking (0.931), power equipment & new energy (0.925), communication (0.92), steel (0.871), and electronics (0.864). Industries with the most significant weekly changes include power equipment & new energy (+0.083), petrochemicals (+0.082), and light manufacturing (+0.078)[23][24][25] - GRU factor weekly tracking ranks industries such as comprehensive (7.22), basic chemicals (3.37), building materials (2.7), transportation (2.36), power & utilities (1.96), and food & beverages (1.94) as top performers. Industries with notable weekly increases include power & utilities, non-bank finance, and basic chemicals[30][33][37]

Market Liquidity Overview - R007 decreased from 1.4923% to 1.4677%, a reduction of 2.46 basis points; DR007 fell from 1.4551% to 1.4130%, down 4.21 basis points. The spread between R007 and DR007 increased by 1.75 basis points [9][13] - The net inflow of funds this week was 7.831 billion, a decrease of 24.527 billion from the previous week. Fund supply was 16.023 billion, while fund demand was 8.192 billion. Specifically, fund supply decreased by 65.002 billion, with net financing purchases down by 21.016 billion and stock dividends down by 12.308 billion [13][16] Industry Sector Liquidity Tracking - Most sectors in the CITIC first-level industry index rose this week, with the electric equipment and new energy sector leading with a weekly increase of 5.11%. Other sectors like steel and oil & petrochemicals also saw slight increases. Conversely, the pharmaceutical and computer sectors led the declines, with decreases of 2.36% and 2.08% respectively [18][21] - The electric equipment and new energy sector received the most net leveraged capital inflow, totaling 2.196 billion, while the electronic sector experienced a net outflow of 2.501 billion [21][24] Style Sector Liquidity Tracking - Most style indices rose this week, with both cyclical and stable styles leading with an increase of 1.85%. The consumer style was the only one to decline, down 0.70%. The growth style was the most active, accounting for 56.88% of the average daily trading volume [32][36] - The main funds in the style sectors were predominantly reduced, with the largest reduction in the growth style amounting to 10.957 billion, followed by the cyclical style with a reduction of 5.597 billion [33][36]

Quantitative Models and Construction Methods 1. Model Name: Macro Timing Strategy - **Model Construction Idea**: The model is designed to provide equity allocation recommendations based on macroeconomic indicators, including economic growth and monetary liquidity[5][42] - **Model Construction Process**: - The model evaluates the strength of signals from two dimensions: economic growth and monetary liquidity - For each dimension, a percentage signal strength is assigned (e.g., 0% for economic growth and 50% for monetary liquidity in October)[42][43] - The model aggregates these signals to determine the recommended equity allocation percentage (e.g., 25% for November)[42][43] - **Model Evaluation**: The model has achieved a year-to-date return of 13.55%, underperforming the Wind All A Index, which returned 25.61% during the same period[42][45] --- Model Backtesting Results 1. Macro Timing Strategy - **Equity Allocation Recommendation**: 25% for November[42][43] - **Year-to-Date Return**: 13.55%[42][45] - **Benchmark (Wind All A Index) Return**: 25.61%[42][45] --- Quantitative Factors and Construction Methods 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures the relative valuation of stocks to identify undervalued opportunities[48][60] - **Factor Construction Process**: - Includes metrics such as book-to-price ratio (BP_LR), earnings-to-price ratio (EP_FTTM), and sales-to-enterprise value ratio (Sales2EV)[60] - **Factor Evaluation**: The value factor performed strongly in the past week, with an IC mean of 12.38% in the CSI 300 stock pool and 31.97% in the CSI 500 stock pool[48][49] 2. Factor Name: Volatility Factor - **Factor Construction Idea**: Captures the risk and price fluctuation characteristics of stocks[48][61] - **Factor Construction Process**: - Includes metrics such as 60-day return volatility (Volatility_60D) and CAPM residual volatility (IV_CAPM)[61] - **Factor Evaluation**: The volatility factor showed strong performance, with an IC mean of 19.89% in the All A-share stock pool and 22.41% in the CSI 1000 stock pool[48][49] 3. Factor Name: Technical Factor - **Factor Construction Idea**: Utilizes historical price and volume data to identify trading opportunities[48][61] - **Factor Construction Process**: - Includes metrics such as 20-day turnover mean (Turnover_Mean_20D) and 240-day return skewness (Skewness_240D)[61] - **Factor Evaluation**: The technical factor achieved an IC mean of 13.68% in the All A-share stock pool and 8.17% in the CSI 500 stock pool[48][49] 4. Factor Name: Growth Factor - **Factor Construction Idea**: Focuses on identifying stocks with high growth potential based on financial metrics[48][60] - **Factor Construction Process**: - Includes metrics such as single-quarter net income growth (NetIncome_SQ_Chg1Y) and single-quarter operating income growth (OperatingIncome_SQ_Chg1Y)[60] - **Factor Evaluation**: The growth factor underperformed, with an IC mean of -6.34% in the All A-share stock pool and -10.06% in the CSI 500 stock pool[48][49] 5. Factor Name: Quality Factor - **Factor Construction Idea**: Identifies stocks with strong financial health and operational efficiency[48][61] - **Factor Construction Process**: - Includes metrics such as operating cash flow to current debt ratio (OCF2CurrentDebt) and gross margin (GrossMargin_TTM)[61] - **Factor Evaluation**: The quality factor underperformed, with an IC mean of -14.36% in the All A-share stock pool and -14.07% in the CSI 500 stock pool[48][49] --- Factor Backtesting Results 1. Value Factor - **IC Mean**: 12.38% (CSI 300), 31.97% (CSI 500), 22.41% (CSI 1000)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 2. Volatility Factor - **IC Mean**: 19.89% (All A-shares), 22.41% (CSI 1000)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 3. Technical Factor - **IC Mean**: 13.68% (All A-shares), 8.17% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Positive across all stock pools[48][49] 4. Growth Factor - **IC Mean**: -6.34% (All A-shares), -10.06% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Negative across all stock pools[48][49] 5. Quality Factor - **IC Mean**: -14.36% (All A-shares), -14.07% (CSI 500)[48][49] - **Multi-Long-Short Portfolio Return**: Negative across all stock pools[48][49]

Group 1 - The sentiment index for A-shares and Hong Kong stocks is declining from high levels, with a decrease in the VIX for the Shanghai 50, CSI 300, CSI 500, and CSI 1000 [1] - Current institutional focus is on the defense and military industry, while attention in the telecommunications sector has decreased from high levels [1] - There has been an increase in institutional interest in the "oil and petrochemicals," "coal," "steel," "retail," and "non-bank financial" sectors over the past week [1] Group 2 - Many industries are currently at the threshold of triggering congestion indicators, including liquidity, constituent stock diffusion, and constituent stock consistency [1] - The relative returns for electric power and utilities, basic chemicals, electric equipment and new energy, electronics, and computers are expected to be favorable by November 2025 [1] - The VIX for gold, silver, copper, and crude oil has decreased, with a long-term bullish outlook on gold [1]

Core Insights - The "Fixed Income + Growth" strategy has shown remarkable performance this year, with significant gains in both fund performance and scale, particularly in high-risk asset allocation within the technology growth sector [1][2]. Fund Performance and Scale - As of the end of Q3, the total scale of "Fixed Income +" funds reached 2.5 trillion yuan, an increase of over 770 billion yuan from the end of last year, with the number of products rising to 1,775 [2]. - The average net value growth rate for 1,795 "Fixed Income +" products this year is 5.57%, with 244 funds increasing by over 10% [2]. - The top-performing product, Huazhang Zhilian A, has a net value growth rate of 48.26%, primarily investing in the AI industry chain with a stock allocation of 45% [2][3]. Investment Strategies - The "Fixed Income + Growth" strategy has outperformed other strategies, with a median return of 7.18% in Q3, while the "Fixed Income + Technology" strategy achieved a median return of 10.29% [4]. - High-risk "Fixed Income +" funds with equity allocations of 25% or more had a median return of 6.45% in Q3, compared to 3.13% and 0.78% for balanced and conservative strategies, respectively [4]. Sector Focus - In Q3, "Fixed Income +" products increased their holdings in electronics, power equipment, new energy, non-ferrous metals, and machinery, while reducing exposure to banking, utilities, basic chemicals, and home appliances [4]. - The focus on high-elasticity sectors is expected to continue, with AI narratives and macroeconomic conditions favoring growth styles [5][6]. Future Outlook - Industry experts recommend maintaining a focus on high-elasticity sectors and "Fixed Income + Growth" strategies, emphasizing the importance of selecting quality targets based on valuation and growth certainty [5][6]. - The investment strategy will prioritize sectors such as technology growth, cycles, manufacturing, pharmaceuticals, and consumer goods, with an increasing allocation to midstream manufacturing as the economy recovers [6].

Core Insights - Public funds have shown an overall trend of increasing positions in equity assets during the third quarter, particularly in the TMT and power equipment sectors, amidst a rising technology stock bull market [1][3] - There is a notable divergence in the strategies of active equity funds, with some aggressively increasing their positions to capitalize on the bull market, while others have opted to reduce their holdings after achieving certain gains [1][3] Fund Positioning - As of the end of the third quarter, the average stock position of all public funds was 83.28%, an increase of 2.13 percentage points from the end of the second quarter. Mixed open-end funds had an average position of 82.15%, up 1.24 percentage points, while stock open-end funds averaged 90.14%, up 2.26 percentage points [3] - The concentration of holdings among public funds has increased, with stock open-end funds and mixed open-end funds seeing concentration levels rise by 0.94 percentage points and 2.1 percentage points to 56.81% and 57.72%, respectively [3] - By the end of the third quarter, 27 fund companies had products with an average stock position exceeding 90%, with Allianz Fund, Zhuque Fund, and Fidelity Fund having stock positions over 94% [3] Investment Style and Sector Allocation - According to a report by CICC, the market capitalization and growth style preferences of active equity funds have risen in tandem, while value style has seen a significant decline. The concentration of holdings has increased, indicating a more unified market perspective [4] - The TMT sector received an overall increase in allocation during the third quarter, with power equipment, new energy, and non-ferrous metals also seeing significant increases, while reductions were mainly in consumer, financial real estate, and manufacturing sectors [4] Notable Fund Performance - Several funds have significantly increased their positions, with some exceeding 99% stock allocation, including Huaxia Panyi One-Year Mixed Fund and CITIC Construction Investment North Exchange Selected Two-Year Mixed Fund [6] - Funds like Wanji New Opportunities Value-Driven Fund adjusted their holdings from consumer and financial stocks to defensive dividend stocks and domestic technology manufacturing companies, resulting in a stock position increase to 93% by the end of the third quarter [7] - Other funds, such as GF Industry Selection and Jin Xin Quality Growth, also made bold increases in their positions, achieving over 20% gains during the third quarter [8] Cautionary Strategies - Some active equity products have chosen to lock in profits by reducing their positions at high levels, with examples including Huashang Fund's products, which saw a stock position drop to 51% after a significant quarterly gain of approximately 48% [10] - Fund managers have expressed cautious views regarding high valuations in growth sectors, leading to a temporary reduction in positions to manage portfolio volatility, with plans to optimize once market styles shift [10]

Core Viewpoint - In the third quarter, public funds showed an overall trend of increasing positions in equity assets, particularly in the TMT and power equipment sectors, amidst a rising technology stock bull market [1][3]. Fund Positioning and Trends - Active equity funds displayed significant differentiation in their strategies, with some funds aggressively increasing their positions to capitalize on the bull market, while others opted to reduce their positions after achieving certain gains [2][10]. - The overall risk appetite of public funds has increased, with an average stock position of 83.28% by the end of the third quarter, up 2.13 percentage points from the end of the second quarter. Mixed open-end funds had an average position of 82.15%, while stock open-end funds reached 90.14%, an increase of 2.26 percentage points [3]. - The concentration of holdings in public funds has risen, with stock open-end funds and mixed open-end funds seeing increases in concentration by 0.94 and 2.1 percentage points, respectively, reaching 56.81% and 57.72% [3]. Sector Allocation - According to research from CICC, there was a simultaneous increase in the market value and growth style preference of active equity funds in the third quarter, while the value style saw a notable decline. The TMT sector received an overall increase in allocation, with power equipment, new energy, and non-ferrous metals also seeing significant increases, while reductions were mainly in consumer, financial real estate, and manufacturing sectors [4]. Notable Fund Performances - Several equity funds significantly increased their positions, with some funds exceeding 99% stock allocation by the end of the third quarter, including products managed by Huaxia and CITIC [6]. - For instance, the Wanji New Opportunities Value-Driven Fund increased its stock position from 22% at the end of the second quarter to 93% by the end of the third quarter, benefiting from a shift towards technology manufacturing companies [7][8]. - Other funds, such as Guangfa Industry Selection and Jin Xin Quality Growth, also chose to increase their positions and achieved over 20% gains during the third quarter [8]. Caution Among Some Funds - Conversely, some active equity products opted to lock in profits and reduce their positions as the market approached the 4000-point mark. For example, Huashang Fund's products reduced their stock positions from over 90% to 51% by the end of the third quarter, securing gains from the previous quarter [10].

Group 1: A-Share Market - The A-share market experienced an overall increase this week, with the Shanghai Composite Index rising by 1.08%, while the North China 50 index fell by 3.79%, indicating significant market differentiation [6] - The market style showed a clear shift towards value and cyclical sectors, driven primarily by traditional energy and materials industries, with substantial profit improvements in the steel sector during Q3 providing solid performance support [6][7] - Future expectations for the A-share market suggest a continuation of structural trends supported by policy and liquidity, with a focus on "developing new productive forces" as outlined in the 14th Five-Year Plan, emphasizing anti-involution and technology [7] Group 2: Hong Kong Market - The Hong Kong market also saw an overall increase, with the Hang Seng Index rising by 1.29%, while the Hang Seng Technology Index fell by 1.20%, reflecting significant internal differentiation [8] - The performance of the Hong Kong market was influenced by two main factors: increased correlation with the A-share market and strong earnings in energy and financial sectors benefiting from "dual carbon" policy expectations [8] - Looking ahead, the Hong Kong market is expected to navigate between "Chinese fundamentals" and "overseas liquidity," with energy and financial sectors likely to remain stabilizers, while technology stocks may face pressure from overseas market trends [8]

- The report tracks stocks, industries, and sectors that have reached new highs, which can be seen as market indicators. It highlights the effectiveness of momentum and trend-following strategies[11] - The report uses the 250-day high distance to represent new highs, calculated as follows: $ 250 \text{ day high distance} = 1 - \frac{Closet}{ts\_max(Close, 250)} $ where Closet is the latest closing price, and ts_max(Close, 250) is the maximum closing price over the past 250 trading days[11] - As of November 7, 2025, the 250-day high distances for major indices are: Shanghai Composite Index 0.47%, Shenzhen Component Index 2.34%, CSI 300 1.45%, CSI 500 2.93%, CSI 1000 1.39%, CSI 2000 1.36%, ChiNext Index 3.49%, and STAR 50 Index 8.02%[12][13] - The report identifies 1018 stocks that reached new 250-day highs in the past 20 trading days, with the highest numbers in the machinery, basic chemicals, and electronics industries[19] - The highest proportions of new high stocks are in the coal, non-ferrous metals, and steel industries[19] - The report tracks "stable new high" stocks based on analyst attention, relative strength, trend continuity, price path stability, and new high sustainability[27] - The screening criteria for stable new high stocks include: - Analyst attention: At least 5 buy or hold ratings in the past 3 months - Relative strength: Top 20% in market performance over the past 250 days - Price stability: Evaluated using the absolute value of price changes over the past 120 days and the sum of absolute daily price changes over the past 120 days[27] - The report lists 50 stable new high stocks, with the highest numbers in the cyclical and technology sectors[28]

- The report introduces a quantitative model called "250-day new high distance" to track market trends and identify investment hotspots. The model is based on the idea that stocks nearing their 52-week high tend to outperform those far from their 52-week high, as supported by research from [George@2004] and other experts[11][18]. The formula for calculating the 250-day new high distance is: $ 250 \text{ day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ where $\text{Close}_{t}$ represents the latest closing price, and $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days. If the latest closing price reaches a new high, the distance is 0; otherwise, it is a positive value indicating the degree of decline from the high[11] - The report evaluates the model positively, highlighting its effectiveness in identifying market trends and leading stocks that drive market cycles[11][18] - The report also introduces a factor-based screening method for "stable new high stocks" using criteria such as analyst attention, relative stock strength, price path smoothness, and new high sustainability. The screening process includes: 1. Analyst attention: At least 5 buy or overweight ratings in the past 3 months 2. Relative stock strength: Top 20% of market-wide 250-day price change 3. Price path smoothness: Evaluated using metrics like absolute value of price changes over the past 120 days and cumulative absolute price changes over the same period 4. New high sustainability: Average 250-day new high distance over the past 120 days 5. Trend continuation: Average 250-day new high distance over the past 5 days[25][27] - The report positively evaluates the factor-based screening method, citing research that smooth price paths and sustained momentum are associated with stronger returns[25][27] --- - The backtesting results for the "250-day new high distance" model show that as of November 7, 2025, major indices such as the Shanghai Composite Index, Shenzhen Component Index, CSI 300, CSI 500, CSI 1000, CSI 2000, ChiNext Index, and STAR 50 Index have respective 250-day new high distances of 0.47%, 2.34%, 1.45%, 2.93%, 1.39%, 1.36%, 3.49%, and 8.02%[2][12][32] - The backtesting results for the "stable new high stocks" factor show that 50 stocks were selected based on the screening criteria. Among these, the cyclical and technology sectors had the highest number of stocks, with 21 and 16 stocks respectively. Within the cyclical sector, the non-ferrous metals industry had the most new high stocks, while the electric equipment and new energy industry led the technology sector[3][28][33]