Market Liquidity Overview - R007 increased from 1.4945% to 1.4952%, a rise of 0.07 basis points, while DR007 decreased from 1.4673% to 1.4408%, a drop of 2.65 basis points. The spread between R007 and DR007 widened by 2.72 basis points [9] - The net outflow of funds this week was 40.114 billion yuan, with net inflow decreasing by 23.562 billion yuan compared to last week. Fund supply was 53.787 billion yuan, while demand was 93.901 billion yuan [11] Industry Sector Liquidity Tracking - All sectors in the CITIC first-level industry index experienced declines, with the comprehensive sector showing the most significant drop of 9.47%. The power equipment and new energy, as well as basic chemicals sectors, also saw slight declines [16] - The defense industry received the highest net inflow of leveraged funds at 0.507 billion yuan, while the electronics sector experienced the most significant net outflow of 10.594 billion yuan [18] Style Sector Liquidity Tracking - The style indices generally fell, with the cyclical and growth styles leading the decline at 6.02% and 5.73%, respectively. The growth style was the most active, accounting for 54.58% of the average daily trading volume [33] - The main funds in the style sectors showed a predominant trend of reduction, with the stable style seeing an increase of 0.378 billion yuan, while the growth style saw a reduction of 31.3 billion yuan [32]

- The report discusses the "Volume Timing Signal" model, which indicates a cautious view for all indices as of November 21, 2025[24][25] - The "Number of Rising Stocks in the CSI 300 Index" sentiment indicator is used to gauge market sentiment by calculating the proportion of stocks with positive returns over a certain period[25][26] - The "Number of Rising Stocks in the CSI 300 Index" timing tracking involves smoothing the indicator over two different periods to capture its trend, with a bullish view when the short-term line is above the long-term line[27][28][29] - The "Moving Average Sentiment Indicator" uses the eight moving averages system to assess the trend state of the CSI 300 Index, assigning values based on the position of the moving average range[33][34][35] - The "Moving Average Sentiment Indicator" shows that the CSI 300 Index is currently in a non-prosperous sentiment range as of November 21, 2025[33][36][37] Model Backtest Results - Volume Timing Signal: All indices show a cautious view as of November 21, 2025[24][25] - Number of Rising Stocks in the CSI 300 Index: The indicator has recently declined, with the proportion of rising stocks slightly above 50%, indicating cooling market sentiment[25][26] - Number of Rising Stocks in the CSI 300 Index Timing Tracking: Both the fast and slow lines are declining, with the fast line below the slow line, indicating a cautious view for the near future[27][28][29] - Moving Average Sentiment Indicator: The CSI 300 Index is in a non-prosperous sentiment range as of November 21, 2025[33][36][37] Factor Construction and Evaluation - Cross-sectional volatility: The recent week saw a decline in cross-sectional volatility for CSI 300 and CSI 500 index constituents, indicating a deteriorating short-term alpha environment, while the CSI 1000 index constituents saw an increase, indicating an improving short-term alpha environment[2][38] - Time-series volatility: The recent week saw a decline in time-series volatility for CSI 300 index constituents, indicating a deteriorating alpha environment, while the CSI 500 and CSI 1000 index constituents saw an increase, indicating an improving alpha environment[2][39][40] Factor Backtest Results - Cross-sectional volatility: - CSI 300: 2.28% (recent quarter average), 83.44% (recent quarter average as a percentile of the past two years) - CSI 500: 2.44% (recent quarter average), 78.57% (recent quarter average as a percentile of the past two years) - CSI 1000: 2.60% (recent quarter average), 83.67% (recent quarter average as a percentile of the past two years)[39] - Time-series volatility: - CSI 300: 0.73% (recent quarter average), 77.23% (recent quarter average as a percentile of the past two years) - CSI 500: 0.53% (recent quarter average), 80.16% (recent quarter average as a percentile of the past two years) - CSI 1000: 0.27% (recent quarter average), 82.07% (recent quarter average as a percentile of the past two years)[42]

- The report does not mention any specific quantitative models or factors, nor does it provide details on their construction, evaluation, or backtesting results. The content primarily focuses on market trends, fund flows, and sector allocations without delving into quantitative methodologies or factor-based analyses. [1][2][3]

Group 1 - The report tracks stocks, industries, and sectors that are reaching new highs, indicating market trends and hotspots [1][4][24] - As of November 21, 2025, the distance to the 250-day new high for major indices is as follows: Shanghai Composite Index 4.83%, Shenzhen Component Index 8.65%, CSI 300 6.20%, CSI 500 9.69%, CSI 1000 7.59%, CSI 2000 7.40%, ChiNext Index 12.16%, and STAR 50 Index 16.45% [5][24] - Among the CITIC primary industry indices, the sectors closest to their 250-day new highs include petroleum and petrochemicals, textiles and apparel, basic chemicals, home appliances, and steel [8][24] Group 2 - A total of 1,127 stocks reached a 250-day new high in the past 20 trading days, with the highest number of new highs in the basic chemicals, machinery, and power equipment and new energy sectors [2][13][24] - The highest proportion of new high stocks is found in the textiles and apparel, coal, and non-ferrous metals sectors, with respective proportions of 41.41%, 38.89%, and 38.71% [13][24] - The cyclical and manufacturing sectors had the most new high stocks this week, with 364 and 315 stocks respectively [15][24] Group 3 - The report identifies 15 stocks that have shown stable new highs, including Heertai, Sry New Materials, and Cangge Mining, with the manufacturing and cyclical sectors contributing the most stocks [3][20][25] - The construction industry had the highest number of new highs within the manufacturing sector, while the non-ferrous metals industry led in the cyclical sector [20][25]

Domestic Market Overview - The domestic market experienced a pullback after an initial rise, with the Shanghai Composite Index closing at 3931.05 points, down 0.4% [10][9] - The Shenzhen Component Index closed at 12980.82 points, down 0.76%, while the ChiNext Index fell by 1.12% [10][4] - Among 30 first-level industries, only 5 saw gains, with construction materials, banking, and telecommunications leading the increases, while coal, power equipment, new energy, and oil and petrochemicals faced significant declines [10][11] Overseas Market Overview - The US stock market saw all three major indices decline, with the Dow Jones down 0.84%, S&P 500 down 1.56%, and Nasdaq down 2.15% [2] - Notable declines included Nvidia dropping over 3% and Boeing falling more than 3% [2] - Chinese concept stocks also faced widespread declines, with Canadian Solar down nearly 19% and Xinyi Technology down over 14% [2] Key Economic Data - The report highlights that the number of college graduates in China for 2026 is expected to reach 12.7 million, an increase of 480,000 from the previous year [23] - In the US, non-farm employment increased by 119,000 in September, exceeding expectations, but the unemployment rate unexpectedly rose to 4.4%, the highest since October 2021 [23] Industry Developments - The Guangdong provincial government aims for the core digital economy industries to account for over 16% of GDP by 2027, with plans to create three trillion-level digital industry clusters and achieve an annual growth rate of over 15% in the data industry [19][20] - The report emphasizes the importance of digital transformation for over 60,000 industrial enterprises in Guangdong, with a focus on enhancing the region's digital economy [19][20]

Group 1 - The current institutional focus is on the basic chemical, defense, automotive, textile and apparel, non-bank financial, and media industries, while the telecommunications sector has seen a decline in institutional attention [1] - In the past week, there has been an increase in institutional interest in the "petroleum and petrochemical," "coal," "steel," "light manufacturing," and "non-bank financial" sectors [1] - Many industries are currently at the threshold of triggering congestion indicators (liquidity, consistency of constituent stocks) [1] Group 2 - 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 risen, and the medium to long-term outlook for gold remains bullish [1]

Group 1 - The Shenzhen Growth 40 Index has shown strong performance, with a 2.50% increase, and key stocks such as Upstream Electric and Zhongcai Technology have risen significantly, indicating a robust growth trend in the market [1][3] - The top three industries represented in the Shenzhen Growth 40 Index are Power Equipment and New Energy (31.10%), Basic Chemicals (13.74%), and Communications (12.51%), highlighting the sectors driving growth [1] - Domestic power battery installation volume reached 578 GWh from January to October this year, a year-on-year increase of 42.4%, while global energy storage battery shipments grew by 90.7% in the same period, indicating a strong upward trend in the battery industry [1] Group 2 - Citic Securities predicts that global energy storage installations will reach approximately 290 GWh by 2025 and could reach 1.17 TWh by 2030, showcasing significant growth potential in the energy storage sector [2] - The domestic energy storage industry chain is gaining a competitive edge, with increasing global market share in battery cells and storage systems, supported by favorable policies that are accelerating marketization [2] - The basic chemicals sector is expected to experience a cyclical recovery driven by profit improvements, with factors such as capacity cycle recovery and policy support contributing to this trend [2] Group 3 - The top ten weighted stocks in the Shenzhen Growth 40 Index account for 69.02% of the index, with leading companies including CATL and Xinyu Technology, indicating concentrated investment in key growth firms [3]

- 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]