Core Viewpoint - The report from CITIC Securities indicates that factors driving the appreciation of the Renminbi are increasing, leading to heightened market attention. Investors need to gradually adapt their asset allocation strategies in an environment of sustained Renminbi appreciation [1] Group 1: Historical Context and Market Behavior - Over the past 20 years, there have been seven cycles of Renminbi appreciation, and exchange rates have not been the decisive factor in industry allocation decisions. However, certain industries tend to perform better during the initial stages of sustained appreciation expectations, suggesting a potential for market memory to replicate past behaviors [1] - Approximately 19% of industries are expected to see an increase in profit margins due to Renminbi appreciation, which will gradually attract investor interest [1] Group 2: Policy Implications and Industry Allocation - Policy responses aimed at curbing rapid unilateral appreciation trends are considered more significant factors influencing industry allocation than the appreciation itself [1] - In the context of ongoing Renminbi appreciation, industry allocation can be guided by three key factors: short-term memory effects, changes in profit margins, and policy changes [1]

Core Viewpoint - The report from CITIC Securities indicates that factors driving the appreciation of the Renminbi are increasing, leading to heightened market attention. Investors need to adapt their asset allocation strategies in a continuously appreciating Renminbi environment [1] Group 1: Historical Context and Market Behavior - Over the past 20 years, there have been seven cycles of Renminbi appreciation, and exchange rates are not the decisive factor in industry allocation decisions. However, certain industries may perform better in the early stages of a sustained appreciation expectation, suggesting a potential for market memory to replicate past behaviors [1] - Approximately 19% of industries are expected to see profit margin improvements due to the appreciation of the Renminbi, which will gradually attract investor attention [1] Group 2: Policy Implications and Industry Allocation - Policy responses aimed at curbing rapid unilateral appreciation trends are considered more significant in influencing industry allocation than the appreciation itself [1] - In the context of ongoing Renminbi appreciation, three key drivers for industry allocation should be monitored: short-term memory effects, profit margin changes, and policy changes [1]

Quantitative Models and Construction Methods 1. Model Name: ERP and DRP Standardized Equal-Weight Model for A-Share Odds - **Model Construction Idea**: The model calculates A-share odds using standardized values of ERP (Equity Risk Premium) and DRP (Default Risk Premium) with equal weighting[12] - **Model Construction Process**: - Standardized values of ERP and DRP are calculated - These values are equally weighted to derive the A-share odds - As of the end of November, the A-share odds declined to near the zero axis, indicating a neutral level[12] - **Model Evaluation**: The model reflects a neutral positioning for A-shares, with odds returning to a balanced state[12] 2. Model Name: Bond Odds Indicator - **Model Construction Idea**: The model uses the expected return difference between long-term and short-term bonds to construct a bond odds indicator[19] - **Model Construction Process**: - The expected return difference between long-term and short-term bonds is calculated - This difference is standardized to derive the bond odds indicator - Recently, the bond odds indicator has significantly rebounded but remains at a low level of -0.9 standard deviations[19] - **Model Evaluation**: The model effectively captures the rebound in bond odds, though it remains at a relatively low level[19] 3. Model Name: AIAE Indicator for US Stocks - **Model Construction Idea**: The AIAE (Asset Implied Allocation Efficiency) indicator measures the historical positioning of US stocks to assess risk and return[20] - **Model Construction Process**: - The AIAE indicator is calculated based on historical data - Currently, the AIAE indicator is at 55%, the highest point in its history, corresponding to 2.4 standard deviations above the mean - Historical analysis shows that when the AIAE indicator exceeded 50% in 2000 and 2022, the S&P 500 experienced significant corrections of 46% and 25%, respectively[20] - **Model Evaluation**: The model highlights elevated risks for US stocks, with the AIAE indicator at a historically high level[20] 4. Model Name: Federal Reserve Liquidity Index - **Model Construction Idea**: Combines quantity and price dimensions to construct a liquidity index for asset allocation[20] - **Model Construction Process**: - The index incorporates multiple factors, including net liquidity, Federal Reserve credit support, market expectations, and announcement surprises - After the October FOMC meeting, the announcement surprise signal turned negative, and net liquidity continued to decline - Other indicators showed easing signals, and the liquidity index returned to a moderately high level of 20%[20] - **Model Evaluation**: The model provides a comprehensive view of liquidity conditions, highlighting mixed signals in the current environment[20] --- Model Backtesting Results 1. ERP and DRP Standardized Equal-Weight Model for A-Share Odds - Odds: Neutral (near zero axis)[12] - Win Rate: 16%[12] 2. Bond Odds Indicator - Odds: -0.9 standard deviations (low level)[19] - Win Rate: -4% (medium level)[19] 3. AIAE Indicator for US Stocks - Odds: 2.4 standard deviations (historically high level)[20] - Win Rate: Not explicitly mentioned[20] 4. Federal Reserve Liquidity Index - Liquidity Index: 20% (moderately high level)[20] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Evaluates small-cap stocks based on odds, trends, and crowding levels[21] - **Factor Construction Process**: - Odds: 0.2 standard deviations (neutral level) - Trend: 1.2 standard deviations (high level) - Crowding: -1.4 standard deviations (low level) - Comprehensive score: 4[21] - **Factor Evaluation**: The factor shows strong trends and low crowding, making it attractive for allocation[21] 2. Factor Name: Value Factor - **Factor Construction Idea**: Assesses value stocks using odds, trends, and crowding levels[23] - **Factor Construction Process**: - Odds: 0.8 standard deviations (moderately high level) - Trend: 0.1 standard deviations (neutral level) - Crowding: -1.3 standard deviations (low level) - Comprehensive score: 3[23] - **Factor Evaluation**: The factor ranks high among others, suggesting it is worth focusing on[23] 3. Factor Name: Quality Factor - **Factor Construction Idea**: Evaluates quality stocks based on odds, trends, and crowding levels[26] - **Factor Construction Process**: - Odds: 1.2 standard deviations (high level) - Trend: -0.6 standard deviations (low level) - Crowding: 0.1 standard deviations (medium level) - Comprehensive score: 0[26] - **Factor Evaluation**: The factor's weak trend reduces its allocation value, requiring confirmation of a right-side trend[26] 4. Factor Name: Growth Factor - **Factor Construction Idea**: Analyzes growth stocks using odds, trends, and crowding levels[29] - **Factor Construction Process**: - Odds: 0.1 standard deviations (neutral level) - Trend: 0.5 standard deviations (moderately high level) - Crowding: 1.5 standard deviations (high level) - Comprehensive score: -1.2[29] - **Factor Evaluation**: The factor's high crowding level indicates elevated trading risks[29] --- Factor Backtesting Results 1. Small-Cap Factor - Odds: 0.2 standard deviations[21] - Trend: 1.2 standard deviations[21] - Crowding: -1.4 standard deviations[21] - Comprehensive Score: 4[21] 2. Value Factor - Odds: 0.8 standard deviations[23] - Trend: 0.1 standard deviations[23] - Crowding: -1.3 standard deviations[23] - Comprehensive Score: 3[23] 3. Quality Factor - Odds: 1.2 standard deviations[26] - Trend: -0.6 standard deviations[26] - Crowding: 0.1 standard deviations[26] - Comprehensive Score: 0[26] 4. Growth Factor - Odds: 0.1 standard deviations[29] - Trend: 0.5 standard deviations[29] - Crowding: 1.5 standard deviations[29] - Comprehensive Score: -1.2[29]

Core Viewpoint - The article discusses the current state of the Chinese stock market, highlighting a decrease in trading activity and concentration, while noting an increase in foreign capital inflow into A-shares and Hong Kong stocks [3][4]. Market Pricing Status - Market sentiment has declined, with average daily trading volume dropping to 2 trillion yuan and the average number of daily limit-up stocks decreasing to 68.4 [3] - The proportion of stocks that increased in value has risen to 54.77%, with the median weekly return for all A-shares increasing to 0.6% [3] - Industry trading concentration has decreased, with only one industry (electric power equipment and new energy) having a turnover rate above 95% [3] A-Share Fund Flow - The issuance of new equity funds has decreased to 21.84 billion yuan, with overall stock positions slightly reduced [4] - The private equity confidence index has slightly declined, but positions are nearing the highest levels of the year [4] - Foreign capital inflow reached 800 million USD, with northbound trading accounting for 27.4% of total trading volume [4] - The IPO fundraising for the period was 3.59 billion yuan, with a future lock-up release scale of 24.73 billion yuan [4] - Net buying in margin trading has decreased to 11.63 billion yuan, accounting for 10.8% of total trading volume [4] A-Share Industry Allocation - Foreign capital primarily flowed into the electronics sector, with a net inflow of 6.32 million USD, while the power equipment sector saw a net inflow of 6.83 billion yuan [5] - The non-bank financial sector and pharmaceutical sector saw significant net inflows in ETFs, while the electronics and power equipment sectors experienced net outflows [5] Hong Kong and Global Fund Flow - Southbound capital inflow increased to 38.68 billion yuan, reaching the 89th percentile since 2022 [6] - Global capital flows showed a net outflow from developed markets and a net inflow into emerging markets, with significant inflows into Asian stock markets, particularly in Japan and China [6]

1. Report Industry Investment Rating - Not provided in the content 2. Core View of the Report - In Q3 2025, funds reduced their positions in the Main Board and increased their positions in the Science and Technology Innovation Board and the ChiNext Board. The overall stock market value ratio of four types of active equity funds slightly increased, and the concentration of fund holdings rose. The allocation of leading companies showed differentiation, with a continuous decrease in the allocation of first - tier leaders and a recovery in the allocation of second - and third - tier leaders. Funds significantly increased their allocation to communication and information technology and reduced their allocation to optional consumption, necessary consumption, and finance. Each scale of funds shifted from consumption, financial real estate to TMT [11][14][19][29]. 3. Summary According to the Table of Contents 3.1 Position Slightly Increased, Concentration Declined Again - **Sector Allocation**: In Q3 2025, funds reduced their positions in the Main Board by 5.13 percentage points to 67.39% and increased their positions in the ChiNext Board by 3.84 percentage points to 19.04% compared with Q2 2025. The proportion of Hong Kong stock holdings continued to increase [11]. - **Stock Market Value Ratio**: The overall stock market value ratio of four types of active equity funds slightly increased. The proportion of stocks in the total fund assets increased to 85.62% quarter - on - quarter, while the proportion of bonds decreased to 3.95% quarter - on - quarter, and the cash ratio decreased [14]. - **Concentration of Fund Holdings**: In Q3 2025, the concentration of the top 50 fund holdings reached 44.5%. The profitability of fund heavy - holding stocks was acceptable, with the top 10 stocks significantly outperforming the common equity fund index. The average return of the top 10 heavy - holding stocks in Q3 2025 reached 65.8%, significantly outperforming the 26.4% of the common equity fund index [16][17]. - **Allocation of Leading Companies**: In Q3 2025, the proportion of fund holdings in first - tier/second - and third - tier leading companies decreased by 1.23 and increased by 2.07 percentage points quarter - on - quarter to 25.83% and 15.31% respectively. Funds mainly increased their allocation to leading companies in communication, electric power and new energy, and non - ferrous metals industries, and mainly reduced their allocation to leading companies in household appliances, banking, and food and beverage industries [19]. 3.2 Expansion of Public Fund Scale, Contraction of Share - **Overall Scale and Share**: The overall management scale of public funds expanded rapidly, but the share contracted. The scale of each size of funds increased quarter - on - quarter, but the share growth rate showed differentiation. The position adjustment directions of large and small public funds were relatively consistent, and each scale of funds shifted from consumption, financial real estate to TMT [56][60][70]. 3.3 Reduction in Manufacturing, Consumption, and Cyclical Sectors, Increase in TMT - **Industry Allocation Changes**: In Q3, funds significantly increased their allocation to communication and information technology, with an increase of 5.9pct in the information technology sector and 4.6pct in the communication business sector. They reduced their allocation to optional consumption and necessary consumption sectors by 3.2pct and 2.4pct respectively. In terms of heavy - holding allocation ratio changes, the heavy - holding allocation ratios of electronics, communication, computer, and electric power and new energy increased the most, while the ratios of banking, food and beverage, household appliances, and national defense and military industry decreased the most. In terms of over - allocation ratio levels, electronics, communication, electric power and new energy, and medicine had the highest over - allocation ratios, while banking, non - banking, public utilities, and petroleum and petrochemical were still significantly under - allocated [29][31]. - **Sub - industry Allocation**: At the secondary industry level, the heavy - holding allocation ratios of communication equipment, computer equipment, semiconductors, and components increased significantly in Q3, while those of white goods, regional banks, and liquor decreased significantly [46][49].

- The "Odds + Win Rate Strategy" was constructed by combining the risk budgets of the odds strategy and the win rate strategy, resulting in a comprehensive score. The strategy has achieved an annualized return of 6.8% since 2011, with a maximum drawdown of 2.9%. Since 2014, the annualized return was 7.4%, with a maximum drawdown of 2.3%. From 2019 onwards, the annualized return was 6.5%, with a maximum drawdown of 2.3%[3][46][48] - The "Small Cap Factor" is characterized by medium odds (0.1 standard deviation), strong trend (1.3 standard deviation), and low crowding (-1.1 standard deviation). Its comprehensive score has risen significantly to 3.2, indicating improved allocation value[19][20][21] - The "Value Factor" exhibits high odds (1.0 standard deviation), moderate trend (-0.2 standard deviation), and low crowding (-1.4 standard deviation). Its comprehensive score is 3, suggesting it is relatively favorable compared to other factors[21][23][34] - The "Quality Factor" currently shows high odds (1.2 standard deviation), moderate crowding (-0.2 standard deviation), but weak trend (-1.0 standard deviation). Its comprehensive score is -0.6, indicating lower allocation value[24][25][26] - The "Growth Factor" is in a high crowding state, with odds at 0.5 standard deviation, trend at 0.3 standard deviation, and crowding at 1.3 standard deviation. Its comprehensive score has dropped to -0.8, highlighting higher trading risks[27][28][29] - The "Odds-Enhanced Strategy" focuses on overweighting high-odds assets and underweighting low-odds assets under a target volatility constraint. Since 2011, it has achieved an annualized return of 6.7% with a maximum drawdown of 3.1%. From 2014, the annualized return was 7.5%, with a maximum drawdown of 2.8%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 2.8%[40][41][42] - The "Win Rate-Enhanced Strategy" derives macro win rate scores from five factors: currency, credit, growth, inflation, and overseas. Since 2011, it has achieved an annualized return of 7.2% with a maximum drawdown of 3.4%. From 2014, the annualized return was 8.1%, with a maximum drawdown of 2.2%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 1.5%[43][44][45]

Fund Position Monitoring - The median position of ordinary equity funds is 91.98%, and for mixed equity funds, it is 91.33%, showing an increase compared to the previous quarter. The current positions are at historical percentiles of 98.41% and 100% respectively [1][6][11] - The average Hong Kong stock allocation for ordinary equity funds is 13%, and for mixed equity funds, it is 17.11%, both slightly increased from the previous quarter. The number of funds allocating to Hong Kong stocks is 241 for ordinary equity funds and 1,671 for mixed equity funds, accounting for 59.55% of the total [1][11][9] Fund Holding Concentration Monitoring - The proportion of heavy-weight stocks in equity allocation is 54.96%, up from 52.46% in the previous period, indicating a significant increase in concentration. The total number of stocks held by fund managers decreased to 2,377 from 2,507, suggesting reduced diversity in holdings [10][1][6] Sector Allocation Monitoring - The main board allocation weight is 47.54%, the ChiNext board is 19.29%, the Sci-Tech Innovation board is 13.91%, and Hong Kong stocks are 19.26%. The main board weight has decreased significantly, while the ChiNext and Sci-Tech boards have increased [21][24] - The technology sector saw a substantial increase in allocation, rising by 12.97% to a historical high of 50.51%. In contrast, the consumer and financial sectors saw significant reductions of 6.08% and 3.48%, respectively, reaching historical lows [24][27] Industry Allocation Monitoring - The top three industries by allocation weight are electronics (23.93%), electric power equipment and new energy (10.27%), and pharmaceuticals (9.81%). The industries with the most active increases in allocation are communication, computer, and electronics, with increases of 2.93%, 1.97%, and 1.85% respectively [26][27][28] Individual Stock Allocation Monitoring - The three stocks with the highest absolute market value allocation are Ningde Times (740 billion), Tencent Holdings (682 billion), and Xinyi Technology (559 billion) [31][32] Performance Fund and Billion Fund Industry Allocation Monitoring - The top three industries allocated by performance funds are electronics (41.18%), communication (38.25%), and computer (8.57%). For billion-scale funds, the top three industries are electronics (26.6%), pharmaceuticals (13.97%), and food and beverage (11.41%) [35][36]

- The report does not contain any quantitative models or factors for analysis

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The trading enthusiasm in the market declined this period. In terms of funds, the issuance of equity - oriented funds increased marginally, the inflow of margin trading funds accelerated, while foreign funds had a slight outflow from A - shares and Hong Kong stocks [1][5]. 3. Summary by Related Catalogs 3.1 Market Pricing Status: The trading enthusiasm declined marginally - **Market sentiment**: The trading turnover rate decreased, the average daily trading volume of the entire A - shares dropped to 1.8 trillion, the average daily number of limit - up stocks rose to 73.2, the maximum consecutive limit - up number was 7, the limit - up board rate rose to 78.6%, and the number of stocks on the Dragon and Tiger List decreased to 59 [5]. - **Profit - making effect**: The proportion of rising stocks increased to 81.2%, and the median weekly return of all A - share stocks rose to 3.1% [5]. - **Trading concentration**: The trading concentration of industries declined. There were 4 industries with the historical percentile of industry turnover rate above 90%, among which the turnover rates of the coal and petroleum and petrochemical industries were above 95% [5]. 3.2 A - share Capital Flow - **Public funds**: The newly - issued scale of equity - oriented funds rose to 12.15 billion, and various public funds reduced their stock positions compared with the previous period [5]. - **Private funds**: In October, the confidence index of private funds decreased slightly, and the positions continued to approach the highest level of the year (as of October 17) [5]. - **Foreign capital**: There was a slight outflow of 120 million US dollars, among which active foreign capital inflowed 16 million US dollars (as of October 22), and the historical percentile of the trading proportion of north - bound funds rose to 38.7% [5]. - **Industrial capital**: The initial public offering (IPO) raised 2.54 billion yuan this period, the private placement scale was 21.151 billion yuan, and the restricted - share lifting scale was 48.76 billion yuan [5]. - **ETF**: Passive funds suddenly turned to net outflow, with a net outflow of 14.7 billion yuan. The passive trading proportion decreased to 6.9% month - on - month, and the premium/discount rate of stock ETFs decreased [5]. - **Margin trading**: The net purchase this period was 21.09 billion yuan, and the trading volume proportion decreased to 11% [5]. - **Retail investors**: Alternative indicators showed that the activity of retail investors increased marginally [5]. 3.3 A - share Industry Allocation - **Foreign capital**: (As of October 22) Non - ferrous metals (+47.3 million US dollars) and electronics (+29.0 million US dollars) had the highest net inflows, while food and beverages (-15.3 million US dollars) and transportation (-13.2 million US dollars) had net outflows [5]. - **Margin trading**: (As of October 23) Electronics (+8.23 billion yuan) and communication (+3.42 billion yuan) had the highest net inflows, while non - ferrous metals (-1.43 billion yuan) had a net outflow [5]. - **ETF**: The passive capital flow behavior of primary industries was concentrated. The non - banking sector (+770 million yuan) had the highest net inflow; among secondary industries, securities and traditional Chinese medicine had net inflows. Power equipment (-4.52 billion yuan) and electronics (-3.24 billion yuan) had the highest net outflows, and among secondary industries, batteries and semiconductors had net outflows. The ETFs with the highest increase this period included securities ETFs and STAR Market 50 ETFs, etc. The 7 - 10 - year China Bond ETF and 0 - 3 - year China Bond ETF had the highest margin trading net purchases; the ChiNext ETF and CSI 300 ETF had the highest net redemptions, and the CSI Overseas Internet ETF and Hang Seng Technology ETF had margin trading net sales [5]. - **Dragon and Tiger List funds**: Machinery, electronics, and power equipment were the top three industries on the Dragon and Tiger List [5]. 3.4 Hong Kong Stocks and Global Capital Flow - **South - bound capital**: The net purchase of south - bound capital per week rose to 17.28 billion yuan, at the 59th percentile since 2022 (MA5) [5]. - **Global capital flow**: This period (as of October 22), the net flow of active/passive funds in developed markets was -6.53 billion/21.88 billion US dollars, and the net flow of active/passive funds in emerging markets was -610 million/-660 million US dollars. From the perspective of foreign capital only, global foreign capital marginally flowed into non - US developed markets this period, with the UK (+1.01 billion US dollars) and France (+550 million US dollars) having the highest inflows, while the US (-132 million US dollars) continued to have an outflow. From the perspective of the overall global flow including domestic capital of each country, the US had the highest inflow, while China and the UK had outflows. North American funds had a large net subscription, and US technology/industrial funds had the highest net subscriptions [5].

Group 1 - The article emphasizes a balanced approach to factor selection, integrating macroeconomic quantitative insights with factor momentum to identify resonant factors while adjusting for discrepancies between macro and micro indicators [1] - Current macroeconomic indicators show signs of economic recovery, slightly loose liquidity, and improved credit metrics, leading to a revised outlook of economic improvement, weak liquidity, and loose credit [1] - The article presents a table summarizing the performance of various factors across different indices, indicating that growth factors remain strong in the CSI 300, while the CSI 500 shows a more balanced factor selection [2][3] Group 2 - Economic leading indicators are beginning to rise, with the PMI and new orders showing increases, suggesting a slight upward trend in economic activity expected to continue into early 2026 [5][9] - The liquidity environment is assessed as slightly loose despite rising interest rates, with a comprehensive liquidity signal indicating a mixed outlook [11][15] - Credit indicators are showing weakness, with a slight positive shift in overall credit metrics, indicating a complex credit environment [15][16] Group 3 - The article suggests a preference for asset allocation towards gold due to strong momentum, while equity allocations are slightly reduced, reflecting a cautious stance on A-shares [16] - The focus of market attention is shifting from liquidity to economic indicators, with recent trends indicating a growing concern for economic performance over liquidity conditions [17] - Industry selection is advised to favor sectors sensitive to economic changes but less affected by liquidity, with public utilities and coal being highlighted as top sectors based on their sensitivity scores [19]