- 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]

- The "ERP and DRP standardized equal-weight calculation model" is used to compute A-share odds, which as of September end, declined to 0.2 standard deviations, indicating a neutral level[10] - The "macro victory rate scoring card model" synthesizes asset victory rates based on factors like credit and PMI pulses, which recently bottomed out, pushing A-share victory rates to 19%[10] - The "bond odds model" is constructed using the expected yield difference between long and short bonds, with recent bond odds retreating to -0.9 standard deviations, reflecting valuation risks for long bonds[11] - The "bond victory rate model" integrates credit and growth expansion data, showing a decline to -6%, indicating low victory rates[11] - The "AIAE indicator model" for US stocks is at 54%, its historical peak, corresponding to 2.4 standard deviations, signaling high pullback risks[15] - The "Federal Reserve liquidity index model" combines quantity and price dimensions, showing a tightening liquidity index at 20%, a medium-high level[15] Model Backtesting Results - ERP and DRP model: A-share odds at 0.2 standard deviations, victory rate at 19%[10] - Bond odds model: -0.9 standard deviations, victory rate at -6%[11] - AIAE indicator model: 54% historical peak, 2.4 standard deviations[15] - Federal Reserve liquidity index: 20% medium-high level[15] Factor Construction and Evaluation - Value factor: High odds (0.9 SD), medium trend (-0.3 SD), low crowding (-1.4 SD), comprehensive score 3, recommended for focus[19][22] - Small-cap factor: Medium odds (-0.2 SD), strong trend (1.6 SD), medium-low crowding (-0.5 SD), comprehensive score 2.2, configuration value improved[20][23] - Quality factor: High odds (1.4 SD), weak trend (-1.2 SD), medium-low crowding (-0.5 SD), comprehensive score 0.6, recommended for long-term attention[24][26] - Growth factor: Medium-high odds (0.8 SD), medium trend (0.1 SD), high crowding (1.0 SD), comprehensive score 0.1, recommended for standard allocation[27][28] Factor Backtesting Results - Value factor: Odds 0.9 SD, trend -0.3 SD, crowding -1.4 SD, score 3[19][22] - Small-cap factor: Odds -0.2 SD, trend 1.6 SD, crowding -0.5 SD, score 2.2[20][23] - Quality factor: Odds 1.4 SD, trend -1.2 SD, crowding -0.5 SD, score 0.6[24][26] - Growth factor: Odds 0.8 SD, trend 0.1 SD, crowding 1.0 SD, score 0.1[27][28] Strategy Construction and Evaluation - "Odds-enhanced strategy" allocates assets based on odds indicators under volatility constraints, achieving annualized returns of 6.6%-7.5% and maximum drawdowns of 2.4%-3.0% since 2011[39][41] - "Victory rate-enhanced strategy" uses macro victory rate scoring to allocate assets, achieving annualized returns of 6.3%-7.7% and maximum drawdowns of 2.3%-2.8% since 2011[42][44] - "Odds + victory rate strategy" combines risk budgets from both strategies, achieving annualized returns of 7.0%-7.6% and maximum drawdowns of 2.7%-2.8% since 2011[45][47] Strategy Backtesting Results - Odds-enhanced strategy: Annualized returns 6.6%-7.5%, max drawdowns 2.4%-3.0%[39][41] - Victory rate-enhanced strategy: Annualized returns 6.3%-7.7%, max drawdowns 2.3%-2.8%[42][44] - Odds + victory rate strategy: Annualized returns 7.0%-7.6%, max drawdowns 2.7%-2.8%[45][47]

Core Insights - The report indicates that the market is currently in an upward trend, with the WIND All A index showing a positive money-making effect of approximately 0.87% [2][10][15] - The report suggests maintaining a portfolio allocation of 80% in absolute return products based on the current valuation levels of the WIND All A index, which is at the 85th percentile for PE and the 50th percentile for PB, indicating a moderate valuation [11][8] Market Overview - The WIND All A index experienced a slight decline of 0.18% over the past week, with small-cap stocks represented by the CSI 2000 down by 0.02%, mid-cap stocks in the CSI 500 up by 0.32%, and large-cap indices like the CSI 300 and SSE 50 down by 0.44% and 1.98% respectively [9][10] - The report highlights strong performance in sectors such as power equipment and new energy, with new energy stocks rising by 3.61%, while the banking sector saw a decline of 4.09% [9][10] Timing System Analysis - The distance between the short-term (20-day) and long-term (120-day) moving averages continues to widen, indicating a sustained upward trend in the market, with the latest figures showing a 13.57% difference [2][10] - The report emphasizes that as long as the money-making effect remains positive, there is potential for continued inflow of incremental funds into the market [2][10][15] Sector Recommendations - The report recommends focusing on sectors that are likely to benefit from policy-driven growth, including innovative pharmaceuticals, new energy, and chemicals, while also suggesting a renewed focus on precious metals [2][10][15] - The TWO BETA model continues to recommend technology sectors, particularly in computing power and consumer electronics [2][10][15]

Group 1 - The report indicates that the current WIND All A index is in an upward trend, with the trend line positioned around 6106 points and a positive earning effect of approximately 1.9% [2][10] - The report suggests maintaining a balanced allocation in response to increased market volatility, especially as the market enters a significant event window [2][10] - The report highlights that the market's short-term moving average (20-day) is above the long-term moving average (120-day), with the distance between them increasing from 12.15% to 13.19%, indicating a continued upward trend [2][9] Group 2 - The industry allocation model recommends focusing on sectors that are expected to benefit from policy-driven growth, such as chemicals and innovative new energy, while also continuing to support the Hong Kong innovative pharmaceutical sector [2][10] - The report emphasizes the importance of the market's earning effect in sustaining mid-term incremental capital inflows, as long as the earning effect remains positive [2][10] - The report identifies technology sectors, particularly those related to computing power and batteries, as areas of interest based on the TWO BETA model [2][10]

Core Insights - The report indicates that the market is in an upward trend, with the WIND All A index showing a significant distance of 12.15% between the short-term (20-day) and long-term (120-day) moving averages, suggesting a continued bullish environment [2][4][11] - The current market environment is characterized by a positive profit effect of 1%, and as long as this remains positive, there is potential for continued inflow of incremental funds [2][4][11] - The report highlights the importance of maintaining a balanced portfolio due to increased market volatility, recommending adjustments to holdings in favor of defensive sectors [3][4][11] Market Performance - The WIND All A index experienced a decline of 1.37% over the past week, with small-cap stocks (CSI 2000) down 1.72%, mid-cap stocks (CSI 500) down 1.85%, and large-cap stocks (CSI 300) down 0.81% [10] - Notable sector performance included a 5.91% increase in the electric equipment and new energy sector, while the defense and military sector saw a decline of 11.61% [10] Investment Strategy - The report recommends maintaining a high position in the market, suggesting an 80% allocation to absolute return products based on the current market conditions [3][11] - The industry allocation model suggests a focus on sectors that are likely to benefit from policy support, such as chemicals, non-ferrous metals, and innovative new energy, while also recommending investments in Hong Kong innovative pharmaceuticals and securities insurance [3][4][11] - The report advises against chasing high-flying stocks and instead suggests increasing exposure to previously lagging sectors to mitigate risks during market adjustments [3][4][11]

Group 1 - The article discusses a macro quantitative framework that combines economic, liquidity, credit, and inflation factors for asset allocation and industry/style configuration [1][3] - The framework has been adjusted based on the changing mapping of macro variables to assets, with a focus on economic and liquidity indicators [1][5] - The performance of aggressive portfolios since 2013 shows an annualized return of approximately 8.5%, with a 0.6% excess return compared to the benchmark [3][5] Group 2 - The article highlights the impact of macroeconomic conditions on industry and style configurations, incorporating credit sensitivity into the analysis [5][7] - The macro-sensitive industry configuration has shown varying performance, with a notable decline since 2022, indicating the need for adjustments in selection criteria [7][10] - The article emphasizes the importance of market expectations in influencing macroeconomic indicators and their relationship with asset performance [13][18] Group 3 - The Factor Mimicking model is introduced to capture market expectations regarding macro variables, using a refined stock pool for better representation [19][20] - The construction of the Factor Mimicking portfolio aims to reflect the market's implicit views on economic, liquidity, inflation, and credit variables [19][23] - The article discusses the need for additional micro mappings to enhance the representation of macro variables, particularly in relation to corporate earnings and valuations [28][30] Group 4 - The article outlines the adjustments made to the macro variables based on market expectations, focusing on economic, liquidity, and credit dimensions [34][36] - The revised indicators are expected to improve asset allocation strategies, particularly in the context of equity markets [39][40] - The performance of the revised industry and style configurations indicates a positive impact from incorporating market expectations into the analysis [46][54]