- The report introduces a timing radar model based on six dimensions, including liquidity, economic conditions, valuation, capital flow, technical signals, and crowding metrics, to generate a comprehensive timing score ranging from [-1,1][2][7][9] - Liquidity factors include "monetary direction" and "monetary strength" indicators. The monetary direction factor is calculated using central bank policy rates and short-term market rates, comparing their average changes over the past 90 days. If the factor > 0, it indicates monetary easing. The monetary strength factor is derived from the deviation of DR007 relative to the 7-year reverse repo rate, smoothed and standardized using z-score. If the factor < -1.5 standard deviations, it signals a future easing environment[11][15][16] - Credit factors include "credit direction" and "credit strength" indicators. The credit direction factor is calculated using monthly long-term loan data, analyzing the past 12-month increment and year-over-year changes. If the factor rises compared to three months ago, it signals a positive trend. The credit strength factor measures deviations in new RMB loans relative to expectations, standardized using z-score. If the factor > 1.5 standard deviations, it indicates a significant credit surprise[18][22][24] - Economic factors include "growth direction" and "growth strength" indicators. The growth direction factor is based on PMI data, calculating the 12-month average and year-over-year changes. If the factor rises compared to three months ago, it signals a positive trend. The growth strength factor measures deviations in PMI relative to expectations, standardized using z-score. If the factor > 1.5 standard deviations, it indicates significant growth surprises[25][28][30] - Inflation factors include "inflation direction" and "inflation strength" indicators. The inflation direction factor combines CPI and PPI data, calculating a weighted average and comparing changes over three months. If the factor decreases, it signals a favorable environment for monetary easing. The inflation strength factor measures deviations in CPI and PPI relative to expectations, standardized using z-score. If the factor < -1.5 standard deviations, it indicates significant inflation surprises[32][35][36] - Valuation factors include "Shiller ERP," "PB," and "AIAE" indicators. Shiller ERP is calculated using inflation-adjusted earnings over six years, subtracting the 10-year government bond yield, and standardized using z-score. PB is processed similarly, with a 1.5 standard deviation truncation. AIAE measures equity allocation proportion, calculated as total market capitalization divided by the sum of market capitalization and total debt, standardized using z-score[38][40][44] - Capital flow factors include "margin financing increment" and "trading volume trend" for domestic capital, and "China sovereign CDS spread" and "overseas risk aversion index" for foreign capital. Margin financing increment compares the 120-day average increment to the 240-day average increment. CDS spread measures the 20-day difference in smoothed CDS levels, while the risk aversion index uses Citi RAI data to assess overseas sentiment[47][53][56] - Technical factors include "price trend" and "new highs and lows." Price trend uses moving average distances (ma120/ma240-1) to assess market direction and strength. New highs and lows measure the difference between the number of new highs and lows among index constituents, smoothed over 20 days[60][63][64] - Crowding metrics include "option implied premium," "VIX," "SKEW," and "convertible bond pricing deviation." Option implied premium and VIX assess market sentiment based on recent returns and percentile rankings. SKEW measures the implied skewness of options, while convertible bond pricing deviation calculates the deviation of bond prices from model estimates, standardized using z-score[66][72][74] - Current scores for key factors: monetary direction (1), monetary strength (-1), credit direction (1), credit strength (0), growth direction (1), growth strength (-1), inflation direction (-1), inflation strength (0), Shiller ERP (0.07), PB (-0.46), AIAE (-0.77), margin financing increment (1), trading volume trend (0), CDS spread (1), risk aversion index (-1), price trend (0), new highs and lows (-1), option implied premium (-1), VIX (-1), SKEW (-1), convertible bond pricing deviation (-1)[12][19][26][29][42][43][45][48][50][54][57][62][65][68][72][75]

Core Viewpoint - The article discusses the investment philosophy of buying stocks during downturns, emphasizing the need for psychological resilience and a strategy that aligns with individual comfort levels in investing [1][2][3] Group 1: Investment Strategy - The approach involves purchasing stocks that are experiencing problems, which goes against human nature, as investors typically avoid such stocks [1] - Investors are encouraged to buy more shares when the price declines, demonstrating a willingness to accept unrealized losses [2] - The strategy focuses on acquiring a diversified portfolio of stocks with limited risk, contrasting with the more concentrated approach of some renowned investors [3] Group 2: Investment Philosophy - The investment style leans towards quantitative analysis, similar to Tweedy Browne, rather than the qualitative approach of Warren Buffett [2] - The belief is that owning a diverse group of stocks serves as a defense against a lack of knowledge or information about individual companies [3]

- The report mentions the construction of A-share sentiment index based on market volatility and transaction volume changes, dividing the market into four quadrants. Only the quadrant with "volatility up - transaction down" shows significant negative returns, while others show positive returns. This sentiment index includes bottom warning and top warning signals[36][39][42] - The A-share sentiment index currently indicates bearish signals for both bottom warning (price) and top warning (volume), resulting in an overall bearish outlook for the market[39][42] - The A-share prosperity index is constructed using the YoY growth of net profit attributable to the parent company of the Shanghai Composite Index as the Nowcasting target. The index shows a slow upward trend, indicating the current upward cycle[29][33][35] - The prosperity index value as of September 19, 2025, is 21.21, which has increased by 15.79 compared to the end of 2023, confirming the upward cycle[33][35] - The report applies the BARRA factor model to construct ten major style factors for the A-share market, including SIZE, BETA, MOM, RESVOL, NLSIZE, BTOP, LIQUIDITY, EARNINGS_YIELD, GROWTH, and LVRG[57][58][60] - Among style factors, BETA factor shows high excess returns, while RESVOL factor demonstrates significant negative excess returns. High BETA and high growth stocks perform well, whereas non-linear size and value factors underperform[58][59][61] - The report analyzes the performance attribution of major indices using factor models. Indices like CSI 500, ChiNext Index, and Wind All A exhibit strong exposure to BETA factor, leading to favorable performance in style factors. Conversely, indices like Shanghai Composite Index and SSE 50 show weaker exposure to BETA factor, resulting in poor performance in style factors[66][67][73] - The CSI 500 enhanced portfolio has generated a cumulative excess return of 48.55% relative to the CSI 500 index since 2020, with a maximum drawdown of -5.73%. However, its weekly performance is -0.24%, underperforming the benchmark by 0.56%[45][47][49] - The SSE 300 enhanced portfolio has achieved a cumulative excess return of 38.48% relative to the SSE 300 index since 2020, with a maximum drawdown of -5.86%. Its weekly performance is -0.95%, underperforming the benchmark by 0.50%[52][53][55]

Core Insights - The talent competition in the fund industry has intensified, with over 20 public fund companies initiating campus recruitment for the 2026 graduating class, indicating a significant expansion compared to previous years [2][3][4] - Leading companies are signaling an "expansion" in hiring, with E Fund opening over 40 positions, while mid-sized firms are opting for a more targeted approach [2][4][5] - The demand for AI and quantitative roles has surged, reflecting the industry's shift towards technology-driven investment strategies [6][7][8] Recruitment Trends - Major fund companies like E Fund, GF Fund, and Harvest Fund are actively recruiting across various functions, including research, marketing, operations, and technology [3][4] - The recruitment process follows a structured five-step approach, with rolling offers being a common practice among top firms [3] - Smaller firms are focusing on key positions to enhance efficiency and competitive differentiation, avoiding the pitfalls of indiscriminate expansion [5] AI and Quantitative Roles - AI has transitioned from a supplementary role to a dedicated focus in recruitment, with several firms hosting specialized hiring events for AI talent [6][7] - The need for quantitative and index-related positions has increased significantly, as firms recognize the importance of technology in enhancing investment research efficiency [7][8] Marketing and Sales Positions - There is a notable rise in demand for marketing roles, driven by the implementation of personal pension systems and the launch of direct sales platforms [8] - Companies are seeking "data-driven" marketing professionals who can integrate data analysis with content creation and channel management [8] Talent Requirements - The industry is increasingly in need of versatile talent who possess a blend of financial theory, technological skills, and practical experience [9] - Key areas of expertise include quantitative modeling, AI applications, global market understanding, and compliance technology [9] - The ultimate challenge for firms is to convert talent advantages into product and scale advantages in the competitive landscape [9]

Quantitative Models and Construction 1. Model Name: CSI 500 Enhanced Portfolio - **Model Construction Idea**: The model aims to generate excess returns relative to the CSI 500 index by leveraging a quantitative strategy based on factor models and portfolio optimization techniques [45] - **Model Construction Process**: - The portfolio is constructed using a strategy model that selects stocks based on specific quantitative factors [45] - The portfolio weights are optimized to maximize the expected return while controlling for risk and tracking error relative to the CSI 500 index [45] - The model's performance is evaluated on a weekly basis, and adjustments are made to the portfolio as needed [45] - **Model Evaluation**: The model has demonstrated significant excess returns over the CSI 500 index since 2020, though it experienced underperformance in the most recent week [45] 2. Model Name: CSI 300 Enhanced Portfolio - **Model Construction Idea**: Similar to the CSI 500 Enhanced Portfolio, this model seeks to outperform the CSI 300 index using quantitative factor-based strategies and portfolio optimization [51] - **Model Construction Process**: - Stocks are selected based on quantitative factors, and portfolio weights are optimized to achieve excess returns while managing risk and tracking error relative to the CSI 300 index [51] - The portfolio is reviewed and adjusted periodically to align with the strategy model's recommendations [51] - **Model Evaluation**: The model has achieved consistent excess returns over the CSI 300 index since 2020, with a slight outperformance in the most recent week [51] --- Model Backtesting Results CSI 500 Enhanced Portfolio - Weekly return: 1.82% - Underperformance relative to the benchmark: -1.56% - Cumulative excess return since 2020: 49.43% - Maximum drawdown: -4.99% [45] CSI 300 Enhanced Portfolio - Weekly return: 1.40% - Outperformance relative to the benchmark: 0.02% - Cumulative excess return since 2020: 39.41% - Maximum drawdown: -5.86% [51] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements, capturing the systematic risk of the stock [55] - **Factor Construction Process**: - Beta is calculated using regression analysis of a stock's returns against the market index returns over a specified period [55] - The formula is: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ where $R_i$ is the stock return, $R_m$ is the market return, Cov is covariance, and Var is variance [55] - **Factor Evaluation**: High Beta stocks have recently outperformed, reflecting a market preference for higher systematic risk [56] 2. Factor Name: Residual Volatility (RESVOL) - **Factor Construction Idea**: Captures the idiosyncratic risk of a stock, representing the volatility of its returns unexplained by market movements [55] - **Factor Construction Process**: - Residual volatility is derived from the standard deviation of the residuals in a regression of stock returns on market returns [55] - The formula is: $ \text{RESVOL} = \sqrt{\frac{\sum (R_i - \alpha - \beta R_m)^2}{n-2}} $ where $R_i$ is the stock return, $R_m$ is the market return, $\alpha$ is the intercept, $\beta$ is the slope, and $n$ is the number of observations [55] - **Factor Evaluation**: Residual volatility has shown a significant negative excess return in the recent period, indicating underperformance of high idiosyncratic risk stocks [56] 3. Factor Name: Nonlinear Size (NLSIZE) - **Factor Construction Idea**: Captures the nonlinear relationship between stock size and returns, complementing the traditional size factor [55] - **Factor Construction Process**: - Nonlinear size is calculated as the square of the logarithm of market capitalization: $ \text{NLSIZE} = (\log(\text{Market Cap}))^2 $ [55] - **Factor Evaluation**: Nonlinear size has underperformed recently, reflecting a lack of market preference for mid-sized stocks [56] --- Factor Backtesting Results Beta Factor - Weekly pure factor return: Positive [56] Residual Volatility Factor - Weekly pure factor return: Negative [56] Nonlinear Size Factor - Weekly pure factor return: Negative [56]

- The timing radar six-dimensional model is constructed based on multiple dimensions including liquidity, economic fundamentals, valuation, capital flow, technical signals, and crowding indicators, with 21 metrics categorized into four major groups: "valuation cost-effectiveness," "macro fundamentals," "capital & trend," and "crowding & reversal" to generate a comprehensive timing score ranging from [-1,1][1][6][8] - Liquidity dimension includes factors such as monetary direction, monetary strength, credit direction, and credit strength. For example, the monetary direction factor is calculated using the average change in central bank policy rates and short-term market rates over the past 90 days. If the factor is greater than 0, it indicates a loose monetary policy environment[12][15][17] - Economic dimension includes growth direction, growth strength, inflation direction, and inflation strength factors. For instance, the growth direction factor is derived from PMI data, calculating the 12-month average and year-over-year change. If the factor shows an upward trend compared to three months ago, it signals a positive outlook[24][27][31] - Valuation dimension includes metrics such as Shiller ERP, PB, and AIAE. For example, Shiller ERP is calculated as $1/Shiller PE - 10-year government bond yield$, with a z-score based on the past six years' data[36][38][42] - Capital flow dimension is divided into domestic and foreign capital metrics. Domestic metrics include margin trading increment and turnover trend, while foreign metrics include China's sovereign CDS spread and overseas risk aversion index. For instance, the CDS spread factor signals foreign capital inflow when the 20-day differential is less than 0[45][52][55] - Technical dimension includes price trend and new highs/lows metrics. For example, the price trend factor is calculated using the moving average distance $(ma120/ma240-1)$, with scores determined by the trend direction and strength[58][61][63] - Crowding dimension includes derivative signals such as implied premium, VIX, SKEW, and convertible bond pricing deviation. For instance, the implied premium factor is derived from the 50ETF's 5-day return and percentile ranking, signaling market crowding levels[64][65][70] - Current scores for the six dimensions are as follows: liquidity 0.25, economic fundamentals -0.25, valuation -0.40, capital flow 0.00, technical signals -0.50, and crowding 0.00, resulting in a comprehensive timing score of -0.15[7][8][10]

Group 1 - The core viewpoint of the article emphasizes skepticism towards reports of continuous capital inflow into stocks, suggesting that such information may be misleading and that true opportunities often lie hidden [1][3]. - Historical patterns in the stock market indicate that significant capital inflow data can lead to poor investment decisions, as seen in the 2015 market situation where retail investors were left holding losses [3][4]. - The article warns that the information available to investors is often curated to present a specific narrative, similar to a magician's trick, where the real action is concealed [4][10]. Group 2 - The article highlights the importance of quantitative data over visual market indicators, suggesting that true market behavior is revealed through data analysis rather than superficial price movements [10][12]. - It points out that while Yunnan Energy Investment has seen continuous net capital inflow for 17 days, this does not necessarily indicate the stock's quality but rather the presence of large institutional trading [12]. - The discussion of the 124 stocks with continuous net capital inflow serves as a reminder that appearances can be deceiving, and investors should be cautious of potential traps hidden behind seemingly positive data [12].

Market Overview - The average increase of "gold-related ETFs" this year is 75.51%, outperforming 87.7% of individual stocks [3][11] - Six gold-related ETFs have seen an average increase of over 5%, with one ETF rising by 6.10% [2][3] Institutional vs Retail Investor Behavior - There is a significant gap in understanding between institutional investors and retail investors, with the latter often focusing too much on news rather than on capital behavior [5][11] - Institutional funds began positioning in certain assets as early as late August to early September 2024, despite a generally low market sentiment [7][11] Quantitative Analysis Insights - Quantitative analysis tools indicate that institutional funds are active even when the market appears stagnant, highlighting the importance of monitoring capital flows rather than market sentiment [7][11] - Three gold stock ETFs have doubled in shares, with the largest seeing a 184% increase in scale, suggesting that such capital movements are unlikely to be driven by retail investors [11] Behavioral Finance Implications - Behavioral finance suggests that irrational behaviors among market participants can create specific patterns, which can be analyzed to uncover the true intentions of institutional funds [9][11] Recommendations for Investors - Investors are advised to avoid blindly chasing market trends and instead focus on analyzing capital flows using quantitative tools [12] - Establishing a personal investment logic framework and maintaining independent thinking is crucial for navigating the market effectively [12]

Group 1 - The core viewpoint is that the shift in the Federal Reserve's stance reflects a broader trend of market manipulation and expectation management, similar to the behavior observed in the A-share market [1][3] - The article highlights that market movements are often driven by unseen "puppet masters" rather than the apparent news and data, suggesting that retail investors are misled by surface-level information [3][4] - It emphasizes the importance of understanding institutional trading behaviors, which can create opportunities for profit despite market volatility [4][10] Group 2 - The concept of "institutional shaking" is introduced, where institutions deliberately create market fluctuations to accumulate shares, which may appear as random volatility to retail investors [8][11] - The article suggests that recognizing these institutional behaviors can provide insights into market trends and help investors navigate through market turbulence [11][13] - It concludes that both the Federal Reserve's communication and institutional trading practices serve the purpose of managing market expectations, urging investors to look beyond superficial market movements [11][13]

- Model Name: Timing Radar Hexagon; Model Construction Idea: The model is based on a multi-dimensional timing framework, considering factors such as liquidity, economic conditions, valuation, capital flow, technical indicators, and crowding. It generates a comprehensive timing score between [-1,1][1][6] - Model Construction Process: The model selects 21 indicators from six dimensions (liquidity, economic conditions, valuation, capital flow, technical indicators, and crowding) and categorizes them into four major categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal." The comprehensive timing score is then generated within the range of [-1,1][1][6] - Model Evaluation: The model provides a neutral view with a current comprehensive score of -0.06, indicating a balanced market outlook[1][6] Model Backtest Results - Timing Radar Hexagon, Comprehensive Score: -0.06[1][6] - Liquidity Score: 0.25[1][8] - Economic Conditions Score: 0.25[1][8] - Valuation Score: -0.29[1][8] - Capital Flow Score: 0.00[2][8] - Technical Indicators Score: -0.50[2][8] - Crowding Score: 0.00[2][8] Factor Construction and Process 1. Factor Name: Monetary Direction Factor; Factor Construction Idea: This factor aims to determine the direction of current monetary policy by calculating the average change in central bank policy rates and short-term market rates over the past 90 days. If the factor is greater than 0, it indicates a loose monetary policy; if less than 0, it indicates a tight monetary policy[12] - Factor Construction Process: $$ \text{Monetary Direction Factor} = \text{Average Change in Policy Rates and Market Rates over 90 Days} $$ - Factor Evaluation: This week, the monetary direction factor is greater than 0, indicating a bullish signal with a score of 1[12] 2. Factor Name: Monetary Strength Factor; Factor Construction Idea: Based on the "interest rate corridor" concept, this factor measures the deviation of short-term market rates from policy rates. If the factor is less than -1.5 standard deviations, it indicates a loose environment for the next 120 trading days; if greater than 1.5 standard deviations, it indicates a tight environment[15] - Factor Construction Process: $$ \text{Monetary Strength Factor} = \frac{\text{DR007}}{\text{7-Year Reverse Repo Rate}} - 1 $$ - Factor Evaluation: This week, the monetary strength factor indicates a bearish signal with a score of -1[16] 3. Factor Name: Credit Direction Factor; Factor Construction Idea: This factor measures the tightness of credit transmission from commercial banks to the real economy using long-term loan indicators. If the factor shows an upward trend compared to three months ago, it indicates a bullish signal; otherwise, it indicates a bearish signal[18] - Factor Construction Process: $$ \text{Credit Direction Factor} = \text{Year-over-Year Change in Long-Term Loans over the Past 12 Months} $$ - Factor Evaluation: This week, the credit direction factor shows an upward trend, indicating a bullish signal with a score of 1[18] 4. Factor Name: Credit Strength Factor; Factor Construction Idea: This factor captures whether credit indicators significantly exceed or fall short of expectations. If the factor is greater than 1.5 standard deviations, it indicates a significantly above-expectation environment for the next 60 trading days; if less than -1.5 standard deviations, it indicates a significantly below-expectation environment[21] - Factor Construction Process: $$ \text{Credit Strength Factor} = \frac{\text{New RMB Loans - Median Expectation}}{\text{Standard Deviation of Expectation}} $$ - Factor Evaluation: This week, the credit strength factor shows no significant signal with a score of 0[21] 5. Factor Name: Growth Direction Factor; Factor Construction Idea: This factor is based on PMI data and measures the year-over-year change in the 12-month average PMI. If the factor shows an upward trend compared to three months ago, it indicates a bullish signal; otherwise, it indicates a bearish signal[22] - Factor Construction Process: $$ \text{Growth Direction Factor} = \text{Year-over-Year Change in 12-Month Average PMI} $$ - Factor Evaluation: This week, the growth direction factor shows an upward trend, indicating a bullish signal with a score of 1[22] 6. Factor Name: Growth Strength Factor; Factor Construction Idea: This factor captures whether economic growth indicators significantly exceed or fall short of expectations. If the factor is greater than 1.5 standard deviations, it indicates a significantly above-expectation environment for the next 60 trading days; if less than -1.5 standard deviations, it indicates a significantly below-expectation environment[26] - Factor Construction Process: $$ \text{Growth Strength Factor} = \frac{\text{PMI - Median Expectation}}{\text{Standard Deviation of Expectation}} $$ - Factor Evaluation: This week, the growth strength factor indicates a bearish signal with a score of -1[26] 7. Factor Name: Inflation Direction Factor; Factor Construction Idea: This factor measures the current inflation level and its impact on monetary policy. If the factor shows a downward trend compared to three months ago, it indicates a bullish signal; otherwise, it indicates a bearish signal[27] - Factor Construction Process: $$ \text{Inflation Direction Factor} = 0.5 \times \text{Smoothed CPI Year-over-Year} + 0.5 \times \text{Original PPI Year-over-Year} $$ - Factor Evaluation: This week, the inflation direction factor shows a downward trend, indicating a bullish signal with a score of 1[27] 8. Factor Name: Inflation Strength Factor; Factor Construction Idea: This factor captures whether inflation indicators significantly exceed or fall short of expectations. If the factor is less than -1.5, it indicates a significantly below-expectation environment for the next 60 trading days; if greater than 1.5 standard deviations, it indicates a significantly above-expectation environment[30] - Factor Construction Process: $$ \text{Inflation Strength Factor} = \frac{\text{CPI and PPI Expectation Difference}}{\text{Standard Deviation of Expectation}} $$ - Factor Evaluation: This week, the inflation strength factor shows no significant signal with a score of 0[30] 9. Factor Name: Shiller ERP; Factor Construction Idea: This factor calculates the Shiller PE based on the average inflation-adjusted earnings over the past six years and then calculates the Shiller ERP. The score is the z-score of the past six years[31] - Factor Construction Process: $$ \text{Shiller ERP} = \frac{1}{\text{Shiller PE}} - \text{10-Year Treasury Yield} $$ - Factor Evaluation: This week, the Shiller ERP shows an upward trend, with the score rising to 0.18[31] 10. Factor Name: PB; Factor Construction Idea: This factor calculates the z-score of the past six years for the PB ratio, standardized to ±1 after capping at 1.5 standard deviations[35] - Factor Construction Process: $$ \text{PB Score} = \text{z-score of PB over the past 6 years} $$ - Factor Evaluation: This week, the PB score rises to -0.39[35] 11. Factor Name: AIAE; Factor Construction Idea: This factor measures the aggregate investor allocation to equities, reflecting overall market risk appetite. The score is the z-score of the past six years[37] - Factor Construction Process: $$ \text{AIAE} = \frac{\text{Total Market Cap of CSI All Share}}{\text{Total Market Cap of CSI All Share + Total Debt}} $$ - Factor Evaluation: This week, the AIAE score decreases to -0.66[37] 12. Factor Name: Margin Trading Increment; Factor Construction Idea: This factor measures the market sentiment and leverage by calculating the average increment of margin trading over the past 120 days compared to the past 240 days. If the short-term increment is greater than the long-term increment, it indicates a bullish signal; otherwise, it indicates a bearish signal[40] - Factor Construction Process: $$ \text{Margin Trading Increment} = \text{Average Increment of Margin Trading over 120 Days - Average Increment over 240 Days} $$ - Factor Evaluation: This week, the margin trading increment indicates a bearish signal with a score of -1[40] 13. Factor Name: Trading Volume Trend; Factor Construction Idea: This factor measures the market trading activity by calculating the moving average distance of logarithmic trading volume. If the maximum distance of short-term moving averages is greater than the long-term moving averages, it indicates a bullish signal; otherwise, it indicates a bearish signal[43] - Factor Construction Process: $$ \text{Trading Volume Trend} = \frac{\text{ma120}}{\text{ma240}} - 1 $$ - Factor Evaluation: This week, the trading volume trend indicates a bearish signal