- The sentiment timing model indicates that the market trend has been broken, issuing a negative signal[1][2][6] - The liquidity shock indicator for the CSI 300 index was 0.84 on Friday, lower than the previous week's 1.57, indicating that current market liquidity is 0.84 standard deviations above the average level of the past year[2][7] - The PUT-CALL ratio of the SSE 50ETF options trading volume decreased to 0.72 on Friday from the previous week's 1.07, indicating an increase in short-term optimism among investors regarding the SSE 50ETF[2][7] - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.19% and 1.66%, respectively, indicating a decrease in trading activity compared to previous periods[2][7] - The SAR indicator shows that the Wind All A Index broke below the reversal indicator on October 17[2][11] - The moving average strength index calculated from the Wind secondary industry indices scored 197, which is at the 71.2% percentile since 2023[2][11] - The sentiment model score is 2 out of 5, the trend model signal is negative, and the weighted model signal is negative[2][11][14] - The small-cap factor congestion level increased to 0.41, the low-valuation factor congestion level was -0.26, the high-profitability factor congestion level was -0.15, and the high-growth factor congestion level was 0.35[4][15][16][18]

Group 1: Market Sentiment Model Insights - The market sentiment score has slightly increased to 2.2 as of October 24, compared to 1.9 the previous week, indicating a partial recovery in market sentiment [9][12]. - The overall market sentiment is showing increased differentiation, with a decline in price-volume consistency, suggesting reduced capital activity and a cautious risk appetite among investors [12][19]. - The total trading volume for the entire A-share market has significantly decreased compared to the previous week, with a peak trading volume of 1,991.617 billion RMB on October 24 [19][22]. Group 2: Industry Trends and Insights - As of October 24, 2025, industries such as banking, oil and petrochemicals, transportation, public utilities, and construction decoration have shown an upward trend in short-term scores, with coal being the strongest at a score of 93.22 [40][41]. - The model indicates that the banking sector's short-term score has rapidly increased, maintaining a favorable signal for both value and large-cap styles [40][41]. - The analysis of industry crowding shows that sectors like electronics and power equipment have high returns but also high capital crowding, which may pose volatility risks [43][44]. Group 3: Technical Indicators and Market Dynamics - The Relative Strength Index (RSI) has shown a decline, indicating weak upward momentum and reduced buying interest in the market [32][35]. - The main capital inflow has improved, suggesting an increase in institutional buying power and a gradual warming of market sentiment [35][37]. - The model maintains a signal indicating that large-cap and value styles are currently dominant, although the strength of this signal may weaken in the future [52][53].

Core Viewpoints - The report indicates that the market is currently in a consolidation phase, with a need for confirmation signals to return to an upward trend [2][4][9] - The macroeconomic environment remains uncertain due to ongoing US-China trade tensions and upcoming Federal Reserve meetings, which may suppress market risk appetite [2][4][10] - The overall market (WIND All A Index) experienced a weekly increase of 3.47%, with small-cap stocks (CSI 2000) rising by 3.75% and mid-cap stocks (CSI 500) by 3.46% [10][11] Market Timing System - The distance between the 20-day moving average (MA) and the 120-day MA has narrowed, with the 20-day MA at 6264 points and the closing price at 6320 points, indicating a need for the 5-day MA to rise above the 20-day MA for confirmation [2][11][18] - The current market is characterized by a consolidation pattern, with risk preference being a key observation indicator [2][4][11] Industry Configuration - The industry trend configuration model shows that storage chips and construction machinery are still in an upward trend, while sectors benefiting from policy support include real estate and photovoltaics [3][12][18] - The TWO BETA model continues to recommend the technology sector, focusing on domestic computing power and gaming [3][12][18] Valuation Indicators - The overall PE ratio of the WIND All A Index is around the 85th percentile, while the PB ratio is at the 50th percentile, indicating a moderate valuation level [3][12] - Based on short-term trend assessments, the report suggests maintaining a 60% allocation in absolute return products based on the WIND All A Index [3][12]

Quantitative Models and Construction Methods - **Model Name**: Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy **Model Construction Idea**: The model explores the impact of China's macroeconomic factors on the overall performance and trends of Hong Kong-listed consumer companies, using dynamic macro event factors to construct a timing strategy framework [2][3][20] **Model Construction Process**: 1. **Macro Data Selection**: Select 20+ macroeconomic indicators across four dimensions: economy, inflation, currency, and credit, such as PMI, PPI, M1, etc [21][23] 2. **Data Preprocessing**: - Align data frequency to monthly frequency by either taking the last trading day of the month or calculating the monthly average for daily data - Fill missing values using the median of the first-order difference of the past 12 months added to the previous value $ X_{t}=X_{t-1}+Median_{diff12} $ [27] - Apply filtering using one-sided HP filter to avoid future data leakage $ \hat{t}_{t|t,\lambda}=\sum\nolimits_{s=1}^{t}\omega_{t|t,s,\lambda}\cdot y_{s}=W_{t|t,\lambda}(L)\cdot y_{t} $ [28] - Derive factors using transformations such as year-on-year, month-on-month, and moving averages [29] 3. **Macro Event Factor Construction**: - Determine event breakthrough direction by calculating the correlation between data and next-period asset returns - Identify leading or lagging relationships by deriving lagged event factors (0-4 periods) and selecting the most suitable lag period - Generate event factors using three types: data breaking through moving average, data breaking through median, and data moving in the same direction, with different parameters (e.g., moving average length: 2-12, rolling window: 2-12, same direction period: 1-5) [30][32] 4. **Event Factor Evaluation and Screening**: - Use two metrics: win rate of returns and volatility-adjusted returns during opening positions - Initial screening criteria: t-test significance at 95% confidence level, win rate >55%, occurrence frequency > rolling window period/6 [31][32] 5. **Combining Event Factors**: Select the highest win rate event factor as the base factor, then combine it with the second-highest win rate factor with a correlation <0.85. If the combined factor improves the win rate, it is selected; otherwise, the base factor is used [33] 6. **Dynamic Exclusion**: If no event factor passes the screening, the macro indicator is marked as empty for the period and excluded from scoring [33] 7. **Optimal Rolling Window Determination**: Test rolling windows of 48, 60, 72, 84, and 96 months to find the most suitable parameter for each macro indicator based on volatility-adjusted returns during opening positions [33] 8. **Final Macro Indicators**: Five macro factors were selected based on their performance in the sample period: - PMI: Raw Material Prices (96-month rolling window) - US-China 10Y Bond Spread (72-month rolling window) - Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY (48-month rolling window) - M1: YoY (48-month rolling window) - New Social Financing: Rolling 12M Sum: YoY (96-month rolling window) [34][35] 9. **Timing Strategy Construction**: - If >2/3 of factors signal bullishness, the category factor signal is marked as 1 - If <1/3 of factors signal bullishness, the category factor signal is marked as 0 - If the proportion of bullish signals falls between these ranges, the category factor is marked with the specific proportion - The score of each category factor is used as the timing position signal for the period [3][35] **Model Evaluation**: The strategy effectively captures systematic opportunities and avoids systematic risks, demonstrating superior performance compared to the benchmark in terms of annualized returns, maximum drawdown, Sharpe ratio, and return-drawdown ratio [2][3][20] --- Model Backtesting Results - **Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy** - **Annualized Return**: 10.44% - **Annualized Volatility**: 18.47% - **Maximum Drawdown**: -29.72% - **Sharpe Ratio**: 0.59 - **Return-Drawdown Ratio**: 0.35 [2][11][22] --- Quantitative Factors and Construction Methods - **Factor Name**: PMI: Raw Material Prices **Factor Construction Idea**: Use raw data to capture macroeconomic trends affecting asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] - **Factor Name**: US-China 10Y Bond Spread **Factor Construction Idea**: Reflect the impact of interest rate differentials on asset returns [35] **Factor Construction Process**: Utilize raw data with a 72-month rolling window [35] - **Factor Name**: Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY **Factor Construction Idea**: Measure credit expansion and its influence on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: M1: YoY **Factor Construction Idea**: Capture monetary supply changes and their impact on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: New Social Financing: Rolling 12M Sum: YoY **Factor Construction Idea**: Reflect credit growth and its effect on asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] **Factor Evaluation**: The selected factors demonstrated strong performance in the sample period, with high win rates and volatility-adjusted returns during opening positions [34][35] --- Factor Backtesting Results - **PMI: Raw Material Prices** - **Rolling Window**: 96 months [35] - **US-China 10Y Bond Spread** - **Rolling Window**: 72 months [35] - **Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY** - **Rolling Window**: 48 months [35] - **M1: YoY** - **Rolling Window**: 48 months [35] - **New Social Financing: Rolling 12M Sum: YoY** - **Rolling Window**: 96 months [35]

Group 1: Market Sentiment Model Insights - The market sentiment score slightly rebounded to 1.9 as of October 17, up from 1.75 the previous week, indicating a neutral sentiment perspective [10][4] - Multiple indicators have turned negative this week, with a rapid decline in price-volume consistency, suggesting a significant drop in the degree of price-volume matching [13][16] - The total trading volume of the A-share market decreased significantly compared to the previous week, indicating a decline in market activity, with the highest trading volume recorded at 25,965.85 billion RMB on October 14 [16][4] Group 2: Sector Performance and Trends - The banking, coal, steel, public utilities, and environmental protection sectors have shown an upward trend in short-term scores, indicating strong short-term trends [37][38] - The short-term score for non-ferrous metals is currently the highest at 89.83, reflecting strong short-term performance in this sector [37][38] - The model indicates that sectors with high trading congestion, such as banking and coal, are experiencing high volatility risks due to valuation and sentiment adjustments [47][42] Group 3: Investment Style and Strategy - The model suggests a preference for large-cap stocks, with signals indicating a shift towards large-cap style dominance, although the strength of this signal is weak [52][51] - The model maintains a value style preference, with increasing strength in the signal, suggesting that value stocks may outperform in the near term [52][51] - The relative strength index (RSI) indicates a shift towards caution in market sentiment, with a decrease in buying momentum and a potential for short-term adjustments [30][33]

Market Performance - The Sci-Tech 50 Index decreased by 6.46% over the last five trading days, while the ChiNext Index fell by 5.71%. In contrast, the large-cap value stocks rose by 2.08%, and large-cap growth stocks declined by 3.90%. The Shanghai Stock Exchange 50 Index dropped by 0.24%, and the small-cap stocks represented by the CSI 2000 fell by 4.69%. The banking and coal sectors performed well, while electronics and media lagged behind [1]. Risk Premium and Valuation Levels - As of October 17, 2025, the static PE of the CSI All Share Index indicates a risk premium of 2.97%, calculated as the inverse of the PE minus the yield of ten-year government bonds. The two standard deviation boundary is set at 4.75%. The valuation levels show that the CSI All Share Index's PETTM is at the 77th percentile, with the Shanghai 50 and CSI 300 at 73% and 70%, respectively. The ChiNext Index is close to the 47th percentile, while the CSI 500 and CSI 1000 are at 60% and 54% [1]. Fund Flows and Trading Activity - In the last five trading days, ETF inflows amounted to 68.6 billion yuan, and the margin trading balance increased by approximately 70.5 billion yuan. The average daily trading volume across both markets was 2.1746 trillion yuan [2]. Thematic Indexes - The latest thematic allocations focus on low-carbon economy, new energy, and semiconductor materials. Specific indices include the CSI Mainland Low-Carbon Economy Theme Index, ChiNext New Energy Index, and the Shanghai Stock Exchange Sci-Tech Board Semiconductor Materials Equipment Theme Index [2][3]. Long-Term Market Sentiment - The report includes observations on the proportion of stocks above the 200-day moving average, indicating long-term market sentiment trends [13]. Financing Balance - The report tracks the financing balance, which reflects the overall leverage and risk appetite in the market [16].

Core Viewpoint - The recent instability in the Sino-US trade environment has led to a valuation correction in certain stocks, resulting in a rise in market risk aversion. The market is expected to maintain a volatile trend in the short term [1]. Market Overview - The market is anticipated to remain volatile in the short term. The liquidity shock indicator for the CSI 300 index was 1.57, higher than the previous week's 1.36, indicating current market liquidity is 1.57 times the average level over the past year [2]. - The put-call ratio for the SSE 50 ETF options increased to 1.07 from 0.85, reflecting heightened caution among investors regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 1.42% and 1.93%, respectively, consistent with the trading activity levels since 2005 [2]. - The RMB exchange rate fluctuated last week, with onshore and offshore rates showing weekly changes of -0.05% and 0.29%, respectively [2]. - In September, China's CPI decreased by 0.3% year-on-year, slightly better than the previous -0.4%, but worse than the consensus expectation of -0.15%. The PPI was -2.3%, also better than the previous -2.9% but below the expected -2.4% [2]. - New RMB loans in September amounted to 1.29 trillion yuan, lower than the expected 1.39 trillion yuan but higher than the previous 590 billion yuan. M2 growth was 8.4%, below both the expected 8.51% and the previous 8.8% [2]. Technical Analysis - The SAR indicator for the Wind All A index broke downwards on October 17, indicating a bearish trend [2]. - The market score based on the moving average strength index is currently at 141, which is at the 49.9% percentile for 2023 [2]. - The sentiment model score is 2 out of 5, indicating moderate market sentiment, while the trend model signal is positive and the weighted model signal is negative [2]. Performance Summary - For the week of October 13-17, the SSE 50 index fell by 0.24%, the CSI 300 index dropped by 2.22%, the CSI 500 index decreased by 5.17%, and the ChiNext index declined by 5.71% [3]. - The overall market PE (TTM) stands at 22.0 times, which is at the 74.0% percentile since 2005 [3]. Industry Insights - The industry crowding levels are relatively high in sectors such as non-ferrous metals, comprehensive, power equipment, telecommunications, and electronics. The crowding levels in the steel and public utilities sectors have increased significantly [4].

- The report introduces a timing system model based on the distance between the 120-day long-term moving average and the 20-day short-term moving average of the WIND All A Index. The model's construction involves calculating the difference between the two moving averages, with the short-term average currently above the long-term average. The formula for the distance is expressed as: $ Distance = \frac{Short\ Term\ MA - Long\ Term\ MA}{Long\ Term\ MA} $ where Short Term MA represents the 20-day moving average and Long Term MA represents the 120-day moving average. The current distance is 12.26%, down from 12.89% last week, and remains significantly above the threshold of 3%[2][11][17] - The report evaluates the timing system model as effective in identifying market trends, noting that the recent shift from an upward trend to a volatile trend is captured by the model. The model's core observation focuses on changes in risk appetite during volatile periods[2][11][17] - The report highlights the "TWO BETA" model for industry allocation, which recommends focusing on technology sectors, including domestic computing power and the Hang Seng Internet sector. The model emphasizes policy-driven sectors such as photovoltaics and chemicals, alongside dividend assets[3][12][17] - The report suggests using a position management model to adjust stock allocation based on the WIND All A Index. The model recommends a 60% allocation for absolute return products, considering the index's PE at the 85th percentile and PB at the 50th percentile, indicating a medium valuation level[3][12][17] - The timing system model's backtesting results show that the current WIND All A Index trend line is at 6264 points, while the closing price is 6108 points, significantly below the trend line. The market's profitability effect indicator has turned negative for the first time in six months, signaling a potential end to the upward trend[2][11][17]

Core Insights - The article highlights the investment strategies of Chen Bo, a fund manager at Shangyin Fund, who successfully manages both dividend and technology-focused funds, demonstrating a unique ability to navigate different asset classes [1][2]. Group 1: Investment Strategy - Chen Bo employs a "barbell strategy" that combines dividend and technology assets, allowing investors to switch between aggressive and defensive positions based on market conditions [2][17]. - The strategy has performed well during market fluctuations in 2023 and 2024, showcasing its adaptability [2]. - Key investment principles include "small but beautiful Alpha," high Return on Equity (ROE), and a focus on dynamic portfolio rebalancing to optimize risk-reward ratios [3][11][26]. Group 2: Performance Metrics - Chen Bo's fund, Shangyin Future Life Flexible Allocation A, has received a dual five-star rating for its performance over three and five years, ranking in the top 10% of its peers [1]. - The fund's performance metrics include a three-year ranking of 101 out of 1718 and a five-year ranking of 249 out of 1488 [1]. Group 3: Investment Philosophy - The investment philosophy emphasizes the importance of high ROE as a criterion for selecting quality companies, with a long-term view on maintaining above-average returns [3][19]. - Chen Bo believes that both dividend and technology assets benefit from a low-interest-rate environment, which supports their growth potential [2][18]. - The focus on identifying companies with clean balance sheets and high growth potential is central to the investment approach [11][12]. Group 4: Market Outlook - Chen Bo expresses optimism about the Chinese equity market, anticipating a systemic revaluation of risk assets, which could lead to significant wealth transfer as market conditions improve [27]. - The article suggests that various asset styles, including both dividend and growth stocks, will perform well in a true bull market [27].

Group 1 - Market sentiment indicators showed a slight decline, with the sentiment score at 1.75 as of October 10, down from 1.85 on September 26, indicating a bearish outlook [8][11] - The trading volume for the entire A-share market increased slightly compared to the previous week, with a peak trading volume of 26,718.18 billion RMB on October 9, indicating improved market activity [14][16] - The financing balance ratio continued to rise, reflecting an increase in market leverage sentiment and improved trading atmosphere among investors [24][26] Group 2 - The model indicates a preference for small-cap value style, with a weak signal strength due to a slight decline in the 5-day RSI relative to the 20-day RSI, suggesting further observation is needed [30][41] - The short-term trend scores for industries such as banks, steel, public utilities, and construction decoration have shown upward trends, with non-ferrous metals currently having the highest short-term score of 98.31 [30][32] - High trading congestion in sectors like non-ferrous metals and coal, alongside lower price increases in sectors like automobiles and electronics, suggests potential volatility risks and opportunities for gradual allocation in low-congestion sectors like pharmaceuticals and beauty care [37][36]