Core Viewpoint - The GuoXin Quantitative Team has made significant research contributions over the past year, focusing on various investment strategies and market trends, showcasing their effectiveness and potential for investment opportunities [1]. Team Overview - The GuoXin Quantitative Team consists of 7 members specializing in areas such as active quantitative stock selection, index enhancement, factor research, FOF investment, fund research, industry rotation, asset allocation, Hong Kong stock investment, and CTA strategies [1]. Research Highlights - The team has produced a selection of research reports that cover a wide range of investment strategies, including: - Active quantitative stock selection strategies - Factor-based stock selection and index enhancement strategies - Market trend analysis and research on hot sectors - FOF and fund research series [1][8][10]. Performance Metrics - The "Super Expectation Selected Portfolio" has achieved an annualized return of 36.04% since 2010, outperforming the CSI 500 Index by 32.90% [11][12]. - The "Growth Steady Portfolio" has maintained an annualized return of 41.15% since 2012, exceeding the CSI 500 Index by 34.84% [14][16]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" has delivered an annualized return of 21.78%, consistently ranking in the top 30% of active equity funds since 2018 [19][21]. Strategy Insights - The "Small Cap Selected Portfolio" has generated an annualized return of 39.22% since 2014, outperforming the CSI 2000 Index by 28.66% [25][27]. - The "Stable Selected Portfolio" has achieved an annualized return of 26.18% since 2012, with a lower maximum drawdown compared to the CSI Dividend Total Return Index [30][32]. - The "Multi-Strategy Enhanced Portfolio" has recorded an annualized return of 23.43% since 2013, with a significant information ratio of 2.60 [34]. Sector Rotation Strategies - The "Key Moment Leading Sheep Strategy" has identified strong momentum effects in the A-share market, achieving an annualized return of 25.29% since 2013, outperforming the CSI All Index by 19.65% [39][40].

Group 1 - The core viewpoint indicates that the A-share market shows a low probability of rising in the five trading days before the National Day holiday, but the last trading day before the holiday has a 70% probability of an increase, while the market tends to rise after the holiday [1][6] - Historical data from 2015 to 2024 shows that the Shanghai Composite Index has a 70% probability of rising on the first trading day after the holiday and a 60% probability of rising in the following five trading days [2][6] - The leading sectors in the A-share market before and after the National Day holiday exhibit significant rotation, covering various fields such as consumption, pharmaceuticals, and technology [6][7] Group 2 - The leading sectors for the five trading days before the holiday from 2020 to 2024 include Food & Beverage, Social Services, and Defense & Military, while the sectors leading after the holiday include Electronics, Automotive, and Pharmaceuticals [4][6] - The market is expected to maintain a volatile pattern before the holiday, influenced by factors such as the Federal Reserve's interest rate decisions and potential profit-taking by investors [6][7] - The financing trend typically shows a pattern of "contraction before the holiday and explosion after," indicating a shift in risk appetite post-holiday [7]

Quantitative Models and Construction Methods 1. Model Name: Convertible Bond Random Forest Strategy - **Model Construction Idea**: Utilizes the Random Forest machine learning method to identify convertible bonds with potential for excess returns by leveraging decision trees[16][17] - **Model Construction Process**: 1. Data preprocessing and feature engineering to prepare convertible bond datasets 2. Training a Random Forest model with historical data to identify patterns of excess return potential 3. Selecting bonds with the highest predicted scores for portfolio construction 4. Weekly rebalancing of the portfolio based on updated predictions[17] - **Model Evaluation**: Demonstrated strong performance in generating excess returns, indicating high predictive accuracy[16] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: Combines macro, meso, micro, and derivative signals to create a four-dimensional non-linear timing model for market positioning[18][19] - **Model Construction Process**: 1. Macro signals: Derived from liquidity, interest rates, credit, economic growth, and exchange rates 2. Meso signals: Based on industry-level business cycle indicators 3. Micro signals: Captures structural risks using valuation, risk premium, volatility, and liquidity factors 4. Derivative signals: Generated from the basis of stock index futures 5. Aggregation: Signals are synthesized into a composite timing signal[18][19][24] - **Model Evaluation**: Effective in identifying market trends and providing actionable signals, with the latest signal indicating a bullish stance[19][24] 3. Model Name: Industry Rotation Strategy 2.0 - **Model Construction Idea**: Constructs an industry rotation strategy based on economic quadrants and multi-dimensional industry style factors[69] - **Model Construction Process**: 1. Define economic quadrants using corporate earnings and credit conditions 2. Develop industry style factors such as expected business climate, earnings surprises, momentum, valuation bubbles, and inflation beta 3. Test factor effectiveness within each quadrant 4. Allocate to high-expected-return industries based on factor signals[69][71] - **Model Evaluation**: Demonstrates strong adaptability to the A-share market, with annualized excess returns of 9.44% (non-exclusion version) and 10.14% (double-exclusion version)[71] 4. Model Name: Genetic Programming Index Enhancement Models - **Model Construction Idea**: Uses genetic programming to discover and optimize stock selection factors for index enhancement strategies[88][93][97] - **Model Construction Process**: 1. Stock pools: Defined for CSI 300, CSI 500, CSI 1000, and CSI All Share indices 2. Training: Genetic programming generates initial factor populations and iteratively evolves them through multiple generations 3. Factor selection: Top-performing factors are combined into a composite score 4. Portfolio construction: Selects top 10% of stocks within each industry based on scores, with weekly rebalancing[88][93][97][102] - **Model Evaluation**: - CSI 300: Annualized excess return of 17.91%, Sharpe ratio of 1.05[91] - CSI 500: Annualized excess return of 11.78%, Sharpe ratio of 0.85[95] - CSI 1000: Annualized excess return of 17.97%, Sharpe ratio of 0.93[98] - CSI All Share: Annualized excess return of 24.84%, Sharpe ratio of 1.33[103] --- Model Backtest Results 1. Convertible Bond Random Forest Strategy - Weekly excess return: 0.64%[16] 2. Multi-Dimensional Timing Model - Latest composite signal: Bullish (1)[19][24] 3. Industry Rotation Strategy 2.0 - Annualized excess return (non-exclusion version): 9.44% - Annualized excess return (double-exclusion version): 10.14%[71] 4. Genetic Programming Index Enhancement Models - CSI 300: - Annualized excess return: 17.91% - Sharpe ratio: 1.05[91] - CSI 500: - Annualized excess return: 11.78% - Sharpe ratio: 0.85[95] - CSI 1000: - Annualized excess return: 17.97% - Sharpe ratio: 0.93[98] - CSI All Share: - Annualized excess return: 24.84% - Sharpe ratio: 1.33[103] --- Quantitative Factors and Construction Methods 1. Factor Name: Industry Business Climate Index 2.0 - **Factor Construction Idea**: Tracks industry fundamentals by analyzing revenue, pricing, and cost dynamics[27] - **Factor Construction Process**: 1. Analyze industry revenue and cost structures 2. Calculate daily market-cap-weighted industry indices 3. Aggregate indices into a composite business climate index[27][30] - **Factor Evaluation**: Demonstrates predictive power for A-share earnings expansion cycles[28] 2. Factor Name: Barra CNE6 Style Factors - **Factor Construction Idea**: Evaluates market performance using 9 primary and 20 secondary style factors, including size, volatility, momentum, quality, value, and growth[45] - **Factor Construction Process**: 1. Calculate factor returns for each style factor 2. Aggregate factor performance to assess market trends[45][46] - **Factor Evaluation**: Size factor performed well during the week, while volatility factor underperformed[46] 3. Factor Name: Industry Rotation Factors - **Factor Construction Idea**: Captures industry rotation dynamics using factors like expected business climate, earnings surprises, momentum, and valuation bubbles[69] - **Factor Construction Process**: 1. Define and calculate individual factors 2. Test factor effectiveness within economic quadrants 3. Combine factors for industry allocation[69] - **Factor Evaluation**: Demonstrates strong historical performance, with factors like expected business climate and momentum showing significant returns[57][59] --- Factor Backtest Results 1. Industry Business Climate Index 2.0 - Current value: 0.913 - Excluding financials: 1.288[28] 2. Barra CNE6 Style Factors - Size factor: Strong performance during the week[46] 3. Industry Rotation Factors - Historical annualized returns: - Expected business climate: 0.40% - Momentum: -0.95% - Valuation beta: 2.37%[57]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Industry Rotation Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries through a diffusion index[26][27] - **Model Construction Process**: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Select top industries for allocation based on their rankings 4. Adjust the portfolio monthly or weekly based on updated diffusion index rankings[26][27] - **Model Evaluation**: The model has shown stable performance in certain years (e.g., 2022 with an annual excess return of 6.12%) but struggled during market reversals or concentrated market themes, such as in 2024 and 2025[26][33] 2. Model Name: GRU Factor Industry Rotation Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[38] - **Model Construction Process**: 1. Input high-frequency volume and price data into the GRU network 2. Train the GRU model on historical data to identify patterns in industry rotation 3. Generate factor scores for industries based on the GRU model's output 4. Rank industries by their GRU factor scores and allocate to top-ranked industries[38][34] - **Model Evaluation**: The model performs well in short cycles but struggles in long cycles or extreme market conditions. It has shown difficulty in capturing excess returns in concentrated market themes during 2025[33][38] --- Model Backtesting Results 1. Diffusion Index Industry Rotation Model - **Weekly Average Return**: -1.74%[30] - **Excess Return (Weekly)**: -1.41%[30] - **Excess Return (September 2025)**: -1.88%[30] - **Excess Return (2025 YTD)**: 2.76%[25][30] 2. GRU Factor Industry Rotation Model - **Weekly Average Return**: -0.72%[36] - **Excess Return (Weekly)**: -0.38%[36] - **Excess Return (September 2025)**: -0.10%[36] - **Excess Return (2025 YTD)**: -7.78%[33][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of price momentum across industries to identify upward trends[26][27] - **Factor Construction Process**: 1. Calculate the proportion of stocks in an industry with positive price momentum 2. Aggregate these proportions to derive the diffusion index for the industry 3. Rank industries based on their diffusion index values[27][28] - **Factor Evaluation**: Effective in capturing upward trends but vulnerable to reversals and underperformance in counter-trend markets[26][33] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and generate predictive scores for industry rotation[38] - **Factor Construction Process**: 1. Input high-frequency trading data into the GRU network 2. Train the model to recognize patterns in industry rotation 3. Output factor scores for industries based on the model's predictions[38][34] - **Factor Evaluation**: Strong in short-term predictions but less effective in long-term or extreme market conditions[33][38] --- Factor Backtesting Results 1. Diffusion Index - **Top Industries (Weekly)**: Non-ferrous Metals (0.978), Banking (0.968), Communication (0.946), Electronics (0.877), Automotive (0.874), Retail (0.873)[27] - **Bottom Industries (Weekly)**: Food & Beverage (0.354), Real Estate (0.46), Coal (0.487), Transportation (0.543), Construction (0.574), Building Materials (0.618)[27] 2. GRU Factor - **Top Industries (Weekly)**: Non-ferrous Metals (7.4), Petrochemicals (5.38), Coal (4.17), Steel (4.15), Building Materials (3.46), Non-banking Financials (3.08)[34] - **Bottom Industries (Weekly)**: Comprehensive Finance (-19.42), Utilities (-13.41), Electronics (-13.18), Pharmaceuticals (-11.14), Automotive (-10.07), Consumer Services (-10.04)[34]

Market Overview - The market experienced fluctuations last week, with the Shanghai Composite Index falling by 1.30%, while sectors like power equipment, electronics, and communications continued to lead in gains, contrasting with the underperforming banking, non-banking, and food and beverage sectors [1] Broker Strategies - Guotai Junan Securities believes that the recent market adjustment presents an opportunity, asserting that the Chinese stock market will not stop here. They highlight the positive implications of the recent US-China talks and the potential for capital market reforms to accelerate, suggesting that the A/H share indices may reach new highs [2] - Guojin Securities indicates that a bull market is in the making, with a focus on cyclical opportunities in manufacturing and a shift from technology-driven growth to export-oriented growth as liquidity constraints ease [2] - Zheshang Securities anticipates continued consolidation in the Shanghai Composite Index, recommending a cautious approach and suggesting adjustments in sector allocations, particularly reducing exposure to technology and media while increasing positions in real estate and infrastructure [3] - Everbright Securities expects the A-share market to maintain a volatile pattern leading up to the National Day holiday, with a focus on structural balance amid potential profit-taking [4] - China Merchants Securities notes a historical pattern of financing trends around the National Day holiday, suggesting a potential rebound in market sentiment post-holiday, with a focus on sectors like solid-state batteries and AI [5] - Industrial Securities emphasizes a rotational investment strategy to navigate market volatility, advocating for a diversified approach across multiple sectors [6][7] - CITIC Securities highlights the clarity in market trading themes following the Fed's interest rate cut, with a focus on AI and domestic demand recovery as key drivers [8] - Huaxia Securities maintains a positive long-term outlook despite short-term fluctuations, emphasizing the importance of structural support from policies aimed at stabilizing the stock market [9] - Galaxy Securities recommends four main investment themes in the construction sector during the 14th Five-Year Plan period, focusing on urban renewal and digital transformation in construction [11]

Group 1 - The multi-dimensional timing model by Huatai Jin Gong has achieved a cumulative return of 40.77% since the beginning of the year, indicating a bullish outlook for the A-share market despite relatively high valuations [1][2] - The model predicts that the strongest performing sectors for the upcoming trading week will be precious metals, liquor, food, steel, and banking, reflecting a balanced allocation across consumption, cyclical, and financial sectors [1] - The technology sector remains active, benefiting from domestic "AI+" policies, while the US stock market's positive performance, particularly the Nasdaq's 2.21% increase, has boosted confidence in the A-share market [1][2] Group 2 - The ChiNext 50 ETF rose by 2.84% last week, and the Sci-Tech Innovation ETF increased by 2.47%, driven by expectations of Federal Reserve rate cuts and domestic policy support [2] - The automotive ETF emerged as a leader with a 4.26% increase, supported by a growth plan for the automotive sector released by eight departments, enhancing sales expectations for new energy vehicles [2] - The multi-dimensional timing model indicates that the A-share market remains in a bullish window, with a year-to-date increase of 26.98% for the Wind All A index, outperforming the model's 40.77% return [2][3] Group 3 - The timing model signal briefly switched to bearish on September 17 but quickly returned to bullish, influenced by the member holding ratio signal, which indicates strong market sentiment [3] - The industry rotation model shows optimism for specific sectors, with a cumulative return of 36.07% this year, surpassing the industry equal-weight benchmark by 17.01 percentage points [3] - The absolute return ETF simulation portfolio has increased by 7.34% since the beginning of the year, maintaining a positive overall performance despite a slight decline of 0.10% last week [3]

Group 1 - The report indicates that the Federal Reserve's recent interest rate cut of 25 basis points aligns with market expectations, but the overall hawkish tone from Powell has dampened market risk appetite [3][12][13] - Economic data from August shows a significant slowdown, with domestic demand weakening and GDP growth for Q3 projected at around 4.9%, prompting expectations for policy support to stabilize the economy [4][15][19] - The report emphasizes the importance of monitoring potential new policies aimed at boosting consumption and the real estate sector, as the current economic environment necessitates additional support [4][15][21] Group 2 - The report highlights a strong focus on the AI industry as a key investment theme, alongside sectors with robust economic support such as rare earths, precious metals, military, and financial IT [5][7][27] - It identifies that in a rising industry rotation intensity, growth style is likely to continue its upward trend for at least one month after reaching a peak, while financial style may weaken and cyclical style may strengthen [5][27][28] - The analysis of past growth cycles indicates that after peaks in industry rotation intensity, strong growth sectors tend to maintain their leading positions, suggesting a favorable outlook for AI and related industries [5][27][28]

Core Insights - Since July 2023, after a round of technology-driven market performance, the speed of industry rotation has decreased, yet it remains at a historical median level over the past decade. The proprietary macro-friendly indicator system indicates a correlation of 0.7 with industry rotation speed, suggesting strong explanatory power. For Q4 2025, the macro drivers of industry rotation are expected to slightly increase, with rotation speed projected to be lower than in 2024 but higher than in 2021, indicating a potential for moderate levels of rotation. A relatively balanced allocation strategy may be a better choice under the expectation of continued structural market conditions [1][4]. Group 1 - The current industry rotation speed is at a historical median level, with the indicator based on the rolling cumulative excess returns of 30 primary industries relative to the Wind All A index. Since July 2023, following a technology-led market rally, the rotation speed has declined, with market consensus expectations gradually strengthening. The current indicator is near the 50th percentile, indicating a moderate level of industry rotation [2][11]. - The macro-friendly indicator system has been developed to construct the industry rotation macro driver indicator. This indicator is defined as the difference between the Chinese financial cycle friendliness and inventory cycle friendliness, adjusted by the US macro friendliness. The correlation between the Chinese financial-inventory cycle and industry rotation speed exceeds 0.6, while the US macro friendliness has a correlation close to -0.6. The combined industry rotation macro driver shows a correlation of 0.7 with industry rotation speed, which is at a historical median as of August 2025 [3][18]. Group 2 - For Q4 2025, the macro drivers of industry rotation are expected to slightly increase, with both the Chinese financial cycle and inventory cycle friendliness anticipated to rise to varying degrees. The US macro friendliness is also expected to increase slightly due to a more favorable financial cycle and a recovery in the inventory cycle. Overall, the macro drivers of industry rotation are projected to experience slight fluctuations, with rotation speed expected to be lower than in 2024 but higher than in 2021, suggesting a moderate level of rotation. A relatively balanced allocation strategy may be more favorable in the context of ongoing structural market expectations [4][21][22].

Quantitative Factors and Construction Methods 1. Factor Name: Market Style Factor - **Construction Idea**: This factor tracks the market's preference for different styles, such as large-cap vs. small-cap and value vs. growth, as well as the volatility of these styles[13][15] - **Construction Process**: - **Size Style**: Measure the relative performance of small-cap stocks against large-cap stocks - **Value-Growth Style**: Measure the relative performance of growth stocks against value stocks - **Volatility**: Calculate the changes in the above style preferences over time to assess their stability[13][15] - **Evaluation**: The factor effectively captures the market's shifting preferences and provides insights into style rotations[13][15] 2. Factor Name: Market Structure Factor - **Construction Idea**: This factor evaluates the dispersion and rotation within industry indices, as well as the concentration of trading activity[13][15] - **Construction Process**: - **Industry Dispersion**: Calculate the excess return dispersion across industry indices - **Industry Rotation**: Measure the speed of rotation among industries - **Trading Concentration**: Assess the proportion of trading volume concentrated in the top 100 stocks and the top 5 industries[13][15] - **Evaluation**: The factor provides a comprehensive view of market dynamics, including sectoral shifts and trading behavior[13][15] 3. Factor Name: Market Activity Factor - **Construction Idea**: This factor tracks the overall market activity through volatility and turnover rates[14][15] - **Construction Process**: - **Volatility**: Measure the index-level volatility over the observation period - **Turnover Rate**: Calculate the turnover rate of the market index to gauge trading activity[14][15] - **Evaluation**: The factor is useful for understanding the market's risk appetite and liquidity conditions[14][15] 4. Factor Name: Commodity Market Factor - **Construction Idea**: This factor evaluates the performance and dynamics of commodity markets, focusing on trend strength, basis momentum, volatility, and liquidity[21][26] - **Construction Process**: - **Trend Strength**: Assess the strength of price trends in commodity sectors like metals and energy - **Basis Momentum**: Measure the changes in the basis (spot price vs. futures price) across sectors - **Volatility**: Calculate the price volatility for each commodity sector - **Liquidity**: Evaluate the trading liquidity and its fluctuations across sectors[21][26] - **Evaluation**: The factor provides a detailed view of commodity market conditions, highlighting sector-specific trends and risks[21][26] 5. Factor Name: Option Market Factor - **Construction Idea**: This factor analyzes the implied volatility and skewness in the options market, focusing on indices like SSE 50 and CSI 1000[30] - **Construction Process**: - **Implied Volatility**: Track the implied volatility levels for SSE 50 and CSI 1000 options - **Skewness**: Measure the skewness in the implied volatility distribution to assess market sentiment[30] - **Evaluation**: The factor captures market sentiment and risk perception, particularly in large-cap and small-cap indices[30] 6. Factor Name: Convertible Bond Market Factor - **Construction Idea**: This factor evaluates the performance and valuation of the convertible bond market, focusing on premium rates and trading activity[33] - **Construction Process**: - **Premium Rates**: Analyze the parity premium and low-premium bond proportions - **Trading Activity**: Measure the total trading volume and its changes over time[33] - **Evaluation**: The factor provides insights into the convertible bond market's valuation and liquidity conditions[33] --- Factor Backtesting Results 1. Market Style Factor - **Size Style**: Small-cap preference increased - **Value-Growth Style**: Growth style outperformed value - **Volatility**: Size style volatility increased, while value-growth style volatility decreased[15] 2. Market Structure Factor - **Industry Dispersion**: Increased - **Industry Rotation**: Accelerated - **Trading Concentration**: Top 100 stocks' trading share rose, while top 5 industries' share remained stable[15] 3. Market Activity Factor - **Volatility**: Increased - **Turnover Rate**: Increased[15] 4. Commodity Market Factor - **Trend Strength**: Metals and energy sectors strengthened - **Basis Momentum**: Declined across all sectors - **Volatility**: Declined in the black sector, stable in others - **Liquidity**: Fluctuated but remained stable overall[26] 5. Option Market Factor - **Implied Volatility**: SSE 50 remained stable, CSI 1000 declined - **Skewness**: CSI 1000 skewness and implied volatility recovered quickly[30] 6. Convertible Bond Market Factor - **Premium Rates**: Parity premium remained stable, low-premium bond proportion unchanged - **Trading Activity**: Slight decline in trading volume, but still supported[33]

Group 1 - The recent stock market has experienced increased volatility, while the bond market shows signs of improvement but remains oscillatory. The optimistic expectation for the resumption of government bond trading operations has contributed to this recovery, with the ten-year government bond yield dropping below 1.75% [2][5] - The market style has slightly shifted towards small-cap stocks, with growth styles prevailing. The volatility of market styles has increased, while the volatility of value and growth styles has decreased [7][8] - In the commodity market, the strength of the non-ferrous and energy chemical sectors has increased, while the trend strength of other sectors remains stable. The basis momentum across all sectors has decreased [3][20][23] Group 2 - In the options market, the implied volatility of the Shanghai Stock Exchange 50 index remains stable, while the implied volatility of the CSI 1000 index has begun to decline. The market experienced a brief pullback in early September, particularly affecting small-cap stocks, but current sentiment has eased [28] - The convertible bond market showed a relatively flat performance, with the index primarily oscillating. The premium rate for convertible bonds remains stable, and the proportion of low premium convertible bonds has not changed significantly [30]