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]

Core Viewpoint - The article emphasizes the importance of macroeconomic policies and industry trends in investment strategies, highlighting the ability to identify investment opportunities through careful analysis of policy changes and market dynamics [1][3][17]. Group 1: Investment Philosophy - The investment philosophy of the company is characterized by a balance between aggressive and defensive strategies, adapting to market conditions through a dynamic asset allocation approach [6][11]. - The company focuses on capturing economic cycles and industry rotations, selecting 3 to 5 high-prospect industries for investment each year [6][7]. - The investment strategy includes a mix of long-term holdings and opportunistic trades, with a keen eye on emerging trends and sectors [15][17]. Group 2: Historical Context and Experience - The company’s investment manager, Yin Tao, has a rich background in macroeconomic research and media, which has shaped his investment acumen over 27 years [3][4]. - Yin Tao transitioned from journalism to investment management in 2008, where he developed skills in asset allocation and risk management during the global financial crisis [4][5]. - The company has successfully navigated multiple market cycles, demonstrating resilience and adaptability in its investment approach [5][12]. Group 3: Performance Metrics - The company’s managed funds have shown significant performance, with a net value growth rate of 19.18% over the past six months and 18.70% over the past year [5]. - According to data from Galaxy Securities, the company’s products ranked in the top 5% of their category over the past two years, indicating strong competitive performance [5]. Group 4: Sector Focus - The company identifies key sectors for investment, including AI, innovative pharmaceuticals, and new consumption, which are seen as the main drivers of future growth [17]. - Yin Tao has successfully invested in leading stocks within these sectors, capitalizing on trends such as the rise of electric vehicles and innovative consumer products [14][15]. Group 5: Research and Analysis - The company employs a rigorous research methodology, including in-depth analysis of financial statements and market conditions, to identify high-quality growth stocks [10][9]. - Yin Tao emphasizes the importance of patience and thorough investigation in investment decisions, often revisiting companies to validate investment theses [9][10]. Group 6: Market Adaptation - The company adapts its strategies based on market conditions, employing a flexible approach to portfolio management that allows for quick adjustments in response to market volatility [12][13]. - Yin Tao’s strategy includes a focus on maintaining a balanced portfolio, with adjustments made to stock allocations based on performance and market signals [12][13].

Macro Strategy Highlights - The market consensus has been strong since August, with industry rotation intensity showing a seasonal decline, while September is traditionally a window for upward industry rotation intensity [1] - As the third-quarter report disclosure period approaches in late September to October, the correlation between stock prices and performance will gradually increase, marking a phase of enhanced effectiveness for cyclical investments [1] - Key areas to focus on include Hong Kong internet stocks, innovative pharmaceuticals, and new energy sectors, which are expected to benefit from interest rate cuts and industry catalysts [1][2][3] Industry Tracking - The Hong Kong internet sector is positioned to benefit from interest rate cuts and AI expansion, with platforms that have the best social scenarios and ecosystems likely to see early gains [1] - The innovative pharmaceutical sector has reached a moderate level of crowding, with sentiment sufficiently digested, and is expected to see catalysts from industry conferences in September and Q4 [1] - The new energy sector is driven by technological breakthroughs and anti-involution trends, providing a flexible new direction [2] - The new consumption sector has high odds currently, with seasonal catalysts and improved cyclical expectations enhancing success rates [3] - The cyclical sectors, particularly non-ferrous metals and chemicals, are experiencing multiple catalysts, with leading chemical companies showing a high safety margin in valuations [3]

Group 1 - The market has entered a rotation and diffusion phase, which is expected to continue in the near future [1][2] - Investment effectiveness is improving as the market shifts its focus towards economic conditions and industry trends [1][2] - The report suggests five key areas for investment: Hong Kong internet stocks, innovative pharmaceuticals, breakthroughs in new energy technology, new consumption, and cyclical industries driven by multiple catalysts [1][2] Group 2 - The report indicates that the market is looking for signs of economic improvement as it approaches the earnings season [1][2] - The sentiment towards innovative pharmaceuticals has fully digested, leading to a revaluation driven by business development and commercialization [1][2] - New consumption is highlighted as having high odds, with seasonal catalysts expected to enhance success rates [1][2]

Group 1: Macroeconomic Expectations - Global inflation and tightening monetary policy have led to increased concerns about liquidity, putting pressure on risk assets such as stocks and bonds, indirectly affecting fund performance [3] - Domestic CPI data for March fell below expectations, raising doubts about the strength of economic recovery and leading to downward adjustments in profit expectations for certain industries [3] Group 2: Geopolitical Conflicts - Recent tensions in the Middle East and ongoing Russia-Ukraine conflict have driven up prices of commodities like oil, increasing global supply chain uncertainties and heightening investor risk aversion [5] Group 3: Industry and Policy Adjustments - Regulatory changes have intensified scrutiny on certain sectors, such as real estate and platform economy, causing significant declines in related sectors like Chinese concept stocks and real estate bonds, which in turn drag down the net value of related thematic funds [6] - Rumors of a "fund fee reform" could further compress management fee income, raising concerns about the industry's profit model [6] - High-performing sectors in Q1, such as technology and new energy, have experienced profit-taking, leading to a shift of funds towards defensive assets like consumer goods and utilities, putting short-term pressure on growth-oriented funds [6] Group 4: Market Sentiment and Fund Flows - A wave of redemptions triggered by net value declines has forced fund managers to sell holdings, exacerbating market downturns, particularly in small-cap stocks and less liquid bonds [8] - Since March, foreign capital has continuously reduced holdings in A-shares, with a cumulative net outflow exceeding 20 billion, negatively impacting the performance of blue-chip stocks and the overall market index [8] Group 5: Short-term Technical Factors - The end of the quarter has led to portfolio adjustments by institutions, amplifying market volatility [8] - The derivatives market has seen a chain reaction with expanded index futures discounts and soaring options volatility, intensifying market panic [8]

Group 1 - The core viewpoint is that the A-share market is experiencing a "slow bull" trend, with high-growth sectors being the preferred choice for investment [1][2] - Policy support is expected to strengthen with the upcoming Fourth Plenary Session in October, particularly in hard technology and new productivity sectors [1][2] - Recent increases in overseas AI industry capital expenditure are positively influencing market sentiment [1][2] Group 2 - A total of 12 out of the 15 leading companies with the highest gains since June are linked to overseas expansion, particularly in the AI supply chain and innovative pharmaceuticals [2][3] - The market consensus has been strong since August, but the intensity of sector rotation has decreased to a new low since April of the previous year [2][3] - The focus should be on high-growth sectors such as solid-state batteries, energy storage, and innovative pharmaceuticals, while also considering new consumption trends [1][2] Group 3 - The current market sentiment is characterized by a high degree of volatility, with a potential for a significant upward trend if new catalysts emerge [3][4] - The upcoming October meeting is anticipated to clarify the direction of the "14th Five-Year Plan," likely emphasizing technological innovation and new productivity [3][4] - The market is expected to see a shift towards cyclical trades as the economy transitions from service to manufacturing sectors [4]

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]

Report Summary 1) Report Industry Investment Rating No information provided on the industry investment rating in the report. 2) Core Viewpoints - The convertible bond market showed positive trends on September 12, 2025, with more than half of the industries rising and the valuation increasing. The trading sentiment in the convertible bond market also heated up, and small - cap growth stocks were relatively dominant [1]. - The central price of convertible bonds increased, while the proportion of high - price bonds decreased. The overall valuation of convertible bonds rose, with the conversion premium rate of various types of convertible bonds increasing [2]. 3) Summary by Related Catalogs Market Overview - Index Performance: The CSI Convertible Bond Index rose 0.17% month - on - month, while the Shanghai Composite Index, Shenzhen Component Index, ChiNext Index, and Shanghai 50 Index all declined. The CSI 1000 Index rose 0.31% [1]. - Market Style: Small - cap growth stocks were relatively dominant. Among them, small - cap growth stocks rose 0.32%, while large - cap growth and value stocks declined [1]. - Capital Performance: The trading volume in the convertible bond market reached 82.886 billion yuan, a 7.82% increase month - on - month. The total trading volume of the Wind All - A Index was 2.548312 trillion yuan, a 3.40% increase. The net outflow of main funds from the Shanghai and Shenzhen stock markets was 37.278 billion yuan, and the yield of the 10 - year Treasury bond decreased by 0.73bp to 1.87% [1]. Convertible Bond Price and Valuation - Price: The weighted average closing price of convertible bonds was 131.41 yuan, a 0.17% increase. The proportion of high - price bonds (above 130 yuan) decreased by 0.29pct, and the proportion of bonds in the 110 - 120 yuan range increased by 1.0pct. There were no bonds with a closing price below 100 yuan [2]. - Valuation: The conversion premium rate of 100 - yuan par - value convertible bonds was 29.94%, a 0.38pct increase. The overall weighted par value decreased by 0.52%. The conversion premium rates of all types of convertible bonds (including partial - equity, partial - debt, and balanced) increased [2]. Industry Performance - Stock Market: Among A - share industries, the top three decliners were communication (-2.13%), beauty care (-1.52%), and banking (-1.52%); the top three gainers were non - ferrous metals (+1.96%), real estate (+1.51%), and steel (+1.41%) [3]. - Convertible Bond Market: 18 industries in the convertible bond market rose. The top three gainers were environmental protection (+2.86%), non - ferrous metals (+1.51%), and communication (+1.37%); the top three decliners were machinery and equipment (-2.08%), media (-1.56%), and agriculture, forestry, animal husbandry, and fishery (-0.48%) [3]. - Different Industry Indicators: In terms of closing price, large - cycle industries rose 0.89%, while manufacturing industries declined 0.22%. In terms of conversion premium rate, all industries increased to varying degrees. In terms of conversion value, large - cycle industries rose 0.87%, while manufacturing industries declined 1.60% [3].