- The report tracks various market environment factors, including stock market factors, commodity market factors, options market factors, and convertible bond market factors [1][7][11] - **Stock Market Factors**: - **Market Style**: The style of large-cap and small-cap stocks was balanced, while the value-growth style leaned towards growth [11][13] - **Market Style Volatility**: Both large-cap/small-cap and value-growth style volatilities increased [11][13] - **Market Structure**: Industry excess return dispersion increased, industry rotation speed decreased, and the proportion of rising constituent stocks increased [11][13] - **Trading Concentration**: The transaction amount of the top 100 stocks slightly decreased, while the transaction amount of the top 5 industries remained unchanged compared to the previous period [11][13] - **Market Activity**: Market volatility increased, and market turnover rate decreased [12][13] - **Commodity Market Factors**: - **Trend Strength**: Precious metals and agricultural products showed a decline in trend strength, while other sectors experienced an increase [26][32] - **Basis Momentum**: Basis momentum increased across all sectors [26][32] - **Volatility**: Volatility rose in all sectors except for the black sector [26][32] - **Liquidity**: Liquidity decreased in precious metals, non-ferrous metals, and agricultural products [26][32] - **Options Market Factors**: - **Implied Volatility**: Implied volatility for the SSE 50 and CSI 1000 indices decreased, reflecting a moderation in market expectations regarding Trump's tariff policies [35] - **Implied Discount Rate**: The implied discount rate for CSI 1000 narrowed, but the market did not turn fully optimistic [35] - **Option Holdings**: Both put and call option holdings increased, indicating persistent market uncertainty [35] - **Convertible Bond Market Factors**: - **Market Recovery**: The convertible bond market showed slight recovery last week [37] - **Valuation**: Pure bond premium rates remained stable, while the premium rate for 100-yuan convertible bonds steadily increased [37] - **Low Premium Convertible Bonds**: The proportion of low premium convertible bonds decreased significantly [37] - **Market Turnover**: Market transaction volume stabilized without further contraction [37]

Quantitative Models and Construction Methods 1. Model Name: Stock Timing Model - **Construction Idea**: The model is based on the comprehensive judgment of economic growth and liquidity easing[18] - **Construction Process**: - Construct timing factors from two core dimensions: economic growth and liquidity easing[18] - Factors include PMI YoY smoothed value, manufacturing fixed asset investment completion amount cumulative YoY, CPI YoY smoothed value, and new medium and long-term loans cumulative value YoY[19] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using CSI 800 total return as the benchmark[19] - **Evaluation**: The model effectively captures stock market cycles, avoiding downturns[21] 2. Model Name: Bond Timing Model - **Construction Idea**: The model analyzes from the perspective of monetary liquidity supply and demand[23] - **Construction Process**: - Factors include DR007, SHIBOR, and social financing scale stock YoY smoothed value[24] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if short-term average < long-term average} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using ChinaBond Treasury Total Net Price Index as the benchmark[24] - **Evaluation**: The model captures bond market trends, minimizing drawdowns[25] 3. Model Name: All-Weather Strategy - **Construction Idea**: The model adjusts risk budgets for different assets based on timing signals[17] - **Construction Process**: - Use a risk parity model to allocate risk contributions of assets[30] - Adjust risk budgets based on stock and bond timing signals[32] - Optimize the model: $$ \begin{array}{c} \min \sum_{i=1}^{N} \left( RC_i - b_i \sigma_p \right)^2 \\ \text{s.t.} \sum_{i=1}^{N} \omega_i = 1 \\ 0 \leq \omega_i \leq 1 \end{array} $$ - Backtest using a combination of CSI 800, ChinaBond Treasury Total Wealth Index, CSI Convertible Bond Index, S&P 500 ETF, and AAA Credit Bonds[31] - **Evaluation**: The strategy provides higher absolute returns while controlling risk[38] Model Backtest Results Stock Timing Model - Annualized Return: 14.1%[21] - Benchmark Annualized Return: 5.4%[21] - Excess Annualized Return: 8.7%[21] - Monthly Win Rate: 56.7%[21] Bond Timing Model - Annualized Return: 2.3%[25] - Benchmark Annualized Return: 1.1%[25] - Excess Annualized Return: 1.1%[25] - Monthly Win Rate: 68.3%[25] All-Weather Strategy - Annualized Return: 6.1%[38] - Benchmark Annualized Return: 5.1%[38] - Excess Annualized Return: 1.0%[38] - Maximum Drawdown: 2.6%[38] - Sharpe Ratio: 2.04[38] Quantitative Factors and Construction Methods 1. Factor Name: Value-Growth Rotation Factor - **Construction Idea**: The factor is based on economic recovery, liquidity, and market sentiment[47] - **Construction Process**: - Factors include manufacturing fixed asset investment completion amount, PPI YoY smoothed value, M2 YoY smoothed value, social financing YoY smoothed value, medium and long-term loan growth YoY smoothed value, market turnover rate, and margin balance percentile[48] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using the National Growth Index and National Value Index[48] - **Evaluation**: The factor captures the cyclical characteristics of value and growth styles[47] 2. Factor Name: Size Rotation Factor - **Construction Idea**: The factor is based on economic prosperity, liquidity, and market sentiment[55] - **Construction Process**: - Factors include manufacturing fixed asset investment completion amount, PPI YoY smoothed value, gold daily return rate, government bond yield, credit spread, M1 YoY smoothed value, market turnover rate, and margin balance percentile[56] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using the CSI 300 Index and CSI 1000 Index[57] - **Evaluation**: The factor captures the cyclical characteristics of large-cap and small-cap styles[55] Factor Backtest Results Value-Growth Rotation Factor - Annualized Return: 9.2%[51] - Benchmark Annualized Return: 1.7%[51] - Excess Annualized Return: 7.5%[51] - Monthly Win Rate: 60.2%[51] Size Rotation Factor - Annualized Return: 9.2%[59] - Benchmark Annualized Return: 0.1%[59] - Excess Annualized Return: 9.0%[59] - Monthly Win Rate: 58.3%[59] Industry Rotation Solution 1. Factor Name: Macro Factor - **Construction Idea**: The factor is based on the second-order changes in economic growth and liquidity[67] - **Construction Process**: - Factors include PMI, social financing scale, manufacturing fixed asset investment completion amount, CPI, M2 growth rate, 10-year government bond yield, and credit spread[70] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using industry indices[73] - **Evaluation**: The factor captures the marginal inflection points of macro trends[67] 2. Factor Name: Fundamental Factor - **Construction Idea**: The factor is based on historical prosperity, prosperity changes, and prosperity expectations[79] - **Construction Process**: - Factors include industry component stock median, industry profitability, and industry consensus profit expectations[79] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using industry indices[82] - **Evaluation**: The factor captures the core of industry prosperity[79] 3. Factor Name: Technical Factor - **Construction Idea**: The factor is based on index momentum, leading stock momentum, and K-line patterns[87] - **Construction Process**: - Factors include industry index relative excess return IR, leading stock sharp ratio, and K-line pattern score[89] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using industry indices[96] - **Evaluation**: The factor captures the technical evaluation of industry trends[87] 4. Factor Name: Crowding Factor - **Construction Idea**: The factor is based on financing inflows, turnover rate, and transaction proportion[100] - **Construction Process**: - Factors include industry financing buy amount, industry turnover rate, and industry transaction amount proportion[101] - Use the formula: $$ \text{Factor} = \begin{cases} 1 & \text{if indicator improves} \\ 0 & \text{otherwise} \end{cases} $$ - Backtest using industry indices[104] - **Evaluation**: The factor captures the crowding level of industries[100] Industry Rotation Backtest Results Macro Factor - Annualized Return: 42.9%[73] - Benchmark Annualized Return: -22.8%[73] - Excess Annualized Return: 65.7%[73] Fundamental Factor - Annualized Return: 11.3%[85] - Benchmark Annualized Return: 2.8%[85] - Excess Annualized Return: 8.5%[85] - IC Mean: 8.2%[85] Technical Factor - Annualized Return: 9.7%[97] - Benchmark Annualized Return: 2.8%[97] - Excess Annualized Return: 6.9%[97] - IC Mean: 8.2%[97] Crowding Factor - Annualized Return: -2.9

- The diffusion index model has been tracking out-of-sample performance for four years, with notable results in 2021 when momentum strategies captured industry trends, achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2022, the strategy maintained stable returns with an annual excess return of 6.12%. However, in 2023, excess returns declined to -4.58%, and in 2024, a major drawdown occurred after September due to the model's focus on upward trends, missing rebound industries, resulting in an annual excess return of -5.82%[24][28] - The diffusion index model suggests allocating to industries such as non-bank finance, construction, and defense military, which showed significant week-on-week improvement in rankings. The top six industries based on diffusion index rankings as of October 24, 2025, are non-bank finance (0.988), banking (0.967), steel (0.952), communication (0.946), comprehensive (0.913), and non-bank finance (0.9)[25][26][27] - The GRU factor model, based on minute-level volume and price data processed through GRU deep learning networks, has shown strong performance in short cycles but weaker performance in long cycles. The model has been effective in capturing trading information since 2021, achieving significant excess returns. However, since February 2025, the model has faced challenges in generating excess returns due to market focus on thematic trading[31][37] - The GRU factor model ranks industries based on their GRU factor scores. As of October 24, 2025, the top six industries are non-bank finance (1.13), banking (1), electric power and utilities (0.54), textile and apparel (0.03), automotive (-0.58), and machinery (-0.73). Industries with the lowest GRU factor scores include food and beverage (-17.79), non-ferrous metals (-10.81), basic chemicals (-8.82), agriculture (-8.76), coal (-6.57), and building materials (-6.48)[6][13][32] - The GRU factor model's weekly industry rotation suggests allocating to non-bank finance, electric power and utilities, textile and apparel, transportation, steel, and petrochemicals. For the week ending October 24, 2025, the model achieved an average return of 1.89%, underperforming the equal-weighted return of the CSI first-tier industries by -0.77%. For October, the model's excess return is 1.80%, while the year-to-date excess return stands at -6.41%[6][34][39]

Core Insights - The report highlights the acceleration of industry rotation in the A-share market, indicating a shift from sustained single-line trends to rapid sector changes, with the industry rotation index showing increased activity since 2023 [13][14] - It emphasizes the scarcity of fundamentally strong investment opportunities, suggesting that while growth investment remains relevant, the range of viable options has significantly narrowed compared to the past two decades [20][19] - The report identifies the importance of "crowding" and "calendar effects" as tools for navigating the current market dynamics, with a focus on how these metrics can guide investment strategies [37][38] Group 1: Industry Rotation Dynamics - The A-share market has experienced a notable increase in industry rotation speed, with the duration of dominant trends decreasing from 6-12 months in previous years to approximately 2 months in 2023 [13][14] - The report outlines that the current market environment is characterized by a blend of "extreme rotation" and "absolute low volatility," where thematic investments and stable fundamental assets coexist [4][5] - The report provides a comparative analysis of industry performance, indicating that sectors such as military, robotics, and software are expected to benefit from low crowding and catalysts in the near term [6] Group 2: Investment Strategies and Sector Focus - For active funds, the report suggests focusing on sectors with strong growth trends and catalysts, particularly in the context of the upcoming quarterly reports [6] - It recommends maintaining positions in sectors like computing power, innovative pharmaceuticals, and non-ferrous metals, while also noting the potential for increased allocations in dividend-paying sectors such as banks and home appliances as the year-end approaches [6] - The report highlights the significance of calendar effects, suggesting that both active and long-term investors may find opportunities for positioning in the market during specific periods [5][6]

Quantitative Models and Construction Methods 1. Model Name: Stock Index Futures Timing Model - **Model Construction Idea**: The model uses the basis of stock index futures to reflect market sentiment changes and constructs daily frequency timing signals based on this correlation[7] - **Model Construction Process**: - The model groups and tests the correlation trend between the basis of stock index futures and the index itself - Constructs daily frequency timing signals based on this correlation - As of October 17, 2025, the timing signal based on the basis of the CSI 500 stock index futures remained at 1[31] - **Model Evaluation**: The model effectively captures market sentiment changes and provides timely signals for trading decisions[7] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: The model integrates macro, micro, meso, and derivative signals to form a four-dimensional non-linear timing model[33] - **Model Construction Process**: - The A-share market is divided into nine states based on macro, micro, and meso signals, each corresponding to long and short signals to form a three-dimensional large cycle timing signal - On this basis, the derivative signal generated by the basis of stock index futures is superimposed to form a four-dimensional non-linear timing model - The latest composite multi-dimensional timing signal is long (1)[34] - **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, enhancing the accuracy of timing signals[33] 3. Model Name: Style Enhancement Model - **Model Construction Idea**: The model enhances returns by adding enhancement factors to the multi-style strategy, suppressing single-style fluctuations, and achieving stable excess returns in different cycles[41] - **Model Construction Process**: - The model is based on the multi-style strategy and adds enhancement factors - It dynamically adjusts the weights of different styles to achieve stable excess returns - As of October 17, 2025, the low volatility enhancement strategy achieved an excess return of 6.05%[42] - **Model Evaluation**: The model effectively enhances returns while controlling risks, providing stable performance across different market cycles[41] Model Backtesting Results Stock Index Futures Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Multi-Dimensional Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Style Enhancement Model - **Absolute Return**: Not specified - **Excess Return**: 6.05%[8] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Quantitative Factors and Construction Methods 1. Factor Name: High-Frequency Factor - **Factor Construction Idea**: The factor captures market valuation and sentiment risks using high-frequency data[11] - **Factor Construction Process**: - The factor uses high-frequency data to measure market depth, spread, and price elasticity - Constructs indicators such as average depth, spread, and price elasticity to reflect market liquidity and sentiment - For example, the average depth is calculated as: $$ avg_{depth} = \frac{av1 + bv1}{2} $$ where av1 and bv1 are the sell and buy volumes at the first level of the order book[98] - **Factor Evaluation**: The factor effectively captures market liquidity and sentiment changes, providing valuable insights for trading decisions[11] Factor Backtesting Results High-Frequency Factor - **Absolute Return**: Not specified - **Excess Return**: Not specified - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Industry and ETF Rotation Strategy 1. Strategy Name: Industry Rotation Strategy - **Strategy Construction Idea**: The strategy uses quantitative fundamental drivers, quality low volatility style drivers, and distressed reversal industry discovery methods to construct an industry rotation strategy[76] - **Strategy Construction Process**: - Combines industry fundamental rotation, quality low volatility, and distressed reversal three-dimensional industry rotation strategies into an equal-weight portfolio - Selects industries from different dimensions to achieve factor and style complementarity, reducing the risk of a single strategy - As of October 17, 2025, the annualized excess return of the industry rotation strategy based on three-strategy integration was 10.59%, with a Sharpe ratio of 0.74[80] - **Strategy Evaluation**: The strategy effectively combines multiple dimensions to enhance returns while controlling risks, providing stable performance across different market cycles[76] Strategy Backtesting Results Industry Rotation Strategy - **Absolute Return**: Not specified - **Excess Return**: 14.75%[10] - **Annualized Return**: 10.59%[80] - **Sharpe Ratio**: 0.74[80]

Report Industry Investment Rating No information provided in the report regarding industry investment rating. Core Viewpoints of the Report - The US ETF market has seen the issuance of multiple asset - allocation products. The inflow of gold ETFs is significant, and precious - metal stock ETFs have performed significantly better than precious - metal ETFs. - In the US ordinary public - offering fund market, the outflow of domestic stock funds remains around $20 billion, while the inflow of bond products is stable, slightly exceeding $10 billion [3]. Summary by Relevant Catalog 1. US ETF Innovation Products: Multiple Asset - Allocation Products Issued - Last week, 22 new products were issued in the US, including various types such as downside protection, leverage, theme, allocation, and rotation products [6]. - There were 7 new downside protection products, including Calamos' laddered downside protection products linked to Bitcoin. Arrow Funds also issued a Bitcoin strategy product [6]. - 4 single - stock leverage products were issued, linked to Figma, Futu, JD.com, and Lemonade [7]. - GMO issued a dynamic asset - allocation ETF, with 40 - 80% invested in stocks and the rest in fixed - income and liquid alternative assets, based on GMO's 7 - year asset return forecast [7]. - AlphaDroid issued two strategy products, a momentum strategy and an industry rotation product [8]. - American Century issued 2 fundamental active ETFs, for small - cap value and small - cap growth [8]. - Pictet issued 3 stock ETFs, entering the US ETF market, with one using an AI strategy and two being theme products [8]. 2. US ETF Dynamics 2.1 US ETF Funds: Significant Inflow into Gold ETFs - In the past week, US ETFs maintained a high - speed inflow of nearly $50 billion, with domestic stocks inflowing over $25 billion and commodity ETFs mainly composed of gold also having a large inflow [9]. - The inflow of US broad - based stock products was stable last week, and the gold ETF GLD ranked second in the inflow of all products. Among bond products, comprehensive products had relatively more inflows, while high - yield bonds and alternative bond products had outflows [9]. 2.2 US ETF Performance: Precious - Metal Stock ETFs Significantly Outperform Precious - Metal ETFs - Due to frequent global situation changes this year, precious - metal ETFs led by gold have continuously risen significantly, and precious - metal - related stock ETFs such as gold - mining stocks have had significantly higher increases, with many products rising around 150% [3]. 3. Recent Capital Flows of US Ordinary Public - Offering Funds - In August 2025, the total amount of non - money public - offering funds in the US was $22.98 trillion, an increase of $0.41 trillion compared to July 2025. The S&P 500 rose 1.91% in August, and the scale of domestic stock products increased by 1.62%, with the redemption pressure easing [14]. - According to weekly ICI statistics, the outflow of US domestic stock funds last week remained around $20 billion, while the inflow of bond products was stable, slightly exceeding $10 billion [14].

- The diffusion index model tracks industry rotation based on momentum principles, focusing on upward trends in industry performance. It has been monitored for four years, with notable excess returns in 2021 (25% before September) and stable returns in 2022 (6.12%). However, it faced significant drawdowns in 2023 (-4.58%) and 2024 (-5.82%) due to market reversals. For October 2025, recommended industries include non-ferrous metals, banking, communication, steel, electronics, and automobiles[26][30] - The GRU factor model utilizes GRU deep learning networks to analyze minute-level volume and price data, aiming to capture industry rotation. It has shown strong adaptability in short cycles but struggles in long cycles and extreme market conditions. For October 2025, industries such as building materials, electric power and utilities, textiles and apparel, transportation, steel, and petrochemicals are recommended[33][36] - Diffusion index model weekly tracking shows top industries as non-ferrous metals (0.979), communication (0.931), banking (0.929), steel (0.849), electronics (0.833), and electric power equipment & new energy (0.816). Industries with the largest weekly changes include consumer services (0.271), coal (0.251), and retail trade (0.127)[27][28][29] - GRU factor weekly tracking highlights top industries as textiles and apparel (4.22), comprehensive (2.68), transportation (2.16), steel (2), electric power and utilities (1.84), and petrochemicals (1.08). Industries with the largest weekly improvements include food and beverage, electric power and utilities, and real estate[34][37] - Diffusion index model performance: weekly average return -3.42%, excess return over equal-weighted industry index -0.85%, October excess return -1.21%, year-to-date excess return 3.42%[30] - GRU factor model performance: weekly average return -1.74%, excess return over equal-weighted industry index 0.86%, October excess return 2.51%, year-to-date excess return -5.40%[36]

- **A-share multi-dimensional timing model**: The model evaluates the overall directional judgment of the A-share market using four dimensions: valuation, sentiment, funds, and technical indicators. Each dimension provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. Valuation and sentiment dimensions adopt a mean-reversion logic, while funds and technical dimensions use trend-following logic. The final market view is determined by the sum of the scores across all dimensions [9][15][16] - **Style timing model for dividend style**: The model uses three indicators to time the dividend style relative to the CSI Dividend Index and CSI All Share Index. The indicators include relative momentum, 10Y-1Y term spread, and interbank pledged repo transaction volume. Each indicator provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. The final view is based on the sum of the scores across all dimensions. When the model favors the dividend style, it fully allocates to the CSI Dividend Index; otherwise, it allocates to the CSI All Share Index [17][21] - **Style timing model for large-cap and small-cap styles**: The model uses momentum difference and turnover ratio difference between the CSI 300 Index and Wind Micro Cap Index to calculate the crowding scores for large-cap and small-cap styles. The model operates in two crowding zones: high crowding and low crowding. In high crowding zones, it uses a small-parameter dual moving average model to address potential style reversals. In low crowding zones, it uses a large-parameter dual moving average model to capture medium- to long-term trends [22][24][26] - **Sector rotation model**: The genetic programming-based sector rotation model selects the top five sectors with the highest multi-factor composite scores from 32 CITIC industry indices for equal-weight allocation. The model updates its factor library quarterly and rebalances weekly. The factors are derived using NSGA-II algorithm, which evaluates factor monotonicity and performance of long positions using |IC| and NDCG@5 metrics. The model combines multiple factors with weak collinearity into sector scores using greedy strategy and variance inflation factor [29][32][33][36] - **China domestic all-weather enhanced portfolio**: The portfolio is constructed using a macro factor risk parity framework, which emphasizes risk diversification across underlying macro risk sources rather than asset classes. The strategy involves three steps: macro quadrant classification and asset selection, quadrant portfolio construction and risk measurement, and risk budgeting to determine quadrant weights. The active allocation is based on macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41] --- Model Backtesting Results - **A-share multi-dimensional timing model**: Annualized return 24.97%, maximum drawdown -28.46%, Sharpe ratio 1.16, Calmar ratio 0.88, YTD return 37.73%, weekly return 0.00% [14] - **Dividend style timing model**: Annualized return 15.71%, maximum drawdown -25.52%, Sharpe ratio 0.85, Calmar ratio 0.62, YTD return 19.53%, weekly return -3.43% [20] - **Large-cap vs. small-cap style timing model**: Annualized return 26.01%, maximum drawdown -30.86%, Sharpe ratio 1.08, Calmar ratio 0.84, YTD return 64.58%, weekly return -2.22% [27] - **Sector rotation model**: Annualized return 33.33%, annualized volatility 17.89%, Sharpe ratio 1.86, maximum drawdown -19.63%, Calmar ratio 1.70, weekly return 0.14%, YTD return 39.41% [32] - **China domestic all-weather enhanced portfolio**: Annualized return 11.66%, annualized volatility 6.18%, Sharpe ratio 1.89, maximum drawdown -6.30%, Calmar ratio 1.85, weekly return 0.38%, YTD return 10.74% [42]

Core Insights - The A-share market has shown an upward trend since October, with the Shanghai Composite Index recovering above 3900 points, coinciding with the third-quarter earnings report disclosure period [1] - As of October 15, 126 companies have released earnings forecasts, with 105 of them expecting year-on-year profit growth, indicating a strong market focus on financial data [1] Group 1: Earnings Forecasts - Two main reasons for companies' positive earnings forecasts are price increases and the ramp-up of product production [2] - Companies like Xianda Co., ShuoBeide, and Chujian New Materials are leading the earnings growth forecast, with increases exceeding 2000% [2] - Resource cycle companies have benefited from significant price increases, while certain tech companies are entering a phase of mass production, driving their earnings growth [2][4] Group 2: Notable Companies - Xianda Co. expects a net profit increase of 2807% to 3211% for the first three quarters, driven by rising market prices for its main product, and operational reforms [2] - Shenghe Resources anticipates a net profit of approximately 740 million to 820 million yuan, reflecting a year-on-year increase of 696.82% to 782.96%, due to favorable market conditions and price increases [3] - ShuoBeide's net profit is projected to increase by 2836.86% to 3203.96%, attributed to enhanced production capacity and successful collaborations with major clients [4] Group 3: Market Trends and Reporting Schedule - The third-quarter earnings report window is short, with only ten trading days left until the reports are due by October 31 [6] - A total of 2352 companies are expected to disclose their earnings in the final week of October, marking a peak in reporting activity [6][11] - Key companies such as NIO, China Telecom, and major banks are scheduled to release their earnings reports between October 21 and October 31 [7][8]

Core Insights - The article highlights the strong performance of the non-ferrous metals sector, driven by multiple factors, including rising international gold prices and robust demand for industrial metals, particularly rare earths due to tightened export controls [4]. Market Overview - As of October 13, 2025, the Shanghai Composite Index and Shenzhen Component Index showed significant percentile rankings of 98.68% and 86.26% respectively, indicating a high valuation level compared to the past decade [1]. - The non-ferrous metals index surged over 3%, with several constituent stocks hitting their daily limit up, reflecting strong market sentiment [3]. Sector Performance - The non-ferrous metals sector is experiencing a favorable environment with both volume and price increases, maintaining high profit growth rates [4]. - The banking sector showed resilience amidst market volatility, with the banking index rising nearly 1% on the same day, attracting defensive capital due to stable dividends and improved yield ratios [4]. Fund Flows - The top three sectors for capital inflows included steel (¥8.92 billion), environmental protection (¥2.49 billion), and agriculture (¥2.46 billion) [2]. - Conversely, the sectors with the highest capital outflows were electronics (¥94.39 billion), electric equipment (¥66.15 billion), and automotive (¥43.09 billion) [2]. ETF Performance - The non-ferrous metals leading ETF (code: 159876) has shown a remarkable increase of 73.41% over the past six months, indicating strong investor interest [3]. - The banking ETF (code: 512800) also demonstrated a solid performance with a 5.71% increase, reflecting its attractiveness in the current market environment [3].