Quantitative Models and Construction Methods 1. Model Name: Industry Relative Strength (RSI) Model - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance over different time windows [10] - **Model Construction Process**: 1. Use 29 first-level industry indices as the configuration targets [10] 2. Calculate the price change rates for the past 20, 40, and 60 trading days for each industry index [10] 3. Rank the industries based on their price change rates for each time window and normalize the rankings to obtain RS_20, RS_40, and RS_60 [10] 4. Calculate the average of the three rankings to derive the final RS value: $ RS = \frac{RS_{20} + RS_{40} + RS_{60}}{3} $ [10] 5. Industries with RS > 90% by the end of April are identified as potential leading industries for the year [10] - **Model Evaluation**: The model successfully identified key annual industry trends, such as high dividend, resource products, exports, and AI, which were validated by market performance throughout the year [10][12] 2. Model Name: Industry Sentiment-Trend-Crowding Framework - **Model Construction Idea**: This framework provides two industry rotation strategies based on market conditions: 1. High sentiment + strong trend, avoiding high crowding (aggressive strategy) 2. Strong trend + low crowding, avoiding low sentiment (conservative strategy) [6][14] - **Model Construction Process**: 1. Evaluate industries based on three dimensions: sentiment, trend, and crowding [6][14] 2. Use sentiment as the core metric for the aggressive strategy, with crowding as a risk control factor [14] 3. Use trend as the core metric for the conservative strategy, avoiding low-sentiment industries [14] 4. Allocate weights to industries based on their scores in the three dimensions [6][14] - **Model Evaluation**: The framework is effective in adapting to different market conditions and has shown strong performance in historical backtests [6][14] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries with potential for recovery by analyzing sectors in distress or those with low inventory pressure and high analyst optimism [24] - **Model Construction Process**: 1. Identify industries currently in distress or recovering from past distress [24] 2. Focus on sectors with low inventory pressure and potential for restocking [24] 3. Incorporate analyst long-term positive outlooks for these industries [24] - **Model Evaluation**: The model effectively captures recovery opportunities in industries undergoing inventory restocking cycles, providing significant absolute and relative returns [24] --- Model Backtesting Results 1. Industry Relative Strength (RSI) Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Industries with RS > 90% by April 2024 included coal, utilities, home appliances, banking, petrochemicals, communication, non-ferrous metals, agriculture, and automotive [10] - These industries showed strong performance, with key themes being high dividends, resource products, exports, and AI [10][12] 2. Industry Sentiment-Trend-Crowding Framework - **Annualized Return**: 22.1% (long-only portfolio) [14] - **Excess Return**: 13.8% (annualized) [14] - **Information Ratio (IR)**: 1.51 [14] - **Maximum Drawdown**: -8.0% [14] - **Monthly Win Rate**: 68% [14] - **Performance Highlights**: - 2023 excess return: 7.3% [14] - 2024 excess return: 5.7% [14] - 2025 YTD excess return: 2.8% [14] 3. Left-Side Inventory Reversal Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: - 2023: 17.0% (relative to equal-weighted industry benchmark) [24] - 2024: 15.4% (relative to equal-weighted industry benchmark) [24] - 2025 YTD: 7.8% (relative to equal-weighted industry benchmark) [24] - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Absolute return: - 2023: 13.4% [24] - 2024: 26.5% [24] - 2025 YTD: 26.4% [24] --- Quantitative Factors and Construction Methods 1. Factor Name: Sentiment Factor - **Factor Construction Idea**: Measures the overall sentiment of an industry to identify high-growth opportunities [14] - **Factor Construction Process**: 1. Evaluate the sentiment of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by sentiment scores [14] - **Factor Evaluation**: Sentiment is a core metric in the aggressive strategy of the Industry Sentiment-Trend-Crowding Framework, providing strong signals for high-growth opportunities [14] 2. Factor Name: Trend Factor - **Factor Construction Idea**: Measures the strength of market trends to identify industries with strong momentum [14] - **Factor Construction Process**: 1. Evaluate the trend of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by trend scores [14] - **Factor Evaluation**: Trend is a core metric in the conservative strategy of the Industry Sentiment-Trend-Crowding Framework, offering a simple and replicable approach to industry allocation [14] 3. Factor Name: Crowding Factor - **Factor Construction Idea**: Measures the level of crowding in an industry to identify overbought or underbought sectors [14] - **Factor Construction Process**: 1. Evaluate the crowding level of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by crowding scores [14] - **Factor Evaluation**: Crowding is used as a risk control factor in both aggressive and conservative strategies of the Industry Sentiment-Trend-Crowding Framework [14] --- Factor Backtesting Results 1. Sentiment Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 3. Crowding Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned

Core Insights - The style rotation model accurately predicted trends in Q3 2025, with signals favoring small-cap and growth stocks for Q4 2025 [1] - The industry rotation model showed positive excess returns in September, with a monthly return of 3.33% and an excess return of 2.43% relative to the benchmark [1] Style Rotation Model - For Q4 2025, the dual-driven rotation strategy indicates a comprehensive score of -1, predicting a preference for small-cap stocks [2] - The growth style is favored in Q4 2025, with a comprehensive score of -3 from the dual-driven rotation strategy [3] Industry Rotation Insights - In September, the composite factor strategy achieved an excess return of 2.43%, while the single-factor multi-strategy had an excess return of -1.02% [3] - For October, the recommended long positions in single-factor multi-strategy include the computer, communication, electronic, non-bank financial, and banking sectors [3] - The composite factor strategy recommends long positions in home appliances, non-ferrous metals, electronics, communication, and computers [3]

Group 1 - The core viewpoint is that the non-bank financial sector is expected to outperform due to rising market trading volumes, with a focus on undervalued leading brokerage firms [1] - The industry rotation model indicates that non-bank financials are included in the long position for October, reflecting improved industry momentum expectations [1] - Leading brokerages may drive industry momentum due to their advantages in transaction amounts, as the sector benefits from increased market activity and low valuation attributes [1] Group 2 - The securities sector is characterized by strong beta attributes, with its performance closely tied to capital market conditions, which are currently favorable due to heightened market risk appetite and liquidity [1] - The securities ETF (512880) is recommended for investment opportunities, especially as it surpasses a scale of 54 billion and continues to lead in liquidity among peers [1][2]

Group 1 - The core viewpoint of the article indicates that the market is favoring small-cap and growth styles, with the style rotation model for Q3 2025 confirming this trend [1][2] - In August, the small-cap stocks outperformed large-cap stocks with a monthly excess return of 1.34%, while growth stocks outperformed value stocks with a monthly excess return of 12.76% [1][3] - The industry rotation model showed that in August, two industry combinations achieved absolute returns exceeding 12%, with excess returns above 4% [1][3] Group 2 - The dual-driven rotation strategy for Q3 2025 indicated a signal for small-cap stocks based on the latest data as of June 30, 2025, with a composite score of -3 [2] - The dual-driven rotation strategy for Q3 2025 also indicated a signal for growth stocks, with a composite score of -5 [3] - In August, the composite factor strategy achieved an excess return of 4.38%, while the single-factor multi-strategy achieved an excess return of 4.59% [3]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - Active equity fund positions have reached the highest level of the year, with continuous increases in the communication industry position. The market's broad - based indices generally rose last week, with the Shanghai Composite Index breaking through 3700 points. TMT industries performed strongly, while dividend - related industries such as banking and coal were weak. [4][12] - Active equity public funds have been continuously increasing their positions, and the overall position has reached the highest level of the year. Even relatively cautious "fixed - income +" funds have been continuously raising their positions. In terms of style, public funds have focused on the growth sector and shifted towards small - cap stocks. [4][12] - Public funds are optimistic about the communication industry, which has seen the most significant position increase in the past three months. The proportion in the consumer sector has decreased, and the allocation ratio of the food and beverage industry has reached a multi - year low. It is recommended to shift the allocation towards the growth sector. [4][12] 3. Summary According to the Table of Contents 3.1 Last Week's Market Review - **Broad - based Index Performance**: Last week (2025/8/11 - 2025/8/15), A - share broad - based indices generally rose, with the ChiNext Index rising significantly. As of 2025/8/15, the Shanghai Composite Index closed at 3696.77 points, up about 1.70% week - on - week; the Shenzhen Component Index closed at 11634.67 points, up about 4.55%; the ChiNext Index closed at 2534.22 points, up about 8.58%; and the CSI 300 closed at 4202.35 points, up about 2.37%. [13] - **Industry Index Performance**: TMT and non - banking industries performed well last week. The top - performing industries in terms of weekly returns were communication, comprehensive finance, non - bank finance, electronics, and computer, with returns of 7.11%, 7.07%, 6.57%, 6.44%, and 6.31% respectively. The bottom - performing industries included banking, steel, textile and apparel, coal, and construction, with returns of - 3.22%, - 2.00%, - 1.36%, - 0.77%, and - 0.59% respectively. [16] 3.2 Public Funds - **Net Value Performance**: The average net value change of active partial - stock funds last week was 3.47%. Among the 4468 funds, 3990 rose, accounting for 89.30%. The top five funds in terms of net value performance were Yongying Digital Economy Smart Selection Hybrid A, SDIC UBS Jinbao Flexible Allocation Hybrid, SDIC UBS Advanced Manufacturing Hybrid, SDIC UBS New Energy Hybrid A, and SDIC UBS Industry Trend Hybrid A, with weekly net value changes of 18.81%, 17.88%, 17.34%, 17.29%, and 17.01% respectively. [4][18] - **Position Calculation**: As of 2025/8/15, the average position of active equity funds was about 89.14%. Among them, the average position of common stock funds was about 91.41% (up 0.86 pct from the previous week), the average position of partial - stock hybrid funds was about 88.93% (up 1.90 pct), the average position of allocation funds was about 88.23% (up 2.61 pct), and the average position of "fixed - income +" funds was about 23.48%, up 0.43 pct from the previous week. [2][22] - **Style Trends**: Recently, public funds have mainly been allocated to the small - cap growth style. As of 2025/8/15, the positions of active partial - stock funds in large - cap growth, large - cap value, mid - cap growth, mid - cap value, small - cap growth, and small - cap value were 27.52% (up 0.19 pct from the previous week), 9.4% (down 0.69 pct), 9.51% (down 0.37 pct), 5.96% (up 0.3 pct), 43% (up 1.06 pct), and 4.62% (down 0.5 pct) respectively. [3][29] - **Industry Trends**: From the perspective of the weighted average of stock - holding market value, the industries with a significant increase in the allocation ratio of active equity funds last week were communication (about 6.19%, up 0.86 pct from the previous week), non - ferrous metals (about 4.31%, up 0.42 pct), petroleum and petrochemicals (about 1.17%, up 0.33 pct), comprehensive (about 0.52%, up 0.30 pct), and real estate (about 1.03%, up 0.24 pct). The industries with a significant decrease were food and beverage (about 3.96%, down 0.62 pct), electronics (about 15.99%, down 0.54 pct), national defense and military industry (about 5.05%, down 0.52 pct), banking (about 3.57%, down 0.43 pct), and textile and apparel (about 1.09%, down 0.32 pct). [4][32] - **ETF Market Tracking**: Last week (2025/8/11 - 2025/8/15), domestic stock ETFs had a net outflow of about 23.799 billion yuan, cross - border ETFs had a net inflow of about 16.335 billion yuan, bond ETFs had a net inflow of about 12.633 billion yuan, and commodity ETFs had a net outflow of about 1.719 billion yuan. [39] - **Newly Established Funds**: This year, 171 active equity funds have been newly issued, with a total scale of about 68.102 billion yuan, about 130.65% of the same period in 2024; 356 passive equity funds have been newly issued, with a total scale of 184.103 billion yuan, about 320.38% of the same period in 2024. [44] 3.3 Main/Active Capital Flows - **Main Capital Flow**: Last week, the main capital flowed into non - bank and electronics sectors and flowed out of national defense and military industry and machinery sectors. [5][56] - **Active Capital Flow**: The net main - buying amount last week was about - 1016.139 billion yuan. Active capital flowed into non - bank and electronics sectors. The industries with the highest net main - buying amounts were non - bank finance, electronics, computer, communication, and non - ferrous metals; the industries with significant outflows were machinery, national defense and military industry, banking, power and public utilities, and medicine. [5][56]

- The report identifies three main industry models: the industry mainline model, the industry rotation model, and the left-side inventory reversal model [1][6][8] - The industry mainline model uses the Relative Strength Index (RSI) to identify leading industries. The construction process involves calculating the price changes over 20, 40, and 60 trading days, normalizing these rankings, and averaging them to get the final RSI. If an industry shows an RSI greater than 90% by the end of April, it is likely to be a leading industry for the year [2][12][14] - The industry rotation model is based on a framework of prosperity, trend, and congestion. It suggests a balanced allocation with specific weights for different industries, such as 20% for banks, 17% for non-ferrous metals, and 15% for steel. The model has shown strong performance, with an annualized excess return of 14.1% and an IR of 1.54 [2][16][18] - The left-side inventory reversal model focuses on industries that are in a state of distress or have recently rebounded. It aims to capture the reversal in industries with low inventory pressure and high analyst expectations. The model has shown significant returns, with a 2023 absolute return of 13.4% and an excess return of 17.0% [27][28][29] - The industry mainline model's backtest results for 2024 showed that industries like coal, electric utilities, home appliances, banks, oil and petrochemicals, telecommunications, non-ferrous metals, agriculture, and automotive had significant returns when their RSI exceeded 90% [2][12][13] - The industry rotation model's backtest results showed an annualized return of 21.2%, an excess return of 14.1%, an IR of 1.54, and a maximum drawdown of -8.0%. The model's performance in 2023, 2024, and 2025 showed excess returns of 7.3%, 5.7%, and 4.1%, respectively [16][17][21] - The left-side inventory reversal model's backtest results showed an absolute return of 25.9% in 2024 and an excess return of 14.8%. In 2025, the model achieved an absolute return of 13.6% and an excess return of 3.5% [27][28][29] - The industry rotation model's ETF configuration showed an annualized excess return of 15.8% and an IR of 1.8. The model's performance in 2023, 2024, and 2025 showed excess returns of 6.0%, 5.3%, and 8.1%, respectively [21][22][26] - The industry prosperity stock selection model showed an annualized return of 25.8%, an excess return of 20.0%, an IR of 1.7, and a maximum drawdown of -15.4%. The model's performance in 2022, 2023, 2024, and 2025 showed excess returns of 10.2%, 10.4%, 4.6%, and 4.7%, respectively [22][23][24] - The recommended industries for the left-side inventory reversal model include agricultural chemicals, general steel, building decoration, precious metals, optical and optoelectronics, special materials, components, and passenger cars [27][28][29]

Group 1 - The market trading volume continues to rise, with mid-year earnings forecasts exceeding expectations, and stablecoins providing a catalyst for growth, indicating a favorable outlook for undervalued leading brokerage firms [1] - Proprietary trading and margin financing businesses are performing steadily, while wealth management transformation shows differentiation and long-term potential [1] - Based on the industry rotation model, the non-bank financial sector has been included in the August industry combination, highlighting its short-term allocation value [1] Group 2 - As the market approaches previous highs, attention should be paid to the trends in non-bank and financial technology sectors [1] - In the current environment of rising uncertainty, stable dividends (including non-bank financials) still hold allocation value [1] - The Securities ETF (512880) tracks the Securities Company Index (399975), which mainly consists of brokerage stocks in the A-share market, reflecting the overall performance of the securities industry [1]

Group 1 - The core viewpoint of the article indicates that the style rotation model has been continuously validated, with macroeconomic factors driving large-cap and value signals in Q2 2025. In May, the market favored large-cap and value styles, with large-cap outperforming small-cap by 0.56% and value outperforming growth by 3.40% [1][2]. - In May, the single-factor multi-strategy model showed a monthly return of 3.31%, with an excess return of 0.33% relative to the benchmark [1][2]. - The dual-driven rotation strategy for large-cap and value signals received a composite score of 3, predicting a favorable outlook for large-cap and value styles in Q2 2025 [1][2]. Group 2 - The industry rotation model for May indicated that the single-factor multi-strategy outperformed with an excess return of 0.33%, while the composite factor strategy had an excess return of -0.64% [2]. - For June, the recommended long positions in the single-factor multi-strategy include non-bank financials, electronics, and banks, while the composite factor strategy recommends non-bank financials, pharmaceuticals, building materials, basic chemicals, and steel [2].

Quantitative Models and Construction 1. Model Name: Macro + Volume-Price Dual-Driver Large-Cap and Small-Cap Rotation Strategy - **Model Construction Idea**: This model integrates macroeconomic factors and micro-level volume-price factors to predict the rotation between large-cap and small-cap styles[6][7] - **Model Construction Process**: - The model uses multiple single-factor signals, including PMI seasonal average difference, social financing growth rate, monetary liquidity, US-China interest rate spread, macro adjustment momentum, and style crowding indicators[7] - Each factor is assigned a signal value: 1 for large-cap signals, -1 for small-cap signals, and 0 for no effective signal[7] - The comprehensive score is calculated by summing the signals of all factors. If the score > 0, the portfolio is fully allocated to the CSI 300 Index; if the score < 0, it is fully allocated to the CSI 1000 Index; if the score = 0, the portfolio is equally weighted between the two indices[7] - **Model Evaluation**: The model demonstrates a high backtest win rate of 82.22% as of Q1 2025, indicating strong predictive power[6] 2. Model Name: Macro + Volume-Price Dual-Driver Value-Growth Rotation Strategy - **Model Construction Idea**: This model integrates macroeconomic factors and micro-level volume-price factors to predict the rotation between value and growth styles[12][13] - **Model Construction Process**: - The model uses multiple single-factor signals, including PMI new orders seasonal average difference, PPI-CPI growth rate, 1-year government bond yield, 3-month US bond yield, macro adjustment momentum, style crowding indicators, and market sentiment[13] - Each factor is assigned a signal value: 1 for value signals, -1 for growth signals, and 0 for no effective signal[13] - The comprehensive score is calculated by summing the signals of all factors. If the score > 0, the portfolio is fully allocated to the CSI Value Index; if the score < 0, it is fully allocated to the CSI Growth Index; if the score = 0, the portfolio is equally weighted between the two indices[13] - **Model Evaluation**: The model demonstrates a backtest win rate of 77.78% as of Q1 2025, showcasing its effectiveness in predicting style rotations[12] 3. Model Name: Industry Rotation Model (Single-Factor Multi-Strategy and Composite Factor Strategy) - **Model Construction Idea**: This model evaluates industry rotation using factors from historical fundamentals, expected fundamentals, sentiment, volume-price technicals, and macroeconomics[18][19] - **Model Construction Process**: - Single-factor multi-strategy: Constructs portfolios based on individual factors and evaluates their performance[18] - Composite factor strategy: Combines multiple factors into a composite score to rank industries and construct portfolios[18] - Both strategies select the top 5 industries from the 30 first-level industries in the CITIC classification and construct equal-weighted long portfolios[18] - **Model Evaluation**: The single-factor multi-strategy outperformed the composite factor strategy in May 2025, with higher monthly absolute and excess returns[20] --- Backtest Results of Models 1. Macro + Volume-Price Dual-Driver Large-Cap and Small-Cap Rotation Strategy - **YTD Return**: -2.41%[11] - **Annualized Return**: -5.83%[11] - **Annualized Volatility**: 17.17%[11] - **Maximum Drawdown**: 10.49%[11] - **Sharpe Ratio**: -0.34[11] - **Calmar Ratio**: -0.56[11] 2. Macro + Volume-Price Dual-Driver Value-Growth Rotation Strategy - **YTD Return**: 1.79%[17] - **Annualized Return**: 4.48%[17] - **Annualized Volatility**: 18.06%[17] - **Maximum Drawdown**: 10.36%[17] - **Sharpe Ratio**: 0.25[17] - **Calmar Ratio**: 0.43[17] 3. Industry Rotation Model - **Composite Factor Strategy**: - **Monthly Absolute Return**: 2.43%[20] - **Monthly Excess Return**: -0.64%[20] - **YTD Absolute Return**: 4.81%[20] - **YTD Excess Return**: 3.98%[20] - **Single-Factor Multi-Strategy**: - **Monthly Absolute Return**: 3.31%[20] - **Monthly Excess Return**: 0.33%[20] - **YTD Absolute Return**: 4.56%[20] - **YTD Excess Return**: 3.83%[20]

Core Insights - The report emphasizes the construction of ETF portfolios based on industry rotation models, highlighting the potential for enhanced returns through strategic sector allocation [1][2] - The industry rotation model has demonstrated stable excess returns over the backtesting period from January 2019 to April 2025, achieving a total return of 212.87%, significantly outperforming major indices like the CSI 300, CSI 500, and CSI 1000 [9][10] Industry Rotation Model - The model incorporates six factors: momentum, main buying amount, turnover rate change, deviation rate, intra-industry return deviation, and volatility, with a monthly rebalancing frequency [5][6] - The model's performance is evaluated across different market phases, showing varying factor effectiveness, with momentum and main buying amount consistently positive across the tested periods [6][8] ETF Market Overview - As of the end of 2024, the total scale of stock ETFs reached 29,259.35 billion yuan, with industry-themed ETFs accounting for 6,161.25 billion yuan, indicating a growing trend towards sector-specific investment strategies [25][26] - The report notes the increasing feasibility of using ETFs as tools for industry rotation strategies due to the expanding variety of newly issued industry-themed ETFs [25] ETF Portfolio Construction - The report outlines the construction of ETF portfolios based on the industry rotation model, recommending specific ETFs that align closely with the identified sectors [32][34] - The recommended ETF combinations for June 2025 include sectors such as oil and petrochemicals, banking, coal, transportation, steel, and agriculture, reflecting the model's latest insights [18][37]