Quantitative Models and Construction Methods 1. Model Name: Industry Mainline Model (Relative Strength Index, RSI) - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance. Industries with RS > 90% are considered potential market leaders [13] - **Model Construction Process**: 1. Use 31 first-level industry indices as the configuration targets [13] 2. Calculate the price change percentages over the past 20, 40, and 60 trading days for each industry [13] 3. Rank the industries based on their price changes for each period and normalize the rankings to obtain RS_20, RS_40, and RS_60 [13] 4. Compute the average of the three rankings to derive the final RS index: $ RS = (RS_{20} + RS_{40} + RS_{60}) / 3 $ [13] 5. Industries with RS > 90% before the end of April are identified as potential leaders for the year [13] - **Model Evaluation**: The model effectively identified leading industries in 2024, such as coal, utilities, home appliances, banks, oil and gas, telecommunications, non-ferrous metals, agriculture, and automobiles. These industries aligned with the market's main themes, including high dividends, resources, exports, and AI [13] 2. Model Name: Industry Sentiment-Trend-Crowding Framework - **Model Construction Idea**: This framework provides two right-side industry rotation strategies based on market sentiment, trend, and crowding levels [17] 1. High Sentiment + Strong Trend, avoiding high crowding (aggressive and synchronized with the market) [17] 2. Strong Trend + Low Crowding, avoiding low sentiment (trend-following and user-friendly) [17] - **Model Construction Process**: 1. Use sentiment as the core metric, combined with trend and crowding levels, to identify industries with strong potential [17] 2. Historical backtesting results show the model's annualized return and risk metrics [17] - **Model Evaluation**: The model demonstrates strong performance, with an annualized return of 22.0%, an annualized excess return of 13.4%, an IR of 1.5, and a maximum drawdown of -8.0%. The monthly win rate is 67% [17] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries in a recovery phase from distress or inventory pressure, aiming to capture turnaround opportunities during restocking cycles [27] - **Model Construction Process**: 1. Focus on industries with current or past distress but showing signs of recovery [27] 2. Evaluate long-term analyst sentiment and inventory pressure to identify industries with restocking potential [27] 3. Historical backtesting results show the model's performance metrics [27] - **Model Evaluation**: The model has shown strong historical performance, with absolute returns of 13.4% in 2023, 26.5% in 2024, and 28.7% in 2025. The excess returns relative to equal-weighted industry benchmarks were 17.0%, 15.4%, and 5.6%, respectively [27] --- Model Backtesting Results 1. Industry Mainline Model (RSI) - **2024**: Industries with RS > 90% included coal, utilities, home appliances, banks, oil and gas, telecommunications, non-ferrous metals, agriculture, and automobiles. These industries aligned with the year's main themes [13] - **2025**: 17 industries showed RS > 90%, including TMT, banks, manufacturing, and some consumer sectors [13] - **2026 (up to February 6)**: 7 industries showed RS > 90%, including media, building materials, oil and gas, non-ferrous metals, basic chemicals, defense, and telecommunications [14] 2. Industry Sentiment-Trend-Crowding Framework - **Annualized Return**: 22.0% [17] - **Annualized Excess Return**: 13.4% [17] - **IR**: 1.5 [17] - **Maximum Drawdown**: -8.0% [17] - **Monthly Win Rate**: 67% [17] - **January 2026 Performance**: Absolute return of 6.5%, excess return of 0.7% [17] 3. Left-Side Inventory Reversal Model - **2023**: Absolute return of 13.4%, excess return of 17.0% [27] - **2024**: Absolute return of 26.5%, excess return of 15.4% [27] - **2025**: Absolute return of 28.7%, excess return of 5.6% [27] - **January 2026**: Absolute return of 10.4%, excess return of 4.8% [27]

Group 1: Industry Index Pool Construction - The report outlines the construction of an industry index pool that combines investability and representativeness, focusing on passive products tracking strong industry attributes [10][12]. - Since 2020, the scale of industry ETFs has experienced explosive growth, increasing from 85.8 billion yuan at the end of 2019 to over 310 billion yuan by the end of 2020, and approaching 900 billion yuan by August 2025 [10]. - The report categorizes various industry ETFs, highlighting that TMT, financial real estate, and pharmaceutical sectors have surpassed 100 billion yuan in ETF scale [10]. Group 2: Economic Prosperity Investment Practices - The report discusses the calculation of expected ROE growth for industries based on analysts' profit forecasts, comparing two fiscal years (FY1 and FY2) [20][21]. - It emphasizes that the FY2 grouping shows stronger monotonicity in performance compared to FY1, indicating better returns for the former [23][24]. - The backtesting period for the economic prosperity factor spans from January 1, 2018, to September 12, 2025, with a focus on marginal changes in industry index prosperity [27]. Group 3: Economic Trend Resonance Strategy - The economic trend resonance strategy combines fundamental marginal improvements with capital consensus, utilizing trend factors to quantify market sentiment [36][38]. - The constructed economic trend resonance portfolio has achieved an annualized return of 12.33% since 2018, outperforming the CSI 800 index by 11.13% [40][42]. - The portfolio's monthly excess return rate stands at 64%, with a profit-loss ratio of 1.30 [45]. Group 4: Economic Trend and Crowding Avoidance Strategy - The strategy integrates economic trend analysis with crowding avoidance to mitigate risks associated with overheated trading [49]. - The three-dimensional strategy has yielded an annualized return of 12.80% since 2018, exceeding the CSI 800 index by 11.60% [52][54]. - The portfolio's monthly excess return rate is 62%, with a profit-loss ratio of 1.47 [57]. Group 5: Current Industry Characteristics - As of August 2025, the report identifies industries that align with the economic trend resonance and crowding avoidance strategy, including the transportation index, home appliances, livestock, media, and oil and gas sectors [60]. - The expected growth rates for these sectors range from 1.1% to 9.6%, with varying levels of crowding and valuation metrics [60].

Group 1 - The core viewpoint of the announcement is the approval of the company's 2025 semi-annual report and the decision to use idle funds for financial products [1][2] - The supervisory board meeting was held on August 22, 2025, with all three members present, and the meeting procedures complied with relevant laws and regulations [1] - The semi-annual report was reviewed and deemed to be true, accurate, and complete without any false records or significant omissions [1] Group 2 - The supervisory board approved the proposal to use idle self-owned funds to purchase financial products, with a limit of up to 800 million yuan [2] - The investment will be in low to medium-risk financial products issued by banks, securities companies, trust companies, and fund management companies, with a maturity of no more than 12 months [2] - The decision aims to enhance the cash management returns on idle funds while ensuring the safety of the funds [2]