Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Strategy - **Model Construction Idea**: The core idea is to identify expectation gaps in the market and enhance portfolio returns by incorporating surprise expectation factors (e.g., SUE, ROE YoY growth) [31] - **Model Construction Process**: - Based on the PB-ROE pricing model derived by Wilcox (1984), stocks with significant expectation gaps are selected to form a pool - From this pool, 50 stocks are selected using factors such as standardized unexpected earnings (SUE) and ROE YoY growth to construct the PB-ROE-50 portfolio [31] - **Model Evaluation**: The strategy achieved positive excess returns across different stock pools, demonstrating its effectiveness in capturing market expectation gaps [31] 2. Model Name: Institutional Research Strategy - **Model Construction Idea**: This strategy leverages public and private institutional research data to extract alpha by analyzing the frequency of company visits and stock performance relative to benchmarks before the visits [39] - **Model Construction Process**: - Public Research Selection: Stocks are selected based on the number of visits by public institutions and their relative performance to the CSI 800 index - Private Research Tracking: Stocks are selected based on the number of visits by well-known private institutions and their relative performance to the CSI 800 index [39] - **Model Evaluation**: Both public and private research strategies generated significant positive excess returns, indicating the value of institutional research data in stock selection [39] --- Model Backtesting Results 1. PB-ROE-50 Strategy - **Excess Return (YTD)**: - CSI 500: 3.62% - CSI 800: 9.73% - All Market: 10.36% [35] - **Excess Return (Last Month)**: - CSI 500: 0.59% - CSI 800: 2.91% - All Market: 2.34% [35] - **Absolute Return (YTD)**: - CSI 500: 12.68% - CSI 800: 15.10% - All Market: 20.07% [35] - **Absolute Return (Last Month)**: - CSI 500: 5.88% - CSI 800: 7.02% - All Market: 6.77% [35] 2. Institutional Research Strategy - **Excess Return (YTD)**: - Public Research: 7.03% - Private Research: 18.00% [42] - **Excess Return (Last Month)**: - Public Research: 3.66% - Private Research: 5.58% [42] - **Absolute Return (YTD)**: - Public Research: 12.26% - Private Research: 23.77% [42] - **Absolute Return (Last Month)**: - Public Research: 7.80% - Private Research: 9.80% [42] --- Quantitative Factors and Construction Methods 1. Factor Name: Percentage of Advancing Stocks (Market Sentiment Indicator) - **Factor Construction Idea**: Strong-performing stocks often exhibit a demonstration effect, and the percentage of advancing stocks can reflect market sentiment. A higher percentage indicates optimism, while an overly high percentage may signal overheating [12] - **Factor Construction Process**: - Formula: $ \text{Percentage of Advancing Stocks (N days)} = \frac{\text{Number of CSI 300 stocks with positive returns over N days}}{\text{Total number of CSI 300 stocks}} $ - The indicator is smoothed using two moving averages (N1 = 50, N2 = 35). When the short-term average (fast line) exceeds the long-term average (slow line), it signals a bullish market sentiment [12][13][15] - **Factor Evaluation**: The indicator effectively captures upward opportunities but struggles to avoid risks in declining markets. It may also miss gains during prolonged market exuberance [12] 2. Factor Name: Moving Average Sentiment Indicator - **Factor Construction Idea**: This factor uses an eight-moving-average system to assess the trend state of the CSI 300 index. By assigning values to different ranges of the moving average, the relationship between indicator states and index trends becomes clearer [20] - **Factor Construction Process**: - Calculate the eight moving averages of the CSI 300 closing price (parameters: 8, 13, 21, 34, 55, 89, 144, 233) - Assign values based on the range of the moving averages: - Range 1/2/3: -1 - Range 4/5/6: 0 - Range 7/8/9: 1 - A bullish signal is generated when the number of moving averages below the current price exceeds 5 [20][26] - **Factor Evaluation**: The indicator provides a clear relationship between sentiment states and index trends, aiding in market timing [20] 3. Factor Name: Leverage Ratios (Debt Indicators) - **Factor Construction Idea**: High leverage ratios indicate greater debt pressure and liquidity risks. Three calculation methods (traditional, strict, and relaxed) are used to assess leverage comprehensively [44] - **Factor Construction Process**: - Traditional Leverage Ratio: $ \text{Traditional Leverage Ratio} = \frac{\text{Short-term Debt + Long-term Debt + Bonds Payable}}{\text{Total Assets}} $ - Strict Leverage Ratio: $ \text{Strict Leverage Ratio} = \frac{\text{Short-term Debt + Interest Payable + Financial Liabilities + Short-term Bonds + Lease Liabilities + Long-term Debt + Bonds Payable + Long-term Payables}}{\text{Total Assets}} $ - Relaxed Leverage Ratio: $ \text{Relaxed Leverage Ratio} = \frac{\text{Strict Leverage Components + Other Current Liabilities + Liabilities Held for Sale + Non-current Liabilities Due Within One Year}}{\text{Total Assets}} $ [44] - **Factor Evaluation**: The relaxed leverage ratio provides more opportunities for short positions compared to traditional metrics [44] 4. Factor Name: Financial Cost Burden Ratio - **Factor Construction Idea**: This factor measures the pressure of interest payments on companies by isolating interest expenses from financial costs, providing a clearer view of financial burdens [48] - **Factor Construction Process**: - Formula: $ \text{Financial Cost Burden Ratio} = \frac{\text{Interest Expenses}}{\text{EBIT}} $ [48] - **Factor Evaluation**: The factor effectively highlights companies with high financial stress, aiding in risk identification [48] --- Factor Backtesting Results 1. Percentage of Advancing Stocks - **Latest Value**: Above 70% as of July 31, 2025, indicating high market sentiment [12] 2. Moving Average Sentiment Indicator - **Latest State**: CSI 300 index is in a sentiment boom zone as of July 31, 2025 [20] 3. Leverage Ratios - **Top Stocks by Relaxed Leverage Ratio**: - Example: Dizhiyiyao-U (64.10%), Shenzhouxibao (64.06%), Zhongyida (59.68%) [45] 4. Financial Cost Burden Ratio - **Top Stocks by Financial Cost Burden**: - Example: Liaoning Chengda (241084.42), Yinbaoshanxin (2314.41), Ashichuang (69.43) [49]

- The report includes multiple quantitative models for A-share market timing, such as the "Volume Model," "Low Volatility Model," "Feature Institutional Model," "Feature Volume Model," "Smart Algorithm Model," and "Long-term Momentum Model" [12][13][14][76] - The "Volume Model" indicates a bullish signal for most broad-based indices in the short term [12][76] - The "Low Volatility Model" provides a neutral signal for the short term [12][76] - The "Feature Institutional Model" shows a bearish signal for the short term [12][76] - The "Feature Volume Model" indicates a bullish signal for the short term [12][76] - The "Smart Algorithm Model" shows bullish signals for the CSI 300 and CSI 500 indices in the short term [12][76] - The "Long-term Momentum Model" flips to bullish for the SSE 50 index in the long term [14][78] - The "Comprehensive Weapon V3 Model" and "Comprehensive Guozheng 2000 Model" indicate bullish signals for the A-share market [15][79] - For the Hong Kong market, the "Turnover-to-Volatility Model" provides a bullish signal for the mid-term [16][80] - Backtesting results for the "Double Bottom Pattern" show a weekly return of 1.73%, outperforming the SSE Composite Index by 0.05% [46][53] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly return of 2.87%, outperforming the SSE Composite Index by 1.2% [46][47]

Core Insights - Quantitative private equity has shown significant performance differentiation in the market this year, with small and mid-cap strategies outperforming large-cap strategies, reflecting structural changes in the market that deeply impact different investment strategies [1] Group 1: Performance of Quantitative Strategies - As of July 11, the Quantitative 1000 index enhancement strategy has performed the best, with Lingjun Investment leading at a 36.79% year-to-date return, while other institutions like Xinhong Tianhe, Longqi, and Qilin also surpassed the 30% mark [3] - The Quantitative 500 index enhancement strategy also performed well, with Xinhong Tianhe and Abama's related products achieving over 30% year-to-date returns [3] - In contrast, the Quantitative 300 index enhancement strategy lagged, with the highest year-to-date return at only 19.13% [3] - The Quantitative stock selection strategy demonstrated the strongest profitability, with Xiaoyong's strategy leading the market at 46.26% year-to-date return, and other institutions like Ruishengming and Ziwuyou also exceeding 40% [3] Group 2: Market Trends and Structural Changes - The market this year has clearly favored small and mid-cap stocks, providing abundant sources of excess returns for related quantitative strategies [4] - The CSI 1000 index, primarily composed of small and mid-cap stocks, has significantly outperformed the CSI 300 index, benefiting from policies favoring specialized and innovative enterprises [4] - The lower research coverage of small and mid-cap stocks leads to more pricing discrepancies, creating opportunities for quantitative strategies to capture excess returns [4] - Increased market volatility has also created a favorable environment for quantitative strategies, as small and mid-cap stocks typically exhibit higher volatility, allowing strategies to profit from capturing liquidity premiums [4] Group 3: Scale Effects and Strategy Differentiation - Billion-yuan private equity firms exhibit clear scale advantages in index enhancement strategies, dominating the top 20 in both the Quantitative 1000 and 500 index enhancement strategies [5] - Large institutions, with assets under management exceeding 5 billion, achieved an average return of 18.30% in their index enhancement products, with a staggering 99.25% of products generating positive excess returns [5] - Medium-sized private equity firms had an average return of 17.30%, while small firms saw their average return drop to 16.41% [5] - The performance differentiation among quantitative private equity firms is increasingly evident, with over a 15 percentage point difference between the highest and the 20th return in the Quantitative 1000 index enhancement strategy [5]

AI基金摩根红利优选股票A（021187）披露2025年二季报，第二季度基金利润64.92万元，加权平均基金份额本期利润0.0195元。报告期内，基金净值增长率 为2.05%，截至二季度末，基金规模为3482.17万元。 该基金属于标准股票型基金，长期投资于周期股票。截至7月21日，单位净值为1.165元。基金经理是胡迪、何智豪和韩秀一。 基金管理人在二季报中表示，报告期内，本基金保持高仓位运作，股票仓位部分以中证红利为基准，通过量化选股模型构建股票组合。未来在公司基本面经 营稳健，股东回报力度持续改善的前提下，我们将维持高仓位运作。量化模型也会在实际运行过程中将进行定期动态调整，力争股票配置最优化，以期持续 超越业绩比较基准收益率的投资目标。 截至7月21日，摩根红利优选股票A近三个月复权单位净值增长率为7.61%，位于同类可比基金13/18；近半年复权单位净值增长率为8.31%，位于同类可比基 金14/18。 通过所选区间该基金净值增长率分位图，可以观察该基金与同类基金业绩比较情况。图为坐标原点到区间内某时点的净值增长率在同类基金中的分位数。 据定期报告数据统计，成立以来平均股票仓位为90.7%，同类平均 ...

Core Viewpoint - The article provides a comprehensive performance tracking of various active quantitative strategies by GuoXin JinGong, focusing on their absolute and excess returns compared to the mixed equity fund index, highlighting the effectiveness of these strategies in outperforming the market [2][3][4]. Group 1: Performance Tracking of Quantitative Strategies - The "Excellent Fund Performance Enhancement Portfolio" achieved an absolute return of 2.75% this week and 10.32% year-to-date, ranking in the 45.63 percentile among active equity funds [1][12]. - The "Super Expectation Selected Portfolio" recorded an absolute return of 3.68% this week and 24.40% year-to-date, ranking in the 11.53 percentile among active equity funds [1][9]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" had an absolute return of 1.91% this week and 14.13% year-to-date, ranking in the 31.39 percentile among active equity funds [1][21]. - The "Growth and Stability Portfolio" posted an absolute return of 2.15% this week and 29.61% year-to-date, ranking in the 7.26 percentile among active equity funds [1][22]. Group 2: Strategy Descriptions - The "Excellent Fund Performance Enhancement Portfolio" aims to outperform the median return of active equity funds by utilizing quantitative methods based on the holdings of top-performing funds [4][34]. - The "Super Expectation Selected Portfolio" selects stocks based on positive earnings surprises and analyst upgrades, focusing on both fundamental and technical analysis [9][38]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" is constructed using a stock pool from brokerage recommendations, optimizing for individual stock and style deviations [16][42]. - The "Growth and Stability Portfolio" employs a two-dimensional evaluation system for growth stocks, prioritizing those with upcoming earnings announcements to capture excess returns [19][47]. Group 3: Historical Performance - The "Excellent Fund Performance Enhancement Portfolio" has achieved an annualized return of 20.31% from January 2012 to June 2025, outperforming the mixed equity fund index by 11.83% [35][37]. - The "Super Expectation Selected Portfolio" has an annualized return of 30.55% since January 2010, exceeding the mixed equity fund index by 24.68% [39][41]. - The "Brokerage Golden Stock Performance Enhancement Portfolio" has an annualized return of 19.34% from January 2018 to June 2025, outperforming the mixed equity fund index by 14.38% [43][46]. - The "Growth and Stability Portfolio" has achieved an annualized return of 35.51% since January 2012, exceeding the mixed equity fund index by 26.88% [48].

Core Viewpoint - The article discusses the evolution and current state of quantitative stock selection strategies in the private equity sector, emphasizing their performance and adaptability in various market conditions [3][4][5]. Group 1: Historical Development - Quantitative stock selection began to gain traction around 2021, as traditional index-enhanced strategies struggled due to a sluggish market, leading many top private equity managers to explore new avenues [4]. - In 2022, the flexibility and anti-drawdown characteristics of quantitative stock selection became apparent, with top-performing products achieving positive returns despite market turbulence [4]. - By 2023, the strategy gained mainstream acceptance, with nearly 90% of quantitative stock selection products yielding positive returns, significantly outperforming major indices [5]. Group 2: Performance and Market Conditions - The first half of 2024 saw a resurgence in quantitative stock selection performance, with some managers reporting returns exceeding 50%, driven by high trading volumes and increased market volatility [6]. - The article highlights that a conducive environment for quantitative strategies includes high trading volumes and volatility, which have been prevalent since the 2023 market rally [8]. Group 3: Investment Timing Concerns - Investors express concerns about entering the market at high points, particularly as the index approaches 3500 points, a level historically associated with bull markets [9][11]. - The article suggests that the timing of entry is less critical for quantitative stock selection, as the strategy is not tied to specific indices and can adapt to various market conditions [13]. Group 4: Specific Fund Analysis - Two private equity funds are highlighted: - Fund A has achieved a 36% return this year and 117% over the past year, utilizing a multi-factor strategy with high turnover and leverage [14][15]. - Fund B has reported a 30% return this year and 83% over the past year, employing a high-frequency trading strategy with low correlation to other market participants [16][17].

Group 1 - The "Bank AH + Small Micro Plate" portfolio has achieved a historical high, increasing by 54.97% from last year, with a maximum drawdown of 13.89% [1] - The portfolio's performance has outpaced major indices, with only the CSI 2000 showing a higher growth rate, but with a larger maximum drawdown of 19.65% [1] - The portfolio consists of 40% Bank AH Preferred ETF (517900), 30% 1000 ETF Enhanced (159680), and 30% CSI 2000 Enhanced ETF (159552), employing a "high dividend base + enhanced growth assets" strategy [2][4] Group 2 - The Bank AH Preferred ETF (517900) has shown significant growth, increasing by 24% since the beginning of 2025, with a 411% surge in fund shares [4][6] - The low interest rate environment and the decline in 10-year government bonds have created a demand for bank stocks due to their high dividend and strong risk-averse attributes [6] - The dynamic adjustment mechanism of the Bank AH index allows for the identification of undervalued bank stocks, enhancing returns while providing stability [6] Group 3 - The portfolio's structure is designed to provide a safety net with high dividends while pursuing growth through small-cap stocks, which combine index beta and excess alpha [7] - The CSI 2000 Enhanced ETF (159552) has achieved a net value growth rate of 29.18% in the first half of the year, ranking first among similar broad-based ETFs [9] - Since its inception, the CSI 2000 Enhanced ETF has accumulated a net value growth of 68.21%, significantly outperforming the CSI 2000 index [10] Group 4 - Two signals support the continuation of the small-cap stock trend: ongoing liquidity support and the release of policy dividends from mergers and acquisitions regulations [11] - The CSI 2000 Enhanced ETF (159552) demonstrates the effectiveness of quantitative discipline in achieving sustained excess returns [12]

Market Sentiment Tracking - As of June 30, 2025, the proportion of rising stocks in the CSI 300 has increased month-on-month, with over 60% of stocks rising, indicating high market sentiment [3] - The momentum sentiment indicator shows a fast line moving upwards and a slow line moving downwards, with the fast line above the slow line, suggesting a bullish outlook in the near term [3] - The short-term CSI 300 index is in a sentiment boom range [3] Fund Separation Degree Tracking - As of June 30, 2025, the fund separation degree has slightly increased and is currently at a low level, indicating a high degree of fund clustering [4] - The excess returns of clustered stocks have slightly increased, while the excess returns of clustered funds have slightly decreased [4] PB-ROE-50 Strategy Tracking - In June 2025, the PB-ROE-50 strategy achieved positive excess returns across various stock pools [5] - The strategy based on the CSI 500 stock pool gained an excess return of 0.92% [5] - The strategy based on the CSI 800 stock pool achieved an excess return of 3.92% [5] - The strategy based on the entire market stock pool obtained an excess return of 4.59% [5] Institutional Research Strategy Tracking - In June 2025, both public and private research selection strategies generated positive excess returns [6] - The public research selection strategy achieved an excess return of 5.55% relative to the CSI 800 [6] - The private research tracking strategy gained an excess return of 1.90% relative to the CSI 800 [6] Negative List - As of June 30, 2025, several stocks with high interest-bearing debt ratios ranked poorly, including Zhongyida, Guiding Compass, and Modern Investment, among others [7] - Stocks with high financial cost burden ratios include Liao Ning Cheng Da, Yin Bao Shan Xin, and A Shi Chuang, with all indicators exceeding 10 times [7]

Quantitative Models and Construction Methods - **Model Name**: Competitive Advantage Portfolio **Construction Idea**: Focuses on analyzing industry competition barriers and identifying companies with unique management advantages in various industry categories such as "Shielded Barriers," "Intense Competition," "Steady Progress," and "Seeking Breakthroughs" [13][14] **Construction Process**: Combines "Shielded Barriers" industries with "Dominant + Cooperative Win-Win" companies and "Efficient Operations" companies in non-barrier industries to form the Competitive Advantage Portfolio [14] **Evaluation**: Provides a differentiated value quantification perspective compared to traditional factor investment [13] - **Model Name**: Safety Margin Portfolio **Construction Idea**: Emphasizes the gap between intrinsic value and market value, focusing on companies with sustainable competitive advantages and high ROIC [18] **Construction Process**: Calculates intrinsic value based on profitability metrics, selects the top 50 stocks with the highest safety margin from a competitive advantage stock pool, and weights them by dividend yield [18][20] **Evaluation**: Highlights the importance of intrinsic value estimation and sustainable profitability [18] - **Model Name**: Dividend Low Volatility Adjusted Portfolio **Construction Idea**: Avoids "high dividend traps" by focusing on sustainable profitability and excluding stocks with extreme price performance or abnormal debt ratios [25] **Construction Process**: Implements predictive models for dividend yield and applies negative screening criteria to optimize the portfolio [25] **Evaluation**: Addresses the risks of chasing high dividend yields without considering long-term value [25] - **Model Name**: AEG Valuation Potential Portfolio **Construction Idea**: Invests in companies with abnormal earnings growth (AEG) that exceed opportunity costs, focusing on undervalued growth potential [30][34] **Construction Process**: Uses the AEG_EP factor to select the top 100 stocks, then narrows down to the top 50 stocks with high dividend reinvestment/P ratios [34] **Evaluation**: Incorporates growth premiums into valuation models, providing a comprehensive perspective on future earnings potential [30][31] - **Model Name**: Cash Cow Portfolio **Construction Idea**: Evaluates companies based on free cash flow (FCF) and cash flow return (CFOR) to assess profitability and cash generation efficiency [38][40] **Construction Process**: Combines CFOR decomposition with ROE decomposition, focusing on high-quality stocks within the CSI 800 index [39][40] **Evaluation**: Enhances traditional DuPont analysis by integrating cash flow metrics for a more comprehensive evaluation [38] - **Model Name**: Large Model AI Stock Selection Portfolio **Construction Idea**: Utilizes FinLLM to process unstructured financial texts and integrates multi-dimensional validation methods such as chain-of-thought reasoning (COT), comparative analysis, and counterfactual reasoning [44][47] **Construction Process**: Applies FinLLM to extract signals from financial texts and uses a triangular validation system to ensure decision-making robustness [47][48] **Evaluation**: Overcomes limitations of traditional models by leveraging AI for non-structured data analysis and improving prediction accuracy [44][47] - **Model Name**: Governance Efficiency Portfolio **Construction Idea**: Analyzes MD&A disclosures to evaluate management transparency, financial consistency, and long-term value creation [53][54] **Construction Process**: Combines short-term profit guidance and financial consistency factors to form a base portfolio, then selects top 50 stocks using PB_ROE factor for valuation and profitability [57] **Evaluation**: Provides insights into management quality and strategic alignment, emphasizing governance as a key alpha source [53][57] --- Model Backtesting Results - **Competitive Advantage Portfolio**: Annualized return 20.41%, Sharpe ratio 0.93, IR 0.12, max drawdown -19.32%, Calmar ratio 1.06 [17] - **Safety Margin Portfolio**: Annualized return 20.27%, Sharpe ratio 1.02, IR 0.13, max drawdown -16.89%, Calmar ratio 1.20 [22] - **Dividend Low Volatility Adjusted Portfolio**: Annualized return 17.36%, Sharpe ratio 1.00, IR 0.15, max drawdown -21.61%, Calmar ratio 0.80 [26] - **AEG Valuation Potential Portfolio**: Annualized return 23.33%, Sharpe ratio 1.11, IR 0.16, max drawdown -24.04%, Calmar ratio 0.97 [36] - **Cash Cow Portfolio**: Annualized return 13.56%, Sharpe ratio 0.66, IR 0.13, max drawdown -19.80%, Calmar ratio 0.68 [42] - **Large Model AI Stock Selection Portfolio**: Annualized return 16.53%, Sharpe ratio 0.71, IR 0.17, max drawdown -33.01%, Calmar ratio 0.50 [49] - **Governance Efficiency Portfolio**: Annualized return 11.00%, Sharpe ratio 0.51, IR 0.23, max drawdown -23.74%, Calmar ratio 0.46 [59]

Quantitative Models and Construction 1. Model Name: Volume Model - **Model Construction Idea**: This model evaluates market timing based on trading volume dynamics[10][64] - **Model Evaluation**: The model currently signals a neutral stance for the short term[10][64] 2. Model Name: Low Volatility Model - **Model Construction Idea**: This model assesses market timing by analyzing low volatility trends in the market[10][64] - **Model Evaluation**: The model currently signals a neutral stance for the short term[10][64] 3. Model Name: Institutional Feature Model (Dragon-Tiger List) - **Model Construction Idea**: This model uses institutional trading features from the Dragon-Tiger list to predict market movements[10][64] - **Model Evaluation**: The model currently signals a bearish outlook for the short term[10][64] 4. Model Name: Feature Volume Model - **Model Construction Idea**: This model leverages specific volume features to predict market trends[10][64] - **Model Evaluation**: The model currently signals a bearish outlook for the short term[10][64] 5. Model Name: Intelligent CSI 300 Model - **Model Construction Idea**: This model applies intelligent algorithms to predict movements in the CSI 300 index[10][64] - **Model Evaluation**: The model currently signals a bullish outlook for the short term[10][64] 6. Model Name: Intelligent CSI 500 Model - **Model Construction Idea**: This model applies intelligent algorithms to predict movements in the CSI 500 index[10][64] - **Model Evaluation**: The model currently signals a bearish outlook for the short term[10][64] 7. Model Name: Limit-Up/Down Model - **Model Construction Idea**: This model evaluates market timing based on the frequency of limit-up and limit-down events[11][65] - **Model Evaluation**: The model currently signals a bullish outlook for the mid-term[11][65] 8. Model Name: Calendar Effect Model - **Model Construction Idea**: This model incorporates calendar-based patterns to predict market movements[11][65] - **Model Evaluation**: The model currently signals a neutral stance for the mid-term[11][65] 9. Model Name: Long-Term Momentum Model - **Model Construction Idea**: This model evaluates long-term market trends using momentum indicators[12][66] - **Model Evaluation**: The model currently signals a neutral stance across all broad-based indices for the long term[12][66] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Model Construction Idea**: This model integrates multiple signals to provide a comprehensive market timing prediction[13][67] - **Model Evaluation**: The model currently signals a bearish outlook for the A-share market[13][67] 11. Model Name: A-Share Comprehensive CSI 2000 Model - **Model Construction Idea**: This model focuses on the CSI 2000 index, combining various timing signals[13][67] - **Model Evaluation**: The model currently signals a neutral stance for the A-share market[13][67] 12. Model Name: Turnover-to-Volatility Model (Hong Kong Market) - **Model Construction Idea**: This model evaluates market timing in the Hong Kong market by analyzing turnover relative to volatility[14][68] - **Model Evaluation**: The model currently signals a bullish outlook for the mid-term[14][68] --- Model Backtesting Results 1. Volume Model - **Short-Term Signal**: Neutral[10][64] 2. Low Volatility Model - **Short-Term Signal**: Neutral[10][64] 3. Institutional Feature Model (Dragon-Tiger List) - **Short-Term Signal**: Bearish[10][64] 4. Feature Volume Model - **Short-Term Signal**: Bearish[10][64] 5. Intelligent CSI 300 Model - **Short-Term Signal**: Bullish[10][64] 6. Intelligent CSI 500 Model - **Short-Term Signal**: Bearish[10][64] 7. Limit-Up/Down Model - **Mid-Term Signal**: Bullish[11][65] 8. Calendar Effect Model - **Mid-Term Signal**: Neutral[11][65] 9. Long-Term Momentum Model - **Long-Term Signal**: Neutral across all broad-based indices[12][66] 10. A-Share Comprehensive Weapon V3 Model - **Comprehensive Signal**: Bearish[13][67] 11. A-Share Comprehensive CSI 2000 Model - **Comprehensive Signal**: Neutral[13][67] 12. Turnover-to-Volatility Model (Hong Kong Market) - **Mid-Term Signal**: Bullish[14][68]