Quantitative Models and Construction Methods 1. Model Name: Timing System Signal (Wind All A Moving Average Distance Model) - **Model Construction Idea**: This model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the Wind All A Index to determine the market's overall trend. A positive and expanding distance indicates an upward trend[2][9]. - **Model Construction Process**: 1. Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the Wind All A Index. - Latest values: 20-day MA = 5658, 120-day MA = 5241[2][9]. 2. Compute the percentage difference between the two moving averages: $ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} \times 100\% $ - Current distance = 7.96%[2][9]. 3. Interpret the signal: If the distance is greater than 3% and positive, the market is in an upward trend[2][9]. - **Model Evaluation**: The model effectively captures the market's upward momentum and provides a clear signal for maintaining high equity positions during positive trends[2][9]. 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies industries with potential for medium-term outperformance based on factors such as policy support, valuation, and growth trends[2][10]. - **Model Construction Process**: 1. Analyze industry-specific drivers, including policy incentives and growth catalysts. 2. Identify sectors with "distressed reversal" characteristics or benefiting from policy-driven growth. 3. Recommend sectors such as innovative pharmaceuticals, securities insurance, photovoltaics, coal, and non-ferrous metals. 4. Use the TWO BETA model to emphasize technology-related sectors, including military, computing power, and batteries[2][10]. - **Model Evaluation**: The model provides actionable insights for sector rotation, aligning with macroeconomic and policy trends[2][10]. 3. Model Name: Position Management Model - **Model Construction Idea**: This model determines optimal equity allocation levels based on valuation metrics and market trends[3][10]. - **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index using PE and PB ratios. - Current PE: 70th percentile (moderate level). - Current PB: 30th percentile (low level)[3][10]. 2. Combine valuation analysis with timing signals (e.g., moving average distance and profit-making effect). 3. Recommend equity allocation levels based on the above factors. - Current recommendation: 80% equity allocation[3][10]. - **Model Evaluation**: The model balances valuation and trend analysis, providing a systematic approach to equity allocation[3][10]. --- Model Backtesting Results 1. Timing System Signal - Moving average distance: 7.96% (greater than the 3% threshold, indicating an upward trend)[2][9]. 2. Industry Allocation Model - Recommended sectors: Innovative pharmaceuticals, securities insurance, photovoltaics, coal, non-ferrous metals, military, computing power, and batteries[2][10]. 3. Position Management Model - PE: 70th percentile (moderate level)[3][10]. - PB: 30th percentile (low level)[3][10]. - Recommended equity allocation: 80%[3][10]. --- Quantitative Factors and Construction Methods 1. Factor Name: Profit-Making Effect - **Factor Construction Idea**: This factor measures the market's ability to generate profits for investors, serving as a key indicator of market sentiment and potential capital inflows[2][10]. - **Factor Construction Process**: 1. Calculate the profit-making effect value based on market performance. - Current value: 3.73% (positive)[2][10]. 2. Interpret the signal: A positive value indicates sustained investor confidence and potential for further capital inflows[2][10]. - **Factor Evaluation**: The factor is a reliable indicator of market sentiment, supporting timing and allocation decisions[2][10]. --- Factor Backtesting Results 1. Profit-Making Effect - Current value: 3.73% (positive, indicating sustained market confidence)[2][10].

Quantitative Models and Construction Methods 1. Model Name: "Three-Dimensional Composite Timing Signal" - **Model Construction Idea**: This model integrates three key timing indicators—"Credit Impulse, Beta Dispersion, and Trading Volume"—to represent three core timing dimensions: economic fundamentals, overall sentiment, and structural risk. It aims to balance high probability and high payoff indicators for superior timing performance[5][12]. - **Model Construction Process**: - **Credit Impulse**: Measures the month-on-month change in credit balance percentile, reflecting economic fundamentals[5][15]. - **Beta Dispersion**: Captures the dispersion of stock betas, representing market sentiment and structural risk[5][12]. - **Trading Volume**: Quantifies market activity and liquidity, serving as a sentiment indicator[5][12]. - The composite signal combines these three indicators to generate timing signals, with historical backtesting showing strong in-sample and out-of-sample performance[12][14]. - **Model Evaluation**: The model demonstrates excellent timing performance in both in-sample and out-of-sample tests, effectively capturing market uptrends[12][14]. 2. Model Name: "Short-Term Timing Strategy" - **Model Construction Idea**: This model uses macroeconomic, valuation, sentiment, and liquidity indicators to generate weekly timing signals[20][23]. - **Model Construction Process**: - **Macroeconomic Indicators**: Includes PMI (>50 for optimism), credit impulse percentile (62.71%), and M1 growth rate percentile (96.61%)[20][23]. - **Valuation Indicators**: PE and PB percentiles (99.59% and 96.36%, respectively) are used to assess valuation levels[21][23]. - **Sentiment Indicators**: Beta dispersion (69.49%), trading volume sentiment (93.80%), and volatility (11.00%) are analyzed for market sentiment[21][23]. - **Liquidity Indicators**: Monetary rate (37.29%), exchange rate expectations (74.58%), and financing data (97.11%) are used to evaluate liquidity conditions[22][23]. - Signals are aggregated to determine overall market positioning[23]. - **Model Evaluation**: The strategy has consistently outperformed the benchmark, with significant annualized returns and lower drawdowns[22][23]. 3. Model Name: "Growth-Value Style Rotation Model" - **Model Construction Idea**: This model evaluates macroeconomic, valuation, and sentiment factors to determine the optimal allocation between growth and value styles[29][30]. - **Model Construction Process**: - **Macroeconomic Factors**: Profit cycle slope (4.17), interest rate cycle level (14.17), and credit cycle changes (-3.33) are analyzed[31]. - **Valuation Factors**: PE and PB valuation spreads (23.99% and 39.00%, respectively) are used to assess relative attractiveness[31]. - **Sentiment Factors**: Turnover and volatility spreads (38.13% and 19.97%, respectively) are considered for sentiment analysis[31]. - Signals are combined to recommend allocations between growth and value styles[31]. - **Model Evaluation**: The model has delivered significant excess returns over the benchmark since 2012, though it underperformed in 2025 YTD[30][32]. 4. Model Name: "Small-Cap vs. Large-Cap Style Rotation Model" - **Model Construction Idea**: This model evaluates macroeconomic, valuation, and sentiment factors to determine the optimal allocation between small-cap and large-cap styles[33][34]. - **Model Construction Process**: - **Macroeconomic Factors**: Profit cycle slope (4.17), interest rate cycle level (14.17), and credit cycle changes (-3.33) are analyzed[35]. - **Valuation Factors**: PE and PB valuation spreads (93.88% and 97.67%, respectively) are used to assess relative attractiveness[35]. - **Sentiment Factors**: Turnover and volatility spreads (81.01% and 51.58%, respectively) are considered for sentiment analysis[35]. - Signals are combined to recommend allocations between small-cap and large-cap styles[35]. - **Model Evaluation**: The model has consistently outperformed the benchmark since 2012, though it underperformed in 2025 YTD[34][36]. 5. Model Name: "Four-Style Rotation Model" - **Model Construction Idea**: This model integrates the conclusions of the growth-value and small-cap-large-cap rotation models to recommend allocations across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[37]. - **Model Construction Process**: - Combines the signals from the growth-value and small-cap-large-cap models to allocate weights across the four styles[37]. - Current recommended allocation: small-cap growth (37.5%), small-cap value (12.5%), large-cap growth (37.5%), and large-cap value (12.5%)[37]. - **Model Evaluation**: The model has delivered significant excess returns over the benchmark since 2012, though it underperformed in 2025 YTD[37][38]. --- Model Backtesting Results 1. "Three-Dimensional Composite Timing Signal" - Annualized Return: 21.26% - Annualized Volatility: 14.46% - Maximum Drawdown: 12.80% - Sharpe Ratio: 1.2676 - Annualized Excess Return: 13.39%[14] 2. "Short-Term Timing Strategy" - Annualized Return: 17.83% - Annualized Volatility: 15.87% - Maximum Drawdown: 22.44% - Sharpe Ratio: 0.9874 - Annualized Excess Return: 13.24%[22][27] 3. "Growth-Value Style Rotation Model" - Annualized Return: 11.76% - Annualized Volatility: 20.77% - Maximum Drawdown: 43.07% - Sharpe Ratio: 0.5438 - Annualized Excess Return: 4.73%[30][32] 4. "Small-Cap vs. Large-Cap Style Rotation Model" - Annualized Return: 12.45% - Annualized Volatility: 22.65% - Maximum Drawdown: 50.65% - Sharpe Ratio: 0.5441 - Annualized Excess Return: 5.21%[34][36] 5. "Four-Style Rotation Model" - Annualized Return: 13.37% - Annualized Volatility: 21.51% - Maximum Drawdown: 47.91% - Sharpe Ratio: 0.5988 - Annualized Excess Return: 5.72%[37][38]

Core Viewpoint - The recent market performance shows a significant increase in the ChiNext and Sci-Tech 50 indices, while large-cap value stocks have declined, indicating a shift in investor sentiment towards growth sectors [1][2]. Market Performance - In the last five trading days, the Sci-Tech 50 index rose by 5.53%, the ChiNext index increased by 8.48%, while the large-cap value index fell by 0.76%. The large-cap growth index rose by 3.63%, and the Shanghai 50 index increased by 1.57%. Small-cap stocks represented by the CSI 2000 index rose by 3.86% [1]. - The communication and electronics sectors performed well, while the banking and steel sectors lagged behind [1]. Risk Premium Analysis - The risk premium, measured as the difference between the inverse of the static PE of the CSI All Share Index and the yield of ten-year government bonds, has reached historical extremes. As of October 28, 2022, the risk premium was at 4.08%, indicating a potential market rebound [1]. - The risk premium has exceeded 4% for the fifth time since 2016, with the latest reading on January 19, 2024, at 4.11% [1]. Valuation Levels - As of August 15, 2025, the CSI All Share Index's TTM PE is at the 72nd percentile, with the Shanghai 50 and CSI 300 at 69% and 63%, respectively. The ChiNext index is at a relatively low valuation level of approximately 33% [2]. - The long-term view of the Deep 100 index suggests a cyclical pattern of bear and bull markets every three years, with the current adjustment phase starting in Q1 2021 showing sufficient time and space for a potential upward cycle [2]. Fund Flow and Trading Activity - In the last five trading days, there was an outflow of 10.4 billion yuan from ETFs, while margin financing increased by approximately 41.8 billion yuan. The average daily trading volume across both markets was 20,767 billion yuan [3]. AI and Trend Observation - The use of convolutional neural networks (CNN) for modeling price and volume data has been explored, with the latest focus on mapping learned features to industry themes, particularly in the communication sector [8].

- The Black-Litterman model (BL model) is used for asset allocation, combining investor views with market equilibrium[17][20] - The construction process of the BL model involves adjusting the expected returns based on investor views and then optimizing the portfolio using mean-variance optimization[17][20] - The Risk Parity model aims to allocate risk equally across all assets in a portfolio, rather than allocating capital equally[27][30] - The construction process of the Risk Parity model involves calculating the risk contribution of each asset and solving an optimization problem to equalize these contributions[28][29][30] - The Counter-Cyclical Allocation model adjusts asset allocation based on economic cycles, aiming to reduce risk during downturns and increase exposure during upturns[11][43] - The Macro Momentum Timing model uses macroeconomic indicators to time market entries and exits, aiming to capture trends and avoid downturns[11][60] - The Sentiment Timing model uses investor sentiment indicators to time market entries and exits, aiming to capitalize on market overreactions[67] Model Performance Metrics - **Black-Litterman Model**: Annualized return 6.58%, maximum drawdown 3.18%, annualized volatility 2.15%, Sharpe ratio 1.86, Calmar ratio 2.07[22][24] - **Risk Parity Model**: Annualized return 6.07%, maximum drawdown 3.78%, annualized volatility 2.26%, Sharpe ratio 1.58, Calmar ratio 1.61[31] - **Counter-Cyclical Allocation Model**: Annualized return 7.36%, maximum drawdown 8.85%, annualized volatility 6.12%, Sharpe ratio 1.13, Calmar ratio 0.85[43][47] - **Macro Momentum Timing Model**: Annualized return 7.06%, maximum drawdown 6.60%, annualized volatility 6.06%, Sharpe ratio 1.13, Calmar ratio 1.97[60] - **Sentiment Timing Model**: Annualized return 7.74%, maximum drawdown 24.91%, annualized volatility 17.49%, Sharpe ratio 1.01, Calmar ratio 0.62[67][87]

Quantitative Models and Construction Methods 1. Model Name: Timing Strategy Based on Dynamic Macro Event Factors for CSI Southbound Consumer Index - **Model Construction Idea**: The model aims to explore the impact of China's macroeconomic environment on the overall performance and trends of Hong Kong-listed consumer companies. It uses dynamic macro event factors to construct a timing strategy framework[3][4][21] - **Model Construction Process**: 1. **Macro Data Selection**: Over 20 macro indicators across four dimensions (economy, inflation, monetary, and credit) were tested, including PMI, PPI, M1, etc.[22][24] 2. **Data Preprocessing**: - Align data frequency to monthly - Fill missing values using the formula: $$ X_{t} = X_{t-1} + Median_{diff12} $$ - Apply filtering (e.g., one-sided HP filter): $$ \hat{t}_{t|t,\lambda} = \sum_{s=1}^{t} \omega_{t|t,s,\lambda} \cdot y_{s} = W_{t|t,\lambda}(L) \cdot y_{t} $$ - Derive factors using transformations like YoY, MoM, and moving averages[28][29][30] 3. **Event Factor Construction**: - Identify event breakout directions based on the correlation between data and asset returns - Generate event factors using methods like data breaking through moving averages, medians, or directional changes - Construct 28 different event factors per indicator[31][33] 4. **Factor Evaluation and Selection**: - Use metrics like "win rate of returns" and "volatility-adjusted returns" for screening - Select the top-performing factors based on statistical significance, win rate (>55%), and occurrence frequency[32][34] 5. **Final Macro Factor Selection**: - Five macro factors were selected based on their performance in the backtest, including "PMI: Raw Material Prices" and "YoY Growth of Aggregate Financing"[35][36] 6. **Timing Signal Construction**: - If >2/3 of factors signal bullish, the category signal is marked as 1 - If <1/3 signal bullish, the category signal is marked as 0 - Intermediate proportions are marked accordingly - Aggregate category scores determine the timing position signal[4][36][38] - **Model Evaluation**: The strategy effectively captures systematic opportunities and mitigates risks, outperforming benchmarks in most years and controlling drawdowns during market downturns[12][21] --- Model Backtest Results 1. Timing Strategy Based on Dynamic Macro Event Factors - **Annualized Return**: 9.31% (2018/11–2025/7)[11][23] - **Maximum Drawdown**: -29.72%[11][23] - **Sharpe Ratio**: 0.54[11][23] - **Return-to-Drawdown Ratio**: 0.31[11][23] - **Average Position**: 43%[11] - **Monthly Return (2025/7)**: 2.79% (vs. benchmark 2.48%)[11][13] --- Quantitative Factors and Construction Methods 1. Factor Name: PMI: Raw Material Prices - **Factor Construction Idea**: Captures inflationary pressures and their impact on consumer sector performance[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 96 months[36] 2. Factor Name: US-China 10Y Bond Spread - **Factor Construction Idea**: Reflects monetary policy divergence and its influence on capital flows[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 72 months[36] 3. Factor Name: YoY Growth of Aggregate Financing (12M Rolling) - **Factor Construction Idea**: Measures credit expansion and its implications for economic growth[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 96 months[36] 4. Factor Name: M1 YoY Growth - **Factor Construction Idea**: Tracks monetary liquidity and its correlation with asset prices[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 48 months[36] 5. Factor Name: YoY Growth of Medium- to Long-Term Loans (12M Rolling) - **Factor Construction Idea**: Indicates long-term credit trends and their impact on investment[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 48 months[36] --- Factor Backtest Results 1. PMI: Raw Material Prices - **Rolling Window**: 96 months[36] 2. US-China 10Y Bond Spread - **Rolling Window**: 72 months[36] 3. YoY Growth of Aggregate Financing (12M Rolling) - **Rolling Window**: 96 months[36] 4. M1 YoY Growth - **Rolling Window**: 48 months[36] 5. YoY Growth of Medium- to Long-Term Loans (12M Rolling) - **Rolling Window**: 48 months[36]

Group 1 - Market sentiment indicators show a slight increase to 3.25, maintaining a high level and a bullish outlook, although there is a need to monitor for potential turning points as scores show a slight decline during the week [9][12][30] - The price-volume consistency indicator remains elevated, indicating high levels of market activity, while the PCR combined with VIX has shifted from positive to negative, suggesting a change in market sentiment [12][23][24] - Total trading volume for the week showed a slight decline but remained strong, with daily trading volumes exceeding 1.6 trillion RMB on most days, indicating robust market activity [17][30] Group 2 - The report highlights that sectors with high trading congestion, such as machinery, defense, and non-ferrous metals, have seen significant price increases, but caution is advised due to potential valuation and sentiment corrections [30][34][36] - The report identifies that the small-cap growth style is currently favored, with the RSI model indicating a preference for growth stocks, although the 5-day RSI shows a rapid decline compared to the 20-day RSI, warranting further observation [30][39][41] - The report provides a detailed analysis of sector performance, with machinery, light industry, and defense showing the strongest short-term trends, particularly machinery scoring a perfect 100 [30][31][32]

Core Viewpoint - The market is expected to maintain a trend of oscillating upward in the coming week, with a notable presence of both bullish and bearish sentiments [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 2.49, indicating current market liquidity is 2.49 standard deviations above the average level of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.92, reflecting a reduced caution among investors regarding the short-term performance of the ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.06% and 1.65%, respectively, indicating a decline in trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced weekly increases of 0.39% and 0.05%, respectively [2]. - In July, China's CPI was reported at 0.0% year-on-year, slightly below the previous value of 0.1% but above the consensus expectation of -0.12%. The PPI remained at -3.6%, matching the previous value and below the consensus expectation of -3.44% [2]. Technical Analysis - The SAR indicator for the Wind All A Index showed an upward breakout on August 6, indicating a potential trend reversal [2]. - The market score based on the moving average strength index is currently at 246, placing it in the 91.5 percentile for 2023 [2]. - The sentiment model score is 3 out of 5, with both trend and weighted models signaling a positive outlook [2]. Market Performance - For the week of August 4-8, the SSE 50 Index rose by 1.27%, the CSI 300 Index increased by 1.23%, the CSI 500 Index grew by 1.78%, and the ChiNext Index saw a rise of 0.49% [3]. - The overall market PE (TTM) stands at 20.7 times, which is in the 67.9 percentile since 2005 [3]. Factor Crowding - The crowding degree for small-cap factors has decreased, with small-cap factor crowding at 0.79, low valuation factor crowding at 0.11, high profitability factor crowding at -0.25, and high growth factor crowding at 0.25 [3]. Industry Crowding - The industries with relatively high crowding degrees include machinery, defense and military, non-ferrous metals, comprehensive, and steel, with notable increases in crowding for defense and machinery sectors [4].

- The report defines the market environment using the distance between the long-term (120-day) and short-term (20-day) moving averages of the WIND All A index, which continues to expand, indicating an upward trend [2][9][10] - The industry allocation model recommends sectors such as innovative drugs in Hong Kong and securities for mid-term allocation, while the TWO BETA model continues to recommend the technology sector, focusing on military and computing power [2][3][10] - The current PE ratio of the WIND All A index is around the 70th percentile, indicating a moderate level, while the PB ratio is around the 30th percentile, indicating a relatively low level [3][10][15] Model and Factor Construction 1. **Model Name: Industry Allocation Model** - **Construction Idea**: Recommends sectors based on mid-term market trends - **Construction Process**: Utilizes historical data and market trends to identify sectors with potential for reversal and growth, such as innovative drugs and securities in the Hong Kong market - **Evaluation**: Effective in identifying sectors with potential for mid-term growth [2][3][10] 2. **Model Name: TWO BETA Model** - **Construction Idea**: Focuses on sectors with high beta values, indicating higher volatility and potential returns - **Construction Process**: Analyzes sectors with high beta values, recommending technology, military, and computing power sectors - **Evaluation**: Continues to recommend high-growth sectors, showing consistency in sector selection [2][3][10] Model Backtesting Results 1. **Industry Allocation Model** - **PE Ratio**: 70th percentile [3][10][15] - **PB Ratio**: 30th percentile [3][10][15] - **Moving Average Distance**: 6.92% [2][9][10] - **Profitability Effect**: 2.30% [2][9][10] 2. **TWO BETA Model** - **PE Ratio**: 70th percentile [3][10][15] - **PB Ratio**: 30th percentile [3][10][15] - **Moving Average Distance**: 6.92% [2][9][10] - **Profitability Effect**: 2.30% [2][9][10]

Group 1 - The market sentiment index has risen to 3.2, up from 1.8 the previous week, indicating a bullish outlook, but caution is advised as high sentiment levels can lead to sensitive directional changes [10][4][8] - The price-volume consistency indicator has increased, suggesting higher capital activity and reduced divergence in market sentiment, while the financing ratio continues to decline [13][4] - The total trading volume for the week remained high, with the peak on Thursday at 1961.849 billion RMB and a significant drop on Friday to 1619.884 billion RMB [17][4] Group 2 - The industry performance shows a clear upward trend in anti-involution related sectors, with basic chemicals and electronics leading the gains, while automotive, environmental, and oil sectors lag behind [26][29] - The short-term scores for most industries have generally decreased, with computer, media, communication, and food and beverage sectors showing slight increases [33][34] - The model indicates a preference for small-cap growth styles, with the RSI model also suggesting a growth style advantage, although the 5-day RSI has decreased compared to the 20-day RSI [36][37]

Quantitative Models and Construction Methods 1. Model Name: Timing System Model - **Model Construction Idea**: The model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index to determine the market trend[2][9] - **Model Construction Process**: - Calculate the 20-day moving average and the 120-day moving average of the WIND All A Index - Compute the percentage difference between the two moving averages: $ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} \times 100\% $ - If the absolute value of the distance is greater than 3% and the short-term moving average is above the long-term moving average, the market is in an upward trend[2][9] - **Model Evaluation**: The model effectively identifies upward market trends and provides actionable signals for investors[2][9] 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies medium-term industry allocation opportunities by focusing on sectors with potential for recovery or growth[2][9] - **Model Construction Process**: - Analyze industry-specific factors such as valuation, growth potential, and market sentiment - Recommend sectors like "distressed reversal" industries, Hong Kong innovative pharmaceuticals, Hang Seng dividend low-volatility sectors, and securities for medium-term allocation[2][9] - **Model Evaluation**: The model provides clear guidance for sector rotation and captures medium-term opportunities in specific industries[2][9] 3. Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on identifying high-growth sectors in the technology domain[2][9] - **Model Construction Process**: - Analyze beta factors related to technology sectors - Recommend sectors such as solid-state batteries, robotics, and military industries based on their growth potential and market trends[2][9] - **Model Evaluation**: The model is effective in capturing high-growth opportunities in the technology sector[2][9] --- Model Backtesting Results 1. Timing System Model - **Key Metrics**: - Moving average distance: 6.06% (absolute value > 3%, indicating an upward trend)[2][9] - WIND All A Index trendline: 5480 points[2][9] - Profitability effect: 1.45% (positive, indicating sustained market inflows)[2][9] 2. Industry Allocation Model - **Key Metrics**: - Recommended sectors: distressed reversal industries, Hong Kong innovative pharmaceuticals, Hang Seng dividend low-volatility sectors, and securities[2][9] 3. TWO BETA Model - **Key Metrics**: - Recommended sectors: solid-state batteries, robotics, and military industries[2][9] --- Quantitative Factors and Construction Methods 1. Factor Name: Profitability Effect - **Factor Construction Idea**: Measures the market's ability to generate positive returns, serving as a key indicator for market sentiment and fund inflows[2][9] - **Factor Construction Process**: - Calculate the profitability effect as a percentage value - Positive values indicate favorable market conditions for sustained fund inflows[2][9] - **Factor Evaluation**: The factor is a reliable indicator of market sentiment and a useful tool for timing investment decisions[2][9] --- Factor Backtesting Results 1. Profitability Effect - **Key Metrics**: - Profitability effect value: 1.45% (positive, indicating favorable market conditions)[2][9]