Quantitative Models and Construction 1. Model Name: Timing Radar Six-Factor Framework - **Model Construction Idea**: The equity market is influenced by multiple dimensions. This model selects 21 indicators from six perspectives: liquidity, economic fundamentals, valuation, capital flows, technical trends, and crowding. These are summarized into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a comprehensive timing score within the range of [-1, 1][1][6][8] - **Model Construction Process**: - The 21 indicators are grouped into six dimensions, and their scores are aggregated into four broader categories. - The final timing score is calculated as a weighted average of these categories, normalized to the range of [-1, 1][1][6][8] - **Model Evaluation**: The model provides a comprehensive and multi-dimensional view of market timing, integrating macroeconomic, technical, and sentiment factors[1][6] --- Quantitative Factors and Construction 1. Factor Name: Monetary Direction Factor - **Factor Construction Idea**: This factor aims to determine the direction of monetary policy by analyzing changes in central bank policy rates and short-term market rates over the past 90 days[12] - **Factor Construction Process**: - Calculate the average change in central bank policy rates and short-term market rates over the past 90 days - If the factor value > 0, monetary policy is deemed accommodative; if < 0, it is deemed tight[12] - **Factor Evaluation**: Effectively captures the directional bias of monetary policy[12] 2. Factor Name: Monetary Strength Factor - **Factor Construction Idea**: Based on the "interest rate corridor" concept, this factor measures the deviation of short-term market rates from policy rates[15] - **Factor Construction Process**: - Compute the deviation as: $ \text{Deviation} = \frac{\text{DR007}}{\text{7-Year Reverse Repo Rate}} - 1 $ - Smooth and normalize the deviation using z-scores - Assign scores based on thresholds: <-1.5 SD indicates a loose environment (score = 1), >1.5 SD indicates a tight environment (score = -1)[15] - **Factor Evaluation**: Provides a quantitative measure of liquidity conditions in the short-term market[15] 3. Factor Name: Credit Direction Factor - **Factor Construction Idea**: Measures the transmission of credit from banks to the real economy using long-term loan data[18] - **Factor Construction Process**: - Calculate the year-over-year growth of long-term loans over the past 12 months - Compare the current value to its level three months ago - If the factor is rising, assign a score of 1; if falling, assign a score of -1[18] - **Factor Evaluation**: Captures the directional trend of credit expansion or contraction[18] 4. Factor Name: Credit Strength Factor - **Factor Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[20] - **Factor Construction Process**: - Compute the z-score of the difference between actual and expected new RMB loans: $ \text{Credit Strength Factor} = \frac{\text{Actual Loans} - \text{Expected Median}}{\text{Expected Standard Deviation}} $ - Assign scores based on thresholds: >1.5 SD indicates a strong credit environment (score = 1), <-1.5 SD indicates a weak credit environment (score = -1)[20] - **Factor Evaluation**: Quantifies the surprise element in credit data[20] 5. Factor Name: Growth Direction Factor - **Factor Construction Idea**: Based on PMI data, this factor identifies the directional trend of economic growth[21] - **Factor Construction Process**: - Compute the year-over-year change in the 12-month moving average of PMI data - Compare the current value to its level three months ago - If the factor is rising, assign a score of 1; if falling, assign a score of -1[21] - **Factor Evaluation**: Tracks the momentum of economic growth effectively[21] 6. Factor Name: Growth Strength Factor - **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[25] - **Factor Construction Process**: - Compute the z-score of the difference between actual and expected PMI values: $ \text{Growth Strength Factor} = \frac{\text{Actual PMI} - \text{Expected Median}}{\text{Expected Standard Deviation}} $ - Assign scores based on thresholds: >1.5 SD indicates strong growth (score = 1), <-1.5 SD indicates weak growth (score = -1)[25] - **Factor Evaluation**: Captures the surprise element in economic growth data[25] 7. Factor Name: Inflation Direction Factor - **Factor Construction Idea**: Reflects the impact of inflation trends on monetary policy and equity markets[26] - **Factor Construction Process**: - Compute the weighted average of smoothed CPI and raw PPI year-over-year changes: $ \text{Inflation Direction Factor} = 0.5 \times \text{CPI} + 0.5 \times \text{PPI} $ - Compare the current value to its level three months ago - If the factor is falling, assign a score of 1; if rising, assign a score of -1[26] - **Factor Evaluation**: Provides insights into the inflationary environment and its implications for monetary policy[26] 8. Factor Name: Inflation Strength Factor - **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[29] - **Factor Construction Process**: - Compute the z-score of the difference between actual and expected CPI and PPI values: $ \text{Inflation Strength Factor} = \frac{\text{CPI Difference} + \text{PPI Difference}}{2} $ - Assign scores based on thresholds: <-1.5 SD indicates low inflation (score = 1), >1.5 SD indicates high inflation (score = -1)[29] - **Factor Evaluation**: Quantifies the surprise element in inflation data[29] --- Factor Backtesting Results 1. Monetary Direction Factor - Current Score: 1[12] 2. Monetary Strength Factor - Current Score: -1[16] 3. Credit Direction Factor - Current Score: -1[18] 4. Credit Strength Factor - Current Score: 1[20] 5. Growth Direction Factor - Current Score: 1[21] 6. Growth Strength Factor - Current Score: 0[25] 7. Inflation Direction Factor - Current Score: 1[26] 8. Inflation Strength Factor - Current Score: 1[29]

Group 1: Banking Sector Insights - The quantitative backtest results indicate that undervalued strategies contribute to excess returns while effectively reducing drawdowns [1] - High ROE and the strategy based on "provision coverage ratio - non-performing loan ratio - attention ratio" show superior performance, while short-term improvement strategies underperform [1] - The combined strategy of high ROE/PB and high "provision coverage ratio - non-performing loan ratio - attention ratio" × dividend yield has achieved over 300% cumulative excess returns since 2011, highlighting the importance of quality and value in bank stock investments [1] Group 2: Dividend Strategy Analysis - Current dividend strategies exhibit significant bottom characteristics, with a rare "negative return - high volatility" feature over the past three months, indicating potential for recovery [2] - The 40-day excess return of dividends is nearly -10% below the annual average, suggesting a high probability of excess return reversion based on historical patterns [2] - The dividend ETF is in a net subscription state with reduced trading volume, typically corresponding to a bottom phase for the strategy [2] Group 3: Copper Industry Outlook - The expectation of increased tariffs on imported copper in the U.S. is likely to push copper prices back to peak levels, with COMEX copper prices reaching new highs compared to LME prices [3] - The tariff impact on domestic demand in China is expected to be limited, but it may restrict imports of refined copper and scrap copper [3] - Positive policy developments and market dynamics are expected to accelerate the convergence of trading and fundamental factors, leading to a bullish outlook for copper prices [3] Group 4: Quantitative Strategy Improvements - The traditional asset rotation framework has been improved to address issues such as low flexibility and fixed scoring standards, enhancing the model's comprehensiveness and adaptability [4] - The industry rotation model constructed from 26 indicators achieved a 32% annualized absolute return during the backtest period from 2017 to January 2025, outperforming the Shanghai and Shenzhen 300 index [5] Group 5: U.S. Stock Market Strategy - U.S. stock markets are experiencing downward pressure due to uncertainties surrounding Trump's policies and tariffs, with major indices giving back all gains since the Fed's rate cuts in September 2023 [7] - Economic indicators from the U.S. have underperformed expectations, and trade tensions may further weaken the economic fundamentals, leading to capital rotation out of U.S. equities [7] - The outlook for U.S. stocks is expected to remain volatile until late March or early April, with recommendations to focus on healthcare, consumer services, traditional telecommunications, and utilities sectors [7] Group 6: Bond Market Insights - The demand for bond ETFs is increasing due to heightened market volatility, offering investors a more convenient and diversified investment tool compared to traditional bond allocations [8] - Local government bond ETFs are noted for their potential yield enhancement and better drawdown control compared to other bond ETF types [8] Group 7: Magnesium Alloy Market Potential - The demand for magnesium alloys in China is expected to grow due to rich domestic magnesium production and the lightweighting needs in automotive and robotics sectors [10] - The semi-solid magnesium alloy forming technology is anticipated to open new growth avenues for leading companies in the industry [10] Group 8: Dairy Industry Forecast - The potential implementation of child-rearing subsidies by 2025 may improve birth rates, positively impacting the demand for infant formula and cheese products [11] - The expected increase in the population of children aged 0-6 years is likely to boost the market outlook for children's cheese products [11]