Market Performance - The recent 5 trading days saw the Sci-Tech 50 Index decline by 1.29%, and the ChiNext Index drop by 1.12%, while the large-cap value index rose by 0.28% and the large-cap growth index increased by 0.04% [1] - The healthcare and agriculture sectors performed well, whereas the computer and defense industries lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium, which has historically reached extreme levels at two standard deviations above the mean during significant market bottoms [1] - As of January 19, 2024, the risk premium indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1] Valuation Levels - As of March 28, 2025, the CSI All Share Index's PE TTM percentile was at 53%, with the SSE 50 and CSI 300 at 58% and 48% respectively, while the ChiNext Index was close to 14% [2] - The ChiNext Index's valuation is relatively low compared to historical averages [2] Long-term Market Trends - The Shenzhen 100 Index has experienced bear markets approximately every three years, followed by bull markets, with declines ranging from 40% to 45% [2] - The current adjustment cycle, which began in Q1 2021, appears to have sufficient time and space for a potential upward trend [2] Fund Flow and Trading Activity - In the last 5 trading days, ETF inflows totaled 16.2 billion yuan, while margin financing decreased by approximately 24.8 billion yuan [3] - The average daily trading volume across both markets was 1.2346 trillion yuan [3] Thematic Investment Focus - As of March 28, 2025, the recommended investment themes include construction materials and low-volatility dividend stocks [2][8]

Quantitative Models and Construction Methods - **Model Name**: Timing Radar Six-Factor Framework **Model Construction Idea**: The model evaluates equity market performance by integrating 21 indicators across six dimensions: liquidity, economic fundamentals, valuation, capital flows, technical trends, and crowding. These are further categorized into four major groups: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a composite timing score within the range of [-1,1][1][5][7] **Model Construction Process**: 1. Select 21 indicators across six dimensions to represent market characteristics 2. Group indicators into four categories: - Valuation Cost-Effectiveness - Macro Fundamentals - Capital & Trend - Crowding & Reversal 3. Normalize the scores of each indicator to a range of [-1,1] 4. Aggregate the scores to compute a composite timing score within [-1,1][1][5][7] **Model Evaluation**: The model provides a comprehensive multi-dimensional perspective for market timing, offering insights into market trends and sentiment[1][5][7] Model Backtesting Results - **Timing Radar Six-Factor Framework**: - Composite Timing Score: -0.21 (Neutral to slightly bearish)[1][5][7] - Liquidity Score: -1.00 (Significant bearish signal)[1][7][9] - Economic Fundamentals Score: 0.00 (No significant signal)[1][7][9] - Valuation Score: -0.17 (Neutral signal)[1][7][9] - Capital & Trend Score: 0.50 (Significant bullish signal)[1][7][9] - Technical Trends Score: 0.00 (No significant signal)[1][7][9] - Crowding & Reversal Score: -0.69 (Significant bearish signal)[1][7][9] Quantitative Factors and Construction Methods Liquidity Factors - **Factor Name**: Monetary Direction Factor **Construction Idea**: Measures the direction of monetary policy using central bank policy rates and short-term market rates **Construction Process**: - Calculate the average change in policy and market rates over the past 90 days - If the factor > 0, monetary policy is deemed expansionary; if < 0, it is contractionary **Current View**: The factor is < 0, signaling a bearish outlook with a score of -1[11][13] - **Factor Name**: Monetary Intensity Factor **Construction Idea**: Based on the "interest rate corridor" concept, measures the deviation of short-term market rates from policy rates **Construction Process**: - Compute deviation = DR007/7-year reverse repo rate - 1 - Smooth and normalize using z-score - If the factor < -1.5 standard deviations, it indicates a bullish environment; if > 1.5, it is bearish **Current View**: The factor signals a bearish outlook with a score of -1[14][15][16] - **Factor Name**: Credit Direction Factor **Construction Idea**: Reflects the transmission of credit from banks to the real economy using long-term loan data **Construction Process**: - Calculate the 12-month incremental change in long-term loans - Compare the year-over-year change to three months prior - If the factor is rising, it is bullish; if falling, it is bearish **Current View**: The factor signals a bearish outlook with a score of -1[17][19] - **Factor Name**: Credit Intensity Factor **Construction Idea**: Captures whether credit metrics significantly exceed or fall short of expectations **Construction Process**: - Compute = (New RMB loans - median forecast) / forecast standard deviation - Normalize using z-score - If the factor > 1.5 standard deviations, it is bullish; if < -1.5, it is bearish **Current View**: The factor signals a bearish outlook with a score of -1[20][22] Economic Factors - **Factor Name**: Growth Direction Factor **Construction Idea**: Based on PMI data, measures the trend of economic growth **Construction Process**: - Calculate the 12-month moving average of PMI data - Compare the year-over-year change to three months prior - If the factor is rising, it is bullish; if falling, it is bearish **Current View**: The factor signals a bullish outlook with a score of 1[23][24] - **Factor Name**: Growth Intensity Factor **Construction Idea**: Captures whether economic growth metrics significantly exceed or fall short of expectations **Construction Process**: - Compute PMI surprise = (PMI - median forecast) / forecast standard deviation - Normalize using z-score - If the factor > 1.5 standard deviations, it is bullish; if < -1.5, it is bearish **Current View**: The factor signals a bearish outlook with a score of -1[25][27] - **Factor Name**: Inflation Direction Factor **Construction Idea**: Measures the trend of inflation using CPI and PPI data **Construction Process**: - Compute = 0.5 × smoothed CPI year-over-year + 0.5 × raw PPI year-over-year - Compare the change to three months prior - If the factor is falling, it is bullish; if rising, it is bearish **Current View**: The factor signals a bearish outlook with a score of -1[28][30] - **Factor Name**: Inflation Intensity Factor **Construction Idea**: Captures whether inflation metrics significantly exceed or fall short of expectations **Construction Process**: - Compute CPI and PPI surprises = (Reported value - median forecast) / forecast standard deviation - Average the two surprises to form the factor - If the factor < -1.5, it is bullish; if > 1.5, it is bearish **Current View**: The factor signals a bullish outlook with a score of 1[31][33] Valuation Factors - **Factor Name**: Shiller ERP **Construction Idea**: Adjusts for economic cycles to evaluate market valuation **Construction Process**: - Compute Shiller PE = average inflation-adjusted earnings over the past six years - Compute ERP = 1/Shiller PE - 10-year government bond yield - Normalize using z-score over the past three years **Current View**: The factor score decreased to 0.39[34][38] - **Factor Name**: PB **Construction Idea**: Similar to ERP, evaluates market valuation using price-to-book ratio **Construction Process**: - Compute PB × (-1) - Normalize using z-score over the past three years - Truncate to ±1 range **Current View**: The factor score decreased to -0.49[36][39] - **Factor Name**: AIAE **Construction Idea**: Reflects market-wide equity allocation and risk appetite **Construction Process**: - Compute AIAE = total market cap of CSI All Share Index / (total market cap + total debt) - Multiply by (-1) and normalize using z-score over the past three years **Current View**: The factor score decreased to -0.41[40][42] Capital Flow Factors - **Factor Name**: Margin Trading Increment **Construction Idea**: Measures market leverage and sentiment using margin trading data **Construction Process**: - Compute = financing balance - short selling balance - Compare the 120-day moving average increment to the 240-day moving average increment - If the short-term increment > long-term increment, it is bullish; otherwise, bearish **Current View**: The factor signals a bullish outlook with a score of 1[44][46] - **Factor Name**: Turnover Trend **Construction Idea**: Measures market activity and capital flow using turnover data **Construction Process**: - Compute log turnover moving average distance = ma120/ma240 - 1 - If the maximum of the 10, 30, and 60-day distances is positive, it is bullish; otherwise, bearish **Current View**: The factor signals a bullish outlook with a score of 1[47][49] - **Factor Name**: China Sovereign CDS Spread **Construction Idea**: Reflects foreign investors' sentiment towards China's credit risk **Construction Process**: - Compute the 20-day difference of smoothed CDS spreads - If the difference < 0, it is bullish; otherwise, bearish **Current View**: The factor signals a bullish outlook with a score of 1[50][51] - **Factor Name**: Overseas Risk Aversion Index **Construction Idea**: Captures global risk sentiment using Citi RAI Index **Construction Process**: - Compute the 20-day difference of smoothed RAI - If the difference < 0, it is bullish; otherwise, bearish **Current View**: The factor signals a bearish outlook with a score

Quantitative Models and Construction Methods - **Model Name**: Three-Dimensional Sentiment Model **Model Construction Idea**: The model observes market sentiment from three perspectives: sentiment expectation, sentiment temperature, and sentiment concentration, corresponding to high-frequency, medium-frequency, and low-frequency sentiment fluctuations respectively [7] **Model Construction Process**: 1. **Sentiment Expectation**: - **Indicator Significance**: Reflects short-term market sentiment through futures and options data. Futures basis rate and the inverse of options PCR (Put-Call Ratio) are used to measure sentiment [6][8] - **Formula**: $ \text{Futures Basis Rate} = \frac{\text{Futures Price} - \text{Spot Price}}{\text{Spot Price}} $ $ \text{Sentiment Expectation Composite Indicator} = \text{Mean Value + Principal Component Analysis} $ - **Evaluation**: Sentiment expectation rising indicates optimistic short-term market sentiment, while a decline suggests cautious sentiment [6][8] 2. **Sentiment Temperature**: - **Indicator Significance**: Quantifies market trading heat and fund activity, focusing on institutional/main funds as the core force. Uses "main fund buy-in rate" smoothed and calculated as a three-year rolling percentile [12] - **Formula**: $ \text{Main Fund Buy-in Rate} = \frac{\text{Large Buy-in Amount}}{\text{Total Market Turnover}} $ - **Evaluation**: Sentiment temperature rising indicates increased fund activity, while a decline suggests cooling sentiment [12] 3. **Sentiment Concentration**: - **Indicator Significance**: Measures the correlation of multi-assets in the A-share market. Uses the first principal component variance contribution rate of the CITIC three-level industry system index, smoothed with a rolling window [16] - **Evaluation**: Higher sentiment concentration indicates increased asset correlation, suggesting stronger emotional influence on the market. Extreme values above the warning line (0.83) may signal long-term market turning points [16] Model Backtesting Results - **Sentiment Expectation**: Current value as of February 20, 2025: 0.7696, up 31.02% from the previous week [9][22] - **Sentiment Temperature**: Current value as of February 20, 2025: 0.6952, up 4.43% from the previous week [13][22] - **Sentiment Concentration**: Current value as of February 20, 2025: 0.6884, down 2.17% from the previous week [18][22]

Quantitative Models and Factor Construction Multi-Horizon Factor - **Factor Name**: Multi-Horizon Factor - **Construction Idea**: This factor captures short-term reversal, medium-term momentum, and long-term reversal effects by analyzing moving average (MA) data across multiple time horizons [2][14][21] - **Construction Process**: - Calculate moving averages for different time horizons \( L = [3, 5, 10, 20, 30, 60, 90, 120, 180, 240, 270, 300] \) using the formula: \[ A_{j t,L} = \frac{P_{j,\,d-L+1}^{t} + \cdots + P_{j,d}^{t}}{L} \] where \( P_{j,d}^t \) represents the price of stock \( j \) at time \( t \) [21] - Standardize the moving average factor: \[ \tilde{A}_{j t,\,L} = \frac{A_{j t,\,L}}{P_{j}^{t}} \] [22] - Perform cross-sectional regression of stock returns on lagged standardized moving average factors: \[ r_{j,t} = \beta_{0,t} + \Sigma_{i}\beta_{i,t}\tilde{A}_{j t-1,L_{i}} + \epsilon_{j,t} \] [23] - Predict next-period regression coefficients by averaging the past 25 weeks' coefficients: \[ E\left[\beta_{i,\,t+1}\right] = \frac{1}{25}\,\sum_{m=1}^{25}\,\beta_{i,t+1-m} \] [24] - Use predicted coefficients and new factor values to estimate next-period returns: \[ E\left[r_{j,t+1}\right] = \Sigma_{i}\,E\left[\beta_{i,\,t+1}\right]\tilde{A}_{j t,\,L_{i}} \] [25] - Rank stocks by predicted returns and construct long-short portfolios [26] - **Evaluation**: The factor demonstrates strong predictive power for stock returns across different market segments, with positive IC values dominating [30][32] LLT Trend Factor - **Factor Name**: LLT Trend Factor - **Construction Idea**: To address the lagging sensitivity of MA, the LLT (Low-Lag Trendline) indicator is used as a replacement. LLT reduces delay and better captures momentum and reversal effects [14][76] - **Construction Process**: - LLT is calculated using a second-order linear filter with the recursive formula: \[ LLT = \begin{cases} P(T), & T=1,2 \\ (2-2\alpha)LLT(T-1) - (1-\alpha)^2LLT(T-2) + \left(\alpha-\frac{\alpha^2}{4}\right)P(T) \\ + \left(\frac{\alpha^2}{2}\right)P(T-1) - \left(\alpha-\frac{3}{4}\alpha^2\right)P(T-2), & \text{else} \end{cases} \] where \( \alpha = \frac{2}{1+N} \) and \( N \) is the smoothing parameter [76] - Replace MA with LLT in the multi-horizon factor construction process [76] - **Evaluation**: LLT-based factors outperform MA-based factors in terms of IC mean, positive IC ratio, and predictive power for asset returns [82][84] --- Backtesting Results Multi-Horizon Factor - **Annualized Return**: 25.40% [3][48] - **Annualized Volatility**: 14.12% [48] - **Maximum Drawdown**: 13.31% [48] - **IR**: 1.81 [48] LLT Trend Factor - **Annualized Return**: 29.58% [4][103] - **Annualized Volatility**: 10.46% [103] - **Maximum Drawdown**: 11.57% [103] - **IR**: 2.51 [103]