The provided content does not contain any specific quantitative models or factors, nor does it include detailed construction processes, formulas, or backtesting results for such models or factors. The report primarily focuses on market trends, style performance, valuation metrics, and other financial indicators. Therefore, no summary of quantitative models or factors can be generated from this content.

Quantitative Models and Construction Methods 1. Model Name: Changjiang Momentum Index - **Model Construction Idea**: The index uses the momentum effect in the A-share market, selecting stocks with strong momentum characteristics and relatively high liquidity[26][27] - **Model Construction Process**: - Momentum indicator = (1-year stock return) - (1-month stock return, excluding stocks with price limits)[26][27] - Select the top 100 stocks in the A-share market with the strongest momentum characteristics and relatively high liquidity as index constituents[26][27] - **Model Evaluation**: The index effectively represents the overall trend of stocks with the strongest momentum characteristics in the A-share market[26][27] 2. Model Name: Changjiang Reversal Index - **Model Construction Idea**: The index captures the reversal effect in the A-share market, selecting stocks with strong reversal characteristics and good liquidity[28] - **Model Construction Process**: - Screening indicator = 1-month stock return[28] - Select the top 100 stocks in the A-share market with the strongest reversal characteristics and good liquidity as index constituents[28] - Weight the constituents based on their average daily trading volume over the past three months[28] - **Model Evaluation**: The index aims to accurately represent the overall performance of stocks with high reversal characteristics in the A-share market during different phases[28] --- Model Backtesting Results 1. Changjiang Momentum Index - **Relative Return (Momentum vs. Reversal)**: - 1 week: -0.2% - 1 month: 5.5% - Year-to-date: 8.5%[26][27] 2. Changjiang Reversal Index - **Relative Return (Reversal vs. Momentum)**: - 1 week: 0.2% - 1 month: -5.5% - Year-to-date: -8.5%[26][27] --- Quantitative Factors and Construction Methods 1. Factor Name: Style Crowdedness - **Factor Construction Idea**: Measures the crowdedness of different investment styles (e.g., growth, dividend, small-cap, large-cap) based on turnover rates[34][120] - **Factor Construction Process**: - Calculate the z-score standardized turnover rate of each style index over the past n trading days[120] - Subtract the turnover rate of the Wind All A Index from the style index turnover rate[120] - Compute the rolling y-year percentile of the difference[120] - Parameters: - 6-month crowdedness: n = 126, rolling window = 3 years - 1-year crowdedness: n = 252, rolling window = 6 years[120] - **Factor Evaluation**: Provides insights into the relative popularity and valuation of different investment styles over time[34][120] 2. Factor Name: Style Excess Cumulative Net Value - **Factor Construction Idea**: Measures the relative performance of style indices compared to the Wind All A Index[121] - **Factor Construction Process**: - Base date: January 4, 2016[121] - Daily cumulative net value = (style index closing value) / (base date closing value)[121] - Excess cumulative net value = (style index cumulative net value) / (Wind All A cumulative net value)[121] - **Factor Evaluation**: Tracks the relative performance trends of different styles over time[121] --- Factor Backtesting Results 1. Style Crowdedness - **Growth vs. Dividend**: - Growth crowdedness: 0% (1-year percentile), unchanged from last week[34] - Dividend crowdedness: 16% (1-year percentile), down from 22% last week[34] - **Small-cap vs. Large-cap**: - Small-cap crowdedness: 0% (1-year percentile), down from 1% last week[38] - Large-cap crowdedness: 29% (1-year percentile), down from 32% last week[38] - **Micro-cap vs. Fund-heavy**: - Micro-cap crowdedness: 6% (1-year percentile), unchanged from last week[40] - Fund-heavy crowdedness: 6% (6-month percentile), unchanged from last week[40] 2. Style Excess Cumulative Net Value - **Growth vs. Dividend**: - 1 week: +0.4% - 1 month: +2.3% - Year-to-date: +0.6%[26][34] - **Small-cap vs. Large-cap**: - 1 week: -1.4% - 1 month: -0.5% - Year-to-date: +1.5%[26][38] - **Micro-cap vs. Fund-heavy**: - 1 week: +1.3% - 1 month: +10.8% - Year-to-date: +26.5%[26][40]

Investment Rating - The report maintains an "Overweight" rating for the sector [1] Core Viewpoints - The market is expected to experience a period of volatility, with a focus on relative opportunities in the pharmaceutical and dividend sectors [19][21] - The model predicts a potential shift to a downward trend for the Wande All A Index, indicating a possible adjustment phase in May [19][26] - The pharmaceutical sector is highlighted for its relative stability and potential for returns, while the dividend sector is also expected to perform well after a short-term adjustment [19][21] Summary by Sections A-share Market Overview (May 12-16, 2025) - The top three broad indices were: North Certificate 50 (3.13%), Wande Micro-Pan Daily Equal Weight Index (1.58%), and ChiNext Index (1.38%) [10] - The bottom three indices were: Sci-Tech Innovation 100 (-1.29%), Sci-Tech Innovation 50 (-1.10%), and Sci-Tech Comprehensive Index (-1.00%) [10] A-share Market Outlook (May 19-23, 2025) - The Wande All A Index's daily model shifted from a positive to a negative signal on May 15, suggesting a potential adjustment phase [19][26] - The monthly model for May scored -2.5, indicating a slight adjustment in the A-share market [19][26] - The report anticipates a "V-shaped" market movement, with ongoing pressure on trading volumes [19] Fund Allocation Recommendations - The report suggests a defensive ETF allocation strategy, focusing on the pharmaceutical and dividend sectors for relative returns [19][21]

Group 1 - The report introduces a quantitative timing strategy for style indices based on valuation, liquidity, and crowding models, emphasizing that "efficient markets" are dynamic processes rather than static states [1][6] - The quantitative timing model effectively captures the characteristics of style index bottoms and tops while mitigating risks associated with crowded trades, achieving an average annual return of 18.54% and an average excess annual return of 16.46% since 2011 [1][6] - The report highlights the performance of the mixed style index model, which has achieved an annual return of 20.10% and an excess annual return of 16.24% since December 2013 [1][6] Group 2 - The style index valuation model includes factors such as PB, PE, PBPE, and equity risk premium, with an average annual return of 10.38% and an average excess annual return of 8.30% since 2011 [1][6][17] - The market liquidity model incorporates factors like buy and sell impact costs and liquidity indices, showing a significant accuracy in bottom timing with an average rebound return of 6.86% [1][6][19] - The trading crowding model serves as a top-timing hedge factor, effectively complementing the valuation and liquidity models, achieving an excess annual return of 4.87% since 2011 [1][6][19] Group 3 - The report outlines a quantitative timing research framework that includes data processing, model factor calculation, model testing, and composite model synthesis [1][6][19] - The valuation factors are constructed by calculating the historical percentile levels of the style index valuation factors, which are then compared against set thresholds to trigger buy or sell signals [1][6][21] - The report emphasizes the need for timing factors to be logical and mean-reverting, with specific thresholds established for different style indices to determine market conditions [1][6][20]