- The report does not contain any specific quantitative models or factors for analysis[1][2][3] - The report provides a detailed overview of A-share market trends, including style performance, crowding levels, valuation metrics, and fund flows[20][26][36] - Style crowding levels and excess cumulative net values are calculated using z-score standardization of daily turnover rates over historical data, with a rolling window of 6 years for crowding levels and cumulative net values relative to Wind All A Index[123][124] - Institutional research activity percentile is calculated using z-score standardization of daily institutional research counts over rolling historical windows of 6 years for long-term and 3 years for short-term[125] - The report highlights the relationship between U.S. bond yields and style indices, noting deviations from historical patterns in recent weeks[47][49][50] - Fund flow analysis indicates that active equity funds are in a long-term decline phase, with reversal outperforming momentum over the long term, but recent trends show deviations from this pattern[51][52] - A-share valuation metrics show PE_TTM at historically high percentiles, with core indices like CSI 300 and CSI 500 also at high percentiles, while the equity risk premium (ERP) remains at balanced levels[64][74][80] - The report tracks major commodity indices, showing mixed performance in Chinese and U.S. markets over the past week[120][121][122]

- The report does not contain any specific quantitative models or factors for analysis[1][2][3] - The report primarily focuses on market trends, style performance, valuation metrics, and fund flows without detailing quantitative models or factor construction[4][5][6] - Key metrics such as PE_TTM, ERP, and fund issuance are discussed, but these are general market indicators rather than specific quantitative factors or models[10][11][12]

- The report tracks the performance of various stock market indices, including A-shares, Hong Kong stocks, and US stocks, highlighting their weekly, monthly, and year-to-date changes[20][21][23] - The report discusses the performance of different stock market styles, such as growth vs. dividend, small-cap vs. large-cap, and micro-cap vs. fund-heavy stocks, providing their excess returns and crowding levels[2][27][28] - The report evaluates the valuation and risk-return trade-off of A-shares, indicating that the current PE_TTM of A-shares is at a historically high percentile, with marginal upward movement[10][64][66] - The report tracks the main funds' indices, showing their absolute and relative returns, and highlights the recent performance of the main funds' indices compared to the Wind All A index[82][83][86] - The report provides insights into the activity levels of institutional research, showing the historical percentiles of institutional research activity for various indices, sectors, and industries[108][110] - The report includes data on the bond market, showing the current and historical levels of Chinese and US government bond yields, as well as the China-US yield spread[111][112][113] - The report covers the foreign exchange market, indicating the recent appreciation of the onshore and offshore RMB against the USD, and provides data on the USD index and RMB exchange rates[118][120] - The report discusses the commodity market, showing the weekly performance of various commodity indices in China and the US, including the South China Commodity Index and the CRB Composite Index[122][123][124]

- The report does not contain any specific quantitative models or factors for analysis[1][2][3]

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]