Quantitative Models and Construction 1. Model Name: Liquidity Shock Indicator - **Model Construction Idea**: The model measures market liquidity by assessing deviations from the average liquidity level over the past year[4][8] - **Model Construction Process**: The liquidity shock indicator is calculated based on the standard deviation of the current market liquidity relative to the average liquidity over the past year. For the CSI 300 Index, the indicator value on Friday was 3.32, which is 3.32 standard deviations above the average liquidity level of the past year[4][8] - **Model Evaluation**: Indicates that the current market liquidity is significantly higher than the historical average, suggesting a favorable environment for trading[4][8] 2. Model Name: Sentiment Model - **Model Construction Idea**: The model evaluates market sentiment using factors such as limit-up and limit-down board data to assess the strength of market sentiment[4][14] - **Model Construction Process**: The sentiment model score is derived from various sub-factors, including: - Net limit-up ratio - Next-day return after limit-down events - Proportion of limit-up boards - Proportion of limit-down boards - High-frequency board-hitting returns The overall sentiment score is 2 out of 5, indicating a moderate sentiment level[4][14][19] - **Model Evaluation**: The model reflects a weakening in market sentiment but still indicates a positive trend[4][14] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy/sell signals for major broad-based indices[4][14] - **Model Construction Process**: The model tracks the capital flow trends for indices such as CSI 300, CSI 500, and CSI 1000. Based on the data, the model generates signals for aggressive long, aggressive short, conservative long, and conservative short positions. For all three indices, the signals are consistently positive, indicating a "buy" recommendation[4][14][19] - **Model Evaluation**: The model suggests that the major indices are in a "buy" cycle, supporting a positive market outlook[4][14] --- Model Backtesting Results 1. Liquidity Shock Indicator - CSI 300 Index: Indicator value = 3.32 (3.32 standard deviations above the historical average)[4][8] 2. Sentiment Model - Overall sentiment score: 2/5 - Sub-factor signals: - Net limit-up ratio: 1 - Next-day return after limit-down events: 0 - Proportion of limit-up boards: 1 - Proportion of limit-down boards: 0 - High-frequency board-hitting returns: 0[4][14][19] 3. High-Frequency Capital Flow Model - CSI 300 Index: All signals (aggressive long, aggressive short, conservative long, conservative short) = 1 - CSI 500 Index: All signals = 1 - CSI 1000 Index: All signals = 1[4][14][19] --- Quantitative Factors and Construction 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Measures the performance of small-cap stocks relative to the market[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using four metrics: - Valuation spread - Pairwise correlation - Market volatility - Return reversal The composite score for the small-cap factor is 0.20[20][21] - **Factor Evaluation**: The factor's crowding level is stable, indicating no significant risk of factor failure[20][21] 2. Factor Name: Low-Valuation Factor - **Factor Construction Idea**: Tracks the performance of low-valuation stocks[20][21] - **Factor Construction Process**: Similar to the small-cap factor, the crowding level is calculated using the same four metrics. The composite score for the low-valuation factor is -0.75[20][21] - **Factor Evaluation**: The negative score suggests a potential risk of underperformance due to crowding[20][21] 3. Factor Name: High-Profitability Factor - **Factor Construction Idea**: Focuses on stocks with high profitability metrics[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using the same four metrics. The composite score for the high-profitability factor is 0.35[20][21] - **Factor Evaluation**: Indicates moderate crowding but still within acceptable levels[20][21] 4. Factor Name: High-Growth Factor - **Factor Construction Idea**: Targets stocks with high growth potential[20][21] - **Factor Construction Process**: The factor's crowding level is calculated using the same four metrics. The composite score for the high-growth factor is 0.55[20][21] - **Factor Evaluation**: Suggests a favorable environment for high-growth stocks[20][21] --- Factor Backtesting Results 1. Small-Cap Factor - Valuation spread: 0.43 - Pairwise correlation: 0.22 - Market volatility: -0.28 - Return reversal: 0.41 - Composite score: 0.20[20][21] 2. Low-Valuation Factor - Valuation spread: -1.22 - Pairwise correlation: -0.05 - Market volatility: 0.26 - Return reversal: -2.01 - Composite score: -0.75[20][21] 3. High-Profitability Factor - Valuation spread: -0.55 - Pairwise correlation: 0.31 - Market volatility: -0.01 - Return reversal: 1.65 - Composite score: 0.35[20][21] 4. High-Growth Factor - Valuation spread: 1.09 - Pairwise correlation: 0.46 - Market volatility: -0.29 - Return reversal: 0.95 - Composite score: 0.55[20][21]

- The report introduces a **market timing system** that uses the distance between the 20-day moving average and the 120-day moving average of the WIND All A Index to determine market trends. The system identifies an uptrend when the short-term moving average is above the long-term moving average, with a significant distance threshold of 3%[2][6][11] - The **industry trend allocation model** is highlighted, which signals opportunities in specific sectors. For the medium term, the "distressed reversal expectation model" suggests focusing on innovative healthcare. The "TWO BETA model" continues to recommend the technology sector, particularly AI applications and commercial aerospace after adjustments. In the short term, the "earnings trend model" points to opportunities in computing power (e.g., Sci-Tech Chip ETF, code 588200) and energy storage batteries (e.g., Energy Storage Battery ETF, code 159566)[2][5][7] - The **position management model** is used to determine stock allocation levels. Based on the WIND All A Index's valuation and trend, the model recommends an 80% stock allocation for absolute return products[5][7] - The **valuation indicators** for the WIND All A Index are also discussed. The PE ratio is at the 90th percentile, indicating a relatively high valuation, while the PB ratio is at the 50th percentile, representing a medium level[5][7][11]

基于动态宏观事件因子的中证港股通大消费指数择时策略 为了探索中国宏观经济对香港大消费主题上市公司整体状况和走势的影响，我们选取中证港股通大消费主题指数作为 研究对象，尝试从动态宏观事件因子的角度构建择时策略框架。我们用经济、通胀、货币和信用四维度的 20 余个宏 观数据指标，基于数据样本内时间段的收益率胜率指标和开仓波动调整收益率指标数值，筛选出这些宏观数据每期最 优的事件因子和最优的数据处理方式，并且从中挑选出了 5 个对中证港股通大消费主题指数择时效果较好的宏观因 子。 在选定了最终使用的宏观指标之后，我们使用这些宏观数据构建的宏观事件因子来搭建择时策略：当大于 2/3 的因子 发出看多信号，则当期该大类因子的信号标记为 1；当少于 1/3 的因子发出看多信号时，则当期大类因子信号标记为 0；若当因子发出看多信号的比例处于两个区间之后，则大类因子标记为对应具体的比例。将每期大类因子的得分作 为当期的择时仓位信号。 建议关注标的介绍：泰康中证港股通大消费 A（006786.OF） 目前跟踪中证港股通大消费主题指数的基金为泰康中证港股通大消费主题基金，是择时策略可投资的标的，该基金成 立于 2019 年 5 ...

- The report discusses the "Liquidity Shock Indicator" for the CSI 300 Index, which measures market liquidity. The indicator was 0.60 on Friday, higher than the previous week's 0.34, indicating that current market liquidity is 0.60 standard deviations above the average of the past year [2][8] - The "PUT-CALL Ratio" for SSE 50ETF options is analyzed, showing a decline to 0.64 on Friday from 0.88 the previous week, reflecting increased short-term optimism among investors regarding the SSE 50ETF [2][8] - The "Turnover Rate" for the SSE Composite Index and Wind All A Index is highlighted, with 5-day average turnover rates of 1.41% and 2.24%, respectively, corresponding to the 79.01% and 87.08% percentiles since 2005, indicating increased trading activity [2][8] - The "Moving Average Strength Index" is introduced as a technical indicator, with the current market score at 261, placing it in the 95.22% percentile since 2023, suggesting strong market momentum [14][19] - The "Sentiment Timing Model" is discussed, which incorporates factors such as net limit-up ratio, next-day return after limit-down, and high-frequency board trading returns. The sentiment model score is 4 out of 5, with both the trend and weighted models showing positive signals [14][17] - The "Factor Crowding Index" is analyzed for various factors, including small-cap, low-valuation, high-profitability, and high-growth factors. The composite crowding scores are 0.37, -0.57, 0.63, and 1.09, respectively, with high-growth factors showing the highest crowding level [18][20][21] - The report evaluates "Industry Crowding Levels," identifying sectors such as communication, comprehensive, non-ferrous metals, defense, and electronics as having relatively high crowding levels. Defense and comprehensive sectors show the largest increases in crowding compared to the previous month [23][25][26]

Market Overview - The market is expected to experience short-term fluctuations next week due to technical indicators showing a high strength index and historical calendar effects indicating poor performance of major indices in the latter half of January [1][2]. Quantitative Indicators - The liquidity shock indicator for the CSI 300 index was 0.60, higher than the previous week's 0.34, indicating current market liquidity is 0.60 standard deviations above the average level of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options decreased to 0.64 from 0.88, suggesting increased investor optimism regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 1.41% and 2.24%, respectively, indicating increased trading activity, positioned at the 79.01% and 87.08% percentiles since 2005 [2]. Macroeconomic Factors - The official manufacturing PMI for December was reported at 50.1, surpassing the previous value of 49.2 and aligning with the consensus forecast of 50.05 [2]. - The December CPI year-on-year was 0.8%, higher than the previous value of 0.7% and the consensus forecast of 0.75% [2]. - The PPI year-on-year was -1.9%, better than the previous -2.2% and the consensus forecast of -2% [2]. Market Performance - The SSE 50 Index rose by 3.4%, the CSI 300 Index increased by 2.79%, the CSI 500 Index surged by 7.92%, and the ChiNext Index climbed by 3.89% during the week of January 5-9, 2026 [3]. - The overall market PE (TTM) stands at 23.2 times, positioned at the 81.9% percentile since 2005 [3]. Factor Crowding - The crowding degree for high-growth factors has increased, with small-cap factors at 0.37, low-valuation factors at -0.57, high-profitability factors at 0.63, and high-profit growth factors at 1.09 [3]. Industry Crowding - The industries with relatively high crowding degrees include telecommunications, comprehensive sectors, non-ferrous metals, defense and military industry, and electronics, with significant increases noted in the crowding degrees of defense and military as well as comprehensive sectors [4].

Group 1 - Market sentiment is steadily recovering, with the sentiment indicator reaching 1.6 as of January 9, up from 1.35 the previous week, indicating a bullish outlook from a sentiment perspective [8][12] - The average daily trading volume for the entire A-share market increased significantly by 34.00% week-on-week, reaching an average of 28,519.51 billion yuan, with January 9 marking a recent high of 31,523.68 billion yuan [16][19] - The industry score trends show that sectors such as pharmaceuticals, coal, real estate, media, and environmental protection have seen upward trends in short-term scores, with defense and military industries scoring the highest at 100 [41][42] Group 2 - The correlation between industry congestion and weekly price fluctuations is high at 0.37, indicating that sectors with high congestion, such as defense and petrochemicals, have experienced significant gains, while sectors like retail and non-bank financials, despite high congestion, have shown lower price increases [44][47] - The current model indicates a preference for small-cap and value styles, with the 5-day RSI relative to the 20-day RSI continuing to rise, suggesting potential for enhanced signals in the future [52]

- The report introduces a **market timing system** based on the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index. The system identifies market trends by observing whether the short-term moving average is above the long-term moving average and the absolute difference exceeds 3%. The latest data shows the 20-day moving average at 6394 and the 120-day moving average at 6142, with a difference of 4.10%, indicating an upward trend[6][11]. - The **profitability effect** is used as a core indicator to assess market conditions. The current profitability effect is 5.28%, which is significantly positive, suggesting that the market is likely to continue its upward trend[6][11]. - The **industry trend allocation model** highlights sectors with strong upward trends, including AI applications, commercial aerospace, computing power, industrial metals, and energy storage. Additionally, the **mid-term reversal expectation model** signals opportunities in media and innovative healthcare sectors[6][11]. - The **TWO BETA model** recommends focusing on technology sectors, particularly AI applications and commercial aerospace[6][11]. - The **valuation metrics** for the WIND All A Index show that the PE ratio is near the 90th percentile, indicating a relatively high valuation, while the PB ratio is at the 50th percentile, reflecting a moderate valuation level. Based on these metrics and the market trend, the allocation model suggests an 80% equity position for absolute return products[7][11]. - Backtesting results for the market timing system show that the WIND All A Index increased by 5.11% over the past week, with small-cap stocks (CSI 1000) rising by 7.03%, mid-cap stocks (CSI 500) by 7.92%, and large-cap indices (HS300 and SSE50) by 2.79% and 3.4%, respectively. Sector-wise, defense and media performed strongly, with defense rising by 14.56%, while banking and transportation lagged, with banking declining by 1.88%[2][5][6].

Market Performance - The Sci-Tech 50 Index increased by 9.80% over the last five trading days, while the ChiNext Index rose by 3.89%. The large-cap value index grew by 0.47%, and the large-cap growth index increased by 2.82%. The Shanghai 50 Index saw a 3.40% rise, and the small-cap index represented by the CSI 2000 gained 7.21% [1]. Valuation Levels - As of January 9, 2026, the static PE of the CSI All Share Index is at a percentile of 83%. The Shanghai 50 and CSI 300 both stand at 76%, while the ChiNext Index is close to 62%. The CSI 500 and CSI 1000 are at 68% and 67%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. Risk Premium - The risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of the 10-year government bond, is at 2.52% as of January 9, 2026. The two standard deviation boundary is at 4.69% [1]. ETF Fund Flow - In the last five trading days, there was an outflow of 1.6 billion yuan from ETFs, while the margin trading balance increased by approximately 64.2 billion yuan. The average daily trading volume across the two markets was 28.26 billion yuan [2]. Thematic Investment Focus - The latest thematic investment focus includes sectors such as satellites and semiconductors, with specific indices like the CSI Satellite Industry Index and the Shanghai Stock Exchange Sci-Tech Board Semiconductor Materials and Equipment Theme Index being highlighted [2][3]. Long-term Market Sentiment - The proportion of stocks above the 200-day moving average indicates a positive long-term market sentiment, suggesting a bullish outlook for the market [13]. Financing Balance - The financing balance has shown significant changes, reflecting the market's risk appetite and investor behavior [16]. Individual Stock Performance - Statistics on individual stock performance year-to-date based on return intervals indicate varying levels of performance across different stocks, providing insights into market dynamics [18]. Oversold Indices - Certain indices are identified as oversold, which may present potential buying opportunities for investors looking for value [20].

- The growth-value rotation model suggests overweighting growth stocks this week, while the small-cap large-cap rotation model suggests overweighting small-cap stocks, leading to a comprehensive recommendation of small-cap growth style[3] - The short-term market timing signal has turned optimistic this week, with a positive outlook on the macro fundamentals, cautious on valuations, neutral on sentiment, and optimistic on liquidity[15][16] - The growth-value rotation model is based on the quantitative economic mid-cycle analysis framework, where a steep profit cycle slope, low comprehensive interest rate cycle level, and rising credit cycle favor growth style, while the opposite favors value style[27] - The small-cap large-cap rotation model is constructed from 11 effective rotation indicators, with the latest results maintaining a bullish stance on small-cap style due to the dispersion of market trading themes and improved small-cap price-volume trends[31] - The short-term market timing strategy has an annualized return of 16.64% since the end of 2012, with an annualized excess return of 11.60% and a maximum drawdown of only 15.05%, significantly outperforming the benchmark strategy[17][18] - The growth-value rotation strategy has an annualized return of 13.21% since the end of 2012, with an annualized excess return of 4.90%, significantly outperforming the benchmark strategy[28][30] - The small-cap large-cap rotation strategy has generated positive annual excess returns every year since 2014, with an annualized excess return of 1.93% in 2026 so far[32][33] Model Backtest Results - Short-term market timing strategy: Annualized return 16.64%, annualized volatility 14.80%, maximum drawdown 15.05%, Sharpe ratio 0.9793, return-drawdown ratio 1.1058, monthly win rate 66.46%, quarterly win rate 61.11%, annual win rate 80.00%[22] - Growth-value rotation strategy: Annualized return 13.21%, annualized volatility 20.77%, maximum drawdown 43.07%, Sharpe ratio 0.6058, return-drawdown ratio 0.3067, monthly win rate 58.60%, quarterly win rate 60.38%[30] - Small-cap large-cap rotation strategy: Annualized return 20.51%, annualized excess return 12.89%, maximum drawdown 40.70%, average turnover interval 20 trading days, win rate (by trade) 50.11%[33]

Group 1 - Market sentiment is gradually recovering, with the sentiment index reaching 1.35 as of December 31, up from 1.1 the previous week, indicating a bullish outlook [2][7] - The overall trading activity in the market has shown signs of reversal, with a notable increase in trading volume, which rose by 8.30% week-on-week, averaging 21,283.35 billion yuan in daily trading volume [13] - The price-volume consistency indicator has improved significantly, reflecting a recovery in market sentiment and a stronger correlation between capital attention and stock price increases [10][11] Group 2 - The short-term scores for industries such as machinery, media, computers, beauty care, and automobiles have shown upward trends, with defense and non-ferrous metals leading with scores of 88.14 [34] - The model indicates a preference for small-cap and growth styles, with the 5-day RSI relative to the 20-day RSI continuing to rise, suggesting potential strengthening of these signals [44] - The industry crowding indicator shows a positive correlation with weekly price changes, particularly in sectors like defense and petrochemicals, which have seen significant inflows and price increases [40][42]