Group 1 - Market sentiment indicators showed a slight decline, with the sentiment score at 1.75 as of October 10, down from 1.85 on September 26, indicating a bearish outlook [8][11] - The trading volume for the entire A-share market increased slightly compared to the previous week, with a peak trading volume of 26,718.18 billion RMB on October 9, indicating improved market activity [14][16] - The financing balance ratio continued to rise, reflecting an increase in market leverage sentiment and improved trading atmosphere among investors [24][26] Group 2 - The model indicates a preference for small-cap value style, with a weak signal strength due to a slight decline in the 5-day RSI relative to the 20-day RSI, suggesting further observation is needed [30][41] - The short-term trend scores for industries such as banks, steel, public utilities, and construction decoration have shown upward trends, with non-ferrous metals currently having the highest short-term score of 98.31 [30][32] - High trading congestion in sectors like non-ferrous metals and coal, alongside lower price increases in sectors like automobiles and electronics, suggests potential volatility risks and opportunities for gradual allocation in low-congestion sectors like pharmaceuticals and beauty care [37][36]

Group 1: Market Sentiment Indicators - The market sentiment index as of October 10 is 1.75, a slight decrease from 1.85 on September 26, indicating a bearish sentiment [8][11] - The financing balance ratio continues to rise, reflecting an increase in market leverage sentiment and improving trading atmosphere [27][11] - The industry trading volatility continues to decline, suggesting a slowdown in fund switching activity and a decrease in market participants' divergent views on short-term industry value [21][11] Group 2: Timing Model Insights - The model indicates a preference for small-cap value style, with a weak signal strength due to a slight decline in the 5-day RSI relative to the 20-day RSI [45][46] - The short-term trend scores for industries such as non-ferrous metals, power equipment, real estate, machinery, and electronics are notably strong, with non-ferrous metals scoring the highest at 98.31 [34][36] - The model maintains a strong signal for value style, suggesting potential for further strengthening in the future [45][46] Group 3: Industry Crowding and Performance - Recent high returns in non-ferrous metals and coal are accompanied by high fund crowding, indicating potential volatility risks due to valuation and sentiment corrections [42][41] - Industries like automotive and electronics show high crowding but lower returns, while sectors with low crowding such as pharmaceuticals and beauty care may present long-term investment opportunities as risk appetite increases [42][41] - The average crowding levels for industries as of October 10 show automotive, environmental protection, real estate, power equipment, and electronics as the highest, while agriculture, computers, defense, beauty care, and pharmaceuticals are the lowest [40][41]

Quantitative Models and Construction Methods 1. Model Name: Market Sentiment Timing Model - **Model Construction Idea**: The model is built from a structural perspective to quantify market sentiment using various sub-indicators[7] - **Model Construction Process**: - The model uses sub-indicators such as industry trading volatility, trading crowding, price-volume consistency, Sci-Tech Innovation Board (STAR 50) trading proportion, industry trend, RSI, main buying force, PCR combined with VIX, and financing balance ratio[8] - Each sub-indicator is scored based on its sentiment direction and position within Bollinger Bands. Scores are categorized as (-1, 0, 1)[8] - The final sentiment structural indicator is the 20-day moving average of the summed scores. The indicator fluctuates around 0 within the range of [-6, 6][8] - **Model Evaluation**: The model effectively captures market sentiment trends and provides actionable insights for timing decisions[8] 2. Model Name: Moving Average Scoring System (MASS) - **Model Construction Idea**: This model evaluates long-term and short-term trends of indices using N-day moving averages to generate timing signals[18] - **Model Construction Process**: - For N moving averages (N=360 for long-term, N=60 for short-term), scores are assigned based on the relative position of adjacent moving averages. If a shorter moving average is above a longer one, it scores 1; otherwise, it scores 0[18] - The scores are standardized to a 0-100 scale and averaged to derive the trend score at a specific time point[18] - Long/short-term timing signals are generated based on the crossover of the trend score with its 100/20-day moving average[18] - **Model Evaluation**: The model provides clear signals for sector rotation and market style preferences, favoring value and defensive sectors in the current environment[18] 3. Model Name: RSI Style Timing Model - **Model Construction Idea**: The model uses the Relative Strength Index (RSI) to compare the relative strength of different market styles (e.g., growth vs. value, small-cap vs. large-cap)[22] - **Model Construction Process**: - For two indices A and B, calculate the standardized ratio of their net values over a fixed period[22] - Compute the average gain (Gain) and average loss (Loss) over N days, where gains on down days are treated as 0 and losses on up days are treated as 0[22] - RSI formula: $ RSI = 100 - 100 / (1 + Gain / Loss) $ - RSI values range from 0 to 100, with values above 50 indicating stronger buying pressure[22] - The model calculates 5-day, 20-day, and 60-day RSI values. When the 20-day RSI exceeds the 60-day RSI, the numerator style is favored; otherwise, the denominator style is favored[22] - **Model Evaluation**: The model effectively identifies style dominance, currently favoring large-cap and value styles while noting short-term strengthening of growth and small-cap styles[22] --- Model Backtesting Results 1. Market Sentiment Timing Model - Sentiment indicator value as of April 30, 2025: 0.8, indicating a recovery in market sentiment[9] 2. Moving Average Scoring System (MASS) - Short-term signals: Positive for sectors like beauty care (72.88), utilities (86.44), banking (74.58), and oil & petrochemicals (22.03)[19] - Long-term signals: Positive for sectors like banking (95.54), machinery (78.55), and steel (51.25)[19] 3. RSI Style Timing Model - Growth/Value (300 Growth/300 Value): RSI 20-day = 53.02, RSI 60-day = 50.42, favoring value[25] - Small-cap/Large-cap (SW Small/SW Large): RSI 20-day = 48.84, RSI 60-day = 53.62, favoring large-cap[25] --- Quantitative Factors and Construction Methods 1. Factor Name: RSI - **Factor Construction Idea**: Measures the relative strength of buying and selling forces over a specific period[22] - **Factor Construction Process**: - Calculate the average gain (Gain) and average loss (Loss) over N days[22] - Formula: $ RSI = 100 - 100 / (1 + Gain / Loss) $ - RSI values range from 0 to 100, with higher values indicating stronger buying pressure[22] - **Factor Evaluation**: Provides a robust measure of market momentum and style preferences[22] --- Factor Backtesting Results 1. RSI - Growth/Value (300 Growth/300 Value): RSI 20-day = 53.02, RSI 60-day = 50.42, favoring value[25] - Small-cap/Large-cap (SW Small/SW Large): RSI 20-day = 48.84, RSI 60-day = 53.62, favoring large-cap[25]

市场情绪自3月20日持续调整，于4月18日下降至低点，数值为0.1。本周市场情绪指标在接近0轴处开始向上反弹，回升至0.5，数值较上周五(4/18)上升0.4，模型转多，市场 情绪有所缓和。 本周A股市场提示市场情绪有一定修复，较上周明显发生变化的指标有科创50成交占比、主力买入力量和期权波动率。主力流出速率减缓和VIX指标体现的恐慌程度减弱是本 周市场情绪回升的主要原因。 科创50成交占比、行业涨跌趋势性、主力买入力量和PCR结合VIX，分别代表了市场风险偏好程度下降，市场情绪不确定性增强，主力流出速度 减缓和期权市场恐慌情绪缓和。其他指标维持和上周一致的判断。 资金当前对成长高估值板块观点不确定性较强。 自上周科创50成交占比指标快速下跌至下轨以下后，本周科创50成交占比指标仍在持续下降。本周主力资金持续从科创板块 流出，累计净流出超过32亿人民币。 投资者信心逐渐恢复，市场的活跃度和投资者参与度都有了明显提升。 除了看到主力资金本周流出科创板，主力资金本周在全A仍然呈现净流出的态势，但流出速度较上周有 所减缓，主力流出主力买入力量指标有所回升。从主力资金净流出绝对量看，主力资金本周累计净流出超过370亿 ...