Group 1 - The core viewpoint emphasizes that market analysts often react to events after they have occurred, and valuable insights come from data that has not yet been fully digested by the market [3][4] - Morgan Stanley analysts set a target price of $115 for Tesla, citing a shrinking European market and unclear U.S. policies, but these factors are already reflected in the stock price [3] - The article highlights a phenomenon where institutional investors often exit the market before significant downturns, relying on quantitative analysis of trading behaviors [4] Group 2 - The article discusses the misconception among retail investors who panic and sell during market downturns, while institutional investors may be quietly accumulating shares [8] - It illustrates the importance of quantitative data in investment decisions, comparing it to a night vision device that helps see through market fog [9] - The case of Tesla serves as a reminder that while warnings from Wall Street should be taken seriously, the focus should be on underlying data trends, such as delivery declines and competitive pressures [11]

Group 1 - The core viewpoint of the article highlights the uncertainty in the market driven by macroeconomic factors such as potential interest rate cuts by the Federal Reserve and geopolitical tensions, leading to a cautious approach from large investors [1][3] - The market is characterized by narrow fluctuations in indices and erratic movements in individual stocks, reflecting a lack of clear direction amidst the prevailing "policy fog" [3][4] - Retail investors face a dilemma in this volatile environment, often either exiting prematurely due to short-term fluctuations or holding onto losing positions, which can lead to missed opportunities [4][6] Group 2 - The article emphasizes that stock price fluctuations are merely surface-level indicators, with the real dynamics being driven by institutional fund movements and strategies [6][10] - Quantitative analysis is suggested as a tool to decode market behaviors, allowing investors to identify clear trading signals amidst the noise of market volatility [6][10] - Data reveals that during periods of price oscillation, institutional funds have been quietly increasing their positions, indicating potential opportunities for discerning investors [10][12] Group 3 - The discussion returns to the implications of Jerome Powell's statements, suggesting that while the market debates interest rate timing, savvy investors are already positioning themselves for future movements [12] - The article advocates for retail investors to leverage data analytics to navigate the complexities of the market, focusing on fund behavior rather than attempting to predict policy changes [12]

Quantitative Models and Construction Methods Model Name: Timing Radar Six-Factor Model - **Model Construction Idea**: The model aims to capture the performance of the equity market through multiple dimensions, including liquidity, economic conditions, valuation, capital flows, technical indicators, and crowding. It summarizes these into four categories: "Valuation Cost-Effectiveness," "Macroeconomic Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a comprehensive timing score between [-1,1][1][6]. - **Model Construction Process**: - **Liquidity**: Includes indicators like monetary strength and credit strength. For example, the monetary direction factor is calculated based on the average change in central bank policy rates and short-term market rates over the past 90 days[12][15][18][21]. - **Economic Conditions**: Includes indicators like inflation direction and growth direction. For instance, the growth direction factor is based on PMI data, calculated as the 12-month moving average and year-over-year change[22][26][27][31]. - **Valuation**: Includes indicators like Shiller ERP, PB, and AIAE. For example, Shiller ERP is calculated as 1/Shiller PE minus the 10-year government bond yield, with a z-score over the past three years[32][36][39]. - **Capital Flows**: Includes indicators like margin trading increment and trading volume trend. For example, the margin trading increment is calculated as the difference between the 120-day and 240-day moving averages of financing balance minus short selling balance[41][44][47][49]. - **Technical Indicators**: Includes indicators like price trend and new highs and lows. For example, the price trend is measured using the distance between the 120-day and 240-day moving averages[51][54]. - **Crowding**: Includes indicators like implied premium/discount from derivatives and convertible bond pricing deviation. For example, the implied premium/discount is derived from the put-call parity relationship in options[57][62][65]. - **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, making it a robust tool for market timing[1][6]. Model Backtesting Results - **Current Comprehensive Score**: -0.10, indicating a neutral view overall[1][6]. - **Liquidity Score**: 0.00, indicating a neutral signal[8]. - **Economic Conditions Score**: 0.00, indicating a neutral signal[8]. - **Valuation Score**: -0.11, indicating a slightly bearish signal[8]. - **Capital Flows Score**: 0.00, indicating a neutral signal[8]. - **Technical Indicators Score**: -0.50, indicating a bearish signal[8]. - **Crowding Score**: -0.13, indicating a slightly bearish signal[8]. Quantitative Factors and Construction Methods Factor Name: Monetary Direction Factor - **Factor Construction Idea**: To determine the direction of current monetary policy by comparing central bank policy rates and short-term market rates over the past 90 days[12]. - **Factor Construction Process**: - Calculate the average change in central bank policy rates and short-term market rates over the past 90 days. - If the factor is greater than 0, it indicates a loose monetary policy; if less than 0, it indicates a tight monetary policy[12]. - **Factor Evaluation**: Provides a clear indication of the monetary policy direction, which is crucial for market timing[12]. Factor Name: Credit Direction Factor - **Factor Construction Idea**: To measure the tightness of credit transmission from commercial banks to the real economy using long-term loan indicators[18]. - **Factor Construction Process**: - Calculate the monthly value of long-term loans. - Compute the year-over-year change over the past 12 months. - If the factor is rising compared to three months ago, it indicates a bullish signal; otherwise, it indicates a bearish signal[18]. - **Factor Evaluation**: Effectively captures the credit conditions in the economy, which is vital for assessing market liquidity[18]. Factor Backtesting Results - **Monetary Direction Factor**: Score of 1, indicating a bullish signal[12]. - **Credit Direction Factor**: Score of 1, indicating a bullish signal[18]. - **Monetary Strength Factor**: Score of -1, indicating a bearish signal[15]. - **Credit Strength Factor**: Score of -1, indicating a bearish signal[21]. - **Growth Direction Factor**: Score of -1, indicating a bearish signal[22]. - **Growth Strength Factor**: Score of -1, indicating a bearish signal[26]. - **Inflation Direction Factor**: Score of 1, indicating a bullish signal[27]. - **Inflation Strength Factor**: Score of 1, indicating a bullish signal[31]. - **Shiller ERP**: Score of 0.16, indicating a slightly bearish signal[32]. - **PB**: Score of -0.38, indicating a bearish signal[36]. - **AIAE**: Score of -0.11, indicating a slightly bearish signal[39]. - **Margin Trading Increment**: Score of -1, indicating a bearish signal[41]. - **Trading Volume Trend**: Score of -1, indicating a bearish signal[44]. - **China Sovereign CDS Spread**: Score of 1, indicating a bullish signal[47]. - **Overseas Risk Aversion Index**: Score of 1, indicating a bullish signal[49]. - **Price Trend**: Score of 0, indicating a neutral signal[51]. - **New Highs and Lows**: Score of -1, indicating a bearish signal[54]. - **Implied Premium/Discount**: Score of 1, indicating a bullish signal[57]. - **Implied Volatility (VIX)**: Score of 0, indicating a neutral signal[58]. - **Implied Skewness (SKEW)**: Score of -1, indicating a bearish signal[62]. - **Convertible Bond Pricing Deviation**: Score of -0.51, indicating a bearish signal[65].

Quantitative Models and Construction Methods 1. Model Name: Timing Radar Six-Facet Chart - **Model Construction Idea**: The model evaluates equity market performance through a multi-dimensional framework, incorporating liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding indicators. These dimensions are aggregated into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a composite timing score within the range of [-1, 1][1][6][8] - **Model Construction Process**: - Select 21 indicators across six dimensions - Aggregate indicators into four categories - Normalize the composite score to the range of [-1, 1][1][6][8] - **Model Evaluation**: The model provides a comprehensive and systematic approach to timing equity markets, offering insights into multiple influencing factors[1][6] --- Quantitative Factors and Construction Methods 1. Factor Name: Monetary Direction Factor - **Factor Construction Idea**: This factor assesses the direction of monetary policy by analyzing changes in central bank policy rates and short-term market rates over the past 90 days[12] - **Factor Construction Process**: - Calculate the average change in central bank policy rates and short-term market rates over the past 90 days - If the factor value > 0, monetary policy is deemed accommodative; if < 0, it is deemed restrictive[12] - **Factor Evaluation**: Effectively captures the directional stance of monetary policy[12] 2. Factor Name: Monetary Strength Factor - **Factor Construction Idea**: Based on the "interest rate corridor" concept, this factor measures the deviation of short-term market rates from policy rates to assess liquidity conditions[15] - **Factor Construction Process**: - Compute deviation = DR007/7-year reverse repo rate - 1 - Smooth and z-score the deviation - Assign scores: >1.5 standard deviations = -1 (tight liquidity), <-1.5 standard deviations = 1 (loose liquidity)[15] - **Factor Evaluation**: Provides a quantitative measure of liquidity conditions in the market[15] 3. Factor Name: Credit Direction Factor - **Factor Construction Idea**: Measures the trend in credit transmission to the real economy using medium- and long-term loan data[17] - **Factor Construction Process**: - Calculate the year-over-year growth of medium- and long-term loans over the past 12 months - Compare the current value to the value three months ago - Assign scores: upward trend = 1, downward trend = -1[17] - **Factor Evaluation**: Captures the directional trend of credit transmission effectively[17] 4. Factor Name: Credit Strength Factor - **Factor Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[20] - **Factor Construction Process**: - Compute Credit Strength Factor = (New RMB loans - median forecast) / forecast standard deviation - Assign scores: >1.5 standard deviations = 1, <-1.5 standard deviations = -1[20] - **Factor Evaluation**: Quantifies the surprise in credit data relative to expectations[20] 5. Factor Name: Growth Direction Factor - **Factor Construction Idea**: Uses PMI data to assess the directional trend of economic growth[23] - **Factor Construction Process**: - Calculate the 12-month moving average of PMI data - Compute year-over-year growth - Compare the current value to the value three months ago - Assign scores: upward trend = 1, downward trend = -1[23] - **Factor Evaluation**: Provides a timely measure of economic growth trends[23] 6. Factor Name: Growth Strength Factor - **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[26] - **Factor Construction Process**: - Compute Growth Strength Factor = (PMI - median forecast) / forecast standard deviation - Assign scores: >1.5 standard deviations = 1, <-1.5 standard deviations = -1[26] - **Factor Evaluation**: Captures the magnitude of surprises in economic growth data[26] 7. Factor Name: Inflation Direction Factor - **Factor Construction Idea**: Assesses the directional trend of inflation using CPI and PPI data[30] - **Factor Construction Process**: - Compute Inflation Direction Factor = 0.5 × smoothed CPI YoY + 0.5 × raw PPI YoY - Compare the current value to the value three months ago - Assign scores: downward trend = 1, upward trend = -1[30] - **Factor Evaluation**: Reflects the directional trend of inflation effectively[30] 8. Factor Name: Inflation Strength Factor - **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[31] - **Factor Construction Process**: - Compute Inflation Strength Factor = average of CPI and PPI forecast deviations - Assign scores: <-1.5 standard deviations = 1, >1.5 standard deviations = -1[31] - **Factor Evaluation**: Quantifies inflation surprises relative to expectations[31] 9. Factor Name: Shiller ERP - **Factor Construction Idea**: Adjusts for economic cycles to evaluate equity valuation[35] - **Factor Construction Process**: - Compute Shiller PE = inflation-adjusted average earnings over the past 6 years - Compute Shiller ERP = 1/Shiller PE - 10-year government bond yield - Standardize using a 3-year z-score[35] - **Factor Evaluation**: Provides a cyclically adjusted measure of equity valuation[35] 10. Factor Name: PB - **Factor Construction Idea**: Measures valuation using the price-to-book ratio[37] - **Factor Construction Process**: - Compute PB × (-1) - Standardize using a 3-year z-score, truncating at ±1.5 standard deviations[37] - **Factor Evaluation**: Offers a simple and effective valuation metric[37] 11. Factor Name: AIAE - **Factor Construction Idea**: Reflects market-wide equity allocation and risk appetite[41] - **Factor Construction Process**: - Compute AIAE = total market cap of CSI All Share Index / (total market cap + total debt) - Standardize using a 3-year z-score[41] - **Factor Evaluation**: Captures overall market risk appetite[41] --- Factor Backtest Results 1. Monetary Direction Factor - Current score: 1[12] 2. Monetary Strength Factor - Current score: -1[15] 3. Credit Direction Factor - Current score: 1[17] 4. Credit Strength Factor - Current score: -1[20] 5. Growth Direction Factor - Current score: -1[23] 6. Growth Strength Factor - Current score: -1[26] 7. Inflation Direction Factor - Current score: 1[30] 8. Inflation Strength Factor - Current score: 1[31] 9. Shiller ERP - Current score: 0.30[39] 10. PB - Current score: -0.18[37] 11. AIAE - Current score: 0.15[41]

Group 1 - The core viewpoint is that the A-share market is heavily influenced by external news, leading to erratic stock price movements, which can be likened to a "puppet show" controlled by information [1] - The Federal Reserve's decision to maintain interest rates is seen as a significant factor affecting market sentiment, with the dot plot indicating a lack of imminent rate cuts, which could lead to prolonged market uncertainty [2][3] - There is a growing concern regarding the credibility of the US dollar, as international capital begins to lose faith in it due to the weaponization of the dollar settlement system [3][5] Group 2 - The market's reaction to the Federal Reserve's decision illustrates the characteristics of an "external leverage market," where neutral news is exaggerated in a fragile market environment, leading to significant volatility [6] - Retail investors often fall into the trap of emotional trading, reacting to short-term market movements rather than focusing on underlying data, which contributes to their losses [9] - The use of quantitative analysis tools has revealed the importance of understanding institutional trading activity, particularly through "institutional inventory" data, which reflects the true market dynamics [10][12] Group 3 - Observations of specific stocks demonstrate that price movements can be misleading; a stock that experiences a rapid rise may not have institutional support, while a stock that declines may have strong institutional backing, leading to a rebound [12][14] - The ability to visualize data and analyze institutional inventory alongside price charts can provide clearer insights into market trends, moving beyond superficial analysis [14][17] - The focus on interest rate expectations may obscure deeper funding trends, highlighting the need for investors to identify hidden opportunities within the market [15]

Group 1 - The core point of the article is that the recent U.S. CPI data for May came in lower than expected, leading to increased market speculation about potential interest rate cuts by the Federal Reserve [2][5] - The U.S. CPI year-on-year rate was reported at 2.4%, below the expected 2.5%, while the core CPI increased by 2.8%, also lower than the anticipated 2.9% [2][6] - Following the CPI release, the probability of a rate cut in September surged to 70%, with expectations for at least two cuts within the year [5] Group 2 - Despite the excitement in the market, the probability of a rate cut in June is only 2.4%, indicating that significant actions may still be months away [6] - The article discusses that a decrease in inflation suggests a potential economic slowdown, prompting the Federal Reserve to consider lowering interest rates to stimulate the economy [7] - It highlights that institutional investors typically do not wait for favorable conditions but instead leverage market expectations to position themselves, often causing market volatility before actual rate cuts occur [8][10] Group 3 - The article emphasizes the importance of understanding institutional trading behaviors rather than relying solely on market sentiment or technical analysis [10][12] - It provides examples of past stock movements, illustrating that significant price increases often follow periods of institutional accumulation, while lack of institutional support can lead to price declines [12][15] - The key takeaway is that recognizing and analyzing data related to institutional activity is crucial for making informed investment decisions [15][17]

Quantitative Factors and Models Summary Quantitative Factors and Construction Methods 1. **Factor Name**: DL_EM_Dynamic - **Construction Idea**: Extract intrinsic stock attributes from public fund holdings using matrix decomposition, and combine these attributes with LSTM-generated factor representations to create a dynamic market state factor[19][21]. - **Construction Process**: - Matrix decomposition is applied to fund-stock investment networks to derive intrinsic attributes of funds and stocks. - Static intrinsic attributes are updated semi-annually using fund reports and transformed into dynamic attributes by calculating their similarity to the market's current style preferences. - These dynamic attributes are combined with LSTM outputs and fed into an MLP model to enhance factor performance[19][21]. - **Evaluation**: The factor effectively captures dynamic market preferences and improves model performance[19][21]. 2. **Factor Name**: Meta_RiskControl - **Construction Idea**: Integrate factor exposure control into deep learning models to mitigate risks during rapid style shifts, leveraging meta-incremental learning for market adaptability[25][28]. - **Construction Process**: - Multiply model outputs by corresponding stock factor exposures and include this in the loss function. - Add penalties for style deviation and momentum to the IC-based loss function. - Use an ALSTM model with style inputs as the base model and apply a meta-incremental learning framework for periodic updates[25][28]. - **Evaluation**: The factor reduces style deviation and volatility, effectively controlling model drawdowns[25][28]. 3. **Factor Name**: Meta_Master - **Construction Idea**: Incorporate market state information into the model, leveraging deep risk models and online meta-incremental learning to adapt to dynamic market conditions[35][37]. - **Construction Process**: - Use deep risk models to calculate new market states and construct 120 new features representing market preferences. - Replace the loss function with weighted MSE to improve long-side prediction accuracy. - Apply online meta-incremental learning for periodic model updates, enabling quick adaptation to recent market trends[35][37]. - **Evaluation**: The factor demonstrates significant improvements in long-side prediction accuracy and market adaptability[35][37]. 4. **Factor Name**: Deep Learning Convertible Bond Factor - **Construction Idea**: Address the declining excess returns of traditional convertible bond strategies by using GRU neural networks to model the complex nonlinear pricing logic of convertible bonds[50][52]. - **Construction Process**: - Introduce convertible bond-specific time-series factors into the GRU model. - Combine cross-sectional attributes of convertible bonds with GRU outputs to predict future returns[50][52]. - **Evaluation**: The factor significantly enhances model performance compared to traditional strategies[50][52]. Factor Backtesting Results 1. **DL_EM_Dynamic Factor** - **RankIC**: 12.1% (May 2025)[9][12] - **Excess Return**: 0.6% (May 2025), 10.4% YTD[9][23] - **Annualized Return**: 29.7% (since 2019)[23] - **Annualized Excess Return**: 23.4% (since 2019)[23] - **IR**: 2.03[23] - **Max Drawdown**: -10.1%[23] 2. **Meta_RiskControl Factor** - **RankIC**: 12.8% (May 2025)[9][14] - **Excess Return**: -0.7% (HS300), 0.8% (CSI500), 0.5% (CSI1000) in May 2025; 3.0%, 4.8%, and 8.3% YTD respectively[9][30][34] - **Annualized Return**: 20.1% (HS300), 26.1% (CSI500), 34.1% (CSI1000) since 2019[30][32][34] - **Annualized Excess Return**: 15.0% (HS300), 19.2% (CSI500), 27.0% (CSI1000) since 2019[30][32][34] - **IR**: 1.58 (HS300), 1.97 (CSI500), 2.36 (CSI1000)[30][32][34] - **Max Drawdown**: -5.8% (HS300), -9.3% (CSI500), -10.2% (CSI1000)[30][32][34] 3. **Meta_Master Factor** - **RankIC**: 14.7% (May 2025)[9][17] - **Excess Return**: -0.5% (HS300), 0.5% (CSI500), 0.4% (CSI1000) in May 2025; 4.2%, 3.3%, and 5.0% YTD respectively[38][44][47] - **Annualized Return**: 22.0% (HS300), 23.8% (CSI500), 30.7% (CSI1000) since 2019[38][44][47] - **Annualized Excess Return**: 17.5% (HS300), 18.2% (CSI500), 25.2% (CSI1000) since 2019[38][44][47] - **IR**: 2.09 (HS300), 1.9 (CSI500), 2.33 (CSI1000)[38][44][47] - **Max Drawdown**: -7.2% (HS300), -5.8% (CSI500), -8.8% (CSI1000)[38][44][47] 4. **Deep Learning Convertible Bond Factor** - **Absolute Return**: 1.7% (偏股型), 2.6% (平衡型), 1.7% (偏债型) in May 2025[52][55] - **Excess Return**: 0.1% (偏股型), 1.0% (平衡型), 0.2% (偏债型) in May 2025[52][55] - **Annualized Return**: 13.2% (偏股型), 11.8% (平衡型), 12.7% (偏债型) since 2021[52][55] - **Annualized Excess Return**: 5.8% (偏股型), 4.0% (平衡型), 4.4% (偏债型) since 2021[52][55]

6月份了，市场里到处是科技回暖的呼声，但真相藏在哪儿呢？别急，我来帮你拨开迷雾。文章最后还有关键彩蛋，保准让你眼前一亮！ 一、科技回暖的迷雾：是希望还是泡沫？ 大家最近都在琢磨科技板块能不能回暖，从市场表现看，科技板块前阵子确实有点蔫儿，成交量都跌到冰点了。 卖方机构们可没闲着，他们使劲儿吆喝：科技要翻身啦！逻辑是啥？就是这成交量冰点，意味着市场情绪触底，反弹在即。 朋友们！今天咱们聊聊一个让股民们心痒痒的话题——科技板块的春天啥时候来？ A股现在有点拧巴，好多人都等着出个王炸级应用再跟风买科技股。 但美股早就用数据说话了——微软3月Token量直接飙到前两个月总和，谷歌4月Token量同比猛涨，就靠这俩数据，微软股价直接刷了新高。 咱国内也没闲着，阿里云日Token量最近也狂涨，说白了，不管中美，应用端都在闷头搞测试、冲用量，这明摆着是科技行业要热闹起来的信号啊！ 但普通股民往往后知后觉——等行情出来再追，黄花菜都凉了，大家一窝蜂跟风，结果总慢半拍。散户在信息链末端，容易错失先机。 二、机构资金的隐形游戏：温水煮青蛙 话说回来，光听机构喊口号可不行。股市里，真正有戏的个股，早被机构盯上了。 他们像老练的猎手 ...

Group 1: Real Estate Market Trends - The current new home prices have not yet reached the bottom, with second-hand home prices generally 25% to 40% lower than new homes, and continuing to decline. In April, first-tier cities saw a 0.2% month-on-month decrease in second-hand home prices, while second and third-tier cities experienced a 0.4% decline [1][3] - Concerns about a prolonged downturn similar to Japan's are unfounded, as China's real estate market is currently in an adjustment phase following rapid growth from 2015 to 2017. This adjustment does not equate to a market collapse, as cyclical recovery is expected [3] Group 2: Stock Market Insights - Both the stock and real estate markets exhibit cyclical behavior, with bull markets often emerging during periods of market despair. Despite recent index declines, underlying support mechanisms remain [4] - The presence of institutional investors in stocks does not guarantee profitability for retail investors, as institutional strategies may shift with market conditions. The focus should be on the trading behavior of institutions rather than mere participation [6] Group 3: Understanding Institutional Trading - Institutional trading is characterized by large volumes and discreet operations, making it essential to utilize quantitative analysis to uncover their true actions, such as accumulation or distribution of shares [8] - Indicators such as the density of orange bars (indicating active institutional trading) and blue circles (indicating potential washout tactics) can provide insights into institutional strategies. For instance, repeated downward movements may signal preparation for a significant upward movement [10][13] Group 4: Identifying Market Signals - To determine the end of a washout phase, it is crucial to analyze two sets of data: a shift from blue candlesticks to blue bars indicates a return of previously sold funds, while dense orange bars suggest concentrated institutional holdings [13] - Retail investors often face losses due to a lack of understanding of institutional trading behaviors. By interpreting data accurately, investors can avoid being shaken out of positions during volatile periods [15]

择时雷达六面图：信用指标弱化，拥挤度分数下行 择时雷达六面图：基于多维视角的择时框架。权益市场的表现受到多维度 指标因素的共同影响，我们尝试从流动性、经济面、估值面、资金面、技 术面、拥挤度选取二十一个指标对市场进行刻画，并将其概括为"估值性 价比"、"宏观基本面"、"资金&趋势"、"拥挤度&反转"四大类，从而生成 [-1,1]之间的综合择时分数。 证券研究报告 | 金融工程 gszqdatemark 2025 05 17 年 月 日 量化分析报告 本周综合打分。本周市场的估值性价比、宏观基本面、资金&趋势、拥挤 度&反转这四个维度分数均有所下降，综合打分位于[-1,1]之间，当前的综 合打分为 0.18 分，整体为中性偏多观点。当前六面图各个维度的观点如 下： 流动性。本周货币强度、信用方向、信用强度发出看空信号，货币方向 发出看多信号，当前流动性得分为-0.50 分，综合来看发出看空信号。 经济面。本周增长方向、通胀方向与通胀强度指标发出看多信号，当前 经济面得分为 0.75 分，综合来看发出看多信号。 估值面。本周席勒 ERP、PB 与 AIAE 指标的打分均下降，当前市场的 估值面得分为 0.28 分 ...