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]

Quantitative Models and Construction Methods 1. Model Name: Timing Radar Hexagon Model - **Model Construction Idea**: The equity market's performance is influenced by multiple dimensions of factors. The model selects 21 indicators from six dimensions: liquidity, economic fundamentals, valuation, capital flow, technical trends, and crowding. These indicators are then categorized into four major dimensions: "Valuation Cost-Effectiveness," "Macroeconomic Fundamentals," "Capital & Trend," and "Crowding & Reversal," to generate a comprehensive timing score ranging between [-1, 1][1][6][8] - **Model Construction Process**: - The model aggregates the scores of 21 indicators into four major categories - Each indicator is normalized and scored based on its historical performance and deviation from the mean - The final comprehensive timing score is calculated as the weighted average of the four major categories, with the score ranging from -1 (bearish) to 1 (bullish)[1][6][8] - **Model Evaluation**: The model provides a multi-dimensional perspective on market timing, offering a comprehensive view of market conditions[1][6] --- Quantitative Factors and Construction Methods 1. Factor Name: Monetary Direction Factor - **Factor Construction Idea**: This factor aims to determine the current direction of monetary policy by analyzing changes in central bank policy rates and short-term market interest rates over the past 90 days[10] - **Factor Construction Process**: - Calculate the average change in central bank policy rates and short-term market interest rates over the past 90 days - If the factor value > 0, it indicates an expansionary monetary policy; if < 0, it indicates a tightening monetary policy[10] - **Factor Evaluation**: The factor effectively captures the direction of monetary policy and its implications for market sentiment[10] 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 interest rates from policy rates to assess the monetary environment[14] - **Factor Construction Process**: - Calculate the deviation: $ \text{Deviation} = \frac{\text{DR007}}{\text{7-year reverse repo rate}} - 1 $ - Smooth the deviation and apply z-score normalization to form the monetary strength factor - If the factor value is below -1.5 standard deviations, it indicates a loose monetary environment; if above 1.5 standard deviations, it indicates a tight monetary environment[14] - **Factor Evaluation**: The factor provides a quantitative measure of the monetary environment's tightness or looseness[14] 3. Factor Name: Credit Direction Factor - **Factor Construction Idea**: This factor reflects the tightness of credit transmission from commercial banks to the real economy, using long-term loan data[17] - **Factor Construction Process**: - Calculate the monthly value of long-term loans - Compute the 12-month incremental change and year-over-year growth - If the factor value rises compared to three months ago, it is bullish (score = 1); otherwise, it is bearish (score = -1)[17] - **Factor Evaluation**: The factor captures the credit environment's directional changes effectively[17] 4. Factor Name: Credit Strength Factor - **Factor Construction Idea**: This factor measures whether credit indicators significantly exceed or fall short of expectations[22] - **Factor Construction Process**: - Calculate the credit strength factor: $ \text{Credit Strength Factor} = \frac{\text{New RMB Loans (current month) - Expected Median}}{\text{Expected Standard Deviation}} $ - If the factor value > 1.5 standard deviations, it indicates a significantly above-expectation credit environment (score = 1); if < -1.5 standard deviations, it indicates a below-expectation environment (score = -1)[22] - **Factor Evaluation**: The factor provides insights into the magnitude of credit surprises[22] 5. Factor Name: Growth Direction Factor - **Factor Construction Idea**: This factor is based on PMI data to assess the direction of economic growth[26] - **Factor Construction Process**: - Use PMI data (e.g., manufacturing and non-manufacturing PMI) - Calculate the 12-month moving average and year-over-year growth - If the factor value rises compared to three months ago, it is bullish (score = 1); otherwise, it is bearish (score = -1)[26] - **Factor Evaluation**: The factor effectively captures the directional trend of economic growth[26] 6. Factor Name: Growth Strength Factor - **Factor Construction Idea**: This factor measures whether economic growth indicators significantly exceed or fall short of expectations[28] - **Factor Construction Process**: - Calculate the growth strength factor: $ \text{Growth Strength Factor} = \frac{\text{PMI - Expected Median}}{\text{Expected Standard Deviation}} $ - If the factor value > 1.5 standard deviations, it indicates a significantly above-expectation growth environment (score = 1); if < -1.5 standard deviations, it indicates a below-expectation environment (score = -1)[28] - **Factor Evaluation**: The factor captures the magnitude of economic growth surprises[28] 7. Factor Name: Inflation Direction Factor - **Factor Construction Idea**: This factor assesses the direction of inflation and its implications for monetary policy[31] - **Factor Construction Process**: - Calculate the inflation direction factor: $ \text{Inflation Direction Factor} = 0.5 \times \text{CPI (smoothed)} + 0.5 \times \text{PPI (raw)} $ - If the factor value decreases compared to three months ago, it indicates a deflationary environment (score = 1); otherwise, it indicates an inflationary environment (score = -1)[31] - **Factor Evaluation**: The factor provides a clear signal of inflation trends and their impact on monetary policy[31] 8. Factor Name: Inflation Strength Factor - **Factor Construction Idea**: This factor measures whether inflation indicators significantly exceed or fall short of expectations[32] - **Factor Construction Process**: - Calculate the inflation strength factor: $ \text{Inflation Strength Factor} = \frac{\text{CPI or PPI - Expected Median}}{\text{Expected Standard Deviation}} $ - If the factor value < -1.5, it indicates a significantly below-expectation inflation environment (score = 1); if > 1.5 standard deviations, it indicates an above-expectation environment (score = -1)[32] - **Factor Evaluation**: The factor captures the magnitude of inflation surprises[32] --- Backtesting Results of Factors 1. Monetary Direction Factor - Current score: 1 (bullish signal)[11] 2. Monetary Strength Factor - Current score: -1 (bearish signal)[14] 3. Credit Direction Factor - Current score: -1 (bearish signal)[18] 4. Credit Strength Factor - Current score: -1 (bearish signal)[22] 5. Growth Direction Factor - Current score: 1 (bullish signal)[26] 6. Growth Strength Factor - Current score: 0 (neutral signal)[28] 7. Inflation Direction Factor - Current score: 1 (bullish signal)[31] 8. Inflation Strength Factor - Current score: 1 (bullish signal)[33]

Quantitative Models and Construction 1. Model Name: Timing Radar Six-Factor Framework - **Model Construction Idea**: The equity market is influenced by multiple dimensions. This model selects 21 indicators from six perspectives: liquidity, economic fundamentals, valuation, capital flows, technical trends, and crowding. These are summarized into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a comprehensive timing score within the range of [-1, 1][1][6][8] - **Model Construction Process**: - The 21 indicators are grouped into six dimensions, and their scores are aggregated into four broader categories. - The final timing score is calculated as a weighted average of these categories, normalized to the range of [-1, 1][1][6][8] - **Model Evaluation**: The model provides a comprehensive and multi-dimensional view of market timing, integrating macroeconomic, technical, and sentiment factors[1][6] --- Quantitative Factors and Construction 1. Factor Name: Monetary Direction Factor - **Factor Construction Idea**: This factor aims to determine 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 tight[12] - **Factor Evaluation**: Effectively captures the directional bias 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[15] - **Factor Construction Process**: - Compute the deviation as: $ \text{Deviation} = \frac{\text{DR007}}{\text{7-Year Reverse Repo Rate}} - 1 $ - Smooth and normalize the deviation using z-scores - Assign scores based on thresholds: <-1.5 SD indicates a loose environment (score = 1), >1.5 SD indicates a tight environment (score = -1)[15] - **Factor Evaluation**: Provides a quantitative measure of liquidity conditions in the short-term market[15] 3. Factor Name: Credit Direction Factor - **Factor Construction Idea**: Measures the transmission of credit from banks to the real economy using long-term loan data[18] - **Factor Construction Process**: - Calculate the year-over-year growth of long-term loans over the past 12 months - Compare the current value to its level three months ago - If the factor is rising, assign a score of 1; if falling, assign a score of -1[18] - **Factor Evaluation**: Captures the directional trend of credit expansion or contraction[18] 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 the z-score of the difference between actual and expected new RMB loans: $ \text{Credit Strength Factor} = \frac{\text{Actual Loans} - \text{Expected Median}}{\text{Expected Standard Deviation}} $ - Assign scores based on thresholds: >1.5 SD indicates a strong credit environment (score = 1), <-1.5 SD indicates a weak credit environment (score = -1)[20] - **Factor Evaluation**: Quantifies the surprise element in credit data[20] 5. Factor Name: Growth Direction Factor - **Factor Construction Idea**: Based on PMI data, this factor identifies the directional trend of economic growth[21] - **Factor Construction Process**: - Compute the year-over-year change in the 12-month moving average of PMI data - Compare the current value to its level three months ago - If the factor is rising, assign a score of 1; if falling, assign a score of -1[21] - **Factor Evaluation**: Tracks the momentum of economic growth effectively[21] 6. Factor Name: Growth Strength Factor - **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[25] - **Factor Construction Process**: - Compute the z-score of the difference between actual and expected PMI values: $ \text{Growth Strength Factor} = \frac{\text{Actual PMI} - \text{Expected Median}}{\text{Expected Standard Deviation}} $ - Assign scores based on thresholds: >1.5 SD indicates strong growth (score = 1), <-1.5 SD indicates weak growth (score = -1)[25] - **Factor Evaluation**: Captures the surprise element in economic growth data[25] 7. Factor Name: Inflation Direction Factor - **Factor Construction Idea**: Reflects the impact of inflation trends on monetary policy and equity markets[26] - **Factor Construction Process**: - Compute the weighted average of smoothed CPI and raw PPI year-over-year changes: $ \text{Inflation Direction Factor} = 0.5 \times \text{CPI} + 0.5 \times \text{PPI} $ - Compare the current value to its level three months ago - If the factor is falling, assign a score of 1; if rising, assign a score of -1[26] - **Factor Evaluation**: Provides insights into the inflationary environment and its implications for monetary policy[26] 8. Factor Name: Inflation Strength Factor - **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[29] - **Factor Construction Process**: - Compute the z-score of the difference between actual and expected CPI and PPI values: $ \text{Inflation Strength Factor} = \frac{\text{CPI Difference} + \text{PPI Difference}}{2} $ - Assign scores based on thresholds: <-1.5 SD indicates low inflation (score = 1), >1.5 SD indicates high inflation (score = -1)[29] - **Factor Evaluation**: Quantifies the surprise element in inflation data[29] --- Factor Backtesting Results 1. Monetary Direction Factor - Current Score: 1[12] 2. Monetary Strength Factor - Current Score: -1[16] 3. Credit Direction Factor - Current Score: -1[18] 4. Credit Strength Factor - Current Score: 1[20] 5. Growth Direction Factor - Current Score: 1[21] 6. Growth Strength Factor - Current Score: 0[25] 7. Inflation Direction Factor - Current Score: 1[26] 8. Inflation Strength Factor - Current Score: 1[29]

Quantitative Models and Construction Methods - **Model Name**: Timing Radar Six-Dimensional Framework **Model Construction Idea**: The model evaluates equity market performance through a multi-dimensional perspective, incorporating liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding dimensions. These 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**: The model selects 21 indicators across the six dimensions and aggregates them into the four categories mentioned above. Each category is scored based on its respective indicators, and the final composite score is calculated as the weighted average of these categories[1][6][8] **Model Evaluation**: The model provides a comprehensive and systematic approach to market timing by integrating multiple dimensions, offering a balanced view of market conditions[1][6][8] Quantitative Factors and Construction Methods - **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[10] **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[10] **Factor Evaluation**: Effectively captures the directional stance of monetary policy, providing insights into liquidity conditions[10] - **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[13] **Factor Construction Process**: - Calculate the deviation as DR007/7-year reverse repo rate - 1 - Smooth and standardize the deviation using z-scores - Assign scores based on thresholds: <-1.5 SD indicates accommodative conditions (score = 1), >1.5 SD indicates restrictive conditions (score = -1)[13] **Factor Evaluation**: Provides a quantitative measure of short-term liquidity conditions relative to policy rates[13] - **Factor Name**: Credit Direction Factor **Factor Construction Idea**: Measures the transmission of credit from banks to the real economy using medium- and long-term loan data[14] **Factor Construction Process**: - Calculate the monthly value of medium- and long-term loans - Compute the 12-month incremental change and its year-over-year growth - Compare the factor value to its level three months ago: an increase indicates a positive signal (score = 1), while a decrease indicates a negative signal (score = -1)[14] **Factor Evaluation**: Captures the directional flow of credit, reflecting economic support from the banking sector[14] - **Factor Name**: Credit Strength Factor **Factor Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[18] **Factor Construction Process**: - Calculate the deviation of new RMB loans from their median forecast, normalized by the forecast's standard deviation - Assign scores based on thresholds: >1.5 SD indicates a positive surprise (score = 1), <-1.5 SD indicates a negative surprise (score = -1)[18] **Factor Evaluation**: Quantifies the strength of credit data relative to expectations, offering insights into market surprises[18] - **Factor Name**: Growth Direction Factor **Factor Construction Idea**: Based on PMI data, this factor evaluates the trend in economic growth over the past 12 months[20] **Factor Construction Process**: - Compute the 12-month moving average of PMI data (including manufacturing and non-manufacturing indices) - Calculate the year-over-year change and compare it to its level three months ago: an upward trend indicates a positive signal (score = 1), while a downward trend indicates a negative signal (score = -1)[20] **Factor Evaluation**: Effectively captures the directional trend in economic growth, providing a macroeconomic perspective[20] - **Factor Name**: Growth Strength Factor **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[22] **Factor Construction Process**: - Calculate the deviation of PMI data from its median forecast, normalized by the forecast's standard deviation - Assign scores based on thresholds: >1.5 SD indicates a positive surprise (score = 1), <-1.5 SD indicates a negative surprise (score = -1)[22] **Factor Evaluation**: Quantifies the strength of economic growth data relative to expectations, offering insights into market surprises[22] - **Factor Name**: Inflation Direction Factor **Factor Construction Idea**: Evaluates the trend in inflation levels, which influence monetary policy constraints[25] **Factor Construction Process**: - Calculate the weighted average of smoothed CPI and raw PPI year-over-year changes - Compare the factor value to its level three months ago: a downward trend indicates a positive signal (score = 1), while an upward trend indicates a negative signal (score = -1)[25] **Factor Evaluation**: Provides insights into inflationary trends and their potential impact on monetary policy[25] - **Factor Name**: Inflation Strength Factor **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[26] **Factor Construction Process**: - Calculate the deviation of CPI and PPI data from their median forecasts, normalized by the forecast's standard deviation - Compute the average of these deviations to form the factor value - Assign scores based on thresholds: <-1.5 indicates a positive signal (score = 1), >1.5 indicates a negative signal (score = -1)[26] **Factor Evaluation**: Quantifies the strength of inflation data relative to expectations, offering insights into market surprises[26] Factor Backtesting Results - **Monetary Direction Factor**: Current score = -1[10] - **Monetary Strength Factor**: Current score = -1[13] - **Credit Direction Factor**: Current score = -1[14] - **Credit Strength Factor**: Current score = 1[18] - **Growth Direction Factor**: Current score = 1[20] - **Growth Strength Factor**: Current score = 0[22] - **Inflation Direction Factor**: Current score = 1[25] - **Inflation Strength Factor**: Current score = 1[26]

资产配置月报 202505 五月配置视点：黄金见顶了吗？ 2025 年 05 月 05 日 ➢ 黄金见顶了吗？ 美国经济在关税政策影响下一季度增速转负，结构上韧性减弱，市场对于美国经 济衰退的预期上升；美国就业市场温和降温，对黄金有正面影响但较弱；美国财 政方面近期虽然增速有所放缓，但是主要由国防支出减少导致，非国防消费支出 和投资依旧维持增长，财政长期扩张趋势未完全扭转，依旧支撑黄金表现；技术 层面黄金过去积累对应的上涨空间已基本兑现，未来价格继续上行需要进一步积 累或者有新增增量资金入场，短期或较为疲软。综合来说，黄金短期或阶段性休 整，但是长期上涨逻辑不变（或由单一财政逻辑转向叠加经济衰退的逻辑）。 ➢ 大类资产量化观点 1. 权益：Q1 财报景气度回升，五月积极应对。景气度 4 月整体走平，金融中 银行、非银景气度都进一步下降，工业景气度有所回升；上市公司 2024 年年报 以及 2025 年一季报反映了积极变化。信用或继续稳步扩张，政府债券仍占主导； 从结构来看，高增主要来源于去年同期的低基数，政府债券继续支撑社融增长。 4 月市场如我们预期先下后上，目前市场遇强支撑，5 月静待成交放量。 2. 利 ...

- The timing radar six-dimensional model is constructed based on multiple dimensions including liquidity, economic fundamentals, valuation, capital flow, technical signals, and crowding indicators. It aggregates 21 indicators into four categories: "valuation cost-effectiveness," "macro fundamentals," "capital & trend," and "crowding & reversal," generating a comprehensive timing score ranging from [-1,1][1][6][8] - Liquidity dimension includes factors such as monetary direction, monetary intensity, credit direction, and credit intensity. 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. If the factor is greater than 0, it indicates monetary easing; otherwise, it signals tightening[12][15][17] - Economic dimension includes growth direction and intensity factors, as well as inflation direction and intensity factors. For instance, the growth direction factor is derived from PMI data, calculating the 12-month average and year-over-year changes. If the factor shows an upward trend compared to three months ago, it signals a positive outlook[23][30][31] - Valuation dimension includes indicators such as Shiller ERP, PB, and AIAE. Shiller ERP is calculated as 1/Shiller PE minus the 10-year government bond yield, with a z-score applied over the past three years. PB and AIAE indicators follow similar z-score normalization methods[36][38][41] - Capital flow dimension is divided into domestic and foreign capital indicators. Domestic indicators include margin trading increment and trading volume trends, while foreign indicators include China's sovereign CDS spread and overseas risk aversion index. For example, the CDS spread factor signals foreign capital inflow when the 20-day difference is less than 0[44][51][54] - Technical dimension captures trends and reversal signals, such as price trends and new highs/new lows. The price trend factor uses moving average distances (ma120/ma240-1) to measure market trends and strength. The new highs/new lows factor evaluates the difference between the number of new highs and new lows among index constituents over the past year[56][59] - Crowding dimension includes derivative signals such as implied premium/discount, VIX, and SKEW, as well as convertible bond pricing deviation. For instance, the implied premium/discount factor is derived from the 50ETF's recent 5-day returns and percentile rankings, signaling market crowding levels[62][68][71] - Current timing radar scores for each dimension are as follows: liquidity (-0.50), economic fundamentals (0.50), valuation (0.32), capital flow (-0.75), technical signals (0.00), and crowding (0.76). The overall timing score is 0.08, indicating a neutral-to-positive market outlook[7][8][10]

Core Viewpoint - The stock market is experiencing low trading volumes, leading many investors to adopt a wait-and-see approach, but this calmness may be hiding accumulating risks [1][2]. Group 1: Market Conditions - The stock market has been quiet, with low trading activity and a lack of enthusiasm among traders [2]. - Two main reasons for the current market stagnation are the anticipation of key news and the typical post-spring farming market pause in A-shares [4]. - Investors are particularly cautious ahead of the upcoming May Day holiday, leading to a continued lack of market activity [4]. Group 2: Investment Opportunities - While many retail investors are waiting for a market upturn, there are hidden risks that could lead to significant losses if not addressed [5]. - The market is not uniformly rising or falling; some stocks and sectors may be quietly accumulating risks during this sideways market [5]. - Understanding institutional trading data can provide insights into potential investment opportunities, as institutional investors often have better market insights than retail investors [7]. Group 3: Institutional Trading Insights - Data analysis reveals that stocks with active institutional trading tend to perform better, as seen with "Huarong Chemical," which saw an 80% price increase due to institutional accumulation [9]. - Conversely, "ST Zhongqingbao" experienced a nearly 50% drop with no institutional involvement, highlighting the importance of institutional sentiment in stock performance [10]. - A comprehensive analysis of over 5,000 stocks can help identify which stocks are being actively managed by institutions, providing a clearer picture of potential investment opportunities [10].

- The report constructs a bottom-up framework for forecasting social financing (社融) by analyzing sub-items based on economic logic, high-frequency data, and seasonal characteristics. This approach allows for detailed predictions of both total social financing and its structural components[7][8] - The framework includes predictive methods for various sub-items such as RMB loans, government bonds, corporate bonds, and others. For example, RMB loans are forecasted using PMI and Tangshan steel plant capacity utilization rates as independent variables, while government bonds are tracked using high-frequency issuance and maturity data[8] - Specific predictive methods include rolling regression for RMB loans and corporate bonds, using past averages for items like foreign currency loans and entrusted loans, and high-frequency tracking for trust loans and ABS net financing[8] - The March 2025 forecast for new social financing is approximately 5.68 trillion RMB, with RMB loans contributing 3.78 trillion RMB, government bonds 1.53 trillion RMB, and corporate bonds 0.01 trillion RMB. Structural predictions also include detailed breakdowns such as enterprise loans, resident short-term loans, and resident long-term loans[8][16] - The report highlights that government bonds continue to drive social financing growth, while corporate credit shows signs of recovery. PMI data and real estate sales are used to support these predictions[9]

指数回来了，钱却没回来 昨天市场出现了一个值得玩味的现象： 虽然三大指数最终收红，但微盘股却领跌全市场，明明指数是涨的，为什么自己反而亏了大钱？ 这种指数与个股表现背离的情况，今天我来给大家好好分析一下。 文末有重要干货提示，千万不要错过！ 一，微盘股领跌 表面上看，微盘股的下跌可以归因于监管边际趋严、缺乏热点题材等短期因素。 但究其根本，是市场内部的杠杆水平和真实赚钱效应之间的扭曲程度已经达到了一个极点。 过去一段时间，部分微盘股凭借资金推动和概念炒作积累了过高涨幅，而随着市场回归理性，均值回归的规律开始发挥作用。 值得庆幸的是，昨天下午两点半的那波强力拉升，避免了市场重演去年12月17日的单边下跌剧情，这显示出市场仍具备一定的自我修复能力。 二，指数涨了，钱却亏了 这种指数与个股分化的现象给我们一个重要启示： 单纯盯着指数涨跌来判断市场好坏是远远不够的。 即便指数上涨，如果选错了板块和个股，依然可能面临亏损。 这就是为什么专业投资者更关注机构资金的动向——因为机构往往能更早感知市场风向的变化。 机构掌握着股价的定价权，对于市场中稀有的有价值的信息能够提前掌握并分析。 当前市场正处于一个关键转折期，前期涨幅 ...