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 ...

证券研究报告 | 金融工程 gszqdatemark 2025 04 26 年 月 日 量化分析报告 择时雷达六面图：本周打分无显著变化 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。 本周综合打分。本周市场的估值性价比有所下降，宏观基本面中性，市场 的资金&趋势信号有所弱化信号偏空，拥挤度&反转指标显著偏多，综合打 分位于[-1,1]之间，当前的综合打分为 0.08 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。本周货币方向、货币强度、信用方向均发出看空信号，信用强 度发出看多信号，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，而增 长强度发出看空信号，当前经济面得分为 0.50 分，综合来看发出看多信 号。 估值面。由于市场上行，本周席勒 ERP、PB 与 AIAE ...

最近股市成交量越来越少，不少人觉得行情没劲，打算先观望，等涨起来再进场。 可大家都忽略了件大事：横盘看着平静，实则风险正在偷偷积累！ 别以为现在不操作就万事大吉。 今天我来和大家讨论一下。文末准备了超实用的股市数据干货，错过真的亏大了！ 一，变量 过去一周，股市跟睡着了似的，看着每天成交额，但实际买卖的人都提不起劲，交易冷清得很。 为啥最近股市这么没劲？我看主要是两方面原因。一是市场在等关键消息，二是A股每年春耕行情后，都会歇一歇缓口气。 具体等啥呢？重点看两个信号。首先是财报风险，业绩好的公司早早就报喜了，那些业绩差的都憋着，就等五一放假前最后几天才公布，到时候肯定有不少 雷。 再就是正策动向，比如最近地产圈就传得沸沸扬扬，大家都在赌会不会出利好正策。但到底有没有，咱们只能边走边看。 机构消息灵通、分析专业，看市场比散户准得多，跟着他们的操作思路走准没错。 现在行情看着平淡，其实不少股票已经悄悄拉开差距了。只要能摸清机构的交易套路，提前布局，轻松跑赢大盘！不信你看下面这张图！ 而且五一假期快到了，节前资金都不敢乱动，市场估计还得继续冷清一阵子。 二，现在就有机会 别人都嫌行情太沉闷，我却觉得暗藏危机！不少散 ...

- 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 ...