最近沪深两市ETF两融余额突破1200亿元的消息刷屏了，富国中债ETF单日净买入2.56亿元。看着这些数字，我突然想起十年前在 复旦图书馆啃量化金融教材的日子——那时候我就明白，市场里真正赚钱的永远是那些看得懂数据的人。 一、牛市幻觉与残酷现实 每次看到融资余额创新高，总有人高喊"牛市来了"。但作为一个量化交易的老兵，我必须说句难听的：90%的散户根本分不清什么 是真牛市。你们知道吗？2025年关税摩擦后的反弹行情，指数都快创新高了，可实际个股上涨的还不到五成！ 这就像挤地铁，看着站台上乌泱泱的人群都往车厢里冲，结果你挤上去才发现是辆区间车。我见过太多人拿着不涨的股票安慰自 己"迟早会轮到的"，最后等来的却是退市通知。这不是危言耸听，看看下面这两组数据对比： （第一只股票机构资金持续活跃） 三、从ETF数据看聪明钱流向 回到开头的新闻，为什么富国中债ETF能获2.56亿净买入？这帮机构可不是傻子。债券ETF在利率下行周期就是避风港，而恒生科 技ETF被抢筹说明资金在押注港股估值修复。这些逻辑散户根本不会看，他们只关心"明天哪个涨停"。 我建议每个散户都去学学基础的量化分析。不需要多复杂，先把融资融券数据、大宗 ...

一、半导体江湖的风云突变 那天刷到敏芯半导体董秘亢娜跳槽中元股份的消息时，我正在喝咖啡。新闻里说这位在敏芯待了七年的女将突然转投同行，而敏芯的IPO进程也在同一 时间从湖北证监局的辅导名单中神秘消失。这杯咖啡顿时变得意味深长起来。 记得三年前敏芯半导体刚启动上市辅导时，朋友圈里那些投资经理们个个眉飞色舞。作为国内首家全系列光芯片独立供应商，高瓴、中芯聚源这些顶级 机构的加持让它一度被称为"科创板潜龙"。谁能想到，三年后的今天，中金公司的辅导报告突然停更，核心高管转身离去。 这让我想起去年参加清华校友会时，一位在投行工作的师兄说的话："现在看企业不能只看表面风光，要看清资金流动的痕迹。"当时我还不太理解，直 到后来用上了量化系统，才发现原来市场上每个动作背后都有迹可循。 二、震荡中的生存法则 在证券市场上混迹多年，我渐渐明白一个道理：好股票就像武林高手，最厉害的不是一路高歌猛进，而是能在震荡中保持内力。任何标的都会面临两个 致命挑战——跟风盘越来越多，获利盘越来越厚。这两座大山不搬走，再好的行情也走不远。 记得刚入行时跟着师傅学艺，他总说："丫头，记住，牛市里的大牛股都是'洗'出来的。"当时不明白什么叫"洗"， ...

证券研究报告 | 金融工程 gszqdatemark 2025 11 15 年 月 日 量化分析报告 择时雷达六面图：本周资金面好转 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、趋势 &资金分数上升，宏观基本 面、拥挤度&反转分数下降，综合打分位于[-1,1]之间，当前的综合打分为 0.03 分，整体为中性观点。当前六面图各个维度的观点如下： 流动性。本周货币方向、信用方向发出看多信号，货币强度信号中性， 信用强度发出看空信号，当前流动性得分为 0.25 分，综合来看发出中性 偏多信号。 经济面。本周增长方向指标发出看多信号，通胀方向、通胀强度发出看 空信号，当前经济面综合得分为-0.25 分，综合来看发出中性偏空信号。 估值面。本周席勒 ERP、PB、AIAE 指标分数均上升，当前市场的估 值面得分为-0.47 分 ...

Core Insights - The recent surge in Japanese and South Korean stock markets, particularly in semiconductor stocks like SK Hynix and Samsung Electronics, contrasts with the stagnant performance of A-shares, leading to mixed feelings among retail investors [1][2] Market Performance - The Shanghai Composite Index has risen by 19.6% from April 7 to October 30, yet less than 40% of individual stocks have outperformed the index [2] - Among 4,200 rising stocks during this period, over 4,000 experienced a price fluctuation exceeding 30%, indicating a lack of ability to capitalize on market opportunities [2][4] Investment Behavior - Retail investors often rely on outdated technical analysis methods, while institutional investors utilize advanced tools like quantum computing for market predictions [2][4] - The disparity in market participation is evident, with institutional investors in Japan and South Korea comprising over 70% of the market, compared to a retail-dominated market in China [6][7] Institutional Influence - The analysis of stock rebounds shows that the first three rebounds were driven by retail investors, while the fourth rebound indicated significant institutional involvement [6] - The presence of institutional investors is crucial for market stability and growth, as evidenced by their ability to create clear signals in a mature market [7] Future Outlook - The ongoing structural transformation in the Chinese market necessitates a shift in investment strategies, with a focus on data-driven decision-making to avoid losses [7] - The evolution of the market suggests that individual investors must enhance their understanding of data analytics to remain competitive against institutional players [7]

Group 1 - The article emphasizes the importance of efficient reading and provides a structured approach for new readers to navigate the content [1] - It highlights four main collections available on the public account: Principles and Formulas Collection, Quantitative Analysis Collection, Beast Stock Research Collection, and Book Notes Collection [1] - The article mentions that the top two selected articles serve as key resources, focusing on the underlying logic of stock selection and methods for quickly identifying trending stocks [4] Group 2 - The public account has garnered significant engagement, with articles receiving thousands of reads and numerous likes, indicating a strong interest in stock selection strategies [2] - The community is encouraged to engage with the content, with some readers reportedly studying and printing key articles for deeper understanding [4]

- The report mentions the construction of the A-share sentiment index, which is based on market volatility and trading volume changes. The sentiment index divides the market into four quadrants, with only the "volatility up-trading volume down" quadrant showing significant negative returns, while the other quadrants show significant positive returns. This index includes bottom warning and top warning signals[37][43][46] - The A-share sentiment index currently indicates bearish signals for both bottom and top warnings. The overall sentiment signal is bearish[40][43][46] - The A-share prosperity index is constructed using the YoY growth of the net profit attributable to the parent company of the Shanghai Composite Index as the Nowcasting target. The index shows a slow upward trend, indicating the current market is in an upward cycle[32][35][36] - The report highlights the performance of style factors, including residual volatility (RESVOL), momentum (MOM), and others. Residual volatility factor showed high excess returns, while momentum factor exhibited significant negative excess returns. High-value stocks performed well, whereas size and non-linear size factors underperformed[61][62][69] - The report uses the BARRA factor model to construct ten style factors for the A-share market, including size (SIZE), beta (BETA), momentum (MOM), residual volatility (RESVOL), non-linear size (NLSIZE), valuation (BTOP), liquidity (LIQUIDITY), earnings yield (EARNINGS_YIELD), growth (GROWTH), and leverage (LVRG)[61][62][64] - The report evaluates the performance of enhanced index portfolios. The CSI 500 enhanced portfolio achieved a weekly return of 0.40%, outperforming the benchmark by 0.45%. Since 2020, the portfolio has generated an excess return of 52.65% relative to the CSI 500 index, with a maximum drawdown of -5.73%[48][51][53] - The CSI 500 enhanced portfolio's holdings include stocks such as Hongfa Shares (600885.SH) with a weight of 3.47%, Gujin Securities (600109.SH) with a weight of 2.74%, and others[53][55][56] - The CSI 300 enhanced portfolio achieved a weekly return of 0.84%, outperforming the benchmark by 0.02%. Since 2020, the portfolio has generated an excess return of 39.36% relative to the CSI 300 index, with a maximum drawdown of -5.86%[56][57][60] - The CSI 300 enhanced portfolio's holdings include stocks such as Yunnan Baiyao (000538.SZ) with a weight of 6.04%, Fangzheng Securities (601901.SH) with a weight of 5.73%, and others[60][56][57]

Core Insights - MSCI's recent adjustment has led to the inclusion of 26 Chinese stocks, indicating a continued influx of foreign capital into A-shares [1][3] - The scale of this adjustment is 2-3 times larger than previous ones, with 17 new A-shares entering the index, prompting upgrades in expectations from firms like Goldman Sachs and Nomura [3] - The market behavior suggests that while institutions may be withdrawing, retail investors are still hesitant about chasing high prices, reflecting a common pattern of profit-taking during bullish trends [3][6] Market Dynamics - The market is characterized by complex trading behaviors, where perceived upward movements may actually indicate institutional selling, while downward trends could signal accumulation by large funds [3][6] - Quantitative analysis reveals that five instances of profit-taking signals (yellow bars) have historically coincided with market peaks, leading to significant corrections [6][8] - Conversely, four instances of bullish signals (blue bars) have marked key buying opportunities, suggesting that understanding these signals can provide strategic advantages [8] Investment Strategy - The MSCI adjustment serves as both a conclusion and a new beginning, emphasizing the importance of understanding market language rather than focusing solely on individual stocks [8] - Investors are encouraged to utilize quantitative data to track foreign capital flows and avoid the pitfalls of relying solely on traditional price movement analysis [8][9] - The ability to interpret market signals and data is highlighted as a crucial skill for navigating the complexities of the current investment landscape [8][9]

- The "Timing Radar Six-Dimensional Chart" is a multi-dimensional timing framework that evaluates the equity market using 21 indicators categorized into four dimensions: "Valuation Cost-Effectiveness," "Macroeconomic Fundamentals," "Funds & Trends," and "Crowdedness & Reversal" These dimensions generate a composite timing score ranging from [-1,1][1][6][8] - The "Liquidity" dimension includes factors such as "Monetary Direction" and "Credit Direction," which signal a slightly bullish outlook this week with a score of 0.50[8][11][17] - The "Economic" dimension uses factors like "Growth Direction" and "Inflation Direction" to assess macroeconomic conditions This week, the score is neutral at 0.00, with "Growth Direction" signaling bullish and "Inflation Direction" signaling bearish[8][22][27] - The "Valuation" dimension evaluates equity cost-effectiveness using indicators like "Shiller ERP," "PB," and "AIAE" This week, the score is -0.49, indicating a slightly bearish outlook[8][30][35][37] - The "Funds" dimension analyzes capital flows through indicators such as "Margin Financing Increment" and "China Sovereign CDS Spread" This week, the score is neutral at 0.00, with domestic capital showing bullish signals and foreign capital showing bearish signals[8][39][46][48] - The "Technical" dimension captures market trends and reversals using indicators like "Price Trend" and "New Highs and Lows" This week, the score is neutral at 0.00, with "Price Trend" signaling bullish and "New Highs and Lows" signaling bearish[8][51][53] - The "Crowdedness" dimension measures market sentiment using derivative signals like "Option Implied Premium," "VIX," and "SKEW," as well as "Convertible Bond Pricing Deviation" This week, the score is 0.50, indicating a slightly bullish sentiment[8][57][63][67]

Core Insights - The article highlights the importance of understanding market dynamics beyond surface-level trends, emphasizing that true investment opportunities lie in recognizing underlying data and behaviors [3][10]. Group 1: Market Dynamics - The recent increase in margin financing on the Sci-Tech Innovation Board, particularly the 236 million yuan net buy by Aters, signals significant institutional interest at a critical market juncture [1][9]. - Despite the Shanghai Composite Index rising by 19.6% from April 7 to October 30, only 40% of stocks outperformed the index, indicating a disparity between overall market performance and individual stock success [3][9]. - The volatility of stocks, with over 4000 out of 4200 rising stocks showing fluctuations greater than 30%, suggests that while opportunities exist, timing and insight are crucial for capitalizing on them [3][8]. Group 2: Investment Behavior - The article contrasts two stocks that appeared similar in their recovery after a 20% pullback, revealing that one was driven by institutional support while the other was merely a retail-driven rebound [4][6]. - The analysis of financing activities indicates that significant inflows often correlate with institutional repositioning, which can lead to sustained stock performance [11]. - The distinction between genuine market movements and superficial trends is critical, as many investors may misinterpret data without a deeper analytical framework [10][11]. Group 3: Analytical Framework - Establishing a data-driven mindset is essential for investors, focusing on tracking capital flows rather than solely relying on analyst opinions [11]. - Recognizing that only a fraction of observed volatility is meaningful can help investors differentiate between effective market movements and noise [11]. - The value of analytical tools and frameworks is emphasized, suggesting that finding a suitable analysis method is more important than attempting to predict market movements [11].

Quantitative Models and Construction Methods 1. Model Name: Timing Radar Six-Sided Chart - **Model Construction Idea**: The performance of the equity market is influenced by multiple dimensions. This model attempts to describe the market using 21 indicators from liquidity, economic, valuation, capital, technical, and congestion perspectives, summarized into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Congestion & Reversal," generating a comprehensive timing score between [-1,1][2][7]. - **Model Construction Process**: - **Liquidity**: Indicators include monetary direction, monetary strength, credit direction, 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[13]. - **Economic**: Indicators include growth direction, growth strength, inflation direction, and inflation strength. For example, the growth direction factor is based on PMI data, calculated as the 12-month average of PMI and its year-over-year change[23]. - **Valuation**: Indicators include Shiller ERP, PB, and AIAE. For example, Shiller ERP is calculated as 1/Shiller PE - 10-year government bond yield, with a z-score over the past 6 years[35]. - **Capital**: Indicators include margin financing increment, trading volume trend, China sovereign CDS spread, and overseas risk aversion index. For example, the margin financing increment is calculated as the difference between the 120-day and 240-day moving averages of financing balance minus short-selling balance[44]. - **Technical**: Indicators include price trend and new highs and lows. For example, the price trend factor is calculated as the distance between the 120-day and 240-day moving averages[54]. - **Congestion**: Indicators include implied premium/discount, VIX, SKEW, and convertible bond pricing deviation. For example, the implied premium/discount is derived from the put-call parity relationship[59]. - **Model Evaluation**: The model provides a comprehensive view of the market by integrating multiple dimensions, making it a robust tool for market timing[2][7]. Model Backtesting Results - **Current Comprehensive Score**: -0.13, slightly down by 0.01 from last week, indicating a neutral view[2][7]. - **Liquidity Score**: 0.25, indicating a slightly bullish signal[9]. - **Economic Score**: -0.25, indicating a slightly bearish signal[9]. - **Valuation Score**: -0.50, indicating a slightly bearish signal[9]. - **Capital Score**: 0.00, indicating a neutral signal[9]. - **Technical Score**: 0.00, indicating a neutral signal[9]. - **Congestion Score**: 0.00, indicating a neutral signal[9]. Quantitative Factors and Construction Methods 1. Factor Name: Monetary Direction Factor - **Factor Construction Idea**: To determine the direction of current monetary policy using central bank policy rates and short-term market rates[13]. - **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 bullish signal[13]. - **Factor Evaluation**: Provides a clear indication of the direction of monetary policy, useful for assessing liquidity conditions[13]. 2. Factor Name: Monetary Strength Factor - **Factor Construction Idea**: Based on the "interest rate corridor" concept to measure the deviation of short-term market rates from policy rates[16]. - **Factor Construction Process**: Calculate the deviation as DR007/7-year reverse repo rate - 1, smooth and z-score it to form the monetary strength factor. If the factor is less than -1.5 standard deviations, it indicates a bullish signal[16]. - **Factor Evaluation**: Useful for assessing the strength of monetary policy and its impact on market liquidity[16]. 3. Factor Name: Credit Direction Factor - **Factor Construction Idea**: To measure the tightness of credit transmission from commercial banks to the real economy using medium and long-term loan data[19]. - **Factor Construction Process**: Calculate the year-over-year change in the 12-month increment of medium and long-term loans. If the factor is rising compared to three months ago, it indicates a bullish signal[19]. - **Factor Evaluation**: Provides insights into the credit environment and its impact on economic activity[19]. 4. Factor Name: Growth Direction Factor - **Factor Construction Idea**: Based on PMI data to measure the direction of economic growth[23]. - **Factor Construction Process**: Calculate the 12-month average of PMI and its year-over-year change. If the factor is rising compared to three months ago, it indicates a bullish signal[23]. - **Factor Evaluation**: Useful for assessing the direction of economic growth and its impact on market sentiment[23]. 5. Factor Name: Shiller ERP - **Factor Construction Idea**: To adjust for the impact of economic cycles on corporate earnings and market valuation[35]. - **Factor Construction Process**: Calculate Shiller PE as the average inflation-adjusted earnings over the past 6 years, then calculate Shiller ERP as 1/Shiller PE - 10-year government bond yield, and z-score it over the past 6 years[35]. - **Factor Evaluation**: Provides a long-term perspective on market valuation, useful for identifying overvalued or undervalued conditions[35]. Factor Backtesting Results - **Monetary Direction Factor**: Score of 1, indicating a bullish signal[13]. - **Monetary Strength Factor**: Score of -1, indicating a bearish signal[17]. - **Credit Direction Factor**: Score of 1, indicating a bullish signal[19]. - **Growth Direction Factor**: Score of 1, indicating a bullish signal[23]. - **Shiller ERP**: Score of -0.04, indicating a slightly bearish signal[35].