6月26日，和讯投顾刘金锁表示，今日盘面冲高回落，呈倒锤头形态，个股调整明显。从盘面看，整体 较昨日弱了许多，出现分化。昨日之前就提示短线昨日不宜追高，因其为第三根阳线。今日下跌个股超 3600只，上涨1600多只，涨停数量比昨日少十几只，涨幅榜与跌幅榜情况显示行情较昨日弱。不过这种 情况正常，不少人前日、昨日指数涨时个股不涨，今日指数调整个股也跟着调整，对此不必担心着急。 经短线调整后，最快明天下午部分个股或止跌企稳，慢则等下周一，因明天是周末。若明天再有小调整 且缩量，鉴于今日部分个股调整已缩量，明天再缩量，短线部分个股将迎来买点。下周先看明天情况， 若明天如期小幅缩量调整，下周一多数个股或止跌企稳或再次拉升，毕竟权重股已搭好台，小票将轮动 表现，当下行情为轮动行情，集体性牛市概率不大。 操作节奏上，仍应追跌杀涨，买跌不买涨，卖涨不卖跌，有调整可短线买入，但切勿追第三根以上的阳 线，尤其是相对底部区域的个股，若追高短线被套，调整时间可能较长。高位个股反弹时建议回避风 险，如之前因某些因素大涨的石油、采掘、航运港口、燃气等行业个股，短线刚被套反弹回本时，尽量 回避。若一直被套，仍以持股待涨、耐心等待解套为主 ...

文 | 银行螺丝钉 （转载请注明出处） 大盘连续3天上涨后，今天略微下跌，还在4.9星。 大中小盘股都下跌。 银行指数上涨。 红利等价值风格波动较小。 港股开盘下跌。不过到收盘跌幅缩小。 港股科技股相对坚挺。 在2014-2015年，是一轮小盘股牛市，当时小盘股等上涨较多，银行等大盘股比较低迷。 所以当时对大蓝筹股，有的投资者就起了个外号"大烂臭"。 到了2016-2017年，小盘股因为估值泡沫连续大跌。 大盘股、蓝筹股出现慢牛行情，这个词也就很少提了。 到了2019-2021年，出现成长股牛市。 成长指数上涨超过150%，A股整体上涨80%。 银行等价值风格指数也上涨，但涨幅比较小，大幅跑输市场。 银行等指数又被称为"三傻"。 到了2022-2024年，价值风格逐渐强势。 这几年，银行、红利等品种也整体上涨，创下指数点数的新高。 严格来说，红利和银行指数还是有区别的。 一方面，银行指数是按照银行市值大小分配比例；红利是按照股息率高低分配比例，越低估的股票占比越高。 另一方面，有的红利类指数则金融股很少。例如红利质量、自由现金流（不含金融股）。 1. 银行指数最近几周比较强势，创下历史新高。 也有朋友问银行指 ...

我们必须看到，A股的赌民正在慢慢的专业化，或者被动的被消灭，或者主动的求提升，市场 的有效性正在提高，就像一个人，读书读到了四年级，就再也不会倒回到一年级的水平。同 时，国家队已经大量买入各种宽基指数，而A股的衍生品数量，也在逐步的增多中。 追求利润的经济体，利润可能会变成无源之水，而追求规模的经济体，未来也可能会通过规 模来调节利润。时代的齿轮正在转动，涓涓细流汇向大海。在未来，我们会迎来更专业，更 理性的投资者，会迎来更有效的市场，那么，那时候的A股生态又会变成什么样子呢？那时候 的土地，又会开出来什么样的花朵呢？我们有什么办法提前拿到未来的入场券么？ 最后用一首杜荀鹤的《小松》结束此文 自小刺头深草里， 而今渐觉出蓬蒿。 时人不识凌云木， 直待凌云始道高。 关注集思录微信 我以为，中国资本市场真正的特点有三个： 1.拥有全球最大规模的"赌民" 2.中国上市公司整体盈利能力偏低 3.中国缺少做空工具 一、中国拥有全球最大规模的"赌民"。这样的人群，广泛分布在我们的周围，我的亲戚 A,B,C，我的同学D,E，我的朋友F,G，都是这样的人。这样的人作为一个群体，拿着自己起早 贪黑的钱，就敢赌妖股，听消息，押重 ...

出品 | 妙投APP 作者 | 丁萍 头图 | 视觉中国 最近这几天，A股涨得让人猝不及防，权重金融股带动指数强势突破，上证指数再次站上了3400点，最 高摸到了3462点。 这波行情最大的催化剂，就是国泰君安国际拿下了香港证监会的"虚拟资产交易牌照"。现在，客户不仅 能在平台上直接交易比特币、以太坊，还能买卖USDT等稳定币。这个动作，无疑给市场注入了一针强 心剂。 基本面超预期 首先一个超预期的事情是，自4月中美贸易战之后，原本市场悲观预期Q2经济全面承压，但实际上并没 有出现。一是中美贸易冲突在4-5月并未体现在外贸数据上，相反因为抢出口，出口数据表现相对不 错；二是消费"以旧换新"政策的延续，整体的经济数据可以定性为"稳弱"。 所以我们看到，指数在经过第一天的大幅低开高走之后，缓慢修复了最初的跌幅。 所以在低估值的背景之下，经济数据没有明显的回落，资本市场在经历了最初两天的恐慌之后，除了最 直接被冲击的若干细分行业之外，指数逐渐回暖并逐渐反弹至中美贸易冲突开始的位置。 展望下半年，虽然中美贸易冲突已经大大缓和（这也是近期指数反弹的宏观背景之一），但是关税的实 际影响力度还是增加的，叠加4-5月抢出口之 ...

尤其港股银行H股平均股息率更高，如银行AH指数最新股息率为4.7%，对险资、社保等"长钱"而言，这类"类固收"资产是抵御低利率的天然避 风港，因此尤其青睐。 6月25日，工商银行、建设银行、交通银行等十余家银行股价再创历史新高。这并不是昙花一现——年初至今，申万银行指数已上涨14.11%，在31个一 级行业中高居榜首，市值较年初增长2.05万亿元，几乎相当于"再造两个宁德时代"。 | 序号 | 证券代码 | 证券简称 | 区间涨跌幅 | | | --- | --- | --- | --- | --- | | | | | [区间首日] 2025-1-1 [区间居日] 2025-6-25 | | | | | | [单位] %] | | | 1 | 801780.SI | 银行(申万) | | 15.77 | | 2 | 801050.SI | 有色金属(甲力) | | 115.34 | | 3 | 801760.SI | 传媒(申万) | | 9.97 | | 4 | 801880.51 | 汽车(甲万) | | 9.70 | | 5 | 801890.51 | 机械设备(申万) | | 8.23 | | 6 | ...

Core Insights - The report introduces a quarterly rebalancing industry rotation strategy based on traditional quantitative multi-factor scoring, focusing on "valuation," "quality," "liquidity," and "momentum" [1][11] - The composite strategy achieved an annualized return of 19.64% during the backtesting period (April 1, 2014 - June 6, 2025), significantly outperforming the industry equal-weight benchmark which returned 7.55%, resulting in an annualized excess return of 12.09% [1][68] - The strategy prioritizes low valuation, low crowding, improving economic conditions, upward price momentum over the past year, and industries that have been at low price levels for the past three years [1][11] Industry Factor Backtesting Framework - The backtesting period spans from January 2010 to September 2024, with a quarterly rebalancing approach using data from the last trading day of each quarter [12] - The strategy excludes industries with a weight of less than 2% in the CSI 800 index for risk control, retaining approximately 15-16 major industries for rotation calculations [12][3] Industry Rotation Strategy Overview Valuation Factors - Valuation factors include PE_TTM, PB_LF, PCF_TTM, PEG, and dividend yield, evaluated through various methods such as historical percentiles and marginal changes [15] - Notable factors include: - Dividend yield ranking over three years (4.0% annualized excess for TOP-5) [16] - PE_TTM marginal change over two months (5.8% annualized excess for TOP-5) [16] Quality Factors - Quality factors are based on ROE and ROA, focusing on profitability and financial stability [19] - Key factors include: - ROA_TTM marginal change over one quarter (4.3% annualized excess for TOP-5) [20] - ROE_FY2 (4.7% annualized excess for TOP-5) [20] Liquidity Factors - Liquidity factors are derived from turnover rates of freely circulating shares, assessed through various time frames [21] - Effective factors include: - 21-day average turnover rate (4.3% annualized excess for TOP-5) [22] - Margin of turnover rates over two months (4.6% annualized excess for TOP-5) [22] Momentum Factors - Momentum factors are calculated based on recent returns over different periods, showing varying characteristics [24] - Significant factors include: - One-month momentum (7.7% annualized excess for TOP-5) [26] - Three-month momentum (1.9% annualized excess for TOP-5) [26] Factor Combination - The report explores both z-score and rank equal-weight combinations of selected factors to enhance model performance [27] - The top-performing combinations include: - z-score combination with PE_TTM marginal change, ROE marginal change, and one-year momentum [32] - rank combination with PE_TTM three-year ranking, ROE marginal change, and 21-day momentum [37] Recommended Factors - The report recommends specific factors for the composite strategy: - Momentum: 252_momentum (one-year) and 756_momentum (three-year) [68] - Liquidity: TURNOVER_FREE_m (21-day average) and TURNOVER_FREE_Q_margin (quarterly margin) [68] - Valuation: 股息率_3Y_rank (three-year dividend yield ranking) and PB_LF_d2m (two-month marginal change) [68] - Quality: ROE_TTM_d1q (one-quarter marginal change) and ROE_FY2 (next year's expected ROE) [68]

Group 1 - The median return of conservative fixed income + products was 0.09% for the week of June 16-20, 2025, with 294 products reaching historical net value highs [2][20] - The total market size of fixed income + funds reached 1,692.127 billion, with 1,173 products available as of June 20, 2025 [2][10] - The performance of various fund types showed divergence, with median returns for mixed bond type funds being 0.10% for level one and -0.02% for level two [2][12] Group 2 - The macro environment forecast for Q2 2025 indicates inflation, with the Shanghai and Shenzhen 300 index, the China government bond index, and gold showing respective increases of 0.17%, 0.71%, and 1.28% since June [2][3] - The recommended industry ETFs for June 2025 include those focused on securities companies, semiconductors, banks, and major consumer sectors, achieving a combined return of 0.21% for the week [2][3] Group 3 - The stock-bond mixed strategy showed a return of 0.03% for the 20/80 rebalancing strategy, while the risk parity strategy yielded a return of 0.15% [3][3] - The small-cap value style within the stock-bond 20/80 combination performed best with a year-to-date return of 5.17% [3][3] - The cumulative return for the small-cap value combination, adjusted for macro momentum, was 2.55% [3][3]

午盘中证1000里9只涨停股，中证2000里28只涨停股，放量上攻的更是一大堆，因此比三大宽基指数走的都要强，分别收涨0.47%和0.72%。 小盘指数给力，对应的指增产品也获得大额资金流入。数据显示，1000ETF增强（159680）今天上午获得一笔300万的大额申购，近两个交易日共计获得 2243万的净流入。 2000增强也是，比较热门的中证2000增强ETF（159552）年内份额一直在稳步增长。 此外，数据还显示，从今年年初至6月25日，1000ETF增强（159680）和中证2000增强ETF（159552）分别跑赢标的指数7.36%、13.41%，超额收益都很优 秀。 这背后的动力，源自三重引擎的推动： 01 流动性与政策的双轮驱动 宽松货币政策如同小微盘的"加速器"。年内多次降准降息后，市场流动性持续充裕，对利率敏感的小微企业率先受益。 政策端更是送来东风，并购重组新规为众多专精特新企业打开成长空间，而密集出台的AI、机器人、军工、半导体、医药等产业政策，也恰好覆盖了中证 1000/2000指数中绝大部分成分股。 02 增强策略的收益"炼金术" 最好主仓搭配一下红利或银行平衡风险，体验感会更好。 ...

价值和成长两类股票具有明显基本面差异，价值类股票往往具备更好的安全边际，而成长类股票则可能 具备更好的盈利前景。成长与价值的盈利增速差和两者收益率差呈现高度正相关性，当成长与价值的盈 利增速差值扩大时，成长表现将会超过价值。因此，观察风格间的相对业绩增速趋势，有助于进行风格 配置。除此之外，市场中也有投资者通过估值指数来衡量价值与成长之间的风格轮动。 风格轮动是依据ETF特征进行交易的行为，常见的风格轮动有大小盘轮动、成长价值轮动等。风格轮动 的逻辑也依赖于权益资产价格的两个驱动因素——盈利和估值。盈利是主导风格强弱的关键因素，绝对 差值和边际变化也同样是判断风格强弱的重要指标。 （1）价值成长轮动策略 大小盘轮动通常根据市场环境和经济周期的变化来进行，并根据月频公告的宏观经济数据来进行辅助判 断。大盘股占国民经济中的比重更高，因此大盘股相比于小盘股更容易受到经济周期的影响。在经济增 长上行阶段，大盘股盈利上升速度大概率高于小盘股，大盘风格表现就更为强势。在经济下行阶段，大 盘股受到影响更大，表现可能相对弱势。另外，流动性环境对股票估值有重要影响：在流动性充裕的市 场中，资金会外溢到小盘股中，因此小盘股对流动 ...

Quantitative Models and Construction Methods Single Strategy Models - **Model Name**: High Prosperity Industry Rotation Strategy **Construction Idea**: Tracks industry profitability expectations using multi-factor models based on analysts' consensus data to select industries with upward profitability trends [13][15][16] **Construction Process**: 1. Constructs three types of factors: - Type 1: Long-term profitability factors (e.g., ROE_FY2, ROE_FY1) - Type 2: Quarterly changes in profitability (e.g., EPS_F2_qoq, EPS_F3_mom) - Type 3: Monthly changes in profitability (e.g., EPS_F3_qoq_d1m) 2. Filters industries with extreme valuations using PB percentile thresholds [30] 3. Selects top 3 industries based on composite factor rankings and allocates equally [21][30] **Evaluation**: Demonstrates strong performance in tracking industry cycles and avoiding valuation bubbles [13][26] - **Model Name**: Implicit Sentiment Momentum Strategy **Construction Idea**: Captures "unverified sentiment" by removing the relationship between turnover rate changes and returns, aiming to identify market sentiment-driven opportunities [32][33] **Construction Process**: 1. Uses OLS regression to remove "expected sentiment" from daily industry returns, leaving residuals as "unverified sentiment" [34] 2. Constructs momentum factors based on cumulative "unverified sentiment" returns over various time windows (e.g., 1 month, 12 months) [35] 3. Enhances the strategy by neutralizing fundamental impacts, adjusting for volatility, and applying composite factor methods [36] **Evaluation**: Effectively captures sentiment-driven market dynamics ahead of fundamental data releases [32][37] - **Model Name**: Macro Indicator Style Rotation Strategy **Construction Idea**: Uses macroeconomic indicators to predict industry styles (e.g., value, momentum) and maps them to industry selection [43][44] **Construction Process**: 1. Constructs macro indicators (e.g., PMI, CPI, M1) using historical positioning, surprise, and marginal change metrics [48][49] 2. Builds style factors (e.g., Value, Beta, Momentum) based on industry exposures [50][51] 3. Maps style predictions to industry scores and selects top industries [61] **Evaluation**: Addresses limitations of traditional top-down models by incorporating style-based predictions [43][61] - **Model Name**: Mid-to-Long-Term Momentum Reversal Strategy **Construction Idea**: Explores the "momentum-reversal" structure in industry returns, combining short-term momentum and long-term reversal factors [70][71] **Construction Process**: 1. Constructs momentum factors based on single-month returns and reversal factors based on multi-month returns (e.g., 12-month momentum, 24-36 month reversal) [76][78] 2. Combines factors using rank-weighted methods and adjusts for turnover rates [80][85] **Evaluation**: Balances short-term trends and long-term recovery opportunities effectively [70][84] - **Model Name**: Fund Flow Industry Rotation Strategy **Construction Idea**: Tracks institutional and tail-end fund flows to identify industry momentum [91][92] **Construction Process**: 1. Constructs "institutional trend strength factors" based on net buy amounts [93][94] 2. Constructs "tail-end inflow strength factors" based on post-14:30 net inflow data [96][103] 3. Combines factors and excludes high-concentration industries [100][101] **Evaluation**: Enhances stability by avoiding crowded trades [91][101] - **Model Name**: Financial Report Failure Reversal Strategy **Construction Idea**: Utilizes mean-reversion characteristics of long-term effective financial factors after short-term failures [108][109] **Construction Process**: 1. Constructs financial factors (e.g., ROA, YOY) using profit and balance sheet data [110][114] 2. Identifies "long-term effective factors" and "recently failed factors" based on rolling windows [116][117] 3. Combines factors using zscore methods [117] **Evaluation**: Captures recovery opportunities in temporarily underperforming factors [108][118] - **Model Name**: Traditional Low-Frequency Multi-Factor Scoring Strategy **Construction Idea**: Combines factors from four dimensions (momentum, valuation, liquidity, quality) for quarterly industry rotation [122][123] **Construction Process**: 1. Selects top-performing factors from each dimension (e.g., 1-year momentum, ROE_TTM) [124][125] 2. Combines factors using rank-weighted methods [135] 3. Filters industries with low weights in the CSI 800 index [135] **Evaluation**: Suitable for long-term holding with robust risk control [122][129] Composite Strategy Models - **Model Name**: Volatility-Controlled Composite Strategy **Construction Idea**: Allocates funds across single strategies based on inverse negative volatility [138][139] **Construction Process**: 1. Calculates negative volatility for each strategy over a rolling window (e.g., 63 days) [139][140] 2. Allocates funds proportionally to inverse negative volatility [139][147] 3. Adjusts allocation frequencies to match individual strategy cycles (weekly, monthly, quarterly) [141][146] **Evaluation**: Balances risk and return effectively, achieving high annualized excess returns [138][144] --- Model Backtest Results Single Strategy Results - **High Prosperity Strategy**: Annualized excess return 16.69%, max drawdown -12.95%, IR 1.29 [26] - **Implicit Sentiment Strategy**: Annualized excess return 18.61%, max drawdown -17.83%, IR 1.04 [37] - **Macro Style Strategy**: Annualized excess return 7.01%, max drawdown -23.46%, IR 0.30 [63] - **Momentum Reversal Strategy**: Annualized excess return 11.42%, max drawdown -14.91%, IR 0.77 [84] - **Fund Flow Strategy**: Annualized excess return 11.64%, max drawdown -12.16%, IR 0.96 [101] - **Financial Report Strategy**: Annualized excess return 9.13%, max drawdown -10.54%, IR 0.87 [118] - **Low-Frequency Multi-Factor Strategy**: Annualized excess return 12.00%, max drawdown -13.25%, IR 0.91 [129] Composite Strategy Results - **Volatility-Controlled Composite Strategy**: Annualized excess return 12.2%, max drawdown -6.8%, IR 1.80 [144][147]