策略研究 / 2025.06.30 全球资本流向非美,国内杠杆资金加快扩张 方奕(分析师) 021-38676666 -投资者微观行为洞察手册 · 6月第 3 期 登记编与 S0880520120005 本报告导读: 郭胤含(分析师) 021-38676666 本期市场交易热度大幅抬升,融资资金大幅流入,ETF与外资资金转为流出,偏股 基金新发规模边际下降。南下资金流入持续抬升。 合记编与 S0880524100001 投资要点: 田开轩(分析师) 市场定价状态:市场整体交易热度快速抬升,赚钱效应明显。1)市 0 场情绪(上升):本期国泰海通资金流入强度指数整体持续上升, 食记编与 S0880524080006 市场交易热度底部抬升,全 A 日均成交额从 1.2 万亿上升至 1.5 万 亿,上证指数换手率分位数上升至85%,科创板换手率分位数上升 至 40%。日均涨停家数上升至 71 家,最大连板数为 6个,封板率上 升至 77.2%,龙虎榜上榜家数降至 61 家。2）赚钱效应(上升): 行业交易集中度回升,本期行业换手率历史分位数处于 90%以上的 行业有 7个,环比增加5个,其中换手率分位数超 95%的行 ...

证券研究报告：金融工程报告 研究所 金工周报 分析师:肖承志 SAC 登记编号:S1340524090001 Email:xiaochengzhi@cnpsec.com 研究助理:李子凯 SAC 登记编号:S1340124100014 Email:lizikai@cnpsec.com 近期研究报告 《基于相对强弱视角下的扩散指数择 时模型》 - 2025.06.25 《ETF 资金大幅净流入金融地产，石油 油气扩散指数环比提升靠前——行业 轮动周报 20250629》 – 2025.06.23 《OpenAI 发布 o3-pro，Mistral 推出推 理模型 Magistral ——AI 动态汇总 20250616【中邮金工】》 - 2025.06.16 《融资资金持续大幅净流入医药，GRU 行业轮动调出银行——行业轮动周报 20250615》 – 2025.6.16 《资金博弈停牌个股大幅流入信创 ETF，概念轮动速度较快——行业轮动 周报 20250608》 - 2025.06.09 《综合金融受益于稳定币表现突出， ETF 资金逢高净流出医药和消费——行 业轮动周报 20250601》 – 2025. ...

Group 1 - The core viewpoint of the articles indicates that the A500 index and its ETF are experiencing positive momentum, with notable increases in specific constituent stocks [1][2] - The A500 ETF fund has shown active trading, with a turnover rate of 13.7% and a transaction volume of 22.13 billion yuan, indicating a vibrant market [1] - The A500 index is designed to reflect the performance of the 500 largest and most liquid stocks across various industries, with the top ten stocks accounting for 21.21% of the index [2][4] Group 2 - The macroeconomic fundamentals have not fundamentally changed compared to late 2024 and early 2025, suggesting a potential shift from small-cap to large-cap value stocks as market conditions evolve [2] - Future investment strategies may focus on growth sectors that benefit from policy support, particularly in technology and healthcare, such as AI, robotics, and innovative pharmaceuticals [2] - The top ten weighted stocks in the A500 index include major companies like Kweichow Moutai, CATL, and Ping An, with varying performance metrics [4]

点击小程序查看报告原文 2024年四季度以来，虽然港股整体表现"可圈可点"并大幅跑赢A股，但也存在一定问题：1）每次反弹都是脉冲式的冲高回落，虽然底部不断抬升，以及 2）表现过于集中在少数行业甚至个股且轮动较快，都使得投资者在这一环境中要获得超额收益不容易，但反过来，如果每一波节奏和主线都能把握准 确，那么收益将会明显放大。 这一指数层面的反复与较为极致的结构行情也印证了我们过去半年持续提示 "反弹是间歇，结构是主线，在低迷的左侧布局，在亢奋的右侧适度获利" 《2025下半年展望：资金盛与资产荒》 。那么今年以来的结构性行情到底有多强？是什么样的宏观环境导致了这一局面？以及如何去寻找并刻画行业轮 动的线索与规律？ 行业轮动的脉络："924"非银地产顺周期、"DeepSeek"AI互联网、"对等关税"后新消费与创新药 去年924之前，是国内信用周期和市场情绪的最低点，自此之后，分别迎来了财政政策、AI产业趋势等几个好消息，推动市场出现几轮反弹，但也容易冲 高透支后回落，而每一次反弹的主线也因为驱动因素不同而有别：1）去年924宏观政策转向，推动非银与地产等顺周期板块领涨，交易的是总量政策； 2）到春节后Deep ...

【金融工程】传统多因子打分行业轮动策略*郭策 李腾。本报告介绍了一种 季频换仓偏配置思路的行业轮动策略，采用传统量化多因子打分的方式，分 别从"估值"、"质量"、"流动性"、"动量"四个维度下各优选 2 个单 因子，再进行等权 rank 复合，形成复合因子。整体策略思路偏配置，优先选 择低估值、低拥挤度、景气度上行、近一年价格动量向上，近 3 年价格处于 低位的行业持有。最终复合策略在回测区间（2014/4/1-2025/6/6）实现年化 收益 19.64%，行业等权基准实现年化收益 7.55%，对应年化超额 12.09%。 期间超额累计净值最大回撤-13.25%。 【机械设备】芯碁微装*苏凌瑶。芯碁微装公告新签 1.46 亿元大单，约占 2024 年营收的 15%。AI 基建热潮投推动 PCB 投资热，公司有望受益于 PCB 厂商 积极扩产潮。 行业表现（申万一级） | 指数名称 | 涨跌% | 指数名称 | 涨跌% | | --- | --- | --- | --- | | 银行 | 1.01 | 汽车 | (1.37) | | 通信 | 0.77 | 非银金融 | (1.20) | | 国防军工 | 0 ...

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 ...

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]

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。 ...

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]

大盘指数已经很明显了，和2020年6月那波差不多，大概率是主旋律是证券。上半年的业绩太好了，市场提前拉升，这一波的证券涨完，本轮牛市的证券大 概率就差不多了。 洗盘！A股年内新高近了！ 证券不要觉得涨多了，才刚刚开始。目前只是香港证券涨幅很明显，从800多点反弹到1200多点了，从4月到现在涨幅近50%。 相比2024年，上半年的业绩增速太明显了，股价要与估值匹配，目前拉升也很正常。白酒这轮的行情应该不会很好，它要用来等市场涨完了，接替银行护 盘…… 不会有股灾，也没有疯牛，就是行业轮动上涨，大盘指数震荡向上。地产随时会动，这次也不做太多期待，白酒、地产的节奏还是接近四季度比较理想。 大盘指数，今年都不会有问题，几个权重行业的相互压制就是慢牛，也是指数牛。如果买错了，就会很麻烦。 今日，三大指数又上涨，本周3连阳了，证券已经连续拉升。如果不是白酒、银行压着指数，大概率已经创年内新高了。 不是出货，如果是出货只需要拉升，今年的624行情堪比去年的924行情，确实非常让人期待。无论是周期、时空、逻辑、消息、估值等都对上了。 准备迎接上涨了 不出意外，接下来会继续上涨，大家都不看好了，早盘又有人离场了，很明显是诱空 ...