随着美股本周再次向历史新高发起冲击，资金似乎正在纷至沓来。高盛和巴克莱发布报告称，考虑到衰 退担忧缓和，资金正在以近一年来最快的速度出售欧洲资产，并将重点重新转向美国。野村证券发现， 基于波动率的交易策略可能在短期内吸引千亿规模的量化资金回流，有望给美股进一步上涨带来动能。 欧股遭遇抛售 高盛团队分析截至6月20日的交易数据发现，欧洲股票空头交易规模创下近一年最高水平，对冲基金大 举建立个股和宏观产品的新空头头寸，推动了这一趋势。 近几个月来，欧洲股市表现明显优于华尔街。例如，德国DAX 30指数在2025年迄今上涨了近19%，因 为投资者对政府支出增加的迹象感到振奋，而同期道指涨幅不到2%。最近，欧洲表现突出的是国防板 块，这得益于该地区北约成员国增加军费开支的提议。例如，德国装甲车制造商莱茵金属公司的股价在 过去12个月里飙升近240%。 然而，高盛观察到，在本次北约会议之前，欧洲国防股明显被净卖出。事实上，自4月以来，对冲基金 一直在净卖出德国市场。 这家华尔街巨头表示，投资者对欧洲市场的担忧主要集中在两个关键问题：缺乏短期催化剂和增长动力 不足。欧股过去12个月的表现完全依赖于价值重估和股息贡献。欧股 ...

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]

股市的反弹震荡剧烈、上涨面狭窄，这些都是不健康上涨的信号。 这种局面更适合采取"试探性入场"而非"全力投入"的选股策略。目前，欧洲基准指数中有高达95%的成 分股站上了其10日均线，这是一个罕见的现象，通常意味着"轻松的上涨空间"已经被消耗殆尽。美国总 统Donald Trump在关税问题上语气趋缓，帮助欧洲Stoxx 600指数升至4月初以来最高点，而S&P 500则 刚刚录得2025年迄今为止的最佳单周表现。 Cau指出，自特朗普宣布"解放日"关税以来，主要股指已经收复超过一半的跌幅，"这在很大程度上是因 为空头回补"。他补充称，如果波动性持续缓解，投资者可能会进一步回补仓位（regrossing），即增加 股票敞口。 确实，Citigroup的策略师Beata Manthey领导的团队表示，是时候"有选择地试探性布局那些被严重抛售 的周期性板块"。不过他们的策略仍强调防御性配置，比如超配医疗类股票。 过去几日，市场内部结构有所改善。经历了因美国对中国等国迅速加征关税引发的暴跌之后，对冲基金 和部分长线投资者重新回到买方阵营。波动率虽已下降，但仍处于高位。VIX和VSTOXX仍都在20以 上，市场仍需政策 ...