因子协方差矩阵更新。 股票协方差矩阵估计是股票组合风险预测的核心。利用多因子模型，可以将股票协方差矩阵拆解为因子协方差矩阵和股票特质风险矩 阵的结合，从而完成较为准确的估计。本文更新了最新一期（ 2025/10/31 ）的因子协方差矩阵。股票协方差矩阵构建详见报告《 A 股风险模型实践：股票 协方差矩阵估计篇 _20240530 》。 风险提示： 量化模型基于历史数据构建，而历史规律存在失效风险。 报告导读： 本报告对大小盘轮动月度策略、价值成长轮动月度策略以及风格因子表现进行 跟踪。综合量化模型信号和日历效应， 11 月建议超配小盘风格，价值风格。 大小盘风格轮动月度策略。 10 月底量化模型信号为 -0.17 ，指向大盘；日历效应上，历史 11 月小盘相对占优；建议 11 月超配小盘风格。中长期观点： 当前市值因子估值价差为 0.88 ，相对历史顶部区域 1.7~2.6 仍有距离，中长期并不拥挤，继续看好小盘。本年以来 大小盘轮动量化模型收益为 27.85% ， 相对等权基准的超额收益为 2.86% 。结合主观观点的策略收益为 26.6% ，超额收益为 1.61% 。策略构建详见报告《量化视角多维度构建大 ...

黄金再创新高，基于宏观因子的资产配置策略本 月收益 0.48% 2025.10.16 根据量化模型信号，10 月建议超配大盘风格，均 衡配置价值和成长风格 2025.10.12 国内权益资产领涨，国内资产 BL 策略本月收益 0.95% 2025.09.04 根据量化模型信号，9 月建议超配小盘风格，均 衡配置价值和成长风格 2025.09.03 7 月权益资产表现优异，风险平价策略本年收益 达 2.65% 2025.08.08 综合量化模型信号和日历效应，11 月建 议超配小盘风格、价值风格 ——风格轮动策略月报第 7 期 本报告导读： 本报告对大小盘轮动月度策略、价值成长轮动月度策略以及风格因子表现进行跟踪。 综合量化模型信号和日历效应，11 月建议超配小盘风格，价值风格。 投资要点： | [Table_Authors] | 郑雅斌(分析师) | | --- | --- | | | 021-23219395 | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 张雪杰(分析师) | | | 0755-23976751 | | | zhangxu ...

Group 1: Core Insights - The report suggests an overweight allocation to small-cap stocks for September, based on a quantitative model signal of 0.17 at the end of August, indicating a preference for small-cap style [1] - The long-term view remains optimistic for small-cap stocks, with the current market capitalization factor valuation spread at 1.01, which is still below the historical peak range of 1.7 to 2.6 [1] - Year-to-date, the small-cap rotation strategy has yielded a return of 28.19%, with an excess return of 4.24% compared to benchmarks like CSI 300 and CSI 2000 [1] Group 2: Value and Growth Style Rotation - The monthly quantitative model signal for value and growth style is 0, suggesting an equal-weight allocation for September [1] - The year-to-date return for the value and growth style rotation strategy is 14.33%, with an excess return of 1.35% relative to equal-weight benchmarks [1] Group 3: Factor Performance Tracking - Among eight major factors, volatility and large-cap factors showed positive returns in August, while liquidity and quality factors had negative returns [2] - Year-to-date, volatility and momentum factors have performed positively, whereas liquidity and large-cap factors have shown negative returns [2] - In August, beta, large-cap, and short-term reversal factors had positive returns, while profitability quality, seasonality, and liquidity factors had negative returns [2] Group 4: Factor Covariance Matrix Update - The report updates the stock covariance matrix, which is crucial for predicting portfolio risk, using a multi-factor model to combine factor covariance and stock-specific risk matrices [2]