指增组合本周超额回撤 分析师 缪铃凯 SAC 证书编号：S0160525060003 miaolk@ctsec.com 相关报告 1. 《沪深 300 增强超额收益创年内新高》 2025-08-09 2. 《 指 增 组 合 本 周 抗 跌 效 果 显 著 》 2025-08-02 3. 《深度学习因子选股体系》 2025- 08-01 证券研究报告 量化选股策略周报/ 2025.08.16 核心观点 ❖ 风险提示：因子失效风险，模型失效风险，市场风格变动风险。 请阅读最后一页的重要声明！ ❖ 本周市场指数表现：截至 2025-08-15，本周上证指数上涨 1.70%，深证 成指上涨 4.55%，沪深 300 上涨 2.37%，上证指数创 2022 年以来新高。 ❖ 我们基于深度学习框架构建 alpha 和风险模型，打造 AI 体系下的低 频指数增强策略，组合周度调仓，年单边换手率约 5.5 倍。最终，通 过组合优化勾连深度学习 alpha 信号与风险信号构建沪深 300、中证 500 和中证 1000 指数增强组合。 ❖ 截至 2025-08-15，今年以来沪深 300 指数上涨 6.8%，沪深 300 指 ...

Core Insights - The report emphasizes the development of a deep learning factor selection system for stock prediction and portfolio optimization, shifting from traditional logic-driven methods to data-driven approaches [7][10]. - The system integrates diverse data sources, including daily and minute market data, to enhance the performance of alpha signals [7][10]. - The report outlines the construction of multiple models that utilize different network architectures to extract unique alpha signals, demonstrating low correlation among them [8][54]. Data and Network - The input data consists of three categories: daily market data, minute market data, and manually crafted features, with neural networks independently extracting alpha features from each dataset [11]. - The report describes the use of Long Short-Term Memory (LSTM) networks combined with self-attention mechanisms to capture long-term dependencies in time series data [19]. - A Graph Attention Network (GAT) is employed to model the complex relationships between stocks, providing a global analysis perspective [20]. Alpha Models - The report presents various alpha models, including simple equal-weight, tree model weighting, and network weighting, with a focus on combining multiple signals to enhance robustness [3][3.1][3.2]. - The average Information Coefficient (IC) for the combined factors since 2019 is reported as 11.3% for 5-day IC and 12.4% for 10-day IC, indicating strong predictive power [31][32]. Risk Models - The report highlights the use of neural networks to identify high-dimensional non-linear risk patterns directly from raw price and volume data, enhancing risk control in portfolio construction [9]. Index Enhancement Strategies - The report details the performance of enhanced index strategies based on deep learning alpha signals, with annualized returns reported as follows: - CSI 300 enhanced portfolio: 18.2% annualized return, 14.2% excess return over the index [3][5.1]. - CSI 500 enhanced portfolio: 22.4% annualized return, 17.2% excess return over the index [3][5.2]. - CSI 1000 enhanced portfolio: 29.8% annualized return, 24.5% excess return over the index [3][5.3].