高频选股因子周报（20260112-20260116）：大部分高频因子多头录得正收益，多粒度因子多头反弹显著。AI 增强组合表现分化，1000增强回撤显著缩窄。-20260118

• The report discusses high-frequency stock selection factors, deep learning factors, and AI-enhanced portfolios, summarizing their historical and 2026 performance in terms of IC, RankMAE, long-short returns, long-only excess returns, and monthly win rates[9][10][11] - High-frequency factors include intraday skewness, downside volatility proportion, post-opening buying intention proportion, post-opening buying intensity, net large-order buying proportion, net large-order buying intensity, improved reversal, end-of-day trading proportion, average single-order outflow proportion, and large-order-driven price increase[7][9][10] - Deep learning factors include GRU(50,2)+NN(10), residual attention LSTM(48,2)+NN(10), multi-granularity models with 5-day and 10-day labels, which are trained using advanced machine learning techniques like AGRU[7][9][10] - AI-enhanced portfolios are constructed based on deep learning factors, specifically the multi-granularity 10-day label model, and include four combinations: CSI 500 AI-enhanced wide constraint, CSI 500 AI-enhanced strict constraint, CSI 1000 AI-enhanced wide constraint, and CSI 1000 AI-enhanced strict constraint. These portfolios aim to maximize expected returns under specific constraints such as turnover, industry, and market cap limits[73][74][75] - The optimization objective for AI-enhanced portfolios is defined as maximizing the expected excess return, represented by the formula: $\operatorname*{max}_{w_{i}}\sum\mu_{i}w_{i}$ where $w_i$ is the weight of stock $i$ in the portfolio, and $\mu_i$ is the expected excess return of stock $i$[74][75] - Performance results for high-frequency factors show positive long-short returns for most factors in January and 2026, with notable results for factors like intraday skewness (1.55%), downside volatility proportion (1.65%), and post-opening buying intensity (2.86%)[5][9][10] - Deep learning factors also demonstrate strong performance, with GRU(50,2)+NN(10) achieving a long-short return of 2.79% in 2026, and the multi-granularity 5-day label model achieving 2.13%[5][9][10] - AI-enhanced portfolios show mixed results, with the CSI 500 AI-enhanced wide constraint portfolio recording a -4.47% excess return in 2026, while the CSI 1000 AI-enhanced strict constraint portfolio achieved a relatively better performance of -1.57%[5][14][73]