高频选股因子周报（20260224- 20260227）-20260302

• The report highlights the performance of high-frequency factors, deep learning factors, and AI-enhanced portfolios, summarizing their historical and 2026 metrics such as IC, RankMAE, multi-long-short returns, and monthly win rates[9][10][11] - High-frequency factors include intraday skewness, downside volatility proportion, post-open buying intention proportion, post-open buying intention intensity, post-open large-order net buying proportion, post-open large-order net buying intensity, improved reversal factor, end-of-day transaction proportion, average single-order outflow proportion, and large-order-driven price increase factor[7][9][10] - Deep learning factors include improved GRU(50,2)+NN(10), residual attention LSTM(48,2)+NN(10), multi-granularity model with 5-day labels, and multi-granularity model with 10-day labels[7][9][10] - AI-enhanced portfolios are constructed based on deep learning factors, combining multi-granularity models (5-day labels and 10-day labels) with constraints such as stock weight, industry weight, market cap, PB, ROE, SUE, volatility, turnover rate, and constituent stock limits. The optimization goal is to maximize expected returns using the formula $max\sum\mu_{i}w_{i}$, where $w_i$ is the stock weight and $\mu_i$ is the expected excess return[73][74][76] - The report provides detailed backtesting results for high-frequency factors, deep learning factors, and AI-enhanced portfolios, including weekly, monthly, and annual returns, as well as win rates across different timeframes[9][10][11]