行业轮动周报：ETF大幅流出红利，成长GRU行业因子得分提升较大-20250519

• Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the observation of industry diffusion indices; Detailed Construction Process: The model tracks the weekly changes in diffusion indices for various industries, ranking them based on their performance. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Advancing Stocks}}{\text{Total Number of Stocks}} $; Model Evaluation: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[6][14][27] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Detailed Construction Process: The model ranks industries based on GRU factor scores, which are derived from the GRU network's analysis of trading data. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Model Evaluation: The model has achieved substantial excess returns by capturing trading information, though it has faced challenges in certain market conditions[7][14][34] Model Backtest Results - Diffusion Index Model, Average Weekly Return: 0.72%, Excess Return: 0.11%, Year-to-Date Excess Return: -2.26%[32] - GRU Factor Model, Average Weekly Return: 1.07%, Excess Return: 0.44%, Year-to-Date Excess Return: -3.71%[37] Factor Construction and Evaluation - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is constructed using GRU deep learning networks to analyze minute-level trading data; Detailed Construction Process: The factor scores are calculated based on the GRU network's output, which evaluates the trading data to rank industries. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Factor Evaluation: The factor has shown significant improvements in certain industries, indicating its effectiveness in capturing trading information[7][14][35] Factor Backtest Results - GRU Industry Factor, Top Industries: Automotive (2.84), Steel (1.85), Media (1.48), Power Equipment and New Energy (1.35), Communication (0.88), Coal (0.66)[7][14][35]