基于财报盈利增速的行业配置模型

Quantitative Models and Construction - Model Name: Industry Allocation Model Based on Profit Growth Rate Model Construction Idea: The model uses profit growth rate as the primary criterion for industry selection, supplemented by valuation and trading crowding metrics as risk indicators[7][27][29] Model Construction Process: 1. Profit Growth Metrics: - Single-quarter net profit year-on-year growth rate - Marginal change in single-quarter net profit year-on-year growth rate Formula for marginal change: $ \text{Marginal Change} = \text{2025 Q3 Single-quarter YoY Growth} - \text{2024 Q3 Single-quarter YoY Growth} $[15][29] 2. Valuation Metric: - Historical PE_TTM percentile (2020 to present) is used to measure valuation levels across industries[18][21][29] 3. Trading Crowding Metric: - Standard deviation of turnover rate over the past three months is calculated to assess trading crowding[6][24][29] 4. Comprehensive Scoring: - Each metric is ranked, and weights are assigned: - Profit growth metrics: 0.3 each - Risk metrics (valuation and trading crowding): 0.2 each Formula for comprehensive scoring: $ \text{Comprehensive Score} = 0.3 \times \text{Net Profit YoY Growth} + 0.3 \times \text{Marginal Change} + 0.2 \times \text{Valuation Percentile} + 0.2 \times \text{Turnover Rate Std Dev} $[31][32] Model Evaluation: The model effectively identifies industries with high profit growth and moderate risk levels, providing actionable allocation recommendations[7][27][32] Model Backtesting Results - Industry Allocation Model: - Portfolio return: 2.38% - Benchmark (Wind All A Index) return: 0.63% - Excess return: 1.75%[7][32] Quantitative Factors and Construction - Factor Name: Profit Growth Rate Factor Construction Idea: Measures industry profitability through single-quarter net profit growth and marginal changes in growth rates[7][29] Factor Construction Process: 1. Single-quarter net profit year-on-year growth rate 2. Marginal change in single-quarter net profit year-on-year growth rate Formula: $ \text{Marginal Change} = \text{2025 Q3 Single-quarter YoY Growth} - \text{2024 Q3 Single-quarter YoY Growth} $[15][29] Factor Evaluation: Effectively captures industries with strong profitability and growth momentum[7][29] - Factor Name: Valuation Percentile Factor Construction Idea: Uses historical PE_TTM percentiles to compare valuation levels across industries[18][21] Factor Construction Process: 1. Calculate PE_TTM for each industry 2. Determine historical percentile (2020 to present) for PE_TTM values[18][21] Factor Evaluation: Provides a standardized comparison of valuation levels across industries, mitigating biases from absolute PE differences[21][29] - Factor Name: Turnover Rate Standard Deviation Factor Construction Idea: Measures trading crowding by assessing the volatility of turnover rates over the past three months[6][24] Factor Construction Process: 1. Calculate daily turnover rates for each industry over the past three months 2. Compute standard deviation of turnover rates[6][24] Factor Evaluation: Identifies industries with extreme trading behaviors, serving as a risk indicator[6][24] Factor Backtesting Results - Profit Growth Rate Factor: - Steel: 203.31% YoY growth, 380.75% marginal change[10][15] - Electronics: 57.42% YoY growth, 59.99% marginal change[10][15] - Media: 58.63% YoY growth, 82.75% marginal change[10][15] - Defense: 29.52% YoY growth, 83.60% marginal change[10][15] - Utilities: 17.77% YoY growth, 19.81% marginal change[10][15] - Valuation Percentile Factor: - Steel: 99.72%[21][29] - Electronics: 98.94%[21][29] - Media: 90.40%[21][29] - Defense: 97.10%[21][29] - Utilities: 55.31%[21][29] - Turnover Rate Standard Deviation Factor: - Steel: 50.48%[6][29] - Electronics: 96.51%[6][29] - Media: 84.50%[6][29] - Defense: 82.79%[6][29] - Utilities: 25.21%[6][29]