“薪火”量化分析系列研究（五）：如何利用DeepSeek辅助降低跟踪误差

Quantitative Models and Construction Methods Model 1: Core-Satellite Strategy - Model Name: Core-Satellite Strategy - Model Construction Idea: Increase the weight of benchmark constituent stocks to reduce tracking error[2] - Model Construction Process: - Allocate a portion of the portfolio directly to the benchmark index, while the remaining portion is actively managed[2] - Use grid search to find optimal parameters and construct the portfolio[2] - Example code provided by DeepSeek to achieve this[16] - For cases where the benchmark index has too many constituents, construct a substitute portfolio using methods like core leading stocks + industry representatives[20][23] - Model Evaluation: Effective in reducing tracking error by increasing the weight of benchmark constituent stocks[2][16] Model 2: Industry Neutralization - Model Name: Industry Neutralization - Model Construction Idea: Focus on stock selection to outperform the industry index by filling the weights of structurally underweighted sectors[3] - Model Construction Process: - Adjust individual stock weights to ensure the portfolio's industry exposure matches the benchmark index[31] - Use DeepSeek to generate the code for industry neutralization[31] - Model Evaluation: Significantly reduces tracking error by ensuring industry exposure consistency with the benchmark index[3][31] Model 3: Style Neutralization - Model Name: Style Neutralization - Model Construction Idea: Adjust stock weights to minimize style deviation from the benchmark index without changing the original holdings[4] - Model Construction Process: - Construct an optimization equation to solve for individual stock weights[4] - Use DeepSeek to generate the code for style neutralization, including multi-objective optimization[36][37] - Model Evaluation: Effective in reducing style deviation and improving portfolio performance relative to the benchmark[4][36] Model 4: Barbell Strategy - Model Name: Barbell Strategy - Model Construction Idea: Balance extreme growth and extreme value strategies to reduce tracking error[5] - Model Construction Process: - Implement a multi-strategy approach, such as equally weighting growth and value indices[5] - Use DeepSeek to generate the code for constructing and backtesting the barbell strategy[43] - Model Evaluation: Successfully reduces portfolio volatility and enhances performance by balancing different investment styles[5][46] Model Backtesting Results - Core-Satellite Strategy: - Average daily deviation reduced from 2.27% to 1.12% after adding 50% benchmark index weight[18] - Substitute portfolio tracking error relative to the benchmark index is 2.91%[28] - Industry Neutralization: - Maximum daily deviation reduced from 6.39% to 0.96% after industry neutralization[33] - Style Neutralization: - Average daily deviation reduced from 1.49% to 1.06% after style neutralization[38] - Relative to the benchmark index, the optimized portfolio's excess return improved from -9.55% to 3.55%[38] - Barbell Strategy: - Excess maximum drawdown reduced from over 30% to 19.88% after implementing the barbell strategy[46] - Annualized return and other performance metrics improved[50]