金融工程专题研究：量化赋能中盘宽基，精筑稳健超额Alpha——博时中证500增强策略ETF投资价值分析

Quantitative Models and Construction Methods - Model Name: Bosera CSI 500 Enhanced Strategy ETF Model Construction Idea: The model aims to track the CSI 500 Index while employing quantitative enhancement strategies to achieve returns exceeding the benchmark index through strict investment procedures and quantitative risk management techniques[3][85] Model Construction Process: 1. The fund tracks the CSI 500 Index, which consists of 500 stocks with the highest market capitalization after excluding the CSI 300 constituents and the top 300 stocks by market capitalization[28] 2. The fund employs quantitative enhancement strategies, including stock selection and risk control, to generate excess returns over the benchmark index[85] 3. The fund ensures that the daily tracking deviation between the fund's net value growth rate and the benchmark is less than 0.35%, and the annual tracking error does not exceed 6.5%[51] Model Evaluation: The model demonstrates stable excess returns and strong risk-adjusted performance, with a focus on high-growth and high-profitability stocks[85][86] Model Backtesting Results - Bosera CSI 500 Enhanced Strategy ETF: - Annualized excess return: 7.76% - Annualized tracking error: 3.84% - Maximum relative drawdown: 6.66% - Information ratio (IR): 1.79 - Sharpe ratio: 0.71 - Monthly win rate: 65.63%[3][85][55] Quantitative Factors and Construction Methods - Factor Name: High Growth and High Profitability Exposure Factor Construction Idea: The fund emphasizes stocks with high growth potential and strong profitability metrics, aiming to generate excess returns through factor exposures[86][67] Factor Construction Process: 1. The fund identifies stocks with positive exposures to growth, long-term momentum, and profitability factors[67] 2. Stocks with negative exposures to non-linear size and liquidity factors are de-emphasized[67] 3. The fund's portfolio construction ensures that 89.40% of individual stock deviations from the CSI 500 Index are within 0.5%, and 98% are within 1%[58] Factor Evaluation: The factor strategy effectively captures excess returns, particularly in industries like computing, electronics, and renewable energy, demonstrating strong stock selection capabilities[65][86] Factor Backtesting Results - High Growth and High Profitability Factor: - Positive exposures to growth, long-term momentum, and profitability factors - Negative exposures to non-linear size and liquidity factors - Strong stock selection performance in computing, electronics, and renewable energy industries[65][67][86]