工银FOF产品巡礼系列一：工银价值稳健聚焦多元资产配置，基金稳健增值范式

Quantitative Models and Construction Methods 1. Model Name: Multi-Asset Allocation Model - Model Construction Idea: The model adopts a top-down allocation approach, strategically allocating 80% to stable assets and 20% to risk assets. It incorporates low-correlation assets such as equity funds, US equity QDII, USD bond QDII, low-volatility dividend ETFs, and gold to diversify risks[9][16]. - Model Construction Process: 1. The model uses a strategic allocation ratio of 80% stable assets and 20% risk assets, with a tactical adjustment range of ±5%[16]. 2. Risk assets are allocated among equity funds, US equities, low-volatility dividend ETFs, and gold in a ratio of 6:6:6:2[16][18]. 3. Stable assets include medium-to-long-term pure bond funds, passive bond index funds, bond QDII funds, and money market funds. Adjustments are made based on credit spreads, term spreads, and the relative attractiveness of US Treasuries[16]. 4. Historical correlations among asset classes were calculated using representative indices such as the Wind Equity Hybrid Fund Index, Wind Medium-to-Long-Term Pure Bond Fund Index, S&P 500 ETF, and SGE Gold 9999[19]. 5. A backtest was conducted using the allocation ratio of 80:6:6:6:2 for stable and risk assets, respectively[19][21]. - Model Evaluation: The model demonstrates strong diversification, reducing the volatility of single risk asset exposure and maintaining stable net value growth[20]. --- Model Backtest Results 1. Multi-Asset Allocation Model - Annualized Return: 5.68%[21] - Annualized Volatility: 3.51%[21] - Maximum Drawdown: 10.30%[21] - Annualized Return-to-Volatility Ratio: 1.62[21] - Annualized Calmar Ratio: 0.55[21] --- Quantitative Factors and Construction Methods 1. Factor Name: Fund Selection Alpha Factor - Factor Construction Idea: The factor combines quantitative and qualitative methods to select funds, focusing on alpha generation and risk control. It emphasizes historical backtesting of selection indicators and fund manager due diligence[9][23]. - Factor Construction Process: 1. Funds are categorized by risk level, investment region, and strategy (e.g., equity, balanced, fixed income)[24][25]. 2. Quantitative screening is performed using metrics such as stock-picking ability and drawdown control[28]. 3. Qualitative due diligence includes analyzing fund managers' tenure, experience, and adaptability to market changes[28]. 4. Internal fund products are prioritized to reduce fees and enhance alpha generation[31]. 5. Excess return contributions are calculated using the formula: $E R_{p}=w_{p}\sum_{i=1}^{n}w_{i}(R_{i}-R_{b})$ where $w_{p}$ is the proportion of the category in the portfolio, $w_{i}$ is the normalized weight of the fund, $R_{i}$ is the fund return, and $R_{b}$ is the benchmark return[53][54]. - Factor Evaluation: The factor demonstrates strong fund selection capabilities, particularly in mid-level configurations, with cumulative excess returns of 1% in passive index funds and positive contributions across other fund types[56]. --- Factor Backtest Results 1. Fund Selection Alpha Factor - Passive Index Fund Excess Return: 1.02% (cumulative)[57] - Pure Bond Fund Excess Return: 0.07% (cumulative)[57] - Fixed Income Plus Fund Excess Return: 0.33% (cumulative)[57] - Active Equity Fund Excess Return: 0.43% (cumulative)[57] - Total Excess Return: 1.85% (cumulative)[57]