课程伊始，余剑峰便指出投资实践中的一个核心难题：精准预测资产未来回报极其困难，相比而言，对 风险的预测更为可行。他通过对股票、债券、商品等大类资产的历史数据图表分析指出，短期内波动率 呈现出明显的"集聚"效应，当期的波动率对未来具有指示意义。他认为，优秀的投资并非源于对收益的 盲目追逐，而是建立在对风险的深刻理解以及有效管理波动率的基础上。 12月1日，在中国证券投资基金业协会的指导下，东方红资产管理"一司一省一高校"投教活动在复旦大 学管理学院持续推进，第六届"东方红固定收益实务课程"第四讲顺利开课。本次课程由东方红资产管理 绝对收益投资部基金经理余剑峰主讲。作为连续三年参与授课并受到广泛好评的讲师，他继续以《风险 管理与量化资产配置》为主题，为同学们系统剖析了如何通过对风险的精细管理来构建稳健的投资组 合。 余剑峰进一步将理论框架与实务操作相结合，回顾了从马科维茨投资组合理论到资产定价模型 （CAPM）、套利定价理论（APT）等资产定价模型的演变。他强调，资产定价模型提供了理解资产价 值的基础，而资产配置的本质是比较不同资产的风险收益特征。应用于实际投资中，大型机构投资者的 成熟框架通常包含战略资产配置（ ...

Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. **Model Name**: Nonlinear Multi-Factor Alpha Model **Model Construction Idea**: This model addresses the nonlinear relationship between factors and stock returns by transforming nonlinear factors into a linear form through polynomial functions[3][23]. **Model Construction Process**: - The model uses a third-degree polynomial transformation for factors with nonlinear characteristics. - The polynomial function is expressed as: $ r_{i}=aF^{3}+bF^{2}+cF+d $ where $a$, $b$, $c$, and $d$ are coefficients determined through least squares fitting[24][25]. **Model Evaluation**: This method is simple and intuitive but relies heavily on historical data and lacks rigorous economic logic[23][99]. 2. **Model Name**: Alpha Model with Additional Factors **Model Construction Idea**: This model introduces auxiliary factors to explain the nonlinear characteristics of primary factors, improving their predictive power[4][75]. **Model Construction Process**: - Identify auxiliary factors that interact with primary factors. For example, market capitalization is used as an auxiliary factor for turnover rate. - Construct a dummy variable $d_{high\_cap}$, which takes values of 0 or 1 depending on whether the stock belongs to a high or low market capitalization group. - The adjusted model is expressed as: $ r_{i}=v_{i0}+v_{i1}F_{i}+v_{i2}d_{i}F_{i}+\varepsilon_{i} $ where $d_{i}$ represents the dummy variable[78][79]. **Model Evaluation**: This method provides a more reasonable economic explanation and achieves significant improvements in factor effectiveness. However, it requires extensive pairwise comparisons and is labor-intensive[5][99]. Model Backtesting Results 1. **Nonlinear Multi-Factor Alpha Model**: - **Annualized Return**: 19.36% (in-sample), 19.19% (out-of-sample) - **Annualized Volatility**: 13.07% (in-sample), 8.97% (out-of-sample) - **IR**: 1.48 (in-sample), 2.14 (out-of-sample) - **Maximum Drawdown**: 15.33% (in-sample), 2.26% (out-of-sample)[63][67][76] 2. **Alpha Model with Additional Factors**: - **Annualized Return**: 30.90% (in-sample), 17.20% (out-of-sample) - **Annualized Volatility**: 15.43% (in-sample), 11.01% (out-of-sample) - **IR**: 2.00 (in-sample), 1.56 (out-of-sample) - **Maximum Drawdown**: 8.23% (in-sample), 1.70% (out-of-sample)[96][97][98] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Debt-to-Asset Ratio **Factor Construction Idea**: This factor exhibits a nonlinear relationship with stock returns, where both high and low values are suboptimal[27]. **Factor Construction Process**: - Transform the factor using a third-degree polynomial function. - Compare cumulative returns before and after transformation. - Annualized return improved from -0.5% to 3% after transformation[27][28]. 2. **Factor Name**: Turnover Rate **Factor Construction Idea**: The factor shows a nonlinear characteristic at low turnover levels[30]. **Factor Construction Process**: - Apply a polynomial transformation to enhance linearity. - Annualized return increased from 21.7% to 28.4% after transformation[30][36]. 3. **Factor Name**: Earnings-to-Price Ratio (EP) **Factor Construction Idea**: The factor demonstrates nonlinear behavior at low EP levels[34]. **Factor Construction Process**: - Use a polynomial transformation to improve linearity. - Annualized return increased from 11% to 13.5% after transformation[34][40]. 4. **Factor Name**: Total Assets **Factor Construction Idea**: The factor is generally linear but shows nonlinear characteristics at low asset levels[38]. **Factor Construction Process**: - Apply a polynomial transformation. - Annualized return improved from 8.1% to 10% after transformation[38][44]. 5. **Factor Name**: Fixed Ratio **Factor Construction Idea**: The factor exhibits a "middle is better" nonlinear pattern[42]. **Factor Construction Process**: - Transform the factor using a polynomial function. - Annualized return increased from 2.75% to 6.57% after transformation[42][48]. 6. **Factor Name**: Current Ratio **Factor Construction Idea**: Similar to the fixed ratio, this factor also shows a "middle is better" pattern[46]. **Factor Construction Process**: - Apply a polynomial transformation. - Annualized return improved from 3.76% to 5.32% after transformation[46][52]. Factor Backtesting Results 1. **Debt-to-Asset Ratio**: Annualized return improved from -0.5% to 3%[27][28] 2. **Turnover Rate**: Annualized return improved from 21.7% to 28.4%[30][36] 3. **Earnings-to-Price Ratio (EP)**: Annualized return improved from 11% to 13.5%[34][40] 4. **Total Assets**: Annualized return improved from 8.1% to 10%[38][44] 5. **Fixed Ratio**: Annualized return improved from 2.75% to 6.57%[42][48] 6. **Current Ratio**: Annualized return improved from 3.76% to 5.32%[46][52]