SWHY(SZ:000166) Shenwan Hongyuan Securities·2025-11-18 12:13Quantitative Models and Construction Methods 1. Model Name: Mean-Variance Model - Model Construction Idea: The model determines the optimal portfolio by balancing expected returns and risks, based on the mean and variance of asset returns[8] - Model Construction Process: 1. Define the portfolio return as a random variable 2. Use the expected return ($E[R]$) and variance ($Var[R]$) to measure the portfolio's performance 3. Solve the optimization problem to maximize expected return for a given level of risk or minimize risk for a given level of return - Formula: $ \text{Minimize: } \sigma_p^2 = \sum_{i=1}^n \sum_{j=1}^n w_i w_j \sigma_{ij} $ $ \text{Subject to: } \sum_{i=1}^n w_i = 1 $ Where $w_i$ is the weight of asset $i$, $\sigma_{ij}$ is the covariance between assets $i$ and $j$[8] - Model Evaluation: Flexible in adjusting portfolios based on expected returns and risks, but struggles to incorporate new market dynamics and subjective views[8] 2. Model Name: Black-Litterman Model - Model Construction Idea: Combines the Bayesian framework with the mean-variance model to incorporate subjective views into the portfolio optimization process[8] - Model Construction Process: 1. Start with a prior distribution of expected returns based on market equilibrium 2. Incorporate subjective views as additional constraints 3. Use the Bayesian approach to update the prior distribution with subjective views to form a posterior distribution - Formula: $ \Pi = \tau \Sigma w_{mkt} $ $ E[R] = \left( \tau \Sigma^{-1} + P^T \Omega^{-1} P \right)^{-1} \left( \tau \Sigma^{-1} \Pi + P^T \Omega^{-1} Q \right) $ Where $\Pi$ is the implied equilibrium return, $\tau$ is a scaling factor, $\Sigma$ is the covariance matrix, $w_{mkt}$ is the market portfolio weights, $P$ is the view matrix, $\Omega$ is the uncertainty matrix, and $Q$ is the view vector[8] - Model Evaluation: Flexible and allows integration of subjective views, but requires strong assumptions about return distributions and is computationally complex[8] 3. Model Name: Risk Parity Model - Model Construction Idea: Focuses on balancing the risk contribution of each asset in the portfolio rather than their weights[7] - Model Construction Process: 1. Calculate the risk contribution of each asset: $RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p}$ 2. Adjust weights to equalize the risk contributions across all assets - Formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p} $ Where $RC_i$ is the risk contribution of asset $i$, $w_i$ is the weight of asset $i$, $\sigma_i$ is the standard deviation of asset $i$, and $\rho_{i,p}$ is the correlation between asset $i$ and the portfolio[7] - Model Evaluation: Enhances risk control and can incorporate multiple risk dimensions, but lacks a mechanism to optimize returns and may struggle with unrecognized risks[7] 4. Model Name: All-Weather Model (Bridgewater) - Model Construction Idea: Aims to achieve stable performance across all economic environments by focusing on risk parity under growth and inflation sensitivity[11] - Model Construction Process: 1. Classify assets based on their sensitivity to growth and inflation 2. Allocate weights to achieve risk parity across these dimensions - Formula: Not explicitly provided, but the model emphasizes balancing risk rather than returns[11] - Model Evaluation: Stable allocation structure with a focus on low-risk assets, but may underperform in specific market conditions due to its heavy reliance on bonds and cash[15] --- Model Backtesting Results 1. Mean-Variance Model - Maximum Drawdown: Exceeded 4% in some periods (e.g., 2018-2019), but quickly recovered[57] - Sharpe Ratio: Higher than benchmarks in optimistic scenarios, demonstrating strong risk-adjusted returns[57] 2. Black-Litterman Model - Maximum Drawdown: Similar to the mean-variance model, with better adaptability to subjective views[57] - Sharpe Ratio: Improved compared to the mean-variance model due to the integration of subjective views[57] 3. Risk Parity Model - Maximum Drawdown: Generally lower than the mean-variance model, reflecting its focus on risk control[57] - Sharpe Ratio: Moderate, as the model does not explicitly optimize returns[57] 4. All-Weather Model - Maximum Drawdown: Comparable to fixed-ratio models, with a focus on stability[15] - Sharpe Ratio: Similar to benchmarks, reflecting its conservative allocation[15] --- Quantitative Factors and Construction Methods 1. Factor Name: Monthly Frequency Slicing - Factor Construction Idea: Use historical slices of monthly data to reflect maximum drawdown and market sentiment[41] - Factor Construction Process: 1. Extract rolling 20-day returns for each year 2. Use the bottom 20% quantile to estimate pessimistic scenarios and maximum drawdown - Formula: $ \text{Max Drawdown} = \text{Min} \left( \frac{P_t - P_{peak}}{P_{peak}} \right) $ Where $P_t$ is the price at time $t$, and $P_{peak}$ is the peak price[41] - Factor Evaluation: Effective in capturing extreme market conditions, but limited in predicting long-term trends[41] 2. Factor Name: BootStrap State Space - Factor Construction Idea: Use BootStrap sampling to create a state space of asset returns under different scenarios[45] - Factor Construction Process: 1. Sample historical data with replacement to create new sequences 2. Calculate return distributions for pessimistic, neutral, and optimistic scenarios - Formula: $ F = B - \alpha \cdot C $ Where $F$ is the objective function, $B$ is the expected return under risk constraints, $C$ is the penalty for exceeding risk constraints, and $\alpha$ is the penalty parameter[50] - Factor Evaluation: Provides a robust framework for scenario analysis, but computationally intensive[45] --- Factor Backtesting Results 1. Monthly Frequency Slicing - Maximum Drawdown: Successfully captured extreme drawdowns in historical data, with 90% coverage for A-shares and Hong Kong stocks[40] - Sharpe Ratio: Not explicitly provided, but the factor is more focused on risk control[40] 2. BootStrap State Space - Maximum Drawdown: Achieved a 4% maximum drawdown target in most scenarios, with only minor deviations in extreme conditions[57] - Sharpe Ratio: Optimized under different scenarios, with higher ratios in optimistic environments[57]