Orient Securities(SH:600958) Orient Securities·2025-04-06 08:13Quantitative Models and Factor Analysis Quantitative Factors and Construction Methods - Factor Name: Non-liquidity Shock Construction Idea: Measures the impact of illiquidity on stock returns Construction Process: Calculated as the average absolute daily return over the past 20 trading days divided by the corresponding daily trading volume[6][16][19] Evaluation: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing illiquidity effects[6][19][21] - Factor Name: Six-Month UMR Construction Idea: Captures momentum adjusted for risk over a six-month window Construction Process: Risk-adjusted momentum is calculated using a six-month rolling window, incorporating volatility adjustments[6][16][19] Evaluation: Consistently performed well in recent periods, showing robustness across different market conditions[6][19][21] - Factor Name: One-Year UMR Construction Idea: Similar to Six-Month UMR but uses a one-year window for risk-adjusted momentum Construction Process: Momentum is adjusted for risk using a one-year rolling window, factoring in volatility[6][16][19] Evaluation: Effective in capturing long-term momentum trends, though performance varies by index[6][19][21] - Factor Name: Three-Month Volatility Construction Idea: Measures short-term price fluctuations Construction Process: Calculated as the standard deviation of daily returns over the past 60 trading days[6][16][19] Evaluation: Demonstrated strong negative correlation with returns, indicating its utility in identifying high-risk assets[6][19][21] - Factor Name: One-Month Turnover Construction Idea: Reflects trading activity and liquidity over a short period Construction Process: Average daily turnover rate over the past 20 trading days[6][16][19] Evaluation: Effective in capturing liquidity dynamics, though performance varies across indices[6][19][21] Factor Backtesting Results - Non-liquidity Shock: - Recent Week: 0.58% (HS300), 0.91% (CSI500), 0.93% (CSI800), 0.87% (CSI1000), 1.14% (CSI All)[19][23][27][31][42] - Recent Month: 0.31% (HS300), 0.64% (CSI500), 0.77% (CSI800), 2.40% (CSI1000), 1.33% (CSI All)[19][23][27][31][42] - Six-Month UMR: - Recent Week: 0.54% (HS300), -0.09% (CSI500), 0.57% (CSI800), 0.73% (CSI1000), 0.73% (CSI All)[19][23][27][31][42] - Recent Month: 1.53% (HS300), 2.09% (CSI500), 2.35% (CSI800), 3.49% (CSI1000), 3.85% (CSI All)[19][23][27][31][42] - One-Year UMR: - Recent Week: 0.46% (HS300), 0.06% (CSI500), 0.88% (CSI800), 0.52% (CSI1000), 0.76% (CSI All)[19][23][27][31][42] - Recent Month: 1.15% (HS300), 2.19% (CSI500), 2.50% (CSI800), 2.85% (CSI1000), 3.74% (CSI All)[19][23][27][31][42] - Three-Month Volatility: - Recent Week: 0.24% (HS300), 0.78% (CSI500), 0.59% (CSI800), 0.65% (CSI1000), 0.86% (CSI All)[19][23][27][31][42] - Recent Month: 0.84% (HS300), 3.24% (CSI500), 2.17% (CSI800), 3.63% (CSI1000), 3.60% (CSI All)[19][23][27][31][42] - One-Month Turnover: - Recent Week: -0.05% (HS300), 0.48% (CSI500), 0.04% (CSI800), 0.57% (CSI1000), 0.50% (CSI All)[19][23][27][31][42] - Recent Month: 0.19% (HS300), 2.47% (CSI500), 0.19% (CSI800), 3.87% (CSI1000), 1.65% (CSI All)[19][23][27][31][42] Quantitative Model Construction - Model Name: Maximized Factor Exposure Portfolio (MFE) Construction Idea: Optimizes portfolio weights to maximize exposure to a single factor while controlling for constraints Construction Process: - Objective Function: Maximize $f^T w$, where $f$ is the factor value and $w$ is the weight vector - Constraints: Include style exposure, industry deviation, stock weight limits, turnover, and full investment constraints - Formula: $\begin{array}{ll}max&f^{T}w\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\ &h_{l}\leq H(w-w_{b})\leq h_{h}\ &w_{l}\leq w-w_{b}\leq w_{h}\ &b_{l}\leq B_{b}w\leq b_{h}\ &0\leq w\leq l\ &1^{T}w=1\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[57][58][61] Evaluation: Provides a robust framework for testing factor effectiveness under realistic constraints[57][58][61] Model Backtesting Results - MFE Portfolio: - Demonstrated strong performance in capturing factor-specific returns while adhering to constraints such as turnover and industry exposure[57][58][61]