市场形态周报（20250908-20250912）：本周指数普遍上涨-20250916

Quantitative Models and Construction Methods 1. Model Name: Heston Model - Model Construction Idea: The Heston model is used to calculate the implied volatility of near-month at-the-money options, serving as a market fear index. Implied volatility reflects market participants' expectations of future volatility[7] - Model Construction Process: The Heston model is a stochastic volatility model where the variance of the underlying asset follows a mean-reverting square-root process. The model is defined by the following equations: $dS_t = \mu S_t dt + \sqrt{v_t} S_t dW_t^S$ $dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^v$ Here: - $S_t$: Underlying asset price - $v_t$: Variance of the asset price - $\mu$: Drift term - $\kappa$: Speed of mean reversion - $\theta$: Long-term variance - $\sigma$: Volatility of variance - $W_t^S, W_t^v$: Two Wiener processes with correlation $\rho$[7] --- Model Backtesting Results 1. Heston Model - Implied Volatility Results: - SSE 50: 19.61% (down 0.12% from last week) - SSE 500: 23.23% (down 0.17% from last week) - CSI 1000: 25.45% (down 0.49% from last week) - CSI 300: 20.56% (up 0.29% from last week)[8] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Sector Timing Factor (Scissors Difference) - Factor Construction Idea: This factor is based on the difference in the number of stocks with bullish and bearish signals within sector indices. It aims to identify timing opportunities by analyzing the relative strength of bullish versus bearish signals[14] - Factor Construction Process: - For each sector index, calculate the number of stocks with bullish and bearish signals daily - If no bullish signals exist, set the bullish count to 0; similarly, if no bearish signals exist, set the bearish count to 0 - Compute the scissors difference as the difference between the bullish and bearish counts - Normalize the scissors difference to obtain a ratio for comparison across sectors[14] - Factor Evaluation: The backtesting results show that the timing model based on this factor outperforms the respective sector indices in all cases, demonstrating excellent historical performance[14] --- Factor Backtesting Results 1. Multi-Sector Timing Factor - Performance Metrics: - The timing model outperformed the respective sector indices in 100% of cases - Specific sector examples: - Building Materials: Annualized return 25.14%, maximum drawdown -37.98%, index annualized return 2.98%, index maximum drawdown -58.37% - Light Manufacturing: Annualized return 21.94%, maximum drawdown -37.91%, index annualized return 3.35%, index maximum drawdown -67.79% - Electric Power & Utilities: Annualized return 17.15%, maximum drawdown -41.46%, index annualized return 2.6%, index maximum drawdown -67.22%[14][15][17]