- The report utilizes the **Heston model** to calculate the implied volatility of near-month at-the-money options, which serves as the market's fear index. Implied volatility reflects market participants' expectations of future volatility[7] - The **broad-based timing strategy** signals indicate a "bullish" outlook for indices such as the ChiNext Index, SSE 50, CSI 800, Wind Microcap Index, CSI 500, CSI 300, Hang Seng Financials, Hang Seng Hong Kong 35, Hang Seng Sustainable Development Enterprises Index, Hang Seng Equal Weight Index, Hang Seng Index, and Hang Seng China Enterprises Index. Other broad-based indices are marked as "neutral"[12][13] - The **industry timing strategy** is constructed based on the scissors difference ratio of long and short signals for industry index constituent stocks. If no bullish or bearish signals are present on a given day, the respective count is set to zero. If both counts are zero, the scissors difference and its ratio are also zero. This forms the basis for the industry timing strategy. Backtesting results show that the timing model outperforms respective industry indices in all cases, demonstrating excellent historical performance[14] - The **industry timing strategy signals** indicate a "bullish" outlook for sectors such as home appliances, comprehensive finance, comprehensive, power equipment and new energy, basic chemicals, national defense and military, construction, textiles and apparel, non-ferrous metals, electric utilities, steel, transportation, and coal. Other sectors are marked as "neutral"[16]

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]

- The Heston model is used to calculate the implied volatility of near-month at-the-money options, serving as the market's fear index. Implied volatility reflects market participants' expectations of future volatility[9] - The industry timing strategy is constructed based on the scissors difference ratio of long and short positions in industry index constituent stocks. If no bullish or bearish signals are present on a given day, the scissors difference value and ratio are set to zero. This model outperformed respective industry indices in backtesting, achieving a 100% outperformance rate[16] - Six technical stock patterns are summarized, including "Golden Needle Bottom," "Rocket Launch," "Full Red," "Hanging Line," "Paradise Line," and "Cloud Line." Positive patterns like "Golden Needle Bottom," "Rocket Launch," and "Full Red" show strong positive signals. Specific stocks with these patterns include Youde Precision, Huicheng Vacuum, and Mingzhi Technology[23][27] - The brokerage gold stock shape signal strategy combines monthly gold stock recommendations with timing signals. Observations show that shape analysis significantly improves portfolio returns and reduces maximum drawdowns. Stocks with 70% bullish shape signals this week include Xianju Pharmaceutical, Jiejie Microelectronics, Ningde Times, Xiechuang Data, and Fosun Pharma[28][29]

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. It reflects market participants' expectations of future volatility[8] - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price 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 $ where: - \( S_t \): Asset price - \( v_t \): Variance of the asset price - \( \mu \): Drift term - \( \kappa \): Rate of mean reversion - \( \theta \): Long-term variance - \( \sigma \): Volatility of variance - \( W_t^S, W_t^v \): Two Wiener processes with correlation \( \rho \)[8] - **Model Evaluation**: The model effectively captures market fear and volatility expectations, providing a robust measure of implied volatility[8] --- Model Backtesting Results 1. Heston Model - **Implied Volatility Results**: - SSE 50: 20.3% (+2.93% WoW)[10] - SSE 500: 22.36% (+2.82% WoW)[10] - CSI 1000: 25.91% (+4.86% WoW)[10] - CSI 300: 19.21% (+1.12% WoW)[10] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Signal Shape Timing Factor - **Factor Construction Idea**: This factor is based on the frequency and success rate of positive and negative signals derived from historical shape patterns. It aims to predict future highs and lows in the market[12] - **Factor Construction Process**: - Positive signals and negative signals are identified based on historical shape patterns - The success rate of these signals in predicting future highs and lows is calculated as: $ \text{Success Rate} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} \times 100\% $ - For the period from August 11 to August 15, 2025: - Positive signals: 3365 occurrences, average success rate of 70.33% - Negative signals: 3167 occurrences, average success rate of 27.82%[12] - **Factor Evaluation**: The factor demonstrates strong predictive power for positive signals, with a high success rate in identifying future market highs[12] 2. Factor Name: Industry Multi-Long-Short Shape Timing Factor - **Factor Construction Idea**: This factor is constructed by calculating the difference in the number of long and short signals within industry index constituent stocks. It aims to outperform respective industry indices through timing strategies[15] - **Factor Construction Process**: - For each industry index, the number of long and short signals is calculated daily - If no long or short signals are present, the respective count is set to zero - The difference between long and short signals (scissor difference) is calculated, and the ratio of this difference is used to construct the timing strategy[15] - **Factor Evaluation**: The factor outperforms all respective industry indices in backtesting, demonstrating excellent historical performance[15] --- Factor Backtesting Results 1. Multi-Signal Shape Timing Factor - **Positive Signal Success Rate**: 70.33%[12] - **Negative Signal Success Rate**: 27.82%[12] 2. Industry Multi-Long-Short Shape Timing Factor - **Performance Metrics**: - Outperformed respective industry indices in 100% of backtests[15] - **Examples of Industry Results**: - Machinery: Strategy annualized return 19.72%, maximum drawdown -42.41%; Index annualized return 4.63%, maximum drawdown -72.59%[16] - Retail: Strategy annualized return 19.75%, maximum drawdown -43.39%; Index annualized return -0.9%, maximum drawdown -77.37%[16] - Electronics: Strategy annualized return 22.54%, maximum drawdown -44.99%; Index annualized return 11.13%, maximum drawdown -58.54%[16] --- Additional Observations - **Special Positive Shape Signals**: Specific K-line patterns such as "Golden Needle Bottom," "Rocket Launch," and "Full Red" exhibit strong positive predictive effects[22][23] - **Brokerage Golden Stock Shape Signals**: Combining fundamental analysis with shape-based buy signals significantly improves portfolio returns and reduces maximum drawdowns[27]

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 [8] - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price 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^1 $ $ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^2 $ Here: - \( S_t \): Asset price - \( v_t \): Variance process - \( \mu \): Drift rate of the asset price - \( \kappa \): Rate of mean reversion of variance - \( \theta \): Long-term variance - \( \sigma \): Volatility of variance - \( W_t^1, W_t^2 \): Two Wiener processes with correlation \( \rho \) [8] - **Model Evaluation**: The Heston model is widely recognized for its ability to capture the stochastic nature of volatility, making it suitable for modeling market fear indices [8] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Long-Short Ratio Scissor Difference - **Factor Construction Idea**: This factor is based on the difference between the number of long and short signals within industry index constituent stocks. It is used to construct industry timing strategies [15] - **Factor Construction Process**: - Define the number of long and short signals for each industry index constituent stock on a given day - If no long signals are present, set the long signal count to 0; similarly, if no short signals are present, set the short signal count to 0 - Calculate the scissor difference as the difference between the long and short signal counts - Normalize the scissor difference to obtain the scissor difference ratio [15] - **Factor Evaluation**: The backtesting results show that the timing model based on this factor outperforms the respective industry indices in all cases, demonstrating excellent historical performance [15] --- Backtesting Results of Models 1. Heston Model - **Implied Volatility Results**: - SSE 50: 13.63% (down 2.78% WoW) - SSE 500: 15.75% (down 3.31% WoW) - CSI 1000: 17.15% (down 3.26% WoW) - CSI 300: 13.96% (down 2.31% WoW) [10] --- Backtesting Results of Factors 1. Multi-Long-Short Ratio Scissor Difference - **Performance Metrics**: - Timing models based on this factor outperformed their respective industry indices in all cases, achieving a 100% success rate in backtesting [15]

- The report utilizes the Heston model to calculate implied volatility for near-month at-the-money options, serving as a market fear index. Implied volatility reflects market participants' expectations of future volatility[6] - Positive signals appeared 3052 times from July 14 to July 18, with an average future high-point success rate of 68.64%. Negative signals appeared 3426 times, with an average future low-point success rate of 27.06%[10] - The broad-based timing strategy signals indicate "bullish" for indices such as ChiNext Index, SSE 50, Wind Microcap Index, Hang Seng Equal Weight Index, Hang Seng Tech Index, Hang Seng Hong Kong 35, Hang Seng Index, Hang Seng Financials, Hang Seng Sustainable Development Enterprises Index, Hang Seng China Enterprises Index, and Hang Seng China (Hong Kong-listed) 100[12] - The industry timing strategy is constructed using the scissors difference ratio of long-short signals for industry index constituent stocks. If no bullish or bearish signals appear on a given day, the scissors difference value and ratio are set to zero. The model outperformed all respective industry indices historically, demonstrating excellent backtesting results[13] - Industry timing strategy signals show "bullish" for sectors such as building materials, light manufacturing, home appliances, textiles and apparel, non-ferrous metals, power and utilities, steel, consumer services, transportation, coal, pharmaceuticals, agriculture, forestry, animal husbandry, and fishing, and petrochemicals[15]

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. It reflects market participants' expectations of future volatility [7] - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price follows a mean-reverting square-root process. The model is defined by the following equations: $$ dS_t = \mu S_t dt + \sqrt{v_t} S_t dW_t^S $$ $$ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^v $$ where: - \( S_t \): 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 Evaluation**: The Heston model is widely recognized for its ability to capture the stochastic nature of volatility, making it suitable for modeling market fear indices [7] --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Industry 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 an industry. It aims to identify timing opportunities by analyzing the divergence between bullish and bearish signals [14] - **Factor Construction Process**: - Define the number of stocks with bullish signals (\(N_{bullish}\)) and bearish signals (\(N_{bearish}\)) in an industry on a given day - If no bullish or bearish signals are present, set the respective count to 0 - Calculate the scissors difference as: $$ \text{Scissors Difference} = N_{bullish} - N_{bearish} $$ - Normalize the scissors difference to obtain a ratio: $$ \text{Scissors Ratio} = \frac{N_{bullish} - N_{bearish}}{N_{bullish} + N_{bearish}} $$ - Use this ratio to construct an industry timing strategy [14] - **Factor Evaluation**: The backtesting results show that the scissors difference timing model outperforms the respective industry indices in all cases, demonstrating excellent historical performance [14] --- Model Backtesting Results 1. Heston Model - Implied volatility for major indices: - **Shanghai 50**: 13.5% (down 0.91% from last week) - **Shanghai 500**: 15.29% (down 0.11% from last week) - **CSI 1000**: 16.79% (down 1.3% from last week) - **CSI 300**: 13.65% (down 0.83% from last week) [9] --- Factor Backtesting Results 1. Multi-Industry Timing Factor (Scissors Difference) - Backtesting results for selected industries: - **Real Estate**: Strategy annualized return 13.18%, maximum drawdown -34.3%; Index annualized return -1.21%, maximum drawdown -75.09% - **Light Manufacturing**: Strategy annualized return 21.84%, maximum drawdown -37.91%; Index annualized return 2.76%, maximum drawdown -67.79% - **Coal**: Strategy annualized return 28.73%, maximum drawdown -24.76%; Index annualized return -0.1%, maximum drawdown -69.7% - **Pharmaceuticals**: Strategy annualized return 19.22%, maximum drawdown -42.71%; Index annualized return 6.69%, maximum drawdown -55.37% [15][16]

Quantitative Models and Construction 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, which serves as a market fear index. It reflects market participants' expectations of future volatility[7]. - **Model Construction Process**: The Heston model is a stochastic volatility model where the variance of the asset price 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^1 $ $ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^2 $ Here: - \( S_t \): Asset price - \( v_t \): Variance process - \( \mu \): Drift rate of the asset price - \( \kappa \): Rate of mean reversion of variance - \( \theta \): Long-term variance - \( \sigma \): Volatility of volatility - \( W_t^1, W_t^2 \): Two Wiener processes with correlation \( \rho \)[7]. - **Model Evaluation**: The Heston model is widely used in financial markets for its ability to capture the stochastic nature of volatility, making it a robust tool for implied volatility estimation[7]. --- Quantitative Factors and Construction 1. Factor Name: Multi-Industry Timing Factor (Scissor Difference) - **Factor Construction Idea**: This factor is based on the difference between the number of stocks with bullish and bearish signals within an industry. It is used to construct an industry timing strategy[15]. - **Factor Construction Process**: - Define the number of stocks with bullish signals as \( N_{bullish} \) and bearish signals as \( N_{bearish} \). - Compute the scissor difference as: $ \text{Scissor Difference} = N_{bullish} - N_{bearish} $ - Normalize the scissor difference by the total number of stocks in the industry to obtain the scissor difference ratio: $ \text{Scissor Difference Ratio} = \frac{N_{bullish} - N_{bearish}}{N_{total}} $ - Use this ratio to construct an industry timing strategy[15]. - **Factor Evaluation**: The backtesting results show that the timing model outperforms the respective industry indices in all cases, demonstrating excellent historical performance[15]. --- Backtesting Results of Models and Factors 1. Heston Model - **Implied Volatility Results**: - SSE 50: 14.41% (+2.91% WoW)[9] - SSE 500: 15.4% (+0.83% WoW)[9] - CSI 1000: 18.09% (+1.24% WoW)[9] - CSI 300: 14.48% (+3.15% WoW)[9] 2. Multi-Industry Timing Factor - **Performance Metrics**: - The timing model outperformed the respective industry indices in all cases, with a 100% success rate in backtesting[15]. - Specific industries with bullish signals include retail, light manufacturing, home appliances, and others[18].

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 asset price 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^1 $ $ dv_t = \kappa (\theta - v_t) dt + \sigma \sqrt{v_t} dW_t^2 $ Here: - \( S_t \): Asset price - \( v_t \): Variance process - \( \mu \): Drift rate of the asset price - \( \kappa \): Rate of mean reversion - \( \theta \): Long-term variance - \( \sigma \): Volatility of volatility - \( W_t^1, W_t^2 \): Two Wiener processes with correlation \( \rho \) [7]. --- Quantitative Factors and Construction Methods 1. Factor Name: Multi-Long-Short Ratio Scissor Difference - **Factor Construction Idea**: This factor is based on the number of stocks with bullish and bearish signals within an industry index. The scissor difference ratio is used to construct an industry timing strategy [15]. - **Factor Construction Process**: - Define the number of stocks with bullish signals as \( N_{bull} \) and bearish signals as \( N_{bear} \). - If \( N_{bull} = 0 \), set the bullish count to 0. Similarly, if \( N_{bear} = 0 \), set the bearish count to 0. - The scissor difference is calculated as \( N_{bull} - N_{bear} \), and the ratio is \( \frac{N_{bull} - N_{bear}}{N_{bull} + N_{bear}} \) [15]. - **Factor Evaluation**: The backtesting results show that this factor outperforms the respective industry indices in all cases, indicating excellent historical performance [15]. --- Backtesting Results of Models and Factors 1. Heston Model - **Implied Volatility Results**: - SSE 50: 13.48% (+1.11% WoW) - SSE 500: 16.97% (+0.72% WoW) - CSI 1000: 21.37% (+1.76% WoW) - CSI 300: 13.25% (-0.12% WoW) [9]. 2. Multi-Long-Short Ratio Scissor Difference - **Industry Timing Strategy Results**: - The timing model outperformed the respective industry indices in 100% of cases [15]. - **Specific Industry Examples**: - Retail Trade: Strategy Annualized Return 19.5%, Max Drawdown -43.39%, Index Annualized Return -1.49%, Max Drawdown -77.37% - Home Appliances: Strategy Annualized Return 16.27%, Max Drawdown -38.25%, Index Annualized Return 11.06%, Max Drawdown -48.96% - Comprehensive: Strategy Annualized Return 24.05%, Max Drawdown -40.81%, Index Annualized Return 0.11%, Max Drawdown -81.18% [16][18].

- The Heston model is used to calculate the implied volatility of near-month at-the-money options, serving as the market's fear index. Implied volatility reflects market participants' expectations of future volatility[9] - Positive signals appeared 3465 times between April 30, 2025, and May 9, 2025, with an average future high-point success rate of 59.95%. Negative signals appeared 2996 times, with an average future low-point success rate of 37.32%[14] - Broad-based timing strategy signals indicate bullish signals for the SSE 50 and Hang Seng Sustainable Development Enterprise Index, while other broad-based signals remain neutral[16] - Industry timing strategy signals are constructed using the scissors difference ratio of long and short positions in industry index constituent stocks. If no bullish or bearish signals appear on a given day, the scissors difference value and ratio are set to zero. This model outperforms respective industry indices in all cases, demonstrating excellent historical backtesting performance[17] - Industry timing strategy signals show bullish signals for sectors such as retail, home appliances, textiles and apparel, utilities, consumer services, transportation, and banking, while other industry signals remain neutral[20]