EBSCN·2026-01-31 14:30Quantitative Models and Construction Methods 1. Model Name: Volume Timing Model - Model Construction Idea: The model uses volume-based timing signals to assess market sentiment and provide trading signals[23] - Model Construction Process: - The model evaluates the volume timing signals of major broad-based indices - Signals are categorized as "cautious" or "optimistic" based on volume trends - As of January 30, 2026, all major indices (e.g., SSE Composite Index, CSI 300, etc.) showed "cautious" volume timing signals[24] - Model Evaluation: The model provides a straightforward approach to gauge market sentiment but may lack granularity in capturing nuanced market dynamics[23][24] 2. Model Name: Momentum Sentiment Indicator - Model Construction Idea: This model identifies market sentiment by analyzing the proportion of stocks with positive returns in the CSI 300 Index over a specific period[24] - Model Construction Process: - The indicator is calculated as: $ \text{CSI 300 N-day Upward Stock Proportion} = \frac{\text{Number of CSI 300 stocks with positive returns over N days}}{\text{Total number of CSI 300 stocks}} $ - The indicator is smoothed using two moving averages with different window periods (N1 = 50, N2 = 35) to create a "fast line" and a "slow line" - A buy signal is generated when the fast line exceeds the slow line, and a neutral signal is generated when the fast line falls below the slow line[26][28] - Model Evaluation: The indicator is effective in capturing upward market opportunities but may fail to predict downturns accurately. It also tends to miss gains during prolonged market exuberance[25] 3. Model Name: Moving Average Sentiment Indicator - Model Construction Idea: This model uses an eight-moving-average system to assess the trend state of the CSI 300 Index and generate trading signals[32] - Model Construction Process: - Calculate the eight moving averages of the CSI 300 Index closing price with parameters: 8, 13, 21, 34, 55, 89, 144, and 233 - Assign values to the indicator based on the number of moving averages the current price exceeds: - If the price exceeds more than five moving averages, the sentiment is bullish - Generate a buy signal when the current price exceeds five moving averages[36] - Model Evaluation: The model provides a clear framework for trend analysis but may oversimplify complex market dynamics[36] --- Model Backtesting Results 1. Volume Timing Model - All major indices (e.g., SSE Composite Index, CSI 300, CSI 500, etc.) showed "cautious" volume timing signals as of January 30, 2026[24] 2. Momentum Sentiment Indicator - The CSI 300 N-day upward stock proportion indicator was above 60% as of January 30, 2026, indicating high market sentiment[25] - The fast line was above the slow line, suggesting a bullish outlook for the CSI 300 Index[26] 3. Moving Average Sentiment Indicator - The CSI 300 Index was in a "sentiment prosperity zone" as of January 30, 2026, indicating a bullish sentiment[36] --- Quantitative Factors and Construction Methods 1. Factor Name: Cross-sectional Volatility - Factor Construction Idea: Measures the dispersion of returns among index constituents to assess the Alpha environment[37] - Factor Construction Process: - Calculate the cross-sectional volatility of index constituents (e.g., CSI 300, CSI 500, CSI 1000) - Compare the recent quarter's average volatility to historical percentiles to evaluate the Alpha environment[38] - Factor Evaluation: The factor effectively captures short-term Alpha opportunities but may not fully reflect long-term trends[37] 2. Factor Name: Time-series Volatility - Factor Construction Idea: Measures the volatility of index constituents over time to assess the Alpha environment[38] - Factor Construction Process: - Calculate the time-series volatility of index constituents (e.g., CSI 300, CSI 500, CSI 1000) - Compare the recent quarter's average volatility to historical percentiles to evaluate the Alpha environment[41] - Factor Evaluation: The factor provides insights into market stability but may be less effective in highly volatile markets[38] --- Factor Backtesting Results 1. Cross-sectional Volatility - CSI 300: Recent quarter average volatility at 2.14%, in the 69.55th percentile of the past two years[38] - CSI 500: Recent quarter average volatility at 2.45%, in the 50.79th percentile of the past two years[38] - CSI 1000: Recent quarter average volatility at 2.61%, in the 66.93rd percentile of the past two years[38] 2. Time-series Volatility - CSI 300: Recent quarter average volatility at 0.96%, in the 57.20th percentile of the past two years[41] - CSI 500: Recent quarter average volatility at 1.22%, in the 50.79th percentile of the past two years[41] - CSI 1000: Recent quarter average volatility at 1.17%, in the 64.94th percentile of the past two years[41]