Core Viewpoint - The article discusses a quantitative timing research framework for style indices, focusing on the identification of market bottoms and tops through valuation, liquidity, and trading congestion models, highlighting their effectiveness in generating returns since 2011 [1][2][3]. Group 1: Style Index Quantitative Timing Research Framework - The style index includes large-cap, small-cap, value, growth, and dividend indices, with a focus on their valuation and market liquidity characteristics [1]. - The average annualized return for the long positions in the style index valuation model since 2011 is 10.38%, with an average excess annualized return of 8.30% [1]. Group 2: Market Liquidity Model - The market liquidity factors include buy and sell impact costs, as well as liquidity indices for rising and falling markets, with bottom timing showing more significant accuracy compared to top timing [2]. - The average rebound return from liquidity factor bottom timing is 6.86%, and the average annualized return for the long positions in the liquidity model since 2011 is 12.38%, with an average excess annualized return of 10.30% [2]. Group 3: Trading Congestion Model - Trading congestion is identified as a top-timing risk factor, effectively complementing the valuation and liquidity models [2]. - The excess annualized return for the congestion composite model since 2011 is 4.87% [2]. Group 4: Application of Quantitative Timing Models - The combined application of valuation, liquidity, and congestion models has accurately captured style index bottoms and tops, while effectively mitigating risks from trading congestion [3]. - The average annualized return for the long positions in the timing model since 2011 is 18.54%, with an average excess annualized return of 16.46% and a SHARP ratio of 1.06, achieving an excess return win rate of 87% [3].

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The core idea is that "the decline in a shrinking volume market is significantly greater than the rise in a shrinking volume market, so avoiding shrinking volume signals can achieve higher trading odds"[3][22][24] - **Model Construction Process**: 1. Calculate the rolling 60-day average and standard deviation of the turnover and turnover rate of the index or market[23] 2. Standardize the daily turnover data: - If the turnover is within ±2 standard deviations, map the score to -1~+1 - If the turnover exceeds ±2 standard deviations, assign a score of +1/-1 3. Combine the scores of turnover and turnover rate equally[23] 4. Generate signals based on the combined score: - Method 1: Go long if the score > 0, stay out if the score < 0 - Method 2: Use the rolling 5-year or 3-year percentile of the score; go long if above the 50th percentile, stay out if below[23] 5. The report adopts the simpler method of directly judging whether the score is greater than 0[23] - **Model Evaluation**: The model is not a high-win-rate strategy but achieves relatively high odds by avoiding significant market adjustments during shrinking volume periods[24] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of growth and value styles based on macroeconomic cycles, valuation differences, and market sentiment[52][54] - **Model Construction Process**: 1. **Fundamentals**: - Growth is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Value is favored under the opposite conditions[52] 2. **Valuation**: - Growth is favored when the PE and PB valuation differences between growth and value are in the lower percentiles and mean-reverting upward[52] 3. **Sentiment**: - Growth is favored when turnover and volatility differences between growth and value are low[52] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between growth and value[52] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[53][55] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates the relative attractiveness of small-cap and large-cap styles based on macroeconomic cycles, valuation differences, and market sentiment[56][58] - **Model Construction Process**: 1. **Fundamentals**: - Small-cap is favored when the profit cycle slope is steep, interest rate levels are low, and the credit cycle is rising - Large-cap is favored under the opposite conditions[56] 2. **Valuation**: - Large-cap is favored when the PE and PB valuation differences between small-cap and large-cap are in the higher percentiles and mean-reverting downward[56] 3. **Sentiment**: - Small-cap is favored when turnover differences are high - Large-cap is favored when volatility differences are mean-reverting downward[56] 4. Combine signals from fundamentals, valuation, and sentiment to determine the allocation between small-cap and large-cap[56] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance[57][60] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines the conclusions of the growth-value and small-cap-large-cap rotation models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[61][63] - **Model Construction Process**: 1. Use the growth-value model to determine the allocation between growth and value 2. Use the small-cap-large-cap model to determine the allocation between small-cap and large-cap 3. Combine the two models to allocate across the four styles[61] - **Model Evaluation**: The model has shown significant improvement over the benchmark in terms of annualized returns and risk-adjusted performance, with consistent outperformance in most years[61][63] --- Model Backtest Results 1. Volume Timing Signal - **Win Rate**: 47.34%[24] - **Odds**: 1.75[24] - **Annualized Excess Return**: 6.87% (based on next-day open price)[34] - **Maximum Drawdown**: 31.40%[34] - **Return-to-Drawdown Ratio**: 0.4634[34] 2. Growth-Value Style Rotation Model - **Annualized Return**: 11.76%[55] - **Annualized Volatility**: 20.77%[55] - **Maximum Drawdown**: 43.07%[55] - **Sharpe Ratio**: 0.5438[55] - **Return-to-Drawdown Ratio**: 0.2731[55] 3. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.45%[60] - **Annualized Volatility**: 22.65%[60] - **Maximum Drawdown**: 50.65%[60] - **Sharpe Ratio**: 0.5441[60] - **Return-to-Drawdown Ratio**: 0.2459[60] 4. Four-Style Rotation Model - **Annualized Return**: 13.37%[63] - **Annualized Volatility**: 21.51%[63] - **Maximum Drawdown**: 47.91%[63] - **Sharpe Ratio**: 0.5988[63] - **Return-to-Drawdown Ratio**: 0.2790[63]

Quantitative Models and Construction Methods 1. Model Name: Short-term Quantitative Timing Model - **Model Construction Idea**: The model integrates macroeconomic fundamentals, valuation, sentiment, and liquidity indicators to generate short-term market timing signals[24][25][26] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Manufacturing PMI: Current value at 49.70, 44.92% percentile over the past 5 years, indicating neutral sentiment[24] - Long-term RMB loan growth: 0.00% percentile, indicating weak credit growth and cautious signals[24] - M1 growth rate: 94.92% percentile, indicating strong growth and optimistic signals[24] - **Valuation**: - PE median: 43.18, 97.19% percentile, indicating high valuation and neutral signals[25] - PB median: 2.85, 86.77% percentile, indicating high valuation and neutral signals[25] - **Sentiment**: - Beta dispersion: -0.59%, 40.68% percentile, indicating neutral sentiment[25] - Volume sentiment score: 0.98, 99.59% percentile, indicating strong sentiment and optimism[25] - Volatility: 7.53% (annualized), 0.17% percentile, indicating optimism[25] - **Liquidity**: - Monetary rate: -0.10, 33.90% percentile, indicating relative ease and optimism[26] - Exchange rate expectations: -0.09%, 40.68% percentile, indicating neutrality[26] - 5-day average financing: 50.66 billion RMB, 95.53% percentile, indicating neutral leverage signals[26] - **Model Evaluation**: The model demonstrates strong performance with significant excess returns and reduced drawdowns compared to benchmarks[26][30] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates growth and value styles based on macroeconomic fundamentals, valuation, and sentiment indicators to recommend allocation[35] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Profit cycle slope: High, favoring growth[37] - Interest rate cycle: High, favoring value[37] - Credit cycle: Weak, favoring value[37] - **Valuation**: - PE difference: 19.57% percentile, favoring growth[37] - PB difference: 38.03% percentile, favoring growth[37] - **Sentiment**: - Turnover difference: 38.13% percentile, favoring value[37] - Volatility difference: 17.73% percentile, favoring balanced allocation[37] - **Model Evaluation**: The model has historically delivered significant excess returns over benchmarks, though recent performance has been mixed[36][39] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model assesses small-cap and large-cap styles using macroeconomic fundamentals, valuation, and sentiment indicators to suggest balanced allocation[40] - **Model Construction Process**: - **Macroeconomic Fundamentals**: - Profit cycle slope: High, favoring small-cap[42] - Interest rate cycle: High, favoring large-cap[42] - Credit cycle: Weak, favoring large-cap[42] - **Valuation**: - PE difference: 78.86% percentile, favoring large-cap[42] - PB difference: 96.59% percentile, favoring large-cap[42] - **Sentiment**: - Turnover difference: 72.56% percentile, favoring small-cap[42] - Volatility difference: 62.60% percentile, favoring large-cap[42] - **Model Evaluation**: The model has consistently outperformed benchmarks, delivering significant excess returns over time[41][44] 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: Combines insights from growth-value and small-cap-large-cap models to allocate across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[45] - **Model Construction Process**: - Allocation recommendation: Small-cap growth (12.5%), small-cap value (37.5%), large-cap growth (12.5%), large-cap value (37.5%)[45] - **Model Evaluation**: The model has historically generated significant excess returns, though recent performance has been slightly below benchmarks[45][46] --- Model Backtest Results 1. Short-term Quantitative Timing Model - Annualized return: 16.98% - Annualized volatility: 14.55% - Maximum drawdown: 27.70% - Sharpe ratio: 1.0138 - Excess return (2024 onwards): 2.26%[26][30][33] 2. Growth-Value Style Rotation Model - Annualized return: 11.82% - Annualized volatility: 20.79% - Maximum drawdown: 43.07% - Sharpe ratio: 0.5457 - Excess return (2025 YTD): -2.32%[36][39] 3. Small-Cap vs. Large-Cap Style Rotation Model - Annualized return: 12.38% - Annualized volatility: 22.69% - Maximum drawdown: 50.65% - Sharpe ratio: 0.5408 - Excess return (2025 YTD): -5.11%[41][44] 4. Four-Style Rotation Model - Annualized return: 13.29% - Annualized volatility: 21.53% - Maximum drawdown: 47.91% - Sharpe ratio: 0.6001 - Excess return (2025 YTD): -3.25%[45][46]

Core Viewpoint - The article discusses the significant discount in the futures market compared to previous years and the higher implied volatility of put options compared to call options, suggesting a potential pessimistic outlook among investors. It proposes a quantitative timing strategy based on the synthetic underlying price of ETF options to address these issues [1]. Group 1: Concepts of Premium and Discount - The premium and discount of stock index futures is defined as the difference between futures prices and spot prices, with a positive value indicating a premium and a negative value indicating a discount. The annualized premium rate is often used for better comparison [2]. - The seasonal discount phenomenon in stock index futures is attributed to dividend payouts from constituent stocks, which can lead to a natural decline in the index and is particularly evident from May to September [2]. Group 2: Synthetic Underlying of ETF Options - The price of the synthetic underlying for ETF options can be expressed using the call option price, strike price, and put option price. The premium or discount rate is calculated as the difference between the synthetic price and the underlying ETF price [3]. - There is a strong positive correlation (over 0.97) between the annualized premium rate of the synthetic underlying of ETF options and the annualized premium rate of stock index futures after excluding dividends, indicating that the synthetic underlying may provide a more accurate reflection of market expectations [3]. Group 3: Quantitative Timing Strategy Backtest Results - The strategy suggests that when the valuation of put options is significantly higher than that of call options, it does not necessarily indicate a market downturn. Instead, it may present a buying opportunity [4]. - The strategy is based on the premise that when the ETF synthetic underlying futures premium is at a historical low, it indicates excessive pessimism, and a potential rebound may occur, prompting a buy signal for the next trading day [4]. Group 4: Historical Backtest Performance - The strategy has shown significant outperformance compared to the underlying ETFs since 2018, with an annualized return of 19.05% and a maximum drawdown of -17.83% when trading the Huatai-PineBridge 300 ETF [6]. - The cumulative return of the timing strategy reached 142.9%, significantly higher than the 51.8% return of the IC monthly contract and 2.52% of the 500 ETF [6]. Group 5: Summary - The article highlights the relationship between the synthetic underlying of ETF options and stock index futures, emphasizing the effectiveness of a quantitative timing strategy based on the synthetic premium. The results indicate that significant discounts in the futures market do not necessarily signal a sell-off but rather present opportunities for long positions [12].

Quantitative Models and Construction Methods 1. Model Name: Short-term Timing Model - **Model Construction Idea**: The model aims to provide short-term market timing signals based on various market indicators. - **Model Construction Process**: - **Fundamental Indicators**: - Manufacturing PMI: Current value is 49.70, at the 44.92% percentile over the past 5 years, giving a neutral signal[17] - RMB medium and long-term loan balance growth rate: Current value is 6.78%, at the 0.00% percentile over the past 5 years, giving a cautious signal[17] - M1 growth rate: Current value is 2.30%, at the 77.97% percentile over the past 5 years, giving an optimistic signal[17] - **Valuation Indicators**: - PE median: Current value is 40.16, at the 92.80% percentile over the past 5 years, giving a neutral signal[18] - PB median: Current value is 2.68, at the 71.05% percentile over the past 5 years, giving a neutral signal[18] - **Sentiment Indicators**: - Beta dispersion: Current value is -0.59%, at the 40.68% percentile over the past 5 years, giving a neutral signal[20] - Volume sentiment score: Current value is 0.30, at the 72.70% percentile over the past 5 years, giving an optimistic signal[20] - Volatility: Current value is 11.57% (annualized), at the 12.99% percentile over the past 5 years, giving a neutral signal[20] - **Liquidity Indicators**: - Monetary rate indicator: Current value is -0.10, at the 33.90% percentile over the past 5 years, giving an optimistic signal[20] - Exchange rate expectation indicator: Current value is -0.09%, at the 40.68% percentile over the past 5 years, giving a neutral signal[20] - Average new financing amount over 5 days: Current value is 23.20 billion, at the 80.81% percentile over the past 5 years, giving a neutral signal[20] - **Model Evaluation**: The model provides a comprehensive view of short-term market conditions by integrating fundamental, valuation, sentiment, and liquidity indicators. 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model aims to rotate between growth and value styles based on economic cycles and market conditions. - **Model Construction Process**: - **Fundamental Indicators**: - Profit cycle slope: High, favoring growth[32] - Interest rate cycle level: High, favoring value[32] - Credit cycle trend: Weak, favoring value[32] - **Valuation Indicators**: - PE valuation difference: 5-year percentile is 15.19%, favoring growth[32] - PB valuation difference: 5-year percentile is 34.08%, favoring growth[32] - **Sentiment Indicators**: - Turnover difference: 5-year percentile is 21.01%, favoring value[32] - Volatility difference: 5-year percentile is 20.58%, favoring balanced allocation[32] - **Model Evaluation**: The model effectively captures the rotation between growth and value styles by considering fundamental, valuation, and sentiment factors. 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model aims to rotate between small-cap and large-cap styles based on economic cycles and market conditions. - **Model Construction Process**: - **Fundamental Indicators**: - Profit cycle slope: High, favoring small-cap[36] - Interest rate cycle level: High, favoring large-cap[36] - Credit cycle trend: Weak, favoring large-cap[36] - **Valuation Indicators**: - PE valuation difference: 5-year percentile is 80.60%, favoring large-cap[36] - PB valuation difference: 5-year percentile is 99.59%, favoring large-cap[36] - **Sentiment Indicators**: - Turnover difference: 5-year percentile is 54.26%, neutral[36] - Volatility difference: 5-year percentile is 83.71%, favoring large-cap[36] - **Model Evaluation**: The model provides a balanced approach to rotating between small-cap and large-cap styles by integrating fundamental, valuation, and sentiment indicators. 4. Model Name: Four-Style Rotation Model - **Model Construction Idea**: The model combines the growth-value and small-cap vs. large-cap rotation models to provide a comprehensive allocation across four styles. - **Model Construction Process**: - **Allocation Recommendation**: - Small-cap growth: 12.5%[41] - Small-cap value: 37.5%[41] - Large-cap growth: 12.5%[41] - Large-cap value: 37.5%[41] - **Model Evaluation**: The model offers a diversified approach to style rotation, leveraging insights from both growth-value and small-cap vs. large-cap models. Model Backtest Results Short-term Timing Model - Annualized Return: 16.58%[26] - Annualized Volatility: 14.57%[26] - Maximum Drawdown: 27.70%[26] - Sharpe Ratio: 0.9889[26] - Monthly Win Rate: 69.74%[26] - Quarterly Win Rate: 69.23%[26] - Annual Win Rate: 85.71%[26] Growth-Value Style Rotation Model - Annualized Return: 11.67%[35] - Annualized Volatility: 20.84%[35] - Maximum Drawdown: 43.07%[35] - Sharpe Ratio: 0.5387[35] - Monthly Win Rate: 58.28%[35] - Quarterly Win Rate: 60.78%[35] Small-Cap vs. Large-Cap Style Rotation Model - Annualized Return: 12.21%[40] - Annualized Volatility: 22.73%[40] - Maximum Drawdown: 50.65%[40] - Sharpe Ratio: 0.5336[40] - Monthly Win Rate: 60.93%[40] - Quarterly Win Rate: 58.82%[40] Four-Style Rotation Model - Annualized Return: 13.17%[43] - Annualized Volatility: 21.58%[43] - Maximum Drawdown: 47.91%[43] - Sharpe Ratio: 0.5895[43] - Monthly Win Rate: 59.60%[43] - Quarterly Win Rate: 62.75%[43] - Annual Win Rate: 69.23%[43]