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海外指数对国内股指预测有效性研究:期货择时系列专题(三)
Guo Lian Qi Huo· 2025-09-23 09:33
Report Industry Investment Rating - Not mentioned in the provided content Core Viewpoints of the Report - The study explores the effectiveness of the NASDAQ Golden Dragon China Index in predicting the short - term trends of domestic stock indices. The quantitative timing strategy based on the previous night's performance of the NASDAQ Golden Dragon China Index can significantly outperform the corresponding benchmark indices, with a smoother net - value curve, enhancing returns and reducing the maximum historical drawdown, especially for the CSI 500 and CSI 1000 indices [4][37]. - This research expands investors' strategy toolkits and helps futures and options intraday traders optimize trading decisions and improve trading win - rates [4][37]. Summary by Relevant Catalogs 1. NASDAQ Golden Dragon China Index Introduction - It is a stock index compiled by the NASDAQ to track the stock price performance of Chinese companies listed in the US, regarded as a "barometer" of Chinese new - economy enterprises in US stocks. As of September 23, 2025, it has 73 constituent stocks, including Alibaba and Baidu, covering new - economy sectors such as the Internet, new energy, and consumer services. In terms of the number of constituent stocks, the optional consumer and information technology sectors have relatively large shares [9]. 2. Correlation Analysis between NASDAQ Golden Dragon China Index and Domestic Stock Indices - There is a significant positive correlation (correlation coefficients above 0.65) between the NASDAQ Golden Dragon China Index and the Shanghai 50, SSE 300, CSI 500, and CSI 1000 indices in the past three years, indicating that the previous night's movement of the NASDAQ Golden Dragon China Index affects the next - day movement of domestic stock indices [12][13]. - The Granger causality test on the NASDAQ Golden Dragon China Index and the SSE 300 and CSI 1000 indices shows that the lagged first - order NASDAQ Golden Dragon China Index has a certain predictive effect on domestic stock indices, and it can be used to predict the next - day movement of domestic stock indices statistically [16]. 3. Quantitative Timing Strategy Based on NASDAQ Golden Dragon China Index 3.1 Strategy Basic Logic - When (closing price - opening price)/opening price of the NASDAQ Golden Dragon China Index on the previous day is greater than X%, indicating that the K - line entity is at least a medium - sized positive line, go long on domestic stock indices at the opening price the next day and hold until closing [17]. 3.2 Historical Back - test Performance - **Shanghai 50 Index Timing Strategy**: Since 2018, the strategy has significantly outperformed the Shanghai 50 Index, with a compound annualized return of 7.63% (compared to 0.22% of the Shanghai 50 Index), and the maximum drawdown has decreased from - 44.43% to - 13.21% [19][22]. - **SSE 300 Index Timing Strategy**: The compound annualized return of the strategy is 8.42% (compared to 1.28% of the SSE 300 Index), and the maximum drawdown has decreased from - 45.6% to - 10.07% [23][24]. - **CSI 500 Index Timing Strategy**: The compound annualized return of the strategy can reach 11.05% (compared to 1.65% of the CSI 500 Index), and the maximum drawdown has decreased from - 41.68% to - 9.44% [28][29]. - **CSI 1000 Index Timing Strategy**: The compound annualized return of the strategy can reach 12.74% (compared to 0.63% of the CSI 1000 Index), and the maximum drawdown has decreased from - 45.38% to - 10.51% [33][36]. 4. Conclusion - The strategy based on the NASDAQ Golden Dragon China Index can significantly outperform the corresponding benchmark indices in the past seven - plus years, with a smoother net - value curve, enhancing returns and reducing the maximum historical drawdown, especially effective for the CSI 500 and CSI 1000 indices [37]. - The research expands investors' strategy toolkits and helps futures and options intraday traders optimize trading decisions and improve trading win - rates [37].
申万金工量化择时策略研究系列之三:“趋势”、“震荡”环境的划分与择时策略:以上证指数为例
Group 1 - The report emphasizes the importance of identifying the current market state as either "trend" or "range," which aids in timing and stock selection strategies [4][8][83] - A two-phase, layered diagnostic algorithm is employed to define the index state, utilizing a "zig-zag" algorithm combined with breakpoint correction to distinguish historical performance [4][10][11] - Six feature variables are constructed from price, volume, and volatility dimensions, and machine learning models such as logistic regression and decision trees are used for state prediction, achieving over 80% accuracy in future market state predictions after smoothing [4][31][32] Group 2 - A dynamic position management strategy is designed based on model predictions, switching between "momentum" logic in trend states and "mean-reversion" logic in range states, with weekly adjustments to balance risk and return [4][55][83] - The decision tree model-driven strategy yielded a total return of 77.26% and a Sharpe ratio of 1.12 during the backtesting period from 2020 to 2025, significantly outperforming the buy-and-hold benchmark return of 14.68% [4][55][78] - The report concludes that the decision tree model's strategy not only outperforms the benchmark but also demonstrates lower volatility and maximum drawdown, achieving a Sharpe ratio of 1.12, indicating robust performance [4][83]
广州天钲瀚:量化择时策略,捕捉个股机遇 | 打卡100家小而美私募
私募排排网· 2025-09-15 07:00
本文首发于公众号"私募排排网"。 (点击↑↑ 上图查看详情 ) 编 者按 私募排排网数据显示,截至2025年8月底,管理规模在20亿以下的私募管理人有7200余家,占比超90%,是私募行业数量庞大的中坚力量。私募排 排网推出 「打卡100家小而美私募」 栏目,聚焦管理规模适中、策略特色鲜明的优质私募基金管理人。通过深度解析其投资方法论、风控体系及能力 圈建设,为投资者提供差异化的视角与洞察。本期打卡—— 广州天钲瀚 。 私募排排网数据显示,截至2025年8月底,广州天钲瀚旗下产品1-8月平均收益达***%, 位列广东地区量化私募榜Top10 ; ( 点击查看收益 ) 在0-5亿规模私募中,广州天钲瀚旗下产品1-8月平均收益达***%, 位列量化私募榜Top10。 ( 点击查看收益 ) Part.1 公司概况 广州天钲瀚私募基金管理有限公司成立于2017年3月,坐标广州市天河区。公司深耕于量化投资领域,专注量化标的择时策略,通过模型预测标 的的未来走势,追求通过标的低买高卖产生绝对收益为投资目标。核心团队成员具有多年实操经验,专业覆盖计算机、数学、金融等。 Part.2 核心团队 投资模式 成立时间 管理规模 ...
从结构化视角全新打造市场情绪择时模型——申万金工量化择时策略研究系列之一
申万宏源金工· 2025-08-26 08:01
Core Viewpoint - The article discusses the limitations of traditional market sentiment indicators and proposes a new approach to measure market sentiment through structural indicators, aiming to provide more detailed insights for market timing decisions. Group 1: Market Sentiment Measurement - The existing market sentiment indicators lack sensitivity and are not effective in signaling market reversals, as they are influenced heavily by a limited number of metrics [1][3][9] - The proposed sentiment temperature model consists of five indicators: total turnover rate, trading volume, northbound capital inflow, and volatility indices for options [1][3] - The methodology for constructing the sentiment temperature involves averaging the VIX percentiles and smoothing the data over a five-day period [1] Group 2: Structural Indicators - The article emphasizes the need for structural indicators to better capture market trading characteristics, especially in weak trend environments where investment hotspots shift rapidly [9][10] - Key structural indicators include: - **Industry Turnover Rate Consistency**: Measures the degree of consensus among funds regarding industry sectors, indicating whether market trading behavior is consistent or shifting [11][14] - **Industry Concentration**: Reflects the degree of trading activity concentration in specific sectors, with higher values indicating a lack of diversification in fund preferences [18][20] - **Industry Performance and Turnover Consistency**: Assesses whether the performance of leading sectors aligns with their trading volumes, indicating market sentiment stability [21][24] - **Growth Board Activity**: Indicates risk appetite among investors, with higher activity in the growth sector suggesting bullish sentiment [25][28] Group 3: Financing Data - The financing balance to free float market value ratio serves as a long-term sentiment indicator, with increases suggesting bullish sentiment and decreases indicating bearish sentiment [29][32] - The article also discusses the use of the Relative Strength Index (RSI) as a sentiment indicator, where values above 50 indicate strong buying power [33][34] Group 4: Timing Strategy - The sentiment structure indicators have been tested for their effectiveness in timing strategies, with daily strategies outperforming weekly ones in terms of annualized returns and risk management [91][92] - The backtesting results show that the sentiment indicators can provide significant excess returns compared to the benchmark index, with a notable reduction in drawdown and volatility [91][92]
美元越贬值,A股越新高?美元汇率如何影响大盘走向?
Hu Xiu· 2025-08-25 09:30
Group 1 - The article discusses the relationship between the depreciation of the US dollar and the performance of the stock market, suggesting that a weaker dollar can lead to better stock market performance [1] - It raises the question of whether the exchange rate of the Chinese yuan against the US dollar can influence the A-share market [1] - The article explores the potential for constructing quantitative timing strategies based on macroeconomic indicators and whether these strategies can outperform the market [1]
国泰海通|金工:“2+1”风格择时模型——通过估值、流动性和拥挤度构建量化择时策略
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].
A股趋势与风格定量观察:维持中性看多,兼论量能择时指标有效性
CMS· 2025-08-10 14:39
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]
A股趋势与风格定量观察20250727:估值和情绪尚未过热,维持看多观点-20250727
CMS· 2025-07-27 09:39
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]
ETF期权合成标的在期指多头策略中的应用
Qi Huo Ri Bao Wang· 2025-07-21 00:53
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].
A股趋势与风格定量观察20250706:短期看好但估值压力渐显,低估板块或需接力
CMS· 2025-07-06 08:32
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]