期货研究报告|量化专题报告 2025-12-17 ETF 视角下的股指期货择时 研究院 量化组 研究员 高天越 李逸资 0755-23887993 liyizi@htfc.com 从业资格号：F03105861 投资咨询号：Z0021365 李光庭 0755-23887993 liguangting@htfc.com 从业资格号：F03108562 投资咨询号：Z0021506 联系人 黄煦然 0755-23887993 huangxuran@htfc.com 从业资格号：F03130959 王博闻 0755-23887993 wangbowen@htfc.com 请仔细阅读本报告最后一页的免责声明 摘要 本报告基于股票型 ETF 数据，通过对不同风险分层下 ETF 交易、份额等数据的分析 与结合进行策略构建。四大品种经不同策略优化后，均取得较好的收益效果。其中， 四因子模型下IH实现年化收益23.64%，夏普1.14，换手率15.35%；IF年化收益21.88%， 夏普 1.05，换手率 14.33%；IC 年化收益 22.17%，夏普 0.94，换手率 15.12%；IM 年 化收益 32.33%，夏普 ...

Quantitative Models and Construction Methods 1. Model Name: Stock Index Futures Timing Model - **Model Construction Idea**: The model uses the basis of stock index futures to reflect market sentiment changes and constructs daily frequency timing signals based on this correlation[7] - **Model Construction Process**: - The model groups and tests the correlation trend between the basis of stock index futures and the index itself - Constructs daily frequency timing signals based on this correlation - As of October 17, 2025, the timing signal based on the basis of the CSI 500 stock index futures remained at 1[31] - **Model Evaluation**: The model effectively captures market sentiment changes and provides timely signals for trading decisions[7] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: The model integrates macro, micro, meso, and derivative signals to form a four-dimensional non-linear timing model[33] - **Model Construction Process**: - The A-share market is divided into nine states based on macro, micro, and meso signals, each corresponding to long and short signals to form a three-dimensional large cycle timing signal - On this basis, the derivative signal generated by the basis of stock index futures is superimposed to form a four-dimensional non-linear timing model - The latest composite multi-dimensional timing signal is long (1)[34] - **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, enhancing the accuracy of timing signals[33] 3. Model Name: Style Enhancement Model - **Model Construction Idea**: The model enhances returns by adding enhancement factors to the multi-style strategy, suppressing single-style fluctuations, and achieving stable excess returns in different cycles[41] - **Model Construction Process**: - The model is based on the multi-style strategy and adds enhancement factors - It dynamically adjusts the weights of different styles to achieve stable excess returns - As of October 17, 2025, the low volatility enhancement strategy achieved an excess return of 6.05%[42] - **Model Evaluation**: The model effectively enhances returns while controlling risks, providing stable performance across different market cycles[41] Model Backtesting Results Stock Index Futures Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Multi-Dimensional Timing Model - **Absolute Return**: Not specified - **Excess Return**: 4.33%[9] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Style Enhancement Model - **Absolute Return**: Not specified - **Excess Return**: 6.05%[8] - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Quantitative Factors and Construction Methods 1. Factor Name: High-Frequency Factor - **Factor Construction Idea**: The factor captures market valuation and sentiment risks using high-frequency data[11] - **Factor Construction Process**: - The factor uses high-frequency data to measure market depth, spread, and price elasticity - Constructs indicators such as average depth, spread, and price elasticity to reflect market liquidity and sentiment - For example, the average depth is calculated as: $$ avg_{depth} = \frac{av1 + bv1}{2} $$ where av1 and bv1 are the sell and buy volumes at the first level of the order book[98] - **Factor Evaluation**: The factor effectively captures market liquidity and sentiment changes, providing valuable insights for trading decisions[11] Factor Backtesting Results High-Frequency Factor - **Absolute Return**: Not specified - **Excess Return**: Not specified - **Annualized Return**: Not specified - **Sharpe Ratio**: Not specified Industry and ETF Rotation Strategy 1. Strategy Name: Industry Rotation Strategy - **Strategy Construction Idea**: The strategy uses quantitative fundamental drivers, quality low volatility style drivers, and distressed reversal industry discovery methods to construct an industry rotation strategy[76] - **Strategy Construction Process**: - Combines industry fundamental rotation, quality low volatility, and distressed reversal three-dimensional industry rotation strategies into an equal-weight portfolio - Selects industries from different dimensions to achieve factor and style complementarity, reducing the risk of a single strategy - As of October 17, 2025, the annualized excess return of the industry rotation strategy based on three-strategy integration was 10.59%, with a Sharpe ratio of 0.74[80] - **Strategy Evaluation**: The strategy effectively combines multiple dimensions to enhance returns while controlling risks, providing stable performance across different market cycles[76] Strategy Backtesting Results Industry Rotation Strategy - **Absolute Return**: Not specified - **Excess Return**: 14.75%[10] - **Annualized Return**: 10.59%[80] - **Sharpe Ratio**: 0.74[80]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The report explores the application value of the intraday characteristics of the 1 - day Treasury bond repurchase rate (GC001) in stock index futures timing. The high - frequency data of GC001 can capture real - time market capital changes, and the information on the capital side has the ability to predict the short - term trend of stock index futures. The constructed high - frequency repurchase rate index has achieved stable performance on four stock index futures, providing a new perspective for stock index timing [2][64]. - The information contained in the repurchase rate can penetrate the overall market liquidity and risk preference. It can provide effective signals leading traditional volume - price indicators, especially during periods of sudden changes in market liquidity, policy windows, or sentiment turning points [5]. - The sensitivity of small and medium - cap stock indices to GC001 signals is higher than that of large - cap stocks, providing a new theoretical basis for cross - variety arbitrage and style rotation strategies [64]. 3. Summary According to the Table of Contents 3.1 Macro and Capital - side Information Wind Vane: Treasury Bond Repurchase Rate - Traditional stock index futures daily - frequency timing research mostly focuses on volume - price factors, while the exploration of factors based on macro - liquidity or short - term capital is relatively insufficient. The repurchase rate contains rich short - cycle market information and can provide effective signals leading traditional volume - price indicators [5]. - The importance of the repurchase rate includes liquidity monitoring, risk - preference mapping, and policy transmission. Its intraday abnormalities can indicate short - term changes in the capital side, and its changes are related to market risk preference and policy adjustments [6]. 3.2 Basic Concepts 3.2.1 GC001 Basic Concept - GC001 represents the trading rate at which investors lend funds through the Shanghai Stock Exchange with Treasury bonds as collateral for a 1 - day term. It can better reflect short - term (1 - day) changes in the capital side, which is consistent with the logic of daily - frequency stock index futures timing [7]. 3.2.2 GC001 Indicator Logic - GC001 reflects the behavior and attitude of institutional investors. When it is at a high level, the capital market is a "seller's market"; when it is at a low level, it is a "buyer's market". Using the minute - level data at the end of the session to construct daily timing factors can effectively capture valuable information [8][9]. 3.2.3 Derivative Indicator Ideas - Basic feature derivatives: Constructed several basic GC001 derivative features at the minute frequency, including average level, volatility level, growth rate, speed, and volume - price correlation coefficient [10]. - Time - stamp factor derivatives: Expanded a series of factor - construction methods containing time and event information based on previous reports and practiced them during the end - of - session trading period of GC001, which can provide more detailed market behavior analysis [11]. 3.3 Back - testing Settings - Target variable: The goal is to construct a daily - timing strategy for stock indices. The back - test includes four stock index futures varieties, and the prediction target is their corresponding open - to - open returns for the next day [12]. - Back - testing time interval: From May 15, 2019, to April 19, 2025 [12]. - Position - adjustment frequency: Daily adjustment. If the timing signal is always long (short), hold long (short) positions without closing [13]. - Margin: 100%, 1 - times leverage [14]. - Handling fee: Two - sided 0.02% [15]. 3.4 Factor Timing Strategy Test - For each of the four stock index futures (IH, IF, IC, IM), multiple excellent repurchase - rate timing factors were presented. Using the direct timing method, the Sharpe ratios in the full sample were all greater than 0.7 [18][29][34][38]. 3.5 Composite Indicator: High - Frequency Repurchase Rate Index - A high - frequency repurchase rate index was constructed for each stock index futures variety, which can provide a timing signal from the perspective of the macro - fundamental aspects such as the capital side, liquidity, and institutional behavior. - The performance of the high - frequency repurchase rate index in long - short timing was shown. For example, in the full - sample long - short trading of all stock index futures varieties, the annualized return was 22.2%, the annualized return in the past 60 days was 35.7% (with a win rate of 55.0%), and the annualized return in the past 120 days was 23.9% (with a win rate of 51.7%) [61]. 3.6 Conclusion - Based on the high - frequency data of the Treasury bond repurchase rate (GC001), timing factors were systematically constructed, and their effectiveness was verified on stock index futures. The intraday characteristics of GC001, especially the end - of - session fluctuations, can provide a unique source of Alpha for stock index timing [64]. - The high - frequency repurchase index achieved stable performance on four stock index futures, and the sensitivity of small and medium - cap stock indices to GC001 signals is higher than that of large - cap stocks [64].