Quantitative Models and Construction Methods 1. Model Name: Composite Signal Model - **Model Construction Idea**: The composite signal model integrates multiple factors, including supply, demand, inventory, and price spread, to generate a comprehensive signal for market positioning [3][7] - **Model Construction Process**: - The model aggregates signals from individual factors such as supply, demand, inventory, and price spread - Each factor contributes to the overall signal based on its respective weight and directional strength - The final composite signal is determined by summing the weighted contributions of these factors [3][7] - **Model Evaluation**: The model provides a balanced view by incorporating multiple dimensions of market dynamics, but its effectiveness depends on the relative strength and alignment of individual factors [3][7] 2. Model Name: Momentum Model - **Model Construction Idea**: The momentum model captures price trends over time (time-series momentum) and across assets (cross-sectional momentum) to identify potential trading opportunities [3][4] - **Model Construction Process**: - **Time-Series Momentum**: Measures the directional strength of price movements within a single asset over a specific period - **Cross-Sectional Momentum**: Compares the relative performance of multiple assets to identify outperformers and underperformers - The model assigns scores to assets based on their momentum strength and aggregates these scores for sector-level analysis [3][4] - **Model Evaluation**: The model effectively identifies trend-following opportunities but may underperform in mean-reverting or range-bound markets [3][4] --- Backtesting Results of Models 1. Composite Signal Model - Weekly Return: 1.33% [7] - Monthly Return: 2.04% [7] 2. Momentum Model - Sector-Level Performance: - Black Metals: Time-Series Momentum (0), Cross-Sectional Momentum (0.09) [4] - Non-Ferrous Metals: Time-Series Momentum (0.05), Cross-Sectional Momentum (-0.21) [4] - Energy and Chemicals: Time-Series Momentum (-0.02), Cross-Sectional Momentum (0.18) [4] - Agricultural Products: Time-Series Momentum (0.13), Cross-Sectional Momentum (0.35) [4] - Stock Indices: Time-Series Momentum (-0.71), Cross-Sectional Momentum (0.46) [4] - Precious Metals: Time-Series Momentum (0.12), Cross-Sectional Momentum (N/A) [4] --- Quantitative Factors and Construction Methods 1. Factor Name: Supply Factor - **Factor Construction Idea**: Measures the supply-side dynamics of commodities, including production levels and inventory changes [3][7] - **Factor Construction Process**: - Tracks production data, such as operating rates and output levels - Incorporates inventory trends to assess supply-side pressure - Aggregates these metrics into a single supply signal [3][7] - **Factor Evaluation**: The factor effectively captures supply-side influences but may lag in responding to sudden disruptions [3][7] 2. Factor Name: Demand Factor - **Factor Construction Idea**: Evaluates demand-side conditions using consumption data and downstream activity levels [3][7] - **Factor Construction Process**: - Monitors downstream consumption metrics, such as procurement volumes and production activity - Aggregates these indicators to generate a demand signal [3][7] - **Factor Evaluation**: The factor provides insights into consumption trends but may be influenced by seasonal variations [3][7] 3. Factor Name: Inventory Factor - **Factor Construction Idea**: Tracks inventory levels to assess market balance and potential price pressure [3][7] - **Factor Construction Process**: - Collects inventory data from key regions and aggregates it into a composite signal - Differentiates between regional inventory trends to identify localized imbalances [3][7] - **Factor Evaluation**: The factor is useful for identifying supply-demand mismatches but may not fully capture speculative inventory behavior [3][7] 4. Factor Name: Price Spread Factor - **Factor Construction Idea**: Analyzes price differentials across contracts or regions to identify arbitrage opportunities [3][7] - **Factor Construction Process**: - Calculates the spread between near-month and far-month contracts or between regional prices - Normalizes the spread to account for seasonal and structural differences - Aggregates the spread data into a directional signal [3][7] - **Factor Evaluation**: The factor is effective in identifying relative value opportunities but may be sensitive to short-term noise [3][7] 5. Factor Name: Profit Factor - **Factor Construction Idea**: Measures profitability dynamics in production and processing activities [7] - **Factor Construction Process**: - Tracks input costs and output prices to calculate profit margins - Aggregates margin data across regions and production methods to generate a profit signal [7] - **Factor Evaluation**: The factor captures economic incentives but may lag in responding to rapid cost or price changes [7] --- Backtesting Results of Factors 1. Supply Factor - Weekly Return: 1.92% [7] - Monthly Return: 2.04% [7] 2. Demand Factor - Weekly Return: -1.28% [7] - Monthly Return: 1.14% [7] 3. Inventory Factor - Weekly Return: 0.00% [7] - Monthly Return: 2.77% [7] 4. Price Spread Factor - Weekly Return: 0.00% [7] - Monthly Return: 1.50% [7] 5. Profit Factor - Weekly Return: 1.89% [7] - Monthly Return: 0.26% [7]

Quantitative Models and Construction 1. Model Name: Short-Cycle Model - **Model Construction Idea**: Focuses on high-frequency financial data, including market style, external factors, and liquidity[16] - **Model Construction Process**: - Selects effective and relatively independent factors from high-dimensional data - Incorporates subjective analysis frameworks to build models with out-of-sample generalization capabilities - Signals are derived from the weighted combination of three independent models, with signal strength ranging from 0 to 1[16][17] - Formula for signal combination: $\text{Comprehensive Signal Strength} = w_1 \cdot \text{Model 1 Signal} + w_2 \cdot \text{Model 2 Signal} + w_3 \cdot \text{Model 3 Signal}$ - Positions are determined based on signal thresholds: - Long positions for signals ≥ 0.6 - Short positions for signals ≤ 0.4[17] - **Model Evaluation**: Focuses on high-frequency data and provides actionable signals for short-term trading[16] 2. Model Name: Long-Cycle Model - **Model Construction Idea**: Focuses on low-frequency macroeconomic data and market expectations[16] - **Model Construction Process**: - Similar to the short-cycle model, selects independent factors from high-dimensional data - Incorporates macroeconomic indicators to capture long-term trends - Signals are combined with weights similar to the short-cycle model[16][17] - **Model Evaluation**: Provides insights into long-term market trends and complements the short-cycle model[16] 3. Model Name: Cross-Asset Arbitrage Strategy (N-S Model) - **Model Construction Idea**: Combines a fundamental three-factor model with a trend regression model to generate trading signals[22] - **Model Construction Process**: - Fundamental model based on Nelson-Siegel instantaneous forward rate function: $\mathbf{R}(t) = \beta_{0} + \beta_{1}\frac{1-e^{-t/\tau}}{t/\tau} + \beta_{2}\left(\frac{1-e^{-t/\tau}}{t/\tau} - e^{-t/\tau}\right)$ - $\beta_0$: Level factor, representing market expectations of forward rates - $\beta_1$: Slope factor, representing bond risk premiums - $\beta_2$: Curvature factor, representing convexity deviations - Principal Component Analysis (PCA) and logistic regression are used to classify signals into three categories: - '1': Large spread likely to decrease - '0': Uncertain or oscillating spread - '-1': Large spread likely to increase - Trend regression model filters signals, and trades are executed when signals resonate - Duration-neutral adjustments are made for 10Y-5Y spreads with a 1:1.8 ratio[22] - **Model Evaluation**: Combines fundamental and trend-based approaches, enhancing signal reliability[22] --- Backtesting Results of Models 1. Short-Cycle Model - **Comprehensive Signal Strength**: - IF: 0.53 - IH: 0.52 - IC: 0.53 - IM: 0.55 - T: 0.53 - TF: 0.54[17] 2. Long-Cycle Model - **Comprehensive Signal Strength**: - IF: 0.52 - IH: 0.51 - IC: 0.53 - IM: 0.54 - T: 0.50 - TF: 0.48[17] 3. Cross-Asset Arbitrage Strategy (N-S Model) - **Trading Signals**: - May 27: -1 (N-S Model), 0 (Trend Regression) - May 28: 0 (N-S Model), 0 (Trend Regression) - May 29: 0 (N-S Model), 0 (Trend Regression) - May 30: 0 (N-S Model), 0 (Trend Regression) - May 31: -1 (N-S Model), 0 (Trend Regression)[25] --- Quantitative Factors and Construction 1. Factor Name: Inflation Indicators - **Factor Construction Idea**: Measures inflationary pressures using commodity prices and indices[3] - **Factor Construction Process**: - Includes metrics such as vegetable basket price index, refined copper prices, and natural gas import prices - Historical percentiles and correlations with stock and bond indices are calculated[3] - **Factor Evaluation**: Captures inflation trends and their impact on financial markets[3] 2. Factor Name: Liquidity Indicators - **Factor Construction Idea**: Tracks short-term liquidity conditions using interbank rates and the USD index[4] - **Factor Construction Process**: - Includes DR007, DR001, SHIBOR, and USD index - Historical percentiles and correlations with stock and bond indices are calculated[4] - **Factor Evaluation**: Reflects the availability of liquidity in the financial system[4] 3. Factor Name: Market Sentiment Indicators - **Factor Construction Idea**: Measures investor sentiment using financing balances and trading volumes[6][7] - **Factor Construction Process**: - Stock market sentiment: Financing balances, margin trading balances, and net purchases via Stock Connect - Bond market sentiment: 10Y government bond yields, credit spreads, and trading volumes[6][7] - **Factor Evaluation**: Provides insights into market risk appetite and sentiment shifts[6][7] --- Backtesting Results of Factors 1. Inflation Indicators - **Historical Percentiles**: - Vegetable Basket Index: 0.05 - Refined Copper Prices: 0.98 - Natural Gas Import Prices: 0.40[3] 2. Liquidity Indicators - **Historical Percentiles**: - DR007: 0.45 - DR001: 0.68 - USD Index: 0.70[4] 3. Market Sentiment Indicators - **Historical Percentiles**: - Stock Market Sentiment: Financing Balance: 0.27, Margin Trading Balance: 0.00 - Bond Market Sentiment: 10Y Government Bond Yield: 0.04, Credit Spread: 0.29[6][7]

Quantitative Models and Construction Methods 1. Model Name: Composite Signal Model - **Model Construction Idea**: The model integrates multiple factors to generate a comprehensive signal for trading decisions[3][7] - **Model Construction Process**: - **Demand Factor**: Influenced by the utilization rate of MTO capacity and the ex-factory price of acetic acid[3] - **Supply Factor**: Determined by methanol port arrivals and inland methanol plant operations[3] - **Inventory Factor**: Based on significant port destocking[3] - **Price Spread Factor**: Influenced by the price spread between different regions[6] - **Profit Factor**: Affected by the daily income of float glass production[7] - **Model Evaluation**: The model provides a comprehensive signal by balancing multiple factors, which helps in making informed trading decisions[3][7] Model Backtesting Results Composite Signal Model - **Demand Factor**: Last week's return: 0.31%, Monthly return: 1.82%[7] - **Supply Factor**: Last week's return: 0.00%, Monthly return: 1.01%[7] - **Inventory Factor**: Last week's return: 0.00%, Monthly return: -0.11%[7] - **Price Spread Factor**: Last week's return: 0.00%, Monthly return: 1.49%[7] - **Profit Factor**: Last week's return: -0.39%, Monthly return: -1.05%[7] Quantitative Factors and Construction Methods 1. Factor Name: Momentum Factor - **Factor Construction Idea**: The factor is based on the momentum of different commodity sectors[1][4] - **Factor Construction Process**: - **Time-Series Momentum**: Measures the momentum over a specific period[1][4] - **Cross-Sectional Momentum**: Compares the momentum across different commodities[1][4] - **Formula**: $$ \text{Momentum} = \frac{P_t - P_{t-n}}{P_{t-n}} $$ where \( P_t \) is the current price and \( P_{t-n} \) is the price \( n \) periods ago[1][4] - **Factor Evaluation**: The momentum factor helps in identifying the strength and direction of price movements, aiding in trend-following strategies[1][4] 2. Factor Name: Term Structure Factor - **Factor Construction Idea**: The factor is based on the term structure of futures contracts[1][4] - **Factor Construction Process**: - **Near-Month vs. Far-Month Contracts**: Compares the prices of near-month and far-month futures contracts[1][4] - **Formula**: $$ \text{Term Structure} = \frac{F_{near} - F_{far}}{F_{far}} $$ where \( F_{near} \) is the price of the near-month contract and \( F_{far} \) is the price of the far-month contract[1][4] - **Factor Evaluation**: The term structure factor helps in understanding the market's expectations of future price movements and the cost of carry[1][4] 3. Factor Name: Open Interest Factor - **Factor Construction Idea**: The factor is based on the open interest of futures contracts[1][4] - **Factor Construction Process**: - **Open Interest Analysis**: Measures the total number of outstanding contracts[1][4] - **Formula**: $$ \text{Open Interest} = \sum_{i=1}^{N} OI_i $$ where \( OI_i \) is the open interest of the \( i \)-th contract[1][4] - **Factor Evaluation**: The open interest factor indicates the level of market participation and liquidity, which can signal the strength of a trend[1][4] Factor Backtesting Results Momentum Factor - **Black Sector**: Time-Series: 1.46, Cross-Sectional: 0.33[4] - **Non-Ferrous Sector**: Time-Series: 0.85, Cross-Sectional: 2.49[4] - **Energy Sector**: Time-Series: -0.38, Cross-Sectional: 0.00[4] - **Agricultural Sector**: Time-Series: -0.60, Cross-Sectional: 0.81[4] - **Stock Index Sector**: Time-Series: 0.06, Cross-Sectional: 0.78[4] - **Precious Metals Sector**: Time-Series: 1.01, Cross-Sectional: 2.56[4] Term Structure Factor - **Black Sector**: 1.10[4] - **Non-Ferrous Sector**: 0.83[4] - **Energy Sector**: 1.03[4] - **Agricultural Sector**: 1.83[4] - **Stock Index Sector**: -0.25[4] Open Interest Factor - **Black Sector**: 0.33[4] - **Non-Ferrous Sector**: 0.92[4] - **Energy Sector**: 1.46[4] - **Agricultural Sector**: -1.82[4] - **Stock Index Sector**: 1.37[4]

金融衍生品周度报告 主观多头走强 CTA周报 国投安信期货有限公司 第 1 页 版权所有，转载请注明出处 2024年5月20日 截至2024/05/10当周，权益、债券与商品市场周度涨跌幅分别为 1.59%、-0.13%、-0.40%； 私募基金市场方面，本周主要策略指 数延续上升趋势，权益类策略收益表现最佳。CTA方面，本周CTA 趋势、套利以及复合指数涨跌幅分别为0.15%、-0.05%与0.67%。 策略拥挤方面，本周权益策略走高，债券策略持平，CTA小幅上行。 子策略部分，CTA中量化与主观趋势有所分化，其中主观趋势维持在 较高拥挤水平，权益部分指增与量化多头上升，主观多头走低，市场 中性策略小幅下降。 近一周除周期外其余风格管理均跑赢市场，其中稳定风格管理人表现 最佳，超额收益率为2.64%，拥挤度波动回升，稳定与金融风格升幅 显著。 Barra因子：截止2024/5/17当周，本周分红与ALPHA因子表现较 优，超额收益率为2.63%。结合模型评分变化结果周期与消费风格环 比上升，稳定风格走弱，当前整体偏好消费与成长风格。本周五风格 择时策略收益率为-0.51%，对比基准均衡配置暂无超额。 基金市 ...