金融工程专题报告：长期布局商品指数，短期增配CTA

Quantitative Models and Construction Methods 1. Model Name: Self-developed Price-Volume Model - Model Construction Idea: The model is designed to assess the short-term trend sustainability and potential reversal risks in the commodity market by analyzing price and volume data[28] - Model Construction Process: The model evaluates two key indicators: 1. Market Trend Smoothness: This measures the smoothness of price trends across various commodities. When the smoothness indicator reaches historical highs, it suggests that most commodities have extended trends and are at risk of trend reversal. 2. Market Expectation Degree: This measures the degree to which market prices are driven by expectations or sentiment rather than fundamental factors. A high expectation degree indicates that prices are primarily sentiment-driven, increasing the risk of trend reversal if market sentiment changes. Both indicators are calculated using historical data and are benchmarked against their historical distributions to identify extreme values[28][30][32] - Model Evaluation: The model effectively identifies extreme market conditions, providing valuable insights for risk management and allocation adjustments[28][30] --- Model Backtesting Results 1. Self-developed Price-Volume Model - Market Trend Smoothness: The indicator is currently near its historical maximum, signaling a high risk of trend reversal[30] - Market Expectation Degree: The indicator is also at a historically high level, suggesting that current price levels are heavily influenced by market sentiment rather than fundamentals[32] --- Quantitative Factors and Construction Methods 1. Factor Name: Commodity Index/CTA Long-Term Ratio - Factor Construction Idea: This factor measures the relative performance of the commodity index compared to long-term CTA strategies, providing insights into allocation preferences under different market conditions[9][12] - Factor Construction Process: The ratio is calculated as: $ \text{Commodity Index/CTA Long-Term Ratio} = \frac{\text{Commodity Index Value}}{\text{CTA Long-Term Strategy Index Value}} $ Historical data from 2019 to 2024 is used to analyze the ratio's trends and identify key phases of performance divergence between the two[9][12][35] - Factor Evaluation: The factor highlights the complementary nature of commodity indices and CTA strategies, with indices performing better in bull markets and CTAs excelling in bear markets[9][12] --- Factor Backtesting Results 1. Commodity Index/CTA Long-Term Ratio - Phase I (2019/1/2 - 2019/12/27): Ratio increased by 12%[35] - Phase II (2020/4/3 - 2020/9/4): Ratio increased by 13%[35] - Phase III (2020/9/25 - 2021/5/7): Ratio increased by 26%[35] - Phase IV (2021/11/5 - 2022/3/25): Ratio increased by 19%[35] - Phase V (2022/7/15 - 2023/3/3): Ratio increased by 31%[35] - Phase VI (2023/5/26 - 2024/5/17): Ratio increased by 19%[35]