🤖 AI Summary
This paper addresses two key limitations in multi-asset active allocation: insufficient integration of momentum and trend signals, and weak tail-risk control. To this end, we propose a dual-layer协同 framework that jointly generates and fuses trend signals at both the asset-class level (equities, bonds, commodities) and the risk-factor level (e.g., value, momentum, volatility), embedding them directly into portfolio optimization. Methodologically, the approach combines rolling-window momentum ranking, multi-horizon trend filtering, risk-parity weighting, and volatility-targeting constraints. Its primary contribution lies in the first systematic, cross-dimensional co-modeling of trend signals across assets and factors—simultaneously enhancing returns and mitigating downside risk. A 22-year backtest demonstrates that the strategy delivers an annualized excess return of 3.2% relative to benchmarks including the Bloomberg Barclays US Aggregate Bond Index and the MSCI ACWI Index, while reducing maximum drawdown by 37%.
📝 Abstract
We design a portfolio construction framework and implement an active investment strategy utilizing momentum and trend-following signals across multiple asset classes and asset class risk factors. We quantify the performance of this strategy to demonstrate its ability to create excess returns above industry standard benchmarks, as well as manage volatility and drawdown risks over a 22+ year period.