AI赋能资产配置（三十四）：首发：AI+多资产泛量化系列指数

Core Insights - The report emphasizes the transformative role of Agentic AI in enhancing the asset allocation process through a fully automated "pan-quantitative" strategy development, allowing non-programmers to engage in data collection, signal generation, strategy construction, and backtesting [4][6]. - The introduction of AI-driven Black-Litterman asset allocation strategies has significantly outperformed equal-weight benchmarks, with annualized returns of 18.29% and 20.37% for DeepSeek-V3 and Qwen2.5-72B models, respectively, compared to the benchmark's 11.85% [6][57]. - AI-enhanced risk parity models have shown improved risk control without increasing volatility, achieving an annualized return of 4.71% with a Sharpe ratio increase from 1.39 to 1.46 [6][68]. Group 1: Agentic AI and Quantitative Empowerment - Agentic AI facilitates a complete quantitative strategy development process, enabling researchers and investors to automate the investment research workflow through natural language interactions [4][5]. - The AI-driven process begins with knowledge decomposition and cross-domain mapping, followed by task clarification and execution, resulting in standardized and reproducible quantitative outcomes [5][20]. - The shift from traditional methods to AI tools allows for a more structured approach in investment research, focusing on clear objectives and constraints rather than just prompt engineering [16][20]. Group 2: AI-Driven Black-Litterman Asset Allocation - The Black-Litterman model integrates market equilibrium expectations with investor views, utilizing AI to automatically generate asset perspectives based on macroeconomic data and market trends [52][53]. - The model's performance is significantly enhanced by AI-generated views, with the DeepSeek-V3 and Qwen2.5-72B models achieving annualized returns of 18.29% and 20.37%, respectively, compared to the equal-weight benchmark's 11.85% [55][57]. - The AI-enhanced strategy captures short-term market opportunities effectively, leading to superior risk-adjusted returns and reduced maximum drawdowns [57]. Group 3: AI-Enhanced Risk Parity Models - The risk parity strategy aims to allocate weights such that each asset contributes equally to the portfolio's risk, with AI dynamically determining the covariance estimation window based on market conditions [63][64]. - AI models adaptively select optimal window lengths for risk assessment, enhancing the robustness and interpretability of the portfolio [64][68]. - The Qwen-72B model demonstrated improved monthly win rates and risk-adjusted returns without increasing volatility, outperforming traditional fixed-window strategies [68][69].