AI赋能资产配置（三）：DeepSeek与风险“再平价”

Core Insights - The report emphasizes the integration of AI in optimizing risk parity strategies, enhancing both annualized returns and Sharpe ratios across various asset classes [5][6][10] - DeepSeek's approach involves adjusting risk contributions, dynamically modifying lookback periods, and optimizing ETF selections to improve portfolio management efficiency and risk control [5][6][10] Group 1: AI Empowered Risk Parity - DeepSeek combines macroeconomic data, capital market indicators, and analyst opinions to optimize asset risk contributions, enhancing the potential returns of portfolios [5] - The annualized return of domestic stock-bond-commodity portfolios improved from 3.85% to 4.2% with a Sharpe ratio increase to 1.137 through risk contribution adjustments [6] - For overseas portfolios, the annualized return rose from 8.11% to 14.15%, with the Sharpe ratio increasing from 0.590 to 1.018 [6] Group 2: Dynamic Lookback Period Adjustments - DeepSeek dynamically adjusts the lookback period based on market cycles, optimizing the risk parity strategy's time window using AI to learn from historical data [12][14] - The report highlights that traditional fixed lookback periods may not adapt well to market changes, while AI can provide a more responsive approach [12][14] - The adjustments led to a significant increase in the annualized return from 3.85% to 4.46% and improved the Sharpe ratio from 1.059 to 1.137 [73] Group 3: ETF Selection Optimization - DeepSeek utilizes traditional indicators and forward-looking market judgments to optimize ETF selections, resulting in an annualized return of 7.18% compared to 6.75% for non-AI selected ETFs [6][10] - The AI-driven selection process considers tracking errors, premium rates, and market volatility to enhance investment outcomes [93][94] - The report outlines a systematic approach to selecting ETFs that minimizes risks associated with market speculation and currency fluctuations [16][93] Group 4: Global Risk Parity Strategy - The report discusses a three-dimensional structure for global risk parity, balancing domestic and overseas assets to achieve risk contribution equilibrium [8][9] - It emphasizes the importance of optimizing asset weights based on volatility and covariance calculations to ensure balanced risk contributions across different asset classes [8][9] - The strategy aims to achieve a stable performance across various market conditions by ensuring that different assets contribute equally to overall risk [8][9] Group 5: Future Outlook and Conclusion - The report concludes that AI's integration into risk parity strategies represents a significant advancement, allowing for more precise adjustments and improved performance metrics [38][41] - It suggests that the ongoing evolution of AI applications in finance will continue to enhance investment strategies and risk management practices [38][41] - The findings indicate a strong potential for AI-driven models to outperform traditional risk parity approaches, highlighting the need for continuous adaptation to market dynamics [38][41]