Group 1: Overseas Quantitative Dynamics - The impact of tariff events has led to significant drawdowns for quantitative hedge funds, with Renaissance Institutional Equities Fund experiencing an approximately 8% decline in early April despite a 22.7% increase in 2024 [1][2] - Man Group's trend-following strategy also faced over a 10% drawdown, prompting a return to in-office work for some researchers to enhance strategy intervention [1] - Systematica Investments, founded by Leda Braga, saw a 20% drawdown in early April, highlighting the vulnerability of trend-following strategies during such events [1] Group 2: Adoption of AI in Quantitative Strategies - AQR has begun to embrace AI in investment decisions, acknowledging its potential for higher returns despite challenges in explanation during drawdowns [3] - In contrast, domestic private quantitative firms in China are utilizing AI more extensively, with teams like Baiont Quant employing fully self-developed AI algorithms for minute-to-hour level return predictions [3] Group 3: Market Uncertainty and Quantitative Strategies - BlackRock emphasizes the importance of adjusting models to cope with increasing global uncertainty, identifying three main uncertainties in tariff policies: target, scale, and timeline [6] - The evolution of BlackRock's quantitative investment system has led to a more granular approach to risk exposure, now incorporating over a thousand risk factors [7] - BlackRock's strategy focuses on maintaining neutrality in risk exposure while seeking short-term reversal opportunities in a high uncertainty environment [8] Group 4: Macro Hedge Fund Perspectives - Bridgewater highlights the impact of "modern mercantilism" on investment portfolios, noting the challenges posed by chaotic implementation processes and the unique risks facing U.S. assets [10] - Despite recent market volatility, Bridgewater believes that asset prices have not undergone substantial adjustments, indicating potential future opportunities [10] - The interaction between AI development and modern mercantilism is seen as a new dynamic, with AI potentially offsetting some negative impacts on productivity [11] Group 5: AQR's Investment Focus - AQR suggests that high volatility factors, while challenging to maintain, can yield significant long-term Sharpe ratios, advocating for the acceptance of these factors [12][16] - The firm recommends focusing on small-cap stocks, particularly in emerging markets, due to their lower valuations and potential for higher returns compared to U.S. large-cap stocks [19] Group 6: Performance Tracking of Quantitative Products - Factor rotation products from BlackRock and Invesco have outperformed their respective indices over the past five years, with BlackRock's adaptive factor selection demonstrating resilience [21][24] - The performance of machine learning-based ETFs has varied, with QRFT showing strong results in certain months while AIEQ continues to experience significant drawdowns [39] - Bridgewater's All Weather ETF faced notable drawdowns due to tariff events but has since recovered, indicating resilience in its strategy [40]

- AQR has started to embrace AI in its investment decisions, using more AI algorithms to potentially provide higher returns despite occasional difficulties in explaining drawdowns[11] - BlackRock's quantitative system aims to identify more granular risk factors and maintain neutrality to most risks, while seeking short-term reversal opportunities in dense market trading to outperform the market[1][15][16] - Bridgewater is focusing on the impact of "modern mercantilism" on asset prices, noting that U.S. assets still face significant uncertainty and highlighting the strong allocation value of gold[21][22] Quantitative Models and Construction Methods 1. **Model Name: BlackRock's Safety Engineering System** - **Construction Idea**: To handle high uncertainty by identifying more granular risk factors and maintaining neutrality to most risks - **Construction Process**: - The system has evolved to control risk exposure not only to conventional factors like market cap, momentum, and growth value but also to thousands of more granular risk factors such as Japan export factor and domestic demand stock factor - The system adjusts these factors based on macroeconomic changes and increases the frequency of monitoring event-related factors to hourly or minute levels - **Evaluation**: The system's performance during the pandemic demonstrated that broader data dimensions and more precise risk control are more important than complex models[15][16][17] 2. **Model Name: AQR's High Volatility Factor Model** - **Construction Idea**: To embrace high volatility factors for their long-term Sharpe ratio despite short-term drawdowns - **Construction Process**: - AQR uses the variance ratio to measure the volatility level of factors: $ \text{Variance Ratio} = \frac{\text{Annual Factor Return Variance}}{\text{Monthly Factor Return Variance} \times 12} $ - Factors with higher variance ratios are considered high volatility factors - AQR analyzed 13 major categories and 153 sub-factors for their variance ratios and Sharpe ratios - **Evaluation**: Long-term high volatility factors show a significant positive correlation with Sharpe ratios, suggesting that quantitative managers should embrace these factors and use diversification to reduce short-term volatility[23][24][25] Model Backtesting Results 1. **BlackRock's Safety Engineering System** - **Information Ratio (IR)**: - Economic regime: 1.02 - Valuation: 0.77 - Sentiment: 0.43 - Growth timing: 1.06 - Aggregate signal: 1.83 - **Max Drawdown**: - Economic regime: -2.5% - Valuation: -3.4% - Sentiment: -4.2% - Growth timing: -2.7% - Aggregate signal: -1.9%[40] 2. **AQR's High Volatility Factor Model** - **Variance Ratio**: - Debt Issuance: 1.8 - Accruals: 1.6 - Profitability: 1.5 - Low Leverage: 1.4 - Investment: 1.4 - Profit Growth: 1.4 - Value: 1.4 - Core Stream Size: 1.2 - Quality: 1.2 - Seasonality: 1.1 - Low Risk: 1.0 - Momentum: 1.0 - Short-Term Reversal: 0.9 - **Sharpe Ratio**: - Debt Issuance: 0.7 - Accruals: 0.6 - Profitability: 0.3 - Low Leverage: 0.0 - Investment: 0.4 - Profit Growth: 0.4 - Value: 0.4 - Core Stream Size: 0.0 - Quality: 0.4 - Seasonality: 0.2 - Low Risk: 0.1 - Momentum: 0.3 - Short-Term Reversal: 0.1[24][25][27]