Core Insights - The Bloomberg Investment Management Forum in Shanghai highlighted the rapid transformation of the asset management industry through quantitative research strategies, emphasizing Bloomberg's commitment to this field over the past 30 years [1][4]. Group 1: Macro Quantitative Scenario Analysis - Bloomberg has developed a factor-based macro quantitative scenario analysis model that integrates macroeconomic variables with underlying drivers such as credit risk and demand changes, utilizing a large covariance matrix updated daily to detail asset correlations and risk transmission [4][6]. - Users can customize macro variable impacts and driver weight distributions to simulate investment portfolio performance under various economic conditions [6]. Group 2: Risk Budgeting in Equity Allocation - The application of risk budgeting strategies in global and A-share markets can help mitigate losses during market volatility by adjusting allocations based on low correlation and volatility differences among A-share stocks [7][9]. - This approach aims to create a more balanced and resilient investment portfolio by ensuring each asset contributes equally to overall portfolio risk rather than focusing solely on weight [9]. Group 3: Cross-Asset Investment and Strategy Index Development - The discussion on cross-asset investment strategies highlighted the increasing demand for diversified asset allocation and risk premium management among institutions, with a focus on innovation to inspire investors and reduce risks [10][12]. - Bloomberg supports quantitative teams with data integration, risk analysis, and scenario simulation to enhance strategy development and risk management efficiency [12]. Group 4: Risk Management in Investment Decisions - Effective risk management is crucial in investment decision-making, with strategies based on risk perspectives aiding in capturing alpha and facilitating quantitative backtesting [13][15]. - The use of risk parity methods combined with asset correlations can enhance portfolio robustness, addressing the challenges of return forecasting [15]. Group 5: Factor Investment and Alternative Data - The exploration of factor investment frameworks and the use of alternative data and machine learning to tackle the "factor zoo" challenge were discussed, with innovative factors developed from Bloomberg's supply chain data [17][19]. - The application of deep learning models for dynamic beta estimation shows significant performance improvements over traditional methods, enhancing the predictive capabilities for future volatility and variance [19].