Core Insights - The report emphasizes the importance of asset allocation strategies based on both long-term and short-term perspectives, utilizing historical data to optimize investment portfolios [21][22][23]. Asset Performance Overview - Equity assets showed strong performance, with the CSI 300 Index and Nasdaq 100 Index rising by 3.5% and 2.4% respectively over the past month, while the Indian Sensex 30 Index declined by 2.9% [7][6]. - In the bond market, government bond yields increased, leading to a 0.2% decline in the government bond index, while corporate bond indices remained stable due to narrowing credit spreads [12][11]. - Commodity assets experienced a 3.8% increase in the South China Commodity Index in July, although gold prices fluctuated, ending the month nearly flat [17][16]. Asset Allocation Strategies - The report suggests a debt-oriented asset allocation strategy comprising 10% Asia-Pacific emerging market stocks, 80% corporate bonds, and 10% gold [28]. - A mixed asset allocation strategy is recommended, including 23% Nasdaq 100 Index, 7% CSI 300 Index, 40% corporate bonds, and 30% commodities [28]. Strategy Performance Tracking - From April 2015 to July 2025, the mean-variance model strategy achieved an annualized return of 6.81% with a maximum drawdown of 3.6% and a Sharpe ratio of 2.76 [25]. - The strategy's performance from January 2025 to July 2025 yielded a cumulative return of 1.97%, with a notable return of -0.15% in July due to insufficient bond contributions and declines in the Indian market index [25][27]. Model Utilization - The report employs a mean-variance model for long-term asset allocation, which outperforms constant mix strategies, and integrates the Black-Litterman model to enhance return stability by combining historical and recent performance data [22][23][24].

Summary of Key Points from the Conference Call Industry or Company Involved - The conference call focuses on the **AI investment strategies** and **ETF market** in the context of the **capital market** as discussed by **Huatai Securities** during their **2025 Spring Strategy Meeting**. Core Points and Arguments - **AI Strategies in Investment**: AI strategies significantly enhance traditional multi-factor models by processing vast amounts of data and complex factors, particularly in volume and price data analysis, optimizing investment decisions [1][4][9]. - **Acceptance of AI in Asset Management**: The asset management industry is increasingly accepting AI strategies, particularly those based on statistical models, due to their strong performance. However, the ability of reasoning-based large language models to reach expert-level performance remains to be validated [1][13][14]. - **ETF Market Growth**: The ETF market has surpassed **3.8 trillion yuan**, with a focus on smart beta strategies to achieve stable returns through industry rotation and asset allocation models [1][22]. - **Investment Strategy Focus**: Huatai Securities emphasizes a robust return strategy, primarily focusing on bond investments, and utilizes global asset allocation models and qualitative analysis for market judgment [1][27]. - **Industry Rotation Strategy**: The industry rotation strategy combines macro, meso, and micro factors with AI identification and qualitative analysis, favoring technology, consumer, and pharmaceutical sectors while adjusting investment targets based on significant events like the Two Sessions [3][31]. - **AI's Role in Financial Engineering**: AI enhances traditional multi-factor frameworks by integrating diverse data types, leading to more precise and efficient data analysis, thus optimizing portfolio design and improving returns while reducing risks [7][18]. - **Performance of AI in Quantitative Investment**: AI strategies outperform traditional multi-factor methods by effectively aggregating information and conducting global analyses, leading to superior excess returns [9][12]. - **Future of Large Models in Finance**: Large models like DeepSeek and ChatGPT show potential in subjective analysis, suggesting a new paradigm of combining subjective and quantitative investment approaches, although their expert-level capabilities need further validation [11][15]. - **ETF Product Development**: Huatai Securities is committed to providing ETF products and solutions, focusing on smart beta strategies and offering professional services, including market reports and strategy analyses [1][23]. Other Important but Possibly Overlooked Content - **Historical Context of AI in Quantitative Investment**: The application of AI in quantitative investment began around 2003, evolving through various phases, with significant adoption starting in 2017, leading to substantial investment returns [2][13]. - **Impact of Two Sessions on Market**: The analysis of the Two Sessions' impact on the market involves reviewing historical key topics and market performance, indicating that different time periods around the event affect market dynamics [32]. - **Investment Heat and Valuation Levels**: The current investment heat in AI-related sectors is at historical highs, with significant trading activity and valuation levels, necessitating cautious investment strategies [62][64]. - **Differentiation of Index Products**: Index products vary significantly in valuation levels and stock resonance, suggesting that investors should choose based on their risk appetite and investment strategy [68][70]. - **Performance of Active Equity Fund Managers**: Different fund managers exhibit varying performance in the AI sector, categorized into stable allocation, focused sector, and flexible adjustment types, highlighting the importance of selecting managers based on their stability and risk-return profile [73][74]. This summary encapsulates the essential insights from the conference call, providing a comprehensive overview of the discussions surrounding AI investment strategies and the ETF market.