Summary of Key Points from the Conference Call Industry Overview - The conference call focuses on the Asia Pacific ex Japan and ex China A-shares market, utilizing a quantitative model named MOST to identify stock opportunities. Core Insights - **Quantitative Model MOST**: The model ranks stocks based on over 70 quant factors, focusing on quality, value, and sentiment, and employs a market-/sector-neutral methodology [4][12] - **Performance Metrics**: MOST achieved a 0.5% increase in January 2025 and a cumulative alpha of 19.8% over the past 12 months. The annualized return over the last five years is 12.4% with a hit ratio of 63% [11][17][18] Notable Stock Recommendations Top-Ranked Stocks 1. **Hyundai MOBIS (012330.KS)**: Upgraded to Overweight (OW) due to improved operational outlook and shareholder return plans after two consecutive quarters of earnings beats [5][11] 2. **Coforge Ltd (COFO.NS)**: Maintained OW rating, expected to command a valuation premium due to strong EPS CAGR projections [5][11] Bottom-Ranked Stocks 1. **ASX Limited (ASX.AX)**: Downgraded to Underweight (UW) due to moderating futures revenue growth and lagging domestic capital market recovery [6][11] 2. **Avenue Supermarts Ltd (AVEU.NS)**: Price target lowered and maintained UW rating due to disappointing margins and management commentary raising growth concerns [6][11] Additional Insights - **Sector Performance**: The report includes a detailed breakdown of stocks with market caps over US$5 billion, categorized by their MOST scores and ratings from industry analysts [12][14] - **Quantamental Connect Platform**: A service launched to help investors customize their stock screening based on specific criteria, enhancing the integration of quantitative factors into investment processes [22][25] Important Metrics - **Performance Metrics for MOST**: - Annualized Return: 12.4% - Information Ratio: 1.11x - Max Drawdown: -11% over a 4-month period [8][18] Conclusion - The conference call emphasizes the effectiveness of the MOST model in identifying investment opportunities in the Asia Pacific market, highlighting both promising and underperforming stocks while providing a platform for tailored investment strategies.

Investment Rating - The report does not explicitly provide an investment rating for the industry of generative AI in higher education Core Insights - The report emphasizes that the emergence of generative AI represents a pivotal moment for higher education, challenging traditional assumptions about teaching, learning, and the purpose of universities [16][17] - It identifies five interdependent elements essential for successful generative AI integration, forming the 'CRAFT' framework: culture, rules, access, familiarity, and trust [18][19] - The report calls for immediate sector-wide action, including the formation of collaborative clusters among universities and elevating students as partners in the integration process [20][21] Summary by Sections Executive Summary - The report outlines the need for transformative change in higher education to adapt to generative AI, moving from reactive to proactive strategies [16][17] - It highlights the urgency for universities to integrate AI responsibly while maintaining educational integrity [17] Introduction - The introduction discusses the varied reactions of higher education to generative AI since its introduction, noting an initial moral panic followed by a growing acceptance of its permanence [25][26] - It positions AI as a general-purpose technology with the potential for widespread impact across society and education [26] Current State and Future Directions - The report notes that universities are currently responding to generative AI in a piecemeal manner, often focusing on immediate concerns rather than systematic integration [17][30] - It identifies key insights from workshops that emphasize the importance of transparency, trust, and equitable access to AI [32] Five Areas for Action - The report outlines three core areas of focus for universities: rules, access, and familiarity, which are essential for responsible AI integration [48][49] - It emphasizes the foundational role of trust in fostering effective engagement with generative AI [49] Rules - Establishing meaningful rules is critical for responsible AI use, including principles and guidelines that govern engagement with the technology [52][55] - The report provides case studies illustrating how institutions have begun to establish rules around generative AI usage [53][54] Access - Equitable access to generative AI applications is essential to prevent exacerbating existing digital divides [72][73] - The report discusses the financial barriers to accessing advanced AI capabilities and the need for collaborative efforts to ensure availability [74]