Core Insights - The public fund industry faces challenges such as performance volatility, reliance on individual capabilities, and a lack of cohesive decision-making processes, which hinder investors' ability to achieve stable excess returns [1][2] - Tianhong Fund is implementing a digital and integrated investment research platform called TIRD (TianHong Investment Research Decision) to address these issues and enhance the investment experience for clients [1][2] Group 1: TIRD Platform Development - The TIRD platform is a key reform in Tianhong's investment research field, aiming to transform the traditional investment research model from a fragmented approach to a more integrated and efficient system [2][3] - TIRD incorporates a closed-loop management process that includes tracking, feedback, review, and iteration across all stages of research, decision-making, investment, trading, and performance analysis [2][4] - The platform features an intelligent dashboard for fund managers, providing real-time insights and recommendations for portfolio adjustments based on various market scenarios [2][4] Group 2: Efficiency and Collaboration - TIRD enhances research efficiency by automating the monitoring of key industry indicators, allowing for timely responses to market changes that may not be captured through traditional methods [4][5] - The platform facilitates better internal communication and collaboration among research teams by ensuring that all interactions are documented and traceable, improving the overall efficiency of information exchange [5][6] - The system has shown a high level of usability and engagement within Tianhong's investment research operations, indicating its effectiveness in streamlining processes [3][4] Group 3: Future Directions - Tianhong plans to expand the TIRD platform's capabilities to cover index-enhanced investment areas and initiate digital transformation in fixed income sectors [6][7] - Future developments include the introduction of specialized AI-driven research assistants to enhance the depth of analysis and support human researchers in tracking multiple stocks [6][7] - The overarching goal is to establish a system-level capability in asset management, moving away from reliance on individual talent to a more structured and predictable investment research process [7]