Group 1 - The core viewpoint emphasizes that AI is transforming asset allocation by achieving computational equity and enhancing investment research capabilities, particularly through the DeepSeek model [1][11][14] - AI is positioned as an "enhanced tool" in investment research, improving data processing efficiency and optimizing strategy execution, but it cannot fully replace human judgment in complex market scenarios [2][3][21] - The report highlights the importance of integrating AI with traditional investment frameworks to enhance decision-making processes while maintaining human oversight [20][21][30] Group 2 - The application of DeepSeek in asset allocation involves a systematic optimization of traditional strategies, focusing on risk parity and dynamic market timing [16][17][18] - The report discusses the integration of AI in A-share market strategies, utilizing macroeconomic indicators and sentiment analysis to enhance market timing and sector rotation [17][18] - AI's role in ESG investment is explored, demonstrating how it can dynamically incorporate ESG factors into asset allocation frameworks to balance returns and sustainability [18][19] Group 3 - The report outlines the deployment of AI in hedge funds and quantitative trading, showcasing how AI-driven models can assist in decision-making and strategy development [49][54][58] - It emphasizes the need for specialized AI models tailored to financial tasks, such as the Fin-R1 model, which is designed for complex financial reasoning and can be deployed on consumer-grade hardware [65][66] - The success of AI-driven investment strategies is illustrated through case studies, including Minotaur Capital, which achieved significant returns using AI for stock selection [70][74]