Group 1 - The core viewpoint is that quantitative strategies in investment rely on diverse data sources, including traditional financial reports and non-traditional data such as social media sentiment and supply chain information, to generate forward-looking investment signals [1][2] - The performance of growth factors has been relatively strong in the current year, indicating a favorable market environment for growth-oriented investments [1] - A rich factor library is essential for diversifying sources of returns and enhancing cyclical resilience, necessitating regular updates to the factor pool to include both economically supported and algorithmically derived factors [1] Group 2 - Continuous model iteration and an open attitude towards new technologies, particularly AI, are crucial for improving the efficiency of factor development and constructing stronger predictive signals [2] - The application of AI in quantitative investment processes has become increasingly prevalent, including the use of large models for text data analysis and advanced models like transformers for end-to-end factor mining [2]