主动量化投资策略赋能 掘金硬科技与新成长

Core Insights - The article emphasizes that technological innovation and industrial upgrading are key drivers of high-quality economic development in China, with the Sci-Tech Innovation Board and the Growth Enterprise Market as primary platforms supporting this innovation [1][2] Investment Focus - The newly launched Guojin Sci-Tech Innovation and Entrepreneurship Quantitative Stock Fund aims to leverage active quantitative investment strategies, focusing on "hard technology" and "new growth" sectors to capitalize on economic growth and industrial transformation [1][2] - The fund will invest at least 80% of its non-cash assets in the Sci-Tech Innovation Board and the Growth Enterprise Market, allowing for diversified allocation across sectors and market capitalizations [1][2] Sector Characteristics - The "double innovation" sector is characterized by information explosion, high specialization, and strong volatility, making stock selection challenging due to rapid technological changes and varying company quality [3] - Companies in this sector exhibit high R&D investment, leading to performance growth that significantly outpaces market averages, as evidenced by the Wind Double Innovation Index outperforming major indices like CSI 300 and CSI 500 since 2020 [2][3] Quantitative Investment Strategy - The Guojin fund employs a systematic quantitative investment approach, which includes broad stock selection through technical models, strict risk control, and optimization of portfolios to achieve smoother long-term excess returns [3][4] - Unlike traditional actively managed funds, the quantitative fund relies on model-driven strategies and comprehensive market coverage rather than deep fundamental research [3][4] Team and Technology - Guojin Fund has a strong quantitative research team with extensive experience and a solid mathematical background, having established its quantitative investment framework since 2013 [4][5] - The team utilizes a wide range of data sources, including research reports and trading data, to build dynamic predictive models and enhance strategy effectiveness [4][5]