国金基金马芳：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][3]. Group 1: Investment Strategy - The Guojin Sci-Tech Innovation and Entrepreneurship Quantitative Stock Fund aims to leverage active quantitative investment strategies focused on the "Double Innovation" sector, selecting individual stocks to capitalize on "hard technology" and "new growth" opportunities [1][3]. - The fund invests 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 [3][4]. Group 2: Market Characteristics - The "Double Innovation" sector is characterized by high levels of information, specialization, and volatility, making investment challenging; however, it also presents significant growth potential due to high R&D investments driving performance well above market averages [4][5]. - The Wande Double Innovation Index has shown significantly higher returns and risk-adjusted performance metrics compared to mainstream indices like the CSI 300 and the CSI 500 since 2020 [4]. Group 3: Quantitative Investment Approach - The fund employs a systematic model and strict risk control to address the challenges of investing in the "Double Innovation" sector, enhancing the probability of capturing quality growth stocks while minimizing non-systematic risks [5][6]. - Unlike traditional actively managed funds, the quantitative fund relies on model-driven strategies and comprehensive market coverage rather than deep fundamental research [5][6]. Group 4: Team and Technology - The Guojin Fund has a well-established quantitative research team with a strong background in mathematics and investment, having built its quantitative investment framework since 2013 [7]. - The team utilizes a wide range of data sources, including research reports and trading data, to construct dynamic predictive models that identify market opportunities that traditional strategies may overlook [7].