Research and Analysis on the Application of NLP in Quantitative Investment in the Stock Market

The continuous iteration and innovation of Natural Language Processing (NLP) technology offer a technical foundation for quantitative investment in the stock market to break through the inherent limitations of traditional structured data analysis, successfully achieving deep mining, semantic analysis, and quantitative transformation of various unstructured financial texts, such as research reports, financial news, market stock reviews, and company announcements. This paper systematically reviews the domestic and international research findings and empirical conclusions of NLP in the field of quantitative stock investment over the past 3-5 years. Beginning with the core NLP application technologies for customized financial text processing, it focuses on analyzing the technically adaptive strategies and application effects in two key practical scenarios, respectively periodic stock price prediction and stock selection strategy optimization. It further deeply analyses industry controversies and practical limitations in the application of NLP technologies in this area, such as information validity, model interpretability, and factor stability. Combined with the development characteristics of financial markets and technological iteration trends, this paper proposes targeted future optimization directions and research paths, aiming to offer both theoretical sight and practical value for future research, technical implementation, and practical applications at the intersection of NLP and quantitative stock investment.