ARCHIVES

Abstract: This research paper provides a comprehensive review of the use of Natural Language Processing (NLP) in stock market analysis, based on a systematic exploration of Scopus-indexed literature. The primary focus is on how NLP technologies have been applied to extract and analyze textual data from financial news, reports, and social media to forecast market trends and guide trading decisions. Through a meta-analysis of documented case studies and empirical research, this paper evaluates the effectiveness of various NLP techniques, including sentiment analysis, topic modelling, and event detection, in enhancing market prediction models compared to traditional quantitative approaches. Results indicate that while NLP presents a significant advantage in interpreting unstructured data, its integration into trading algorithms also poses challenges such as data inconsistency, model overfitting, and the need for adaptive learning mechanisms. The paper concludes by identifying future research directions that focus on improving the accuracy, robustness, and real-time capabilities of NLP applications in financial trading, underscoring the potential of NLP to transform financial market analytics.