Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Abstract

For highly time-constrained, very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations between authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull–bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors, while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull–bear words using a futures corpus detection method that extracts valuable information from financial news, allowing investors to quickly understand market trends. Generative AI models are trained to provide real-time bull–bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with random forest and SVMs achieving an impressive 80% accuracy rate. MLP and deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.