Abstract Summary/Description
This paper seeks to explore the use of LLMs – large language models for sentiment analysis in the quarterly earnings reports of Cummins Inc. This research leverages the Linq platform to predict the impact of press releases of earnings announcements on stock price changes. To carry out this research, data was gathered from Cummin’s press release site across four periods. The Linq platform was then used to test these unstructured data sources for sentiment analysis: positive, negative, or neutral. Price fluctuations were then assessed for 5 trading days before the earnings press release, on the day the press statement was released, and 5 trading days after the release of each earnings report and then compared to the sentiment predictions from Linq. The analyzed data in this paper shows some connection between the predictions made by the Linq platform and real-life market performance. This study emphasizes the value of LLMS in giving real-time financial insights. This paper also stresses the importance of artificial intelligence in financial applications while admitting the complex dynamics associated with financial markets and the need for more development of these AI models for broader applications.
