Large Language Models for Smarter Market Sentiment and Trend Prediction

The fast development of financial sectors has provided an enormous amount of unstructured data which ranges from the organization files, reports collected by the analyst for the social media discussion and the real-time news. Thus, collecting the useful information is very complex task for traders, financial organizations and the investors. The current advancements in the Large Language Models (LLMs) provides a promising way forward. LLM understands the tone, context and other hidden signals inside the text. It can also identify the market sentiment and enhances the trend prediction accuracy. In the traditional models, the dictionaries which are predefined will be considered or it uses the shallow statistical methods, but in LLM it offers a richer insight by identifying the subtle changes in the investor sentiment and uncovers the potential risks or opportunities. This research examines how the LLMs will be used for the financial sentiment analysis and the trend forecasting by reviewing the core architectures, benchmark datasets and various evaluation strategies. It also points out the specific challenges such as biased data, privacy concerns and regulatory compliance. once these challenges are addressed, LLM-powered systems have the potential to provide smarter, more adaptive, and human-like financial insights, enabling faster and more confident decisionmaking in dynamic market environments. To demonstrate the proposed approach, the Financial Phrase Bank dataset will be considered for implementation, with the aim of evaluating LLM-based models against traditional approaches and showcasing measurable improvements in sentiment detection and trend prediction accuracy.