This project evaluates multiple approaches to sentiment classification on financial text data, ranging from traditional ML models to zero-shot large language models (LLMs) and fine-tuned LLMs. Further application of downstream use case multivariate time series forecasting to stock price prediction using fine-tuned LLM sentiment analysis.