This paper describes the team GMU-MU submission to the Financial Misinformation Detection challenge. The goal of this challenge is to identify financial misinformation and generate explanations justifying the predictions by developing or adapting LLMs. The participants were provided with a dataset of financial claims that were categorized into six financial domain categories. We experiment with the Llama model using two approaches; instruction-tuning the model with the training dataset, and a prompting approach that directly evaluates the off-the-shelf model. Our best system was placed 5th among the 12 systems, achieving an overall evaluation score of 0.6682.