  Financial sentiment analysis plays a crucial role in uncovering latent
patterns and detecting emerging trends, enabling individuals to make
well-informed decisions that may yield substantial advantages within the
constantly changing realm of finance. Recently, Large Language Models (LLMs)
have demonstrated their effectiveness in diverse domains, showcasing remarkable
capabilities even in zero-shot and few-shot in-context learning for various
Natural Language Processing (NLP) tasks. Nevertheless, their potential and
applicability in the context of financial sentiment analysis have not been
thoroughly explored yet. To bridge this gap, we employ two approaches:
in-context learning (with a focus on gpt-3.5-turbo model) and fine-tuning LLMs
on a finance-domain dataset. Given the computational costs associated with
fine-tuning LLMs with large parameter sizes, our focus lies on smaller LLMs,
spanning from 250M to 3B parameters for fine-tuning. We then compare the
performances with state-of-the-art results to evaluate their effectiveness in
the finance-domain. Our results demonstrate that fine-tuned smaller LLMs can
achieve comparable performance to state-of-the-art fine-tuned LLMs, even with
models having fewer parameters and a smaller training dataset. Additionally,
the zero-shot and one-shot performance of LLMs produces comparable results with
fine-tuned smaller LLMs and state-of-the-art outcomes. Furthermore, our
analysis demonstrates that there is no observed enhancement in performance for
finance-domain sentiment analysis when the number of shots for in-context
learning is increased.
