  The integration of Large Language Models (LLMs) into financial analysis has
garnered significant attention in the NLP community. This paper presents our
solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs
within three critical areas of financial tasks: financial classification,
financial text summarization, and single stock trading. We adopted Llama3-8B
and Mistral-7B as base models, fine-tuning them through Parameter Efficient
Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches. To enhance model
performance, we combine datasets from task 1 and task 2 for data fusion. Our
approach aims to tackle these diverse tasks in a comprehensive and integrated
manner, showcasing LLMs' capacity to address diverse and complex financial
tasks with improved accuracy and decision-making capabilities.
