  The task of financial analysis primarily encompasses two key areas: stock
trend prediction and the corresponding financial question answering. Currently,
machine learning and deep learning algorithms (ML&DL) have been widely applied
for stock trend predictions, leading to significant progress. However, these
methods fail to provide reasons for predictions, lacking interpretability and
reasoning processes. Also, they can not integrate textual information such as
financial news or reports. Meanwhile, large language models (LLMs) have
remarkable textual understanding and generation ability. But due to the
scarcity of financial training datasets and limited integration with real-time
knowledge, LLMs still suffer from hallucinations and are unable to keep up with
the latest information. To tackle these challenges, we first release AlphaFin
datasets, combining traditional research datasets, real-time financial data,
and handwritten chain-of-thought (CoT) data. It has a positive impact on
training LLMs for completing financial analysis. We then use AlphaFin datasets
to benchmark a state-of-the-art method, called Stock-Chain, for effectively
tackling the financial analysis task, which integrates retrieval-augmented
generation (RAG) techniques. Extensive experiments are conducted to demonstrate
the effectiveness of our framework on financial analysis.
