The paper "AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework" addresses the challenges in financial analysis, particularly in stock trend prediction and financial question answering. It introduces AlphaFin, a dataset that combines traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data to enhance the performance of large language models (LLMs) in financial analysis. The authors propose the Stock-Chain framework, which integrates retrieval-augmented generation (RAG) techniques to improve the accuracy and interpretability of LLMs. The framework is designed to provide both stock trend predictions and comprehensive financial analyses, leveraging real-time market data and macroeconomic news. Extensive experiments demonstrate that Stock-Chain outperforms baseline methods in terms of annualized rate of return (ARR) and accuracy, showcasing its effectiveness in financial analysis tasks. The paper also includes ablation studies, preference evaluations with human and GPT-4, and case studies to validate the framework's performance.The paper "AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework" addresses the challenges in financial analysis, particularly in stock trend prediction and financial question answering. It introduces AlphaFin, a dataset that combines traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data to enhance the performance of large language models (LLMs) in financial analysis. The authors propose the Stock-Chain framework, which integrates retrieval-augmented generation (RAG) techniques to improve the accuracy and interpretability of LLMs. The framework is designed to provide both stock trend predictions and comprehensive financial analyses, leveraging real-time market data and macroeconomic news. Extensive experiments demonstrate that Stock-Chain outperforms baseline methods in terms of annualized rate of return (ARR) and accuracy, showcasing its effectiveness in financial analysis tasks. The paper also includes ablation studies, preference evaluations with human and GPT-4, and case studies to validate the framework's performance.