This project built a pipeline that pulls news articles from the News API, company background from Wikipedia, and stock price data from Yahoo Finance for ten major companies and developed a simple but effective template that converts stock data into natural language narratives.
Abstract
Stock market analysts and investors face a daily challenge: too much financial news, too little time. Manually reading and synthesizing hundreds of company-specific articles is impractical, yet missing key information can directly affect investment decisions. This project, conducted at George Washington University in Fall 2023, explores whether Large Language Models can automate this process reliably. We built a pipeline that pulls news articles from the News API, company background from Wikipedia, and stock price data from Yahoo Finance for ten major companies (AAPL, MSFT, GOOGL, AMZN, META, TSLA, JPM, NVDA, WMT, DIS). Because LLMs cannot directly process numerical tables, we developed a simple but effective template that converts stock data into natural language narratives. We then tested two summarization approaches (Summarize Chains and Retrieval-Augmented Generation with FAISS) across three open-source models (Falcon-7B-Instruct, DistilBART-CNN-12-6, BART-Large-XSum) for news, and GPT (text-davinci-003) for stock summaries. Falcon-7B with Summarize Chains gave the best results, covering all news events accurately and coherently. RAG, while promising in theory, caused severe repetition in Falcon and hallucinated facts in BART-Large when k was large. Both LLM-based approaches outperformed a simple Lead-3 baseline on ROUGE-1. We also built a Streamlit dashboard for interactive stock visualization. The work was done in Fall 2023, before RAG-based financial tools became widespread, and the failure modes we document, particularly hallucination under RAG in smaller models, remain relevant today.
Financial sentiment analysis has long relied on labeled data to fine-tune models like FinBERT, a process that is both costly and time-consuming. The arrival of large language models (LLMs) has changed the landscape: with zero-shot and few-shot prompting, one can now extract sentiment from financial texts using few or no annotated examples. This survey takes stock of how LLMs are being applied to this task. It begins by clarifying the core ideas behind in-context learning and chain-of-thought prompting. It then examines a range of prompt designs that have been developed to cope with the peculiarities of financial writing, such as numerical expressions, implicit sentiment and long documents. A comparison of LLM performance on standard benchmarks against fine-tuned domain models shows that general-purpose LLMs are often competitive, especially when prompts are carefully crafted. Yet three problems remain unresolved: numerical reasoning errors, hallucination, and the practical hurdles of cost, latency, and privacy. These challenges are discussed in detail, and retrieval-augmented generation, trustworthiness frameworks, and efficient open-source models are pointed out as the most promising paths forward. Overall, LLMs offer a flexible and annotation-light alternative to traditional fine-tuning, but their successful deployment in finance will depend on robust prompt engineering and solid factual grounding.
Liyang Gao· Applied and Computational En...· 0 citations
Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce \textbf{FinED-Bench}, the first publicly \textbf{Bench}mark for \textbf{Fin}ancial \textbf{E}rror \textbf{D}etection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at https://github.com/hedyHe/FinED-Bench.
Ying He, Zhouhong Gu, Zhecheng Hu et al.· Annual Meeting of the Associ...· 2 citations
This survey presents a systematic review of 121 references spanning 2002 to 2026, tracing the evolution of TextRank-based approaches into hybrid LLM pipelines and advancing three qualified arguments.
Ahmed J. Jabur, Asmaa Abdul Azeez Dakhil, Israa Saad Mohammed et al.· Iraqi Journal for Computers...· 0 citations
This paper presents a framework that preserves semantics in LLM-based opinion summarization while minimizing token usage and computational cost and demonstrates that this method significantly reduces token usage and computational cost while consistently outperforming traditional AI-based and standard LLM summarization baselines in terms of content coverage, balance, and semantic preservation.
It is taken as initial evidence for market time series as an input modality in financial text classification on the task of classifying sentences from Federal Reserve communication as hawkish, dovish, or neutral.
Michael Schlee, Fabian Lukassen, Christoph Weisser· 0 citations
Obvious performance discrepancies exist across LLMs; high-quality annotations significantly boost downstream classification accuracy, which highlights the importance of selecting competent LLMs for sentiment labeling tasks.
Wenjing Pi, Changxian He· Computers and artificial int...· 0 citations