Utilizing Modern Large Language Models (LLM) for Financial Trend Analysis and Digest Creation

• URL: http://arxiv.org/abs/2510.01225v1
• Date: Mon, 22 Sep 2025 21:04:39 GMT
• Title: Utilizing Modern Large Language Models (LLM) for Financial Trend Analysis and Digest Creation
• Authors: Andrei Lazarev, Dmitrii Sedov,
• Abstract summary: This paper introduces an innovative framework for automatically generating insightful financial digests using the power of Large Language Models (LLMs), specifically Google's Gemini Pro.<n>By leveraging a combination of data extraction from OpenAlex, strategic prompt engineering, and LLM-driven analysis, we demonstrate the automated example of creating a comprehensive digests.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exponential growth of information presents a significant challenge for researchers and professionals seeking to remain at the forefront of their fields and this paper introduces an innovative framework for automatically generating insightful financial digests using the power of Large Language Models (LLMs), specifically Google's Gemini Pro. By leveraging a combination of data extraction from OpenAlex, strategic prompt engineering, and LLM-driven analysis, we demonstrate the automated example of creating a comprehensive digests that generalize key findings, identify emerging trends. This approach addresses the limitations of traditional analysis methods, enabling the efficient processing of vast amounts of unstructured data and the delivery of actionable insights in an easily digestible format. This paper describes how LLMs work in simple words and how we can use their power to help researchers and scholars save their time and stay informed about current trends. Our study includes step-by-step process, from data acquisition and JSON construction to interaction with Gemini and the automated generation of PDF reports, including a link to the project's GitHub repository for broader accessibility and further development.

Related papers

• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Toward Automated and Trustworthy Scientific Analysis and Visualization with LLM-Generated Code [6.068120728706316]
  Large language models (LLMs) offer a promising solution by generating code from natural language descriptions.<n>We construct a benchmark suite of domain-inspired prompts that reflect real-world research tasks.<n>Our findings show that, without human intervention, the reliability of LLM-generated code is limited.
  arXiv  Detail & Related papers  (2025-11-26T21:27:03Z)
• Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions [22.880788190504827]
  Large Language Models (LLMs) perform well in natural language understanding and generation, but suffer from "hallucination" issues when processing structured knowledge.<n>This paper proposes a multi-dimensional data analysis method based on the interactions between LLM agents and Knowledge Graphs.
  arXiv  Detail & Related papers  (2025-10-17T02:38:44Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models [0.0]
  This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks.<n>We introduce an exploratory framework that uses LLMs to move beyond classification and perform semantic analysis.<n>The results demonstrate that LLMs can function as powerful "reliability co-pilots," moving beyond labelling to synthesise textual information and actionable, expert-level hypotheses.
  arXiv  Detail & Related papers  (2025-09-26T14:00:20Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.<n>Recent research has focused on graph foundation models (GFMs)<n>GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Towards a RAG-based Summarization Agent for the Electron-Ion Collider [0.5504260452953508]
  A Retrieval Augmented Generation (RAG)--based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data.
  arXiv  Detail & Related papers  (2024-03-23T05:32:46Z)
• Large Language Models for Data Annotation and Synthesis: A Survey [49.8318827245266]
  This survey focuses on the utility of Large Language Models for data annotation and synthesis.<n>It includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.
  arXiv  Detail & Related papers  (2024-02-21T00:44:04Z)
• Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering [0.0]
  We introduce a benchmarking framework focused on knowledge graph engineering (KGE) We show that while being a useful tool, Large Language Models are yet unfit to assist in knowledge graph generation with zero-shot prompting.
  arXiv  Detail & Related papers  (2023-08-31T10:31:19Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Enhancing Knowledge Graph Construction Using Large Language Models [0.0]
  This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text.
  arXiv  Detail & Related papers  (2023-05-08T12:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.