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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | LLM Analysis of 10-K and 10-Q Filings: RAG Results |
| Authors Name: | Shyam Balagurumurthy Viswanathan , Amritha Arun Babu Mysore , Arjun Karat , Raghavan Muthuregunathan |
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IJRTI_188987
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| Published Paper Id: | IJRTI2401091 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | In the field of financial analytics, Large Language Models (LLMs) have become significant tools for analyzing and interpreting complex, unstructured text, like the content found in financial reports. This paper focuses on the use of LLMs to study 10-K and 10-Q filings, which are essential financial statements required from publicly traded companies. These reports thoroughly view a company's financial situation and future outlook, targeting a broad audience, including investors, analysts, regulators, employees, and the general public. The main part of this research involves combining Retrieval-Augmented Generation (RAG) models and knowledge graphs with LLMs to improve the analysis of these financial documents. A key challenge in understanding 10-K and 10-Q reports is figuring out which companies are competitors and how they fit into the market, something that regular LLM methods don't fully address. Our approach uses RAG models and knowledge graphs to overcome this issue, offering a better understanding of how companies compete and where they stand in their industry based on these reports. This study aims to show that integrating LLMs with RAG and knowledge graphs can significantly improve how we analyze financial data, leading to a deeper and more complete understanding of a company's position in the market, its competition, and potential risks. This research goes beyond just analyzing financial reports, suggesting a new way to use AI in business intelligence and market research. |
| Keywords: | Artificial Intelligence, Machine Learning, LLM, Finance, Financial Reports |
| Cite Article: | "LLM Analysis of 10-K and 10-Q Filings: RAG Results", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.594 - 602, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401091.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2401091
Registration ID:188987
Published In: Volume 9 Issue 1, January-2024
DOI (Digital Object Identifier):
Page No: 594 - 602 Country: Acton, MA, United States Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2401091 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2401091 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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