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A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models by Daimi, Syed Affan; Iqbal, Asma is a scholarly article available to read on EtoBox.

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The number of companies listed on the NYSE has been growing exponentially, creating a significant challenge for market analysts, traders, and stockholders who must monitor and assess the performance and strategic shifts of a large number of companies regularly. There is an increasing need for a fast, cost-effective, and comprehensive method to evaluate the performance and detect and compare many companies' strategy changes efficiently. We propose a novel data-driven approach that leverages large language models (LLMs) to systematically analyze and rate the performance of companies based on their SEC 10-K filings. These filings, which provide detailed annual reports on a company's financial performance and strategic direction, serve as a rich source of data for evaluating various aspects of corporate health, including confidence, environmental sustainability, innovation, and workforce management. We also introduce an automated system for extracting and preprocessing 10-K filings. This system accurately identifies and segments the required sections as outlined by the SEC, while also isolating key textual content that contains critical information about the company. This curated data

Author

Daimi, Syed Affan; Iqbal, Asma

Published

2024

Language

EN