Collecting Financial Data From Online Sources: Enhancing Large Language Models With Real-Time Search

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Podrobná bibliografie
Vydáno v:	Journal of Organizational and End User Computing vol. 37, no. 1 (2025), p. 1-24
Hlavní autor:	Li, Yang
Vydáno:	IGI Global
Témata:	Information processing Data processing Data collection Accounting Research design Human performance Retrieval Research methodology Automation Data retrieval Chatbots Large language models Artificial intelligence Language modeling Real time
On-line přístup:	Citation/Abstract Full Text - PDF
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Popis
Abstrakt:	Timely and accurate access to financial data is crucial for empirical research in accounting and finance. However, current data collection processes are often manual, inconsistent, and difficult to scale. This study asks: How can large language models (LLMs) be effectively used to automate financial data collection? Using design science research methodology (DSRM), the author develops a modular architecture that integrates a real-time search API and auxiliary information processing into LLM workflows. The study applies the model to two tasks: extracting ESG report release dates and identifying customer firm tickers from COMPUSTAT. The system achieves 96% and 95% accuracy, respectively, comparable to human performance. This study advances LLM applications in accounting by providing a scalable, practical framework for automating financial data retrieval.
ISSN:	1546-2234 1546-5012 1043-6464 1063-2239
DOI:	10.4018/JOEUC.388470
Zdroj:	ABI/INFORM Global