Large Language Models for Semantic Monitoring of Corporate Disclosures:
  A Case Study on Korea's Top 50 KOSPI Companies
Junwon Sung Woojin Heo Yunkyung Byun Youngsam Kim
In the rapidly advancing domain of artificial intelligence, state-of-the-art
language models such as OpenAI's GPT-3.5-turbo and GPT-4 offer unprecedented
opportunities for automating complex tasks. This research paper delves into the
capabilities of these models for semantically analyzing corporate disclosures
in the Korean context, specifically for timely disclosure. The study focuses on
the top 50 publicly traded companies listed on the Korean KOSPI, based on
market capitalization, and scrutinizes their monthly disclosure summaries over
a period of 17 months. Each summary was assigned a sentiment rating on a scale
ranging from 1(very negative) to 5(very positive). To gauge the effectiveness
of the language models, their sentiment ratings were compared with those
generated by human experts. Our findings reveal a notable performance disparity
between GPT-3.5-turbo and GPT-4, with the latter demonstrating significant
accuracy in human evaluation tests. The Spearman correlation coefficient was
registered at 0.61, while the simple concordance rate was recorded at 0.82.
This research contributes valuable insights into the evaluative characteristics
of GPT models, thereby laying the groundwork for future innovations in the
field of automated semantic monitoring.