Aspect-Level Obfuscated Sentiment in Thai Financial Disclosures and Its Impact on Abnormal Returns

TL;DR

This paper develops an Aspect-Based Sentiment Analysis method to decode obfuscated sentiment in Thai financial reports and examines its influence on stock market reactions, revealing that specific report aspects impact market behavior.

Contribution

It introduces a novel ABSA approach tailored for obfuscated financial language in Thai reports and provides a benchmark dataset for this challenging task.

Findings

Strong sentiment classification performance achieved

Market reactions are influenced by specific report aspects

Addressing obfuscation improves sentiment accuracy

Abstract

Understanding sentiment in financial documents is crucial for gaining insights into market behavior. These reports often contain obfuscated language designed to present a positive or neutral outlook, even when underlying conditions may be less favorable. This paper presents a novel approach using Aspect-Based Sentiment Analysis (ABSA) to decode obfuscated sentiment in Thai financial annual reports. We develop specific guidelines for annotating obfuscated sentiment in these texts and annotate more than one hundred financial reports. We then benchmark various text classification models on this annotated dataset, demonstrating strong performance in sentiment classification. Additionally, we conduct an event study to evaluate the real-world implications of our sentiment analysis on stock prices. Our results suggest that market reactions are selectively influenced by specific aspects within the reports. Our findings underscore the complexity of sentiment analysis in financial texts and highlight the importance of addressing obfuscated language to accurately assess market sentiment.