Aspect-Level Obfuscated Sentiment in Thai Financial Disclosures and Its Impact on Abnormal Returns
This work addresses the challenge of accurately assessing market sentiment for investors and analysts by focusing on obfuscated language in financial texts, representing an incremental advancement in domain-specific sentiment analysis.
The paper tackled the problem of decoding obfuscated sentiment in Thai financial annual reports using Aspect-Based Sentiment Analysis, resulting in strong performance in sentiment classification and evidence that market reactions are selectively influenced by specific aspects within the reports.
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.