用细粒度情感分析破解泰语财报中的模糊表达及其对股价影响
Aspect-Level Obfuscated Sentiment in Thai Financial Disclosures and Its Impact on Abnormal Returns
- 基于细粒度情感分析识别财报中刻意模糊的正向表述
- 在100多份财报上验证模型可有效分类情感并关联特定事件
- 适合关注金融文本分析与市场反应研究的研究者
理解财务文件中的情感对洞察市场行为至关重要。这些报告常使用模糊语言呈现积极或中性基调，即使实际状况并不乐观。本文提出一种基于方面的情感分析（ABSA）新方法，用于解码泰语财务年报中的隐晦情感。我们制定了针对此类文本的模糊情感标注指南，并对超过一百份财务报告进行了标注。随后在该标注数据集上对比多种文本分类模型，表现出优异的分类性能。此外，我们通过事件研究评估了情感分析结果对股价的真实影响。结果显示，市场反应受报告中特定方面的信息所驱动。研究揭示了金融文本情感分析的复杂性，强调了处理模糊语言对于准确评估市场情绪的重要性。
原文摘要 · Abstract (English)
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.
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