The Effects of Sentiment Evolution in Financial Texts: A Word Embedding Approach

• 成果类型: Article
• 署名作者: Zheng, Jiexin; Ng, Ka Chung; Zheng, Rong; Tam, Kar Yan
• 署名单位: Hong Kong University of Science & Technology; Hong Kong Polytechnic University
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2023.2301176
• 发表日期: 2024
• 页码: 178-205
• 关键词: disclosure; media; language; earnings; tone; INFORMATION; discussions; READABILITY; frequency; investors

摘要

We examine the evolutionary effects of sentiment words in financial text and their implications for various business outcomes. We propose an algorithm called Word List Vector for Sentiment (WOLVES) that leverages both a human-defined sentiment word list and the word embedding approach to quantify text sentiment over time. We then apply WOLVES to investigate the evolutionary effects of the most popular financial word list, Loughran and McDonald (LM) dictionary, in annual reports, conference calls, and financial news. We find that LM negative words become less negative over time in annual reports compared to conference calls and financial news, while LM positive words remain qualitatively unchanged. This finding reconciles with existing evidence that negative words are more subject to managers' strategic communication. We also provide practical implications of WOLVES by correlating the sentiment evolution of LM negative words in annual reports with market reaction, earnings performance, and accounting fraud.