← Search

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

Jiexin Zheng¹; Ka Chung Ng²; Rong Zheng¹; Kar Yan Tam¹

¹ Department of Information Systems, Business Statistics and Operations Management, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong · ² Department of Management and Marketing, Faculty of Business, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

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.

DOI

10.1080/07421222.2023.2301176

Volume

41 (1)

Pages

178-205

Language

en

Export

BibTeX

Sources

crossref openalex