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

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 te...

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Bibliographic Details
Published inJournal of management information systems Vol. 41; no. 1; pp. 178 - 205
Main Authors Zheng, Jiexin, Ng, Ka Chung, Zheng, Rong, Tam, Kar Yan
Format Journal Article
LanguageEnglish
Published Abingdon Routledge 02.01.2024
Taylor & Francis Ltd
Subjects
Algorithms
Annual reports
Earnings
Embedding
Evolution
financial communication
financial texts
financial word lists
market sentiment
News
sentiment evolution
textual analysis
word corpora
word embedding
Words
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Summary: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.
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ISSN:0742-1222
1557-928X
DOI:10.1080/07421222.2023.2301176