The Effects of Sentiment Evolution in Financial Texts: A Word Embedding Approach
Jiexin Zheng, Ka Chung Ng, Rong Zheng, Kar Yan Tam
Journal of Management Information Systems · 2024 · Volume 41 · Issue 1
DOI: 10.1080/07421222.2023.2301176
Abstract
We examine the evolutionary effects of sentiment words in financial text and their implications for various business outcomes. We propose an algorithm called Wo rd L ist Ve ctor for S entiment (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.
Keywords
financial texts, market sentiment, word embedding, sentiment evolution, textual analysis, financial communication, word corpora, financial word lists
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