Annual report tone and divergence of opinion: evidence from textual analysis
By utilizing web-crawling and text analysis techniques on unstructured big data (text sets), this study examines to what extent investors disagree with the sentiment conveyed in annual reports. The main empirical findings suggest that the tone of annual reports significantly influences investor opin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Journal of Applied Economics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15140326.2024.2354641 |
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| Summary: | By utilizing web-crawling and text analysis techniques on unstructured big data (text sets), this study examines to what extent investors disagree with the sentiment conveyed in annual reports. The main empirical findings suggest that the tone of annual reports significantly influences investor opinions. Specifically, a negative tone in annual reports is associated with high levels of divergence among investors’ opinions, whereas a positive tone correlates with lower divergence. In the robustness tests, the results remain consistent after controlling for various factors. After we control for Management Discussion and Analysis (MD&A), both positive and negative tones in annual reports continue to be significant predictors of divergences in investor opinions. Additionally, after controlling for future earnings quality, future cash flows, and future earnings surprises, investors still present high/low divergence of opinion in response to a negative/positive tone in annual reports. Moreover, the robustness of our analysis is assessed by employing alternative sentiment analysis word lists. |
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| ISSN: | 1514-0326 1667-6726 |