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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Applied Economics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15140326.2024.2354641 |
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| _version_ | 1846160166458753024 |
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| author | Zhihao Qin Menglin Cui |
| author_facet | Zhihao Qin Menglin Cui |
| author_sort | Zhihao Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f71ed5e0bfff47e8b53b868c43d541f8 |
| institution | Kabale University |
| issn | 1514-0326 1667-6726 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Applied Economics |
| spelling | doaj-art-f71ed5e0bfff47e8b53b868c43d541f82024-11-22T09:09:02ZengTaylor & Francis GroupJournal of Applied Economics1514-03261667-67262024-12-0127110.1080/15140326.2024.2354641Annual report tone and divergence of opinion: evidence from textual analysisZhihao Qin0Menglin Cui1School of Business, Chengdu University of Technology, Chengdu, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaBy 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.https://www.tandfonline.com/doi/10.1080/15140326.2024.2354641Divergence of opinioncorporate disclosure tonetextual analysis |
| spellingShingle | Zhihao Qin Menglin Cui Annual report tone and divergence of opinion: evidence from textual analysis Journal of Applied Economics Divergence of opinion corporate disclosure tone textual analysis |
| title | Annual report tone and divergence of opinion: evidence from textual analysis |
| title_full | Annual report tone and divergence of opinion: evidence from textual analysis |
| title_fullStr | Annual report tone and divergence of opinion: evidence from textual analysis |
| title_full_unstemmed | Annual report tone and divergence of opinion: evidence from textual analysis |
| title_short | Annual report tone and divergence of opinion: evidence from textual analysis |
| title_sort | annual report tone and divergence of opinion evidence from textual analysis |
| topic | Divergence of opinion corporate disclosure tone textual analysis |
| url | https://www.tandfonline.com/doi/10.1080/15140326.2024.2354641 |
| work_keys_str_mv | AT zhihaoqin annualreporttoneanddivergenceofopinionevidencefromtextualanalysis AT menglincui annualreporttoneanddivergenceofopinionevidencefromtextualanalysis |