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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林嬋娟(Chan-Jane Lin) | |
| dc.contributor.author | Hung-Wei Chiang | en |
| dc.contributor.author | 江泓葦 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:16:34Z | - |
| dc.date.copyright | 2022-07-27 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85435 | - |
| dc.description.abstract | 本論文利用未來盈餘反應係數 (future earnings response coefficient, FERC) 探討法人說明會語調是否能夠提供增額資訊，以協助投資人修正對於未來盈餘的預期，進而反應在股價上。在語調衡量方面，本論文使用FinBERT 模型及Loughran and Mcdonald (2011)編製的字典來衡量法說會語調。為比較兩個方法，本論文先進行情感分類的測試，從分類測試的結果可以發現FinBERT 模型不僅整體表現較佳，在各個情感類別上的表現也優於字典法。在實證方面，本論文發現越正向的法人說明會語調具有更高的未來反應係數，顯示正向的語調能夠提供較多關於未來盈餘的資訊，此結果與過往研究一致，顯示法人說明會語調確實具有資訊內涵。本論文並未發現法人說明會不同階段的語調對未來盈餘反應係數有明顯差異，顯示不同階段語調所提供有關未來盈餘資訊量差異很小。另外，本論文發現透過FinBERT模型衡量的語調在實證上較顯著，顯示FinBERT模型不僅能更準確地衡量文本情緒，且更適合用於財經及會計方面的情感分析研究。 | zh_TW |
| dc.description.abstract | This paper uses future earnings response coefficient (FERC) to test whether the tone of the conference calls can assist investors in predicting future earnings and reflect the expectations in stock prices. To accurately measure the tone, this paper utilizes the state-of-the-art natural language processing (NLP) algorithm - FinBERT model, and uses the Loughran and Mcdonald (2011) word lists as a benchmark. To check the performance of them, this paper does a classification task first. The results show that the FinBERT model not only achieves better overall performance but also surpasses the word list method in predicting sentiment for each sentiment class. In terms of empirical results, this paper finds weak evidence that firms with more positive tone will have higher FERC, suggesting that positive tone can provide more information and better assist investors in predicting future performance. The findings are consistent with the results in previous research that the linguistic features in conference calls can provide additional information and support the findings that positive tone can reduce uncertainty of firm’s future value. This paper does not find significant FERC difference between the tone in discussion section and the tone in presentation section. The results may suggest that the tone in different section provide almost same amount of information in predicting future earnings. Notably, the paper demonstrates that the tone measured by the FinBERT model gets greater significance than the tone measured by the word list method, suggesting that the FinBERT model not only gauges the sentiment more accurately but is also more suitable for sentiment analysis in financial and accounting research. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:16:34Z (GMT). No. of bitstreams: 1 U0001-1607202214153000.pdf: 1489612 bytes, checksum: 6d073b2c61c378e699eb053570a253b6 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii 英文摘要 iv 1. Introduction 1 2. Literature Review and Hypothesis Development 8 2.1 Literature on information content of Earnings Conference Call 8 2.2 Literature on Earnings Conference Call Tone 10 2.3 Literature on Future earnings response coefficient (FERC) 13 2.4 Hypothesis Development 15 3. Tone measure method 18 3.1 Introduction of Word list method 18 (1) Harvard GI word list 19 (2) DICTION word list 20 (3) Henry word list 21 (4) Loughran and McDonald word list 22 3.2 Introduction of FinBERT model 24 3.3 Comparison of two methods 26 4. Empirical model, Measurement of tone, and Sample selection 29 4.1 Empirical model 29 4.2 Measurement of tone 33 (1) Word list method 34 (2) The FinBERT model 35 4.3 Sample selection and distribution 37 5. Results 39 5.1 Comparison of classification performance 40 5.2 Sample statistics and correlation matrix 44 5.3 Empirical results of H1 47 5.4 Empirical results of H2 50 6. Additional test 52 6.1 Additional test for H1 53 6.2 Additional test for H2 55 7. Conclusion 57 References 62 Appendix: The details about the FinBERT model 66 | |
| dc.language.iso | en | |
| dc.subject | 未來盈餘反應係數 | zh_TW |
| dc.subject | 法人說明會 | zh_TW |
| dc.subject | 情感分類 | zh_TW |
| dc.subject | FinBERT | zh_TW |
| dc.subject | 文本分析 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | Sentiment Classification | en |
| dc.subject | Textual Analysis | en |
| dc.subject | Natural Language Processing (NLP) | en |
| dc.subject | FinBERT | en |
| dc.subject | Earnings Conference Call | en |
| dc.subject | FERC | en |
| dc.title | 法人說明會語調對未來盈餘反應係數之影響 | zh_TW |
| dc.title | The effects of conference call tone on Future Earnings Response Coefficient | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏如君(Ju-Chun Yen),謝昇峯(Sheng-Feng Hsieh) | |
| dc.subject.keyword | 未來盈餘反應係數,法人說明會,情感分類,FinBERT,文本分析,自然語言處理, | zh_TW |
| dc.subject.keyword | FERC,Earnings Conference Call,Sentiment Classification,FinBERT,Textual Analysis,Natural Language Processing (NLP), | en |
| dc.relation.page | 67 | |
| dc.identifier.doi | 10.6342/NTU202201498 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-07-19 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 會計學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-27 | - |
| 顯示於系所單位： | 會計學系 | |
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