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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張景宏 | zh_TW |
| dc.contributor.advisor | Ching-Hung Chang | en |
| dc.contributor.author | 黃世漳 | zh_TW |
| dc.contributor.author | Shi Zhang Ooi | en |
| dc.date.accessioned | 2023-08-15T16:14:10Z | - |
| dc.date.available | 2023-11-10 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-26 | - |
| dc.identifier.citation | Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88423 | - |
| dc.description.abstract | 在已知Brown et al. (2020)的方法可以有效預測財務舞弊案的情況下，我們探討該方法是否也可以用於預測盈餘管理。該方法主要是把一種稱爲隱含狄利克雷分布演算法 (Latent Dirichlet Allocation) 的機器學習方法應用於日漸複雜至人類難以理解的公司年報上。盈餘管理預測能讓公司真正的財務狀況更清楚地展示出來。這可以提供有效的資訊于投資者、債權人、審計師、立法委員等等以便他們可以做出更好的決策。此爲，利用該方式來同時預測盈餘管理以及財務舞弊可以降低成本及時間。 | zh_TW |
| dc.description.abstract | We examine whether the Brown et al. (2020) method that can predict financial fraud using annual report filings is also able to predict earnings management. This model applies Latent Dirichlet Allocation (LDA) an advanced machine learning tool to the annual reports’ qualitative disclosures that have become incomprehensible to humans over time. Predicting earnings management paints a clearer picture of the firm’s financial position. This will allow the investors, creditors, auditors, legislators, and other stakeholders to make higher-quality decisions. Besides, using the proposed approach in the context of both abnormal accruals and financial misreport can save time and money. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:14:10Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T16:14:10Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 1. Introduction and Literature Review 1
1.1 Earnings Management 1 1.1.1 Earnings 1 1.1.2 Types of Earnings Management 1 1.2 Latent Dirichlet Allocation 2 2. Contribution 4 3. Research Method 5 3.1 Modified Jones Model Measure 5 3.2 Main Methodology 6 4. Results and Analysis 9 4.1 AUC 12 4.2 Yearly-basis ROC & PRC 14 4.3 Cutoff Percentile 15 4.4 Cumulative Distribution 17 5. Robustness 18 6. Conclusion 19 7. Future Research & Limitations 19 8. References 21 | - |
| dc.language.iso | en | - |
| dc.subject | 隱含狄利克雷分布演算法 | zh_TW |
| dc.subject | 主題模型 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 年度報告 | zh_TW |
| dc.subject | 盈餘管理 | zh_TW |
| dc.subject | Latent Dirichlet Allocation | en |
| dc.subject | Earnings Management | en |
| dc.subject | 10-K | en |
| dc.subject | Natural Language Processing | en |
| dc.subject | Machine Learning | en |
| dc.subject | Topic Modeling | en |
| dc.title | 通過隱含狄利克雷分布演算法預測盈餘管理 | zh_TW |
| dc.title | Earnings Management Prediction via Latent Dirichlet Allocation on 10-K | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 盧信銘 | zh_TW |
| dc.contributor.coadvisor | Hsin-Min Lu | en |
| dc.contributor.oralexamcommittee | 陳建錦;徐愛恩 | zh_TW |
| dc.contributor.oralexamcommittee | Chien Chin Chen;AI-AN TSUI | en |
| dc.subject.keyword | 隱含狄利克雷分布演算法,盈餘管理,年度報告,自然語言處理,機器學習,主題模型, | zh_TW |
| dc.subject.keyword | Latent Dirichlet Allocation,Earnings Management,10-K,Natural Language Processing,Machine Learning,Topic Modeling, | en |
| dc.relation.page | 25 | - |
| dc.identifier.doi | 10.6342/NTU202302092 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-07-28 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| 顯示於系所單位： | 財務金融學系 | |
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