請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80285完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 石百達(Pai-Ta Shih) | |
| dc.contributor.author | Yung-Yu Chou | en |
| dc.contributor.author | 周永昱 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:03:49Z | - |
| dc.date.available | 2021-07-08 | |
| dc.date.available | 2022-11-24T03:03:49Z | - |
| dc.date.copyright | 2021-07-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-29 | |
| dc.identifier.citation | 1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. 2. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71-111. 3. Boyacioglu, M. A., Kara, Y., Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355-3366. 4. Dong, W., Liao, S. S., Fang, B., Cheng, X., Chen, Z., Fan, W. (2014, June). The Detection of Fraudulent Financial Statements: an Integrated Language Model. In PACIS (p. 383). 5. Dong, W., Liao, S., Zhang, Z. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461-487. 6. Erdogan, B. E. (2013). Prediction of bankruptcy using support vector machines: an application to bank bankruptcy. Journal of Statistical Computation and Simulation, 83(8), 1543-1555. 7. Hua, Z., Wang, Y., Xu, X., Zhang, B., Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440. 8. Huang, Y. P., Yen, M. F. (2019). A new perspective of performance comparison among machine learning algorithms for financial distress prediction. Applied Soft Computing, 83, 105663. 9. Imandoust, S. B., Bolandraftar, M. (2013). Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background. International Journal of Engineering Research and Applications, 3(5), 605-610. 10. Jabeur, S. B., Fahmi, Y. (2018). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3), 1173-1186. 11. Liang, D., Lu, C. C., Tsai, C. F., Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. 12. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 109-131. 13. Salehi, M., Shiri, M. M., Pasikhani, M. B. (2016). Predicting corporate financial distress using data mining techniques: An application in Tehran Stock Exchange. International Journal of Law and Management. 14. Sun, J., Li, H., Huang, Q. H., He, K. Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. 15. 林郁翎, 黃建華. (2009). 考慮公司治理之企業財務危機預警模型. 東吳經濟商學學報 (64), 23-55. 16. 袁同心, 王克陸, 包曉天. (2010). 財務比例, 公司治理與總體經濟對財務危機公司股價之關聯性—運用 Ohlson 模型 (Doctoral dissertation). 17. 黃振豊. 呂紹強. (2000). 企業財務危機預警模式之研究－以財務及非財務因素構建. 當代會計, 27-65. 18. 趙翊茹. (2020). 獨立董事與經理人之學歷專業度影響財務危機之研究—以電子產業為例. 臺灣大學財務金融學研究所學位論文, 1-67. 19. 謝佳芳. (2020). 危機預測之探討–以財務與資訊優勢者異常事件為例. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80285 | - |
| dc.description.abstract | 相較於過去文獻多追求財務危機預測之最佳模型或是最佳變數組合。本研究探討資料預處理對於建立財務危機預測模型之重要性。透過不同定義之產業別來進行樣本配對，產生配對樣本，以建立財務危機預測模型，比較財務危機預測模型的表現。 研究結果顯示，使用較佳的配對樣本進行訓練，可以提升模型的整體表現，且結果於不同的樣本配對比例、分類模型皆穩固。在五個衡量指標：準確率、召回率、精確率、F1-score、ROC AUC中，新產業所訓練出之模型絕大多數都異於且優於舊產業。此外，隨機森林在除精確率外，其餘的衡量指標皆為最佳的模型。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:03:49Z (GMT). No. of bitstreams: 1 U0001-2906202116315200.pdf: 2480300 bytes, checksum: 0e628ff6d61aa4c6dcf0b11edd0e4d4d (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………………………………………………..i 誌謝………………………………………………………………………………….....ii 摘要…………………………………………………………………………………iii Abstract...……………………………………………………………………………...iv 目錄…………………………………………………………………………………….v 圖目錄…………………………………………………………………………………vi 表目錄………………………………………………………………………………vii 第一章 緒論…………………………………………………………………………...1 第二章 文獻回顧……………………………………………………………………...2 2.1危機事件定義及自變數…………………………....…………………………2 2.2 分類模型…………………………………………...…………………………4 第三章 研究設計……………………………………………………………………...6 3.1 資料蒐集及樣本選取………………………………………………………...6 3.2 樣本配對……………………………………………………………………...7 第四章 研究結果…………………………………………………………………….10 4.1 危機樣本分析……………………………………………………………….10 4.2 配對樣本差異…...…………………………………………………………..10 4.3 分類模型結果……………………………………………………………….10 第五章 結論………………………………………………………………………….17 參考文獻…………………………………………………………………….…..…....18 | |
| dc.language.iso | zh-TW | |
| dc.subject | K-近鄰演算法 | zh_TW |
| dc.subject | 財務危機 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 樣本配對 | zh_TW |
| dc.subject | 資料預處理 | zh_TW |
| dc.subject | 邏輯斯迴歸 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 隨機森林 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Data Preprocessing | en |
| dc.subject | Financial distress | en |
| dc.subject | Sample Matching | en |
| dc.subject | K-Nearest Neighbor | en |
| dc.subject | Random forest | en |
| dc.subject | Support vector machines | en |
| dc.subject | Logistic regression | en |
| dc.title | 使用機器學習演算法預測企業財務危機 | zh_TW |
| dc.title | Using machine learning algorithms to predict financial distress | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪偉峰(Hsin-Tsai Liu),盧佳琪(Chih-Yang Tseng) | |
| dc.subject.keyword | 財務危機,機器學習,樣本配對,資料預處理,邏輯斯迴歸,支持向量機,隨機森林,K-近鄰演算法, | zh_TW |
| dc.subject.keyword | Financial distress,Machine learning,Sample Matching,Data Preprocessing,Logistic regression,Support vector machines,Random forest,K-Nearest Neighbor, | en |
| dc.relation.page | 19 | |
| dc.identifier.doi | 10.6342/NTU202101194 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-06-30 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位： | 財務金融學系 | |
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