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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87852完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 石百達 | zh_TW |
| dc.contributor.advisor | Pai-Ta Shih | en |
| dc.contributor.author | 黃靖雯 | zh_TW |
| dc.contributor.author | Ching-Wen Huang | en |
| dc.date.accessioned | 2023-07-24T16:05:57Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-07-24 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2023-05-29 | - |
| dc.identifier.citation | Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23, 589-609.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. Breiman, L. (1966). Random Forests. Machine Learning, 45, 5–32. Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. The Review of Financial Studies, 21, 1339-1369. Erdogan, B. E. (2013). Prediction of bankruptcy using support vector machines: an application to bank bankruptcy. Journal of Statistical Computation and Simulation, 83, 1543-1555. 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, 434-440. 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, 105-663. Queen, M., & Roll, R. (1987). Firm mortality: Using market indicators to predict survival. Financial Analysts Journal, 43, 9-26. 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, 605-610. Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72, 3507–3516. Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29, 449-470. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109-131. Odom, M. and Sharda, R. (1990) A Neural Network for Bankruptcy Prediction. International Joint Conference on Neural Networks, 2, 163-168. 鄭文英, 蘇恩德, 李勝榮, & 何慧清.(2008). 財務危機預警模式建構影響因素之預測能力整合分析. 古永嘉, 陳達新, 陳維寧, & 楊延福. (2007). 以會計資訊衡量企業信用風險: 區別分析與類神經網路模型之比較與應用. 管理科學研究. 棗厥庸, & 李永新. (2010). 公司危機預測: 計量模型與變數選取. 期貨與選擇權學刊, 3, 57-82. 尹賢瑜. 葉立仁. 游雅璇 (2015). 建構企業財務危機預警模型－以財務及非財務因素構建. 德明學報, 39, 37-58. 董律里. (2020). 基於混合模型建立企業風險評估方法. 周永昱. (2021). 使用機器學習演算法預測企業財務危機 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87852 | - |
| dc.description.abstract | 過去文獻較多只用財務變數的組合來探討企業危機預測的機率，較少對於市場變數的組合有著墨，且考慮到全年度財報發佈時間在三月底前，若企業發生危機是在財報發布前，則沒有前一年度的財報可參考。本研究探討運用前兩年度財務變數再加上市場變數，是否能提高財務危機預測模型的準確率。
研究結果顯示，在決大多數的情況下，加入特定市場變數所訓練出的模型能有效提高預測力，且在眾多機器學習模型中，RF的預測能力最穩定，預測能力最準確。 | zh_TW |
| dc.description.abstract | Previous studies usually only use financial variables to establish financial distress forecasting models. However, if companies have financial crises before the financial reports are revealed, investors can’t use them to establish the models. This study will use the financial data of the previous two years and add market variables to build financial distress prediction models.
The results show that adding marketing variables improve the performance of the models in the majority time. Compared to other machine learning algorithms, random forest is the best model in out-of-sample tests. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-07-24T16:05:57Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-07-24T16:05:57Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 Ⅰ
摘要 Ⅱ ABSTRACT III 表目錄 V 圖目錄 VI 壹、緒論 1 貳、文獻回顧 2 一、企業危機定義 2 二、自變數及樣本配對方法 3 三、分類模型 6 參、研究設計 8 一、資料蒐集 8 二、樣本選取與配對 9 三、結果評估 9 肆、研究結果 11 一、樣本配對結果 11 二、樣本敘述統計 13 三、分類模型結果 15 伍、結論 22 參考文獻 24 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 使用機器學習演算法加入市場變數來預測財務危機 | zh_TW |
| dc.title | Using machine learning algorithms to predict financial distress by adding marketing variables | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪偉峰;盧佳琪 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-Feng Hong;Chia-Chi Lu | en |
| dc.subject.keyword | 財務危機,機器學習,市場變數,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,Marketing variables,Logistic regression,Support vector machines,Random forest,K-Nearest Neighbor, | en |
| dc.relation.page | 25 | - |
| dc.identifier.doi | 10.6342/NTU202300872 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-05-30 | - |
| 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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| ntu-111-2.pdf 目前未授權公開取用 | 1.07 MB | Adobe PDF |
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