| dc.contributor.advisor | 翁久幸 | zh_TW |
| dc.contributor.author (作者) | 徐美珍 | zh_TW |
| dc.creator (作者) | 徐美珍 | zh_TW |
| dc.date (日期) | 2003 | en_US |
| dc.date.accessioned | 17-九月-2009 18:46:07 (UTC+8) | - |
| dc.date.available | 17-九月-2009 18:46:07 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-九月-2009 18:46:07 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0091354019 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/33902 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 91354019 | zh_TW |
| dc.description (描述) | 92 | zh_TW |
| dc.description.abstract (摘要) | 由於目前的經濟體制是自負風險的，若是一個企業發生財務上的危機，將會影響到企業本身、員工、往來的廠商、股東、債權人、甚至是整個社會，因此若是可以事先偵測到財務可能發生的危機，對每個層面都是有益的。每一種模型的提供都是為了能避免遭受損失，甚至能提早發現可能出現的失敗。因此本論文主要是利用財務比率的資訊，去衡量企業的財務危機，而期望能達到一個預警的目的。本論文以區別分析法、SVM(Support Vector Machine)、BSVR(Bayesian Support Vector Regression)做為研究的模型。以財務危機發生前一年的財務比率做為選取的變數，利用不同的選取方法，我們選取出4組變數。以發生財務危機前一年的20筆正常公司與20筆財務危機公司為訓練樣本，10筆正常公司與10筆危機公司為測試樣本。得到的結論為: 分別比較4組變數在區別分析模型下的表現，可以看出4組變數在變異數相等的模型下，至少有3組變數的預測正確率是理想的。在SVM模型中，可以看到4組變數在經過尺度化和給定參數 =1， =1/40後，有3組的正確率都可以達到95%以上。在BSVR模型中，可以看到變數的選擇對模型的預測能力是很重要的。 | zh_TW |
| dc.description.tableofcontents | 第一章 緒論 1第一節 研究動機與目的 1第二節 研究架構 2第二章 文獻回顧 3第一節 企業失敗的意涵 3第二節 失敗的預測模型 4第三章 研究方法 9第一節 區別分析法 9第二節 SVM(Support Vector Machine) 14第三節 BSVR(Bayesian Support Vector regression) 20第四章 實證結果 23第一節 資料來源與變數說明 23第二節 變數的選取 26第三節 區別分析模型的實證結果 38第四節 SVM模型的實證結果 45第五節 BSVR模型的實證結果 49第五章 結論與建議 50第一節 重要結論 50第二節 建議與未來研究方向 51參考文獻 54附錄 57 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0091354019 | en_US |
| dc.subject (關鍵詞) | 財務危機 | zh_TW |
| dc.subject (關鍵詞) | 財務比率 | zh_TW |
| dc.subject (關鍵詞) | 區別分析 | zh_TW |
| dc.subject (關鍵詞) | SVM | en_US |
| dc.subject (關鍵詞) | BSVR | en_US |
| dc.title (題名) | 企業財務危機之預測 | zh_TW |
| dc.type (資料類型) | thesis | en |
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