| dc.contributor.advisor | 廖四郎 | zh_TW |
| dc.contributor.advisor | Liao, Szu-Lang | en_US |
| dc.contributor.author (Authors) | 饒宇軒 | zh_TW |
| dc.contributor.author (Authors) | Jao, Yu-Hsuan | en_US |
| dc.creator (作者) | 饒宇軒 | zh_TW |
| dc.creator (作者) | Jao, Yu-Hsuan | en_US |
| dc.date (日期) | 2018 | en_US |
| dc.date.accessioned | 3-Jul-2018 17:26:28 (UTC+8) | - |
| dc.date.available | 3-Jul-2018 17:26:28 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-Jul-2018 17:26:28 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0105352016 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/118238 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 105352016 | zh_TW |
| dc.description.abstract (摘要) | 在2007年的金融危機後，外部評等機構信用評等的可信度受到打擊，外部信用評等機構的信用評等無法反映公司的經營能力。而BASEL II協定中，允許銀行經過主管機關核准後，使用內部模型法評估自身的信用風險。在這樣的條件下，銀行為了加強對自身信用風險的控管，我認為銀行將會開始發展自己內部的信用評等模型。本研究將變數分為財務變數和市場變數，財務變數是根據資產管理能力、獲利能力、財務結構和償債能力這四項因素，選取15項財務指標；市場變數為該公司的股票波動度和違約距離(Distance to Default, DD)作為市場變數。研究樣本為2000年到2008年半導體公司每季的信用評等將其分為三類，使用倒傳遞類神經網路模型進行分析。本研究中有模型A和模型B，模型A為只使用財務變數的倒傳遞類神經網路，模型B為使用財務變數和市場變數的倒傳遞類神經網路，並比較兩個模型的預測準確度。經由實證結果發現加入違約距離後，信用評等為第三類的資料能夠被有效的預測到，這是只使用財務比率為變數的倒傳遞類神經網路所無法辦到的。加入違約距離後，同時也使得整體準確度也由55.56%提升為58.89%。 | zh_TW |
| dc.description.abstract (摘要) | In the financial crisis of 2007, the credibility of external rating agencies was undermined and the credit ratings of external credit rating agencies could not reflect the company`s operating capabilities. In the BASEL II agreement, banks are allowed to pass the approval of the competent authority and use the internal model method to assess their own credit risk. Under such conditions, in order to strengthen the bank`s control over its own credit risk, I think banks will begin to develop their own internal credit rating models.This study divides the variables into financial variables and market variables. The financial variables are based on four factors, asset management capabilities, profitability, financial structure and solvency. In the study, 15 financial indicators are selected as a financial variable. Market variables are the company`s stock volatility and Distance to Default (DD) as a market variable. The sample for the study was divided into three categories for each quarter of the credit rating of semiconductor companies from 2000 to 2008, and was analyzed using back propagation neural network model. In this study, there are Model A and Model B. Model A is a back propagation neural network that only uses financial variables. Model B is a back propagation neural network that uses financial variables and market variables. In the study, prediction accuracy of the two models is compared.Through empirical results, it is found that when the Distance to Default (DD) is added, the credit rating of the third type of data can be effectively predicted. This is impossible to achieve using only the back propagation neural network with financial variables. After adding the Distance to Default (DD), it also increased the overall accuracy from 55.56% to 58.89%. | en_US |
| dc.description.tableofcontents | 第一章、緒論 1第一節、研究動機 1第二節、研究目的 2第三節、研究架構 3第二章、文獻回顧 5第一節、信用評等 5第二節、早期信用評等方法 6第三節、現今信用評等方法 8第三章、研究方法 11第一節、研究樣本與來源 11第二節、研究模型 25第三節、變數定義 36第四章、實證分析 40第一節、研究資料處理與分析 40第二節、實證結果 46第五章、結論與建議 55第一節、研究結論 55第二節、研究建議 57參考文獻 59 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0105352016 | en_US |
| dc.subject (關鍵詞) | 倒傳遞類神經網路模型 | zh_TW |
| dc.subject (關鍵詞) | 信用評等 | zh_TW |
| dc.subject (關鍵詞) | KMV模型 | zh_TW |
| dc.subject (關鍵詞) | 違約距離 | zh_TW |
| dc.subject (關鍵詞) | Back propagation neural network model | en_US |
| dc.subject (關鍵詞) | Credit rating | en_US |
| dc.subject (關鍵詞) | KMV model | en_US |
| dc.subject (關鍵詞) | Distance to default | en_US |
| dc.title (題名) | 市場因子於倒傳遞類神經網路對信用評等之影響 | zh_TW |
| dc.title (題名) | The Effect of Market Factor in the Back Propagation Neural Network on Credit Rating | en_US |
| dc.type (資料類型) | thesis | en_US |
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| dc.identifier.doi (DOI) | 10.6814/THE.NCCU.MB.008.2018.F06 | - |
