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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/115729
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/115729


    Title: 公司財務困境機率之評估—Logistic-SVM模型之應用
    The Evaluation of Companies' Probability of Financial Distress—The Application of Logistic-SVM Model
    Authors: 羅子欣
    Luo, Zi Xin
    Contributors: 陳威光
    林靖庭

    Chen,Wei Kuang
    Lin, Ching Ting

    羅子欣
    Luo,Zi Xin
    Keywords: 製造業
    上市公司
    Logistic模型
    SVM模型
    組合模型
    財務困境
    Manufacturing enterprises
    Listed companies
    Logistic model
    SVM model
    Combined model
    Financial distress
    Date: 2018
    Issue Date: 2018-02-02 10:59:07 (UTC+8)
    Abstract: 近年來,在中國大陸市場有大量公司進行掛牌上市的同時,也有越來越多的公司出現債務逾期甚至是違約的情況。考慮到目前中國經濟增速放緩,處在轉型發展的複雜階段,銀行信貸等資金供應鏈需要謹慎評估企業出現財務困境的風險。但是我們發現金融機構在平常管理信貸業務的時候會盲目地看重高額利潤的回報而忽略借款者潛在的財務危機,而且投資人在進行投資分析的時候往往也會忽略企業的財務狀況而使自己遭受損失,因此從企業的財務狀況入手對其進行財務困境機率的評估有非常重大的現實意義。
    本文通過對企業財務指標進行相關分析以構建公司財務困境機率評估模型。本文選取了不良貸款率最高的製造業作為研究對象,將2015年滬深兩地的124家上市製造業公司的財務資料作為訓練樣本,將2014年120家上市公司的財務資料作為檢驗樣本,將交易所特別處理公司劃分為非正常組公司,其餘為正常組。本文通過篩選得出23個財務指標作為研究變數,引入了 Logistic 模型與 SVM 模型,針對單一模型的預測結果在準確率和穩定性方面不理想的問題引入了基於 Logistic 模型、SVM 模型的組合模型,並用檢驗樣本進行了四個模型的相關實證分析,比較了四個模型之間的準確度。
    對四個模型進行實證分析的結果表明:Logistic模型穩健性好、可解釋性強、建模過程簡單易操作,但分類精度略低於 SVM 模型;SVM雖然分類精度高,但缺乏可解釋性和穩定性,且建模過程依賴專家知識和經驗;Logistic -SVM 組合模型則兼具其優點,預測精確度較單一模型均有提高,而且研究發現異態並行結構優於串型結構。通過本文建立的模型可以計算出企業的陷入財務困境的機率,有效評估企業的違約風險,進而為相關金融機構和投資者提供放款或投資的判斷依據。
    At present, more and more companies are listed in the Chinese mainland market. At the same time, more and more companies are frequently at risk of default and overdue. Given the slowdown in China's economic growth and the complex environment of transformation and development, the supply of funds such as bank loans and other capital needs to be cautious, debt default, loan overdue cases are still likely to occur one after another. However, we find that financial institutions blindly value the return of high profits while ignoring the potential financial crisis of borrowers in the normal management of credit business, it is of great significance to start with the financial status of a company to assess the probability of financial distress.
    This paper builds a company default probability assessment model by analyzing the financial indicators of enterprises. This paper selects the manufacturing industry with the highest NPL as the research object. Taking the financial data of 124 listed manufacturing companies in Shanghai and Shenzhen in 2015 as the training samples, using the financial data of 120 listed companies in 2014 as the test sample, Exchange special treatment companies divided into non-normal group companies, the rest for the normal group. According to the data of its 2015 financial indicators, 23 financial indicators were screened out as research variables, and a comprehensive analysis was carried out. The Logistic model and SVM model were introduced. Combined model was introduced based on the Logistic model and SVM model to solve the problem that the prediction accuracy and stability of the single model were not ideal,. Finally, empirical analysis of the four models is carried out using the sample data of listed companies in 2014, and the accuracy of the four models is compared.
    The results of empirical analysis of the four models show that Logistic regression model has no strict assumptions on the data, a better stability and interpretation, but the classification accuracy is slightly lower than the SVM model. The SVM model has higher classification accuracy, but the disadvantage is the lack of interpretability and stability, the modeling process depends on expert knowledge and experience. In order to balance the stability of Logistic model and the accuracy of SVM model, this paper introduces a combined model based on Logistic model and SVM model. The analysis shows that the prediction accuracy of combined model is higher than that of single model, the combination of Logistic regression model and SVM model based on Parallel structure has a higher prediction accuracy than Sequential structure. The model established in this paper can calculate the default probability of an enterprise, effectively assess companies’ risk of financial distress, and then provide the judgment basis for the relevant financial institutions and investors to lend or invest.
    Reference: 于立勇,詹捷輝。基於Logistic 回歸分析的違約機率預測研究[J]。財經研究,2004(9):15-23。
    石曉軍,邊界Logistic違約率模型及實證研究[J]。管理科學學報。2007,8(6):44-51。
    田秋麗。Logistic 模型在中小企業信用風險度量中的應用分析[J]。中國商貿,2010,08:64-65。
    宋冬梅,沈友娣。運用Logistic 模型評價上市公司信用風險[J]。財會刊,2008,05:51-52。
    李向前,賀卓異。我國商業銀行內部評級的順週期性研究--基於我國上市公司違約率的分析[J]。河北經貿大學學報,2014,02:63-68。
    李裕豐,李倩,於洋。中國上市公司動態違約機率研究--基於KMV 模型的實證檢驗[J]。瀋陽工業大學學報(社會科學版),2011,03:222-227。
    沈沛龍,周浩。基於支持向量機理論的中小企業信用風險預測研究[J]。國際金融研究,2010,08:77-85。
    沙曉。基於Logit 模型的上市公司評級研究[J]。經濟視角(下旬刊),2013,08:126-127。
    胡勝,朱新蓉。我國上市公司信用風險評估研究--基於Logit 模型的分析[J]。中南財經政法大學學報,2011,03:38-41。
    徐大江。組合預測及其應用研究[J]。《統計與預測》,1996,(1):1-2。
    唐亮,張北陽,陳守東。我國上市公司信用風險評價和度量——基於面板資料Logit 模型的實證分析[J]。工業技術經濟,2011,02:140-147。
    夏紅芳,馬俊海。基於KMV 模型的上市公司信用風險預測[J]。預測,2008,06:39-43。
    甄士龍,黎豔。用Logistic 模型估計企業的違約機率[J]。廣西金融研究,2008,11:43-46。
    管杜娟。商業銀行信用風險評估指標體系的構建研究[J]。經濟研究導刊,2011,11:65-66。
    劉瑞霞,張曉麗,郝豔麗。多元有序Logit 模型用於上市公司信用評級探析[J]。財會月刊,2008,02:54-55。
    劉瑞霞,張曉麗,陳小燕,郝豔麗。多元有序Logit 模型用於上市公司信用評級探析[J]。財會月刊,2008,02:54-55。
    遲國泰,曹勇,党均章。基於最優負債係數的上市銀行違約機率測算模型與實證[J]。運籌與管理,2012,03:176-186。
    錢思佳。我國上市公司信用風險度量實證研究[D]。西南財經大學2010。
    韓豔豔,王波。基於logistic 回歸-KMV 模型的上市公司信用風險評價研究[J]。科技與管理,2011,01:104-107。
    龐素琳。Logistic 回歸模型在信川風險分析中的應用。數學的實踐與識,2006(9):129-137。
    寶富,李南。上市公司違約機率的實證研究[J]。商業時代,2007,18:78-79。
    Adnan Khashman, Modeling cognitive and emotional processes: A novel neural network architecture Elsevier Ltd. All rights reserved, 2010, 34: 836-847.
    Alexander Kreinin, Ahmed Nagi. Calibration of the default Probability model. European umal of operational Research, 2008, 85: 1462-1476.
    Altman EI, Marco G, Varetto F.Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks. Journal of Banking and Finance. 1994
    Asghar Ali, Kevin Daly. Macroeconomic determinants of credit risk:Recent evidence from a cross country study, International Review of Financial Analysis, International Review of Financial Analysis 19, 2010: 165-171.
    Cho-Jieh C., Harry P. Unifying Discrete Structural Models and Reduced-Form Models in Credit Risk Using a Jump-Diffusion Process. Insurance, Mathematics& Economics, 2003, 33(2):357.
    David West, Neural net work credit scoring models, Statistica Neerlandica, 2000, 22: 1561-1674.
    Hamilton, Alperton Smith, KMV Modeling term structures of defaultable bonds[J]. Review of financial studies,1999(12).
    Lydian Medema Ruud H.Koning Robert Lensink. A Praetieal approach to validating a PD model. Oumal of Banking&Finanee, 2009, 33: 701 -708.
    Madalla G., Limited Dependent and Qualitative Variables in Econometrics, Uppsala Studies in Economic History, 1986, 91-554.
    Martin, Daniel, Early warning of bank failure: a logistic regression approach, These ratios are then used in disaggregated logistic models to predict bank, 1977, 35:87-93.
    Ohlson. Financial Ratios and The probabilistic of bankruptcy, accounting Research, 1980, 33: 109-131
    Pamela Nickell, William Perraudin, Varotto Simone, Ratings-based credit risk modelling: Anempirical analysis, International Review of Financial Analysis, Elsevier, 2007, vol.16(5), 434-451.
    Pang Su-Lin, Wang Yan-Ming. Credit Decision Model and Mechanism with Default Risk Parameter. SETP, 2008, 8: 81-88.
    Wiginton, Credit Risk Modeling: design and application, 1980, 23: 234-245.
    Description: 碩士
    國立政治大學
    金融學系
    104352042
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104352042
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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