( 您好!臺灣時間:2021/05/19 08:51
字體大小: 字級放大   字級縮小   預設字形  


研究生(外文):Sha-Wei Zhang
論文名稱:利用 XGBoost 建立台灣中小型企業信用風險評估模型
論文名稱(外文):Using XGBoost model to establish a credit assessment model for SMEs in Taiwan
指導教授(外文):Ping-Yu HsuJin-Huei Yeh
外文關鍵詞:XGBoost modelSmall and Medium Enterprises (SMEs)Credit Risk
  • 被引用被引用:0
  • 點閱點閱:164
  • 評分評分:
  • 下載下載:67
  • 收藏至我的研究室書目清單書目收藏:0
基於上述認知,本文對台灣中小型企業的信用違約風險進行了實證研究,採用XGBoost模型分析影響台灣中小企業之重要因素,建立中小企業財務風險的預警模型。本文有以下兩點發現:第一:本文總供挑選22個財務與非財務變數對中小企業的財務風險進行分析預測,發現在前十大變數排行中,僅有2個為非財務變數,其他均為財務變數,因此台灣中小型企業違約風險預測的關鍵預測因數主要依然是財務變數,但非財務變數(公司治理變數/審計品質變數)的作用一樣不可忽視。第二: XGBoost在保留了決策樹優點的同時,減少了決策樹過度擬合的問題。該方法在信用風險評估準確度上明顯優於決策樹與隨機漫步,且隨著訓練樣本的增加,該模型的優勢愈發明顯。
Based on the above cognition, this paper conducts an empirical study on the credit default risk of Taiwan's OTC-listed SMEs; establishes and contrasts early warning models using XGBoost, Decision Tree and Random Forest. Two main findings unfold. Firstly, classification via XGBoost appears to be significantly superior to Decision Tree and Random Forest model in terms of prediction accuracy and the advantage using XGBoost becomes obvious when training sample increases. Given the inherited features from the Decision Tree, XGBoost further circumvents the common problem of overfitting and is thus worthy of attention for applications in credit assessment for SMEs. Secondly, we identify key predictors for the credit risk prediction of Taiwan's SMEs. Among a total of 22 employed financial and non-financial variables, although 2 out of the top 10 important variables are found to be governance-related non-financial variables, financial variables remain crucial for SMEs’ credit worthiness.
中文摘要 II
目錄 IV
圖目錄 VI
表目錄 VII
一、緒論 1
1-1  研究背景與動機 1
1-2  研究目的 2
1-3  研究方法 4
二、文獻探討 6
2-1  企業風險評估變數 6
2-2  中小型企業信用風險評估 11
2-3  XGBOOST的定義與模型 17
三、研究設計 25
3-1  研究架構 25
3-2  研究方法及資料來源 26
3-3  研究變數介紹 28
3-4  模型架構 30
3-5  資料前處理 31
四、實證分析 33
4-1  樣本敘述統計表 33
4-2  模型參數設定 36
五、結論 45
5-1  結論 45
5-2  研究限制與未來建議 46
參考文獻 47
〔1〕 Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. 23(4), 589-609.
〔2〕 Altman, E. I. (2010). The value of non-financial information in small and medium-sized enterprise risk management. 6(2), 1-33.
〔3〕 Altman, E. I., & Sabato, G. (2005). Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs. Journal of Financial Services Research, 28(1-3), 15-42. doi:10.1007/s10693-005-4355-5
〔4〕 Altman, E. I., & Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, 43(3), 332-357. doi:10.1111/j.1467-6281.2007.00234.x
〔5〕 Andrieu, G., Staglianò, R., & van der Zwan, P. (2017). Bank debt and trade credit for SMEs in Europe: firm-, industry-, and country-level determinants. Small Business Economics, 51(1), 245-264. doi:10.1007/s11187-017-9926-y
〔6〕 Babaev, D., Savchenko, M., Tuzhilin, A., & Umerenkov, D. (2019). E.T.-Rnn. Paper presented at the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
〔7〕 Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
〔8〕 Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
〔9〕 Chen, X., Wang, X., & Wu, D. D. (2010). Credit risk measurement and early warning of SMEs: An empirical study of listed SMEs in China. Decision Support Systems, 49(3), 301-310. doi:10.1016/j.dss.2010.03.005
〔10〕 Cheng-Ying, W. (2004). Using non-financial information to predict bankruptcy: A study of public companies in Taiwan. International Journal of Management, 21(2), 194.
〔11〕 Creal, D. D., Gramacy, R. B., & Tsay, R. S. (2014). Market-Based Credit Ratings. Journal of Business & Economic Statistics, 32(3), 430-444. doi:10.1080/07350015.2014.902763
〔12〕 Dietsch, M., & Petey, J. (2004). Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs. Journal of Banking & Finance, 28(4), 773-788. doi:10.1016/s0378-4266(03)00199-7
〔13〕 Duan, J.-C., Sun, J., & Wang, T. (2012). Multiperiod corporate default prediction—A forward intensity approach. Journal of Econometrics, 170(1), 191-209. doi:10.1016/j.jeconom.2012.05.002
〔14〕 Gupta, V. (2017). A Survival Approach to Prediction of Default Drivers for Indian Listed Companies. Theoretical Economics Letters, 07(02), 116-138. doi:10.4236/tel.2017.72011
〔15〕 Kwak, W., Shi, Y., Cheh, J. J., & Lee, H. (2004). Multiple criteria linear programming data mining approach: An application for bankruptcy prediction. Paper presented at the Chinese Academy of Sciences Symposium on Data Mining and Knowledge Management.
〔16〕 Lee, T. S., & Yeh, Y. H. (2004). Corporate governance and financial distress: Evidence from Taiwan. Corporate governance: An international review, 12(3), 378-388.
〔17〕 Ma, X., Sha, J., Wang, D., Yu, Y., Yang, Q., & Niu, X. (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31, 24-39. doi:10.1016/j.elerap.2018.08.002
〔18〕 Mensah, Y. M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study. Journal of accounting research, 380-395.
〔19〕 Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance, 29(2), 449-470.
〔20〕 The New Basel Capital Accord. (2003).
〔21〕 Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
〔22〕 Vallini, C., Ciampi, F., Gordini, N., & Benvenuti, M. (2008). Can credit scoring models effectively predict small enterprise default? Statistical evidence from Italian firms. Paper presented at the Proceedings of the 8th Global Conference on Business & Economics, Association for Business and Economics Research (ABER).
〔23〕 vivounicorn. (2017). 机器学习与人工智能技术分享. Retrieved from https://www.zybuluo.com/vivounicorn/note/446479#4210-adam
〔24〕 李想. (2017). 基于 XGBoost 算法的多因子量化选股方案策划. 上海师范大学,
〔25〕 經濟部中小企業處. (2019). 2019年中小企業白皮書. 臺北市: 經濟部中小企業處
〔26〕 廖益均. (2014). 台灣公司治理指標(TCGI)架構概述. 貨幣觀測與信用評等, 107, 110-123.
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
第一頁 上一頁 下一頁 最後一頁 top