| 摘要: | 2007-2008環球金融危機，導因於2007年8月9日爆發的次級房貸危機，投資人開始對抵押證券的價值失去信心，引發流動性風險。這場金融危機開始失控，並導致多間大型金融機構倒閉或被政府接管並引發經濟衰退。金融機構與財團法人反覆槓桿操作下，財報中很難判讀資產與負債，傳統的檢視方式難以預警破產危機。尤其大如雷曼兄弟控股公司，一旦無預警破產會引發整體金融系統風險，每家金融機構都需要思考新的工具來檢視投資標的。如果預測出會破產就不投資或減碼，在這金融洪流中避開暗礁。
本研究的主要目的是利用機器學習技術建構破產預測的最佳混合模型，在台灣6819家公司為標的資料庫，未破產公司中隨機選出220筆與220家破產公司組合成平衡型資料庫。其中有95種財務指標或分為八類組合：償債能力Solvency / 資本結構Capital Structure ratios / 其他Others / 盈利能力 Profitability / 周轉率 Turnover ratios / 現金流量率 Cash flow ratios / 成長能力 Growth / 償債能力+其他。排列組合各種訓練模型，預期找出最佳的財務指標與分類器組合。此外進一步探討若使用Feature Selection刪減維度來探討模型效能與建模時間成本的影響。
訓練結果發現，八種資料集使用CART與MLP的AUC很接近，SVM不適用因為AUC多接近於0.5不具參考性。Bagging與Adaboost多重分類器的AUC都比單一分類器可略微提昇。Feature Selection刪減維度後又可更進一步提升AUC以及減少建模時間。
;Due to the global financial crisis in 2007 and 2008, cause by August 9, 2017 Subprime mortgage crisis, investors began to lose confidence in the value of mortgage-backed securities, causing a liquidity risk. The financial crisis started out of control and leads to a number of large financial institutions fail or the governments have to take over and lead to a recession. Financial institutions operating lever repeatedly Foundation, the financial statements of assets and liabilities are difficult to interpret, the traditional way of viewing difficult warning bankruptcy. Especially big as Lehman Brothers Holdings Inc., once no warning bankruptcies happen will tiger overall financial system risk, every financial institution needs to think about new tools to review investment targets, which are not in potential bankrupt risk.
The main research objective of this study is using machine learning techniques to construct an optimal bankruptcy prediction model. The research target dataset is based on 6819 companies of Taiwan, which contain 220 non-bankruptcy and bankruptcy companies, respectively. In addition, there are 95 different financial indicators, which are divided into eight categories or combinations including Solvency Solvency / Capital Structure Capital Structure ratios / Other Others / Profitability Profitability / Turnover Turnover ratios / cash flow ratio Cash flow ratios / ability to grow Growth / solvency + other. By constructing different single classifiers and classifier ensembles, the study is expected to find out the best combination of financial indicators and classifier. Moreover, the performance impact when using feature Selection for dimensionality reduction is further examined.
According the experimental results, we found based on the eight kinds of financial type datasets using the CART and MLP classifier has similar AUC. For the SVM classifier, it is not applicable because it AUC is near 0.5 only. On the other hand, classifier ensembles by the Bagging and Adaboost techniques slightly perform better than single classifiers. moreover, feature selection can enhance AUC and reduce the modeling time. |
