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  • 學位論文

考量總體經濟及產業變數的違約預測模型--以台灣面板產業為例

Default prediction with macroeconomics and industrial variables:A case study of Taiwan FPD industry

指導教授 : 林修葳

摘要


本研究從總體面及產業面角度分析何種自變數會影響台灣面板產業整體的違約狀況,挑選與面板業相關的自變數與產業違約率進行迴歸分析,將其中顯著性佳的變數建構違約預測模型,再用以作樣本外測試,檢視預測結果。以期能找出更精準描述面板產業內企業違約的風險因子。 在研究方法上採用線性函數及分段線性函數兩種函數模型去分析解釋變數與應變數之間的關係。結果顯示油價、台幣對日圓貶值率、17吋面板價格、電腦電子光學製品WPI以及銀行對電子零組件製造業放款餘額年增幅變數,均與產業內企業違約率有顯著的相關性。 樣本外測試結果發現建立的預測模型其標準差誤差均相當微小,顯示模型有優良的預測能力,可用以當作修正傳統會計基礎預測模型的評分項目。

並列摘要


To predict the default probability of companies more precisely, this thesis considers several macroeconomic and industry variables to predict the default rate of Taiwan FPD industry. Examining the correlation between dependent and independent variables by using linear function regression and piecewise linear function regression. The result reveals that oil price, new Taiwan dollar depreciation against Yen, 17’’ inches panel price, WPI of computer, electronic and optical products and the growth rate of loans & discounts at all banks to electronic parts & components have significant relevance with industry default rate of Taiwan FPD industry. Out-sample testing shows the prediction error is tiny, which means prediction of the model is excellent. Therefore, in spite of considering accounted-based variables, we can use these external environment variables to amend the score of company’s credit risk.

並列關鍵字

industry credit risk default credit rating

參考文獻


Altman, E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance 23 (September): 589-609.
Coats, P. and Fant, L. (1993), “Recognizing Financial Distress Patterns Using a Neural Network Tool,”Financial Management 22, 142-155.
Crouhy, M., D. Galai, and R. Mark. 2001. “Prototype risk rating system,” Journal of Banking and Finance 25: 47-95.
Hillegeist, S. A., E. B. Keating, D. P. Cram, and K. G. Lundstedt. 2004. “Assessing the Probability of Bankruptcy” Review of Accounting Studies, 9, 5–34
Kim, Neung and Kyungho Kim. 1992. “Predicting Financial Condition of an Industry,” The Journal of Business Forecasting Methods & Systems 3 (Fall): 14-16.

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