| Summary: | 碩士 === 輔仁大學 === 統計資訊學系應用統計碩士班 === 100 === Seeing the financial crisis occurs three years ago, the effect makes a significant impact on investors, banks, as well as government. Therefore, if we can create a financial crisis warning model to credit risk of such crisis, it will be helpful to the financial institution to reduce the possibility of such loss.
The main purpose of this research is to apply CRISP-DM techniques, as well as traditional statistical methods to develop Crisis Warning models. By using the methods of logistic and neural networks to analyze this data. For each method, the Crisis Warning models were built. Correct rates were compared with all of the models. We try to form the best model to find the significant variables that affect financial distress happened.
From the decision trees model, we found that “the number of inquiry in this three months”, “using cash card or not”,” loan using ratio” etc. are the significant variables. It will understand interactive of the significant variables and help government, banks, and investors to assure the characters of financial distress then reduce the possibility of loss.
|
|---|
