遗传规画决策树模型于房贷提前偿还之风险管理-中华民国住宅学会.pdfVIP

遗传规画决策树模型于房贷提前偿还之风险管理-中华民国住宅学会.pdf

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遗传规画决策树模型于房贷提前偿还之风险管理-中华民国住宅学会

63 87 JOURNAL OF HOUSING STUDIES, VOLUME 18 NO. 1, JUNE 2009 Genetic Programming Decision Trees in the Risk Management of Mortgage Loan Prepayment *** Wo-Chiang Lee*, Chien-Chung Nieh** 摘 要 GP-DT Logit GP-DTLogit Logit GP-DT GP-DT500 GP-DT GP-DTLogit Logit-3 GP-DT 500- 1 GP-DT 500-2 關鍵詞 :提前償還、遺傳規畫決策樹模型、風險管理 、成本率、誤判成本 ABSTRACT This paper applies a nonparametric GP-DT model to the risk management of mortgage loan prepayment. While previous empirical studies used the Logit model as a benchmark, the results of this study show that GP-DT models have better accuracy on average than Logit models. When considering different cut-off values, the Logit model is not consistent either in sample or out of sample. However, the GP-DT models present consistent conclusions. The GP- DT 500 has a higher overall average accuracy. In addition, the higher the number of generations, the better the GP-DT model’s performance. Furthermore, the GP-DT model is also consistently superior to the Logit model in terms of misclassification costs. When the ratio costs increase, the misclassification costs also decrease in most models, except the Logit-3. Among the GP-DT models, the GP-DT 500-1 and GP-DT 500-2 models offer the best performance. Using sensitivity analysis, this study also examines the influence of the key variables on prepayment. In order of importance, these variables include the loan purpose, location, type of occupation, loan interest rate, sex, age, the loan amount, year of housing, and loan period. Finally, this paper also proposes a practical risk management system for mortgage loans for reference. Key words: prepayment, GP-DT, risk management, ratio cost, misclassification cost (本文於2008年9月30 日收稿 ,2009年3月24 日審查通過 ,實際出版日期2009年6月

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