Forecasting Loan Default in Europe with Machine Learning
نویسندگان
چکیده
Abstract We use a dataset of 12 million residential mortgages to investigate the loan default behavior in several European countries. model occurrence as function borrower characteristics, loan-specific variables, and local economic conditions. compare performance set machine learning algorithms relative logistic regression, finding that they perform significantly better providing predictions. The most important variables explaining are interest rate characteristics. existence relevant geographical heterogeneity variable importance points at need for regionally tailored risk-assessment policies Europe.
منابع مشابه
Machine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملMachine Learning Strategies for Time Series Forecasting
The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistica...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملForecasting terminal call rate with machine learning methods
This paper deals with the development of a model to predict the products’ terminal call rate (TCR) during the warranty period. TCR represents a key information for a quality management department to reserve the necessary funds for product repair during the warranty period. TCR prediction is often carried out by parametric models such as Poisson processes, ARIMA models and maximum likelihood est...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Financial Econometrics
سال: 2021
ISSN: ['1479-8409', '1479-8417']
DOI: https://doi.org/10.1093/jjfinec/nbab010