This study evaluates the performance of linear model trees to forecast recovery rates of defaulted bonds. The linear model trees are built based on regression trees, with a linear regression model in each leaf. I use bond characteristics, firm characteristics, industry indicators, and macroeconomic indicators as explanatory variables. The relevance of explanatory variables is assessed using the Mutual Information Feature Selection method. The results show that the linear model trees present better out-of-sample forecasts of recovery rates in comparison with some other widely-used models
This master thesis explore the potential of Machine Learning techniques in predicting default of veh...
This paper evaluates the performance of a number of modelling approaches for future mortgage default...
We perform a comparative analysis of two machine learning methods to predict corporate bond return i...
This study evaluates the performance of linear model trees to forecast recovery rates of defaulted b...
We compare the performances of a wide set of regression techniques and machine learning algorithms f...
In this study we implement a neural network to forecast recovery rates of defaulted bonds using bond...
The main requirement for effective credit risk management is the sound quantification of default and...
While previous academic research highlights the potential of machine learning and big data for predi...
A new beta regression model for recovery rates is proposed and implemented on a sample of 3,827 defa...
There have been more studies on recovery rate modeling of bonds than of personal loans and retail cr...
Forecasting partial recovery rates for non-performing loans. Based on real debt portfolio data provi...
This paper is an extended version of the paper originally presented at the International Conference ...
In this paper we explore how predictive modelling can be applied in loan default prediction. The iss...
The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default...
This thesis explores the predictive power of different machine learning algorithms in Swedish firm d...
This master thesis explore the potential of Machine Learning techniques in predicting default of veh...
This paper evaluates the performance of a number of modelling approaches for future mortgage default...
We perform a comparative analysis of two machine learning methods to predict corporate bond return i...
This study evaluates the performance of linear model trees to forecast recovery rates of defaulted b...
We compare the performances of a wide set of regression techniques and machine learning algorithms f...
In this study we implement a neural network to forecast recovery rates of defaulted bonds using bond...
The main requirement for effective credit risk management is the sound quantification of default and...
While previous academic research highlights the potential of machine learning and big data for predi...
A new beta regression model for recovery rates is proposed and implemented on a sample of 3,827 defa...
There have been more studies on recovery rate modeling of bonds than of personal loans and retail cr...
Forecasting partial recovery rates for non-performing loans. Based on real debt portfolio data provi...
This paper is an extended version of the paper originally presented at the International Conference ...
In this paper we explore how predictive modelling can be applied in loan default prediction. The iss...
The aim of this paper is to evaluate a machine learning technique, Random Forest, to predict default...
This thesis explores the predictive power of different machine learning algorithms in Swedish firm d...
This master thesis explore the potential of Machine Learning techniques in predicting default of veh...
This paper evaluates the performance of a number of modelling approaches for future mortgage default...
We perform a comparative analysis of two machine learning methods to predict corporate bond return i...