Suchergebnisse

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

Abstract
Mortality forecasting plays an essential role in designing welfare policies and pricing aged-related financial derivatives. However, most prevailing models do not perform well in mortality forecasting particularly for the elder people. Indeed, the problem of missing category for the elderly is a typical feature in developing countries, because people are shorter-lived in earlier times and hence the mortality is recorded up to fewer age categories. For example, in Taiwan, the mortality is recorded up to an age of 95 before 1997, but as the improvement of life expectancy, the mortality is recorded up to an age of 100 afterwards. This paper proposes several approaches for data imputation to alleviate this systematic missing data problem of the mortality data. Motivated by Lee, and Carter. 1992. "Modelling and Forecasting the Time Series of US Mortality." Journal of the American Statistical Association 87:659–71 and Renshaw, and Haberman. 2006. "A Cohort-Based Extension to the Lee-Carter Model for Mortality Reduction Factors." Insurance: Mathematics and Economics 38:556–70, we employ factor models, in which age, period, and cohort are employed as useful effects. Simulation study and an empirical study using mortality data of Taiwan demonstrate the improvement in forecasting using a suitable data augmentation technique.