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10 sai lầm khi xây dựng mô hình dự báo dữ liệu thời gian thực

Posted by sanghv on 04/03/2014 in Financial Risk, Stock market risk |

Khi xây dựng mô hình dự báo chúng ta thường mắc phải các sai lầm sau: Nếu không kiểm tra cẩn thận các giả định – Không có khả năng hiểu được những lĩnh vực kinh doanh , và tất cả các giả định xung quanh một mô hình dự báo cụ thể có thể […]

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Predicting Stock Market Returns

Posted by sanghv on 10/09/2013 in R, Stock market risk |

Predicting Stock Market Returns: R Code of Chapter 3 (right-click here to save the code in a local file) ################################################### ### The Available Data ################################################### library(DMwR) data(GSPC) ################################################### ### Handling time dependent data in R ################################################### library(xts) x1 <- xts(rnorm(100),seq(as.POSIXct(“2000-01-01″),len=100,by=”day”)) x1[1:5] x2 <- xts(rnorm(100),seq(as.POSIXct(“2000-01-01 13:00″),len=100,by=”min”)) x2[1:4] x3 <- xts(rnorm(3),as.Date(c(‘2005-01-01′,’2005-01-10′,’2005-01-12’))) x3 x1[as.POSIXct(“2000-01-04”)] x1[“2000-01-05”] x1[“20000105”] x1[“2000-04”] x1[“2000-03-27/”] […]

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The evaluation of classification models for credit scoring

Posted by sanghv on 10/09/2013 in Credit risk |

The evaluation of classification models for credit scoring The Contents 1 Introduction ——————————– ——————————– —————————– 1 2 Reasons of the model evaluation ——————————– ——————————– – 3 3 Criterions of the model evaluation ——————————– ——————————- 6 3.1 The classification accuracy ——————————– ——————————– —– 6 3.1.1 The estimation of true error rates ——————————– ————————- 6 3.1.2 Sampling […]

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Filter- versus Wrapper-based Feature Selection For credit scoring

Posted by sanghv on 07/09/2013 in Credit risk, Feature selection, Financial Risk, Machine Learning |

We address the problem of credit scoring as a classification and feature subset selection problem. Based on the current framework of sophisticated feature selection methods, we identify features that contain the most relevant information to distinguish good loan payers from bad loan payers. The feature selection methods are validated on several real world datasets with […]

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