%0 Journal Article
%T Machine Learning Approaches to Predicting Company Bankruptcy
%A Wenhao Zhang
%J Journal of Financial Risk Management
%P 364-374
%@ 2167-9541
%D 2017
%I Scientific Research Publishing
%R 10.4236/jfrm.2017.64026
%X Machine
Learning has undergone a tremendous progress</span><span style=\"font-family:Verdana;\">,</span><span style=\"font-family:Verdana;\"> which is evolutionary
over the last decade. It is widely used to make predictions that lead to the
most valuable decisions. Many experts in economics use models derived from Machine
Learning as important assistance, and many companies would use Neural Network,
a model in bankruptcy prediction, as their guide to prevent potential failure.
However, although Neural Networks can process a tremendous amount of attribute
factors, it results in overfitting frequently when more statistics is taken in.
By using K-Nearest Neighbor and Random Forest, we can obtain better results
from different perspectives. This paper testifies the optimal algorithm for bankruptcy
calculation by comparing the results of the two methods.
%K Neural Networks
%K Random Forest
%K KNN
%K Bankruptcy Prediction
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=81016