TITLE:
Machine Learning Approaches to Predicting Company Bankruptcy
AUTHORS:
Wenhao Zhang
KEYWORDS:
Neural Networks, Random Forest, KNN, Bankruptcy Prediction
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.6 No.4,
December
13,
2017
ABSTRACT: Machine
Learning has undergone a tremendous progress, 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.