Machine Learning Approaches to Predicting Company Bankruptcy - Journal of Financial Risk Management

JFRM > Vol.6 No.4, December 2017

Journal of Financial Risk Management

Volume 6, Issue 4 (December 2017)

ISSN Print: 2167-9533 ISSN Online: 2167-9541

Google-based Impact Factor: 1.09 Citations

Machine Learning Approaches to Predicting Company Bankruptcy ()

HTML XML

Download as PDF (Size: 1518KB) PP. 364-374

DOI: 10.4236/jfrm.2017.64026 2,839 Downloads 5,330 Views Citations

Author(s)

Wenhao Zhang

Affiliation(s)

Catholic Memorial School, West Roxbury, USA.

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.

KEYWORDS

Neural Networks, Random Forest, KNN, Bankruptcy Prediction

Share and Cite:

Zhang, W. (2017) Machine Learning Approaches to Predicting Company Bankruptcy. Journal of Financial Risk Management, 6, 364-374. doi: 10.4236/jfrm.2017.64026.

Cited by

[1]	Predicting accounting fraud using imbalanced ensemble learning classifiers–evidence from China
	Accounting & Finance, 2023

[2]	Klasifikasi Rumah Tangga Penerima Subsidi Listrik di Provinsi Gorontalo Tahun 2019 dengan Metode K-Nearest Neighbor dan Support Vector Machine
	JUSTIN (Jurnal Sistem dan …, 2022

[3]	Machine Learning based Business Risk Analysis for Big Data: A Case Study of Pakistan
	2022

[4]	POLYTECHNIC OF TURIN
	2022

[5]	Comparison Between Convolutional Neural Network and Random Forest as Classifier for Cerebral Infarction
	… Intelligent Systems for …, 2022

[6]	The need for disruption in the credit ratings landscape: a model for machine learning computed credit ratings.
	2022

[7]	Evaluation metrics and dimensional reduction for binary classification algorithms: a case study on bankruptcy prediction
	Pons, J Parra-Dominguez… - The Knowledge …, 2022

[8]	Dynamic Provisioning and Run-time Optimization of Cloud Workloads.
	2022

[9]	Makine öğrenmesi algoritmaları ile kredi temerrüt riskini tahmin etme
	2022

[10]	Sistema De Busca Reversa De Imagens Utilizando Aprendizado Por Contraste
	2022

[11]	The statistical learning methods in image processing and facial recognition
	2021

[12]	Performance of Various Low-level Decoder for Surface Codes in the Presence of Measurement Error
	2021

[13]	An Analysis of Convolutional Neural Network—Random Forest for Liver Cancer CT Scan Images
	… International Conference on …, 2021

[14]	Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine
	2021

[15]	Comparison between Convolutional Neural Network and Convolutional Neural Network-Support Vector Machines as the classifier for Colon Cancer
	2020

[16]	Understanding the Role of Background Knowledge in Predictions
	2020

[17]	Data analysis for bankruptcy prediction: the wholesale and retail sector in Italy.
	2020

[18]	Applications of artificial olfaction and machine learning for detection of volatile gas
	2020

[19]	Machine Learning in Banking Risk Management: A Literature Review
	2019

[20]	Winner Prediction of Blood Bowl 2 Matches with Binary Classification
	2019

[21]	Bankruptcy prediction models: A comparative analysis

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journals Menu

Home

About SCIRP

Service

Policies