• Support
  • Contact Us
  • Corporate website
  • Customer Care
  • Training

  • ScienceWatch Home
  • Inside This Month...
  • Interviews

Featured Interviews
Author Commentaries
Institutional Interviews
Journal Interviews
Podcasts

  • Analyses

Featured Analyses
What's Hot In...
Special Topics

  • Data & Rankings

Sci-Bytes
Fast Breaking Papers
New Hot Papers
Emerging Research Fronts
Fast Moving Fronts
Corporate Research Fronts
Research Front Maps
Current Classics
Top Topics
Rising Stars
New Entrants
Country Profiles

  • About Science Watch

Methodology
Archives
Contact Us
RSS Feeds

 ScienceWatch

2009 : September 2009 - Fast Moving Fronts : Kyoung-jae Kim on on Financial Forecasting Using Support Vector Machines

FAST MOVING FRONTS - 2009

September 2009 Download this article
 
Kyoung-jae Kim talks with ScienceWatch.com and answers a few questions about this month's Fast Moving Front in the field of Engineering.
Kyoung-jae Kim Article: Financial time series forecasting using support vector machines
Authors: Kim, KJ
Journal: NEUROCOMPUTING, 55 (1-2): 307-319 SEP 2003
Addresses: Dongguk Univ, Coll Business Adm, Dept Informat Syst, 3-26 Pil Dong, Seoul 100715, South Korea.
Dongguk Univ, Coll Business Adm, Dept Informat Syst, Seoul 100715, South Korea.

 Why do you think your paper is highly cited?

This paper represented one of the earliest studies on financial forecasting using support vector machines (SVMs), a powerful algorithm based on statistical learning theory. SVMs are usually applied to engineering problems, such as pattern recognition, but this paper applied SVMs to a financial problem.

"...the main theme of my Ph.D. thesis is financial forecasting using various data mining techniques..."

In addition, this paper compared SVMs with other popular data mining techniques, including case-based reasoning and backpropagation neural networks. The paper also performed the McNemar test; a statistical procedure used to compare two proportions which are dependent or correlated, and is one of the most popular nonparametric tests for statistical significance, to validate the generalization of the experimental results. These may be among the primary reasons for the high citation rate.

 Does it describe a new discovery, methodology, or synthesis of knowledge?

This study is the first attempt to apply SVMs to real-world financial forecasting.

 Would you summarize the significance of your paper in layman's terms?

SVMs are the most popular data mining techniques. This study applied SVMs to predict real-world stock price indices. In addition, this study examined the feasibility of applying SVMs in financial forecasting by comparing it with backpropagation neural networks and case-based reasoning.

 How did you become involved in this research and were any particular problems encountered along the way?

My research interests include the application of data mining techniques to business problems, such as financial forecasting, corporate bond rating, credit rating, and customer classification. Thus, the main theme of my Ph.D. thesis is financial forecasting utilizing various data mining techniques.

I have previously published several research papers on financial forecasting using data mining techniques, whose subjects include artificial neural networks, case-based reasoning, support vector machines, and hybrid models of multiple techniques.

Kyoung-jae Kim, Ph.D.
Associate Professor
Department of Management Information Systems
College of Business Administration
Dongguk University
Seoul, Korea

KEYWORDS: ARTIFICIAL NEURAL-NETWORKS; INDEX FUTURES.

Download this article

back to top


2009 : September 2009 - Fast Moving Fronts : Kyoung-jae Kim on on Financial Forecasting Using Support Vector Machines

  • © 2020 Clarivate
  • Careers
  • Copyright
  • Terms of Use
  • Privacy Policy
  • Cookie Policy
Follow us Share to Twitter Share to LinkedIn Share to Facebook Share to Instagram
Previous
left arrow key
Next
right arrow key
Close Move