Elsevier

Decision Support Systems

Volume 64, August 2014, Pages 31-42
Decision Support Systems

A kernel entropy manifold learning approach for financial data analysis☆

https://doi.org/10.1016/j.dss.2014.04.004Get rights and content
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open access

Highlights

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    A kernel entropy manifold learning algorithm for financial data (MLFD)

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    MLFD employs the information metric to measure the relationships between two financial data points.

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    MLFD yields reasonable and accurate low-dimensional embedding of the original financial data set.

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    The accuracy of the financial early warning is improved by MLFD.

Abstract

Identification of intrinsic characteristics and structure of high-dimensional data is an important task for financial analysis. This paper presents a kernel entropy manifold learning algorithm, which employs the information metric to measure the relationships between two financial data points and yields a reasonable low-dimensional representation of high-dimensional financial data. The proposed algorithm can also be used to describe the characteristics of a financial system by deriving the dynamical properties of the original data space. The experiment shows that the proposed algorithm cannot only improve the accuracy of financial early warning, but also provide objective criteria for explaining and predicting the stock market volatility.

Keywords

Manifold learning
Financial analysis
Low-dimensional embedding
Information metric

Cited by (0)

Yan Huang is a doctoral candidate of School of Management and Economics, University of Electronic Science and Technology of China. She received her M.S. degree in Management Science and Engineering from Beijing University of Technology in 2007. Her research interests include data mining, machine learning and information management.

Gang Kou is a Professor and Executive Dean of School of Business Administration, Southwestern University of Finance and Economics. He is the managing editor of International Journal of Information Technology & Decision Making and series editor of Quantitative Management (Springer). Previously, he was a professor of School of Management and Economics, University of Electronic Science and Technology of China, and a research scientist in Thomson Co., R&D. He received his Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha; got his Master's degree in Dept of Computer Science, Univ. of Nebraska at Omaha; and B.S. degree in Department of Physics, Tsinghua University, Beijing, China. He has participated in various data mining projects, including data mining for software engineering, network intrusion detection, health insurance fraud detection and credit card portfolio analysis. He has published more than eighty papers in various peer-reviewed journals and conferences. Gang Kou has been Keynote speaker/workshop chair in several international conferences. He co-chaired Data Mining contest on The Seventh IEEE International Conference on Data Mining 2007 and he is the Program Committee Co-Chair of the 20th International Conference on Multiple Criteria Decision Making (2009) and NCM 2009: 5th International Joint Conference on INC, ICM and IDC. He is also co-editor of special issues of several journals, such as Journal of Multi Criteria Decision Analysis, Decision Support Systems, Journal of Supercomputing and Information Sciences.

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Copyright © 2014 The Authors. Published by Elsevier B.V.