Support Vector Regression with Lévy Distribution Kernel for Stock Forecasting

DOI

10.2991/icacsei.2013.156How to use a DOI?

Keywords

Normal distribution, Lévy distribution, Probability density function, Mercer Condition and Support Vector Regression.

Abstract

Normal distribution has been widely applied in modern financial time series forecast. Lévy distribution is another alternative to normal distribution which this paper would like to explore. It has been demonstrated in this paper that Support Vector Regression (SVR) using Lévy distribution kernel is a robust forecasting tool and performs very well in the following experiments. Three stock Indexes are selected to test the (SVR) forecasting model. They are Hong Kong - Hang Sang Index (HSI), U.S. - Dow Jones Industrial Average Index (DJ) and China – Shanghai Composite Index (SH). It has been discovered that the SVR using Lévy kernel has given better performance in 9 out of 24 tests when compared with that of the commonly used RBF kernel. It shows promising result in general.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lucas K.C.Lai
AU  - James N.K. Liu
AU  - Yan xing Hu
PY  - 2013/08
DA  - 2013/08
TI  - Support Vector Regression with Lévy Distribution Kernel for Stock Forecasting
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 654
EP  - 657
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.156
DO  - 10.2991/icacsei.2013.156
ID  - K.C.Lai2013/08
ER  -