Support Vector Regression with Lévy Distribution Kernel for Stock Forecasting
- 10.2991/icacsei.2013.156How to use a DOI?
- Normal distribution, Lévy distribution, Probability density function, Mercer Condition and Support Vector Regression.
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.
- © 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/).
Cite this article
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 -