JACIII Vol.19 No.3 pp. 397-406
doi: 10.20965/jaciii.2015.p0397


Financial Conditions Index Construction Through Weighted Lp-Norm Support Vector Regression

Ya-Fen Ye*,**, Yue-Xiang Jiang*, Yuan-Hai Shao**, and Chun-Na Li**

*College of Economics, Zhejiang University
38 Zheda Road, Hangzhou 310027, China

**Zhijiang College, Zhejiang University of Technology
182 Zhijiang Road, Hangzhou 310024, China

December 30, 2013
February 25, 2015
May 20, 2015
financial conditions index, support vector regression, Lp-norm, robustness, variable selection
This study proposes weighted Lp-norm support vector regression (WLp-SVR) robust against both noise and outliers. Using Lp-norm enables WLp-SVR to select financial variables for creating the financial conditions index (FCI) reliably. We use a weighted sum method to construct a Chinese FCI. We then evaluate our FCI’s ability to forecast real output based on the Granger-causality and Engle-Granger cointegration tests. Regression results show that our FCI has strong predictive power in forecasting real output, indicating that our FCI is a potential leading indicator of the future state of the economy.
Cite this article as:
Y. Ye, Y. Jiang, Y. Shao, and C. Li, “Financial Conditions Index Construction Through Weighted Lp-Norm Support Vector Regression,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 397-406, 2015.
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