Paper Title:
Portfolio Optimization for Index Investing Based on Self-Organizing Neural Network
  Abstract

Index investing is an important issue for researchers and practitioners. This paper proposes an index portfolio optimization model for index investing via employing CSI 300 as underlying index. Firstly, a self-organizing neural network clustering model is constructed to complete the stock clustering based on stock trend which regards stock price as input. The index portfolio optimization model is proposed to determine the optimal investment proportion of each cluster sampling and achieve the minimum tracking error. The constraint BP algorithm is improved to benefit the optimization calculation of stock weights. Empirical results show that our approach achieves smaller tracking error and better index tracking effect than the random sampling.

  Info
Periodical
Chapter
Chapter 6: Intelligent System
Edited by
Yun-Hae Kim and Prasad Yarlagadda
Pages
1595-1598
DOI
10.4028/www.scientific.net/AMM.303-306.1595
Citation
L. N. Ni, J. Q. Zhang, "Portfolio Optimization for Index Investing Based on Self-Organizing Neural Network", Applied Mechanics and Materials, Vols. 303-306, pp. 1595-1598, 2013
Online since
February 2013
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Price
$35.00
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