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Wavelet evolutionary network for complex-constrained portfolio rebalancing

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  • N.C. Suganya
  • G.A. Pai

Abstract

Portfolio rebalancing problem deals with resetting the proportion of different assets in a portfolio with respect to changing market conditions. The constraints included in the portfolio rebalancing problem are basic, cardinality, bounding, class and proportional transaction cost. In this study, a new heuristic algorithm named wavelet evolutionary network (WEN) is proposed for the solution of complex-constrained portfolio rebalancing problem. Initially, the empirical covariance matrix, one of the key inputs to the problem, is estimated using the wavelet shrinkage denoising technique to obtain better optimal portfolios. Secondly, the complex cardinality constraint is eliminated using k-means cluster analysis. Finally, WEN strategy with logical procedures is employed to find the initial proportion of investment in portfolio of assets and also rebalance them after certain period. Experimental studies of WEN are undertaken on Bombay Stock Exchange, India (BSE200 index, period: July 2001–July 2006) and Tokyo Stock Exchange, Japan (Nikkei225 index, period: March 2002–March 2007) data sets. The result obtained using WEN is compared with the only existing counterpart named Hopfield evolutionary network (HEN) strategy and also verifies that WEN performs better than HEN. In addition, different performance metrics and data envelopment analysis are carried out to prove the robustness and efficiency of WEN over HEN strategy.

Suggested Citation

  • N.C. Suganya & G.A. Pai, 2012. "Wavelet evolutionary network for complex-constrained portfolio rebalancing," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(7), pages 1367-1385.
    • Handle: RePEc:taf:tsysxx:v:43:y:2012:i:7:p:1367-1385
    DOI: 10.1080/00207721.2011.601351
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    Cited by:

    1. Yeu-Shiang Huang & Li-Chen Liu & Jyh-Wen Ho, 2015. "Decisions on new product development under uncertainties," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1010-1019, April.
    2. Bo Zhang & Jin Peng & Shengguo Li, 2015. "Uncertain programming models for portfolio selection with uncertain returns," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2510-2519, October.
    3. Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.
    4. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    5. Ruey-Chyn Tsaur, 2015. "Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 438-450, February.
    6. Fei Ren & Ya-Nan Lu & Sai-Ping Li & Xiong-Fei Jiang & Li-Xin Zhong & Tian Qiu, 2017. "Dynamic Portfolio Strategy Using Clustering Approach," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.

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