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A clustering-based portfolio strategy incorporating momentum effect and market trend prediction

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  • Lu, Ya-Nan
  • Li, Sai-Ping
  • Zhong, Li-Xin
  • Jiang, Xiong-Fei
  • Ren, Fei

Abstract

The hierarchical clustering algorithm has been proved useful in portfolio investment, which is one of the hottest issues in finance. In our new portfolio strategy, central, peripheral and dispersed portfolios constructed from clusters detected using unweighted and weighted modularity are compared according to their past performances, and the optimal portfolio is used in the investment period only if the market index return predicted by the LR, WMA or BP models is positive to avoid losses when the market drops. Our strategy is tested using the daily data of Chinese A-share market from January 4, 2008 and December 31, 2016, and the average investment return during different moving investment periods and 200 repeated runs is calculated. We find that although incorporating dispersed portfolio into our strategy has no significant effect in raising the investment return, it shows a similar performance as the peripheral portfolio, and the strategy constructed using unweighted modularity generally outperforms its counterpart by using weighted modularity. In addition, the market trend prediction can refine the investment return of our strategy. In brief, the strategy constructed using the BP model and unweighted modularity has the best investment return, which also outperforms the Markowitz portfolio.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:117:y:2018:i:c:p:1-15
    DOI: 10.1016/j.chaos.2018.10.012
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    as
    1. Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2016. "Correlation structure and principal components in the global crude oil market," Empirical Economics, Springer, vol. 51(4), pages 1501-1519, December.
    2. Vladimir Boginski & Sergiy Butenko & Oleg Shirokikh & Svyatoslav Trukhanov & Jaime Gil Lafuente, 2014. "A network-based data mining approach to portfolio selection via weighted clique relaxations," Annals of Operations Research, Springer, vol. 216(1), pages 23-34, May.
    3. Barfuss, Wolfram & Massara, Guido Previde & Di Matteo, T. & Aste, Tomaso, 2016. "Parsimonious modeling with information filtering networks," LSE Research Online Documents on Economics 68860, London School of Economics and Political Science, LSE Library.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    7. Kocheturov, Anton & Batsyn, Mikhail & Pardalos, Panos M., 2014. "Dynamics of cluster structures in a financial market network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 523-533.
    8. Jaehyung Choi & Young Shin Kim & Ivan Mitov, 2014. "Reward-risk momentum strategies using classical tempered stable distribution," Papers 1403.6093, arXiv.org, revised Jun 2015.
    9. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    10. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    11. Clifford S. Asness & Tobias J. Moskowitz & Lasse Heje Pedersen, 2013. "Value and Momentum Everywhere," Journal of Finance, American Finance Association, vol. 68(3), pages 929-985, June.
    12. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    13. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    14. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    15. Pafka, Szilárd & Kondor, Imre, 2003. "Noisy covariance matrices and portfolio optimization II," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 487-494.
    16. 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.
    17. Tomaso Aste & T. Di Matteo, 2017. "Sparse Causality Network Retrieval from Short Time Series," Complexity, Hindawi, vol. 2017, pages 1-13, November.
    18. repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
    19. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    20. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    21. 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.
    22. Kang, Joseph & Liu, Ming-Hua & Ni, Sophie Xiaoyan, 2002. "Contrarian and momentum strategies in the China stock market: 1993-2000," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 243-265, June.
    23. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    24. Chen, Kun & Luo, Peng & Sun, Bianxia & Wang, Huaiqing, 2015. "Which stocks are profitable? A network method to investigate the effects of network structure on stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 224-235.
    25. Choi, Jaehyung & Kim, Young Shin & Mitov, Ivan, 2015. "Reward-risk momentum strategies using classical tempered stable distribution," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 194-213.
    26. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    27. Daly, J. & Crane, M. & Ruskin, H.J., 2008. "Random matrix theory filters in portfolio optimisation: A stability and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4248-4260.
    28. Heiberger, Raphael H., 2014. "Stock network stability in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 376-381.
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