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A Study on Assets Categorizations and Optimal Allocation via an Improved Algorithm

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Recent Developments in Data Science and Business Analytics

Part of the book series: Springer Proceedings in Business and Economics ((SPBE))

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Abstract

The efficiency of assets allocation is essential to investment performance. To deal with the dynamic optimization of industry asset allocation, an improved algorithm (the Index Hierarchical Structure Algorithm, IHSA) is applied to 20 industry indexes according to different sample period, and proved to deduce stabilized industry categorizations. Then, a series of different size industry portfolios are constructed by the algorithm, and their monthly rate of returns are compared with 369 open-ended funds (including 263 growth funds and 106 index funds). The results indicate that the performance of each industry portfolio is better than most of the sample funds in the early sample period, especially than index funds. The algorithm provides a useful guidance for industry allocation and active asset management.

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References

  1. Bernanke, B. S., & Kuttner, K. N. (2005). What explains the stock market’s reaction to federal reserve policy? Journal of Finance, 60, 1221–1257.

    Article  Google Scholar 

  2. Kotter, J., & Lel, U. (2011). Friends or foes? Target selection decision and performance effects of sovereign wealth funds. Journal of Financial Economics, 101, 360–381.

    Article  Google Scholar 

  3. Chen, X. X., Chen, W. Z., & Jin, Y. P. (2007). Country and industry factors on the performance of international asset allocation. Finance & Trade Economics, 2, 93–97.

    Google Scholar 

  4. Brands, S., Brown, S. J., & Gallagher, D. R. (2005). Portfolio concentration and investment manager performance. International Review of Finance, 5, 149–174.

    Article  Google Scholar 

  5. Sapp, T., & Yan, X. (2008). Security concentration and active fund management: Do focused funds offer superior performance? Financial Review, 43, 27–49.

    Article  Google Scholar 

  6. Liao, C. Y. (2013). Industry selection for fund asset allocation and fund performance: New evidence about the investment ability of fund managers. Journal of Chong Qing University(Social Science Edition), 19, 53–60.

    Google Scholar 

  7. Benzécri, J. P. (1984). L’analyse des données 1′, La Taxonomie Dumand. Paris.

    Google Scholar 

  8. Song, G. H., & Liu, G. (2013). An empirical study of dynamic asset allocation based on index hierarchical structure algorithm. Soft Science, 27, 32–37.

    Google Scholar 

  9. Mantegna, R. N. (1999). Information and hierarchical structure in financial markets. Computer Physics Communications, 121, 153–156.

    Article  Google Scholar 

  10. Miccich, S., Bonanno, G., Lillo, F., et al. (2002). Degree stability of a minimum spanning tree of price return and volatility. Physica A, 324, 66–73.

    Article  Google Scholar 

  11. Brida, J. G., & Risso, W. A. (2008). Multidimensional minimal spanning tree: The Dow Jones case. Physica A, 387, 5205–5210.

    Article  Google Scholar 

  12. Gilmore, C. G., Lucey, B., & Boscia, M. (2008). An ever-loser union? Examining the evolution of linkages of European equity markets via minimum spanning trees. Physica A, 387, 6319–6329.

    Article  Google Scholar 

  13. Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks: The case of brazil. Physica A, 389, 3240–3249.

    Article  Google Scholar 

  14. Yu, D. F., Gan, C. H., & Zheng, R. G. (2011). Industrial linkages in China——an empirical research based on structural decomposition technique of input-output model. China Industrial Economics, 11, 5–15.

    Google Scholar 

Download references

Acknowledgments

This work is financially supported by the Guangdong Province-sponsored Philosophy and Social Science Funding Program (GD14XYJ16) and the Guangdong Province Education Department—sponsored Platform Funding Program for Humanities and Social Sciences (2014WQNCX074).

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Authors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guang Liu

Authors
  1. Guang Liu
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Editors and Affiliations

  1. Department of Business Systems and Analytics, La Salle University, Philadelphia, Pennsylvania, USA

    Madjid Tavana

  2. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

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Liu, G. (2018). A Study on Assets Categorizations and Optimal Allocation via an Improved Algorithm. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_28

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