Abstract
We introduce a new network-based data mining approach to selecting diversified portfolios by modeling the stock market as a network and utilizing combinatorial optimization techniques to find maximum-weight s-plexes in the obtained networks. The considered approach is based on the weighted market graph model, which is used for identifying clusters of stocks according to a correlation-based criterion. The proposed techniques provide a new framework for selecting profitable diversified portfolios, which is verified by computational experiments on historical data over the past decade. In addition, the proposed approach can be used as a complementary tool for narrowing down a set of “candidate” stocks for a diversified portfolio, which can potentially be analyzed using other known portfolio selection techniques.
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References
Abello, J., Resende, M. G. C., & Sudarsky, S. (2002). Massive quasi-clique detection. In S. Rajsbaum (Ed.), LATIN 2002: theoretical informatics (pp. 598–612). London: Springer.
Balasundaram, B., Butenko, S., & Hicks, I. V. (2011). Clique relaxations in social network analysis: the maximum k-plex problem. Operations Research, 59, 133–142.
Boginski, V., Butenko, S., & Pardalos, P. (2003). On structural properties of the market graph. In A. Nagurney (Ed.), Innovations in financial and economic networks (pp. 29–45). Northampton: Edward Elgar.
Boginski, V., Butenko, S., & Pardalos, P. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431–443.
Boginski, V., Butenko, S., & Pardalos, P. (2006). Mining market data: a network approach. Computers & Operations Research, 33(11), 3171–3184.
Bomze, I. M., Budinich, M., Pardalos, P. M., & Pelillo, M. (1999). The maximum clique problem. In D.-Z. Du & P. M. Pardalos (Eds.), Handbook of combinatorial optimization (pp. 1–74). Dordrecht: Kluwer Academic.
Elton, E. J., & Gruber, M. J. (1997). Modern portfolio theory, 1950 to date. Journal of Banking & Finance, 21, 1743–1759.
Grane, A., & Veiga, H. (2010). Wavelet-based detection of outliers in financial time series. Computational Statistics & Data Analysis, 54, 2580–2593.
Harary, F., & Ross, I. C. (1957). A procedure for clique detection using the group matrix. Sociometry, 20(3), 205–215.
Kim, T., & White, H. (2004). On more robust estimation of skewness and kurtosis. Finance Research Letters, 1, 56–73.
Luce, R. D. (1950). Connectivity and generalized cliques in sociometric group structure. Psychometrika, 15(2), 169–190.
Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 14(2), 95–116.
Luenberger, D. G. (1998). Investment science. New York: Oxford University Press.
Mantegna, R. N., & Stanley, H. E. (2000). An introduction to econophysics: correlations and complexity in finance. Cambridge: Cambridge University Press.
Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7, 77–91.
McClosky, B. (2011) Clique relaxations. In: J. Cochran (Ed.), Encyclopedia of operations research and management science. New York: Wiley.
Mokken, R. J. (1979). Cliques, clubs and clans. Quality and Quantity, 13, 161–173.
Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.
Östergȧrd, P. R. J. (1999). A new algorithm for the maximum-weight clique problem. Electronic Notes in Discrete Mathematics, 3, 153–156.
Pattillo, J., Youssef, N., & Butenko, S. (2013). On clique relaxation models in network analysis. European Journal of Operational Research, 226, 9–18.
Pena, D. (2001). Outliers, influential observations and missing data. In D. Pena, G. Tiao, & R. Tsay (Eds.), A course in time series (pp. 136–170). New York: Wiley.
Seidman, S. B., & Foster, B. L. (1978). A graph theoretic generalization of the clique concept. The Journal of Mathematical Sociology, 6, 139–154.
Shirokikh, O., Pastukhov, G., Boginski, V., & Butenko, S. (2013). Computational study of the U.S. stock market evolution: a rank correlation-based network model. Computational Management Science. doi:10.1007/s10287-012-0160-4.
Trukhanov, S., Balasubramaniam, C., Balasundaram, B., & Butenko, S. (2013, working paper). Exact algorithms for hard node deletion problems in graphs.
Yu, H., Paccanaro, A., Trifonov, V., & Gerstein, M. (2006). Predicting interactions in protein networks by completing defective cliques. Bioinformatics, 22, 823–829.
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Boginski, V., Butenko, S., Shirokikh, O. et al. A network-based data mining approach to portfolio selection via weighted clique relaxations. Ann Oper Res 216, 23–34 (2014). https://doi.org/10.1007/s10479-013-1395-3
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DOI: https://doi.org/10.1007/s10479-013-1395-3