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Minimization of Systemic Risk for Directed Network Using Genetic Algorithm Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Dynamic Portfolio Optimization in Ultra-High Frequency Environment

Authors : Patryk Filipiak, Piotr Lipinski

Published in: Applications of Evolutionary Computation

Publisher: Springer International Publishing

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Abstract

This paper concerns the problem of portfolio optimization in the context of ultra-high frequency environment with dynamic and frequent changes in statistics of financial assets. It aims at providing Pareto fronts of optimal portfolios and updating them when estimated return rates or risks of financial assets change. The problem is defined in terms of dynamic optimization and solved online with a proposed evolutionary algorithm. Experiments concern ultra-high frequency time series coming from the London Stock Exchange Rebuilt Order Book database and the FTSE100 index.

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Footnotes
1
One of the companies, Royal Dutch Shell, was listed as two separate assets throughout the analyzed time period, thus FTSE100 essentially consisted of 101 components.
 
Literature
1.
Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952)
2.
Sharpe, W.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964)
3.
4.
Sortino, F., Price, L.: Performance measurement in a downside risk framework. J. Investing 3, 59–64 (1994)CrossRef
5.
Bacon, C.: Practical Portfolio Performance Measurement and Attribution. Wiley, Hoboken (2008)
6.
Anagnostopoulos, K., Mamanis, G.: A portfolio optimization model with three objectives and discrete variables. Comput. Oper. Res. 37(7), 1285–1297 (2010)MathSciNetCrossRefMATH
7.
Chang, T., Meade, N., Beasley, J., Sharaiha, Y.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000)CrossRefMATH
8.
Mansini, R., Speranza, M.G.: Heuristic algorithms for the portfolio selection problem with minimum transaction lots. Eur. J. Oper. Res. 114(2), 219–233 (1999)CrossRefMATH
9.
Lin, C.-C., Liu, Y.-T.: Genetic algorithms for portfolio selection problems with minimum transaction lots. Eur. J. Oper. Res. 185(1), 393–404 (2008)CrossRefMATH
10.
Tapia, M.G.C., Coello, C.A.C.: Applications of multi-objective evolutionary algorithms in economics and finance: A survey. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), pp. 532–539 (2007)
11.
Coello, C.A.C.: Evolutionary multi-objective optimization and its use in finance, Handbook of Research on Nature Inspired Computing for Economy and Management (2006)
12.
Ponsich, A., Jaimes, A., Coello, C.A.C.: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans. Evol. Comput. 17(3), 321–344 (2013)CrossRef
13.
Skolpadungket, P., Dahal, K., Harnpornchai, N.: Portfolio optimization using multi-objective genetic algorithms. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 516–523 (2007)
14.
Streichert, F., Ulmer, H., Zell, A.: Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), vol. 1, pp. 932–939 (2004)
15.
Chiam, S., Tan, K., Al Mamum, A.: Evolutionary multi-objective portfolio optimization in practical context. Int. J. Autom. Comput. 5(1), 67–80 (2008)CrossRef
16.
Branke, J., Scheckenbach, B., Stein, M., Deb, K., Schmeck, H.: Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. Eur. J. Oper. Res. 199(3), 684–693 (2009)MathSciNetCrossRefMATH
17.
Anagnostopoulos, K., Mamanis, G.: The mean-variance cardinality constrained portfolio optimization problem: an experimental evaluation of five multiobjective evolutionary algorithms. Expert Syst. Appl. 38(11), 14208–14217 (2011)
18.
Krink, T., Paterlini, S.: Multiobjective optimization using differential evolution for real-world portfolio optimization. Comput. Manag. Sci. 8(1–2), 157–179 (2011)MathSciNetCrossRef
19.
Cura, T.: Particle swarm optimization approach to portfolio optimization. Nonlinear Anal.: Real World Appl. 10(4), 2396–2406 (2009)MathSciNetCrossRefMATH
20.
Deng, G., Lin, W., Lo, C.: Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Syst. Appl. 39(4), 4558–4566 (2012)CrossRef
21.
Di Tollo, G., Roli, A.: Metaheuristics for the portfolio selection problem. Int. J. Oper. Res. 5(1), 13–35 (2008)MathSciNetMATH
22.
Gaspero, L.D., Tollo, G.D., Roli, A., Schaerf, A.: Hybrid metaheuristics for constrained portfolio selection problems. Quant. Finance 11(10), 1473–1487 (2011)MathSciNetCrossRefMATH
23.
Armananzas, R., Lozano, J.A.: A multiobjective approach to the portfolio optimization problem. IEEE Congress on Evolutionary Computation, vol. 2, pp. 1388–1395 (2005)
24.
Liu, Q.: On portfolio optimization: how and when do we benefit from high-frequency data? J. Appl. Econ. 24(4), 560–582 (2009)MathSciNetCrossRef
25.
Goumatianosa, N., Christoua, I., Lindgrenb, P.: Stock selection system: building long/short portfolios using intraday patterns. Proc. Econ. Finance 5, 298–307 (2013)CrossRef
26.
Ziegelmann, F.A., Borges, B., Caldeira, J.F.: Selection of minimum variance portfolio using intraday data: an empirical comparison among different realized measures for BM&FBovespa data. Braz. Rev. Econ. 35(1), 23–46 (2015)
27.
28.
Choueifaty, Y., Froidure, T., Reynier, J.: Properties of the most diversified portfolio. J. Investment Strat. 2(2), 49–70 (2013)CrossRef
29.
Cesarone, F., Scozzari, A., Tardella, F.: Efficient algorithms for mean-variance portfolio optimization with hard real-world constraints. AFIR Colloquium (2008)
30.
Deb, K., Rao N., U.B., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007). doi:10.​1007/​978-3-540-70928-2_​60CrossRef
31.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TEC 6(2), 182–197 (2002)
32.
Helbig, M., Engelbrecht, A.P.: Performance measures for dynamic multi-objective optimisation algorithms. Inf. Sci. 250, 61–81 (2013)CrossRefMATH
33.
34.
O’Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995)
35.
Goodhart, C., O’Hara, M.: High frequency data in financial markets: Issues and applications. J. Empirical Finance 4, 73–114 (1997)CrossRef
36.
Lipinski, P., Brabazon, A.: Pattern mining in ultra-high frequency order books with self-organizing maps. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 288–298. Springer, Heidelberg (2014). doi:10.​1007/​978-3-662-45523-4_​24
37.
Lipinski, P., Michalak, K., Lancucki, A.: Improving classification of patterns in ultra-high frequency time series with evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 127–128. ACM (2016)
38.
Michalak, K., Lancucki, A., Lipinski, P.: Multiobjective optimization of frequent pattern models in ultra-high frequency time series: stability versus Universality. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2016) (2016)
Metadata
Title
Dynamic Portfolio Optimization in Ultra-High Frequency Environment
Authors
Patryk Filipiak
Piotr Lipinski
Copyright Year
2017
Book
Applications of Evolutionary Computation

Print ISBN: 978-3-319-55848-6

Electronic ISBN: 978-3-319-55849-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55849-3_3

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