Skip to main content

2017 | OriginalPaper | Buchkapitel

Dynamic Portfolio Optimization in Ultra-High Frequency Environment

verfasst von : Patryk Filipiak, Piotr Lipinski

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
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.
 
Literatur
1.
Zurück zum Zitat Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952) Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952)
2.
Zurück zum Zitat Sharpe, W.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964) Sharpe, W.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964)
3.
Zurück zum Zitat Sharpe, W.: The sharpe ratio. J. Portfolio Manag. 21(1), 49–58 (1994)CrossRef Sharpe, W.: The sharpe ratio. J. Portfolio Manag. 21(1), 49–58 (1994)CrossRef
4.
Zurück zum Zitat Sortino, F., Price, L.: Performance measurement in a downside risk framework. J. Investing 3, 59–64 (1994)CrossRef Sortino, F., Price, L.: Performance measurement in a downside risk framework. J. Investing 3, 59–64 (1994)CrossRef
5.
Zurück zum Zitat Bacon, C.: Practical Portfolio Performance Measurement and Attribution. Wiley, Hoboken (2008) Bacon, C.: Practical Portfolio Performance Measurement and Attribution. Wiley, Hoboken (2008)
6.
Zurück zum Zitat Anagnostopoulos, K., Mamanis, G.: A portfolio optimization model with three objectives and discrete variables. Comput. Oper. Res. 37(7), 1285–1297 (2010)MathSciNetCrossRefMATH Anagnostopoulos, K., Mamanis, G.: A portfolio optimization model with three objectives and discrete variables. Comput. Oper. Res. 37(7), 1285–1297 (2010)MathSciNetCrossRefMATH
7.
Zurück zum Zitat Chang, T., Meade, N., Beasley, J., Sharaiha, Y.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000)CrossRefMATH Chang, T., Meade, N., Beasley, J., Sharaiha, Y.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000)CrossRefMATH
8.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat Krink, T., Paterlini, S.: Multiobjective optimization using differential evolution for real-world portfolio optimization. Comput. Manag. Sci. 8(1–2), 157–179 (2011)MathSciNetCrossRef 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.
Zurück zum Zitat Cura, T.: Particle swarm optimization approach to portfolio optimization. Nonlinear Anal.: Real World Appl. 10(4), 2396–2406 (2009)MathSciNetCrossRefMATH Cura, T.: Particle swarm optimization approach to portfolio optimization. Nonlinear Anal.: Real World Appl. 10(4), 2396–2406 (2009)MathSciNetCrossRefMATH
20.
Zurück zum Zitat 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 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.
Zurück zum Zitat Di Tollo, G., Roli, A.: Metaheuristics for the portfolio selection problem. Int. J. Oper. Res. 5(1), 13–35 (2008)MathSciNetMATH Di Tollo, G., Roli, A.: Metaheuristics for the portfolio selection problem. Int. J. Oper. Res. 5(1), 13–35 (2008)MathSciNetMATH
22.
Zurück zum Zitat 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 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.
Zurück zum Zitat Armananzas, R., Lozano, J.A.: A multiobjective approach to the portfolio optimization problem. IEEE Congress on Evolutionary Computation, vol. 2, pp. 1388–1395 (2005) 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.
Zurück zum Zitat Liu, Q.: On portfolio optimization: how and when do we benefit from high-frequency data? J. Appl. Econ. 24(4), 560–582 (2009)MathSciNetCrossRef 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.
Zurück zum Zitat Goumatianosa, N., Christoua, I., Lindgrenb, P.: Stock selection system: building long/short portfolios using intraday patterns. Proc. Econ. Finance 5, 298–307 (2013)CrossRef 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.
Zurück zum Zitat 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) 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)
28.
Zurück zum Zitat Choueifaty, Y., Froidure, T., Reynier, J.: Properties of the most diversified portfolio. J. Investment Strat. 2(2), 49–70 (2013)CrossRef Choueifaty, Y., Froidure, T., Reynier, J.: Properties of the most diversified portfolio. J. Investment Strat. 2(2), 49–70 (2013)CrossRef
29.
Zurück zum Zitat Cesarone, F., Scozzari, A., Tardella, F.: Efficient algorithms for mean-variance portfolio optimization with hard real-world constraints. AFIR Colloquium (2008) Cesarone, F., Scozzari, A., Tardella, F.: Efficient algorithms for mean-variance portfolio optimization with hard real-world constraints. AFIR Colloquium (2008)
30.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat Helbig, M., Engelbrecht, A.P.: Performance measures for dynamic multi-objective optimisation algorithms. Inf. Sci. 250, 61–81 (2013)CrossRefMATH Helbig, M., Engelbrecht, A.P.: Performance measures for dynamic multi-objective optimisation algorithms. Inf. Sci. 250, 61–81 (2013)CrossRefMATH
34.
Zurück zum Zitat O’Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995) O’Hara, M.: Market Microstructure Theory. Blackwell, Oxford (1995)
35.
Zurück zum Zitat Goodhart, C., O’Hara, M.: High frequency data in financial markets: Issues and applications. J. Empirical Finance 4, 73–114 (1997)CrossRef Goodhart, C., O’Hara, M.: High frequency data in financial markets: Issues and applications. J. Empirical Finance 4, 73–114 (1997)CrossRef
36.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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) 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.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Dynamic Portfolio Optimization in Ultra-High Frequency Environment
verfasst von
Patryk Filipiak
Piotr Lipinski
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-55849-3_3