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Abstract

In this chapter some of the fundamental concepts necessary to understand the developed work are addressed, particularly the domain relative to financial markets. Further, a substantial part of the several methodologies applied to the portfolio problematic are analyzed; throughout the first two sections, the problem related with portfolio theory and investment’s analysis is presented. Subsequently, the evolutionary techniques which can be used to solve this problem are focused. Finally, Sect. 2.4 presents the connection between the presented financial domain and the evolutionary techniques, through an extended analysis on the existing solutions.

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References

  1. Maginn JL, Tuttle DL, McLeavey DW, Pinto JE (2007) Managing investment portfolios: a dynamic process. CFA Institute Investment Series. Wiley, New Jersey

    Google Scholar 

  2. Markowitz HM, Todd GP, Sharpe WF (2000) Mean variance analysis in portfolio choice and capital markets. Wiley, New Jersey

    Google Scholar 

  3. Malkiel B (1973) A random walk down wall street. W. W. Norton & Company, New York

    Google Scholar 

  4. Markowitz HM (1972) Portfolio selection. J Financ 7:77–91

    Google Scholar 

  5. Goodman B (2002) Passive management: it’s not an oxymoron. http://www.thestreet.com/author/1651333/BeverlyGoodman/all.html. Accessed 20 Aug 2009

  6. Aranha C, Hitoshi I (2008) A tree-based GA representation for the portfolio optimization problem. Genetic Evolutio Computation Conference (GECCO), Atlanta, pp 873–880

    Google Scholar 

  7. Achelis S (2000) Technical analysis from A to Z. McGraw-Hill, New York

    Google Scholar 

  8. Hamsapiya SS, Surekha P (2008) Evolutionary intelligence: an introduction to theory and applications with matlab. Springer, Heidelberg

    Google Scholar 

  9. Mitchell M (1999) An introduction to genetic algorithms. MIT Press, Cambridge

    Google Scholar 

  10. Eiben AE, Smith JE (2007) Introduction to evolutionary computing. Springer, Berlin

    Google Scholar 

  11. Black F, Litterman R (1992) Global portfolio optimization. Financ Anal J 48:28–43

    Article  Google Scholar 

  12. Salomons A (2007) The Black-Litterman model hype or improvement? Dissertation, University of Groningen

    Google Scholar 

  13. Sing TF, Ong SE (2000) Asset allocation in a downside risk framework. J Real Estate Portfol Manage 6:213–223

    Google Scholar 

  14. Cheng P, Wolverton M (2001) MPT and the downside risk framework: a comment on two recent studies. J Real Estate Portfol Manage 7:125–131

    Google Scholar 

  15. Gould N, Toint P (2008) Quadratic programming solvers. http://www.numerical.rl.ac.uk/qp/qp.htm. Accessed 15 Aug 2009

  16. Stein M, Branke J, Schmeck H (2005) Portfolio selection: how to integrate complex constraints. J Financ Plan 68–78

    Google Scholar 

  17. Arnone S, Loraschi A, Tettamanzi A (1993) A genetic approach to portfolio selection. Neural Netw World III 6:597–604

    Google Scholar 

  18. Loraschi A, Tettamanzi A, Tomassini M, Verda P (1995) Distributed genetic algorithms with an application to portfolio selection problems. In: Pearson D, Steele N, Albrecht R (ed) In Artificial neural nets and genetic algorithms, Springer-Verlag, Wien

    Google Scholar 

  19. Chang TJ, Meade N, Beasley JE, Sharaiha YM (2007) Heuristics for cardinality constrained portfolio optimization. Comput Oper Res 27:1271–1302

    Article  Google Scholar 

  20. Busetti FR (2000) Metaheuristic approaches to realistic portfolio optimization. Dissertation, University of South Africa

    Google Scholar 

  21. Schaerf A (2002) Local search techniques for constrained portfolio selection problems. J Comput Econ 20:177–190

    Article  MATH  Google Scholar 

  22. Crama Y, Schyns M (2003) Simulated annealing for complex portfolio selection problems. Eur J Oper Res 34:1177–1191

    Google Scholar 

  23. Cura T (2008) Particle swarm optimization approach to portfolio optimization. Nonlinear Anal: Real World Appl 10:2396–2406

    Article  MathSciNet  Google Scholar 

  24. Fernández A, Gómez S (2004) Portfolio selection using neural networks. Comput Oper Res 34:1177–1191

    Article  Google Scholar 

  25. Ehrgott M, Klamroth M, Schwehm C (2004) An MCDM approach to portfolio optimization. Eur J Oper Res 155:752–770

    Article  MathSciNet  MATH  Google Scholar 

  26. Lin C, Gen M (2007) An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Appl Math Sci 1:201–210

    MathSciNet  MATH  Google Scholar 

  27. Streichert F, Ulmer H, Zell A (2003) Evolutionary algorithms and the cardinality constrained portfolio selection problem. Oper Res Proc 3–5

    Google Scholar 

  28. Aranha C, Iba H (2007) Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing. In: Genetic and evolutionary computation conference (GECCO), London, pp 196–203

    Google Scholar 

  29. Streichert F, Ulmer H, Zell A (2004) Comparing discrete and continuous genotypes on the constrained portfolio selection problem. In: Genetic and evolutionary computation conference (GECCO), Seattle, pp 1239–1250

    Google Scholar 

  30. Skolpadungket P, Dahal K, Hampomchai N (2007) Portfolio optimization using multi-objective genetic algorithms. In: Congress evolutionary computation, Singapore, pp 516–523

    Google Scholar 

  31. Aranha C, Hitoshi I (2009) Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios. In: Genetic and evolutionary computation conference (GECCO), Montreal

    Google Scholar 

  32. Branke J, Scheckenbach B, Stein M, Deb K, Schmeck H (2008) Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. Eur J Oper Res 199:684–693

    Article  MathSciNet  Google Scholar 

  33. Wagman L (2003) Stock portfolio evaluation: an application of genetic-programming-based technical analysis. Gen Algorithms Gen Program, Stanford, pp 213–220

    Google Scholar 

  34. Yan W, Sewell M, Clack C (2008) Learning to Optimize Profits Beats Predicting Returns—Comparing Techniques for Financial Portfolio Optimization. Proc 10th annu conf Gen and evol computation, 1681-1688. Atlanta, USA

    Google Scholar 

  35. Hassan G, Clack C (2009) Robustness of multiple objective GP stock-picking in unstable financial markets. In: Genetic and evolutionary computation conference (GECCO), Montreal, pp 1513–1520

    Google Scholar 

  36. Murphy JJ (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. Prentice Hall Press, New York

    Google Scholar 

  37. Férnandez-Blanco P, Bodas-Sagi D, Hidalgo JI (2008) Technical market indicators optimization using evolutionary algorithms. In: Genetic and evolutionary computation conference (GECCO), Atlanta, pp 1851–1858

    Google Scholar 

  38. Bodas-Sagi D, Fernández P, Hidalgo JI, Soltero F, Risco-Martín J (2009) Multiobjective optimization of technical market indicators. In: Genetic and evolutionary computation conference (GECCO), Montreal, pp 1999–2004

    Google Scholar 

  39. Tsang E, Li J (1999) Improving technical analysis predictions: an application of genetic programming. Proceedings of Florida Artificial Intelligence Research Symposium. USA

    Google Scholar 

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Correspondence to António M. S. B. S. Gorgulho .

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Gorgulho, A.M.S.B.S., Neves, R.F.M.F., Horta, N.C.G. (2013). Related Work. In: Intelligent Financial Portfolio Composition based on Evolutionary Computation Strategies. SpringerBriefs in Applied Sciences and Technology(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32989-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-32989-0_2

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