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Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?

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  • Ankit Dangi

Abstract

Financial portfolio optimization is a widely studied problem in mathematics, statistics, financial and computational literature. It adheres to determining an optimal combination of weights associated with financial assets held in a portfolio. In practice, it faces challenges by virtue of varying math. formulations, parameters, business constraints and complex financial instruments. Empirical nature of data is no longer one-sided; thereby reflecting upside and downside trends with repeated yet unidentifiable cyclic behaviours potentially caused due to high frequency volatile movements in asset trades. Portfolio optimization under such circumstances is theoretically and computationally challenging. This work presents a novel mechanism to reach an optimal solution by encoding a variety of optimal solutions in a solution bank to guide the search process for the global investment objective formulation. It conceptualizes the role of individual solver agents that contribute optimal solutions to a bank of solutions, a super-agent solver that learns from the solution bank, and, thus reflects a knowledge-based computationally guided agents approach to investigate, analyse and reach to optimal solution for informed investment decisions. Conceptual understanding of classes of solver agents that represent varying problem formulations and, mathematically oriented deterministic solvers along with stochastic-search driven evolutionary and swarm-intelligence based techniques for optimal weights are discussed. Algorithmic implementation is presented by an enhanced neighbourhood generation mechanism in Simulated Annealing algorithm. A framework for inclusion of heuristic knowledge and human expertise from financial literature related to investment decision making process is reflected via introduction of controlled perturbation strategies using a decision matrix for neighbourhood generation.

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File URL: http://arxiv.org/pdf/1301.4194
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Paper provided by arXiv.org in its series Papers with number 1301.4194.

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Date of creation: Jan 2013
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Handle: RePEc:arx:papers:1301.4194

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Web page: http://arxiv.org/

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  1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  2. Greyserman, Alex & Jones, Douglas H. & Strawderman, William E., 2006. "Portfolio selection using hierarchical Bayesian analysis and MCMC methods," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 669-678, February.
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  8. Harry M. Markowitz, 2010. "Portfolio Theory: As I Still See It," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 1-23, December.
  9. Alois Geyer & Michael Hanke & Alex Weissensteiner, 2009. "A stochastic programming approach for multi-period portfolio optimization," Computational Management Science, Springer, vol. 6(2), pages 187-208, May.
  10. Sergio Ortobelli & Svetlozar T. Rachev & Stoyan Stoyanov & Frank J. Fabozzi & Almira Biglova, 2005. "The Proper Use Of Risk Measures In Portfolio Theory," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(08), pages 1107-1133.
  11. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
  12. Carl Lindberg, 2009. "Portfolio optimization when expected stock returns are determined by exposure to risk," Papers 0906.2271, arXiv.org.
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