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Solution of Multi-objective Portfolio Optimization Problem Using Multi-objective Synergetic Differential Evolution (MO-SDE)

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 584))

Abstract

Portfolio optimization plays an important role in managing the financial assets of an individual. The investments are made such that an individual attains the maximum benefit out of it. In this paper, a bi-objective portfolio optimization model is considered, where the objectives are to maximize the return and minimize the risk, and is solved using multi-objective synergetic differential evolution (MO-SDE).

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References

  1. Gupta, P., et al.: Fuzzy portfolio optimization. Springer (2014)

    Google Scholar 

  2. Saborido, R., et al.: Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Appl. Soft. Comput. 39, 48–63 (2016)

    Google Scholar 

  3. Pouya, A.R., Maghsud, S., Mustafa, J.R.: Solving multi-objective portfolio optimization problem using invasive weed optimization. Swarm Evol. Comput. 28, 42–57 (2016)

    Google Scholar 

  4. Yoshimoto, A.: The mean-variance approach to portfolio optimization subject to transaction costs. J. Oper. Res. Soc. Jpn. 39(1), 99–117 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  5. Konno, H., Suzuki, K.-I.: A mean-variance-skewness portfolio optimization model. J. Oper. Res. Soc. Jpn. 38(2), 173–187 (1995)

    Google Scholar 

  6. Chang, T.-J., et al.: Heuristics for cardinality constrained portfolio optimization. Comput. Oper. Res. 27(13), 1271–1302 (2000)

    Article  MATH  Google Scholar 

  7. Soleimani, H., Golmakani, H.R., Salimi, M.H.: Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using geneticalgorithm. Expert Syst. Appl. 36(3), 5058–5063 (2009)

    Article  Google Scholar 

  8. Golmakani, H.R., Fazel, M.: Constrained portfolio selection using particle swarm optimization. Expert Syst. Appl. 38(7), 8327–8335 (2011)

    Article  Google Scholar 

  9. Sadjadi, S.J., Gharakhani, M., Safari, E.: Robust optimization framework for cardinality constrained portfolio problem. Appl. Soft Comput. 12(1), 91–99 (2012)

    Article  Google Scholar 

  10. Ghahtarani, A., Najafi, A.A.: Robust goal programming for multi-objective portfolio selection problem. Econ. Model. 33, 588–592 (2013)

    Google Scholar 

  11. Katagiri, H., Ishii, H.: Fuzzy portfolio selection problem. In: IEEE SMC’99 Conference Proceedings, International Conference on Systems, Man, and Cybernetics. IEEE (1999)

    Google Scholar 

  12. Goldfarb, D., Iyengar, G.: Robust portfolio selection problems. Math. Oper. Res. 28(1), 1–38 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  13. Oh, K.J., et al.: Portfolio algorithm based on portfolio beta using genetic algorithm. Expert Syst. Appl. 30(3), 527–534 (2006)

    Article  Google Scholar 

  14. Ali, M., Millie, P., Ajith, A.: Improving differential evolution algorithm by synergizing different improvement mechanisms. ACM Trans. Auton. Adapt. Syst. (TAAS) 7.2 (2012)

    Google Scholar 

  15. Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the Congress on Evolutionary Computation 2002, pp. 1145–1150 (2002)

    Google Scholar 

  16. Robic, T., Filipic, B.: DEMO: differential evolution for multiobjective optimization. In: Proceedings of the 3rd International Conference on Evolutionary Multi Criterion Optimization, LNCS 3410, pp. 520–533 (2005)

    Google Scholar 

  17. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  18. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  19. Zaheer, H., Pant, M., Kumar, S., Monakhov, O., Monakhova, E., Deep, K.: A new guiding force strategy for differential evolution. Int. J. Syst. Assur. Eng. Manag. 1–14 (2014). doi:10.1007/s13198-014-0322-6

  20. Zaheer, H., et al.: A portfolio analysis of ten national banks through differential evolution. In: Proceedings of Fifth International Conference on Soft Computing for Problem Solving, pp. 851–862. Springer, Singapore (2016)

    Google Scholar 

  21. Ansari, I.A., Pant, M., Ahn, C.W., Jeong, J.: PSO Optimized multipurpose image watermarking using SVD and chaotic sequence. In: Proceedings of Bio-Inspired Computing–Theories and Applications: 10th International Conference, BIC-TA 2015 Hefei, China, 25–28 Sept 2015, vol. 562. Springer (2016)

    Google Scholar 

  22. Jauhar, S., Pant, M., Deep, A.: Differential evolution for supplier selection problem: a DEA based approach. In: Proceedings of the Third International Conference on Soft Computing for Problem Solving, pp. 343–353. Springer, India (2014)

    Google Scholar 

  23. Singh, N., Chandra, M., Yadav, D.: Formal specification of asynchronous checkpointing using Event-B. In: 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA), pp. 659–664. IEEE (2015)

    Google Scholar 

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Acknowledgements

The reported study was partially supported by DST, research project No. INT/RFBR/P-164.

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Authors and Affiliations

  1. Department of Applied Sciences and Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, India

    Hira Zaheer & Millie Pant

Authors
  1. Hira Zaheer
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  2. Millie Pant
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Corresponding author

Correspondence to Hira Zaheer .

Editor information

Editors and Affiliations

  1. Department of Applied Science and Engineering, IIT Roorkee, Saharanpur, India

    Millie Pant

  2. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  3. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  4. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  5. Surface Characterization Group, NIMS, Nano Characterization Unit, Advanced Key Technologies Division, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Zaheer, H., Pant, M. (2018). Solution of Multi-objective Portfolio Optimization Problem Using Multi-objective Synergetic Differential Evolution (MO-SDE). In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-10-5699-4_19

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  • DOI: https://doi.org/10.1007/978-981-10-5699-4_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5698-7

  • Online ISBN: 978-981-10-5699-4

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