Back
 TI  Vol.4 No.1 B , February 2013
Portfolio Selection by Maximizing Omega Function using Differential Evolution
Abstract: Paper presents alternative solution seeking approach for portfolio selection problem with Omega function performance measure which allows determining capital allocation over the number of assets. Omega function computability is diffi-cult due to substandard structures and therefore the use of standard techniques seems to be relatively complicated. Dif-ferential evolution from the group of evolutionary algorithms was selected as an alternative computing procedure. Al-ternative approach is analyzed on the Down Jones Industrial Index data. Presented approach enables to determine good real-time solution and the quality of results is comparable with results obtained by professional software.
Cite this paper: P. Juraj, B. Ivan, Č. Zuzana and R. Marian, "Portfolio Selection by Maximizing Omega Function using Differential Evolution," Technology and Investment, Vol. 4 No. 1, 2013, pp. 73-77. doi: 10.4236/ti.2013.41B012.
References

[1]   W. F. Sharpe, “The Sharpe Ratio”, The Journal of Port-folio Management, Vol. 21, No. 1, 1994, pp. 49–58.

[2]   J. L. Treynor, “How to Rate Management of Investment Funds”, Harvard Business Review, Vol. 43, No. 1, 1965, pp. 63-75.

[3]   M. C. Jensen, “The Per-formance of Mutual Funds in the Period 1945-1964”, Journal of Finance, Vol. 23, 1968, pp. 389-416.

[4]   F. A. Sortino and R. Meer, “Downside Risk”, The Journal of Portfolio Management, Vol. 17, No. 4, 1991, pp. 27–31.

[5]   T. H. Goodwin, “The Information Ratio”, Investment Performance Measurement: Evaluation and Presenting Results. Hoboken, NJ: John Wiley & Sons, 2009.

[6]   C. S. Pedersen and T. Ruddholm-Alfin, ”Se-lecting risk-adjusted shareholder performance meas-ure”, Journal of Asset Management. Vol. 4, No. 3, 2003, pp. 152-172.

[7]   R. Hentati-Kaffel and J. L. Prigent, “Structured portfolio analysis under SharpeOmega ratio”, Documents de Travail du Centre d’Economie de la Sor-bonne, 2012.

[8]   C. Keating and W. F. Shadwick, “A Universal Performance Measure”, Journal of Perfor-mance Measurement.Vol. 6, 2002, pp. 59-84.

[9]   S. Avouyi-Dovi, A. Morin and D. Neto, “Optimal Asset Allocation with Omega Function”, Technical report, Banque de France, 2004.

[10]   R. Storn and K. Price. “Differential Evolution – A simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, Vol. 11, 1997, pp. 341–359.

[11]   Z.cickova,I. Brezina and J. Pekár, “Al-ternative method for solving traveling salesman problem by evolutionary algorithm”, Management information systems. No. 1, 2008, pp. 17-22.

[12]   I. Brezina, Z.cickova and J. Pekár, “Application of evolutionary ap-proach to solving vehicle routing problem with time windows”, Economic review, Vol. 38, No. 4, 2009, pp. 529-539.

[13]   I. Brezina, Z.cickova and J. Pekár, “Evolutionary approach as an alternative method for solving the vehicle routing problem”, Economic review, Vol. 41, No. 2, 2012, pp. 137-147.

[14]   D. Ardia, K. Boudt, P. Carl, K. M. Mullen and B. G. Peterson, “Differential Evolution with DEoptim”, The R Journal, Vol. 3, No. 1, 2011, pp. 27-34.

[15]   I. Zelinka, “Umělá inteligence v problémech globální optimalizace”, BEN-technická literature, Praha, 2002.

[16]   V.Marik,o.stepankova and J.Lazansky, “Umělá inteligence 4”, Academia Praha, 2003.

[17]   G. C. Onwubolu and B. V. Babu, “New Optimization Techniques in Engineering”, Springer-Verlag, Berlin-Heidelberg, 2004.

[18]   Z.cickova, I. Brezina and J. Pekár, “A memetic algo-rithm for solving the vehicle routing problem”, In Mathematical methods in Economics 2011, 29th international conference, Praha, Professional Publishing, 2011, pp. 125-128.

 
 
Top