Advanced Technologies and Algorithms for Efficient Portfolio Selection
In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.
[1] Armananzas R. and Lozano J. A., (2005) “A Multiobjective Approach to
the Portfolio Optimization Problem,” in 2005 IEEE Congress on
Evolutionary Computation (CEC’2005), vol. 2. Edinburgh, Scotland:
IEEE Service Center, September 2005, pp. 1388–1395.
[2] Black, Fischer; Myron Scholes (1973). "The Pricing of Options and
Corporate Liabilities". Journal of Political Economy 81 (3): 637–654.
[3] Chang, T.J., Meade, N., Beasley, J.E. and Sharaiha, Y.M. (2000),
“Heuristics for cardinality constrained portfolio optimisation”,
Computers and Operations Research, 27, (2000), 1271-1302.
[4] Chang, T.J., Yang S.C. & Chang K.J. (2009) Portfolio optimization
problems in different risk measures using genetic algorithm, Expert
Systems with applications, 36 (2009): 10529-10537.
[5] Crama, Y. & Schyns, M. (2003): Simulated annealing for complex
portfolio selection problems, European Journal of Operational Research
150: 546-571.
[6] Dantzig, George B. (1949). "Programming of Interdependent Activities:
II Mathematical Model". Econometrica 17 (3): 200–211.
[7] Deng, G.F & Lin W.T. (2010) Ant colony optimization for Markowitz
mean-variance portfolio model, B.K. Panigrahi et al. (Eds): SEMCCO
2010, LNCS 6466, pp.238-245, Springer – Verlag Berlin Heidelberg
2010.
[8] Doerner K., Gutjahr W., Hartl R., Strauss C., Stummer C., (2004) Pareto
Ant Colony Optimization: A Metaheuristic Approach to MUltiobjective
Portfolio Selection, Annals of Operations Research 131, 79-99, 2004
[9] Ehrgott M., Klamroth K., and Schwehm C., (2004) “An MCDM approach
to portfolio optimization,” European Journal of Operational Research,
vol. 155, no. 3, pp. 752–770, June 2004.
[10] Fama, Eugene (1965). "The Behavior of Stock Market Prices". Journal of
Business 38: 34–105.
[11] Fogel, L. J. (1966), Artificial Intelligence through Simulated Evolution,
John Wiley, New York, NY.
[12] Golmakani, H. R. & Fazel, M. (2011) Constrained portfolio selection
using particle swarm optimization, Expert Systems with Applications 38
(2011): 8327-8335.
[13] Konno H. and Yamazaki H.(1991) Mean-Absolute Deviation Portfolio
Optimization Model and Its Applications to Tokyo Stock Market,
Management Science, v. 37, 5: 519-531.
[14] Liagkouras, K. & Metaxiotis, K. (2014) A new Probe Guided Mutation
operator and its application for solving the cardinality constrained
portfolio optimization problem, Expert Systems with Applications 41
(14), 6274-6290.
[15] Liagkouras, K and Metaxiotis, K. (2015) Efficient Portfolio Construction
with the Use of Multiobjective Evolutionary Algorithms: Best Practices
and Performance Metric, International Journal of Information
Technology & Decision Making, Vol. 14 (2015) / DOI:
10.1142/S0219622015300013.
[16] Metaxiotis, K. & Liagkouras, K. (2012) Multiobjective evolutionary
algorithms for portfolio management: a comprehensive literature review,
Expert Systems with Applications 39 (14), 11685-11698.
[17] Metaxiotis, K & Liagkouras, K., (2013) A fitness guided mutation
operator for improved performance of MOEAs Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on, pp.
751-754, DOI: 10.1109/ICECS.2013.6815523.
[18] Maringer, D. & Kellerer, H., (2003) Optimization of cardinality
constrained portfolios with a hybrid local search algorithm. OR Spectrum
(2003) 25: 481-495
[19] Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1):77-91.
[20] Schaffer, J. D. (1985). Multiple Objective Optimization with Vector
Evaluated Genetic Algorithms. In Genetic algorithms and their
applications: Proceedings of the first international conference on genetic
algorithms (pp. 93–100). Hillsdale, NJ: Lawrence Erlbaum.
[21] Metaxiotis, K. & Liagkouras, K. (2014) An exploratory crossover
operator for improving the performance of MOEAs, Advances in Applied
and Pure Mathematics, pp. 158-162, ISBN: 978-1-61804-240-8.
[22] Liagkouras, K. & Metaxiotis, K. (2014) Application of Customer
Relationship Management Systems in Business: Challenges and
Opportunities, International Journal of Social, Education, Economics and
Management Engineering Vol:8, No:6, 2014, World Academy of
Science, Engineering and Technology.
[23] Liagkouras, K. & Metaxiotis, K. (2013) The Constrained
Mean-Semivariance Portfolio Optimization Problem with the Support of
a Novel Multiobjective Evolutionary Algorithm, Journal of Software
Engineering and Applications, 2013, 6, 22-29
doi:10.4236/jsea.2013.67B005.
[24] Metaxiotis, K. & Liagkouras, K. (2014) An Expert System Designed to
Be Used with MOEAs for Efficient Portfolio Selection, International
Journal of Computer, Information Science and Engineering Vol:8 No:2,
2014, World Academy of Science, Engineering and Technology.
[25] Liagkouras, K. & Metaxiotis, K. (2012) Modern Portfolio Management
with the Support of Multiobjective Evolutionary Algorithms: An
Analysis of Problem Formulation Issues, World Academy of Science,
Engineering and Technology 69 2012, Paris, France 27 – 28 June 2012.
[26] Liagkouras, K. & Metaxiotis, K. (2013) An Elitist Polynomial Mutation
Operator for Improved Performance of MOEAs in Computer Networks,
pp. 1-5, Computer Communications and Networks (ICCCN), 2013 22nd
International Conference on, DOI: 10.1109/ICCCN.2013.6614105.
[27] Liagkouras K. & Metaxiotis K. (2015) An experimental analysis of a new
two-stage crossover operator for multiobjective optimization, Soft
Computing, DOI: 10.1007/s00500-015-1810-6
[1] Armananzas R. and Lozano J. A., (2005) “A Multiobjective Approach to
the Portfolio Optimization Problem,” in 2005 IEEE Congress on
Evolutionary Computation (CEC’2005), vol. 2. Edinburgh, Scotland:
IEEE Service Center, September 2005, pp. 1388–1395.
[2] Black, Fischer; Myron Scholes (1973). "The Pricing of Options and
Corporate Liabilities". Journal of Political Economy 81 (3): 637–654.
[3] Chang, T.J., Meade, N., Beasley, J.E. and Sharaiha, Y.M. (2000),
“Heuristics for cardinality constrained portfolio optimisation”,
Computers and Operations Research, 27, (2000), 1271-1302.
[4] Chang, T.J., Yang S.C. & Chang K.J. (2009) Portfolio optimization
problems in different risk measures using genetic algorithm, Expert
Systems with applications, 36 (2009): 10529-10537.
[5] Crama, Y. & Schyns, M. (2003): Simulated annealing for complex
portfolio selection problems, European Journal of Operational Research
150: 546-571.
[6] Dantzig, George B. (1949). "Programming of Interdependent Activities:
II Mathematical Model". Econometrica 17 (3): 200–211.
[7] Deng, G.F & Lin W.T. (2010) Ant colony optimization for Markowitz
mean-variance portfolio model, B.K. Panigrahi et al. (Eds): SEMCCO
2010, LNCS 6466, pp.238-245, Springer – Verlag Berlin Heidelberg
2010.
[8] Doerner K., Gutjahr W., Hartl R., Strauss C., Stummer C., (2004) Pareto
Ant Colony Optimization: A Metaheuristic Approach to MUltiobjective
Portfolio Selection, Annals of Operations Research 131, 79-99, 2004
[9] Ehrgott M., Klamroth K., and Schwehm C., (2004) “An MCDM approach
to portfolio optimization,” European Journal of Operational Research,
vol. 155, no. 3, pp. 752–770, June 2004.
[10] Fama, Eugene (1965). "The Behavior of Stock Market Prices". Journal of
Business 38: 34–105.
[11] Fogel, L. J. (1966), Artificial Intelligence through Simulated Evolution,
John Wiley, New York, NY.
[12] Golmakani, H. R. & Fazel, M. (2011) Constrained portfolio selection
using particle swarm optimization, Expert Systems with Applications 38
(2011): 8327-8335.
[13] Konno H. and Yamazaki H.(1991) Mean-Absolute Deviation Portfolio
Optimization Model and Its Applications to Tokyo Stock Market,
Management Science, v. 37, 5: 519-531.
[14] Liagkouras, K. & Metaxiotis, K. (2014) A new Probe Guided Mutation
operator and its application for solving the cardinality constrained
portfolio optimization problem, Expert Systems with Applications 41
(14), 6274-6290.
[15] Liagkouras, K and Metaxiotis, K. (2015) Efficient Portfolio Construction
with the Use of Multiobjective Evolutionary Algorithms: Best Practices
and Performance Metric, International Journal of Information
Technology & Decision Making, Vol. 14 (2015) / DOI:
10.1142/S0219622015300013.
[16] Metaxiotis, K. & Liagkouras, K. (2012) Multiobjective evolutionary
algorithms for portfolio management: a comprehensive literature review,
Expert Systems with Applications 39 (14), 11685-11698.
[17] Metaxiotis, K & Liagkouras, K., (2013) A fitness guided mutation
operator for improved performance of MOEAs Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on, pp.
751-754, DOI: 10.1109/ICECS.2013.6815523.
[18] Maringer, D. & Kellerer, H., (2003) Optimization of cardinality
constrained portfolios with a hybrid local search algorithm. OR Spectrum
(2003) 25: 481-495
[19] Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1):77-91.
[20] Schaffer, J. D. (1985). Multiple Objective Optimization with Vector
Evaluated Genetic Algorithms. In Genetic algorithms and their
applications: Proceedings of the first international conference on genetic
algorithms (pp. 93–100). Hillsdale, NJ: Lawrence Erlbaum.
[21] Metaxiotis, K. & Liagkouras, K. (2014) An exploratory crossover
operator for improving the performance of MOEAs, Advances in Applied
and Pure Mathematics, pp. 158-162, ISBN: 978-1-61804-240-8.
[22] Liagkouras, K. & Metaxiotis, K. (2014) Application of Customer
Relationship Management Systems in Business: Challenges and
Opportunities, International Journal of Social, Education, Economics and
Management Engineering Vol:8, No:6, 2014, World Academy of
Science, Engineering and Technology.
[23] Liagkouras, K. & Metaxiotis, K. (2013) The Constrained
Mean-Semivariance Portfolio Optimization Problem with the Support of
a Novel Multiobjective Evolutionary Algorithm, Journal of Software
Engineering and Applications, 2013, 6, 22-29
doi:10.4236/jsea.2013.67B005.
[24] Metaxiotis, K. & Liagkouras, K. (2014) An Expert System Designed to
Be Used with MOEAs for Efficient Portfolio Selection, International
Journal of Computer, Information Science and Engineering Vol:8 No:2,
2014, World Academy of Science, Engineering and Technology.
[25] Liagkouras, K. & Metaxiotis, K. (2012) Modern Portfolio Management
with the Support of Multiobjective Evolutionary Algorithms: An
Analysis of Problem Formulation Issues, World Academy of Science,
Engineering and Technology 69 2012, Paris, France 27 – 28 June 2012.
[26] Liagkouras, K. & Metaxiotis, K. (2013) An Elitist Polynomial Mutation
Operator for Improved Performance of MOEAs in Computer Networks,
pp. 1-5, Computer Communications and Networks (ICCCN), 2013 22nd
International Conference on, DOI: 10.1109/ICCCN.2013.6614105.
[27] Liagkouras K. & Metaxiotis K. (2015) An experimental analysis of a new
two-stage crossover operator for multiobjective optimization, Soft
Computing, DOI: 10.1007/s00500-015-1810-6
@article{"International Journal of Business, Human and Social Sciences:70839", author = "Konstantinos Liagkouras and Konstantinos Metaxiotis", title = "Advanced Technologies and Algorithms for Efficient Portfolio Selection", abstract = "In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.
", keywords = "Portfolio selection, optimization techniques, financial
models, stochastics, heuristics.", volume = "9", number = "7", pages = "2502-6", }
