Portfolio Optimization Using Teaching-Learning Based Optimization (TLBO) Algorithm in Tehran Stock Exchange (TSE)

Document Type : Research Paper

Authors

1 PhD in Financial Management, University of Tehran, Tehran, Iran

2 MSc. Student in Financial Engineering, University of Tehran, Tehran, Iran

Abstract

Increasing the profits and reducing the risks have always been of the most important issues of concern to the investors in the financial markets. In recent years, many solutions and proposals have been suggested in respect to the frequency of portfolio optimization issue, with the highest return and the lowest possible risk. One of the most prominent suggestions is the Markowitz Model which is mostly known as the Modern Portfolio Theory. On the other hand, the TLBO algorithm which has been presented in 2010 is one of the most efficient meta-heuristic methods to solve the optimization problem. In this study, we are attempting to solve the portfolio optimization problem, according to the framework of the model introduced by Markowitz and using TLBO algorithm. For this purpose, the data related to the returns of 20 companies listed in TSE during the period 2012-2016 were collected. It is worth mentioning that four criteria including variance, mean absolute deviation, semi-variance and conditional value at risk (CvaR) were used in order to measure the risk level in this investigation.
 

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Anagnostopoulos, K.P., Mamanis, G. (2010). A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37 (7), 1285-1297.
Beale, E. M. L. & Forest, J. J. H. (1976). Global optimization using special ordered sets. Mathematical Programming, 10 (1), 52-69.
Bertsimas, D., Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications, 43(1), 1–22.
Chang, T. J., Meade, N., Beasley, J. E. & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimization. Computers & Operations Research, 27 (13), 1271-1302.
Deng, G. F., Lin, W. T. & Lo, C. C. (2012). Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications, 39 (4), 4558 – 4566.
Fernandez, A. & Gomez, S. (2007). Portfolio Selection Using Neural Networks. Computer & Operation Research, 34(4), 1177-1191.
Ghodusi, S., Tehrani, R. & Bashiri, M. (2015). Portfolio optimization with simulated annealing algorithm. Journal of Financial Research, 17(9), 141-158.
Gulpinar, N., An, L.T.H., Moeini, M. (2010). Robust investment strategies with discrete asset choice constraints using DC programming. Optimization, 59(1), 45-62.
Jia, J., Dyer, J. S. (1996). A Standard Measure of Risk and Risk-Value Models, Management Science, 42(12), 1691-1705.
Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1) 77-91.
Navidi, H., Nojoomi, A., Mirzazadeh, H. (2009). Portfolio Selection in Tehran Stock Exchange Market with a Genetic Algorithm. Journal of Economic Research, 44(4), 243-262. (in Persian)
Raei, R. & Alibeiki, H. (2010). Portfolio optimization using particle swarm optimization method. Financial Research, 12 (29), 21-40. (in Persian)
Rao, R. V., Savsani, V. J. & Vakharia, D. P. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1), 1-15.
Shaw, D.X., Liu, S. & Kopman, L. (2008). Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optimization Methods & Software, 23(3), 411-420.
Soleimani, H., Golmakani, H.R., Salimi, M.H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36(3), 5058-5063.
Tehrani, R., Noorbakhsh, A. (2012). Financial Theories (Advanced Financial Management). Tehran, Negah-e-Danesh Publications. (in Persian)
Vielma, J.P., Ahmed, S., Nemhauser, G.L. (2008). A lifted linear programming branchand-bound algorithm for mixed-integer conic quadratic programs. INFORMS Journal on Computing, 20(3), 438-450.
Woodside-Oriakhi, M., Lucas, C., Beasley, J.E. (2011). Heuristic Algorithms for The Cardinality Constrained Efficient Frontier. European Journal of Operational Research, 213(3), 538-550.